Podcasts about docker

Fri, 28 Aug 2026 00:12:00 GMT http://relay.fm/rd/294 http://relay.fm/rd/294 Grumpy 37 294 Merlin Mann and John Siracusa What is the role of loneliness on "Survivor?" What is the role of loneliness on "Survivor?" clean 7434 Subtitle: Loneliness is where John is a Viking.What is the role of loneliness on "Survivor?" Links and Show Notes: Things kick off unexpectedly. For Merlin, anyway. Honestly, that's what had happened. And, then, an old friend from the before times has a Thing It Took Him Too Long to Realize. It involves music and butts. And, while your hosts can recall that the listener's wife was way more famous, they can neither remember her name nor pronounce his. For a main topic, John and Merlin consider the role of loneliness on Survivor. Every dingaling has a plan until they get punched in the face. So, what do we think about all that isolation, privation, paranoia, and self-doubt? Even setting aside the sunburns and the Fijian food, this is not an environment in which Merlin would thrive. In this month's member bonus segment, your hosts discuss self-quantization. What are we measuring, and what is that helping us manage? You can sign up today to hear all the member episodes, get more bonus stuff, and help support our program. (Recorded on Tuesday, August 18, 2026) Credits Audio Editor/Key Master: Jim Metzendorf Admin Assistance: Kerry Provenzano Music: Merlin Mann The Suits: Stephen Hackett, Myke Hurley Get an ad-free version of the show, plus a monthly extended episode. ATP #703: Part of a Healthy BreakfastThe ATP episode where John tried to explain Docker (starting at 1:51:26). A timestamp link to John's Docker explanation in ATP #703 The Survivor 50 cast - People.com Carrot in a Box - YouTube Carrot in a Box: Rematch - YouTube Liz is emotional about Applebee's on Survivor season 46, episode 10 The cast of Veep talks about their fina

Fri, 28 Aug 2026 00:12:00 GMT http://relay.fm/rd/294 http://relay.fm/rd/294 Merlin Mann and John Siracusa What is the role of loneliness on "Survivor?" What is the role of loneliness on "Survivor?" clean 7434 Subtitle: Loneliness is where John is a Viking.What is the role of loneliness on "Survivor?" Links and Show Notes: Things kick off unexpectedly. For Merlin, anyway. Honestly, that's what had happened. And, then, an old friend from the before times has a Thing It Took Him Too Long to Realize. It involves music and butts. And, while your hosts can recall that the listener's wife was way more famous, they can neither remember her name nor pronounce his. For a main topic, John and Merlin consider the role of loneliness on Survivor. Every dingaling has a plan until they get punched in the face. So, what do we think about all that isolation, privation, paranoia, and self-doubt? Even setting aside the sunburns and the Fijian food, this is not an environment in which Merlin would thrive. In this month's member bonus segment, your hosts discuss self-quantization. What are we measuring, and what is that helping us manage? You can sign up today to hear all the member episodes, get more bonus stuff, and help support our program. (Recorded on Tuesday, August 18, 2026) Credits Audio Editor/Key Master: Jim Metzendorf Admin Assistance: Kerry Provenzano Music: Merlin Mann The Suits: Stephen Hackett, Myke Hurley Get an ad-free version of the show, plus a monthly extended episode. ATP #703: Part of a Healthy BreakfastThe ATP episode where John tried to explain Docker (starting at 1:51:26). A timestamp link to John's Docker explanation in ATP #703 The Survivor 50 cast - People.com Carrot in a Box - YouTube Carrot in a Box: Rematch - YouTube Liz is emotional about Applebee's on Survivor season 46, episode 10 The cast of Ve

Topics covered in this episode: Web UIs for your reverse proxy Wagtail 8.0 is hot off the presses RISC-V is now officially supported by CPython Django's annual releases make every version an LTS Extras Joke Watch on YouTube About the show Sponsored by Logfire from Pydantic: pythonbytes.fm/logfire Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Web UIs for your reverse proxy Traefik, nginx, and Caddy all sit in front of a lot of self-hosted infrastructure, and all three are configured by hand-editing files. Three active projects put a control plane on top: Traefik Manager (Python + Flask), Nginx UI (Go + Vue), and caddy/ui (React + Node). All three are additive rather than replacements - none of them take ownership of your config away from you - which is the part that matters when the thing has write access to production routing. Traefik Manager is the Python one: Flask 3.1 and Gunicorn for the control plane, a lightweight Go agent for remote instances, currently v1.10.0 with an Android companion app. Nginx UI is a single Go binary at 11.3k stars, with a block-style config editor, an Ace editor doing LLM completion on nginx syntax, and an MCP server so agents can drive it. caddy/ui runs as two containers next to your existing Caddy, reads and writes your Caddyfile directly, and uses Caddy's /adapt API to validate before reload - no Docker socket required. Each one edits the config the underlying server already reads, so your files stay the source of truth and you can drop the UI without unwinding anything. Undo is a first-class feature across all three - timestamped backups with optional Git history, config version compare and restore, Caddyfile snapshots with one-click rollback. Observability is where they diverge: Traefik Manager does CrowdSec and a visual route map, Nginx UI does server metrics, caddy/ui streams access logs over SSE and pulls p50/p95/p99 off Caddy's Prometheus endpoint. Maturity spread is wide - Nginx UI has 11.3k stars, caddy/ui has 4 and was built in a single Claude session - and caddy/ui ships with auth off by default, so set CADDY_UI_USER and JWT_SECRET before it goes anywhere near a public interface. Calvin #2: Wagtail 8.0 is hot off the presses Link: https://github.com/wagtail/wagtail/releases/tag/v8.0 Custom base page models are now supported, so projects aren't locked into subclassing Wagtail's Page as shipped (Matt Westcott). New v3 REST API handles both read and write CMS operations, a first for Wagtail's API. A global registry for permission policies, plus full customizability for the remaining page views via PageViewSet. AVIF and WebP images are no longer auto-converted to PNG by default, a real behavior change to watch on upgrade. Five security fixes: page admin API restrictions, document identification by SHA1 hash, descendant collections in the Documents/Images API, snippet copy permissions, and the page translation endpoint. Formalized Django 6.1 support, and CI now runs on uv with a lockfile. Sponsor: Logfire from Pydantic Your AI agent failed at 2am. Was it the model? A tool call? The database? Most observability tools can't tell you, because they only see part of your stack. Pydantic Logfire sees all of it. One trace across your agents, LLMs, APIs, and database. Down to the infrastructure: services, Kubernetes, and hosts. It's built on OpenTelemetry, with SDKs for Python, TypeScript, and Rust, and it works with any OTel-compatible language. Every prompt, token count, and cost, right next to your vector searches and API calls. You query everything with Postgres-compatible SQL. And so can your coding agent, through the Logfire MCP server. Stop guessing. Read the trace. Pydantic Logfire. AI, it's still just engineering. Visit pythonbytes.fm/logfire today and sign up today. Get 10M records free every month, no card required. You can even click “Onboard with your coding agent” to copy a prompt to have claude or codex integrate Logfire into your app. Thanks to Pydantic for supporting the show. Calvin #3: RISC-V is now officially supported by CPython Link: https://blog.python.org/2026/08/riscv-now-officially-supported/ CPython added RISC-V as a tier 3 platform under PEP 11, specifically the 64-bit Linux target riscv64-unknown-linux-gnu. RISC-V is an open ISA anyone can implement, unlike x86 and ARM, and its market is projected to quadruple by 2032. The RISE Project donated real RISC-V machines for buildbots; the author's work was funded by a Sovereign Tech Agency fellowship. What changes: the port is now a maintained compatibility target, so CPython changes are less likely to quietly break it. What doesn't: no python.org installers, no binary wheel parity for native extensions. Next up: RISC-V runners in CPython CI for pre-merge feedback, then a push toward tier 2, plus architecture-specific optimizations. The ask is testing. If you have RISC-V hardware, build CPython, run your test suite, file what breaks. Tier 3 is the weakest support tier. PEP 11 tier 3 requires a core developer contact and a buildbot, but failures on tier 3 platforms explicitly do not block a release. Saying "ongoing CI/testing expectations" oversells it. The honest bit is "someone is now on the hook for it, and breakage gets noticed," not "it's guaranteed working." Worth the caveat that this is Linux SBCs, not microcontrollers. A VisionFive 2 counts, an ESP32-C6 or Pico 2 does not. Those are 32-bit non-Linux parts where MicroPython is still the answer. Michael #4: Django's annual releases make every version an LTS Starting with Django 2028, Django will move to one January feature release per year, adopt calendar-based version numbers, and support every release for three years. The old distinction between standard and LTS releases disappears, giving teams a predictable annual upgrade path that aligns more closely with Python's own release and support cadence. Every Django release becomes the safe, long-supported choice, so teams no longer need to wait for a specially designated LTS version or absorb two years of changes at once. Each release gets one year of mainstream bug fixes followed by two years of security and data-loss fixes. New releases support the three latest Python versions and add the next Python release during their first year. Calendar versioning begins with Django 2028, followed by Django 2029 and so on. Three Django versions will be supported at any time, giving third-party packages a clearer rolling target. Nothing changes before 2028, and existing commitments for Django 5.2 LTS and 6.2 LTS remain in place. Extras Calvin: The Python docs now document the time complexity of built-in types https://docs.python.org/3.16/library/time-complexity.html Thinking in Python - Bruce Eckel's free book https://thinkinginpython.com/ Michael: prune_uv_pythons.py - Prune uv-managed Python installs, keeping only the newest patch per minor version Runs automatically in my system “upgrade” script: upgrade-output-2026.png Started using Ollama cloud models for my Hermes assistant. Thanks to Jeff Triplett I learned they are not just local models. Joke: The Tao of Programming - Book Seven: Corporate Wisdom

Jerry Murdock is the Co-Founder of Insight Partners, which manages over $90 billion in assets. Jerry personally backed companies including Twitter, Nest and Docker, while Insight's portfolio includes giants such as Shopify, Wiz and monday.com. Across three decades, Insight has helped produce 55+ IPOs and become one of the most powerful technology investment firms in the world.  AGENDA: 00:00 — Is the AI Bubble About to Burst? 11:00 — Will Half of All Neoclouds Disappear Within 36 Months? 13:00 — Can Open Source Models Actually Beat OpenAI and Anthropic? 22:00 — Are We Entering the Golden Age of Cybersecurity Attacks? 32:00 — Have AI Valuations Completely Lost Touch With Reality? 42:00 — Is AI About to Wipe Out an Entire Generation of SaaS & Private Equity? 47:00 — Should We Ban U.S. Chips From Being Exported to China? 48:00 — Should We Be Fearful of Open-Source Chinese Models? 49:00 — Will Continuous Learning Kill Every AI Model We Use Today? 53:00 — Why Microsoft Remains a Mega Buy 56:00 — Which Mag 7 Giant Would You Short — and Is Apple's AI Strategy a Disaster?  

What does an AI-native CI look like? The Semaphore team joins me to talk about their focus on making your agent harness the gateway to testing, fixing, and deploying your code.Video podcast version here: https://youtu.be/HzXAdtVrW70★Show Links★Semaphore https://semaphore.io/Semaphore open source https://github.com/semaphoreio/semaphoreSLSA Security Checklist https://slsa.dev/Creators & Guests Marcos Filipe - Guest Cristi Cotovan - Editor Bret Fisher - Host Beth Fisher - Producer Marko Gaćeša - Guest (00:00) - Introduction (00:29) - AI Native CI Vision (04:54) - Bret's Update (09:03) - Semaphore AI Journey (22:39) - Open Source Platform Shift (37:28) - Plugins CLI MCP Skills (43:11) - Work Until CI Is Green (45:42) - Test Boxes and Agentic CI Loop (57:39) - Sandboxing and Least Privilege (01:10:12) - Roadmap SLSA and Getting Started

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. THIS IS THE TWEET Q WANTS YOU TO SEE: https://x.com/justh0dl/status/2086202393998291138AOBWhat a fucking weekKeyOS v1.3.1 now publicly availableSomething exciting to share on Friday's FTF (delayed by 2 weeks)NEWSThe Coldcard entropy catastropheSources: Coinkite technical backgrounder / The Rage, L0la L33tz / TRM Labs / coldcard.rip / cktripwire.com / Bitcoin Magazine victim surveyEXPLAINERThe largest self-custody theft on record, and it traces back to a single wrong conditional. In March 2021 a build guard checked whether Coldcard's hardware random number generator was defined rather than whether it was enabled, so seed generation silently fell back to a deterministic software PRNG. Every seed made on an affected device from that point carried roughly 40 bits of entropy on Mk2 and Mk3, and about 72 on Mk4, Mk5 and Q, instead of the intended 128. That is guessable. Someone did the maths offline, derived the addresses, checked them against the public chain, and swept everything with a balance. Somewhere between 1,400 and 1,800 bitcoin gone, depending on whose forensics you trust, with a median victim loss of one BTC. The part people keep missing: updating the firmware does not fix an existing seed. A weak seed is weak forever.ACTION FOR LISTENERSMove funds to a brand new seed BEFORE upgrading firmware (Lopp's guidance, on reports of update problems).Use high fees. If you see your own coins in the mempool, the attacker opted into RBF and you can outbid them. Window is minutes.Multisig users: consider a private mempool like Marathon's Slipstream.Keep the device; the UID may prove ownership in any recovery process.Updating does NOT fix an existing seed. A weak seed is weak forever.You are exempt only if you added 50+ fair independent private dice rolls, or used a strong unique BIP39 passphrase stored separately.BTCPay Server: unauthenticated LND macaroon theft, actively exploitedSources: BTCPay security advisory / v2.4.2 release / CoinDesk / TFTCCRITICAL FRAMING NOTE: this is ONE story, not two. The "BTCPay bug" and the "LND credential exploit" are the same event. The vulnerability is the macaroon leak. The Aug 8-9 wave of coverage is follow-up hardening, not a new incident. Do not present them separately.REMEDIATION (updating alone is NOT enough)Update to v2.4.2. Verify "2.4.2" in the footer.Update NBXplorer to 2.6.10+.Revoke and regenerate LND macaroons. Updating stops new theft but does nothing about already-stolen credentials. Deleting files is insufficient; the macaroon root signing key must be destroyed at node level. v2.4.2 does this automatically for standard Docker deployments. Custom reverse proxies, separate Tor services or port forwarding must rotate manually.Move funds out of any BTCPay-generated on-chain hot wallet and recreate it.Update LND to 0.21.1. Audit for unrecognised channel closures, unknown peers, unexplained balance changes.SIDE EFFECT WORTH FLAGGING: v2.4.2 removes public LND API access on Docker deployments, which breaks remote wallet connections such as Zeus connecting to your own BTCPay node. Intentional, no restoration timeline published.BREAKING CHANGE: Greenfield Basic authentication disabled by default five minutes after account creation (#7492). BTCPay: "We are not aware of any user impacted by this breaking change, as API Keys authentication is generally used."Boltz suspends all swaps indefinitelySources: Boltz statement / canary.boltz.exchange / The Defiant / TFTC / Bull Bitcoin statementCORRECTION TO THE COMMON FRAMING: Boltz has not shut down. It suspended swap services indefinitely. And the canary sequence runs the opposite way to the rumour: lapsed → suspended → renewed clean.The Bitcoin Red TeamSources: Calle and Rob Hamilton on Nostr/X / Bitcoin Magazine / CoinDesk / TFTC / OpenSats Red Team FundSUMMARYThis is the story that explains the other four. After the Coldcard exploit, Calle and Rob Hamilton pointed frontier AI models at the open-source Bitcoin stack and started auditing everything. In 108 hours, 25 developers scanned 501 projects and produced 7,958 findings, 1,280 of them rated high or critical, at a compute cost north of 58,000 dollars. They found the BTCPay bug's neighbours, and Boltz cited exactly this dynamic when it switched itself off. The uncomfortable symmetry is that the same capability doing the defending is what an attacker almost certainly used on Coldcard in the first place. And the bottleneck turns out not to be finding bugs, it is telling anyone: only 19.5% of the projects they scanned even have a SECURITY.md file, and only 13.1% list a security contact. The scanners move at machine speed. Responsible disclosure is still hunting around for an email address.BIP-110: the fork that mined two blocks and frozeSources: bip110monitor.com / Peter Todd code review / Aaron van Wirdum, Bitcoin Magazine / Lopp's Layman's Guide / Saylor essay / CoinDeskRELEASESBitcoin core / protocollibsecp256k1 v0.8.0 - 2026-08-03Adds a native Silent Payments (BIP-352) module directly into the crypto library nearly every self-custody wallet builds on, plus up to ~11% faster signature verification. Quietly the most consequential positive release of the fortnight: it lowers the bar for every wallet to ship reusable static receive addresses.Bitcoin Knots v29.4 - 2026-08-08Non-urgent maintenance: fixes a chainstate DB bug causing repeated large rewrites, and adds corruption-detection safeguards around BIP-110 mandatory signaling. No critical fixes. (No Bitcoin Core release in window; latest is v31.1 from 2026-07-08.)Hardware / signingColdcard Firmware 4.2.0 (Mk2/Mk3) - 2026-08-03The patch for the entropy catastrophe. Affected ranges: Mk2/Mk3 4.0.1 through 4.1.9; Mk4/Mk5 all before 5.6.0; Q all before 1.5.0Q. Companion fixes shipped the same day: 5.6.0 Mk4/Mk5, 1.5.0Q, 6.6.0X Edge, 6.6.0QX Edge Q. Updating does NOT fix an existing seed - changelog says Mk3 users "must regenerate any seeds made on earlier versions as their entropy is critically low at just ~40 bits." TAPSIGNER, OPENDIME and SATSCARD unaffected.Krux 26.08.0 - 2026-08-04Maintainer odudex is stepping down and the project may be archived. "Krux was not created by me: Jeff started it and passed it on to me, and now it is my turn to pass the torch." On succession: "Krux may be carried on by another maintainer, if a proof-of-work backed Krux contributor accepts the role. Otherwise the Krux project will be put in sunset mode and gracefully archived in a few months." Cause is hardware, not drama: "K210 chips are no longer produced, and Canaan dropped the Kendryte line entirely." Substantial release regardless: fixes a heap buffer overflow in the camera entropy module, adds stricter PSBT fee-calculation checks, replaces the Python UR stack with a faster C module, and makes Krux Installer fully offline.Frostsnap v0.3.0 - 2026-08-05FROST threshold-signing device ships reproducible/deterministic builds and "a fresh release signing key as part of an overhauled release-signing pipeline." Well timed in a fortnight where "can you verify what is running on your signer" is the whole conversation. Catch: the new key breaks in-place Android updates, so direct-APK users must uninstall, reinstall, and re-visit their threshold devices to restore.Trezor Suite v26.7.4 - 2026-08-04Lowers minimum Normal-priority fee rate to 0.2 sat/vB and ships updated Safe 7/5/3 and Model T firmware with security improvements.BitBoxApp 4.51.4 - 2026-08-07Bundles new BitBox02 firmware v9.26.5.Specter Desktop v2.1.11 - 2026-08-09Genuinely security-relevant: adds auth and CSRF protection to the HWI bridge settings, restores validation of active API tokens so revoked JWTs are rejected, and warns that Specter's auth layer does not encrypt the data folder. Also ships an in-app Coldcard Mk3 seed-entropy advisory.Bitkey source/2026-08-02-0031 - 2026-08-02Block's consumer hardware wallet, routine source drop.LightningBTCPay Server v2.4.2 - 2026-08-07Actively exploited, funds already stolen. "This release contains fix of a critical vulnerability that is being actively exploited. You need to update as fast as you can." Unauthenticated remote .macaroon disclosure for LND, plus a TOTP 2FA bypass via Greenfield Basic auth. Requires NBXplorer 2.6.10. Breaking change: Basic auth disabled by default five minutes after account creation. See News item 2 for full remediation.lnd v0.21.2-beta.rc1 and v0.20.3-beta.rc1 - 2026-08-08Not security releases and not related to the BTCPay exploit. Migration/stability fixes only: KV-to-SQL payment migration edge case, channeldb migration recovery, invoice handling, data races, bounded memory on graph sync.Zeus v13.1.3 - 2026-07-27Adds LND v0.21.1-beta support for embedded and remote nodes; patches known vulnerabilities in the ws, js-yaml and markdown-it dependencies. (Note: Zeus also shipped an unreleased swap-security sprint on 08-04 - verify…

Welcome to episode 367 of The Cloud Pod, where the forecast is always cloudy! Justin, Ryan, and Matthew are in the studio this week and ready with a lot of news, including passkeys (we know, they've had a rough week), Secrets Manager, Vector Search, and Glimmer (no, not my second favorite character from She-Ra), and even…wait for it…undersea cable news!  We've got a lot to cover, so let's get started!  Titles we almost went with this week AWS Secrets Manager Jenkins Rotation Finally Claude Enterprise Hooks a Ride on Data Loss Prevention Passkeys Take the Wheel, SMS Rides Off Into the Sunset Claude Code Says Trust Falls Are Over Muse Glimmer Shines While Meta’s Wallet Dims Zuckerberg Bets Big on Open Weights, Loses on Free Cash Flow AI is persistently in the news How many ways are there to run vector search in AWS, now 1 more Vector Search is the new Docker on AWS… how many ways are there to run it AWS Says “You get a Vector Search, and you get a Vector Search” You say you’re a Cloud Azure, but “Azure Network Router Appliance” says otherwise Claude now tells the world, I did the AI Slop Open, Closed, Open; Zuckerberg is on the AI Revolving Door Anthropic triples everyone’s productivity with Automode A big thanks to this week's sponsors: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our Slack channel for more info. AI Is Going Great – or How ML Makes Money  01:40 Inference hooks: inline data loss prevention for Claude Enterprise  Anthropic launched inference hooks in beta for Claude Enterprise, providing inline data loss prevention across chat, Claude Code, Claude Cowork, and other Enterprise surfaces through a single configuration point. Technical approach: every inference request routes through a signed WebSocket connection to a customer-controlled security server; Claude sends the prompt and context before generation begins and waits for an allow/deny verdict before proceeding. The same inspection applies to tool call responses, including those from MCP connectors, skills, and plugins. The feature uses an open, webhook-based protocol with a published schema, allowing integration with existing DLP vendors such as Netskope, Palo Alto Networks, Proofpoint, and Zscaler, or custom in-house security servers, without requiring separate per-product integration work. Rollout controls include shadow mode (log without blocking), role-based exclusions, and percentage-based rollouts, along with configurable failure-policy tolerance and timeouts to match organizational risk requirements. This addresses a gap where inline enforcement was previously limited to Claude Code’s client-side hooks, giving compliance teams a unified enforcement layer for sensitive data across all Claude Enterprise channels.  Documentation is available

Container sind überall. In der Cloud, auf deinem Laptop, im Home Lab und ziemlich sicher auch in Systemen, die du täglich benutzt. Aber wie viel Isolation steckt wirklich in einem Container? Wann reicht ein klassischer Docker-Container, wann brauchst du eine VM und warum tauchen plötzlich Begriffe wie Micro-VMs, Firecracker, RunC oder GVisor überall auf? Genau an dieser Stelle wird es spannend, denn hinter dem scheinbar einfachen docker run wartet ein erstaunlich tiefes Rabbit Hole.In dieser Episode sprechen wir mit Sébastien Pahl, Co-Founder der Firma hinter Docker und heute bei Cloudflare wieder tief im Container-Umfeld unterwegs. Gemeinsam zerlegen wir das Container-Ökosystem von unten nach oben. Wir klären, was Namespaces und C-Groups im Linux-Kernel eigentlich machen, wie sich Container, virtuelle Maschinen und Micro-VMs unterscheiden, welche Rolle LXC, OverlayFS, Systemd und RunC spielen und warum Docker der Container-Welt zum Durchbruch verholfen hat. Außerdem schauen wir auf Multi-Tenancy, Security, Kernel-Isolation, Hypervisor-Grenzen und die Frage, warum Bootzeit und Memory-Footprint für moderne Container-Plattformen so entscheidend sind.Besonders spannend wird es beim Blick in die Praxis. Wir sprechen darüber, wie ein maßgeschneidertes Init-System in Rust entsteht, wie AI beim Systems Engineering und beim Schreiben von Tests hilft und warum sich damit Produktionssysteme in wenigen hundert Millisekunden booten lassen. Wenn du Container, Docker, Micro-VMs und Linux-Isolation endlich wirklich verstehen willst, ist diese Folge dein Deep Dive.Bonus: Selbstfahrende Autos, Mars-Fantasien und Advent of Code schaffen es auch noch in die Episode.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

