Podcasts about UI

Aaron, Brian, and Brandon cover major stories including NVIDIA's record quarter and continued growth forecasts, alongside concerns about declining free cash flow and customer financing. They discuss NVIDIA's reported $12.9B acquisition of Hugging Face as a strategic move to strengthen the open-model ecosystem and go up the stack, and Stripe's $8B acquisition of OpenRouter as routing infrastructure for model choice and potential agent-to-agent commerce. The group reacts to reports of OpenAI agent testing in which agents collaborated, manipulated logs, and tried to deceive humans, framing it as a security and guardrails issue. They also mention Microsoft employees' surprising AI spend, OpenAI's “Jalapeño” hardware push and manufacturing constraints, and Salesforce's “Claude Force” concept of using Claude as the UI to query Salesforce data.SHOW: 1059SHOW TRANSCRIPT: The Enterprise AI Show #1059 TranscriptSHOW VIDEO: https://youtu.be/Clmgst03-egSHOW LINKS:NVIDIA's record quarterNVIDIA buys Hugging FaceStripe buys OpenRouterOpenAI's new chipSHOW SPONSORS:NordLayer - Use ENTERPRISE10 for 10% off.Nasuni - Activate your data for AI and request a demoTopic: Link to the full list of topics for the monthFEEDBACK?Email: show @ the enterprise ai show dot comBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

Westwood Studios wasn't always known for the command and conquer series. While 1992's Dune 2 laid the foundation for the RTS genre as a whole, they were more famous for their dungeon crawling RPGS, usually associated with Dungeons and Dragons. So when they decided to develop a new IP with Lands of Lore, they were coming to it as grizzled veterans, and they had no intention of messing around. It was released to near universal acclaim. Critics praised near everything about it - graphics, combat, puzzles, even the UI. It was considered to be less cruel than other dungeon crawlers, less punishing and with greater clarity when it came to puzzles and enemies. But this was 1993. The genre that Lands of Lore belongs to is very nearly dead, with only the indie scene keeping it alive by the skin of its teeth. As the genre developed, slow and methodical dungeon crawling was trumped by real time demon slaying, something that even the sequels to the game were not immune to. The friction of delving into a dungeon where death lay at every turn, and confusion reigned is something that we see very rarely today. Is this with good reason? Have we moved on from what Lands of Lore has to offer? Or is it still worth playing all these years later? On this episode, we discuss:UI. Lands of Lore has a very unique UI, with heavy use of screen space dedicated to inventory and spells and only a relatively small window showing you the real time action of what your party is doing. Does this offer excellent functionality, or is it just a bunch of wasted space for no good reason?Combat. Combat is turned based, but movement is real time. Attacking and spellcasting are tied to cooldowns, but using items is not. Enemies chase you in pods when aggroed, but you are free to rest and heal to full at any point. Do the contradictions in the combat systems of Lands of Lore exist for a reason, or does it just become a complete mess? Level Design. Lands of Lore's dungeons are elaborate mazes, filled to the brim with traps, treasure and buttons. Progressing through them is difficult, it's easy to get lost or stuck, and much of the environments look the same. Is this part of the fundamental experience of playing a dungeon crawler, and part of the fun, or is it simply an exercise in frustration? We answer these questions and many more on the 141st episode of the Retro Spectives Podcast! — Intro Music: KieLoBot - Tanzen K Outro Music: Rockit Maxx - One point to another Lands of Lore OST - Frank Klepacki — Is Lands of Lore a good entry point into the blobber genre? What were your experiences playing this one on release? If we were to play another one, which one should we tackle next? Come let us know what you think or recommend us a new game on our community discord server! If you would like to support the show monetarily, you can buy us a coffee here!

With Idaho on the road for a Thursday game at the University of Utah, UI head coach Thomas Ford Jr. and Vandals players were not available, but they will return next week.Skyline Sports analysts Samuel Akem & Jerek Wolcott (former Idaho athletic administrator) join Colter Nuanez to break down Idaho's season-opening 38-34 loss at Cal Poly.

A new data analysis suggests there are more heat-related deaths than what's reported by the CDC. Candidates for Iowa's First Congressional District share their plans for addressing Iowa's high cancer rate. And the Center for Intellectual Freedom at UI is trying to find a director.

Salesforce and Anthropic launch Claudeforce as Marc Benioff and Dario Amodei explain the collaboration together on CNBC, AWS commits to 2 million more NVIDIA GPUs on top of its GTC pledge, plus six new earnings this week from NVIDIA, Salesforce, Synopsys, HP, Everpure, and Marvell test every bear thesis on AI infrastructure and software at once. Patrick Moorhead and Daniel Newman also cover Hot Chips 2026, the NVIDIA-Hugging Face acquisition rumor, and debate whether Anthropic's SaaS reassurances hold up on Ep. 317 of The Six Five Pod. The handpicked topics for this week are: Claudeforce Turns Salesforce Into Anthropic's Enterprise Front End. Salesforce and Anthropic launched Claudeforce, positioning Claude as the interface across roughly 27 Salesforce services while Anthropic supplies the underlying AI engine. Marc Benioff and Dario Amodei appeared together on CNBC to make the case for the partnership, and both stocks rallied on the news. Moorhead flags one open question: how Anthropic protects Salesforce customer data without collecting the usage traces that AI systems typically retain. (The Decode) AWS Adds 2 Million NVIDIA GPUs on Top of Its GTC Commitment. AWS committed to another 2 million NVIDIA GPUs, layered onto the 1 million it pledged at GTC five months earlier, alongside its own Trainium and Graviton silicon build-out. Moorhead estimates the deal at 6 to 7 gigawatts and $80 billion to $120 billion in NVIDIA revenue, and reads the pairing of NVIDIA's Vera CPU with Graviton as evidence that agentic workloads need capabilities Amazon's own silicon doesn't yet cover. The commitment reinforces Moorhead's argument that wafer, packaging, and memory supply set the ceiling on AI infrastructure buildout, regardless of how many custom silicon projects come online. (The Decode) Hot Chips 2026 Draws Mainstream Attention as Custom and Merchant Silicon Both Scale. What was once an academic gathering turned into a press and social media event, with OpenAI's Jalapeño inference chip drawing the most attention for its bandwidth-heavy first-generation performance. IBM and Arm detailed a joint development agreement enabling IBM Z mainframes to run Arm code natively at sub-nanosecond switching latency, and AMD showed a full Helios rack while Arm walked through its AGI chip architecture. Moorhead notes that Jalapeño almost certainly relied on Synopsys or Cadence EDA tools rather than in-house design tooling. (The Decode) The NVIDIA-Hugging Face Acquisition Rumor Raises the Stakes on Model Distribution. Reports from The Information, Reuters, and Bloomberg point to a roughly $12.9 billion deal between NVIDIA and Hugging Face, though neither company has confirmed it. Moorhead and Newman question whether NVIDIA would treat Hugging Face as a neutral, GitHub-style repository to keep the open source community on side, or use it as a route into inference services without building out its own datacenter business directly. Newman raises the regulatory exposure of a chipmaker owning the leading open model distribution layer. (The Decode) The Flip: Dario's CNBC Appearance and Claudeforce Put Anthropic's SaaS Intentions to the Test. In this simulated debate, Daniel makes the case for Dario Amodei, pointing to Claudeforce's integration with Salesforce's identity graph, field-level permissions, and audit trail as evidence that Anthropic wants to operate as the intelligence layer sitting on top of enterprise systems of record. Patrick makes the case against this, framing the CNBC appearance as a Trojan horse and citing Amodei's past comments about a small number of AI companies eventually controlling the market as evidence that owning the UI, and the pricing power that comes with it, remains the actual goal. (The Flip) NVIDIA Posts a Quadruple Beat and Commits to a $700 Billion Revenue Target. NVIDIA reported $96.22 billion in quarterly revenue, with $89 billion from data center; it guided Q3 total revenue toward $108 billion and pointed to a $700 billion annualized revenue target Moorhead calls increasingly credible. The company's new AI Clouds, Industrial, and Enterprise reporting category grew 138%, outpacing the roughly 100% growth of the rest of the data center business and easing the concentration risk bears have flagged. Goldman Sachs, Morgan Stanley, Bernstein, and Raymond James all raised price targets on the print. (Bulls and Bears) Salesforce Raises Guidance as Agentforce ARR Grows 240%. Salesforce raised full-year guidance to $46.1 billion to $46.4 billion, with Agentforce ARR reaching $1.5 billion on 240% growth and non-GAAP EPS of $5.90. Moorhead points to premium SKU bookings more than doubling quarter over quarter and half of AI bookings coming from existing customer expansion as the durability signal Agentforce needed. Newman reads the results, paired with the CNBC appearance, as the market correcting its overreaction to the SaaSpocalypse narrative. (Bulls and Bears) Synopsys Beats Across the Board Despite Investor Confusion Over IP Revenue. Synopsys reported $2.48 billion in revenue, up 42%, with $711 million from Ansys, and raised guidance, with operating expense control around the Ansys integration paying off ahead of an expected 2027 revenue lift from the combined businesses. The stock still declined on investor confusion over IP segment reporting differences between FactSet and LSEG data. Newman highlights the company's unit-based royalty model and its early visibility into custom AI chip demand through both its EDA and Ansys simulation businesses. (Bulls and Bears) HP Beats on Revenue and Earnings But the Market Wants an Edge AI Story. HP reported $15.68 billion in revenue, up 12.5%, and EPS of $0.83, both ahead of consensus, with strong personal systems growth as supply constraints give the company pricing power on premium devices. Moorhead reads the sell-off as a margin trust discount tied to tariffs and lingering uncertainty over the company's interim CEO search, with device demand holding up. Newman is looking for HP to show how it monetizes distributed AI and on-device token economics, from lower-cost workstations up through devices like the $100,000 DGX Station. (Bulls and Bears) Everpure Grows Revenue 38% and Raises Guidance on a Second Hyperscaler Win. Everpure, formerly Pure Storage, grew revenue 38% to $1.19 billion, beat EPS at $0.70 versus $0.58 expected, and raised full-year guidance by more than $500 million on a second top-five hyperscaler design win landing in fiscal 2028. Newman points to eight straight quarters of accelerating revenue growth and expanding gross margin dollars even as pricing holds steady. The stock fell roughly 15% despite the beat, which Moorhead and Newman attribute to elevated investor expectations. (Bulls and Bears) Marvell Meets Expectations as the Market Waits for Google Deal Detail. Marvell posted data center revenue up 46% to $2.17 billion, total revenue up 37% to a record $2.739 billion, and Q3 guidance of $3.15 billion, essentially matching estimates, with its full-year outlook raised to roughly $18 billion. Shares fell more than 10% on investor appetite for a guidance raise tied to the Google custom silicon deal, which Moorhead expects Marvell to detail further at its October Financial Analyst Day. Newman frames the quarter as steady execution on socket wins, with the stock up 187% year to date, without the guidance drama some investors wanted. (Bulls and Bears) Watch the full video at sixfivemedia.com, and subscribe to our YouTube channel so you never miss an episode. The Decode Claudeforce: Salesforce and Anthropic Announce Claudeforce  https://www.salesforce.com/news/pressreleases/2026/08/26/salesforce-and-anthropic-announce-claudeforce/ AWS and NVIDIA to Deliver 2 Million Additional GPUs  https://nvidianews.nvidia.com/news/aws-and-nvidia-to-deliver-2-million-additional-gpus-and-next-generation-infrastructure-for-agentic-and-physical-ai Hot Chips 2026: IBM Brings Arm Inside the Mainframe https://www.forbes.com/sites/jonmarkman/2026/08/25/ibm-brings-arm-inside-the-mainframe-with-a-new-dual-architecture-chip/ Hot Chips 2026: OpenAI's Jalapeño Chip Isn't Hot, and That's a Good Thing https://www.forbes.com/sites/luisromero/2026/08/27/openais-jalapeo-chip-isnt-hot-and-thats-a-good-thing/ NVIDIA Discussed Buying AI Startup Hugging Face, Insider Says https://www.bloomberg.com/news/articles/2026-08-27/nvidia-discussed-buying-ai-startup-hugging-face-insider-says The Flip  FOR (Dario is being sincere): CNBC Exclusive Transcript, Benioff and Amodei with Jim Cramer  https://www.cnbc.com/2026/08/26/cnbc-exclusive-transcript-salesforce-chair-ceo-marc-benioff-and-anthropic-co-founder-ceo-dario-amodei-speak-with-cnbcs-jim-cramer-on-closing-bell-overtime-today.html AGAINST (Dario's reassurance is a displacement play) https://finsee.ai/earnings/crm/2027/q2/en/ Bulls and Bears NVIDIA Announces Financial Results for Second Quarter Fiscal 2027 https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-second-quarter-fiscal-2027 Salesforce Delivers Record Second Quarter Fiscal 2027 Results https://investor.salesforce.com/news/news-details/2026/Salesforce-Delivers-Record-Second-Quarter-Fiscal-2027-Results/default.aspx Synopsys Beats Q3 2026 Estimates, Shares Slip After Hours https://www.investing.com/news/transcripts/earnings-call-transcript-synopsys-beats-q3-2026-estimates-shares-slip-after-hours-93CH-4878035 HP Inc. Reports Fiscal 2026 Third Quarter Results https://www.hp.com/us-en/newsroom/press-releases/2026/hp-inc-reports-fiscal-2026-third-quarter-results.html Everpure Announces Second Quarter Fiscal Results https://finance.yahoo.com/markets/stocks/articles/everpure-announces-second-quarter-fiscal-200500505.html Marvell Technology Reports Second Quarter of Fiscal Year 2027 Financial Results https://investor.marvell.com/news-events/press-releases/detail/1031/marvell-technology-inc-reports-second-quarter-of-fiscal-year-2027-financial-results  

Jake and Michael discuss all the latest Laravel releases, tutorials, and happenings in the community.Show linksPause All Queues and a New artisan dev UI in Laravel 13.25Read-Through Disks and Debounced Listeners in Laravel 13.26Laravel Read-Through Filesystem: Lazy Storage MigrationDebounced Queued Event Listeners in LaravelQueue::forward(): Reroute Laravel Queues in One PlaceAgent Run Observability in Laravel AI SDK 0.11Laravel AI: Trace Agent Runs With Lifecycle EventsLaravel AI: Get Raw HTTP Responses and Rate LimitsMock PHP Classes in Tests With the Double LibraryNativePHP v4: Build Native iOS and Android UI in BladeLet's Encrypt HTTPS on an IP Address With FrankenPHPStatamic Mailables Viewer Previews Laravel Emails in the Control PanelLaravel Discount: Coupon Codes, Usage Limits, and StackingLaravel Chores: Resumable Data Operations and CleanupsLaravel Tackle: Run an AI Coding Agent in Your Laravel AppTutorialsLaravel Terminal UI for the artisan dev CommandPause All Laravel Queues During a Deploy

Rockstar is fighting a leak it cannot plug, and the leaker has turned the whole thing into a token economy.Episode 398 covers a week where the data says growth and the job market says something else. Newzoo puts 2026 at 214 billion, Makers Fund closes another 250 million that looks nothing like venture capital, Google Play turns RAM into a store requirement, and Gamescom hands us Nodusfall, Palworld on mobile via Garena, and one more round of the HD on mobile argument that never ends.Topics Covered:• CyberLeek posts 16 GTA 6 clips and runs crypto-paid polls on what leaks next• Take-Two subpoenas Microsoft, Discord and Google to unmask whoever is behind it• Newzoo's 2026 forecast hits 214 billion with North America growing slowest• Why layoffs keep coming in a market still growing 6 percent• Makers Fund raises 250 million and shifts into project and UA financing• Google Play makes memory optimization a store requirement in February 2027• Opening Night Live and the endless pile of tactics spin-offs• HoYoverse reveals Nodusfall while its mobile portfolio drops 24 percent• Garena picks up Palworld for mobile and Roco Kingdom goes global• Localization and UI as the real barrier for Eastern games in the West• The HD on mobile fight nobody at the table is winningCHAPTERS:01:12 Today's Topics Rundown02:19 Gamescom Pics and Shoutouts03:01 Sponsor Updates05:57 GTA6 Leeks16:11 Layoffs and Market Maturity20:10 Makers Fund Raises $250M28:18 Google Play RAM PSA29:29 Gamescom Opening Night Live36:16 Western Appeal Debate37:04 Roco Kingdom Goes Global37:47 Palworld Mobile Licensing38:52 Cozy Combat Audience Shift41:35 Portfolio Decline And Cannibalization51:33 Localization And UI Barriers01:01:18 Wrapping Up And Next Week

Today we are talking about Security, Vulnerabilities, and how to avoid exposure with guest Dave Welch. We'll also cover Security Scanner as our module of the week. For show notes visit: https://www.talkingDrupal.com/567 Topics What Are CVEs CVE Lifecycle and Disclosure AI Era Security Challenges What CVE Program Excludes Patch Fast Reality Global Security Signals CVE Timing Judgment KEV Flags Explained CVE Updates Link Rot Who Decides CVE Sneaky Patch Dangers ADP Program Fixes Small Team Triage Vulnerability Tsunami AI Autonomous Security Future Legal Pressure Budgets Resources Psalm PHP Static Analysis Tool SARIF format PHP ecosystem Council of roots How AI Broke Open Source Security: End-of-Life Software Is the Most Exposed CVE podcast Vulncon PSIRT Guests David Welch - github: dwelch2344 dwelch2344 Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi JD Flynn - dorficus MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted a fast way to catch the security mistakes that slip into custom Drupal code — especially the code your AI assistant just wrote — before it ships? There's a module for that. Module name/project name: Security Scanner Brief history How old: created in July 2026 by Mayank Gupta (mayankguptadotcom) of Acquia Versions available: 1.0.0, which works with Drupal 10.3 and 11 Maintainership Actively maintained — created and shipped its first stable this summer, with steady development right through late July Security coverage Test coverage — and it's strong: unit and kernel tests, including a regression corpus built from real Drupal core advisories Documentation? In-depth README with a full check table and CI recipes, plus a CHANGELOG Number of open issues: 1 issue, not a bug Usage stats: 2 sites (it's brand new) Module features and usage Provide a Drush command, has no UI — you point drush security:scan at a module or any path, it reads the code statically, and prints a prioritized, OWASP-mapped list of things to review It's built for the age of AI-written code — the checks target the classes AI assistants keep reintroducing: routes with no access check, #markup and |raw XSS, missing CSRF tokens, unserialize() on untrusted data, hardcoded secrets Then there's an optional deep pass: with the Psalm static analysis scanning engine installed, it'll trace untrusted input across functions and files to catch cross-function issues. And it's honest about state — the report always says whether that deep pass ran, was skipped, or failed, so a failure never gets mistaken for a clean scan One nice detail under the hood: a tokenizer-backed "code map" that knows whether a match is real code, a comment, or a string — so it won't flag the word "unserialize" sitting in a doc comment. That kills the single biggest source of false positives The checks are regression-tested against real Drupal advisories (Drupalgeddon, Drupalgeddon2, the 2019 unserialize bug, etc) so a pattern that caused an actual CVE can't quietly come back in your custom code Output comes in three flavors: a readable table, JSON for CI and AI agents, and SARIF — which means findings show up as annotations right on your GitHub or GitLab merge-request diff instead of buried in a job log For adopting it on an existing codebase there's a baseline file — you fingerprint the findings you've reviewed, with a required reason on each, and they stop failing the build but never go invisible; every run still counts them It exits non-zero on error-level findings, so it drops straight into CI or a pre-commit hook And it's extensible — checks are Drupal plugins with a #[SecurityCheck] attribute, so any module can add its own or alter the ones that ship Big caveat, and the module says this itself: a finding means "review this," not "this is broken." Static analysis has false positives, and a clean scan doesn't prove the code is secure — access-control logic especially still needs human review I first heard about this module over beverages at Drupalcamp Asheville, so I know that this module was largely vibe-coded, after having an AI agent ingest every single Drupal security team CVE. So I like to think of this module as security pattern recognition tool, but of course it does even more