You’ll start with a travel charger that swallows 200-plus countries into one brick, then find out that macOS Tahoe 26.6 is quietly rebuilding your Spotlight index to get ready for macOS 27…and hear the theory on why Apple wants that churn finished before you upgrade. You’ll get talked into finally looking at Raycast (with a nostalgia lap through Quicksilver, LaunchBar, and Alfred), meet Brett Terpstra’s TerminalWidget as the GeekTool replacement you’ve been waiting a decade for, and learn how Paperless-ngx in Docker can run your entire paperless workflow off a Mac or a Synology. There’s Type Switch for anyone living in two keyboard layouts, LibraryThing for the book hoarders, Nixbook for putting an operating system with guardrails on an old Mac, wallet tracker cards that double as digital business cards, and Tailscale Grants so the family you added to your tailnet can’t go wandering your whole network. You’ll also stop fighting the Sonos app and just AirPlay to your speakers, and pick up the Siri fix that ends the “did you mean” death spiral: answer “the first one,” not by repeating yourself. Then it gets real. You’ll sit in on the question every geek eventually faces: Migration Assistant or nuke and pave? You’ll hear why a half-fresh install is the worst of both worlds, how keeping the old machine alive on your network turns a scary afternoon into a safe one, and where to log in and reclaim those third-party licenses when you hit your device limit. You’ll learn what those “free credit monitoring after a breach” offers are actually worth, why freezing your credit at all three bureaus costs nothing by law no matter how hard they upsell you, and the surprise bonus that a credit lock also blocks buy-now-pay-later signups. And when your Synology Assistant app goes Intel-only, finds.synology.com finds your NAS from the browser. Five new things, minimum, so whatever this week throws at you, don’t get caught. 00:00:00 Mac Geek Gab 1154 for Monday, August 10th, 2026 August 10th: National Spoil Your Dog Day MGG Monthly Giveaway – Win a license to Yoink, Screenfloat, Transloader, or DeskMat from Eternal Storms Cool Stuff Found 00:00:01 John-CSF-Anker Nano Travel Plug Adapter for over 200 Countries 00:02:27 Hey there, Pilot Pete! 00:03:28 Allen-CSF-Raycast is the best app launcher, and it’s free! Alfred QuickSilver 00:07:11 macOS Tahoe 26.6 optimizes the Spotlight index to prepare for macOS 27 00:12:26 Jessi-CSF-TerminalWidget to replace GeekTool 00:14:58 Max-CSM-Type Switch for people who regularly type in more than one language 00:16:15 Paul-CSF-Paperless-NGX runs in a docker container 00:18:43 Mike-CSF-Pause Take9 00:19:57 Also…beware of recurring in-app purchases from scammy apps. 00:22:58 KingKelson-CSF-1130-LibraryThing for organizing books (and Kevin, too) 00:23:53 Don't sleep on Sonos with AirPlay 00:28:02 Does Siri on HomePod get smarter with Apple Intelligence? 00:30:22 You can tell Siri AI “the first one” or “the third one” when it gives you an audible list of options for its (potential) translation of your commands 00:33:33 Siri AI is still misgendering people despite the update to their Contact record 00:35:42 Joel-CSF-Ridge Wallet Tracker Card Rolling Square AirCard 00:38:44 CSF-TOS 7.0 for TNAS— Full Native AI NAS OS (leveraging OpenClaw) 00:44:36 WillRun4Fun-CSF-Nixbook for putting linux on older Macs 00:47:04 Jonathan-Tailscale Grants allows you to control what your family can do Sponsor 00:50:13 SPONSOR: CleanMyMac. Get Tidy Today! Try 7 days free and use our code MACGEEK for 20% off at https://clnmy.com/MACGEEK Your Questions Answered – feedback@macgeekgab.com 00:52:29 Mark-Nuke and Pave vs. Partial Migration? 01:06:46 Andy-Should I take the credit monitoring service offers? PIlot Pete says: freeze your credit bureau accounts TransUnion Freeze Equifax Freeze Experian Freeze 01:13:21 Joe-How do I find my Synology server? 01:17:40 MacPaw and Liquid AI bring On-Device AI to Millions of Mac Users 01:18:48 iNET App for local scanning 01:19:47 MGG 1154 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

An old Linux box powers down for the last time as we unwind its history, and the entire show, back to the beginning.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD

We discuss the acquisition of Mirantis by the neocloud company IREN, and use it to examine the current market for infrastructure and AI deployment. We look at Mirantis' consulting business, Kubernetes distribution, and its earlier acquisition of parts of Docker, including the registry and enterprise components. We also consider whether trusted registries could play a role in distributing agents and tools for AI systems, and we compare this idea to package management. We get into an interesting discussion on neoclouds and managed agent providers as part of the operational layer for running AI workloads, including GPU capacity, model hosting, and repeatable deployment. Transcript: https://otter.ai/u/ZBtX6hwwHIqqOBbLDiJwM66wFuM?utm_source=copy_url

Pre-show: The Overcast “kicking machine” Adam Wunn’s blog post Previously in ATP #324: Automatic Kicking Machine Follow-up: Marco gives John the gift of transcript search in build #1169 [The system works! — Ed.] Build #1172 includes Siracusa Replay Mode Be wary of Sonos + Pi-hole or other ad blockers (via Will Smidlein) Ubiquiti High Availability Redundant Power Supply USP-RPS SmartPower Cable USP-Cable “Odd” RAM amounts (via Joe Lion) Multiple Apple accounts (via TJ Usiyan) Some science about Sony’s axe of PlayStation discs SuperDuper 4 Previously in the SuperDuper blog… Apple Upgrade Program John Voorhees at MacStories The Verge with example numbers Apple Upgrade vs. iPhone Upgrade “Restricted Mode” isn’t about this Is it worth it? Joe Rossignol answers macOS 26.4 adds Battery Charge Limit Ask ATP: What are good backup strategies? (via Todd Vaziri) Backblaze (former sponsor) Tailscale (former & future sponsor) What the heck is a Docker container? (also via Todd Vaziri) Wizard zine Post-show: Casey is car shopping again Visor AutoTempest Bimmer.work Driveway Safari extension screenshot Members-only ATP Overtime: OpenAI hardware progress Codex Micro Gruber Quinn’s take Stack Overflow keyboard Rumored speaker Sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code atp. Zocdoc: Find the right doctor, right now. Become a member for ATP Overtime, ad-free episodes, member specials, and our early-release, unedited “bootleg” feed!

Griffin Logue joined the 6PR Call team following North Melbourne's drought-breaking win over the Dogs. The former Docker spoke glowingly to former teammate David Mundy and pondered an even bigger drought-breaking win with a potential Fremantle Premiership.See omnystudio.com/listener for privacy information.

PHP Alive and Kicking – August 4, 2026 Hosts: Chris Miller (standing in solo while Mike is on holiday) Mike escaped to a beach so Chris hosts alone with special guest Derick Rethans. Expect date-time philosophy, PHP 8.6 goodies, Xdebug secrets, deprecation drama, and a heated debate about how to queue at a pub. From University Websites to Committing to PHP Derick traces his PHP origins back to his university days, building websites that needed database connectivity. ASP.NET was an option, but nobody wanted to pay for it — Linux was cool, and PHP was easy to start using. That combination hooked him early. His very first accepted contribution came from a practical need: everyone wanted centered Shockwave Flash films on their pages, back before CSS even existed. Centering meant knowing the width of a file, and while getimagesize() already existed (and still works today), it didn’t understand Flash. Derick added that support. From there he fell into bug triage, spending several years working through the issue tracker alongside Jani Taskinen (whose “Sniper” name still shows up in commit logs). For a long time it was just the two of them reproducing and fixing bugs — an important task that gets neglected easily, which is exactly how backlogs of a thousand issues and pull requests build up. The Eternal Problem of Date and Time Chris and Derick dig into why date-time bugs are so hard to triage: date algorithms are genuinely complicated, and it takes real time to figure out whether a report is a user’s mistake or an actual PHP bug. Crashes are easy to reproduce; subtle date arithmetic is not. The core philosophical question is “what does adding a month even mean?” If it’s January 31st and you add a month, PHP historically just bumps the month number, producing February 31st, which overflows to March 2nd or 3rd. But most people who add a month really just want the next month and don’t care about day counts — so “next month” is conceptually distinct from “add a month.” Derick has been brainstorming better interfaces so people can’t make these mistakes, though it’s a lot of work. They touch on Carbon as a unified interface people understand, but agree that making Carbon the language standard isn’t the right move. At the language level you need something that works for everybody and ideally doesn’t break existing code. What’s Coming in PHP 8.6 The first new time class is landing: a Duration object representing seconds and nanoseconds within a specific range, living in the Time namespace. It’s a trial run for new APIs and immediately useful — for example, passing a duration to sleep or to timeouts in the new polling API, rather than juggling raw integers. Another highlight is partial function application. With the pipe operator from 8.5, piping into anything that takes more than one argument meant wrapping it in a closure (and extra parentheses because of how the parser works). Partials let you pre-fill some arguments so the resulting callable accepts just one — perfect for pipelines. There’s also TLS session resumption support for streams, which lets OpenSSL shortcut the expensive session setup on repeated TLS connections. Bigger stream-layer work by Jakub Zelenka of the PHP Foundation is underway too, but that’s a huge job on old code and won’t make 8.6 — Chris and Derick both note the pain of keeping a year-and-a-half-long project in sync with the master branch. Xdebug in 8.6 and the Xdebug Cloud Xdebug’s recent headline feature (shipped with 8.5) is native path mapping, which lets you configure remote-to-local path mappings inside Xdebug itself instead of your IDE — handy when your code runs in Docker or on a dev machine. Feedback since release is driving quality-of-life improvements. For 8.6, relatively little is broken, though a change to how PHP reports line numbers needs untangling so debugging stops on the correct lines. Derick also has a new code-coverage implementation that gives better, more correct results at roughly a 10% speed cost. He’d rather ship correct output than fast output — and points out that PHPCov may be quicker but skips path and branch coverage entirely. The pair discuss how branch coverage doubles your test count at every branching point, and how Xdebug has a hard limit (around 64K) to avoid running out of memory on pathological code. Finally, Chris asks about Xdebug Cloud — a paid service that enables debugging where networking otherwise wouldn’t, such as remote teams sharing a single development machine (an SSH tunnel only lets one person listen on the debug port). It’s £15/month or £150/year for an individual, priced similarly to Packagist’s private packages, and helps fund Derick’s ongoing Xdebug work. He plans to do more marketing about it this year. Deprecations, Consensus, and Two-Thirds Votes Every year PHP collects a big list of deprecations — around 20 to 25 this cycle — voted on separately. Derick is more reluctant than most to approve them, arguing many offer no clear user benefit. His examples: deprecating metaphone() (whose suggested Composer replacement ironically relies on the internal function) and the dechunk filter (which can’t really be removed because HTTP streams use it internally). The list() deprecation is another sore point — Chris uses it, and the vote is tied 21–21 with one abstention, so it won’t pass under the two-thirds rule. Derick reminds everyone that “yes” needs to be twice “no.” Returning from a finally block is another candidate that makes little sense to use but hurts nothing to keep. Chris and Derick reflect on how voting was originally meant to confirm consensus reached on the mailing list, not to be the decision itself. A 50%+1 result isn’t consensus; two-thirds is the compromise, even if true 75–80% agreement would be ideal. Joe notes many deprecations are groundwork to reserve keywords for future RFCs — which raises the perennial question of how you measure real-world usage across Composer’s dependency chains. Infrastructure, the Website, and the Next Generation The PHP infrastructure has been converted to an Ansible-based repository over the last year and a half (largely by Derick, with help from Joe), replacing a wild mix of individually maintained servers — one was still running FreeBSD 4.3. They’ve also migrated to a new CDN, which brought surprises: a default one-month cache, ignoring the query string in cache keys, and even dark/light mode being an organization-wide setting rather than per-user. On the website side, everything currently lives in a single root directory of 1999-vintage code, and there’s a long-pending patch to restructure it. The PHP Foundation’s ambassadors program — with speaking, marketing, and research streams — aims to make the very techy php.net homepage speak to CTOs and newcomers about who uses PHP and why it’s a thriving project (only 31-ish years old, versus C’s 60). The conversation closes on caretakers and the next generation of contributors. Triage, they stress, doesn’t require deep internals knowledge — just reading reports to see if they still make sense is hugely valuable. Security and infrastructure access necessarily stay gatekept for trust reasons, and both agree a bus factor of one (or even two) isn’t good enough. Chris admits he’s struggling to name people who are both nice and know infra. Links from the show: Join us live in Discord — discord.phparch.com Watch live on YouTube — youtube.com/phparch PHP Tek Conference 2027 — Call for Papers now open PHP Arch Swag Store — store.phparch.com Xdebug — and Xdebug Cloud for shared debugging Magazine discount code ALIVE3 — three free months on an annual digital subscription Host: Chris Miller X: @ccmiller2018 Mastodon: @miller@phpc.social Bluesky: @ccmiller2018.bsky.social PHPArch.me: @miller Mike Page Mastodon: @MikePageDev@phpc.social PHPArch.me: @mikepagedev Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – the team is available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ OurCVEs Your security posture, on autopilot with OurCVEs CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. PHP Architect Consulting Your PHP codebase deserves a partner, not a contractor PHP Architect provides long-term technical partnerships for organizations that need senior-level PHP expertise that you can depend on. https://www.phparch.com/consulting/ Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post PHP Alive and Kicking 2026.08.04 appeared first on PHP Architect.

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats. Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted. Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Docker and Sandboxing AI Agents appeared first on Software Engineering Daily.

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats. Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted. Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Docker and Sandboxing AI Agents appeared first on Software Engineering Daily.

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats. Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted. Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Docker and Sandboxing AI Agents appeared first on Software Engineering Daily.

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats. Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted. Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Docker and Sandboxing AI Agents appeared first on Software Engineering Daily.

The most useful coding agents can mutate their environments by downloading packages, writing files, and connecting to services across the network. However, that freedom also presents dangers, and promises to usher in a new wave of security threats. Docker recently announced Docker Sandboxes, which give each agent its own isolated micro VM while preserving the familiar ergonomics of a container. A standard container shares the host’s kernel, but a micro VM emulates hardware and runs its own kernel, giving a stronger security boundary around code that cannot be trusted. Mark Cavage is the President and COO of Docker, and he previously worked at companies including Stripe, AWS and Oracle. In this episode, Mark joins Gregor Vand for a wide-ranging conversation that includes why agents break the immutability assumptions containers were built on, how micro VMs differ from both containers and traditional VMs, and the still-unsolved challenge of giving agents scoped, trustworthy access to sensitive services and data.Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post Docker and Sandboxing AI Agents appeared first on Software Engineering Daily.

Sebastian und Michelle sprechen in dieser Folge über Cluster-Architektur mit Kubernetes: was das Tool leistet, welche Betriebsvarianten es gibt und für wen sich der Einsatz überhaupt rechnet. Ausgangspunkt sind die typischen Gründe für k8s – Skalierung, Ausfallsicherheit durch Health Checks und Rolling Updates sowie Infrastructure as Code. Danach geht es um die Frage self-hosted (k8s, k3s) oder managed (AWS, GCP, Azure) und um den Unterschied zwischen plain Kubernetes und Red Hat OpenShift. Ein zweiter Schwerpunkt liegt auf dem Zuschnitt der Umgebung: wie viele Cluster sinnvoll sind (pro Stage, pro Produkt) und wie innerhalb eines Clusters mit Namespaces und Network Policies getrennt wird. Zum Schluss diskutieren die beiden Alternativen ohne Kubernetes und die Voraussetzungen, die im Team erfüllt sein müssen.   **Zusammenfassung** Kubernetes verwaltet containerisierte Anwendungen und arbeitet mit Docker zusammen – die beiden Tools sind keine Konkurrenz Hauptargumente für k8s: Skalierung und Ressourcennutzung, Verfügbarkeit über Health Checks (Liveness, Readiness), Rolling Updates und Rollbacks, Konfiguration als Code in YAML Self-hosted (k8s, k3s) bedeutet eigene Versions-Updates und Skills in Systemadministration und Provisionierung; Managed Cluster kosten mehr, reduzieren aber Maintenance und erhöhen die Verfügbarkeitsgarantie OpenShift bringt eigene CLI, UI, Monitoring und Enterprise Support mit (Open-Source-Variante: OKD), plain k8s läuft dafür auf schlankerer Hardware Anzahl der Cluster: mindestens eine Trennung von DEV und PRD, größere Organisationen provisionieren pro Produkt x Stage – jedes zusätzliche Cluster bedeutet mehr Aufwand für Updates, Monitoring und Provisionierung Trennung nach Produkt statt nach Team, weil Zuständigkeiten sich ändern; Namespaces sind die leichtgewichtige Alternative zur vollen Isolation Network Policies funktionieren wie Firewall-Regeln für Pods (Ziel/Quelle, ingress/egress); standardmäßig ist alles erlaubt, sobald ein Pod eine Policy hat, gilt für ihn Deny-All – ein Deny-All pro Namespace ist deshalb Pflicht Alternative für kleine Setups: ein oder zwei VMs mit mehreren Instanzen hinter einem Loadbalancer, Rolling Updates per Skript; entscheidend sind Produkt, Verfügbarkeitsanspruch und vorhandenes Know-how   **Links** Episode #14: Kubernetes https://inwt.podbean.com/e/14-kubernetes/ Training Course by The Linux Foundation: Introduction to Kubernetes (LFS158) https://training.linuxfoundation.org/training/introduction-to-kubernetes/  Kubernetes: https://kubernetes.io/ k3s: https://k3s.io/

Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun will discuss some of the new features around Docker, Testcontainers and the Spring Boot plugin.You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.Show Notes:The Java DocumentaryInside Java Podcast 63: Spring AI with Dan VegaClone. Run. Done: Dan's YouTube VideoSpring Boot Docker Compose SupportPaketo BuildpacksShow Resources:springofficehours.io - episodes, schedule, communitySpring Developer on YouTube - join us live every MondayDan VegaDaShaun CarterThe Spring Blog - the news we cover each week

Send us Fan MailDigital games feel convenient until you ask one uncomfortable question: do you actually own what you paid for? We come back after a couple weeks off and start with real life catch up, because the stuff outside gaming shapes how we think about it. Christopher talks home lab progress and why learning Linux, Docker, and Proxmox scratches that “build it yourself” itch, plus the hands on work of running Cat 6, setting up a PoE switch, and dialing in a home network. We also get into collecting anime DVDs and why physical media still hits differently when everything else is turning into a subscription.Lexi shares what the last few weeks have really looked like, including a deer hitting her Tesla, getting it repaired, and then going straight into a major surgery recovery. We talk through the parts people don't warn you about: long anesthesia time, mobility limits, the mental side of being dependent on help, and the hidden expenses after the surgeon's quote like lymphatic massages, compression confusion, and simple recovery care that somehow never gets explained upfront.Then we shift into the bigger topic: the future of gaming platforms. We break down Xbox layoffs, studio pressure, and why Game Pass can be framed as a “failure” when growth targets miss, even if players love the value. From there, we compare Steam's long term library trust to console storefront uncertainty, dig into digital licensing, and react to the idea of “physical” preorders that are really just a box with a download code. We also debate the push toward $80 games, what it means for GTA 6 and beyond, and whether PC gaming and handheld PCs are slowly becoming the main platform as console pricing and restrictions climb.If you've been feeling weird about where gaming is headed, you're not alone. Subscribe, share this with a friend who cares about game ownership, and leave us a review with your take: are you sticking with consoles, moving to PC, or going back to physical? Support the showhttps://www.carolinaotakus.com/

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Kevin Ball or KBall, is the vice president of engineering at Mento and an independent coach for engineers and engineering leaders. He co-founded and served as CTO for two companies, founded the San Diego JavaScript meetup, and organizes the AI inaction discussion group through Latent Space. Please click here to see the transcript of this episode. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

AI agents have shown remarkable potential to function as persistent digital assistants that are capable of monitoring data, managing communications, and taking action autonomously over long periods. OpenClaw was one of the first serious attempts to fulfill that vision, connecting frontier coding agents to messaging platforms like Slack and WhatsApp and letting them run continuously in the background. However, OpenClaw largely set aside questions of security to pursue that vision, leaving credentials exposed in the agent’s environment and giving agents broad access to data and services far beyond what any given task required. NanoClaw is an open source project that takes a zero trust approach to agent orchestration. Rather than relying on instructions to constrain agent behavior, it isolates each agent in its own Docker container, keeps credentials entirely outside the agent’s environment, and enforces human-in-the-loop approval for sensitive actions. Gavriel Cohen is the founder of NanoClaw and he joins Kevin Ball to discuss the security architecture behind NanoClaw, how the agent sandbox and proxy model work in practice, how agents communicate with each other and with the host orchestration process, how the project approaches context window management and long-lived agent sessions, and more. Sponsorship inquiries: sponsor@softwareengineeringdaily.com The post NanoClaw and the Rise of Personal AI Agents appeared first on Software Engineering Daily.

Erik Seliger (Lead Product Engineer at Sourcegraph) and Robert Lin (Lead Platform Engineer at Sourcegraph) join FINOS Zenith Project Lead Peter Smulovics to break down the mechanics of Agentic Batch Changes. They reveal how implementing a multi-layered agent architecture solves the high economic cost of pure "vibe coding" by combining deterministic scripting pipelines with localized, token-efficient AI coding models.

Sam Docker is an award-winning wedding photographer based in Derby, known for his creative approach that blends documentary storytelling with editorial-inspired imagery. With over a decade of experience and more than 300 weddings photographed across the UK and Europe, Sam has built a reputation for creating authentic, emotional images that capture not just how a wedding looked, but how it felt. Drawing inspiration from art, fashion, architecture, and design, his work is modern, timeless, and deeply personal. Beyond photographing weddings, Sam is passionate about helping photographers build sustainable, long-lasting careers. He regularly educates and mentors creatives, sharing his thoughts on evolving your style, staying inspired, and adapting to an ever-changing wedding industry. Through his podcast and YouTube channel, Another Idea, he explores creativity, business, and the challenges of building a successful career in the creative industries. Based at Brickwork Studio in the heart of Derby, Sam continues to photograph weddings throughout the UK and internationally. His philosophy is simple: create honest, meaningful photographs that stand the test of time while constantly pushing himself creatively with every wedding he captures.

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In this episode of The Effortless Podcast, Dheeraj Pandey sits down with co-host Amit to dissect the dramatic acceleration of AI over the last few months and map out its next major frontier memory.  Moving past prescriptive frameworks and simple prompt-engineering, they unpack how autonomous agents are shifting the industry's focus from "token maxing" to "impact maxing," forcing a complete rethink of computing architecture. The conversation explores how memory within AI agents cannot remain a flat, horizontal file.  Instead, true enterprise intelligence requires a tiered hierarchy of memory spanning episodic, semantic, and procedural layers that mirrors human psychology and classical hardware caching. Drawing a striking parallel between token anxiety and electric vehicle range anxiety, they make the case for a hybrid CPU-GPU future where structured data, governance, and safety rollbacks are critical to preventing autonomous systems from breaking the bank or deleting databases. Key Topics & Timestamps 00:00 – Summer updates and AI's recent "quantum jump". 01:00 – Token maxing vs. impact maxing & autonomous React loops. 03:00 – Model reliability & using Grep, Sed, and Awk for dynamic context. 07:00 – Terminal text-matching tools explained simply. 08:00 – xAI, data center builds, and Neocloud disruption. 10:00 – Cursor's acquisition & the shift to autonomous harnesses. 12:00 – Desktop hurdles: Sandboxing, Docker, and local firewalls. 14:00 – Coding for the "paranoid path" and failure modes. 18:00 – The Core Thesis: Memory as AI's next major frontier. 21:00 – Caching tiers: KV cache vs. CPU/GPU caches and DRAM. 25:00 – Personal vs. enterprise memory: Turning data into goal-oriented meaning. 32:00 – Enterprise memory grammar: Ontology, identity, and work. 41:00 – Psychology of memory: Episodic, semantic, and procedural structures. 45:00 – Hybrid CPU-GPU needs & the EV range anxiety metaphor. 53:00 – Agent safety: Rollbacks, versioning, and transaction protection. 58:00 – Team intelligence: Bringing AI context to Slack and Teams. 1:01:00 – State vs. skill versioning: The derivative of human intelligence. 1:03:00 – Summary: Memory as data reduction & reinforcement learning. 1:09:00 – Final thoughts: Managing atoms vs. bits & the future of labor. Hosts: Amit Prakash – CEO and Founder at AmpUp, former engineer at Google AdSense and Microsoft Bing, with extensive expertise in distributed systems and machine learning. Dheeraj Pandey – Co-founder and CEO at DevRev, former Co-founder & CEO of Nutanix. A tech visionary with a deep interest in AI, systems, and the future of work. Follow the Hosts: Amit Prakash  LinkedIn – https://www.linkedin.com/in/amit-prakash-50719a2/  Twitter/X – https://x.com/amitp42 Dheeraj Pandey  LinkedIn – https://www.linkedin.com/in/dpandey/  Twitter/X – https://x.com/dheeraj  Share Your Thoughts Have questions, comments, or ideas for future episodes?

We've been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we're excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.The cloud was built for developers. But agents are now changing that.The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.However, agents don't have that luxury. Now in this new era of agents, everything has to be tighter.They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:At the time, Modal was just a teeny little company with a $17M Series A.Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don't fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.We go deep on Modal's AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal's capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.We discuss:* Why Kubernetes wasn't built for bursty AI workloads* How Modal started as a better runtime before becoming an AI cloud* Why Modal added GPUs before ChatGPT* The shift from developer experience to agent experience* Why observability matters when agents are writing the code* Elastic inference for custom models across audio, video, robotics, and comp bio* GPU snapshotting, cold starts, and why inference workloads are so bursty* Why RL rollouts can require 100,000 sandboxes* DeFlash, speculative decoding, and frontier-level inference performance* Auto Endpoints and making optimized inference easier to deploy* What Modal adds beyond vLLM, SGLang, and raw GPU rental* Modal's 17-cloud capacity pool and supercloud strategy* Networked sandboxes, sidecars, private IPv6, and RDMA* Serverless multi-node training for post-training and research workloads* Auto-research, model-guided sweeps, and agents launching GPU experiments* Compute strategy, capacity planning, and batch tiers* Why production agents need specialized sandboxes and hard guardrails* Modal's take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal BenchAkshat Bubna* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103* X: https://x.com/akshat_bModal* Website: https://modal.comTimestamps00:00:00 Introduction00:00:39 Modal's origin and why Kubernetes wasn't enough00:04:32 Developer Experience → Agent Experience00:06:21 Modal's AI cloud primitives00:09:14 Sandboxes, agent loops, and proto-Cognition00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes00:15:24 DeFlash, speculative decoding, and Auto Endpoints00:19:59 Production-grade inference beyond raw GPUs00:22:00 Background agents, Ramp Inspect, and the agent lifecycle00:24:08 Modal's 17-cloud supercloud strategy00:26:40 Networked sandboxes, private IPv6, and RDMA00:32:48 Multi-node training, post-training, and auto research00:37:36 Compute strategy, capacity planning, and batch tiers00:40:55 Open models, real-time AI, and production agent infra00:43:06 Hard guardrails, managed agents, and specialized sandboxes00:46:06 Why AI made infrastructure exciting again00:48:30 Model APIs, differentiated products, and agentic video00:51:50 CI, coding-agent infra, SDKs, and Modal Bench00:57:28 Closing ThoughtsTranscriptIntroduction: Modal, Series C, and the Art PartySwyx [00:00:00]: We're here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.Akshat [00:00:10]: Thank you.Swyx [00:00:11]: Your party yesterday was amazing.Akshat [00:00:15]: Yeah.Swyx [00:00:15]: From all the photos and all the swag.Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.Modal's Origin: A New Runtime Beyond KubernetesAkshat [00:00:39]: I first met Eric, who's the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It's because you have to run them on Kubernetes. Kubernetes is hard to manage. It's not built for burstiness and, custom images,Swyx [00:01:03]: YeahAkshat [00:01:03]: It has a terrible developer experience.Swyx [00:01:05]: And I'll, I'll interjectAkshat [00:01:06]: YeahSwyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there's a bit more of the story there from Spotify and all those things.Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.Akshat [00:01:26]: Yeah.Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it's a very useful primitive in itself. It's There's a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would've been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.From Serverless Containers to GPU WorkloadsSwyx [00:02:19]: Nice.Akshat [00:02:19]: We just didn't think it would be that big of a deal.Swyx [00:02:22]: Yeah, just like add A100.Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?Akshat [00:02:28]: Yeah. Primarily it's just, none of the tooling that was out there was built for, one, a really great developer experience, and also there's a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there's just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.Software-Defined Infrastructure and Decorator-Based DXSwyx [00:03:13]: Yeah. Yeah. Be nice. I don't know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.Akshat [00:03:22]: Yeah, the self-provisioningSwyx [00:03:23]: Self-provisioning.Akshat [00:03:24]: Yeah.Swyx [00:03:24]: Yeah. I can't even remember my own post.Swyx [00:03:26]: And then you put me on the landing page.Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?Akshat [00:03:37]: Yeah.Swyx [00:03:37]: Was that a big part of the originalAkshat [00:03:39]: YesSwyx [00:03:39]: Story or it was just like a DX layer?Akshat [00:03:41]: That was, really important because we really didn't want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you're doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that's more expressive and dynamic. and so yeah, that was always a very important part.Swyx [00:04:04]: Then the pushback is this is a DSL.Akshat [00:04:07]: Yeah.Swyx [00:04:07]: It's you're closed source. I am locked into Modal.Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you're using, how you're scaling things up, but you still own the code.Akshat [00:04:27]: And that's, that's been an important, part of our story, even as we do inference now.Swyx [00:04:32]: Yeah.Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there's very agent native primitives that are different than if I'm doing this myself, right?Developer Experience → Agent ExperienceAkshat [00:04:54]: We've changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that's not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.Akshat [00:05:38]: Yeah.Swyx [00:05:38]: Well, the negative thesis now is that nobody's looking at their code anymore, so there's no point.Akshat [00:05:44]: Yeah, people aren't looking at code. one thing we still see is really important is observability.Swyx [00:05:51]: Yeah.Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what's going on and, make judgment calls and whatnot. and that's I feel like, Maybe more important now than looking at the code itself.Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.What Modal Is For: AI Cloud PrimitivesAkshat [00:06:21]: Yeah.Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.Swyx [00:06:31]: People ask you, “What are you for?” You're like, “ I don't know. We can do this, we can do that.”Vibhu [00:06:36]: Well, I'd be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There's a lot you guys do, sandboxes, GPUs, everything. How do you answer?Akshat [00:06:46]: Modal is a cloud platform that's built for, where we've built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.Akshat [00:07:00]: But we're building a lot moreSwyx [00:07:02]: I noticed you didn't say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.Akshat [00:07:09]: Yeah, absolutely. We're, we're not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they're, they're, they're just shaped differently.Working Alongside Frontier StartupsVibhu [00:07:26]: I think you're building a lot of it alongside the startups, right? They're innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they're, they're innovating with you, right? And that's not something AWS is doing directly with.Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We're a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They're like, “Yeah, I just. I am embedded inside of Cog.”Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him overSwyx [00:08:07]: Yeah.Akshat [00:08:07]: Because, the latency of communication was too high otherwise.Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.Vibhu [00:08:16]: Yeah.Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.smol developer, Sandboxes, and Proto-CognitionAkshat [00:08:39]: Yeah, you blew up on Hacker News and,Swyx [00:08:41]: YeahAkshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.Akshat [00:08:55]: Right.Swyx [00:08:56]: If only I had, like, stuck to it.Swyx [00:08:58]: Like, that was like, if - did you say draw the tech treeAkshat [00:09:00]: AbsolutelySwyx [00:09:00]: You're just like, “Yeah, like, probably this will happen.”Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon usSwyx [00:09:04]: I just didn't realize.Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.Swyx [00:09:14]: Yeah.Akshat [00:09:14]: This is like twenty-three.Swyx [00:09:15]: Yeah.Akshat [00:09:16]: So we builtSwyx [00:09:17]: You introduced a new API right after that.Akshat [00:09:18]: Yeah.Swyx [00:09:19]: Yes.Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developerSwyx [00:09:28]: Smol developerAkshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.Swyx [00:09:33]: Loops are hot these days.Vibhu [00:09:34]: It's the looper.Akshat [00:09:34]: Yeah.Vibhu [00:09:35]: Loops in. When was this, twenty-three?Akshat [00:09:38]: Yeah.Vibhu [00:09:39]: A small check.Akshat [00:09:39]: Yeah.Swyx [00:09:39]: It's like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?Swyx [00:09:46]: Like, you're just trying to like. They're not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.Akshat [00:09:55]: Yeah.Akshat [00:09:55]: I don't remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answerVibhu [00:10:08]: Of course they will get betterSwyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environmentAkshat [00:10:15]: RightSwyx [00:10:15]: Sell it for like ten billion dollars to Meta.Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this isAkshat [00:10:23]: Yeah, of courseSwyx [00:10:23]: The funny machine. Like, it's like, it's about the hardware.Akshat [00:10:28]: It's hard to have that inherent conviction that the stuff will get that much better.Swyx [00:10:33]: In retrospect, it's so f*****g obvious.Akshat [00:10:36]: Fair enough.Swyx [00:10:37]: Like, what else were we doing back then? I don't know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn't blow up until, like, last year.Akshat [00:10:49]: Yeah.Swyx [00:10:50]: So there was like a couple years of quietness.Akshat [00:10:52]: Exactly, yeah. We wereVibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he's like, “ there's this cool company, Modal. They'll like spin up a GPU sandbox, we can throw it on there. They'll take a Hugging Face link.” And like there's so much value just right there, right? Like instant hosting, spin it up, spin it down. It'll stay cold, but we run the demo a few days later, it'll come back up and like all this stuff in retrospect, like it's still what we needed like today.Akshat [00:11:27]: Yeah, it's still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it's it's not about scaling from zero to one, but it's how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It's the same shape problem.Elastic Inference, GPU Autoscaling, and Custom ModelsVibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?Akshat [00:11:53]: Yeah.Vibhu [00:11:53]: They had a. “We'll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.Vibhu [00:12:01]: When you look at workloads like that, you're doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That's the example for we do need it, right?Akshat [00:12:12]: Yeah. Well, so I'll, I'll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it's like diurnal. It's Some days, like the company will do a launch and, they'll need like, way more. And it's not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.Akshat [00:13:20]: So that's like our sortVibhu [00:13:22]: And thatAkshat [00:13:22]: YeahVibhu [00:13:22]: That in and of itself is a huge category. There's a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that's carved into its own niche for language models, at least right now.Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.Vibhu [00:13:41]: Yeah.Akshat [00:13:41]: Because we found that it's not universally true that everyone else can autoscale, and we've gone deeper into it on the tech side by, we've incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it's That's why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we'll see, a lot of companies, before they have a training run, they'll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you're doing RL. RL is justRL, Batch Jobs, and 100,000 SandboxesVibhu [00:14:28]: Or commerceAkshat [00:14:28]: Insanely bursty.Vibhu [00:14:29]: Yeah.Akshat [00:14:30]: Yeah. Like when you're doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.Vibhu [00:14:37]: Yeah. I'm curious if you've seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced thisAkshat [00:14:45]: YeahVibhu [00:14:45]: They're, they're trying to do training. That also seems like a different workload, right? If you're doing training twenty-four/seven per se, there's a very weird dynamic of how you're using GPUs between people and whatnot, but seems like something you guys would work for.Akshat [00:15:00]: As you said, we're, we're fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It's possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we're, we're just waiting to seeVibhu [00:15:23]: YeahAkshat [00:15:23]: How it shakes out.Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?LLM Inference, DeFlash, and Speculative DecodingAkshat [00:15:32]: I guess we've been going much deeper into LLM inferenceVibhu [00:15:35]: YeahAkshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren't, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we've been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we've open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we're thinking about hereVibhu [00:16:23]: I thought this wasAkshat [00:16:24]: YeahVibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.Akshat [00:16:33]: Yeah.Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.Vibhu [00:16:44]: Yes.Akshat [00:16:44]: I will, yes.Vibhu [00:16:45]: I think, likeAkshat [00:16:46]: YeahVibhu [00:16:46]: So we've covered like Eagle and all thisAkshat [00:16:47]: YeahVibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.Akshat [00:16:51]: Yeah.Vibhu [00:16:51]: I think it doesn't hurt, right?Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it's faster is if you're predicting, one token at once, you're bound by memory bandwidth. But if you can batch the verification of, the draft model, then you're much more efficient using compute, and it's faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that's, multiple times of, the original model speed. and well, that's what we highlight here. It's Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decreaseVibhu [00:17:47]: Like two to four X.Akshat [00:17:48]: Yeah, exactly.Vibhu [00:17:48]: Without much head-on performance.Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,Vibhu [00:17:57]: I meant quality performanceAkshat [00:17:58]: Probably not by muchVibhu [00:17:58]: But yeah. I thinkAkshat [00:17:59]: So there's no drop in quality performanceVibhu [00:18:01]: YeahAkshat [00:18:01]: Because you're always. You're never accepting a token that the big modelVibhu [00:18:04]: It's strictly betterAkshat [00:18:05]: YeahVibhu [00:18:05]: Or it's same.Akshat [00:18:06]: Exactly.Vibhu [00:18:07]: Right. Yeah.Akshat [00:18:08]: And so we've been working a bunch on DeFlash, which is a block-based speculator. so it's instead of predicting, one token at a time, it's predicting a block. And we've been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it's it's something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we're, we're launching is, as you run an auto endpoint, we shadow trafficAuto Endpoints and Frontier-Level PerformanceVibhu [00:18:54]: Do you want to explain what auto endpoints are?Akshat [00:18:57]: Yeah.Vibhu [00:18:57]: I lovely, yeah.Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don't wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we've made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there's full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It's it's not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that's compatible, you serve open model. If someone was to do this themselves, what's the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What's the delta that plugging into something this, like this offers outside of the benefit of, scaling?Production Inference Beyond Raw GPUsAkshat [00:20:34]: It's interesting because we've taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.Vibhu [00:21:20]: Yeah. And I will say it's not that straightforward to just. like what I said is easier said than done, right?Akshat [00:21:26]: Yeah.Vibhu [00:21:27]: It's I think still for the average person, still hard to just gut check using different. There's, there's quite a bit of combinations you can make there. the trade-offs aren't really known at face value.Akshat [00:21:40]: Yeah. it's it's not just that. I think it's it's that running production-grade inference is a hard infer problem.Vibhu [00:21:49]: YeahAkshat [00:21:49]: Even if you subtract out the autoscalingVibhu [00:21:50]: YeahAkshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.The Model and Agent LifecycleVibhu [00:22:00]: There's a lot of innovation that you can do here. I think, it's very interesting that you're starting to encroach on, like as you become a full cloud, you're starting to encroach on other people's turf.Vibhu [00:22:09]: What will you not do?Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we're focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let's say, sandbox, do persistent storage, a whole bunch of other stuff.Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.Akshat [00:22:42]: Yeah.Vibhu [00:22:42]: And RealInspect also is on Modal.Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.Ramp Inspect and Background AgentsVibhu [00:23:02]: Yeah. That's the new CTO of, Ramp right there.Akshat [00:23:05]: Yeah, Rahul.Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guysThe Inference Inflection: CPU, GPU, and Co-LocationVibhu [00:23:19]: You weren't that much in the GPU game, and now you're all about, inference. And one of the points that I hinged on for Jensen's keynote at GTC this year was, what we're calling like the inference inflection, right? That let's say in AI workloads or machine learning workloads, it used to be like, let's call it eight to one GPU to CPU, and now it's more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.Akshat [00:24:01]: Yeah.Vibhu [00:24:02]: GPU, CPU. And now it's like just constantly, and you just have to locate everything.Seventeen Clouds and the Supercloud StrategyAkshat [00:24:08]: Yeah. And that's one of the things that, again, we see as, something appealing about Modal, which is we've built this capacity pool that spans, 17 cloud providers, so we're, we're very good at Running on various kinds of cloud capacity across the worldSwyx [00:24:24]: You don't have your own data centers?Akshat [00:24:25]: We don't have our own data centers. We just run across a lot of neo cloudsSwyx [00:24:29]: Yeah. AreAkshat [00:24:30]: Metal providers.Swyx [00:24:30]: Yeah. Question mark.Swyx [00:24:31]: Yeah. You're, you're running the math, and you're like, “What's the cutover point where you're like.”Akshat [00:24:36]: Yeah, it's a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it's worked out well because there are so many other people building data centers that we're able to work effectively with them, and again, focus on what makes us, special.Swyx [00:24:55]: Yeah.Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. LikeAkshat [00:25:00]: The,Swyx [00:25:01]: Which was the most interesting one?Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that's why it's something we've invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,Swyx [00:25:30]: YeahAkshat [00:25:30]: You as a user would be able to.Swyx [00:25:32]: It's a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.Akshat [00:25:41]: Yeah. That's, that's, that's the idea. and so I guess when you mentioned colocation, that's, that's another interesting thing where, one thing we've seen is people come to us when they want, very specifically located, CPUs or GPUs, like they wantSwyx [00:25:57]: Oh, they pin it in likeAkshat [00:25:58]: YeahSwyx [00:25:58]: EU?Akshat [00:25:59]: Exactly. Or EU, US.Swyx [00:26:01]: Right. Data resiliencyAkshat [00:26:02]: AustraliaSwyx [00:26:02]: Locality thing or performance or what?Akshat [00:26:04]: It's either data locality or latency, yeah.Swyx [00:26:07]: Yeah.Akshat [00:26:07]: Like, you want your. They're running sandboxes and model. They want them to be right next to aSwyx [00:26:10]: Yeah, it's easy thenAkshat [00:26:11]: YeahSwyx [00:26:12]: To. That is important in all those things. and so, like, you've accidentally, I don't know if it's accident, but, like, you've built the perfect primitive for agents to express themselves. And then, like, it's almost very funny how every extra development just involves more file system, just involves more CPU.Akshat [00:26:30]: Yeah.Swyx [00:26:31]: Just like the things that you already have. I don't know much about, if there's any, like, networking usages that are interesting, but you've also done some good work on networking.Networking, Sidecars, Private IPv6, and SandboxesAkshat [00:26:40]: Yeah, that's exactly right. Like, we're just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.Swyx [00:26:49]: YeahAkshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we haveSwyx [00:26:57]: For like a Docker cluster type thing.Akshat [00:26:59]: Yeah.Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?Akshat [00:27:02]: Compose.Swyx [00:27:03]: Compose type thing.Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.Swyx [00:27:23]: Yeah.Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we've, we've had to build a lot of that stuff ourselves.Swyx [00:27:38]: Yeah.Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we're seeing. We have support for that for a different reason, and yeah, we'll see if that becomes stable.Swyx [00:27:52]: Like, just an open socket. It's a. This is directly like mTLS.Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.Swyx [00:28:01]: Yeah.Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven't talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.Akshat [00:28:28]: So it's like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that's truly serverless. and we did the overlay network for that. But then we've seen that people are using it for other reasons, and, I'm intrigued to yeah, what would people do with it.Swyx [00:28:59]: Build primitives and let people figure it out, right?Akshat [00:29:01]: Yeah, exactly.Swyx [00:29:02]: You put out a pretty interestingAkshat [00:29:03]: They're like, they read the docs webpage. Let me use thatSwyx [00:29:06]: YeahAkshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they're using it.RDMA, Memory Movement, and Distributed TrainingSwyx [00:29:12]: Huh.Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I'm sure someone found it. It's found it to be a lot more efficient before you made a thing out of it, right?Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me becauseAkshat [00:29:51]: YeahSwyx [00:29:51]: So I review 2,200 submissions for the World's Fair.Akshat [00:29:56]: Yeah.Swyx [00:29:57]: And then I got this from John OsterhoutAkshat [00:29:58]: HuhSwyx [00:29:59]: Who I don't know if. Do John Osterhout by name?Akshat [00:30:01]: The name sounds familiar.Swyx [00:30:02]: He published a. He's a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I'm like, you wouldn't think that this guy, who is like operating systems guy, would care about RDMA.Akshat [00:30:20]: I, it makes sense to me because I,Swyx [00:30:24]: This is the cloud, right? YeahAkshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there's a lot of degrees of freedom, and it is a systems problemSwyx [00:30:41]: YeahAkshat [00:30:41]: Moving memory aroundSwyx [00:30:42]: YeahAkshat [00:30:43]: Scheduling.Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.Swyx [00:30:46]: Is this like the domain of WireGuard as well?Akshat [00:30:50]: Not quite.Swyx [00:30:51]: It's adjacent?Swyx [00:30:53]: Explain everything.Akshat [00:30:54]: Sure.Swyx [00:30:56]: How do we move memory around GPUs?Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you've set up.Swyx [00:31:09]: Yeah.Akshat [00:31:09]: Is it like it's a VPN?Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you're right. It is,Akshat [00:31:16]: Right. Yeah, you already moved on to new topicsSwyx [00:31:17]: A similarAkshat [00:31:18]: OkaySwyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,Akshat [00:31:23]: And you don't need encryption.Swyx [00:31:23]: Yeah.Akshat [00:31:24]: Yeah.Swyx [00:31:24]: This is not encrypted. that's the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you're allowed to do it.Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.Swyx [00:31:39]: Yeah.Akshat [00:31:40]: Yeah. I don't know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that's the bottleneck, is your networking fast enough?Swyx [00:31:59]: Yeah. So I guess you're talking about fully distributed training like, Dialog or something which is like cross data centerAkshat [00:32:06]: That would be, yes.Swyx [00:32:07]: That's the extreme.Akshat [00:32:08]: Yeah.Swyx [00:32:08]: You're in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it's a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networkingSwyx [00:32:40]: OkayAkshat [00:32:40]: Which is the standard that's needed.Swyx [00:32:42]: Okay. So I misunderstood whatAkshat [00:32:43]: 50Swyx [00:32:43]: What part of the stack you wereAkshat [00:32:44]: 50 gigs overSwyx [00:32:45]: YeahAkshat [00:32:45]: If you wentSwyx [00:32:45]: YeahAkshat [00:32:46]: RDMA.Swyx [00:32:46]: Okay.Swyx [00:32:48]: Yeah. I, very impressive work.Multi-Node Training, Post-Training, and Auto ResearchSwyx [00:32:52]: So effectively you're extending like the model philosophy to the training cluster, like, yeah.Akshat [00:32:59]: Yeah. And we're, we're not going for like large scale training runs. the thing that we've built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.Akshat [00:33:31]: Yeah. The other use case we've seen for multi-node training is even if you have a big cluster, your researchers are still doing small runsSwyx [00:33:38]: YesAkshat [00:33:39]: Having elasticity thereSwyx [00:33:40]: Right, sureAkshat [00:33:40]: Matters a lot more.Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.Akshat [00:33:51]: We have a blog post on auto resource and model is,Swyx [00:33:55]: YeahAkshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.Swyx [00:33:59]: So my impression is auto research means many things, likeAkshat [00:34:01]: YeahSwyx [00:34:01]: Anything that Andrej coins. Right now it's still science fair, right? Like not like, I don't know how many people are doing this.Akshat [00:34:08]: We're having a golf.Swyx [00:34:08]: Yeah.Akshat [00:34:09]: I thought the same thing.Swyx [00:34:11]: Yeah, you would know.Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we've automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It'll even run like, NVIDIA inside profiler and it'll like tweak configs and it'll arrive the right thing. it'll change your GPUs both from H200 to B200, and works really well.Swyx [00:34:47]: Nice.Akshat [00:34:47]: So yeah.Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.Swyx [00:34:52]: It's very different from forward-deployed engineering from other people.Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they're like applied inference researchers or applied training researchers.Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they're good, they're just like post-sale type of thing?Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with themSwyx [00:35:13]: YeahAkshat [00:35:13]: Matters a lot.Swyx [00:35:14]: They want the same thing.Akshat [00:35:15]: Yeah.Swyx [00:35:15]: ?Akshat [00:35:15]: But it's it's not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.Swyx [00:35:23]: Okay. Let's spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there's all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.Auto Inference and Modal BenchAkshat [00:35:51]: Yeah, like,Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.Akshat [00:35:54]: Yeah, - So the stuff I've seen people do with it is nowhere on the architecture level. It's pretty much tweaking parameters, but it's it's a hyperparameter sweep that's guided by some model intuition, so it's like much more efficient than, whatever other, sweep you would have.Swyx [00:36:12]: Yeah, it's just, it's just a question of where you want to spend your compute?Akshat [00:36:16]: Right.Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you'll bang out Shakespeare?Akshat [00:36:22]: Yeah, infinite monkey.Swyx [00:36:24]: Yeah, so like the very good for model. and I think it's also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.Akshat [00:36:42]: Yeah. They're, they're surprisingly good. I think like pre Cloud 4 they were not, and then now they're able to shot, stuff out of the box. But we're playing around with releasing like a Modal Bench for like the harderSwyx [00:36:55]: YeahAkshat [00:36:55]: Things, that the LLMs cannot do yet and maybeSwyx [00:36:59]: What's an example of that?Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It's reasoning about that. But they're able to shot, likeSwyx [00:37:23]: Yeah. You can just add a skill to it?Compute Strategy and Capacity PlanningAkshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It's to find things like that, so we can address them in our tool.Swyx [00:37:35]: Tune a skill. Yeah.Akshat [00:37:36]: Yeah.Swyx [00:37:36]: No. it's it's good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.Swyx [00:37:44]: Yeah.Akshat [00:37:45]: We have had a lot of growth, which means that, there's - we've had to be much better aboutSwyx [00:37:53]: PlanningAkshat [00:37:54]: Proactive capacity planning.Swyx [00:37:55]: Yeah.Akshat [00:37:55]: So we have,Swyx [00:37:57]: Which by the way, like it's like a MBA's like dreamAkshat [00:38:00]: YesSwyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on thatSwyx [00:38:13]: Compute strategy?Akshat [00:38:13]: Yeah.Swyx [00:38:14]: I think,Akshat [00:38:14]: I feel like,Swyx [00:38:15]: I think the normies call it FP&A or something.Akshat [00:38:18]: Well, it's more It's it's not FP&A. It's it's There's a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,Swyx [00:38:49]: YeahAkshat [00:38:49]: Based on that.Swyx [00:38:50]: Tokenomics.Akshat [00:38:50]: Yeah.Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.Akshat [00:38:59]: Yeah.Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost becauseAkshat [00:39:12]: OhSwyx [00:39:12]: Compared to everyone else.Akshat [00:39:14]: Yeah. I hadn't thought about that.Vibhu [00:39:16]: We're at a fun time too?Akshat [00:39:18]: Yeah. It's. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it's how you can unlock more value for customers. Like, one of the things we're building now is like a way for customers to get, If they don't care about latency, like get much cheaper pricing and they'll get results back in like next 24 hours or something, like a batch tier essentially.Batch Tiers and Latency-Insensitive WorkloadsSwyx [00:39:47]: Yeah.Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficientSwyx [00:39:53]: Yeah. I feel like they're not as popular. Like those, like the Frontier Labs have all those APIs. They're not as popular as they should be.Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals andSwyx [00:40:08]: OkayAkshat [00:40:08]: Synthetic data prep and there it makes sense.Swyx [00:40:10]: Okay.Akshat [00:40:11]: But it's from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don't care about when they get it back.Swyx [00:40:22]: Yeah. And like they have a reasonable. It's it's also like a cousin to the stopping problem of like, will this finish in time?Akshat [00:40:30]: Yeah. You can bound it.Swyx [00:40:33]: Yeah.Akshat [00:40:33]: Like you can give peopleSwyx [00:40:34]: YeahAkshat [00:40:34]: SLAs on it.Swyx [00:40:35]: Yeah. I think what's, what's interesting is like the next phase of model.Swyx [00:40:38]: Like what, do people expect from you, now that you're established and you're like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?What Modal Builds NextAkshat [00:40:55]: We are building primitives that make our users' lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we're thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven't talked to anyone. It looks somewhat different on other verticals. Like, we're also seeing a lot of real-time, audio-video stuff in there, which is why like, we're working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it's,Akshat [00:41:52]: We're still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there's a lot of other things people will need from this agent stack as they build production agents. So yeah, we're thinking about those other things that fit in there.Swyx [00:42:13]: I want to ask what the other things are.Akshat [00:42:15]: Yeah. I probably should share right now.Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.Akshat [00:42:25]: Yeah.Swyx [00:42:25]: Because so far for me, it's fine. so far for the. the first couple generations of cloud, it's fine. What's different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I'll just kinda spew tokens at you until it like hopefully sparks something.Akshat [00:42:43]: Yeah.Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they're like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.Hard Guardrails vs. LLM-Mediated PermissionsVibhu [00:43:03]: Now you're looping it with a goal and letting it roll.Akshat [00:43:06]: Yeah, I'm, I'm skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.Swyx [00:43:16]: Yeah.Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.Swyx [00:43:20]: But likeAkshat [00:43:20]: YeahSwyx [00:43:20]: Maybe that's old school thinking. Maybe we're the dinosaurs.Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.Swyx [00:43:30]: Like it makes you feel uncomfortable.Akshat [00:43:31]: Yeah, I'm, I'm toldSwyx [00:43:32]: But that's what trusting the LLM is. Like imagine a spherical cow perfect LLM.Akshat [00:43:36]: Right.Swyx [00:43:37]: That it.Akshat [00:43:39]: Maybe.Swyx [00:43:41]: I wanna test the boundaries, right?Akshat [00:43:42]: Yeah.Swyx [00:43:42]: Like, and I don't believe that, but I wanna see where I'm wrong ‘cause that's, that's the consensus.Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that's gonna be a lot of mediated.Managed Agents and Specialized SandboxesSwyx [00:44:00]: There. I'll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.Akshat [00:44:17]: Yeah.Swyx [00:44:17]: What's going on?Akshat [00:44:19]: Yeah, we're, very excited to partner with Anthropic and some of the other foundation labs, will not name who we're also working with. the way we see it is the manage agent thing is a great place to start if you're starting out building an agent and, But then when you get to, building something more production grade, like you're a company that's like Ramp that's building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that's the role that we are trying to play.Swyx [00:45:15]: YeahAkshat [00:45:16]: We don't really have an opinion on the harness, whether it runs - it's a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We'll see where people converge with that.Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?Akshat [00:45:31]: You mean like the OpenPipeSwyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can't remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it's kinda pseudo agent cloud type things.Akshat [00:45:50]: I personally have not played around with them.Swyx [00:45:53]: Yeah.Akshat [00:45:53]: Build agents with them.Swyx [00:45:54]: Everything's bullish Modal, as long as it consumes more infra.Akshat [00:45:57]: That's why we're focusing on the infra layer. It's somewhere where our, relative competence is and, also it's a hard problem to solve.Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don't know if - if there's other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn't really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.Why Infrastructure Became Exciting AgainAkshat [00:46:39]: Yeah.Swyx [00:46:40]: And like now everyone gives a crap.Akshat [00:46:42]: That's true. It is a very exciting time, and I think a lot of that's driven by just the amount of scale all of this stuff needs.Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn't necessarily have thought about it myself, which.Akshat [00:47:00]: We need the predictions.Swyx [00:47:02]: I think there's a lot that you just don't even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?Akshat [00:47:10]: What else is coming up for usSwyx [00:47:11]: Yeah. Where do you see things going?Akshat [00:47:13]: Yeah. I, in generalBiotech, Robotics, and Non-LLM AI WorkloadsAkshat [00:47:15]: It's it's clear that there's there's a huge shift happening. I think one thing that's not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?Akshat [00:47:50]: No. We,Swyx [00:47:51]: You should cloud only.Akshat [00:47:51]: Yeah.Swyx [00:47:52]: Yeah. Okay. But yeah, so what you're saying is like because you're focused on primitives and they're good primitives, you find use cases in all these kinds of things.Akshat [00:48:01]: Yeah.Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.Akshat [00:48:05]: Yeah, absolutely. We're, we'- our goal isn't to only serve the LLM inference market.Swyx [00:48:10]: There are a lot just on the website, the audio,Akshat [00:48:12]: Yeah. We said both onSwyx [00:48:14]: Computational bio images. Yeah, there's a lot here. There's QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.Akshat [00:48:24]: Okay. Yeah.Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.Model APIs vs. Differentiated AI ProductsSwyx [00:48:31]: What's your postmortem on what happened?Akshat [00:48:34]: This is one thing we've stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.Swyx [00:48:50]: Yeah.Akshat [00:48:50]: And we've always wanted to build for companies that are building products and need more flexibility that's not just an API.Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you're saying, you can wrap it into a more fully functioning back end that you run.Akshat [00:49:06]: Yeah. So all of our examples, it's not that spin up this model, here's an API token, use it. They're all code.Swyx [00:49:13]: Okay.Akshat [00:49:13]: And so the point is that this is just an example.Swyx [00:49:16]: Starter code.Akshat [00:49:17]: Yeah. But you can tweak it however you want.Swyx [00:49:20]: Yeah.Akshat [00:49:21]: And if you're like a company building a product, like, computational bio whatnot, yeah.Swyx [00:49:26]: I guess I'm trying to tease out for listenersAkshat [00:49:28]: YeahSwyx [00:49:28]: When does it stop becoming, oh, you're just an API call and you're just a wrapper on API to becoming what you call a product, right?Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?Akshat [00:49:46]: I think there's a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that's more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I'm not saying that's, that successful, in that case. But a better example is like, let's say Suno. because Suno, does not use Modal for training.Swyx [00:50:26]: Mikey on the pod. Yeah.Akshat [00:50:27]: But they use Modal for all their inference and that's because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.Swyx [00:50:41]: It's interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it's a better model or agent that orchestrates video models.Video Agents and Production WorkflowsAkshat [00:50:56]: Oh, interesting.Vibhu [00:50:56]: Language model backbone that can use toolsAkshat [00:50:58]: RightVibhu [00:50:59]: And write code.Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.Akshat [00:51:06]: And I'm not going there through normal video gen.Swyx [00:51:10]: Yeah, that's interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.Swyx [00:51:23]: Run FFmpeg. But likeAkshat [00:51:25]: That's not enough.Swyx [00:51:25]: Yeah.Akshat [00:51:26]: You need to give it Adobe.Swyx [00:51:27]: Yeah, I hadn't put it together with like it would be a video production thing. in my mind these things were going more towards editingAkshat [00:51:36]: Yeah.Vibhu [00:51:36]: Well, shout out Mantis.Akshat [00:51:37]: I think about this a lot.Swyx [00:51:38]: .Akshat [00:51:41]: Yeah. Sorry.Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it's a off.Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happensGitpod/Ona, CI, and Runtime SandboxesSwyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very li