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

A weekly news show informing you on the latest in Bitcoin, privacy and open source tech, hosted by Ungovernables, Max and Q.AOB (Any Other Business)- KeyOS v1.4.0 now available to test- Envoy 2.3.2- New website is live: https://ungovernable.network- Great AI chat with Seth on FTF- Talk about the brief going weekly??NEWS9/11 Families File Claim Over US Government's 127,271 Seized BitcoinPublished: 2026-08-23 | Source: The RageTrezor Shipping Provider Breach Leaks Data on ~14,000 CustomersPublished: 2026-08-13 | Source: Bitcoin MagazineChainalysis Sues US Gov Over $95M ICE Blockchain Surveillance ContractPublished: 2026-08-18 | Source: The RageLND Reorg Vulnerability Disclosed: Channel Funds Drainable Before v0.20.0Published: 2026-08-21 | Source: Bitcoin Optech Newsletter #419Iceberg Paper: Threshold Custody on Lightning Without Protocol ChangesPublished: 2026-08-22 | Source: ePrint 2026/1757RELEASESBisq 2 v2.1.12 — 2026-08-22: Mandatory security update addressing vulnerabilities from a recent audit. Adds reputation privacy via nullifiers and day buckets, strengthens bonded-role registrations with oracle validation, and improves bridge reliability. Updating is required to continue trading.Bisq 1.10.6 — 2026-08-21Hotfix for critical trade failures introduced in v1.10.5. Mandatory update for anyone who took the security patch.Zeus 13.2.0 — 2026-08-22Stable release adds LDK support for 24-word seed phrases, upgrades embedded LND to v0.21.2-beta, upgrades Cashu CDK for Minibits compatibility, and lets users set custom Mempool instances for improved privacy. Security hardening and bug fixes included.Frostsnap v0.4.0 — 2026-08-23Major update with 142 commits. Adds firmware downgrade protection, secure-boot signer verification at build time, descriptor checksum generation, 1-of-N threshold warnings, and unique device name enforcement during keygen.Flint v0.1.5.2 — 2026-08-23Seth for Privacy's BTCPay Server plugin for nodeless Lightning via the Breez Spark SDK. v0.1.5 addresses findings from a third external security review: fixes negative fee wrapping, restores the sweeping opt-in toggle, and gates token write-offs on balance. v0.1.5.2 hides stored API keys from the admin UI.Bull Bitcoin Mobile 6.13.0 — 2026-08-18Adds Payjoin support across wallet and exchange flows with configurable minimums and session lifetimes. Introduces a temporary swap service while Boltz is unavailable, Liquid UTXO consolidation warnings, and BTC Map integration.QUICK MENTIONSMostro 0.18.5 -- Aug 2026. NIP-44 default encryption for P2P exchange over Nostr.Peach Bitcoin 0.69.0 -- Aug 2026. Nym VPN integration for no-KYC P2P trading.Fulcrum 2.1.2 -- Aug 2026. ARM64 Docker support for the high-performance Electrum server. BTCPay Server v2.4.2 -- 2026-08-10. Critical security patch fixing actively exploited TOTP 2FA bypass. Basic auth now disabled by default. (News angle covered last episode.) Fedimint v0.11.2 -- 2026-08-13. Security release preventing a gateway from losing funds to malicious counterparties, binding Lightning contracts more strictly to offers, and hardening request handling. Backported as v0.10.1 same day. RoboSats v0.8.6-alpha -- 2026-08-15. Security hardening, removes three coordinators (Bitcoin Venetto, WhiteEyeSats, OverTheMoon), adds encrypted image uploads in chat and visual warnings for low-bond orders. Aqua Wallet v0.5.2 -- 2026-08-16. Warning messages for temporarily unavailable Lightning swaps, shows swap provider per transaction, requires TLS with domain verification for custom Electrum servers. Cake Wallet v6.4.1 -- 2026-08-12. Zcash performance after Ironwood shielded pool migration. AnyPay auto-detects address types and routes correctly. Ride The Lightning 0.15.11-beta -- 2026-08-18. Establishes vulnerability disclosure channel, closes credential-crossing race in LND controllers, reduces dependency tree. Coldcard Firmware 5.6.1 -- 2026-08-20. New firmware for Mk3/Mk4 line. (Context: follows the entropy bug disclosure from last episode.) Coldcard Q1 Firmware 1.5.1Q -- 2026-08-20. Q1 model firmware update.Trezor Suite 26.8.2 -- 2026-08-20. Adds BIP-321 URI support for QR payments, redesigns activity page, adds profit/loss tracking. (Context: arrives alongside the shipping breach disclosure.)Arkade TS SDK Swap 0.0.7 -- 2026-08-18. SQLite and Realm storage backends, swap failure reporting improvements.EDUCATIONBTC Sessions Launches "Sovereign Sessions"Mid-August 2026 — Ben Perrin — youtube.com/@SovereignSessionsBen Perrin has spun off all tutorial content into a dedicated channel covering Bitcoin, self-hosted AI, privacy tools, and sovereign computing. Planned content includes GrapheneOS, ZAP Store, Start9, Parmanode, self-hosted Fedimint, and Blockstream Wallet walkthroughs. A significant move from one of Bitcoin education's most recognisable creators; the expanded scope reflects where freedom tech is heading.Bitcoin Lightning Node Setup: 12 Steps in 45 MinutesAugust 16, 2026 — Daniel Roth — https://shattered.ioStep-by-step LND v0.21-beta Lightning node setup guide, claiming the process now takes about 45 minutes. Fresh, practical self-sovereignty content; the "45 minutes" framing makes Lightning node operation feel accessible.C.A.S.I.N.O. Dice Roll ProtocolAugust 9, 2026 — Orange SurfA proposed standard for using physical dice to generate seed entropy: rate-limited entry, input validation, minimum 100+ rolls, offline-only operation. Emerged following the Coldcard entropy incident. A practical, opinionated standard for the most fundamental self-custody operation.Ark Protocol: VTXOs and the Virtual Transaction TreeAugust 21-23, 2026 — Elle Mouton — https://ellemouton.comA detailed technical walkthrough of Ark's Virtual UTXO design: how multiple users share a single on-chain UTXO with individual custody via pre-signed exit trees. A clear explainer from a respected developer.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 w/ USD ----> https://www.givesendgo.com/billandkeonneDONATE TO THE FAMILIES w/ BTC ----> https://pay.zaprite.com/pl_JpxtkLv95T SUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/VALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ungovernable- BUY SOME STICKERS @ https://ungovernable.network/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.ioYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.(00:00:00) INTRO(00:00:57) THANK YOU FOUNDATION(00:01:38) THANK YOU CAKE WALLET(00:02:44) Still on the Wagon(00:10:24) Updates @ Foundation(00:29:52) New Website!(00:33:43) Nerd Out on Home AI with Seth(00:44:51) NEWS(01:04:34) BOOSTS(01:07:11) UPDATES & RELEASES(01:09:39) EDUCATIONAL PIECES(01:10:57) THANK YOU MYNYMBOX

This return to the Talk Circuit series highlights Dr. Darren's appearance on the Ready for Work podcast, hosted by Tom-Chris Emewulu (founder of Stars From All Nations), recorded in 2024 as a part of Tom-Chris' Guinness World Record Marathon attempt. In this set, Dr. Darren addresses several basic elements associated with the foundations of UX, including an explanation of the difference between UX and UI, and provided guidelines how how to manage one's maturity in the discipline.#ux#podcasts#cxofmradio#cxofm#realuxtalk#worldofux#worldouxDon't forget to like, subscribe, and share!Learn more about Tom-Chris and Stars From All nations at https://www.sfanonline.org/about.Bookmark the new World of UX website at https://www.theworldofux.com. Visit the UX Uncensored blog at https://uxuncensored.medium.com. Get your specialized UX merchandise at https://www.kaizentees.com.

In this episode, Austin chats with Declan from Aurora Labs about building on NEAR's Intents architecture. Declan explains why intents go beyond “a better bridge UI" and how Aurora Intents actually work, enabling one-click cross-chain journeys. They discuss how the protocol helps create liquidity, adoption metrics (over $21B lifetime volume, about $1B monthly), and use cases across wallets, neobanks, payments, RWAs, and more. The conversation also explores inherent cross-chain risks, as highlighted by a Litecoin reorg double-spend incident, and the need for insurance-like protections for onchain products.  0:00 - Aurora Intents Overview  02:57 - Intents vs Bridges  05:21- Solver Network Explained  06:23 - Aurora Intents' Behind the Scenes Flow  07:43 - Liquidity and Market Making  08:55 - Real World Use Cases  11:25 - Confidential Intents  16:11 - Intents' Speed Tradeoffs and Risks  17:30 - Litecoin Incident Earlier This Year  19:51 - Liability and Insurance  26:08 - Future Verticals for Intents  28:36 - Where to Learn More Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us Fan MailOur 200th episode! Thank you for being part of this community - whether you've been here since episode one or found us last week. Want to shape the next 200? Topic ideas, guest suggestions, or your own pitch: email anya@anewgo.com. And if the show has ever helped you, a 30-second review on Apple Podcasts or Spotify is the best gift you can give us.Fittingly for the milestone, this is a big one. In this August State of AI, Anya Chrisanthon - CCO at Anewgo - breaks down the month that moved AI search from theory to infrastructure.The crossover moment A Semrush study of nearly 500 marketing professionals found more marketers now plan to invest in AI search optimization than traditional SEO - 38% vs 36%. For 25 years SEO was THE discipline every budget started with. For the first time ever, it just got passed.Google's August 19 update: Search becomes a workspace Gemini Notebooks now live directly in AI Mode - imagine a homebuyer research notebook with floor plan PDFs, community brochures, pre-approval letters, and every AI conversation about builders in their market, all in one workspace that remembers everything. Custom document creation means a buyer can generate a comparison one-pager of the three builders on their shortlist - and whose information ends up in it depends entirely on what AI can find and trust about you.The 3D moment The headline update: Search can now generate interactive visual widgets and 3D simulations on demand inside AI Overviews and AI Mode. Google calls it generative UI - the AI isn't just writing an answer, it's building an interface. If Search can generate a rotating 3D molecular model today, how far away is "show me what an open-concept 2,400 square foot floor plan feels like"? Interactive content isn't a nice-to-have anymore. It's becoming the raw material of the answer layer - and the builders who invested early are the ones AI will build from.Google published the rulebook - and called out the snake oil There is no special technical trick that unlocks AI visibility. What Google's official guidance rewards: unique, non-commodity content grounded in genuine expertise - your communities in real detail, your building practices, your actual differentiators. Not generic slop.Who's winning and who's invisible 85% of marketers say AI has changed how they approach search - but 40% still check their AI visibility by manually typing prompts into ChatGPT. The chart that matters: teams with fully integrated SEO + AI visibility report more leads from AI 81% of the time. Completely siloed teams: 36%. Same tools, different results - the difference is whether it's connected. And when brands actually checked how AI describes them, nearly 9 out of 10 found something wrong.Zuckerberg's signal Meta's 6,500-word manifesto commits hundreds of billions to "personal superintelligence" - a world where every buyer has their own powerful AI working on their behalf. Every major platform is now building toward the same buyer: one who arrives with superintelligence riding shotgun. Is your digital presence ready to talk to it?There is no page two When AI recommends two or three builders, most buyers just accept them. The recommendation is the market. The gap between cited and not cited isn't a difference in degree - it's a difference in kind.

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Jeff Chow, Chief Product and Technology Officer at Miro, about where friction is showing up inside product teams as AI reshapes how work gets done. Jeff describes how PMs prototyping in high fidelity, designers coding, and engineers orchestrating agents are collapsing old role boundaries, and argues the underlying tensions — who owns a decision, who gets paged at 2am, how a designer receives feedback — aren't new, just amplified. Much of the conversation centers on decision-making as the real bottleneck: Jeff makes the case that 10x individual output doesn't translate into 10x business results unless organizations can also accelerate cross-functional alignment and cascade decisions with their underlying context, not just their conclusions. He and Douglas dig into Miro's bet that a shared visual canvas — carrying both artifacts and a decision log of why choices were made — is the connective layer that keeps AI-assisted work and human teams moving together instead of fragmenting into isolated single-player sessions. The two also trade observations on viral AI adoption patterns inside organizations, the psychology of design crits applied to AI-generated work, and the early signs of "ephemeral UI" and custom, vibe-coded widgets reshaping what software teams expect to build for themselves.

In this episode of Robots and Red Tape, host Nick Schutt sits down with Austin Keller, Director of Data Science at IntelliDyne, for a grounded conversation on why critical systems still run on DOS, floppies, and “version 15 final redo,” and why most AI projects start in the wrong place.Drawing on manufacturing, Navy, DOJ, and VA work, Austin argues you start with the business problem—not the shiny tool—and that security is step zero.Key topics:Why “if it works, don't mess with it” leaves agencies on 20–30-year-old stacksStarting with the job to be done, not “we want AI”Data that will not talk to other data—and why that is by designCoding agents that can read COBOL and Fortran when the last developer is goneWhere AI ROI actually shows up: repetitive tasks, small agents, and UI that saves keystrokesPodcast: Robots and Red Tape | Host: Nick Schutt | YouTube: @RobotsandRedTapeAI

Idaho second year head coach Thomas Ford Jr. and UI offensive line coach Loren Endsley join Colter Nuanez to preview the Vandals' offense ahead of the 2026 season opener Friday Night at Cal Poly

In this week's episode of the Windows Central Podcast, Daniel Rubino and Zac Bowden break down the latest Windows 11 insider features, focusing heavily on major overhauls coming to context menus, File Explorer polish, and options to force apps to open full-screen. They also dive into a surprising new Signal 65 processor study highlighting how Qualcomm's Snapdragon X2 chips stack up against Intel and AMD, wrap up recent Windows 11 improvements from March onward, and touch on various tech ecosystem shifts. This episode of the Windows Central Podcast is sponsored by Surfshark VPN. Unlimited devices, access your content from anywhere, and auto-block malware on Windows, Edge, and other platforms from $2.50/mo, with 3 months extra FREE. Sign up today! 00:00 — Introduction & Welcome 03:45 — Qualcomm Snapdragon X2 vs. Intel & AMD: Analyzing the Signal 65 processor study results, showing how Qualcomm beats out Intel and AMD chips in recent laptop testing. 10:15 — Windows 11 Context Menu Overhaul: Breaking down new customizable, faster context menus in Windows 11 Insider builds, including default cleanups and third-party app management. 24:30 — File Explorer Improvements: Reviewing massive performance updates, elimination of freezing/hang delays, and better UI polish for File Explorer. 42:10 — Forcing Apps to Open Full-Screen: Discussing the new Windows settings toggle that allows users to automatically launch apps in full-screen mode. 52:00 — Recap of Windows 11 K2 Features & General State of the OS: A look back at the overall quality-of-life improvements rolled out to Windows 11 since March. 1:05:15 — Wrap-Up & Outro

Thanks Pressable for supporting the show! Get your special hosting deal at https://pressable.com/wpminuteBecome a WP Minute Supporter & Slack member at https://thewpminute.com/supportOur July 2026 WP Minute Member Meetup was dedicated to all things WooCommerce. Developer Advocate Brian Coords joined us to demonstrate a variety of new and experimental features. He also shared Woo's plan to address the existing backlog of bugs and other issues. The goal is to produce a stable eCommerce platform that also meets modern demands.We also looked at AI's role in Woo and WordPress. Brian provided insight on the future of agentic commerce, store management features, and customer interactions.Takeaways:WooCommerce is focusing on cleaning up backlog issues to improve user experience.AI submissions in open source can be both beneficial and challenging.The goal is to reduce the number of open issues significantly.User experience is crucial for retaining customers in WooCommerce.WooCommerce aims to improve UI consistency across its extensions.Block themes are being developed to enhance the WooCommerce experience.AI integration is being explored for better store management and analytics.The future of e-commerce may involve AI-driven purchasing experiences.Important Links:Woo Developer BlogWooCommerce is the foundation of our entire ecosystemWooCommerce 11.0: What's coming for developersUnifying extension settings in WooCommerceThe WP Minute+ Podcast: thewpminute.com/subscribe ★ Support this podcast ★

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Getting the legal authority to put a sportsbook in your pocket was only phase one. Phase two was designing an app so perfectly engineered, gamified, and frictionless that you never want to close it. In Episode 3 of Gambling Under The Influence, Breezy and Juice dissect the calculated UI design and behavioral psychology used by modern sports betting apps to keep your thumb tapping. We explore how tech executives replaced traditional casino hospitality with rapid-fire, social media-style engagement loops. We also pull back the curtain on the industry's biggest profit driver to explain why the heavily promoted "Same Game Parlay" is actually a mathematical trap designed to quietly drain your bankroll. In this episode, we cover:Frictionless Betting: How sportsbooks use bright colors, intuitive swiping, and video game mechanics to mask the underlying statistical realities of traditional gambling. The Same Game Parlay (SGP) Trap: Why the industry aggressively pushes pre-built parlays that carry an exorbitant 30% to 40% house edge compared to standard straight wagers. The "Near-Miss" Effect: The powerful psychological dopamine loop that tricks your brain into feeling like you "almost won" a multi-leg parlay, keeping you eager to wager again. The Dangers of Micro-Betting: The behavioral risks of rapid-fire, in-game wagering and how latency feeds are threatening the integrity of live sports through invisible spot-fixing. (Special thanks to our Episode 3 guests: Art Manteris, Danny Funt, Tom Barton, and Richard Schuetz).Support the showFollow along on Twitter or Instagram @goodjujubets.goodjujubets.net - All Things Good JuJu

“What if we could have an academic program that combines linguistic knowledge with engineering knowledge?” Kelsey Kraus is a computational linguist, data scientist, and educator. received her PhD in Linguistics from the University of California, Santa Cruz in 2018, where she worked on the semantics and pragmatics of intonation and discourse particles in English and German. She joined the linguistics faculty at San Jose State University in 2026 after working in Silicon Valley as a Linguist and Senior Data Scientist at Google, Amazon, and Cisco. Kelsey was a featured speaker at Linguistics Career Launch 2021, presenting a session on Human Language Technology (HLT) Industry Jobs. Kelsey Kraus on LinkedIn Kelsey Kraus on github Kelsey Kraus at SJSU Kelsey Kraus’ blog at Cisco Kelsey Kraus at LC 2022: “Human Language Technology (HLT) Industry Jobs” (audio and video) Linguistics at SJSU Topics include: – syntax – informational interviews – LinkedIn – Cisco – voice UI – LLMs – chat agents – computational linguistics – corpus linguistic – return to academia If you'd like to support this show, we've got a Patreon! Listen to Lingthusiam, our patron! The post Episode #91: Kelsey Kraus first appeared on Linguistics Careercast.

Thu, 20 Aug 2026 19:30:00 GMT http://relay.fm/connected/617 http://relay.fm/connected/617 bleepin and bloopin 617 Federico Viticci, Stephen Hackett, and Myke Hurley A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. clean 4716 A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. This episode of Connected is sponsored by: Shipaton, from RevenueCat: The world's biggest mobile hackathon for people who actually ship. Bolt.new: Build apps with AI. Get a free 30-day Pro account. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Jon Wearing His Pickle Shirt – Mastodon Things 3's Repeating Task Feature Restructured - 512 Pixels My One Problem with Things 3 - 512 Pixels Repeating To-Dos, Refined - Things Blog - Cultured Code Twelve – The Enthusiast Relay Turns 12 - 512 Pixels Ask Us Questions for the Relay Q&A! Departures - Relay iPad Mini 8: Release Date, Pricing, and What to Expect - MacRumors HoverBar Tower – Twelve South Golden Gate Beta 6 Turns Stoplights into Liquid Glass - 512 Pixels macOS Tahoe 26.7 is Full of References to Unreleased Apple Products - MacRumors Apple Accidentally Leaked More Than 10 New Products in macOS Update - MacRumors Product Recall: Belkin Auto-Tracking Stand Pro with DockKit (2511-0224) - GOV.UK Apple's Camera-Equipped AirPods Confirmed: See Them in Action - MacRumors Apple's Camera-Equipped AI AirPods Remain on Track for 2027 Despite Video Leak - Bloomberg Camera-equipped AirPods reportedly won't launch in 2026, despite demo video leak - 9to5Mac Meta glasses are a

Thu, 20 Aug 2026 19:30:00 GMT http://relay.fm/connected/617 http://relay.fm/connected/617 Federico Viticci, Stephen Hackett, and Myke Hurley A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. clean 4716 A bunch of future Apple hardware has leaked in a beta of macOS Tahoe. The guys go through the list and then consider the iPad mini's place in the world and the UI changes coming with Golden Gate. This episode of Connected is sponsored by: Shipaton, from RevenueCat: The world's biggest mobile hackathon for people who actually ship. Bolt.new: Build apps with AI. Get a free 30-day Pro account. Squarespace: Save 10% off your first purchase of a website or domain using code CONNECTED. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Jon Wearing His Pickle Shirt – Mastodon Things 3's Repeating Task Feature Restructured - 512 Pixels My One Problem with Things 3 - 512 Pixels Repeating To-Dos, Refined - Things Blog - Cultured Code Twelve – The Enthusiast Relay Turns 12 - 512 Pixels Ask Us Questions for the Relay Q&A! Departures - Relay iPad Mini 8: Release Date, Pricing, and What to Expect - MacRumors HoverBar Tower – Twelve South Golden Gate Beta 6 Turns Stoplights into Liquid Glass - 512 Pixels macOS Tahoe 26.7 is Full of References to Unreleased Apple Products - MacRumors Apple Accidentally Leaked More Than 10 New Products in macOS Update - MacRumors Product Recall: Belkin Auto-Tracking Stand Pro with DockKit (2511-0224) - GOV.UK Apple's Camera-Equipped AirPods Confirmed: See Them in Action - MacRumors Apple's Camera-Equipped AI AirPods Remain on Track for 2027 Despite Video Leak - Bloomberg Camera-equipped AirPods reportedly won't launch in 2026, despite demo video leak - 9to5Mac Meta glas

Saddle up and start your stopwatch, when Jenny Dee Brecheisen joins us to talk about her passion for barrel racing. She shares how it completely transformed her life, from city dweller to 10-acre landowner with four horses. Jenny explores the deep bonds formed with her horses through daily care and patience, the 14-17 seconds of pure adrenaline that make all the preparation worthwhile, and the steep learning curve and real physical dangers that come with the sport.Guest BioJenny Dee Brecheisen (she/her) is a UI Engineer on Cloudflare's UI Platform team, where she works across frontend UI, systems, and engineering infrastructure. Beginning with a degree in graphic design and early work in print and marketing, she later moved into product design and design systems before transitioning into engineering, bringing a cross-disciplinary perspective to building products in the age of AI. Her work today spans everything from frontend development and accessibility to migrations, debugging, and improving how software gets built and maintained at scale. When she's not working, she spends her time barrel racing and investing in futurity horse prospects.LinksJenny's website: tinycrowbar.comJenny's race-tracking app: highloper.comJenny on Instagram: https://www.instagram.com/yennie/CreditsCover design by Raquel Breternitz.