Topics covered in this episode: dust - a better du Hermes Agent: The AI agent that grows with you llm-coding-agent 0.1a0 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: dust - a better du du + Rust = dust - a fast, visual, intuitive disk-usage CLI Run dust and immediately see the biggest directories and files without piping through sort, head, or awk Smart recursive output focuses on what matters instead of dumping every folder Colored bars show relative size and parent/child hierarchy, making “where did the space go?” obvious Perfect for Python projects bloated by .venv, caches, Docker volumes, downloaded datasets, and local AI models Install via brew, cargo install du-dust, conda-forge, Scoop, Snap, deb-get, or GitHub releases Calvin #2: A Way better ARchive format for Python packaging war - new archive format spec from Astral (same team as uv/ruff), v0.0.2, still no binary encoding defined yet Header-Index-Store layout: header IDs the file, index maps names to store offsets, store holds compressed data Index uses a finite-state transducer (FST) to dedupe common path prefixes across entry names Supports three entry types (file, directory, link) and three compression modes (store/DEFLATE/zstd), plus an "executable" metadata flag Unpacking is atomic - writes to a temp dir, then renames into place, so a failed extract never leaves a half-unpacked directory Strict name-segment rules (no NUL/control chars, no leading/trailing whitespace, blocks Windows-reserved names like CON/PRN) to avoid path traversal and cross-platform footguns Michael #3: Hermes Agent: The AI agent that grows with you Hermes Agent is an open-source, Python-built AI agent framework from Nous Research - think ChatGPT-style assistant, but connected to your tools, files, shell, browser, calendar, memory, and messaging apps I'm using it in Discord as a long-running agent conversation, not just a one-off chatbot session Hermes can connect through a gateway to platforms like Discord, Telegram, Slack, WhatsApp, email, webhooks, and more - so the same assistant can follow you across surfaces In my setup, I can send Hermes voice/text from Discord, keep project context across turns as threads, and ask it to actually do things: read GitHub repos, run commands, edit files, schedule calendar events, generate drafts, and verify results A fun workflow: I can trigger one-shot actions from an Apple Watch shortcut - dictate a request, send it to Hermes, and have the agent execute it asynchronously Hermes has persistent memory, so it can remember durable preferences and facts - for example, how I like my research formatted It also has “skills,” which are reusable procedures the agent can load later, so Hermes can self-improve over time instead of rediscovering the same workflow repeatedly It supports scheduled jobs / cron-style automations, so it can proactively watch for releases, send summaries, run checks, or remind you about things It's provider-agnostic: OpenRouter, Anthropic, Google, xAI, local models, Nous Portal, and others The big idea: Hermes turns an LLM from “a chat box I visit” into “an agent I can reach from anywhere that knows my workflows and can take real actions and learns over time.” Calvin #4: llm-coding-agent 0.1a0 Simon Willison built a Claude/Codex-style coding agent on top of his llm library, using an alpha of the llm package plus his python-lib-template-repo Built almost entirely via prompted TDD - asked an agent to write a spec.md, then commit + implement with red/green tests, occasionally hitting a real OpenAI key to sanity-check Shipped to PyPI as an alpha: uvx --prerelease=allow --with llm-coding-agent llm code Tool set mirrors familiar coding-agent primitives: read_file, edit_file (exact string replace + diff), write_file, list_files, search_files, execute_command Also exposes a Python API - CodingAgent(model="gpt-5.5", root=..., approve=True).run(...) - which Simon didn't ask for but got anyway Demo: llm code --yolo told GPT-5.5 to build a SwiftUI CLI clock; model correctly noted SwiftUI isn't really CLI-friendly and still produced an ASCII-art time display Extras Calvin: Slides, but for developers https://sli.dev/ Wanna reduce your token usage…. only issue is that its lossy https://github.com/teamchong/pxpipe PEP 772 - Python Packaging Council inaugural election dates set, nominations open July 28, voting September 1-15 Michael: What the pls? revisited! Joke: Min requirements for Linux

Today on The Business of Open Source I spoke with Sam Alba, co-founder of Mendral, formerly of Dagger, and the first hire at Docker. We talked a lot about about how AI is changing the developer tools marketplace. If AI is changing the way software is developed, what does that mean for the companies that previously had made tools for developers? We talked a lot about hype; what is hype and what is not hype? How many people are actually using AI and getting value from it. It's hard to differentiate between the hype and real value, Alba says, but also that while it does seem like AI is a bubble, that doesn't mean there's nothing real there. It's just hard to see where the difference is now, but after the bubble bursts there will still be plenty of changes that stick around. When it comes to building an open source company — which Mendral is not, by the way, Alba talks about how you have to get product-market fit twice. But for AI is absolutely changing things, and there have been companies that are abandoning open source because the game is changing so much and it is harder to make it work. Listen to the entire podcast! And if you want someone to work with on establishing your message and then sharing that message in as many places as possible, get in touch. 

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOB• Holiday season is over• UK no longer melting• Samourai updatesNEWS• GitHub permanently bans Rust Lightning (LDK); Corallo moves Bitcoin dev infra to self-hosted Forgejo - Bitcoin Magazine: Corallo urges Bitcoin projects to exit GitHub after Rust Lightning ban• Sparrow Wallet Apple developer-account scare resolved - Bitcoin Magazine / Craig Raw / Sparrow Wallet• FISA Section 702 lapses for the first time since 2008 - EFF: Victory, 702 Has Expired• Canada's surveillance-and-speech week: Lawful Access bill (C-22) rammed to a June 19 deadline, "Combatting Hate" Act (C-9) gets royal assent June 18 - EFF: Canada Is Forging Ahead with Its Dangerous Surveillance Bill / Canada.ca: stronger hate crime protections become law• US CBDC ban through 2030 heads to the President's desk - House Financial Services Committee• Developer-protection carve-out (BRCA) being sanded down as CLARITY Act stalls; passage odds cut to 50-50 - Bitcoin Magazine: Galaxy Research cuts CLARITY Act passage odds• Bull Bitcoin secures MiCA license in France while keeping self-custody/privacy model - Bitcoin Magazine / Bull Bitcoin• ESMA tells unlicensed crypto providers to wind down as MiCA deadline arrives - ESMA public statement PDF• LND zero-timestamp DoS vulnerability publicly disclosed - Bitcoin Optech #411RELEASESWallets• Envoy v2.3.0-beta - 2026-06-22Redesigned Send flow with inter-account transfers, message signing, Address Explorer, sub-sat fee rates, and custom block-explorer support, plus Passport Prime pairing/Bluetooth reliability fixes. Disclose it is ours.• Zeus v13.1.0 - 2026-06-17Feature release: Cashu multi-mint sends, queue-less Nostr Wallet Connect payments on iOS, CLINK noffer support, and Cashu-token detection in URLs. Strong ecash plus Nostr-native payments stack for a self-custodial Lightning wallet.• Zeus v13.1.1 - 2026-06-26Patch: fixes TLS cert verification for LND/CLN REST over Tor and a currency-converter input bug.• Phoenix 2.8.1 - 2026-06-18Adds an iOS debug screen for signing swap-in transactions and fixes an Android amount-display bug, on updated lightning-kmp.• Blockstream Green iOS 5.5.0 - 2026-06-15Adds support for paying LNURL, BOLT12, and BIP-353 payment instructions, improves Lightning invoice handling, and shows funding fees in the receive flow.• Blockstream Green Desktop 3.4.1 - 2026-06-17Adds BOLT12 offer descriptions and updates GDK to 0.77.6. Secondary to the iOS release, but it reinforces the same BOLT12/BIP-353 direction.• Blink Mobile 3.0.1 - 2026-06-26Pre-release build that continues Blink's self-custodial wallet implementation, including wallet creation, seed backup/recovery, Blink Lightning address, LNURL pay, and Stable Balance with BTC-USD conversion. Mention only as a prerelease / watch item.Lightning / node ops• Core Lightning 26.06.2 - 2026-06-29Point release recommended for minimal OS setups and Docker images lacking root TLS certificates. Late-breaking / record-day item if recording after June 29; not a lead, but useful for node operators.Self-hosting / payments infra• BTCPay Server v2.4.0 - 2026-06-25Major release: multisig wallet setup, loginless passkey auth, and granular per-wallet permissions. Breaking: removes the LNBank and Lightning Charge backends.Bitcoin / wallet infrastructure• Liquid GDK 0.77.6 - 2026-06-16Adds Python 3.14 wheels and fixes singlesig GDK use over Tor that broke after a ureq dependency update. Developer/infrastructure note rather than listener-facing wallet news.Hardware / multisig / inheritance• Nunchuk 2.6.0 - 2026-06-17 (Blog Post: https://nunchuk.io/blog/phased-rollout )Phased rollout of the off-chain inheritance protocol, plus fixes and performance work. Self-custodied estate planning for multisig users.P2P / no-KYC trading• Mostro v0.17.5 - 2026-06-16Anti-abuse bonds harden the Nostr-based P2P marketplace while staying non-custodial. Adds multi-source price aggregation and maker-timeout penalties.• BULL Wallet v6.11.1 - 2026-06-22CSV export, payment notes, auto swap-claim retries, better Electrum reliability, experimental Linux desktop support. • Peach Bitcoin 0.69.0 - 2026-06-25Faster funding-flow sync, but ships a new EU-compliance sign-up step. Worth naming the privacy/compliance tradeoff for a no-KYC-leaning audience.On-chain privacy / coinjoin• Ashigaru Desktop 1.0 - 2026-06-24 Ashigaru Desktop 1.0.0 (June 19, 2026) is the inaugural stable release of the Whirlpool coinjoin desktop wallet, shipping Tx0/zeroleak mixing with Premix/Postmix/Badbank account management, built-in Tor, Mix To for routing postmix funds offline, a receive-only attack-surface reduction model, BIP-329 label import/export, and an offline BIP47 message verifier. It also adds a polished dark-theme UI with live coinjoin progress tracking, full cross-platform packaging (Windows/macOS/Linux plus headless server builds), reproducible builds, and BIP47-signed three-step release verification.• JoinMarket NG 0.33.0 - 2026-06-24Correction/update from earlier 0.32.0 note. 0.33.0 adds forced-address-reuse defences, a config center, TUI send validation, wallet history API work, and fidelity-bond / tumbler fixes.• Wasabi Wallet 2.8.0 - 2026-06-28This release adds P2P sync for compact filters, pay-in-coinjoin, sub-1 sat/vByte fees, payment batching, a Scheme scripting language, Signet support, and Tor forward compatibility. It also broadens platform reach with arm64 Linux, Tails, and Whonix support.• Payjoin Dev Kit payjoin-mailroom 0.1.2 - 2026-06-26Hardens the combined Payjoin Directory and OHTTP Relay with bounded file-descriptor usage, recovery from transient accept errors, database/per-request metrics, and unified mailbox TTL.General wallet / spend• Stack Wallet 2.6.0 - 2026-06-27Adds CakePay gift-card support. EDUCATION• The Fed Is Working on a CBDCThe Rage reports that despite public denials, the Fed is internally researching CBDC design. A concrete surveillance-money piece that pairs directly with the 702 lapse for a “financial panopticon” segment. Also pair with the new ROAD to Housing Act CBDC-ban item: the ban runs only through 2030, which is the punchline.• BIP 110 Fork Simulator / explainerInteractive explainer of the BIP 110 “Reduced Data Temporary Softfork”: activation mechanics, what gets restricted, and how the data-filtering debate could split consensus.• How-To Guide: Running an Ecash MintHands-on Cashu / LNbits + Nutshell mint walkthrough. Ecash is the privacy tech of the moment; a good “run your own mint” counterweight to “just use a wallet.”• Bitcoin Privacy in 2026: A Practical GuideDated 2026-06-04, outside even the grace cutoff, so use as evergreen rather than news. Good “where do listeners actually start” resource.• Bitcoin Optech #411Carries the LND zero-timestamp DoS disclosure plus the usual protocol-discussion roundup. Patch reminder for node runners.• Bitcoin Optech #410Summarises an active dev debate to remove opt-in RBF signaling because it now mainly fingerprints the wallet that created the transaction, plus Sparrow Silent Payments, JoinMarket, and Ark items.KEEP AN EYE ON• EU “Chat Control” final trilogue - Patrick Breyer / fightchatcontrol.euThe fourth and likely final trilogue on the permanent CSAM-scanning regulation is set for Monday 2026-06-29. Member states have reportedly dropped the forced encrypted-message-scanning mandate, but the Council text would still codify “voluntary” scanning and extend the interim derogation, with formal adoption expected in July. Outcome not yet known as of record date.• MiCA enforcement after July 1 - ESMA / Bull BitcoinBull Bitcoin is the self-custody-friendly example to watch, while ESMA's wind-down warning is the enforcement backdrop. After July 1, check which EU-accessible services remain available, which providers stop onboarding, and whether “compliant but non-custodial” survives contact with implementation.TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateHELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/VALUE FOR VALUEThanks for listening you…

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält. Mit dabei: Niklas Dierking, Stefan Porteck Moderation: Jörg Wirtgen Produktion: Tobias Reimer ► Der von Niklas erwähnte Newsletter c't Open Source Spotlight: https://www.heise.de/newsletter/anmeldung.html?id=ct-opensource

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält.

Jetzt habt ihr euer NAS, um eure Dateien lokal zu speichern statt bei Google Drive, OneDrive oder Dropbox. Aber wie denn nun konkret? Bindet man den Netzwerkspeicher einfach als SMB-Share ein? Installiere ich umständlich eine Nextcloud? Bringt das NAS vielleicht was mit, beispielsweise Qsync von Qnap? Und was können diese Peer-2-Peer-Lösungen wie Syncthing, Resilio und Seafile? Oder machen Localsend und KDE Connect den ganzen Kram nicht viel einfacher? Im Podcast gehen die c't-Redakteure Niklas Dierking, Stefan Porteck und Jörg Wirtgen diese Fragen durch. Zuerst die einfachste Lösung, ein Netzwerk-Share. Wir diskutieren die Vor- und Nachteile der gängigsten Protokolle SMB, NFS, Webdav und AFP und gehen dann auf die strukturellen Eigenschaften ein: Die Dateien liegen dann nicht lokal vor, sondern man benötigt ständig eine Netzwerkverbindung, und um von außerhalb des eigenen LANs zuzugreifen, muss beispielsweise der Router ein VPN- oder Wireguard-Netz aufspannen und man muss das irgendwie per DynDNS finden. Auch wie gut das auf dem Smartphone geht, erwähnen wir. Eine Alternative wäre, Nextcloud auf dem NAS zu installieren: Die Daten liegen dann lokal vor, allerdings benötigt man DynDNS und VPN weiterhin. Wir sind einerseits zufrieden damit, auch weil die meisten Nextcloud-Clients ganz gut mit virtuellen Dateien umgehen können (die nur auf Wunsch lokal gepuffert vorliegen). Andererseits haben wir alle drei Zuverlässigkeits- und Geschwindigkeitsprobleme mit Nextcloud festgestellt, vielleicht aber auch aufgrund zu schwachbrüstiger NAS-Geräte. Gerade wenn man einen Reverse Proxy benötigt, der den DynDNS-Namen innerhalb des LANs auf eine lokale IP umbiegt, damit Transfers nicht auf die Geschwindigkeit der Internet-Anbindung beschränkt sind, und wenn man das beispielsweise mit einem PiHole im Docker auf dem NAS löst, könnten einige NAS überfordert sein. Einen interessanten Tipp brachte Niklas: OpenCloud ist ein abgespeckter Fork von Nextcloud, der vielleicht performanter läuft. Was auch einen Blick wert ist: Die Software, die das NAS mitbringt. Qnap beispielsweise kommt mit Qsync, was ein ähnlicher Dienst ist: Clients für Windows, macOS, Linux, Android, iOS; die Clients verbinden sich im LAN automatisch direkt per IP; DynDNS über Qnap-Dienste eingebaut; virtuelle Dateien funktionieren einigermaßen. Weitere Alternativen sind Peer-2-Peer-Lösungen. Eigentlich synchronisieren sie Verzeichnisse zwischen zwei Geräten, die dazu eingeschaltet sein müssen. Auf den ersten Blick klingt es unpraktisch, extra seinen PC einschalten zu müssen, damit das Handy auf aktuellen Stand kommt. Aber der Trick beim NAS ist natürlich, dort einen Client permanent laufen zu lassen. Vorteil der Systeme: Sie fummeln sich von selbst durch die Firewall, sodass man keinen DynDNS benötigt, und der lokale Zugriff erfolgt automatisch schnell. Syncthing machte sich als solches Peer-2-Peer-System bei allen drei gut, auch weil es das für einige NAS als native Anwendung gibt. Allerdings fehlen virtuelle Dateien. Zur Sprache kommen auch die Alternativen Resilio Sync und Seafile. Schließlich sprachen wir über ganz andere Lösungen, um explizit nur einzelne Dateien auszutauschen, aber auch beispielsweise Zwischenablagen, und das ganze auch ohne Usernamen und Passwörter einfach mit Leuten im gleichen Raum – so etwas wie Apple AirDrop oder Google Quick Share, nur systemübergreifend. Gute Erfahrungen gabs mit Localsend und mit KDE Connect – das anders als der Name vermuten lässt, auch Clients für Windows, macOS, Android und iOS bereit hält. Mit dabei: Niklas Dierking, Stefan Porteck Moderation: Jörg Wirtgen Produktion: Tobias Reimer ► Der von Niklas erwähnte Newsletter c't Open Source Spotlight: https://www.heise.de/newsletter/anmeldung.html?id=ct-opensource

Si has estado atento a los últimos episodios del podcast, ya te habrás dado cuenta de que estoy completamente enfocado en exprimir la inteligencia artificial local y el software libre. En concreto, hay dos herramientas que se han convertido en mis compañeras inseparables de fatigas en el día a día: OpenCode, que me ayuda a programar de una forma increíble, y Hermes Agent, un asistente digital del que hoy te lo quiero contar absolutamente todo.El dilema de la instalación: ¿Docker o en tu propia máquina?Como ya me conoces, sabes bien lo mucho que me gusta a mí levantar "al rico contenedor" y solucionar cualquier despliegue con Docker. Sin embargo, en mis pruebas con Hermes Agent he preferido dar un paso atrás y realizar una instalación directa sobre el sistema operativo, utilizando un entorno virtual de Python. El peligro de la ventana de contexto y la sangría de tokensAquí está uno de los grandes secretos que casi nadie te explica al principio. Cuando ejecutas el asistente de configuración inicial de Hermes Agent, te entran ganas de activar absolutamente todas las características que te ofrece: herramientas de visión, utilidades del sistema, navegación web, traducción... ¡todo suena fantástico! Pero hay una trampa invisible en la que es muy fácil caer. El superpoder de los perfiles aislados (Profiles)La solución definitiva a este problema de consumo y rendimiento tiene un nombre: perfiles. Hermes Agent te permite crear tantos perfiles aislados como consideres oportuno. Modelando el Alma y la Memoria de tu AgenteEn el podcast te detallo cómo dar personalidad a tu agente a través del archivo de alma. A mi asistente personal, que he bautizado como Chloe, le he configurado un tono sarcástico, irónico y burlón. Me encanta interactuar con ella de esta manera porque rompe completamente con la clásica respuesta robótica y aburrida de otras inteligencias artificiales comerciales; se siente como hablar con un colega de verdad. Eso sí, te doy pautas para redactar este archivo con cuidado, ya que un "alma" demasiado extensa también te comerá espacio de contexto útil de forma innecesaria.Ampliando fronteras: MCP, Telegram y automatizaciones automáticasPor último, abordamos el fantástico protocolo MCP (Model Context Protocol), que nos permite dotar de "manos y ojos" a nuestro agente. Y para rematar la jugada, la integración con Telegram y Matrix. Es una auténtica delicia poder ir caminando, mandarle un audio desde el móvil a mi bot de Telegram, que este use Whisper en local para transcribir mi voz, procese lo que le pido y me conteste con otro audio sintetizado a la velocidad que yo le he configurado de antemano. Todo ello combinado con tareas programadas (Cron) y un tablero de Kanban interno con el que el propio agente se organiza y ejecuta flujos de trabajo de forma completamente autónoma.Te invito a que te prepares un buen café, te pongas los auriculares y disfrutes de este viaje de configuración avanzada de 0 a 100.CAPÍTULOS DEL AUDIO:00:00:00 Introducción: Mi día a día con OpenCode y Hermes Agent00:01:26 El problema de los tutoriales básicos e instalación00:03:00 Configuración inicial y la sangría de tokens00:04:47 Archivos clave y estructura interna de Hermes00:05:56 Creando "Skills" personalizadas y configurando API Keys00:08:15 Perfiles aislados (Profiles): Qué son y por qué los necesitas00:11:00 Cómo clonar y gestionar tus perfiles sin romper nada00:13:35 soul.md: Diseñando el "Alma" y el tono de tu asistente00:15:28 memory.md: El gran desafío de la memoria y el RAG en Rust00:17:38 Expandiendo capacidades con MCP y conversión de voz00:20:47 Llevando tu agente a Telegram con Cron y Kanban integrado00:27:18 Reglas de oro para optimizar tu contexto y despedida

JDK 26 optimise la JVM dans ses moindres recoins, le SDK Java d'Agent2Agent passe en 1.0, Micronaut 5 est là. Côté terrain, un retour d'expérience après 40 jours à coder avec 100 % d'IA : génie ou junior, Alzheimer numérique et dette technique invisible. Pendant ce temps, GitLab restructure, Microsoft suspend ses licences Claude Code, et un développeur injecte un prompt destructeur dans sa lib JUnit. La révolution IA a un coût et les boites commencent à s'en rendre compte. Enregistré le 12 juin 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-341.mp3 ou en vidéo sur YouTube. News Langages Les améliorations de performance dans le JDK 26 https://inside.java/2026/06/09/jdk-26-performance-improvements/ Côté bibliothèques, l'API LazyConstant (anciennement StableValue) fait son entrée en prévisualisation pour permettre une initialisation paresseuse, sécurisée pour les threads et optimisée par le mécanisme de constant-folding de la JVM. L'extraction de chaînes de caractères via MemorySegment::getString a été revue pour réduire considérablement les allocations intermédiaires et les copies en mémoire off-heap, accélérant fortement les traitements sur les chemins critiques (hot paths). La méthode générée automatiquement hashCode() pour les classes de type record a été optimisée par la JVM pour atteindre un niveau de performance équivalent à une implémentation écrite manuellement. Le ramasse-miettes G1 bénéficie du JEP 522 qui redessine sa table de cartes (card-table) afin de réduire les coûts de synchronisation des barrières d'écriture, offrant un gain de débit de 5 % à 15 % sur les applications manipulant énormément de références d'objets. Grâce au JEP 516 (Project Leyden), le cache d'objets Ahead-of-Time (AOT) adopte un format de flux agnostique, ce qui lui permet d'être compatible avec n'importe quel Garbage Collector, y compris le ramasse-miettes à très faible latence ZGC. Le démarrage de la JVM s'accélère par défaut lorsqu'aucune taille de tas n'est configurée, car HotSpot n'applique plus de pourcentage initial (InitialRAMPercentage) mais démarre directement avec la taille minimale (MinHeapSize) pour éviter d'allouer des métadonnées inutiles. Les threads virtuels gagnent en robustesse en étant désormais capables de céder la main (yield) pendant les phases d'initialisation des classes, éliminant ainsi le risque de famine des threads porteurs (carrier threads). Le compilateur C2 JIT améliore son modèle de coût pour la vectorisation des boucles (SIMD) et se montre maintenant capable de compiler et d'optimiser des méthodes dotées de listes de paramètres extrêmement longues. Librairies Release candidate du A2A Java SDK supportant versions 0.3 et 1.0 en même temps https://medium.com/google-cloud/a2a-java-sdk-1-0-0-cr1-released-f0c651ec9139 Dernière étape avant la GA : Toutes les fonctionnalités prévues pour la version 1.0 sont finalisées. Migration simplifiée depuis la Beta1. Compatibilité v0.3 : Ajout d'une couche de compatibilité permettant aux agents v1.0 de communiquer avec les systèmes v0.3 (via JSON-RPC, gRPC ou REST). Support natif pour Android (nouvel AndroidHttpClient). Uniformisation des clients HTTP pour garantir une cohérence entre les versions. Nouveau parseur SSE (Server-Sent Events) conforme aux spécifications. Ça y est, le SDK Java de l'Agent 2 Agent Protocol est sorti en version 1.0 finale ! (avec compatibilité v0.3 et v1.0) https://medium.com/google-cloud/a2a-java-sdk-1-0-0-final-released-10c05b6aee34 Lancement officiel : Sortie de A2A Java SDK 1.0.0.Final, la première version stable (GA) du protocole Agent2Agent. Objectif du protocole : Standard ouvert (Linux Foundation) permettant aux agents IA de communiquer, déléguer des tâches et collaborer, indépendamment du langage ou du framework. Interopérabilité : Introduction de l'Integration Test Kit (ITK) pour valider la compatibilité entre les SDK (Java, Python, TypeScript, etc.). Transports supportés : Support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Alignement total avec la spécification A2A 1.0.0. Passage aux Java records pour l'immutabilité et moins de code répétitif. Architecture interne basée sur un MainEventBus pour garantir la persistance et éviter les conditions de concurrence. Intégration d'OpenTelemetry pour le suivi et la surveillance. Support d'Android et compatibilité descendante avec la version 0.3. Installation : Gestion des dépendances via Maven BOM (org.a2aproject.sdk). Sortie de Micronaut 5.0 https://micronaut.io/2026/05/20/micronaut-framework-5-0-0-released/ Lancement majeur : Disponibilité générale de Micronaut 5, incluant une refonte de plus de 70 modules et la plateforme BOM. Baselines techniques : Support de Java 25, Groovy 5, Kotlin 2.3 et GraalVM 25.0.3. Optimisations internes : Amélioration significative des performances au démarrage et réduction de la surcharge à l'exécution via une refonte du conteneur IoC et du traitement à la compilation. Architecture HTTP : Support stable de HTTP/3, nouvelle API de formulaires (multipart) et annotations de nullabilité (JSpecify) pour une meilleure interopérabilité Kotlin/IDE. Configuration : Nouveau système d'importation de configuration (remplaçant le Bootstrap Configuration) et validateur de schéma JSON intégré. Fiabilité : Nouvelles API programmatiques pour les politiques de retry et circuit breaker. Sécurité & Outils : Mise à jour majeure des dépendances (Jackson 3, Ktor 3), rafraîchissement du Panneau de contrôle et diagnostics AOT améliorés. Écosystème : Mises à jour complètes pour les bases de données (Data, SQL, R2DBC, MongoDB, Redis), le cloud (AWS, Azure, GCP, OCI) et les tests (JUnit 6, Testcontainers 2.0). Évolutions notables : Intégration HTMX dans Micronaut Views, retrait du support RxJava 2 et migration de divers processeurs d'annotations vers des modules dédiés. Comment rajouter un agent IA dans une app Android, avec le tout nouveau framework ADK pour Kotlin https://glaforge.dev/posts/2026/05/21/wiring-adk-kotlin-agents-in-an-android-application/ Guillaume a participé au développement et au lancement du nouveau runtime ADK pour Kotlin et Android https://developers.googleblog.com/adk-kotlin-android-building-ai-agents/ Tutoriel sur comment intégrer un agent ADK dans une app Dépendances : Ajout du noyau ADK (google-adk-kotlin-core) et du processeur KSP dans build.gradle.kts. Sécurité API : Utilisation de local.properties pour stocker la clé API Gemini et l'exposer via BuildConfig afin d'éviter le hardcoding. Définition de l'agent : Création d'un objet LlmAgent configuré avec le modèle Gemini, des instructions spécifiques et des outils (ex: GoogleSearchTool). Utilisation de InMemoryRunner pour gérer automatiquement le contexte et l'historique de la session. Implémentation de runAsync avec StreamingMode.SSE pour un retour en temps réel dans l'interface. Threading : Exécution des requêtes réseau sur Dispatchers.IO et mise à jour de l'état de l'interface utilisateur sur Dispatchers.Main. Comment développer et hoster des agents IA sur la plateforme d'agents managés de DeepMind https://glaforge.dev/posts/2026/05/21/managed-agents-with-the-gemini-interactions-java-sdk/ L'équipe DeepMind de Google a lancé une plateforme d'agents managés sur son API Gemini Interactions https://blog.google/innovation-and-ai/technology/developers-tools/managed-agents-gemini-api/ Guillaume a implémenté un SDK Java pour utiliser cette API Gemini Interactions, qui donne entre autre accès à tous les modèles mais aussi à cette plateforme managée d'agents IA Agents managés : Permet d'exécuter des agents autonomes qui raisonnent, planifient et exécutent du code dans des environnements isolés (sandboxes), sans gestion d'infrastructure par le développeur. Environnement distant : Utilise des espaces de travail Linux éphémères dans le cloud via le paramètre remote, permettant l'accès réseau et la persistance des fichiers sur plusieurs appels. Agents prédéfinis : Accès immédiat à des agents spécialisés comme deep-research-pro (recherche multi-étapes) ou antigravity (tâches de codage généralistes). Agents personnalisés : Possibilité de configurer ses propres agents avec des instructions système dédiées, des outils spécifiques (exécution de code, recherche Google) et des règles réseau (egress) personnalisées. Architecture basée sur les étapes (Steps) : Utilise une structure de données typée (Step, Content) pour suivre le raisonnement de l'agent, ses appels de fonctions et ses résultats en temps réel. Outils et Schémas : Inclut des utilitaires pour générer des schémas JSON complexes via une interface fluide (DSL), par réflexion Java ou par parsing JSON. Streaming réactif : Support natif des événements en temps réel (SSE) pour suivre la progression de l'agent et recevoir les deltas de contenu au fur et à mesure de la génération. Flexibilité : Fournit un gestionnaire de routage (InteractionsHandler) pour créer facilement des serveurs proxy ou des backends intermédiaires traitant les interactions Gemini. Spring Boot 4.1 https://github.com/spring-projects/spring-boot/wiki/Spring-Boot-4.1-Release-Notes Support natif pour Spring gRPC permettant de créer et tester facilement des applications clientes et serveurs basées sur Netty ou des Servlets via HTTP/2 Introduction du lazy fetching pour les connexions JDBC via la propriété spring.datasource.connection-fetch=lazy afin de ne prendre une connexion du pool que lorsqu'un Statement est réellement exécuté Amélioration de l'auto-configuration de Jackson permettant de définir globalement les contraintes de lecture/écriture pour les formats JSON, XML et CBOR via des propriétés de configuration Sécurisation des clients HTTP bloquants et réactifs face aux attaques SSRF grâce à l'introduction d'un InetAddressFilter bloquant les requêtes sortantes vers des adresses spécifiques Améliorations majeures autour d'OpenTelemetry avec le support complet des variables d'environnement OTel, la possibilité de désactiver le SDK via une propriété globale et l'ajout du support SSL sur les exporters OTLP Ajout de l'auto-configuration pour l'utilisation de Spring Batch avec MongoDB incluant un nouveau starter dédié spring-boot-batch-data-mongo Auto-configuration des endpoints @RedisListener sans nécessiter la déclaration manuelle d'un RedisMessageListenerContainer Dépréciation du support de Apache Derby (projet arrêté), suppression définitive du mode layertools du JAR et réintroduction du support de Spock 2.4 (avec Groovy 5) Upgrade des dépendances majeures de l'écosystème avec notamment Spring Framework 7.0.8, Spring Security 7.1.0 et Micrometer 1.17.0 Outillage Vous êtes plutôt endive ou chicorée ? La librairie Chicory qui permet d'exécuter du code WASM à partir de son application Java est forkée et rejointe la Bytecode Alliance pour continuer son développement https://bytecodealliance.org/articles/endive-and-the-next-chapter-of-webassembly-on-the-jvm Annonce d'Endive : Nouveau projet hébergé par la Bytecode Alliance ; fork de Chicory (moteur WebAssembly pur Java, sans dépendance native). ​Objectif principal : Permettre aux développeurs Java d'intégrer, charger et déployer des modules Wasm nativement via les workflows Java habituels. ​Compilateur "Redline" : Intégration à venir de Redline (basé sur Cranelift) pour compiler le Wasm en code machine natif ; performances comparables à Rust/Wasmtime. ​Zéro dépendance (Java 25+) : Grâce à l'API standard Foreign Function & Memory (Project Panama), l'exécution à vitesse native se fait sans composants externes. ​Modèle de Composants (Component Model) : Support futur prévu pour consommer des composants (Rust, Go, JS, etc.) via des interfaces typées et sécurisées directement dans la JVM. ​Prochaines étapes : Fusion de Redline, conformité stricte aux specs Wasm (dont WasmGC) et amélioration du support WASI. Un visualisateur de sessions de travail avec Antigravity https://glaforge.dev/posts/2026/06/11/antigravity-brain-visualizer/ Un projet open source construit avec Micronaut, LangChain4j et GraalVM pour analyser les sessions de travail avec l'outil de développement agentique Antigravity (de Google) Analyse toutes les étapes, les requêtes utilisateur, les outils utilisés, les erreurs rencontrées, les réponses du modèle Gemini fait une analyse pour comprendre les moments clés de cette session de travail Outil buildé avec l'aide d'Antigravity lui-même SBX-Kits : des environnements de développement simplifiés pour les débutants (et les autres) https://k33g.org/20260501-sbx-kits.html Philippe Charrière (:whale: ) présente SBX-Kits (Sandbox Kits), une initiative personnelle visant à simplifier radicalement la mise en place d'environnements de développement pour les débutants, en éliminant la complexité d'installation des outils traditionnels. Chaque "kit" est une archive prête à l'emploi contenant un outil de développement spécifique (comme un langage, un framework ou une base de données) configuré pour s'exécuter de manière isolée et portable. La philosophie du projet repose sur le principe de "zéro configuration" et "zéro dépendance globale", permettant de tester une technologie ou de commencer à coder immédiatement sans polluer son système d'exploitation. L'approche technique s'appuie sur des scripts légers et des binaires portables pré-packagés, offrant une alternative plus simple et moins gourmande en ressources que les conteneurs Docker ou les configurations d'IDE complexes pour l'apprentissage. L'objectif à terme est de proposer un catalogue de kits couvrant les technologies courantes (JavaScript, Python, petites bases de données) pour faciliter les ateliers de programmation et le prototypage rapide. De nombreux kits sont disponibles sur https://github.com/docker/sbx-kits-contrib ghui: une interface utilisateur en ligne de commande (TUI) interactive pour GitHub https://github.com/kitlangton/ghui ghui est un outil en ligne de commande (TUI) écrit en Rust qui fournit une interface visuelle, interactive et rapide directement dans le terminal pour interagir avec GitHub. Il permet de gérer ses pull requests, ses issues et ses notifications sans avoir à ouvrir son navigateur web ou à taper de longues commandes avec la CLI officielle de GitHub. L'outil propose une navigation fluide au clavier, des raccourcis efficaces, et permet de réaliser des actions courantes comme valider une PR, ajouter des commentaires, attribuer des reviewers ou inspecter les logs des GitHub Actions. Conçu pour être extrêmement réactif, ghui s'intègre naturellement dans le flux de travail des développeurs adeptes du terminal et du mode "sans souris". Sortie de Homebrew 6.0.0 https://brew.sh/2026/06/11/homebrew-6.0.0/ Introduction du mécanisme de sécurité Tap Trust : comme les dépôts tiers (taps) peuvent exécuter du code Ruby arbitraire non sandboxé sur la machine, Homebrew demande désormais une confiance explicite de l'utilisateur avant d'évaluer ou d'exécuter leur code. L'API JSON interne devient le choix par défaut, offrant un système plus léger et beaucoup plus rapide pour les développeurs. Sécurisation renforcée de l'environnement avec l'implémentation du sandboxing sur Linux. Évolution des comportements par défaut basés sur un sondage utilisateur : le mode "ask" est activé par défaut pour les développeurs, affichant un résumé des dépendances et une demande de confirmation avant toute action de brew install ou brew upgrade. Améliorations notables des performances globales, notamment un boost de ~30 % sur la vitesse de la commande brew leaves et la parallélisation de la récupération des bottles (binaires) lors des mises à jour. Ajout du support initial pour la prochaine version d'Apple, macOS 27 (Golden Gate). Multiples optimisations pour brew bundle, incluant une gestion plus sécurisée des installations de paquets npm. Méthodologies Retour d'expérience très détaillé et 100% humain sur 40 jours avec une équipe 100% AI hormis le superviseur https://www.linkedin.com/pulse/jai-vir%C3%A9-mon-%C3%A9quipe-de-dev-pour-une-100-ia-pendant-40-luc-bonnin-jlgjf/ Voici le résumé en bullet points : Expérimentation de 40 jours : remplacer une équipe de dev par 100% IA agentique (Cursor) sur un vrai projet en production (playthatsheet.com, 200k lignes de code legacy) Chiffres bruts : 2,3 milliards de tokens consommés, 1 477 prompts, 260 564 lignes ajoutées (+145%), 59% du code final produit par l'IA ROI vertigineux à court terme : 9 mois de travail humain livrés en 40 jours, coût total 260$ d'abonnement + 15 jours de supervision, ROI x18 Profil psy de l'IA : Alzheimer (oublis de contexte), schizophrène (change de méthodo), ado de 12 ans (refait les mêmes erreurs), oscille entre génie et junior sans prévenir Effet iceberg : la dette technique ne disparaît pas, elle se camoufle et s'accélère ; hallucinations = bombes à retardement détectables uniquement par relecture humaine ligne par ligne Paradoxe du bateau de Thésée : perte de paternité et de maîtrise fine du code, baisse de l'autonomie du dev humain qui valide sans avoir construit Arnaque du "monkey money" : consommation de tokens opaque, non corrélée à la complexité (écart de 350% sur des prompts identiques), facturation imprévisible donc impossible à budgéter Syndrome du bazooka : les devs utilisent l'IA même pour changer une couleur CSS, atrophie progressive des compétences et coût écologique délirant Risque stratégique : dépendance irréversible aux vendeurs de tokens (Nvidia, Anthropic, OpenAI), business non rentable qui devra augmenter ses prix Conseil final : approche Pareto, garder 20% du temps en code "fait main", nommer un responsable stratégie IA, l'humain senior reste irremplaçable pour superviser Une libraries de test JUnit cache un prompt qui demande aux coding agents d'effacer les tests https://arstechnica.com/security/2026/05/fed-up-with-vibe-coders-dev-sneaks-data-nuking-prompt-injection-into-their-code/ Agacé par les « vibe coders », un développeur introduit une injection de prompt destructrice dans son code Le développeur de jqwik (un moteur de tests pour JUnit 5) a volontairement inséré une injection de prompt dans la version 1.10.0 de sa bibliothèque Java pour saboter le travail des agents d'IA. L'instruction injectée via la sortie standard (stdout) ordonne textuellement aux LLM d'ignorer les consignes précédentes et de supprimer l'intégralité du code et des tests jqwik du projet. Pour dissimuler cette action aux yeux des développeurs humains, le mainteneur a utilisé des séquences d'échappement ANSI qui effacent la ligne d'injection dans les émulateurs de terminaux interactifs. La modification a été découverte par un utilisateur qui a pointé du doigt les risques majeurs et disproportionnés pour les machines des utilisateurs, bien que certains outils comme Claude d'Anthropic aient détecté et bloqué la consigne malveillante. Face aux critiques de la communauté et aux accusations de comportement infantile ou potentiellement illégal, le développeur a mis à jour ses notes de version pour documenter explicitement son opposition à l'usage de son outil par des IA, avant de refuser tout commentaire supplémentaire sur conseil de son avocat. La réalité du rôle de Principal Engineer https://leaddev.com/career-development/reality-being-principal-engineer Le passage au rôle de Principal Engineer marque une transition majeure où les compétences techniques ne suffisent plus, l'impact se mesurant désormais à travers l'influence, la stratégie et la capacité à aligner la technique avec les objectifs business. Contrairement aux attentes, le quotidien est souvent marqué par une forme d'isolement, car le poste se situe à l'intersection de la direction (qui attend des solutions) et des équipes techniques (qui attendent des directives), sans appartenance directe à un groupe précis. Le rôle exige d'accepter une grande part d'ambiguïté et l'absence de retours immédiats, les projets et les décisions stratégiques mettant parfois des mois ou des années à porter leurs fruits. La gestion du temps devient un défi critique, nécessitant de savoir naviguer entre les sollicitations constantes, la présence en réunion et le besoin de préserver des moments de réflexion approfondie pour concevoir des visions à long terme. La réussite à ce niveau repose sur le développement de compétences humaines pointues (soft skills), notamment la négociation, la communication vulgarisée auprès des profils non techniques, et la capacité à faire grandir les autres ingénieurs par le mentorat. Sécurité Une attaque de la chaîne d'approvisionnement npm utilise binding.gyp pour compromettre des dizaines de paquets https://cybersecuritynews.com/binding-gyp-supply-chain-attack-compromises-dozens-of-npm-packages/ Une nouvelle variante du ver auto-propageable "Shai-Hulud", baptisée "Miasma", cible l'écosystème npm (et PyPI sous le nom de "Hades") en dissimulant son exécution dans le fichier binding.gyp au lieu des scripts classiques preinstall ou postinstall. La technique, surnommée "Phantom Gyp", exploite le fait que npm lance automatiquement node-gyp rebuild dès qu'un fichier binding.gyp est présent à la racine d'un paquet pour compiler des modules natifs C/C++, exécutant ainsi le code malveillant dès la commande npm install. L'attaque contourne la plupart des outils de sécurité traditionnels car l'injection s'appuie sur l'évaluation récursive de commandes (via la syntaxe ) ou directement sur la fonction eval() de Python sous-jacente à GYP, cachée sous n'importe quelle clé du fichier. Le script malveillant télécharge un runtime alternatif (Bun) pour échapper aux détections comportementales de Node.js, puis moissonne les identifiants et secrets des développeurs et des environnements CI/CD (npm, GitHub, AWS, GCP, Azure, Kubernetes, HashiCorp Vault). Plus de 57 paquets npm (dont le SDK serveur de Vapi ou des outils liés à l'IA) et des dizaines de paquets PyPI ont été infectés via des comptes de mainteneurs compromis, le ver republiant automatiquement de nouvelles versions vérolées en utilisant les jetons volés. Loi, société et organisation Restructuration chez Gitlab https://about.gitlab.com/blog/gitlab-act-2/ GitLab entame une restructuration majeure pour s'adapter à l'ère de l'intelligence artificielle agentique, incluant une réduction d'effectifs planifiée de manière transparente et ouverte. L'entreprise prévoit de réduire de 30 % le nombre de pays où elle maintient de petites équipes, d'aplatir sa hiérarchie en supprimant jusqu'à trois niveaux de gestion, et de réorganiser la R&D en une soixantaine d'équipes plus petites et autonomes. Les processus internes vont être revus en intégrant des agents d'IA pour automatiser les revues, les approbations et les passages de relais afin d'accélérer le rythme de travail. La stratégie repose sur la conviction que le logiciel sera bientôt écrit par des machines et dirigé par des humains, ce qui va multiplier la demande de logiciels et transformer le rôle des ingénieurs vers la résolution de problèmes complexes. Sur le plan technique, GitLab reconstruit son infrastructure sous-jacente (notamment Git) pour supporter la charge massive générée par les agents d'IA, tout en misant sur l'orchestration du cycle de vie, la centralisation du contexte des données et une gouvernance intégrée. Le modèle économique évolue vers un système hybride combinant les abonnements classiques et une tarification à la consommation pour le travail effectué par les agents d'IA. Un LLM local sur un mac pourrait coûter plus cher en électricité qu'un modèle hébergé sur OpenRouter dans le cloud https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html Conclusion : L'inférence locale sur Mac M5 Max est 3x plus chère et 2x plus lente que le cloud (OpenRouter). Électricité : Négligeable (~0,02 $/heure pour 50-100W). Matériel (Le vrai coût) : Achat du Mac à 4 299 $; l'amortissement sur 3 à 5 ans plombe la rentabilité horaire. Coût au million de tokens (Gemma 4 31b) : Mac M5 Max : 0,40 à4, 79 (pour 10-40 tokens/s). OpenRouter : 0,38 à0, 50 (pour 60-70 tokens/s). Verdict pro : Le temps humain perdu à cause de la lenteur locale coûte infiniment plus cher que les tokens cloud. Privilégier les API (Anthropic, OpenRouter). Ai didn't kill your junior pipeline https://andrewmurphy.io/blog/ai-didnt-kill-your-junior-pipeline-you-did L'IA n'a pas tué le recrutement des juniors, les entreprises l'ont fait elles-mêmes, par effet de mode. Sans juniors, pas de futurs seniors : on retire l'échelle qui nous a tous fait monter. Tout le monde pêche dans le même bassin de seniors sans le réapprovisionner, pénurie garantie dans 3-5 ans. Une équipe 100% senior + IA est fragile : un départ et tout le savoir tacite s'évapore. Les juniors posent les "pourquoi ?" qui révèlent les bugs et processus absurdes ; l'IA, elle, exécute sans questionner. Les seniors s'atrophient aussi en déléguant leur réflexion à l'IA, pince à double effet sur les compétences. Dépendre des outils IA, c'est sous-traiter sa stratégie talents à des fournisseurs dont les prix vont tripler. Solution : redéfinir le rôle junior (revue de code IA + mentorat), pas le supprimer. Les rapports internes de Microsoft révèlent la crise des coûts de l'IA : les agents coûtent plus cher que les employés humains https://fortune.com/2026/05/22/microsoft-ai-cost-problem-tokens-agents/ Des données et rapports internes chez Microsoft et d'autres géants de la tech ébranlent la promesse de rentabilité de l'IA, révélant que le déploiement d'agents autonomes à l'échelle de l'entreprise revient souvent plus cher que de payer des humains pour le même travail. Le modèle de tarification à l'usage (basé sur les tokens) se heurte à la nature même des architectures agentiques : contrairement à un simple chatbot, un agent boucle, enchaîne les appels d'outils, crée des sous-agents et auto-évalue son code, ce qui multiplie la consommation de tokens par un facteur de 5 à 30, voire jusqu'à 1 000 fois pour des tâches de programmation complexes. L'impact financier sur les budgets de calcul cloud est immédiat ; par exemple, Uber a entièrement épuisé l'intégralité de son budget annuel 2026 dédié au codage par IA en l'espace de seulement quatre mois. Face à cette explosion des coûts, des retours en arrière drastiques sont observés : Microsoft a ainsi commencé à suspendre une grande partie de ses licences internes Claude Code pour rediriger d'urgence ses milliers de développeurs vers sa propre solution moins onéreuse, GitHub Copilot CLI. Les directeurs techniques (CTO) et acheteurs de solutions logicielles qui ont signé des contrats pluriannuels basés sur des projections de réduction de masse salariale se retrouvent pris au piège, les gains réels de productivité ne parvenant pas à compenser les factures d'infrastructure exorbitantes. Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 26-27 juin 2026 : LeHACK - Paris (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2 juillet 2026 : MCP Connect Travel Edition - Paris (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 25 septembre 2026 : SAP Inside Track Paris 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 8-9 octobre 2026 : Forum PHP 2026 - Marne-la-Vallée (France) 12 octobre 2026 : Dev With AI - Paris (France) 22-23 octobre 2026 : Agile Tour Bordeaux 2026 - Bordeaux (France) 26 octobre 2026 : Agile Tour Montpellier - Montpellier (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 29-30 octobre 2026 : Agile Tour Nantais 2026 - Nantes (France) 29 octobre 2026-1 novembre 2026 : Pycon FR - Biarritz (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 19 novembre 2026 : Agile Laval 2026 - Laval (France) 19 novembre 2026 : OVHcloud Summit - Paris (France) 19 novembre 2026 : Codeurs en Seine - Rouen (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 2-3 décembre 2026 : Cloud Native AI Summit Europe - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) 3 juin 2027 : Cloud Native Days France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Seguro que si eres de los míos, de los que disfrutan pasando el rato en la terminal o montando servicios en casa, te habrás dado cuenta de que acabamos haciendo tareas repetitivas casi sin querer. Para poner fin a este caos cotidiano te traigo una herramienta espectacular que se llama Just. Pero la verdadera razón por la que he querido dedicarle este pódcast a Just de nuevo es por una experiencia divertidísima que he tenido estos últimos días con mi asistente de inteligencia artificial local, al que cariñosamente llamo Hermes. Yo soy una persona bastante perezosa para ciertas tareas repetitivas y me gusta poner a trabajar a las máquinas por mí. Normalmente, al acabar mis entrenamientos de carrera, le dicto un audio a Hermes detallando la distancia, las pulsaciones y el ritmo para que él los registre. Pero el otro día, llevado por la vaguería máxima, decidí simplemente hacer una captura de pantalla de la aplicación del móvil y enviársela por Telegram.Hermes, que es una maravilla de asistente, aplicó un sistema de lectura de imágenes (OCR) llamado Tesseract, extrajo todos los datos de mi carrera y los guardó en un periquete. Yo me quedé encantado y pensé que la vida ya estaba resuelta. Sin embargo, al día siguiente repetí el proceso y... ¡sorpresa! Hermes se había olvidado por completo de cómo lo había hecho. Me preguntó qué quería que hiciese con la imagen y, cuando le recordé lo del día anterior, me soltó que no tenía la herramienta de lectura instalada en su entorno de trabajo. Tuve que guiarle de nuevo de la mano paso a paso.Ahí fue donde se me encendió la bombilla. Las inteligencias artificiales a veces se despistan y tienen una memoria muy volátil para los flujos de trabajo técnicos. La mejor forma de darles estabilidad es crearles un recetario claro, un archivo "justfile" donde tengan todas sus habilidades documentadas y listas para ejecutar con un simple comando. Así, Hermes nunca más olvidará cómo procesar una imagen o cómo gestionar un contenedor, porque solo tiene que invocar la receta correspondiente.En este episodio quiero animarte a que pruebes Just en tu propio día a día, uses o no inteligencia artificial. Capítulos del episodio:00:00:00 Introducción: Olvídate de repetir comandos00:01:33 El problema con Hermes: Por qué las IA también se despistan00:03:04 ¿Qué es Just y cómo funciona?00:04:59 Cómo instalar Just en Linux00:05:31 Comparativa: Just contra Make y Task00:06:42 Gestión de variables, argumentos y funciones00:08:49 Atributos de receta para afinar su comportamiento00:10:00 El comportamiento de las líneas y el poder del Shebang00:11:00 Funciones integradas y ajustes globales00:12:00 Operadores, expresiones y dependencias complejas00:13:00 Usando intérpretes alternativos (Bash, Python, Node) en Just00:14:18 Recetas normales frente a recetas Shebang y scripts00:15:33 Módulos e importación de recetas externas00:16:38 El selector interactivo con búsqueda difusa (just choose)00:17:37 Alias, grupos y autocompletado en tu shell00:18:09 Casos prácticos de uso real (Sysadmin, Docker, Backups)00:19:18 Documentación viva y ejecutable para todo el mundo00:20:17 Control de versiones con Git y límites de Just00:21:10 Una historia de pereza, Hermes, deporte y OCR que se olvida00:22:59 Conclusiones: Simplifica tu vida con este ejecutor de comandos00:24:58 Cierre del episodio y despedidaMás información y enlaces en las notas del episodio