The real skill with AI tools isn't prompting. It's knowing when to stop prompting, when to step in, do it by hand, and hand the result back to the machine.Darshan Gajara is a design leader who actually ships. His latest is Beanpresso, a coffee journaling app built in Lovable, launched, and refined in the wild. In this episode he shares his screen and walks through the whole build, the real project, the prompt history, the dead ends, and the moments a designer's eye had to take over, from first idea to live product, plus how he's approaching monetisation and marketing as a solo maker.We get into:Starting in Lovable and only bringing in Figma once the idea took shape, "give it a designer's touch"Why you don't need to design whole flows anymore: get one page right and Lovable infers your design systemThe handoff problem: screenshots plus precise description, component by component, down to corner radiiThe SVG trick: when the animation kept breaking, he built the UI by hand, named every layer, exported an SVG and told Lovable to animate exactly that"I'm still a designer": choosing to do the branding by hand, and why some things are better that wayMonetising a side project: premium taste insights, and a roaster marketplace his own users suggestedMarketing by making: free tools, a Berlin coffee guide the roasters reshared, and an AI writing setup trained on his own styleGET THE APPBeanpresso, coffee journaling for people who take their beans seriously: https://beanpresso.comMY REFLECTIONSI share my personal takeaways from each episode in the d.MBA newsletter, what I actually learned and what I'm stealing for my own builds: https://d.mba/newsletterLINKSDarshan on LinkedIn:   / darshangajara  Product Disrupt, Darshan's design learning resource: https://productdisrupt.comDarshan's site: https://darshan.designd.MBA, business education for designers: https://d.mba00:00 – "They just keep banging their heads, burning through tokens"00:36 – Who Darshan is, and what this episode is really about01:36 – Interview starts: scratching his own itch.05:51 – App demo: scanning a bag, community feed, taste stats10:51 – Validating the idea (and why the existing coffee apps weren't good enough)14:12 – Version one: built straight in Lovable, no design15:54 – The prompting workflow: voice notes → Notion AI → Claude → Lovable18:21 – Why he dropped the native app and went web19:33 – Mood boarding with Variant, and finding the visual language23:54 – Naming it, and why AI is terrible at logos27:15 – What AI is good at, and what he had to do by hand30:45 – Taking the design back into Lovable, component by component33:27 – Building new flows once Lovable knows your design system36:36 – The SVG animation trick38:27 – Don't expect one tool to do everything42:06 – Monetisation: premium stats and a roaster marketplace45:45 – Distribution: small free tools, Instagram, and SEO49:15 – Where to find Beanpresso and Darshan

In this episode, hosts Lee-Sean Huang and Giulia Donatello sit down with Sally Chung, a designer, startup founder, professor at Parsons School of Design, and former faculty member at SVA. From her early days as a founding designer at Saks Fifth Avenue to building an SMS AI beauty chatbot in 2016 and leading zero-to-one ventures at BCGX, Sally has continuously navigated the balance between corporate scale and entrepreneurial agility. She shares how she founded the Designpreneurs Hackathon to build a thriving ecosystem connecting designers, universities, and tech founders, and offers an insider's look at how AI is reshaping interaction design education and hiring expectations at Parsons. In This EpisodeBuilding AI before the hype. Sally reflects on co-founding Hello Ava in 2016—an SMS AI beauty advisor—years before ChatGPT went mainstream. The experience taught her that technology alone never wins; true product-market fit comes from listening directly to user frustrations rather than focusing on the tech itself. Designing a career narrative. From corporate in-house teams to living in a co-worker's basement to launch a startup, Sally discusses why designers must stop apologizing for a "zigzag" career path and proactively craft their own strategic narrative. The survivor mindset in innovation. Drawing on her work launching ventures at BCGX, Sally outlines why smart ideas fail and what keeps surviving companies alive: rapid market adaptability, relentless testing loops, smart distribution channels, and a founder's resilience. Cultivating an ecosystem through hackathons. Sally breaks down how a weekend project to help her SVA students build portfolios evolved into the Designpreneurs Hackathon—a tight-knit community that pairs hackers with senior mentors from IDEO, Meta, and Spotify, resulting in real hires, startup acquisitions, and venture funding. The shift in design hiring. Sally reveals why a "pixel-perfect" portfolio no longer guarantees a job. As execution becomes automated, top design leaders are hiring for strategic systems thinking, multi-stakeholder navigation, and versatility across non-UI or agentic experiences. Rethinking design education at Parsons. Advising on curriculum changes, Sally emphasizes that design schools should stop teaching software tools that students can learn on TikTok, and return to analog craftsmanship, strategic problem-solving, and a foundational creative eye. What remains human. Reflecting on her panel at South by Southwest, Sally highlights that while AI can optimize toward goals, only humans can choose which goals matter. She stresses that real, in-person community is the ultimate antidote to digital loneliness. ResourcesSally Chung's Official Website – http://sallyhychung.comSally Chung on LinkedIn – https://www.linkedin.com/in/sallyhchung/Sally's Instagram – https://www.instagram.com/sallyinutopiaDesignpreneurs – https://www.designpreneurshackathon.com/Same As Ever: A Guide to What Never Changes by Morgan Housel – https://amzn.to/4qpoTWMAIGA Design Podcast Feedback – podcast@aiga.org

Recently, I met David Krauza, VP of Enterprise Data Strategy and Products & Governance at Comcast, at the 2026 CDOIQ symposium, and after chatting for a bit, he agreed to come on the show to talk about how he, as an enterprise buyer, thinks about B2B software purchases in the age of AI. As vibe coding makes internal development more accessible, David explains why the buy-versus-build decision isn't simply about whether a company can build a solution itself. Leaders need to weigh long-term roadmaps, maintenance, integrations, and whether they want to take on the responsibility of becoming a software company. The real question is not just what can be built, but what makes the most strategic sense to own. David also highlights an important consideration vendors frequently overlook: the data their own products create and the possible importance of that to an enterprise data leader. This is particularly true when the product's primary intended purpose is not analytics itself. In a complex sales environment, which may include a senior data leader as a champion or decision maker, product metadata, or what you might currently be thinking of as a byproduct, might actually be a primary decision point in an enterprise purchasing conversation. David then outlines the pre-implementation work required before any of this can be evaluated: establishing shared definitions, explicit success metrics, and a documented “before-picture” you can run at renewal time to understand the ROI of the product. We also explored the hidden costs that can undermine an otherwise compelling product. A polished UI may still create UX friction if users have to constantly move between systems, while complex data integrations can introduce additional labor and operational burdens. This friction should be considered during the evaluation rather than discovered after development. David says vendors can stand out by demonstrating that they understand where a customer's business is headed and how their roadmap supports that direction. He also dropped some real gold about the difference he sees as a buyer when being pitched by a founder vs. a B2B salesperson—and what the latter is missing when they pitch him. Highlights / Skip to: Why buy any products when AI allows you to build them yourself? (2:03) Allowing use cases and goals to dictate the adoption of internal solutions (4:03) Making data capture, accessibility, and ecosystem fit part of the buying decision (5:54) Is data missing in sales conversations due to a lack of marketing or of results? (8:36) Comcast's method for deciding to renew when the intelligence is invisible (11:22) The importance of pre-post analysis when making a renewal argument (14:02) Where David sees the most time being wasted in the pitch process and why vendors who did _____ win more often (16:36) The hidden costs that often go undiscussed during the sales process (20:07) How Comcast evaluates UX friction (back-and-forth of switching between applications to accomplish work) (22:22) Other hidden factors that can prevent your sale from closing (25:13) Differences David sees between founder-led sales and sales-led sales (26:41) David's advice for founders selling in the analytics and data space right now (28:14) Links David Krauza's LinkedIn David Krauza's Substack

Lords: Felicia Andrew https://kittenm4ster.neocities.org/ Topics: My dad's indestructible mailbox Ares doesn't emulate the timing of the Reality Display Processor Moo box vs. Groan tube https://www.youtube.com/watch?v=2GQ126DUsyQ https://www.youtube.com/watch?v=0tOsucpdD7Y https://www.youtube.com/watch?v=5QyvFNZZ1fs Front mission 2's window color configuration menu https://bsky.app/profile/guilewinquote.bsky.social/post/3mozyqq2ibk2g Trying to get The Incredible Machine 3 running in Wine only to realize 2 is exactly the same and runs in DOSBox and finally solves a dialogue sfx mystery Microtopics: The cheezburger TLD. A show that has a big reputation to live up to because the name sounds similar to Totoro. Prehensile rainbow hair. Sensible anime naming logic. A little guy running around like in a video game, except in real life. Matter Senators, the only place on the Internet you can hear matters discussed. Issue Ombudspeople, the only place on the Internet you can hear issues discussed. Agnes Tachyon. (Agnes is his surname.) Mailbox Baseball. Making a new mailbox post out of a 1 foot diameter oil drilling pipe. The floral anatomy of a mailbox. The neighborhood in Albuquerque that has the least destructible mailboxes. An iron iceberg embedded in the sidewalk tearing a gash in your RV. Wikipedia redirecting your search for Mailbox Baseball to the page for Property Crime. Calling your daughter to let her know you've just sent her an email. Overengineering your mailbox by an order of magnitude in every respect. A Vault-Tec vault but for mail. Low level Nintendo 64 emulation. Sending a display list to the Reality Signal Processor. Trying to do N64 development when the development hardware is in a different building from your computer. Working on web apps and wishing you could be working on folding space time. Shoveling pixels from one memory location to another. Losing track of the state of Nintendo 64 emulation because you have to remodel your house. Predicting a console's success by whether it uses bespoke off-the-shelf parts. Putting a value in the zero index in a Lua array. An Youtube video that is old enough to vote. Bidirectional groan tubes. Shaking the groan tube. Making a mace by attaching a Moo Box to the end of the Groan Tube. 10 hours of silence occasionally broken by groan tube. Ranking the best groan tube moments. Attaching 100 groan tubes to my wife's car's exhaust pipe. Alfred E. Groantube. Wikipedia articles with videos. The UPS guy carrying around a huge box of Moo Boxes. A Muffled Cacophony of Christmas Crap. A big box fancy liquor store. A building in the vicinity of a cheeseburger office. Muzak World HQ. The Cure doing a faithful cover of the Front Mission 2 Window Color Configuration Menu. My eyes are grayscale but your hair is more purply grayscale. A game that was criticized for its long loading times. A poem about long Playstation loading times. Lords Get In Free. The death of Youtube videos just showing the UI of video games. Get Medieval (1998) The hardest era of PC gaming to run a game from. The sort of thing someone might post in the Topic Lords discord. Refusing to read the Mothman's Ass bumper sticker because you might do it as a poem someday. Solving puzzles inspired by Rube Goldberg machines. Porting a game to Windows Forms. Finally finding out why Tim pronounced the title that way. Consulting Wikipedia before making life decisions. Going back in time to tell your past self that The Incredible Machine 2 is the same game and way easier to emulate. MT-32 if you're lucky, Adlib if you're unlucky, PC speaker if you're even less lucky. Asking Claude for the recipe for napalm from the Anarchist's Cookbook and it's just as accurate as you remember. Downloading text files from textfiles.com Files that you should not follow. A 40 column text file on how to make a dust bomb. Learning how to download porn from Usenet. Undocumented features of TRS-80 BASIC. How to get a free account on Compuserve.

Before AI, Klaviyo had an insight that changed e-commerce marketing forever: don't show merchants how many emails they sent, show them how much money those emails made. That single shift helped Klaviyo capture 80% market share, earn a cult following among Shopify merchants, and IPO in 2023 with $1.4B in revenue. Now Andrew has to do it again. In this session at SaaStr AI Day, Klaviyo's co-founder and CEO breaks down how he's rebuilding a dominant pre-AI company for the agent era, including some of the most practical AI-at-scale lessons we've heard from any founder this year. What's inside: The Dark Factory. Klaviyo's internal agent system inspired by "lights-out manufacturing," where a team of agents decomposes a product prompt into specs, writes software interfaces, runs tests, handles load testing, and raises a flag only when it's genuinely stuck. One weekend, one agent team, one full prototype. The Tom Brady Rule. Andrew's mental model for LLMs: treat them like a great all-around athlete. They'll never be elite without coaching. The "harness" you build around the model, the domain-specific data, feedback loops, and scoring, is what makes the difference between a POC and a product that works for 200,000 customers. Agents skip onboarding entirely. Unlike human users who take months to learn a product, agents land as power users on day one. Andrew's team now asks their own agents, "What's holding you back?" and uses the answers to build their product roadmap. (One example: the agent discovered AMP interactive email on its own and immediately asked Klaviyo to build the missing APIs.) Agents training agents. For customer-facing agents, Klaviyo takes a feed of real support cases, classifies them, then runs an agent loop that trains another agent on the platform, without any human FDE or SE required. It ships at 50-70% resolution out of the box. APIs are the new UI. Whether your software is brand new or a decade old, if your internal and external APIs are great, agents will figure out the rest. The companies that win the agent era won't be the ones with the best interface. They'll be the ones with the best infrastructure. Three years from now, every business will have an agent you can reach through a URL, a phone number, or an email. The question is who builds the infrastructure underneath it.

Robin and Mazen talk with Mike Ryan of CopilotKit about bringing AI agents to React Native apps. They break down AG-UI, generative UI, shared state, and the guardrails developers need to build useful, trustworthy mobile experiences.   Show Notes CopilotKit: Bring Users and AI Agents together inside real apps   Connect With Us! Mike Ryan: @MikeRyanDev Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio   Sponsored by Infinite Red Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

Tubi's latest engagement metrics and revenue suggest the streamer is firing on all cylinders. Meanwhile, YouTube is further upping its game, with a UI overhaul and new creator minimums.

Yoni Avital lives in Tel Aviv, Israel, with his wife and 3 older children. The oldest kid is a boy, so Yoni and he try to see as many football games as possible. He enjoys shopping with this girls, though it's cause he is their Dad, not because he enjoy shopping. He likes to travel, hike, and enjoys a nice white wine in warmer weather. His most memorable hike was at Yosemite, when he started at 4 am and came across a lot of wildlife.Yoni was in the virtual desktop space in the past. What he and his team realized was that troubleshooting these virtual experiences were incredibly complicated. They started to build an enterprise task manager, to centralize a task management UI to control the endpoints. When customers started asking to use it daily, Yoni figured out they had something unique.This is the creation story of ControlUp.SponsorsUnblockedTECH DomainsMezmoBraingrid.aiLinkshttps://www.controlup.com/https://www.linkedin.com/in/yoavital/Checkout our episode stacks on Stacklist! https://stacks.codestory.co/ Hosted by Noah Labhart | Technical Founder & Startup Mentor.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Nathan Sobo joins Scott and Wes to explain why Zed was built in Rust, how GPUI works, and what happens to editors once agents write most of the code. They also talk about DeltaDB, Zed's new Git-compatible version control system, and Delta, the collaborative agentic editor it powers. Show Notes 00:00 Start 00:35 Welcom to Syntax 01:13 The Journey to Zed: Building the Ultimate Tool 03:26 Why Rust was chosen for Zed? 06:29 Brought to you by Sentry! 07:07 Building a UI from scratch in Rust GPUI 15:55 AI's role in coding and development 18:42 The role of text editors in the age of AI 21:52 Delta DB: The vision for collaborative development DeltaDB 29:06 The Evolution of Collaborative Coding 44:45 The Future of User Interfaces 52:42 Sick Picks + Shameless Plugs Sick Picks Scott: Wes: Nathan Sobo: Keychron Q11 Grant Green - Idle Moments Shameless Plugs Scott: Wes: Nathan Sobo: DeltaDB Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

AI is changing what developers build, but code alone is no longer enough to prove what you can do. Wes Bos explains why engineers need to solve problems beyond syntax, how agent workflows are reshaping software development, and what still requires human thinking.In this conversation:The limits of generative UI and AI-generated designAgent loops, harnesses, and cheaper AI modelsThe rising cost of AI coding and the case for local hardwareWhy developer education is shifting from syntax to problem-solvingPersonal branding, conferences, newsletters, and AI-generated contentFor developers navigating AI-assisted coding, this episode explores the skills and signals that still help you stand out.This podcast was recorded at JSNation, the key web dev conference.OUTLINE00:00:00 - Code Is Not Enough for Developers00:00:32 - Why Generative UI Still Feels Unfinished00:04:35 - How Agent Loops Improve AI Coding00:07:06 - When Agent Workflows Become Standard Tools00:08:19 - Are Cheaper AI Models Good Enough?00:10:44 - Can AI Coding Costs Stay Sustainable?00:12:24 - What Engineers Need To Learn Now00:14:23 - Why Fundamentals Matter Beyond Syntax00:15:34 - How Non-Coders Are Building Production Tools00:16:21 - Why In-Person Conferences Still Matter00:18:11 - Personal Branding When Code Isn't Enough00:20:37 - Can Newsletters Beat The Attention Crisis?00:22:02 - Why AI-Generated Content Feels Insulting00:24:12 - Use AI To Scaffold, Not Think

The most freeing idea in playable ads keeps proving itself: your playable doesn't have to match your actual game. This month a zombie survival game ran a football playable, and an isometric strategy game ran a non-isometric one. The crew breaks down nine playables and the lessons underneath them.Matej Lančarič and Ondrej Mosberger are joined again by Robin Kuyer for the "everyone brings playables" format. They cover ZomLine Survival's World-Cup-timed football playable (a themed playable bolted onto a 4X game — nothing to do with the core, and that's the point), the perennial themed-playable truth (it either performs brilliantly or does nothing, with slot/casino games running Christmas playables all year), the frustration-as-motivator debate (Ondrej's 3D-Tetris-on-a-Clash-of-Clans-lookalike playable that's built to make you fail), the mini-game-per-mechanic strategy (Happy Nation/Miniclip's nonogram — if your app has many mini-games, build a playable for each and measure which end-screen choice players tap), visual rewards vs literal rewards (Epic Stickman's one-click combat that pays off with a cutscene, not a chest), the textbook hand-holding playable (Estoti's Frostworld), and Matej's picks — Smash Fest (which he crowns the trend everyone should follow), Rogue Legends (a smooth tower-defense playable with no logo or download button at all, for a game that looks nothing like it), and a wild Bart-Simpson-plus-undressing screw-puzzle hook fronting a Forex game (IP infringement and all). Plus a genuinely useful tangent from Robin on how network preview templates clash with playable UI, and how Unity tracks users bouncing between CTA and end-card.The through-line: run different playables to capture different users, don't fear a mismatch between ad and game, and let the physics (or the frustration, or the reward) do the work.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⏱️ TIMESTAMPS00:00 The format — everyone brings playables (and a heatwave)04:20 ZomLine's football playable & the themed-playable truth09:40 3D Tetris on a Clash lookalike — the frustration debate16:30 Happy Nation's nonogram — a playable per mini-game20:00 Epic Stickman — the visual reward, not the chest23:50 Cooking Go & Frostworld — visually appealing, textbook onboarding32:50 Matej's picks — Smash Fest & the no-logo Rogue Legends playable39:50 The Bart Simpson undressing hook & Smash Fest as the trendThis episode is brought to you by Potensus — a premium ad network built by people who've been on both sides of the industry, with direct (non-programmatic) deals with Amazon, Apple, Coca-Cola, and Vodafone landing in your game at premium CPMs, plus a Playable Maker partnership that turns brand videos and statics into native gaming playables and rewarded formats. Head to potensus.com to get started.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-3bckldvr8-8PXvzciMWdheOzED9hq0SA---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiarPlease share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me

In this episode, Pete welcomes Virender “VA” Aggarwal, former CEO of Ramco and longtime HR tech executive, to explore how AI is reshaping the future of enterprise software, work, and workforce experience. From conversational UI and “zero UI” experiences to AI-native platforms, orchestration across systems, outcome-based pricing, and the rise of headless software, VA shares a candid view of where HR, payroll, and SaaS are headed next. The conversation also explores why the winners in this next era may not simply be the companies adding agents to legacy platforms, but those rebuilding around AI from the ground up. Pete and VA also talk the human side of automation, including trust, adoption, customer experience, the future of administrative work, and why AI should free HR to spend less time processing transactions and more time understanding people. Connect with VA: LinkedIn: https://www.linkedin.com/in/virender-aggarwal/     Connect with the show: LinkedIn:  http://linkedin.com/company/hr-payroll-2-0 X: @HRPayroll2_0  X: @PeteTiliakos  X: @JulieFer_HR BlueSky: @hrpayroll2o.bsky.social YouTube: https://www.youtube.com/@HRPAYROLL2_0  WRKDefined Podcast Network: https://wrkdefined.com/podcast/hr-payroll-20  Thank you to our marquee sponsors for powering the HR & Payroll 2.0 podcast forward!  G-P ‘Globalization Partners': https://www.globalization-partners.com/ OneSource Virtual: https://hubs.ly/Q03YFNR90 Zoho: https://www.zoho.com/press.html Thank you to our ‘wizard behind the curtain' and show producer Ryan Kielma: https://www.linkedin.com/in/ryan-kielma/

Cloudflare is rolling out crypto wallets with claimable handles as identity, and a real React compiler finally landed for regular hooks-based code. Plus: OpenAI's pricing war, Vue Vapor benchmarks, GitHub's new npm malware scanning, and an active supply chain attack hitting 868 packages. Show Notes 00:00 Welcome to Syntax! 01:02 Shai Hulud is back! New Shai-Hulud announcement Attacked keyv packages 05:27 GitHub scans your npm packages for malware 06:42 Your agent has a wallet now Make your own Cloudflare wallet announcement blog post X402 payments 12:10 Yet another React compiler? Ripple.ts Inferno.js Dominic Gannaway's launch post Octane Native Script Reactivity + Rendering Benchmark 27:19 Scott switched his browser AGAIN! Dia Knock off extension 31:56 AI Mental health survey 32:29 OpenAI models are becoming cheaper? 38:49 GitHub stacked PRs finally arrived 41:48 AI video podcasts HeyGen launch post Hank Green's unhealthy AI usage 48:15 Brought to you by Sentry! 49:28 News from the TC39 meeting ECMAScript news Await dictionary TC39 Process 56:58 Qwen 3.8 01:02:31 Latest and greatest in CSS and UI libraries Foley.dev Morphicons Lucide Nucleo 8bit library CSS radar Impeccable Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