Conventional wisdom says the hardest part of building a startup is building the product. Shanea Leven says the harder challenge is figuring out what customers will actually pay for. Before co-founding Empromptu.ai, Shanea spent more than 15 years building products at companies including Google, eBay, Docker, and Cloudflare. In this conversation, she explains why sales is every bit as complex as engineering, why customer interviews aren't enough to validate an idea, and why early-stage founders need to spend more time testing demand than perfecting roadmaps. We discuss the case-study approach she uses to find customers, the controversial belief that the only real product validation is money, and what happened when a LinkedIn post generated a 1,000-person waitlist almost overnight.  Shanea also shares how she used more than 100 customer calls to shape Empromptu's direction, why she stopped fundraising when the company took  off, and the go-to-market challenges that still keep her up at night. If you're a technical founder trying to figure out whether you're building something people truly want, this episode offers a practical framework for separating genuine demand from wishful thinking. Listen to this episode if you're trying to figure out: why sales validation should happen before you commit to a roadmap how to find your first customers using the case study method what a viral waitlist can teach you about product-market fit how to distinguish customer feedback from customer demand why technical founders need to learn sales earlier than they think which early traction signals are worth trusting — and which aren't how to validate an idea before spending months building it RUNTIME 49:06   EPISODE BREAKDOWN (3:16) What Is Empromptu.ai, and Who Is it For? (6:25) Sales Is Just as Complicated as Engineering (8:14) The Case Study Method for Finding Early Customers (11:56) Why Al Is Rewriting the Product Playbook (13:37)   The Only Real Product Validation Is Money  (17:10) The S***ty Purple Website That Predicted Impromptu's Viral Launch (21:38) What 100 Waitlist Calls Taught Shanea About Customer Demand (26:28) "We evolved the platform." (34:57) "Ninety-nine percent of VCs are great at one thing." (36:20) The GTM Problem That Still Keeps Shanea Up at Night  (39:02) A Process for Selecting Your First Sales/Marketing Hires (40:59) Why She'd Hire a "Scrappy" Marketer Over a Former Meta Employee "Every Time" (44:05) The Early Traction Signal She No Longer Trusts (45:28) A 30-day Experiment Founders Can Run To Validate Their Idea LINKS Shanea Leven Empromptu.ai Empromptu raises $2M pre-seed to help enterprises build AI apps, 12/9/2025, TechCrunch SUBSCRIBE

OpenChoreo is an opinionated, “batteries included”, AI-native Kubernetes platform stack for Platform Engineers that combines GitOps, Observability, AI Agents, and Workflows into a custom K8s distribution “super pack” that is managed via Backstage, CLI, API, or MCP. Now a CNCF project.Check out the video podcast version here: 

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about the quarkus Insights episode "#337 The Database Cloud" stackgres live demo, StackGres as a Quarkus and GraalVM native kubernetes operator for running Postgres, comparing CloudNativePG (CNPG) by EnterpriseDB to StackGres, Patroni for Postgres high availability, the split-brain risk of relying on Kubernetes and etcd alone, distributed consensus and leader lock election via etcd, why distributed systems and cryptography should not be self-implemented, async, synchronous and quorum (semi-synchronous) Postgres replication trade-offs, cascading and cross-region replication topologies, the false-positive problem and heuristic exceptions in two-phase commit, the ondb ("own your database") project for self-hosted Postgres, losing control with managed cloud services and untestable backups, vanilla unmodified Postgres on StackGres, the "Kubernetes without Kubernetes" (Kubeless) pattern, talking directly to ContainerD through the CRI API, runc and the Docker to ContainerD chain, a self-contained native binary that embeds ContainerD over Unix domain sockets, the slony node-local component named after the Postgres slonik elephant mascot, the Matriarch orchestrator component, reverse gRPC tunnels with Slonies phoning home across NAT and firewalls, a multi-tenant cloud control plane provided as a service, curl-pipe-shell node installation with a token, end-to-end encrypted Postgres protocol tunneling for JDBC from anywhere, psql compiled to wasm in the web console, Tailscale-inspired user experience, unifying nodes, Kubernetes clusters and cloud pools as resources, Slony Kubernetes controller, Java 25 source-mode scripting without dependencies, implementing your own MCP server for Postgres JDBC metadata, the Goose agentic UI donated by Block to the Linux Foundation, AI Rails BCE, Java, Web Components skills Alvaro Hernandez on twitter: @ahachete

In this episode of the Ardan Labs Podcast, Ale Kennedy talks with François Bitouzet, Managing Director of Viva Technology, about the forces shaping the future of technology and innovation. François shares his journey from studying in France to leading one of the world's largest technology and startup events, connecting entrepreneurs, investors, and industry leaders from around the globe.00:00 Introduction02:58 Education and Early Influences08:53 Early Career and Communication17:47 Communication in a Changing World32:25 Innovation and Technology42:40 Creativity and Marketing49:38 Leadership and Career Growth54:54 Adapting to Technological Change59:42 The Future of Events01:06:54 AI and Society01:11:20 Startups and Innovation01:15:35 Deep Tech and the FutureConnect with François: LinkedIn: https://www.linkedin.com/in/fran%C3%A7ois-bitouzet-180a89/Mentioned in this Episode:Viva Technology: https://vivatechnology.comWant more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

Joe Beda and Craig McLuckie co-created Kubernetes, the infrastructure standard that became the default for cloud native computing. Now running Stacklok, they're watching enterprises hit the same identity, permissions, and security problems with AI agents that took the container ecosystem years to resolve, and they're building tools to compress that timeline. In this episode of Founded & Funded, Madrona's Tim Porter sits down with Joe and Craig to talk through what AI adoption actually requires: why MCP is the Docker moment for AI-native applications, how the LLM gateway is becoming a strategic chokepoint for cost, safety, and model flexibility, and why enterprises that don't get the architecture right early will face a familiar trap: vertical integration that looks like productivity and acts like lock-in. They cover: Why the developer workflow is the template for knowledge worker AI adoption, and where the analogy breaks down The mainframe vs. open platform question that will define the AI infrastructure era Why the knowledge worker transition is harder than it looks — and what has to be built differently before developer-grade AI tooling can scale to the rest of your organization The governance gap between human accountability and AI behavior, and what enterprises actually need to build to close it Where to start: MCP controls first, LLM gateway second, and why deploying a platform without staying to close the loop consistently fails Transcript: https://www.madrona.com/the-best-infrastructure-moment-since-cloud Chapters: (0:00) – Introduction (1:04) – Why the Kubernetes Creators Are the Right People to Read This AI Moment (2:18) – Joe's Lesson from Cloud Native: Ignore Conventional Wisdom, Except When You Shouldn't (4:16) – Craig on Enterprises and the Chaos of a New Infrastructure Era (5:32) – Why Joe Rejoined Craig at Stacklok: The Engineer's Case for Getting Your Hands Dirty (7:05) – Developers as Agent Orchestrators: How the Knowledge Worker Transition Will Follow (10:10) – MCP Explained: Craig Sees Docker in 2013 When He Looks at the MCP Spec 1 (7:53) – The Mainframe vs. Open Platform Question That Will Define the AI Era (20:24) – LLM Lock-In Is the Wrong Worry: The Real Risk Is Left of the Model (25:19) – Where Enterprises Actually Start: Developer Posture First, Knowledge Workers Second (29:10) – MCP First, LLM Gateway Second: The Concrete Technical Starting Point (31:19) – How Stacklok Builds Software Now: Agents, Smaller Teams, the Unrecognizable Developer Profile (38:07) – The Recruiter Who Started Building Agents: What AI Tools Do to Role Boundaries

Jim Collison and Gavin Campbell explore AI agents for Home Assistant, comparing OpenClaw and Hermes while covering local vs cloud LLMs, token economics, Mattermost, Docker, Unraid, and home-lab hardware requirements.

PHP Podcast – June 4, 2026 Hosts: Eric Van Johnson & John Congdon Another fun episode of the PHP Podcast! Here’s what we covered: PHP Tek 2027 — New Dates, Bold New Format Mark your calendars: PHP Tek 2027 is happening April 27–29 in Chicago, and Eric and John are shaking things up. Rather than a straight three-day PHP conference, next year gets three tracks — two of which are familiar PHP-focused content, and a third specialty track that rotates each day: one day of JavaScript, one day of DevOps, and one day of Laravel. The Laravel track is specifically focused on how developers actually use the framework day-to-day, not a product pitch. Single-day passes will be available, so if you’re only coming for the DevOps or JS day, you’re covered. One important heads-up: there’s a big convention happening at a venue nearby in Rosemont, so the hotel block could sell out faster than usual. When they open reservations, don’t wait. Holly the Elephant Is Going Fast The PHP Architect conference elephant, named Holly, is now available at store.phparch.com, and demand has been remarkable. Eric woke up one morning to a flood of orders and genuinely couldn’t figure out what happened. The warning from last year applies here: people said they’d grab Tony later, and now Tony is gone forever. Holly ships June 17th for most orders, but if you’ve already ordered, it’s likely on its way. Get yours while you can. PHP Tek TV Is Doing Something Different This Year In past years, conference talk videos would get edited and uploaded weeks (or months) after the event. This year, John is doing things differently: the raw, unedited recordings are going up now, with timestamps in the description so you can jump straight to specific talks — some rooms recorded a seven-hour continuous feed and just left it running. The clean edited versions are still coming (a video editor friend in the UK is on it), but if you want to see a talk right now, the raw version is there. Audio quality varies by room, but it’s watchable. Immich — A Self-Hosted Google Photos That Actually Works John has been running Immich, a self-hosted photo management platform, in a Docker container for about a month and loves it. It does facial recognition, GPS tagging, and auto-uploads from his phone — essentially everything he cares about in Google Photos, without handing his photos to Google or Apple. He’s now planning to use it as the PHP Architect conference photo library, centralizing all the Tech photos in one browsable, shareable place. It’s fully open source, with no licensing cost, and an optional donation tier. If you’re sick of paying ever-increasing storage bills to big tech companies, this is worth a look. Ben Ramsey’s PHP Tek Homecoming Article Is Free to Read The May issue of PHP Architect magazine is now available to digital subscribers, and this month’s free article is Ben Ramsey’s piece on the PHP Tek homecoming experience. Eric reached out to Ben last minute and he delivered. If you’ve never subscribed, this is a low-barrier way to see what the magazine is like. Head to phparch.com, grab the free article, and if you like what you see, subscriptions are not expensive. John Is Resurrecting a Legacy Laravel App — With Claude’s Help John has been grinding away on a Laravel 6 app that was a passion project years ago and has now been revived as an actual client project. Using Claude to methodically baby-step through each version upgrade — starting with writing tests to establish a baseline — he’s worked up through the major Laravel versions. The turning point came when he hit the version where the old event sourcing package (Prooph) was clearly on its way out, and the decision was made to migrate to Verbs, Nuno Maduro’s Laravel-native event sourcing package. John’s now looking forward to it. He’s also accidentally been burning tokens on the company Anthropic account (not his personal account), which Eric caught live on air. They are going to talk about it after the show. Eric’s Mystery Side Project Is Almost Ready — If DNS Would Cooperate Eric teased a new side project last week and intended to reveal it this week, but he’s stuck waiting on DNS propagation. The domain was registered with DigitalOcean DNS already in use by a previous owner, so Eric moved it to Cloudflare — only to discover there may be a conflict because the previous owner was also on Cloudflare. The result: the name servers are stuck on old values. John’s live suggestion was to move it to Route 53, and Eric was immediately sold. The project is almost ready to show the world, DNS gods willing. Meta’s AI Support Bot Got Socially Engineered Eric shared a video demonstrating how someone prompt-injected Meta’s AI customer support bot into sending a verification code to an attacker-controlled email address — and then using that code to add the email to an account, enabling a full password reset and account takeover. The irony: Meta is the company behind Llama and has some of the deepest AI expertise on the planet, and they still shipped a support bot with permissions it shouldn’t have. Eric’s point was pointed: you can fire a human employee who gets social engineered, which creates accountability throughout the team. An AI has no such incentive structure. Crowbarring AI into account-modification workflows without appropriate guardrails is just asking for this. The PHP Foundation Now Publishes Board Meeting Minutes Eric discovered that the PHP Foundation has started publishing their board meeting minutes in a public GitHub repository. Nothing earth-shattering yet, but seeing who attended, what was discussed, and what decisions are being made gives the community a real window into how the foundation operates at scale. It also helps explain something Eric and John have always found interesting: why PHP stalled so hard between versions 5 and 7. There was no foundation, no financial backing, just volunteer hours. Now there’s a paid staff and governance structure — and the minutes show exactly how complex running something at PHP’s scale actually is. The PHP Foundation Has a Dedicated Security Team Now Speaking of the Foundation, it now has a dedicated security team — a sign of how seriously the supply chain attack problem has gotten. AI tools are being deployed by black hat actors to find vulnerabilities in open source projects at a scale that wasn’t possible before. PHP is not just another open source project; it underpins a massive slice of the web, and companies depend on it staying secure. Having a team specifically focused on this is the right call, even if it’s a sobering reminder of where the threat landscape is heading. Moat — Nuno’s GitHub Security Auditing Tool Nuno Maduro (of Laravel fame) quietly shipped a tool called Moat that audits your GitHub presence for security gaps. Install it globally via Brew or Composer, point it at your GitHub org, a specific repo, or even a specific branch, and it gives you a report on where your security posture could be improved. It’s read-only — it won’t change anything — and it’s explicit that it is not a security certification. Eric wants to use it to audit the PHP Architect organization’s repos, many of which haven’t been touched in years. Think of it as a fast, opinionated triage tool, not a replacement for a real security audit. Links from the show: PHP Tek 2027 — Chicago, April 27–29 PHP Architect Store — Holly the Elephant Immich — Self-Hosted Photo Management PHP Architect Magazine Verbs — Laravel Event Sourcing by Thunk Moat — GitHub Security Auditing by Nuno Maduro PHP Foundation on GitHub PHP Architect Discord Host: Eric Van Johnson X: @shocm Mastodon: @eric@phparch.social Bluesky: @ericvanjohnson.bsky.social PHPArch.me: @eric John Congdon X: @johncongdon Mastodon: @john@phparch.social Bluesky: @johncongdon.bsky.social PHPArch.me: @john Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – Joe and the team are available for consulting, infrastructure work, Ansible playbooks, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ PHPScore Put Your Technical Debt on Autopay with PHPScore CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.06.04 appeared first on PHP Architect.