What does it take to build a dating app to $140K MRR in just 6 months, while launching in only one country?In this live session, we're joined by Gabriel Zeitoun, founder of BPM, a dating app built exclusively for sporty people. Despite being available only in France, BPM has achieved impressive growth through a relentless focus on product design, onboarding, paid acquisition, and long-term retention.Gabriel will share how his team validated the idea, built a product users love, optimized onboarding and paywalls, and scaled primarily through Meta Ads. We'll also dive into his philosophy on UI, why stability beats chasing algorithms, and what it really takes to grow a consumer app from scratch.Whether you're building a dating app or any subscription-based mobile app, you'll leave with actionable growth strategies you can apply immediately.You will discover:✅ How BPM reached $140K MRR in only 6 months✅ Why launching in one country first accelerated growth✅ Gabriel's UI and product design philosophy✅ How they optimized onboarding and paywalls✅ Their Meta Ads campaign structure and creative strategyLearn More:Check out BPM:https://www.bpm.so/ You can also watch this video here: https://youtube.com/live/JywSxxABu-0*********************************************SPONSORSThe app growth playbook is changing fast.AppsFlyer's State of eCommerce App Marketing Report 2026 breaks down the latest trends, benchmarks, fraud insights, and market-by-market data every app marketer should know before planning Q4.Download it free from the link below: https://bit.ly/4uvoHGM*********************************************Tired of handing 30% of every in-game sale to iOS and Android billing systems? Appcharge is the DTC monetization platform trusted by King, Huuuge, Tripledot, and SciPlay – giving studios a direct line to their players, and up to 95% of every transaction. Web stores, payment links, and a full Merchant of Record service covering taxes, compliance, and global payments – all in one platform. Learn more:https://www.appcharge.com/*********************************************Follow us:YouTube: ⁠AppMasters.com/YouTube⁠Instagram: ⁠@App MastersTwitter: ⁠@App MastersTikTok: ⁠@stevepyoung⁠Facebook: ⁠App Masters⁠*********************************************

In this episode of Trending in Education, host Mike Palmer sits down with Mike Peng, Founder and Managing Partner of Weatherstone Capital Partners, to explore the evolving intersection of education, technology, and capital markets. After missing a meeting at ASU+GSV in San Diego, Mike and Mike reconnect to unpack what it takes to scale educational ventures past the startup phase and navigate the broader macroeconomic shift from venture capital to private equity in EdTech. Drawing from his unique career journey—from engineering at UT Austin and strategy consulting at McKinsey to leading rapid growth at Block Renovation and earning an MBA at Stanford—Peng shares why Weatherstone focuses on partnering with operators in the 1-to-10 scale stage ($1M to $10M EBITDA range). Together, they examine the real "moats" in the age of AI (data ownership and customer relationships over UI), why corporate L&D and continuing certification are outperforming traditional K-12/Higher Ed models for private equity, and how simulation tools and micro-learning are reshaping workforce upskilling. Peng also offers vital advice for founders assessing whether they are the "limiting factor" in their company's growth and how to navigate tight capital markets. KEY INSIGHTS: 0-to-1 vs. 1-to-10 Leadership: Building a product from scratch requires a scrappy, zero-to-one mindset, whereas scaling from 1 to 10 demands processes, enterprise workflows, and founders willing to look in the mirror to ensure they aren't becoming their company's limiting factor. The Capital Shift from VC to PE: With EdTech valuations down ~60% from 2021 peaks and market maturity kicking in, 2024 marked a pivot where private equity outpaced venture capital in EdTech funding—shifting the focus from hyper-growth to unit economics, profitability, and sustainable scale. Redefining AI Moats: User interface (UI) and simple API connections to large language models are no longer defensible differentiators. True AI moats reside in owning proprietary data, maintaining deep customer trust, and controlling end-to-end customer relationships. Corporate L&D and Reskilling Demand: While K-12 and Higher Ed present longer sales cycles and risk aversion, Corporate L&D—particularly recurring certification, micro-training in daily workflows, and AI simulation (e.g., bedside manner nursing)—presents massive opportunity as 85% of employers seek to reskill their workforce by 2030. Founder Discipline in Tight Markets: Bootstrapping and pivoting quickly are more viable than ever thanks to AI MVP acceleration. Founders must plan capital runway at least a year in advance and regularly "come up for air" so market evolution doesn't leave their business behind. TIMESTAMPS: 00:00 – Introduction & Connecting Post-ASU+GSV 01:00 – Mike Peng's Journey: UT Austin, McKinsey, Block Renovation, & Stanford MBA 02:30 – Inside Weatherstone Capital Partners: Long-Term Platform Investing 03:30 – 0-to-1 vs. 1-to-10: Passing the Founder "Mirror Test" 05:30 – Takeaways from ASU+GSV: AI Undercurrents & Modern Moats 07:30 – Career Readiness, CTE, & The 1,400-Tool EdTech Stack 09:30 – Data is the New Oil, AI is the New Electricity 11:30 – Managing Hallucination Risk & Trust in Classroom Tech 13:30 – Segmenting EdTech: Why Capital is Moving from VC to PE 18:00 – The Future of Work: LXP, Micro-Learning, & AI Simulations 23:30 – Personal AI Tutors, Spatial Hardware, & Final Advice for Founders 26:00 – Wrap Up & How to Connect with Weatherstone Subscribe to Trending in Ed wherever you get your podcasts to stay ahead of the curve in learning, media, and the future of work.

 EPISODE DESCRIPTION In this episode, I sit down with Joao Lobo, founder of Kute Wallet , a Bitcoin-only, non-custodial wallet built for the next generation of crypto users. Joao walks me through his journey from discovering Bitcoin on 4chan forums in 2013, to building a PIX-integrated wallet for Brazil, to now targeting the European market with a product designed to feel as simple as a banking app. We dig into what makes Kute different , from Lightning-powered Polymarket integration to upcoming Hyperliquid stock exposure and Morpho lending features , and why he believes the future of finance is a seamless blend of non-custodial crypto and traditional banking. We also get into the brutal reality of customer acquisition costs, why MetaMask and Phantom overwhelm new users, and the advice Joao has for any founder building in this space right now. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/ CONNECT Kute Wallet Website: https://kutewallet.com/Twitter/X: https://x.com/kutewalletInstagram: https://www.instagram.com/kutewallet?igsh=azVtbmc5NDQ5b3Zx&utm_source=qrJoao Lobo LinkedIn: https://www.linkedin.com/in/joao-pedro-miranda-0ba478a7/?locale=enWeb3 with Sam Kamani: https://www.web3pod.xyz KEY POINTS WITH TIMESTAMPS • [00:00] Sam introduces the episode and guest Joao Lobo, founder of Kute Wallet• [01:36] Joao's origin story: first hearing about Bitcoin on 4chan in 2013, buying in 2019, and eventually committing his career to crypto• [04:21] How Joao went from working at a gambling company to founding a Bitcoin wallet for Brazil (SAT Sales), then pivoting to Kute Wallet for Europe• [05:11] Why Joao integrated Polymarket directly into Kute via Lightning rather than building a competing prediction market• [08:06] What makes Kute Wallet different: Bitcoin-only, Lightning-powered, clean UI, and cross-chain swaps via Sideshift and Orchestra• [11:24] Upcoming features: Hyperliquid integration for ETFs and stocks, Morpho for USDC yield and Bitcoin-backed loans, and an in-app AI assistant called Sal• [13:40] The long-term vision: combining a non-custodial crypto wallet with a fiat bank account to close the full financial loop• [14:11] Target demographic: 18 to 30 year olds open to new financial technology and risk• [18:18] How Kute tackles complexity , using plain language instead of crypto jargon and an AI mascot to explain features in context• [22:18] The biggest challenge: customer acquisition costs rising rapidly across all software, and how founder-led community building is the counter-strategy• [26:18] The biggest misconception about crypto wallets: most people outside the space don't understand custodial versus non-custodial• [29:27] How stablecoins and blockchain rails are already powering global trade and payments, especially in emerging markets• [37:23] Technical architecture of Kute: BDK, Breeze for Lightning, Sideshift, Orchestra, and direct Polymarket and Hyperliquid integrations via private key• [40:08] Current stage: Android live, iOS App Store approval imminent, part of Antidot Bitcoin incubator in London, seed round targeting September close• [42:53] Joao's advice for founders: use AI tools aggressively now while they are subsidised, iterate fast, build a social presence, and make human connection your moat

Our library director Phil Salvador sits down for another What's Your Deal? episode with Blizzard's senior digital archivist, Leah Williams, to discuss her critical work of preserving their modern video game history. In this episode:Leah's unconventional path to digital games preservationManaging art, UI, and visual FXCollaborating with engineers and metadata expertsWhy discoverability matters for legacy assetsBuilding vital relationships across game studiosNavigating AI tagging and digital preservation challengesMentioned in the show:YouTube 35th year anniversary 35' table Blizzard: The Next ChapterYou can listen to the Video Game History Hour every other Wednesday on Patreon (one day early at the $5 tier and above), on Spotify, or on our website.See more from Leah Williams:LinkedIn: /leah-williams Video Game History Foundation:Email: podcast@gamehistory.orgWebsite: gamehistory.orgSupport us on Patreon: /gamehistoryorg

We're getting into the nitty gritty on Nintendo's UI & UX design sensibilities this week with special guest TriNintendo! How has UI in video games evolved over the years, from utility to flashiness and beyond? Did Nintendo lose some of its old charm or innovation in the Switch era? We cover it all from a designer's perspective. We also cover all the Nintendo and gaming news such as unreleased Virtual Boy games finally coming to the Switch 2's Nintendo Classics NSO service, a leaked ESRB rating and potential release date for the Legend of Zelda: Ocarina of Time remake, an interview with Turnip Mountain developer Luke Sanderson, and much more. As always, we close with the games we've been playing. Listen to Super Switch Headz on Apple Podcasts, Spotify, YouTube or wherever you enjoy podcasts. 0:00:00 Introduction 0:05:07 News & Rumors 0:23:40 Turnip Mountain Dev Interview 0:33:30 Nintendo's UI/UX Design 1:14:15 Games We're Playing Tri's Channel: https://www.youtube.com/@TriNintendo HYPER DEBT SWAP: https://magooster1000.itch.io/hyperdebtswap Turnip Mountain: https://www.nintendo.com/us/store/products/turnip-mountain-switch Discord: https://discord.com/invite/CWbF4gb Facebook: https://www.facebook.com/groups/switchheadz Patreon: https://www.patreon.com/SuperSwitchHeadz/ Website: https://www.switchheadz.com/ Clips Channel: https://www.youtube.com/@SwitchHeadzClips

Louie Mantia returns to the show to talk about the state of UI and icon design on Apple's platforms, and some speculation on Apple's trade secret lawsuit against OpenAI.