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!On the product side, everyone is getting Computer - Perplexity, Manus, Cursor, and so on. Meanwhile on the research side, agentic evals like TerminalBench and GDPVal are also assuming computer (Harbor). On both ends, the consolidating LLM OS stack has become a standard toolkit, and Daytona is one of a small set of AI Infra companies that are booming because of it.“The end of localhost” has been Ivan Burazin's obsession for more than a decade.Something that is all too familiar…Long before agents became the default way people talked about software development, Ivan was already chasing the idea that development should not depend on a fragile local machine. CodeAnywhere, one of the first browser-based IDEs, was an early attempt at that future: move the development environment into the cloud, make setup reproducible, and free developers from the endless “works on my machine” tax.The thesis was directionally right, but the market wasn't ready yet.However, agents changed that. They do not care about a laptop, desk setup, or favorite editor. They need a computer they can access through an API: something stateful enough to keep working, fast enough to spin up instantly, flexible enough to resize, isolated enough to be safe, and composable enough to run the messy real-world workflows that real software engineering actually requires.Daytona isn't just selling “sandboxes” in the narrow code-execution sense. It is the latest version of Ivan's original localhost thesis.In this episode, Daytona's CEO joins swyx to explain why AI agents need more than code execution boxes: they need composable computers, stateful sandboxes, instant startup, dynamic resources, and infrastructure that can survive workloads going from zero to 100,000 CPUs.We go deep on the new agent compute market: Daytona's hard pivot from human dev environments to AI sandboxes, the New Year's Eve MVP that customers begged for, why Daytona runs on bare metal with its own scheduler, how one customer runs almost 850,000 sandboxes a day, and why RL/eval workloads went from 0% to roughly 50% of usage in just months. Ivan also explains why agents need Windows and macOS machines, why CLI may matter more than MCP, why Kubernetes is painful for this workload, and why the future AI cloud may look more like Stripe than AWS.We discuss:* How Daytona grew out of CodeAnywhere, Shift, and the “end of localhost” thesis* Why Daytona pivoted from human dev environments to AI sandboxes* Why agents need composable computers instead of disposable code execution boxes* The New Year's Eve MVP that customers chased API keys for* Why Daytona chose bare metal, stateful snapshots, and its own scheduler* How Daytona spins up one sandbox in ~60ms and 50,000 sandboxes in ~75 seconds* Why Daytona's biggest customer runs ~850,000 sandboxes a day* How RL/eval workloads create zero-to-100,000 CPU spikes* Why RL workloads went from 0% to roughly 50% of Daytona usage* Why customers compare Daytona against EKS/GKS and say they're “never going back”* Why every AI agent may need a computer, including Windows and macOS environments* The Apple licensing constraints that make macOS sandboxes hard* Why CLI gives agents more power than MCP* How open source helps agents integrate Daytona* Why agent-generated PRs may break today's CI/CD assumptions* Why AI SaaS companies reselling tokens may face a cold shower* Why the AI cloud may look more like Stripe than AWSIvan Burazin* LinkedIn: https://www.linkedin.com/in/ivanburazin* X: https://x.com/ivanburazinDaytona* Website: https://www.daytona.io* X: https://x.com/daytonaioTimestamps* 00:00:00 Hook* 00:01:12 Introduction* 00:03:15 CodeAnywhere, Shift, and the end of localhost* 00:05:58 What Daytona is: composable computers for AI agents* 00:08:07 The pivot from dev environments to AI sandboxes* 00:10:17 The New Year's Eve MVP and customers begging for API keys* 00:12:56 Bare metal, stateful sandboxes, and Daytona's scheduler* 00:17:28 60ms startup, 50,000 sandboxes, and 850K daily runs* 00:21:53 Spiky RL/eval workloads and the new agent infra problem* 00:28:12 RL workloads, Kubernetes pain, and dynamic resizing* 00:33:31 Why every AI agent needs a computer* 00:38:48 macOS sandboxes and Apple's licensing problem* 00:44:28 Why CLI may matter more than MCP* 00:48:11 Open source, GitHub stars, and agent integration* 00:53:11 Git, CI/CD, and agent collaboration bottlenecks* 00:58:15 Founder life and building a 25-person infra company* 01:02:44 AI SaaS, token resale, and API-first business models* 01:06:10 GPU sandboxes, data centers, and compute growth* 01:09:48 Why the AI cloud may look more like Stripe than AWS* 01:11:26 Closing thoughtsTranscriptIntroduction: Daytona, CodeAnywhere, and the End of LocalhostSwyx [00:00:02]: Okay, we're in the studio with Ivan Burazin, CEO of Daytona. Welcome.Ivan [00:00:07]: Thanks for having me, man.Swyx [00:00:08]: Ivan, you and I go back.Ivan [00:00:10]: Way back.Swyx [00:00:11]: How I don't even know how, you found, did you reach out or, for Shift.Ivan [00:00:17]: I reached out to you. The reason was you - we were just - we were thinking about I was one of the co-founders of CodeAnywhere, the first browser-based IDE, and so we were thinking a long time of, localhost should die. And you had this article.Swyx [00:00:29]: End of localhost.Ivan [00:00:30]: Then I reached out to you because of that, and then we talked, and I was actually at a different job and learning about I was the head of, developer experience, and you were quite well-versed in that, and I actually reached out to you, among other people, how do we go about that? What are the key things and whatnot at this point in time? And you were nice enough to take the call, and I remember I was late on your call with you.Swyx [00:00:51]: I don't remember.Ivan [00:00:52]: I remember because I was with my then I'm thinking of a girlfriend or wife at that point in time, I'm not sure. It's the same person, so that's great, and I was late ‘cause we were, in, Italy on, vacation, and then I was late for something. I felt so bad, and you were so nice to be, good about.Swyx [00:01:10]: The reason I'm nice is because I'm also late to other people, so it's like, who's, who's without sin here, yeah, so I have to, for those who don't know, InfoBip Shift, there's this whole thing that, you did in the past, and, and that was basically one of the inspirations for me starting AI Engineer, which is like, I have to thank you for giving me that push to be like, “Oh, you can, you can build and sell conferences?”Ivan [00:01:34]: I remember you asked you asked me at the beginning to give me advisory shares, and I was so focused on what we were doing, I said no, and I should've took the advisory shares. So I'm sorry, dude. But anyway.Swyx [00:01:43]: We're not, we're not venture backed.Ivan [00:01:44]: No, it doesn't matter.Swyx [00:01:45]: It's Yeah, anyway, so I think what's impressive about you is that CodeAnywhere is the thing that you've been trying to build, and, you kind of put it on hold and then came back after InfoBip. Just give us the story, do you - the story and the origin story, going into Daytona.From CodeAnywhere and Shift to DaytonaIvan [00:02:05]: Sure. Like, really way back, me and my co-founder have been together. I say this, I've said this multiple times, it's like we were married and divorced and married. Some people actually ask me is my co-founder my partner. they thought it literally. It's not literally, but we have done multiple companies together, and to your point, we had this shift where we went from the CodeAnywhere to the conference called Shift, and then back to, Daytona. We originally started stacking servers, doing like virtualization in the early 2000s and, routers and doing basically all these things, at a foundational level, and that was a services company which we sold to focus on what my co-founder actually invented, which was the very first browser-based IDE, right, I say the first. Before us was actually Heroku. They did it for a very short time until they became Heroku. But outside of them, we were the only one, and it was called.Swyx [00:02:55]: There was Cloud9.Ivan [00:02:57]: Cloud9 came out slightly after us. There was Replit, which came out when we stopped doing it, Replit came out, and they have been successful since then, which is great. There was Nitrous.io. There was quite a few that existed at the time, but it was like too early. But the interesting part is that we, at that point in time, because there was no VS Code, there was no Kubernetes, and Docker had just started when we Or I'm not sure if it was even public at that point in time. And so we had to build everything to the whole stack ourselves and that was the key learning that we brought into and that we've been using in Daytona today. So it was super early. There's about 3 million people used CodeAnywhere. It was slightly, it was angel-backed more than venture-backed. We ended up paying everyone back because it didn't have that sort of scale. But, three years ago, we started something similar with Daytona, which is not what we are today, but it was automating dev environments for human engineers, the basically the underlying stack of CodeAnywhere. And then we did a hard pivot last January to sandboxes. And so here we are.Swyx [00:04:01]: Historic pivot, yeah, and, it's one of those things where, I had independently invested in CodeAnywhere, but also in E2B, and then both of you pivoted into the same thing, and I'm like, “F**k.”Ivan [00:04:12]: You invested, you invested in Daytona. You invested in Daytona. But you were the first If we had not got your check, we wouldn't have done it.Swyx [00:04:18]: No way.Ivan [00:04:19]: No, it was like, “We have to get him on board first,” and you were that kicker that we, that got us off the ground.Swyx [00:04:23]: No, because you were putting me on your pitch deck, man. I was like, “Man, this is like a good trip if I don't invest.”Ivan [00:04:29]: That's because it was your quote. It's like we.Swyx [00:04:30]: Yeah. It's the end of localhost.Ivan [00:04:31]: Did a bunch of research about end of localhost and who was interested in that,.Swyx [00:04:34]: No, that's like, I put, I wrote that blog post, and every single company in that field reached out to me, and then every VC who was receiving those pitches then also had to call me and, talk it, talk through it with me.Ivan [00:04:47]: It's finally happening though.Swyx [00:04:48]: It was really super interesting.Ivan [00:04:48]: It's finally happening.Swyx [00:04:49]: It's finally happening.Ivan [00:04:49]: Yeah, it's finally.Swyx [00:04:49]: It's finally happening, with maybe sort of non-human users. Yeah, so what is Daytona today? Let's get like a quick description. I'm wearing the shirt.What Daytona Is Today: Composable Computers for AI AgentsIvan [00:04:58]: You're wearing the shirt. Yes,.Swyx [00:04:59]: It says, I think your branding is very good. Like, it's very consistent. It runs AI code. Like, it cannot be simpler.Ivan [00:05:05]: Exactly, but we're gonna probably have to change that.Swyx [00:05:07]: Oh, s**t.Ivan [00:05:07]: It's also a subset of what we do. Unfortunately, we really love this, Run AI Code is super simple. People interpret it different ways. I think we've given out 5,000, 6,000 of these shirts. People wear them with pride because it doesn't really market about us.Swyx [00:05:21]: Yeah, Daytona's on the back.Ivan [00:05:22]: It markets the back. It markets to the person itself, so I think we did a really good job on that one. But it is also a subset of what we do, because people, when they think about Run AI Code, they just think about these small, let's call it isolates, code execution boxes that, you send some code, you get an output. Whereas what Daytona is today is essentially composable computers for AI agents. It is, the market calls them sandboxes which can be misleading.Swyx [00:05:44]: All these things. All these things on.Ivan [00:05:45]: Yeah, exactly, ‘cause it can be misleading ‘cause people usually think about sandboxes as a demo or a test environment versus a production-grade environment. But what Daytona does, if you think of the laptop that you have in front of you or the computer that's over there, or, my wife is an architect, so she has like a Windows with a 3D graphics card inside to do 3D rendering. Like, as humans, we have different computers or different compositions of computers. And our belief is strongly that agents today and going forward will need all these different compositions of computers to do different types of tasks. And so we offer that basically through an API.Swyx [00:06:19]: Yeah, to give people - I'm trying to sort of front-load all the aha moments or the wow moments so that people can, stay engaged and click like and subscribe. the market is exploding, right? Like, you have been reporting 74% month-on-month growth, and it also, it's just been growing for a while. Like, it's been going like this. And every single - It's not just you guys. It's every single.Ivan [00:06:41]: Everyone, yeah.Swyx [00:06:42]: Sort of, compute provider. I don't know if you agree with me saying compute provider or not.Ivan [00:06:48]: It's fine.Swyx [00:06:48]: Yeah. So like organically PLG-driven growth, but also enterprise is doing super well, I think I wanna rewind to January of last year when you did the pivot. Like, so you obviously called this market early, and you were positioned for it, and you are now one of the market leaders. But what was the insight that made you do the pivot?The Pivot: From Human Dev Environments to Agent SandboxesIvan [00:07:06]: The insight that made us do this pivot is the quarter before that, so end of 2024, when we had - Basically, we did a demo with - I don't I think we discussed this as well, Devin was not public. You actually gave me access to Devin at that time. So Devin.Swyx [00:07:25]: I did?Ivan [00:07:26]: Yeah, you gave me access.Swyx [00:07:26]: I don't think I was supposed.Ivan [00:07:27]: Yeah, exactly.Swyx [00:07:28]: Yeah, I.Ivan [00:07:28]: So it doesn't matter. You.Swyx [00:07:29]: Yeah. I gave like three friends access.Ivan [00:07:31]: Yeah, or it was a call and you showed it to me. It doesn't matter. but OpenDevin was available, which is now called OpenHands. And so we're like, “Oh, this seems to be a thing. This is not public. Let's take our for human automation of dev environments and take, OpenDevin and launch that as a SaaS.” And we did that. Not very many people signed up and used it, but a lot of people reached out that were building agents, and they were like, “Hey, my agent needs a compute sandbox runtime,” whatever you wanna call it. I forgot what it was called at that point. And then we were like, “Oh, amazing. This is a new market. Here is our infrastructure. Here's our product, and go.” And what we found really fast, soon, was that people did not like what we had built. It didn't work. And I remember talking to people at the beginning when we're doing this, the sandbox we're building for agents. People were like, “Oh, why is it different? It's the same thing. We have like EC2, we have VMs, we have all these things.” But we saw that everyone we gave it to, it was like 20, 30 people, they all said, “No.” Like, “This is not what we need. This sort of breaks.” And basically, me and my co-founder not knowing a lot about - ‘cause we're infra people. We're not AI people. So I basically took it upon myself to like watch every single podcast that exists, including all of, all of these and all that, and sort of get up to date, read all the blogs, like get, understand what's going on.Swyx [00:08:45]: Do you wanna shout out who else was useful, just in case people are also looking.Ivan [00:08:49]: Generally we -, I looked at There's a few of podcast, different segments and different types. So there's you guys, No Priors, Bill Gurley's was great while.Swyx [00:09:04]: VG2, yeah.Ivan [00:09:05]: Yeah, while it was around. So there's a few. 20VC is interesting from a different dynamic, and some are different dynamic. But there was, also Red Points.Swyx [00:09:14]: We're not really about the compute market.Ivan [00:09:15]: It was also already - Sorry?Swyx [00:09:16]: You're, you want - You're looking at the agent infra market.Ivan [00:09:19]: I was looking at the agent market and the AI market in general and sort of understanding who are the players, what the perception, and how that goes. And like obviously you complement this with like going to conferences, going to events, going to meetups, reading white papers, like doing all the things that you have to do to understand what's happening. And so when we figured, when we sort of had an idea of what we had to build, literally over the New Year's Eve, literally on New Year's Eve, I half vibe coded the first MVP, first minimal viable product of what Daytona is today. And I went to sleep at like 3:00 AM or something like that. I was doing - I just put my like baby daughter and wife to sleep and, Happy New Year's, and go back to just, doing this. And I sent it to my co-founder, my CTO, and he saw it in the morning. He's like, “This is absolute garbage.” “Do not show this to anybody at all, but the idea is good.” And so he took two weeks, and he rebuilt it.Swyx [00:10:09]: Did it like look like that? Listen, I - It was rough idea.Ivan [00:10:12]: Oh, not even, not even close. Like it was it was way worse. But it was like a very - It was a simplistic view of what it should be. Like, it worked, but it was not ideal. And so he went, we went down the whole, which is his job as CTO, to go, and he came back with this version. We then called all the people that had said like, “This is garbage,” a quarter ago. And we set up these calls, and we gave it to - We just demoed it to everyone. And all the calls went long, every single one. They were 15-minute calls, and they all went to like 25, 30 minutes or whatnot. And everyone said, “We need, we want access.” There was no login, just an API key, ‘cause it was just a beta or an alpha. And they said, “Oh, we want access.” And we're like, “Sure, yeah. Okay, thank you very much.” But after like the next day, if we'd not send it, every single one, like every call that we did, everyone came back, “Where is my API key?” Like everyone wanted it. We're like, “S**t.” Like this is it. Like I've never felt So one, the understanding to your point was like most people thought it was the same infrastructure for humans and agents. We understood a quarter ago it's not. We just didn't know what was the right primitive. And then when we came, and we can talk about what that is, and we gave it to these people, I've never seen, I've never experienced - I've done multiple companies in my life. I've never experienced this, that people literally call you if you do not give them access. Like they want access right now. And so it's like, okay, they don't want this. the thing that they want doesn't seem to exist, or they have not found it, and they really want what we want. And then when we understood that we're onto something, and then when you think about the size of the market, like the market for human engineers and enterprise is a very large market, so think GitLab or whatnot. But the market for every single agent that will exist ever in the future is just like, what is that market? How big is that? And we're like, “We are all in on this.” And so that is where we made sort of the cut between the old product and the new one.Bare Metal, Stateful Sandboxes, and the Lambda + EC2 ModelSwyx [00:12:02]: Yeah. But it wasn't composable at the time?Ivan [00:12:05]: It was very - It was basically just a Linux box that you could change, that you could define number of CPUs, disk, and RAM. Like that is what you could do, but you couldn't have multiple operating systems, you couldn't resize it on the fly, you couldn't add a GPU, you couldn't do like all the things. It was just the, just the first sort of variation of that, yeah.Swyx [00:12:22]: Was it bare metal from the start?Ivan [00:12:24]: It was bare metal from the start. And so the interesting thing that we thought about right away, so our.Swyx [00:12:29]: Which, give people the background, what is the normal path?Ivan [00:12:32]: Yeah, so, basically most providers run this on top of VMs. And also.Swyx [00:12:37]: Firecracker.Ivan [00:12:38]: Yeah, they run on Firecracker and VM. And so we also fire - We can get - We have multiple isolation layers and we can do that. But the common way to do it is that they, one, that the state of the machine, or the hard disk is not part of the sandbox itself. And the other thing is they're not meant to last forever. So most of them are preemptible, like they can There's a time that they can live. And so our thought was when we were going into this is, agents will be like humans in the sense of you don't want your laptop to be shut down until you're done with work. Like, and you want to close the lid and open the lid, it's the same state. So you - Agents would want that, like the pause and come back. They want those two things. But also agents really want speed, right? Can they get it? So when we thought about it's like we need something insanely fast, how to make it fast, how to make it long-running, and stateful. And so those two things, it's like combining a Lambda and an EC2, right? Those two things together. And so we didn't have an idea how others did it, ‘cause we didn't know too that there was a market around this. It was more like, okay, this is what we need, what they need. And we looked at Kubernetes, it wasn't wasn't good enough for that. We looked at Nomad, it didn't enable that. And so our history in rewriting our own scheduler at CodeAnywhere is basically what my CTO came up with. Like, he's like, “Oh, the learnings from there,” and he brought it. And the funny thing is, our third co-founder, when he saw it, he's like, “Dude, what is this? This is like 2008.” Like, we went back in time, and he's like, “Exactly.” And so the reason why Daytona is like super fast, and you see this on benchmarks, is we essentially, we run on bare metal. We have our own scheduler, we use the underlying, disk, CPU, and RAM of the underlying machine, which means your IOPS are insanely fast because there's no, there's no network between an EBS or something like that. But also the snapshot, the point in time, the templates, are also preloaded on the bare metal machines. So when you fire off a sandbox from a template or a snapshot, you're essentially directed to the bare metal machine where that snapshot is based on that NVMe drive, and then it literally just turns on that machine, and it's local. There's no network latency, anything on there. And so that is sort of the specificities that we, when we're thinking from first principles, what a computer would look like for an agent, that is what we came up with, and that's what we created.Benchmarks, 60ms Startup, and 50,000 SandboxesSwyx [00:15:02]: Yeah. I should maybe, I don't know if you endorse this, but there's someone that does compute SDK, you guys do very well on there, with like the TTI, right? I. is this a, is this a is this a relevant benchmark for you guys? I don't know.Ivan [00:15:16]: I don't know, and it changes every day. So today RKL is.Swyx [00:15:18]: I don't know what RKL is. Never heard of it.Ivan [00:15:20]: Yeah. RK, yeah, so it is there.Swyx [00:15:22]: You are, at least a third of the next tier of performance, and then, there's a lot of other better-known names that are very slow to start.Ivan [00:15:31]: Yeah. We've been the number one by far for a long time, and now there's different, there's different definitions also of sandboxes, different isolation patterns, different other things. So RKL runs it literally on the S3, the data, so it's very different, and they spin up a sandbox, spin up a container for that, so it's a different type of thing. So the definition of a sandbox is something that we can all, we all need to get along with. But yeah, we're insanely fast on getting these things, up and running. And so you can see even there that it's a zero point 0.10 to 0.11, so.Swyx [00:16:03]: Close enough. Yeah. what else do you need, right?Ivan [00:16:05]: Yeah. So the benchmarks itself, so, in this, in I don't think the benchmarks equate to market ownership or revenue or anything like that. and I've seen this with multiple benchmarks, not just in sandboxes, but in general benchmarks around.Swyx [00:16:20]: It's table stakes. It's just like.Ivan [00:16:21]: Exactly. But it doesn't hurt.Swyx [00:16:22]: Just roughly check.Ivan [00:16:22]: Like you definitely have to be up there and you have to be competing so that people know that, oh, this is definitely one of the top. Because this is only one dimension of what customers look for. There's other things like how many can you spin up consecutively? There's a feature set, there's support, there's like all different things that people look at, but you definitely have to be there, on the benchmarks.Swyx [00:16:40]: How many people do people spin up consecutively?Ivan [00:16:43]: So we have.Swyx [00:16:43]: Or concurrently, is the Concurrency, right?Ivan [00:16:45]: There's three metrics that we look at. And so one is like time to spin up one, and so our time to spin up one is 60 milliseconds with network latency. So request, spin up, reply, 60, the whole thing, 60 milliseconds. That is one. But if you wanna spin up 50,000 at once, we are now at about 75 seconds. So it takes about 75 seconds to spin up concurrently 50,000. Some others, there's public data around this, like take 2,000 seconds, which is 30 minutes. Like there's different variations of that. And then there is the so it is speed of one, speed of like multiple, and then how many can you consistently have up and running. And so we basically have right now no limit to how much we can add because we basically own our own metal. But the biggest customer of ours does like about 850,000 every single day is sort of where they're, where they're just shy of a million every single day that they're running, we do have a request for half a million concurrent, which is literally half a million CPUs somewhere running. So that's an interesting.Swyx [00:17:44]: They pay by like vCPU seconds.Ivan [00:17:47]: By seconds, yeah.Swyx [00:17:47]: Or whatever. Yeah. Okay, and so and then, and the other thing is, the sleeping and the resuming, ‘cause it's all the stateful resumption of all these things, how, what kind of workload are people putting through this, right? Like how is it Do we measure by gigabytes in memory, gigabytes in storage? I don't In like network attached storage. I, what are the costly ones of, out of all these features?Workload Economics: CPU, RAM, Network, and StorageIvan [00:18:15]: The most expensive thing are CPU.Swyx [00:18:18]: Okay. Yeah, of course.Ivan [00:18:18]: The second one, yeah Then it's RAM, then it's disk. We actually don't charge.Swyx [00:18:22]: Which is snapshotting, right?Ivan [00:18:23]: No, it's actually the, snapshotting's part of it, but basically the size of your hard disk, of your machine. So do you have 10 gigabytes, do you have 20, do you have 50, do you have whatever? And then the transference of that. Right now, currently we don't charge for, network at all at Polychron.Swyx [00:18:37]: Oh, you gotta, yeah, you gotta fix.Ivan [00:18:38]: Yeah. It is very much a it's a larger and larger part of our bill, so we're working around, that part there. Obviously, that is the least, expensive, so the hard disk is the least expensive, so it's basically CPU, RAM, for us network, ‘cause we don't charge the customer, and then hard disk, is how it's split up. But there's also different types of workloads, so we basically split it up into two types of workloads in Daytona. One is what we call background agents or long-running agents. and the other is, basically RLs and evals, which I put sort of together. And so they have very different patterns of usage, and if you look at the usage of a background And I'll just name names of companies, not specifically.Background Agents vs. RL/Evals: Two Usage ShapesSwyx [00:19:21]: Yeah, open, all hands.Ivan [00:19:23]: Yeah. So like a background agent's a Cognition, a Lovable, a like all these things are Harvey. These are all long-running, background agents. And so if you look at their usage patterns, their usage patterns are similar to human, which is like follow the sun. Basically, the usage patterns of that is like noon is probably the highest, and the midnight is the lowest, and then weekends are lower. weekday is higher.Swyx [00:19:42]: Yeah, that's a fun question. How global is it? Is it very US-centric or?Ivan [00:19:46]: The US is a large part, but we have currently, we have Asia, Europe, and the US regions.Swyx [00:19:52]: So it's quite global.Ivan [00:19:53]: Yeah, it's quite global. We have it all over. It's interesting that our I talked to you a bit about this. Our number one city by user.Swyx [00:20:01]: Hmm.Ivan [00:20:02]: Is Singapore.Swyx [00:20:04]: Oh, wow. Amazing.Ivan [00:20:05]: Which is an interesting one, right? Not by revenue, just by just like by individual head count.Swyx [00:20:09]: Really?Ivan [00:20:09]: Just like an interesting thing.Swyx [00:20:10]: Singapore is, Singapore is weirdly high in the adoption charts of AI for the population. It's like an, seven, eight million population. And it's like keeps showing up.Ivan [00:20:20]: No, it's quite interesting. We were quite shocked, and I was like, “Oh, this is interesting.” And also one that's up there.Swyx [00:20:24]: There's a reason I'm doing AI using Singapore. it's because I'm from there.Ivan [00:20:27]: We're there. We're gonna, we're gonna be there as well. and it's interesting that Japan is in the top or like Tokyo's in the top, which is in all the tech cycles it has never been. It has never been, so it's quite interesting that they're.Swyx [00:20:39]: I think the Japanese just love AI. Yeah. It's that, and then it's Brazil. That's it.Ivan [00:20:44]: Brazil has always been in.Swyx [00:20:45]: I think.Ivan [00:20:46]: Even when I look, if you look at like GitHub's data and ask historically with CodeAnywhere, it was always like US, Western Europe, and then you'd have like India, Brazil, China, like that would be there. But like Singapore was not in, specifically Japan was never in sort of that top, that top.Swyx [00:21:01]: Yeah. Weird pockets.Ivan [00:21:01]: Weird. Yeah, so it's very global.Swyx [00:21:02]: Okay, so actually that, but that's helps you to distribute your load through, all time?Ivan [00:21:08]: The interesting thing is like we have those kind of loads, but if you look at the researcher loads, they're quite different. So what they are is like if you give them concurrency of 10,000 or 50,000 or 100,000 CPUs at ARMb, when they fire off a run, it's just 100%. And then it just runs, and then it stops. So it's very, the usage pattern is squares basically, right? And it's also not follow the sun, because people will fire it off at midnight before they go to sleep but then wake up and so it's very unpredictable, so you don't know where that is. So the shapes of the usage are quite different than we have had before. And also what's interesting is when it's sort of a follow the sun, even if you have a high growth company, you can sort of predict your usage patterns and have enough capacity for that, because it's sort of, it grows in a, in a way you can project. When you have companies doing sort of like evals and RL, they're super spiky. So they're gonna come in, it's like, “We're gonna use nothing, then can we have 100,000?” Right? And then go back down. And then 100,000, go back down. So it's very different, right? And.Swyx [00:22:09]: Do you want to lock them into commits so.Ivan [00:22:11]: Yeah, we do.Swyx [00:22:12]: Yeah, okay.Ivan [00:22:12]: We so we have to lock them into some sort of commits to have that capacity, because we have to have, basically we have to have the capacity for peak. Right? And so right now, Daytona's mean utilization is 15%, 1-5.Swyx [00:22:25]: Oh my God.Ivan [00:22:26]: So it's very low.Swyx [00:22:27]: Because it's very spiky.Ivan [00:22:27]: It's very spiky, but we get up to 90%. so we have these things. And so what we're, what we're looking at right now as a company is similar to Cloudflare where you can like geo move things around, but that works really well for basically the background agent where it's follow the sun. But this, it's not. Like it's a very different shape. Obviously with scale you figure these things out, but that's an interesting new problem that we have, as a compute provider in the agent space. And when we were doing the conference recently, and so we talked to like Nikita from Neon and.Swyx [00:22:57]: I should bring it up.Ivan [00:22:58]: Parag from Parallel and whatnot, everyone has the same problem. Whereas the usage is super spiky, and this is something that has not happened before, that you have these types of like it was always, it the amplitudes were not this high, right? So it's quite interesting use case and problem solve.Compute Conference and Spiky Agent InfrastructureSwyx [00:23:12]: Yeah, I don't know if we're gonna bring this up again, but let's just talk about the conference, you had like 1,000 something people at the Warriors game, at the Sorry, where is it? What's.Ivan [00:23:22]: Chase Center.Swyx [00:23:23]: Chase Center.Ivan [00:23:23]: Chase Center.Swyx [00:23:24]: I went. It was, it was very impressive. Obviously, you can, how to throw a conference, what did you learn? you put, you pulled together all these impressive names.Ivan [00:23:33]: What I.Swyx [00:23:34]: What were you looking for?Ivan [00:23:35]: My thesis behind the Compute Conference was let's bring together people that are building infrastructure for AI agents. Because when I think of what we're building, it is the agent is the primary user, what are the ergonomics and usage patterns of agents, and so we can do that. And what I found, this was a theory, it wasn't proven, is that we all have these problems, as I touched onto. And I was, as I was talking on stage, it was like we all have the same underlying infra problems, which is this spiky workloads, unpredictable workloads that we've never had before, in human, compute or human infrastructure. And it's, again, it's the same when I was talking to Parag or when I was talking.Swyx [00:24:20]: Lynn. Nikita.Ivan [00:24:21]: Lynn, Nikita. Lynn especially, I was talking to her the other day as well. Like the It is a very interesting type of problem to solve because I can touch on Cloudflare because there's a lot of like talk about that recently as to how they solve that, which is they have a bunch of geos, and basically, as users work in different places, and depending on your tier, they can move you around the geos. And so that how, that's how they get the higher utilization. But you can sort of predict these, and it's If it's something in You'll rarely get a spike that is 10 orders of magnitude. Like you'll get a like let's say one of your customers has some like an exponential curve. What is that to I'm using Cloudflare as an example. 10%, 20%, whatever it is. I don't, I don't have this data, I'm just assessing. It's surely not 10x, right? It's surely not something there. And so how do you go out and solve this problem? And we're all solving this in different ways. So we have.Swyx [00:25:11]: She also has the same thing.Ivan [00:25:12]: Yeah, I know specifically that like Neon had that issue as well. Like how are we solving these spiky loads and things like that ‘cause we talked about it. And so the interesting thing for me to actually internalize was, yes, everyone that's building for agents first is going through this, and we're all solving similar problems, which is quite.Swyx [00:25:28]: Let me let me double-click on this. Okay. So for example, Neon, I happen to know that they're very sort of S3 oriented, right? so they're just like fully bet on S3. And you get to benefit from S3's distribution and infrastructure. So I would imagine that Neon doesn't have to care, whereas Lynn maybe has to care a bit more because obviously she's doing GPU inference. And, for listeners, we did an episode with her, one and a half years ago. And you have to care. But like, right?Ivan [00:25:54]: Parag cares for sure, and Nikita.Swyx [00:25:58]: And Parag is C of, Parallel.Ivan [00:25:59]: Parallel, yeah.Swyx [00:26:00]: Former CTO of Twitter.Ivan [00:26:01]: Twitter, yeah.Swyx [00:26:02]: They are the search.Ivan [00:26:03]: Yeah, they're search, yeah.Swyx [00:26:03]: I You and I know but the listeners don't know.Ivan [00:26:08]: Yeah, we can put it down in the screen, and so ‘cause we, when we were talking.Swyx [00:26:11]: I'll put it up on the, on the screen.Ivan [00:26:12]: Yeah, right.Swyx [00:26:12]: People can look it up if they need.Ivan [00:26:14]: Look it up. And, yes, but they still have CPU and RAM, allocation that you have to have up and running. And so CPU and RAM, you have to allocate that and have that ready. And so there's basically two ways to do it. One is you either over-provision and you can handle the bursts, or two, you basically have, I don't know if this is a term, just-in-time compute, which is like as your load becomes, as your usage comes in, you can fire off requests for VMs or bare metals at other cloud providers and then get them up and running.Swyx [00:26:43]: This is if you go above 100%, right?Ivan [00:26:45]: Yeah, this is.Swyx [00:26:46]: Like your overflow.Ivan [00:26:46]: If your overflow, like spillage or whatever you do.Swyx [00:26:48]: You probably lose money on it, but it doesn't matter, right?Ivan [00:26:50]: It, not Well, you might, you might not That is a more cost-effective way to do it but it's a slower way to do it. Because basically what you have to do is you have to like queue your requests, spin up these just-in-time compute, get it all ready, provision it, and then get your workload there. And so if the time isn't important that much, that's fine, and you can do that. But if your customer, and especially for, let's say, the RL training runs, the reason why a lot of people come to us is because GPUs are more expensive than CPUs, right? So you want your GPU running at, what, 100% the entire time. And so when you're running runs on CPUs, when the when the CPU cycle is like down and spinning up the next one, you want that to be instantaneous so that your GPU doesn't go down, right? And if you then have to like go out and provision machines, you're essentially telling the GPU that it has to wait, and that's incurring our cost. So there's things that you have to try to solve for there.RL Workloads, Declarative Images, and Kubernetes ReplacementSwyx [00:27:43]: Yeah, let's talk about the different workload, right? You said that, what was it? A few months ago, you had zero RL workload and now it's 50%.Ivan [00:27:52]: It will be this one, 50%, yeah.Swyx [00:27:54]: Let's talk about how different it is, right? Like I imagine, for example, a lot less dynamic code generation of like arbitrary code. Like here, it's probably all the same code. You're just doing parallel runs or something, I don't know.Ivan [00:28:05]: Yeah. So you'll have multiple Depends on the like for each run, you'll have a snapshot. And they, for the most part, they actually do use our declarative image builder, which is like, “Oh, we, the agent wants these dependencies, these env vars.”Swyx [00:28:17]: These ones, yeah.Ivan [00:28:18]: Yeah, the declarative image builder, it.Swyx [00:28:20]: Which is a very modal like thing that they.Ivan [00:28:22]: Yeah. And so we build it on the fly and then we propagate that snapshot, and you can spin up as many sandboxes as you want against that snapshot. And then if you have to do changes, the model can, or like it could be also be automated. It's like, “Oh, now for the next run, we need to install these things or remove these things or whatever to get, a task done,” and then it goes off and runs that. So yes, that is something that it seems that they prefer. The number one reason I found, or should I say, let's take a step back. What we are competing against in that environment is essentially managed Kubernetes. So EKS, GKE, whatever. That is what the vast majority run on. And anyone that has tried Daytona versus GKE, EKS is like, “I'm never going back.” That has always been. There's a few reasons. One is the ergonomics. So if you have, if you're using Kubernetes to spin that up, you have to essentially manage the interface interactions with that. Daytona, although as a compute provider, it's more akin to a Twilio and Stripe from a consumption perspective than it is an AWS. Like you have an API, an SDK, it's quite like easy and seamless to get these things up and running, that's one. The other is the speed to which we spin up, which we mentioned earlier, which is much faster, and the scale to which we can go to. We haven't got into features, but an interesting feature is that it's very hard to OOM, or out of memory, our sandboxes, because we can dynamically on the fly.Swyx [00:29:48]: Resize.Ivan [00:29:49]: Resize, which is like impossible on almost any other thing. There are some technologies that enable you to do that, but it's like a very hard thing. And so we actually saw this when, the Terminal Revenge team is, brought us actually. So thank you, Alex and the team, that brought us into this whole space.Swyx [00:30:05]: It's just very rare that, a framework would just say, “Guys, just use Daytona.”Ivan [00:30:11]: Yeah, I think it says it somewhere. Yeah.Swyx [00:30:13]: Yeah. I was like, “What is this?”Ivan [00:30:15]: There's all, there's multiple there, but they also mention a few other places. and so Daytona specifically-We have, the, just jumping on themes here We, I don't know where it says Data Center.Swyx [00:30:27]: I, there.Ivan [00:30:27]: Doesn't matter.Swyx [00:30:28]: There's a very strong recommendation, which is, very unusual. Which is, it's.Ivan [00:30:33]: We do not pay them for this, just.Swyx [00:30:34]: I know, yeah. They just like you.Ivan [00:30:35]: Yeah, they like us. yeah, and also a thing, so, Data Center has multiple isolation sets underneath. The customer doesn't have to know what they are. But basically we have Docker, which is a container, that's hardened with Sysbox. So it's Docker's, isolation that is a security equivalent to a VM, but it's still a container. And that is the default, and they, especially in these training workloads, really like that as an interface to be able to use just a basic Docker container, and we enable Docker and Docker. Which for these RL runs, if you need to do a Docker compose or Kubernetes, you can spin up a K3S inside of these things, which unlocks a huge amount of workloads that you can do that you cannot do on other providers. So just on that part is much more interesting. And so we went that, through that. We showed them that we could do that, and they enjoyed that quite a bit. They being the general venture people.Swyx [00:31:28]: Those people, yeah.Ivan [00:31:29]: And Harbor people.Swyx [00:31:29]: Harbor people, do are they, are they a company yet?Ivan [00:31:33]: As far, I do not know.Customer Pull, Slack Connect, and the Computer Use BetSwyx [00:31:35]: Okay. All right. Yeah. It's like super obvious that like, there's a lot of excitement and success around these things, okay, so yeah, tell us more, right? Like, this is an exploding workload, Harbor adopted you, which helped speed things along. But what are you learning as this new workload comes online?Ivan [00:31:53]: There's a couple things that we learned, which we chat about in the beginning. We, and this has led our story, as we mentioned, we like talked to a lot of customers along the way, and we add more features and more tool sets as we talk to customers. And it's interesting that And I think it's that the ecosystem is so small and/or the models get smarter, where when we see one user come with a request, we know it goes on a roadmap if like three to five customers come with the same request in that week. It's like very bizarre. It happens so many times, which is.Swyx [00:32:27]: Because they're all friends.Ivan [00:32:28]: Sorry?Swyx [00:32:28]: They all, they're all friends. They're all in the same group chat.Ivan [00:32:30]: Yeah, probably, yeah. ‘Cause and they're like, “Oh, can you do this?” And I'm like, “Okay, this is interesting. We'll put it on a feature request.” And then the next one's like, “Oh, can you do this?” “Okay.” It's all the same, right? It's always the same. And so what we try to do, and I personally try to do, I try to be on as many call, quote-unquote “sales calls” I can. I'm in every Slack channel. We literally have about 1,000 Slack Connect channels, something like that. It's an interesting, there's so many interesting things you find out when you have all the Slack channels. You can also see where people, transfer between companies. You see leave Slack channel, enter Slack channel. It's an interesting thing. Also, just I digress, I feel that Slack Connect is literally LinkedIn what it should be. You have a list.Swyx [00:33:08]: LinkedIn charges you to, use your own connections, but Slack doesn't, right? Slack is like, do it for free. It's more lock-in. It's great.Ivan [00:33:15]: Yeah. It's amazing. Yeah. It's one of the reasons.Swyx [00:33:17]: You're gonna pay Slack for life.Ivan [00:33:18]: Exactly. You're there for life. So that's interesting. And so one of the things, the newer things we were talking about earlier is we made a big bet and put a lot of investment on computer use. that is not seen publicly the light of day. We haven't GA'd that yet, but we have.Swyx [00:33:32]: Is there a thing I can pull up?Ivan [00:33:33]: There is computer use there. It's right up a bit.Swyx [00:33:36]: Oh, yeah. Okay.Ivan [00:33:38]: What we have, what we talked about and what we've seen publicly is there's this theme now about, the human emulator where And Elon from XAI has talked about this publicly, and if you think about the models today, they're actually quite sophisticated and they can do a lot of work, but they still don't have access to all the tools. Like, I'm a strong believer that the most efficient way for an agent to work is essentially headless or through, terminal or whatnot. But if we, if we look at knowledge work in general, there's about 100 million knowledge workers in the US, about a billion in the world, and knowledge workers, and the salaries of them aggregate to 10 trillion in the US 50 trillion worldwide.Swyx [00:34:24]: Wow.Ivan [00:34:25]: Something like that. And if we look at, the five most important sectors of that, so like healthcare and government and financial services and whatnot, that's about 56% of that. So let's say it's about half of that. So in the US it's about 25 trillion, and most of them, most of that work is actually still locked into legacy apps inside of Windows, which is not going anywhere for a very long time. Like, people just won't invest in that. How much of it? our assumption is the following: if, in the RPA market, which is similar market, well, not the same 25% of, these white collar, workers', work is automated. If an agent is more sophisticated, can go through more runs, figure stuff out, let's say it's, 40%, right? And so if you take 40% of that, you get to essentially, $10 trillion a year.Swyx [00:35:17]: That's a TAM.Ivan [00:35:18]: That is a that is a TAM. So that's the TAM of the models, right? That's not our, essentially ours. But you get to that size, and to be able to do that, you essentially have to give agents these computers with the legacy. So computer use, either Mac or Windows or Linux. Linux we also obviously have and others have. But Windows specifically is something very new, and the only option right now is an EC2 with, Windows or on Azure. Both of them take anywhere from three to five minutes to spin up. We've created an actual sandbox, so it's a second instead of milliseconds, but you have, point in time snapshots, you have, forking, you have all the things that you have from a sandbox, but essentially enables you to hopefully unlock all this value. And so that's been our big push and bet, but we've sort of, kept our ear to the ground. What is sort of the next things in the market?RPA Returns: Why Agents Still Need ComputersSwyx [00:36:06]: Yeah, knowledge work, and building, and sort of RPA, the next wave of RPA. I got very excited about RPA kind of during COVID times. The UI path was IPO-ing. And it was, a very hot Isn't it, Eastern European?Ivan [00:36:20]: It is, Romanian.Swyx [00:36:21]: Romanian?Yeah, it might be the only Romanian, big unicorn okay, yeah. This I don't I don't, I don't have like a I think there's, I think there's a stage being set for the resurgence of RPA, ‘cause everyone understands that, yeah, no one wants to deal with these shitty apps and no one's gonna rewrite them. Like, you just have to do, a remote operation and programmatic operation of them.Ivan [00:36:45]: If you wanna unlock it, my own setup was basically the following. So I was doing a board deck recently, last month, whatever, and I'm like, “Okay, let's just, let's just do automated.” So, all our data's in, ClickHouse and PostHog and QuickBooks, where everyone else's is, and I'm basically, connected that all to, my Cloud code, like go off and go Cloud code whatever. Go off and, here's the integrations, go do that. It pulled out the first report, which was great. It connected to Brex and all these things, pulled it, which was great, and then I say, “Okay, now pull out this, and this,” and I kept getting, really well McKinsey-style design reports, but the data said partial data. all the missing data, partial data. Like, it can't access all the things, and I got so frustrated, and so I got, I got, my Mac Mini virtual sandbox with OpenClaw. I gave it its own account in our company, and then I went to all these services and created a read-only account, so literally like an intern in your company. And so I would say, “Now go and do this report,” and it would get the same, or like, “I can't via the MCP or the API or whatever. I can't get all the information.” I'm like, “Go log in.” And it will log into the website, then go in, export the data. It'll export the data and do the thing end to end. So even for things that have today APIs, not all of it is exposed, and I to get value, I get immense value right now, but it has to be a computer usage, unfortunately, and so I spend a bunch of tokens just on that, but I get the job done. And so if even a startup like ours, and using all the hottest tools, still needs a computer agent what hope does, Goldman have to have a headless, right?Swyx [00:38:22]: Yeah, what a - Why isn't Microsoft doing this?Ivan [00:38:27]: I'm pretty sure, Satya had a post yesterday.Swyx [00:38:29]: Oh, okay. I see.Ivan [00:38:29]: Which was like, “Every agent needs a computer.”Swyx [00:38:31]: I see, I see.Ivan [00:38:32]: So they have launched something recently.Swyx [00:38:34]: Yeah, they have Microsoft Power Automate, I'm sure, I'm sure, they're gonna have their version.macOS Sandboxes, Apple Constraints, and the Windows OpportunityIvan [00:38:39]: Version of that, yeah.Swyx [00:38:39]: You're gonna try to do yours, and it - I always know there's always demand for Mac, but I know it's, tricky to host, macOS sandboxes.Ivan [00:38:49]: We will have macOS sandboxes fairly soon. The problem with macOS, OS sandboxes is, I'm deep in this, I don't know how much interesting is.Swyx [00:38:55]: No, it's.Ivan [00:38:56]: MacOS has this problem.Swyx [00:38:57]: It's a licensing thing, right?Ivan [00:38:58]: Licensing thing. So one, you're allowed to run only two parallel VMs per machine, so that's one. Two, you can only license to a different user every 24 hours. So if you come in and theoretically, if I wanna charge you per second and I charge you one second, I have to have it idle for the rest of the day. I can't have anyone else doing that. So the pricing will be different in the sense that I will have to - we would have to charge for 24 hours, and that's not even, that's not even the most difficult thing. But the, thing above that is, from a security perspective, they enable you to do memory snapshot, pause, resume, but only on the same physical drive, physical machine. And so what you can do in, Windows world or Linux world is that I can move in the background, your snapshot from one to the other and manage load, right? Here, if you wanna do that, you essentially have to have your.Swyx [00:39:49]: Yeah, snapshots. Yeah.Ivan [00:39:50]: Your.Swyx [00:39:51]: It's like.Ivan [00:39:51]: Physical machine.Swyx [00:39:52]: You can't break it up.Ivan [00:39:53]: You can't, you can't move things around that, and all of that is, that part is, from a security standpoint, if it is written. Like, I understand the security aspect of that, but it disables you from doing these agentic, like really scalable agentic workloads.Swyx [00:40:08]: You need to do a vibe-coded, clean room implementation on macOS that you can then - That's like Clean OS or something. I don't know.Ivan [00:40:17]: So. We have.Swyx [00:40:18]: ‘cause like Linux was originally like a clean room rewrite of Unix.Ivan [00:40:21]: Okay. Yeah.Swyx [00:40:21]: Or something like that, right? Like same thing to macOS. Someone needs to do it.Ivan [00:40:25]: Someone will do that, and someone will have some long-running agents for a few days to figure this stuff out. But yeah. So definitely we - we're really close to offering something ‘cause people do want it, but the pricing will be different, and the feature set will be sort of stringent.Swyx [00:40:38]: Yeah, nobody's gonna use this. like, the labs, the labs will because they want to automate macOS.Ivan [00:40:42]: They have to do RL. They have to do RL again. But even if you The - So the point is with the RL part, if you, if you do RL on macOS, then the next iteration of the model comes out, it will be able to use these tools significantly. Then you actually need to run those, that somewhere. So you're gonna have to have that, later on. And from, if anyone at Apple is listening, I very much feel that they are shooting themselves in the foot of the scale of the revenue of compute or licensing they could get if they would just enable a concurrency model similar to what you can get on a Windows and a, and Linux.Swyx [00:41:17]: Yeah. Yeah. And I'm sure they've heard this before. They just don't care. Yeah, it's And maybe they will change their mind with the new CEO.Ivan [00:41:24]: Yeah. We'll see.Swyx [00:41:25]: We'll see.Ivan [00:41:25]: High hopes.Swyx [00:41:26]: High hopes.Ivan [00:41:26]: High hopes.Swyx [00:41:27]: Okay. But I, it's very clear the market opportunity is huge in Windows, and you can go for a long time on just Windows, but your customers are gonna want both. and I think, it is interesting to me that, this is the sort of God application of agents, right? Like, I don't It was - How big was OpenClaw for you guys? Like, was it, was there, a significant bump.OpenClaw, Agent Labs, and the B2B2C Sandbox MarketIvan [00:41:54]: Not for us because we.Swyx [00:41:54]: Because you already.Ivan [00:41:55]: We're kind of positioned differently. Whereas although it's completely PLG and we have individual developers that use it, most of the users that use Daytona are sort of a B2B2C. Sort of it's either B2B or B2B2C. So, in the researcher world, it's B2B, so you're selling to, labs and neo labs and things like that. But on the long-running agents, it's mostly, from a scale revenue perspective, it's mostly B2B2C, where you have a app layer agent that uses you at a big scale.Swyx [00:42:26]: Like a Manus. Yeah.Ivan [00:42:28]: Like a Manus Lovable type of thing.Swyx [00:42:31]: Yeah. I think that's the question of, well how, um-Uh, yeah, B2B to C is basically to me what I've been calling an agent lab, which is kind of like you're not in a model lab, but you're making a very good wrapper that is a platform that other people can sign up so they don't have to code those things. Yeah, it sound, it sounds like a much better market than the direct OpenClaw market.Ivan [00:42:56]: I've like - We I've done multiple things. So the CodeAnywhere's part of our career path R in the calendar, was very much an end user developer product. And so that is great. It You can get a lot of developer love, and I feel that we do as a company have a bunch of developer love. But it's a different type, where it's people building these things. Again, it's more akin to a Twilio because you don't really run - As a person, you wouldn't run Twilio. I don't know how many people remember. It was like ask your developer billboard and whatnot. And people really love Twilio, but they only used it inside of like, “Oh, I'm building this app or service for thing.” And so we're very much directly to that. And you also know that I used to work for a competitor for Twilio, so it's kind of ingrained, in my DNA.Swyx [00:43:35]: People don't know InfoBip is that big.Ivan [00:43:38]: Yeah, it's.Swyx [00:43:39]: Because.Ivan [00:43:40]: It's a billion euro.Swyx [00:43:40]: They're all American. They're like, “Whatever's in Europe doesn't matter to me.” But like it's the, it's the same size or bigger? Same size?Ivan [00:43:46]: It's about half the size.Swyx [00:43:47]: Half the size?Ivan [00:43:48]: Yeah, about half the size.Swyx [00:43:48]: It's like, yeah.Ivan [00:43:48]: Still huge. Multiple billions a year. Yes.Swyx [00:43:51]: That's crazy.Ivan [00:43:51]: Exactly, and so that - These are like really interesting and large revenue-generating, very sticky businesses. Whereas when you're selling to the - When your focus is the end developer, it is a very hard sell because they're very price sensitive, very price conscious, very around that. And there's very It's very hard to scale. Your cap is the number of people that are willing to spin up - First of all, wanna spin that up, and then spin up multiple of these. Whereas if you're in the enterprise one, like we know everyone's talking about like how many tokens they're spending, I'm spending. Like a lot of companies today are like, “If this is our company, spend as much as you can.” Like basically that is where we're going. And so if you think about that paradigm, where you're selling to companies that say, “Spend as much as you can to generate, productivity,” versus, “Oh, I'm a single person. I have this much budget, and I'm doing this thing because it's fun or it's helping me out or whatever.” Like it is a different, it's a different go-to-market, I think, strategy.MCP, CLIs, and Sandboxes as the Agent RuntimeSwyx [00:44:50]: Yeah, there's a lot of discussion. I'm just kind of going through like the mental list of things that are in your favor, which is, for example, MCP versus CLI. Like obviously you want CLI. It's been very good for you. I feel like it's maybe a drop in the bucket or maybe it's huge. I'm just checking whether it's like these are big trends.Ivan [00:45:10]: Those things you - work well in our favor, to your point just because every.Swyx [00:45:13]: They're kind of drop in the bucket, right?Ivan [00:45:15]: I think it's like sort of all the things come together. And so there's so many things that impact that. To your point, like OpenClaw wasn't huge for us, but like having the agent SDK, from Anthropic, so or Cloud Claude Code was very interesting. The reason why it was interesting is that a lot of, let's call them app I don't know what to call them, app layer agent companies, essentially they are like, “Oh, I can create this new app, this new agent. All I need, I just use Claude Code, and I throw it into a sandbox, and then I have my interface to the human to that.” And so that enabled so many more companies to actually offer this, and then they would pull on sandbox. So that was, that was interesting. And to your point, like MCP, versus the CLI, the MCP is an interface against an API, whereas the CLI is like you can actually go do things. Like this is it. The difference between integrations and actually running scripts or data or analysis against a thing. So being able to use a CLI very well enables the agent to do more things, and it's because that people will invoke a sandbox, they'll run it in the CLI, and but it'll do anal-analysis on that data and then give you an actual result versus just, pulling data from an API source.Swyx [00:46:29]: Yeah, it's a layer of indirection basically, it's the same thing as agentic search versus RAG, which where you're.Ivan [00:46:34]: Exactly, yeah.Swyx [00:46:34]: Just like you just win whenever people put more agents into their workflow. And so like it doesn't really matter, but I'm just kinda teasing out like what else have people heard about that like it's sort of, “Oh yeah, this is another sandbox use case. Oh yeah, that's another one.” Am I, am I missing any big ones?Ivan [00:46:51]: The thing, the thing that people, which is the computer use stuff, which I think is probably the most interesting one, is, and to your point, we've talked to so many people over the last year. It's like, “Oh, like why do you need a sandbox? Why do you need this? Why this?” And to your point, it's like, “Oh, I need sandbox for this. I need sandbox for that. I need sandbox-” It's like, “Oh, I need it for every single thing.” And so basically what I, what I - and it sounds like a broken record, it's like you use a laptop every single day, right? And you are n of one. It's just you. But now imagine how And by the way, the laptop, the computer PC market, the PC market is about equal to the cloud market in total. So it's about 150, 180 billion a year. Something like that. It's about roughly the three cloud hyperscalers is about equal to like Apple, HP, Lenovo, whatever, It's a little bit less, but it's sort of like that. And now imagine And that's just like, so how big is the addressable market? What, how many people are there in the world now? What's the last data?Swyx [00:47:45]: Let's call it eight billion.Ivan [00:47:46]: Eight billion. And so let's say you can have two computer, like you have one personal and one business, whatever. Like so it's double that, right? and so that's 16 billion, right? How many agents are gonna be running in two years, in 10 years, in 100 years? Like And for every single task, they will need one of these. And so how big is that? That market is essentially quote unquote “infinite”. You will get to the point, and Dylan Patel was at the conference talking about, from SemiAnalysis, that talks usually about GPUs, was also talking about how CPUs will now be a bottleneck because it will be the constraint. You won't be able to grow, or we won't be able to have enough of these because there won't be enough CPUs to basically do.Swyx [00:48:23]: Yeah. Well, I actually had a really good podcast with Doug Oliphant, who, which was his president at SemiAnalysis, where they've basically been like, yeah, it's been a GPU shortage first, but then it's cascaded down to memory and now to CPUs.Ivan [00:48:35]: CPU, yeah.Swyx [00:48:35]: It-What's next? So networking. So, networking actually has been in shortage for a while if you're looking at, just GPU networking. But, yeah, it's really crazy the amount of computer use that's going on, yeah, cool. I, other questions are, just the one very big part is the open sourceness which you didn't have to do, your competitors don't do, like it's not, a lot of people are worried about keeping their projects open source because some competitor can just slot fork it. I don't know if there's any reflections on just being an open source company.Open Source, Trust, and Enterprise ProcurementIvan [00:49:15]: Yeah. There's a bunch. So we the original product that we did was open source.Swyx [00:49:19]: Yeah. CodeAnywhere.Ivan [00:49:20]: So doing that was actually very good for us. There's basically a saying of, What's the saying? Like, companies that are, that are doing really well, measure themselves against, free cashflow, that are kinda okay, it's EBITDA, then, it's, it goes all the way down.Swyx [00:49:36]: The worst is like GitHub stars.Ivan [00:49:37]: GitHub stars. GitHub stars are the worst, yeah. So you go all the way down to GitHub stars. And so our original one was GitHub stars. That's what we talked about, we're at the point we're talking about revenue, so we're we've gone up the stack on that. And so we started.Swyx [00:49:47]: No, profit.Ivan [00:49:48]: Yeah. We haven't, we're, we'll get there. We'll get there. But basically at that point we did stars and GitHub and it was useful, and the original variation that we did, it we split the core into its own repo and it was Apache 2.0, so very, permissive. And then we basically would bundl