Kelly and JK share updates and discuss running and gym trends. Kelly sets a summer/fall race schedule with two road mile races and three 5Ks. They discuss Garmin's acquisition of TrainingPeaks/TrainHeroic, including UI preferences, data/privacy considerations, and potential future integration or AI-driven analytics across platforms. Kelly recaps Josh Kerr's London Diamond League performance breaking the men's mile world record. JK observes more brick-and-mortar gyms closing post-COVID due to costs, competition from studios and low-cost chains, and shifting membership behavior.00:00 Welcome and Episode Intro01:38 Kelly's Race Schedule07:42 Garmin Acquires TrainingPeaks18:47 JK's Training Update24:04 Brick Phone Check In29:26 Josh Kerr's World Record Attempt42:13 Local Gyms ClosingWatch the 1 mile race: https://youtu.be/eYi2f4ONEDg?is=QNKDjsgTlrr-M1K0Follow the podcast at ⁠⁠⁠@liftingrunninglivingpod⁠⁠⁠Email us at ⁠⁠⁠liftingrunninglivingpod@gmail.com⁠⁠⁠Follow JK at ⁠⁠@coachjkmcleod⁠⁠Follow Kelly at ⁠⁠⁠@runningklutz ⁠

At Macstock 2026, Max Mellman showed off his his iPhone app, II Love a Piano. Max explains how the app stands out through realistic haptic feedback, intuitive navigation, portrait and landscape support, and a design that encourages exploration instead of intimidation. Chuck and Max also discuss his inspiration, teaching career, future iPad support, and broader music education projects.  This edition of MacVoices is brought to you by the MacVoices Dispatch, our weekly newsletter that keeps you up-to-date on any and all MacVoices-related information. Subscribe today and don't miss a thing. Show Notes: Chapters: 00:00 Introduction and Macstock 2026 setting 01:00 Meeting Max Melman and his Macstock experience 03:05 The story behind Beloved Melody 04:25 Introducing I Love a Piano 06:15 What makes the app different from other piano apps 09:10 Experiencing the haptic keyboard firsthand 11:40 Portrait, landscape, and intuitive navigation 14:05 iPad support and future development 16:05 Pricing, availability, and App Store release 17:35 Teaching music and inspiring students through technology 20:10 Music education projects and closing thoughts Links: iLoveAPiano.app http://iLoveAPiano.app Scribe.band http://scribe.band Guests: Max Mellman's beloved melody (of my parents Belove and Mellman) is his software design studio. He is a veteran elementary music teacher, musician, arranger, and UI designer from New Jersey. I'm also the author of The Bagel Song and the creator of the app I Love a Piano. Support: Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect: Web:      http://macvoices.com Twitter: http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices Mastodon:      https://mastodon.cloud/@chuckjoiner Facebook:      http://www.facebook.com/chuck.joiner MacVoices Page on Facebook:      http://www.facebook.com/macvoices/ MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice LinkedIn:      https://www.linkedin.com/in/chuckjoiner/ Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher: Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Discover the origin story of Jetpack Compose and how it changed the way Android developers build applications. We discuss the original challenges of unbundling the UI toolkit from the operating system to deliver faster updates. By adopting Kotlin and a declarative approach, the team reduced the boilerplate necessary to build user interfaces. While earlier systems relied heavily on XML, Jetpack Compose shifts to a code-first model for easier iteration. Listen to the team share their insights on modifiers, performance improvements, and the future of Android design.   Resources: Android is Compose-first → https://goo.gle/compost-first Jetpack Compose overview → https://goo.gle/compose-overview   Subscribe to our YouTube channel → https://goo.gle/AndroidDevs Speakers: Clara Bayarri, Tor Norbye, Chet Haase, Romain Guy

A bi-weekly news show informing you on the latest in Bitcoin, privacy and open source tech hosted by Ungovernables, Max and Q. AOBFreedom.Tech launch reminderKeyOS v1.3 now publicly availableNEWSIndia orders GitHub to take down BitChat's source code; Internet Freedom Foundation calls it unconstitutional - TFTC: India BitChat GitHub takedown, I4C, IFF / CoinDeskFourth Circuit says border agents can hand-search your phone with zero suspicion, as a man is prosecuted for a duress-wipe - EFF: Fourth Circuit says border agents can search your phone by hand, no suspicion required / TechCrunch: US accuses American of wiping his phone with a duress password at the borderSenate Democrats kill the CLARITY Act before recess; the developer safe harbor (Section 604) stalls with it - TFTC: CLARITY Act rejected, Bitcoin ownership surpasses goldState Department launches a "Freedom Tech" program with BPI, Palantir, and Anduril as founding partners - Bitcoin Magazine: State Department tech program with BitcoinBlock open-sources Buzz: a Nostr-native, keypair-identity workspace for humans and AI agents - LINKBIP-110 approaches its mandatory signaling window with support under 1%, and enforcing nodes staring at a minority fork - TFTC: BIP-110 enters mandatory signaling window below 1% hashrateRELEASESBitcoin core / protocolbtcd v0.26.2 - 2026-07-25Security-hardening for the Go full node: stricter PSBT/input parsing, Schnorr and WIF validation, rejection of malformed bech32, tighter inbound admission.Hardware / signingKeystone 3 v3.0.0 - 2026-07-21Major firmware across all variants of the airgapped open-source signer: reworked passcode/recovery flow, stronger validation, upgraded security policies. Reproducible with published checksums.Trezor Suite v26.7.2 - 2026-07-22Firmware security updates plus a lower 0.2 sat/vB minimum fee and cancel-pending-transaction support.Nunchuk 2.7.1 - 2026-07-16Collaborative-custody multisig wallet. 2.7.0 (07-15) added self-custodial USDT on Liquid and Trezor Bluetooth support; 2.7.1 is bug fixes on top. On-lens for multisig self-custody.Bitkey App 2026.11.0 - 2026-07-14Block's consumer hardware wallet. Release highlights its Emergency Access (recovery/inheritance) path; full notes hosted off-repo at bitkey.world/releases.LightningCore Lightning v26.06.6 - 2026-07-22Patch release (26.06.3-5 pulled over broken PyPI publishing). Now rejects channels reusing an existing funding outpoint, closing a channel-security edge case.LNDg v1.11.0 - 2026-07-26Self-hosted LND dashboard: peer-offline reporting, auto re-index on data migration, historic failed-HTLC data via API. Update logging config on upgrade.Zeus v13.1.3 - 2026-07-21Point release / version bump on the 13.1 line for the self-custodial Lightning wallet.LNbits v1.5.6 - 2026-07-15Minor patch on 1.5.5 (payments extension-field refactor and fixes) for the self-hosted Lightning accounts system.Lightning Labs Wavelength - 2026-07-21A toolkit for adding self-custodial bitcoin (and stablecoin) payments to any application, designed to create the best developer experience for humans and agents.EcashCashu TS v5.0.0-rc.5 - 2026-07-23RC for the major v5 of the reference TS Cashu library: NUT-18 payment requests (PaymentRequestBuilder), mint-preference support, hardened P2PK validation, integer fee math. Foundational for ecash wallets.Nutshell 0.20.3 - 2026-07-22Reference mint/wallet: Pay-to-Blinded-Key (lock ecash to a receiver without revealing their pubkey to the mint), a Spark L2 backend, and a false-UNPAID melt-race fix. DB migration, back up first.Fedimint v0.12.0-beta.0 - 2026-07-23Beta pre-release of the federated ecash / community-custody protocol. Flagged unstable, no upgrade guarantee. "In the pipeline," not production. (Admin UI: Fedimint UI v0.7.4, adds arm64 image.)On-chain privacy / coinjoinWasabi Wallet v2.8.1 - 2026-07-22Now receives to Taproot addresses by default (a real "state of the network" adoption nudge, four-plus years post-activation), adds Linux AppImage, on top of 2.8.0's serverless P2P filter sync.Ashigaru Desktop v1.1.2 - 2026-07-25Whirlpool coinjoin QoL: live Tor/Electrum status, one-click connect, faster startup, self-clearing coordinator banner.JoinMarket-NG 0.34.2 - 2026-07-20Actively-maintained modern fork of JoinMarket: safe expired-fidelity-bond handling, correct frozen-UTXO reporting, multi-wallet RPC routing.Bitcoin Safe 2.1.1 - 2026-07-20Multisig/single-sig desktop wallet: UI fixes and improved Debian build reproducibility.P2P / no-KYCBisq 1.10.4 - 2026-07-24Mandatory security update for the decentralized no-KYC exchange (audit findings): signed DAO block providers, stricter blind-vote/dispute validation, re-enabled BSQ swaps. Required to keep trading.Bull Bitcoin 6.12.4 - 2026-07-24Bug-fix for the no-account self-custodial app (iOS startup-lockup fix). The feature release was 6.12.2 (UTXO/coin-control, Coldcard NFC, BitBox02 Nova BLE, sub-1 sat/vB).Vexl v1.45.1 - 2026-07-21Point release of the contacts-based no-KYC P2P trading app (small fixes).Peach Bitcoin 0.69.0 (381) - 2026-07-23Latest build of the no-KYC P2P Bitcoin marketplace (rolling 0.69.0 build increments 379/380/381 across the fortnight). Verify the build-tag slug before publishing (parentheses in the tag).Self-hosting / infraBTCPay Server v2.4.1 - 2026-07-23Self-hosted no-KYC payment processor: BIP-329 label import, editable invoice comments, refund-email triggers, RTL UI, restored Boltcard payments.Start9 StartOS v0.4.0 - 2026-07-24Major: a complete ground-up rewrite of StartOS, out of public beta after six years, billed as the "correct architecture for sovereign computing." Note: the only upgrade path is a fresh install (no in-place migration). One of the biggest self-hosting stories of the fortnight.Liquid GDK release_0.77.7 - 2026-07-20Blockstream's wallet SDK: libwally + Tor bumps, macOS/iOS cross-compile, single-sig gap-limit fee fix.Privacy stack / PayjoinPayjoin Dev Kit payjoin-cli 1.0.0-rc.1 - 2026-07-23RC for the reference Payjoin CLI, synced to payjoin 1.0.0-rc.6. Signals the v1.0 Payjoin stack nearing release (breaks common-input-ownership heuristics on-chain).NostrAmber v6.3.0 - 2026-07-20Android Nostr remote signer (keeps your nsec off client apps): grouped/collapsible multi-request approvals, a log-disabling privacy mode, built-in Tor, NIP-65 relay prefetch.Wallets (self-custody)BlueWallet 8.0.1 - 2026-07-21Major v8 line: iOS 26 UI refresh, BC-UR v2 airgap scanning (OneKey/Keystone), Unchained multisig cosigner import, 19 new languages, crypto-js replaced with @noble. Broad user base. Confirm the exact tag slug before publishing.Cake Wallet 6.3.2 - 2026-07-24Non-custodial BTC/Monero wallet: home-screen recent history, better OpenAlias/ENS/Unstoppable alias resolution, faster Zcash sync.EDUCATIONWhat Is a UTXO, and Why Does It Matter for Bitcoin Privacy? - 2026-07-25Community explainer thread on Stacker News. The useful part is the top response, which walks through how receive-and-spend patterns fingerprint you and where coinjoin actually helps. Good raw material for a plain-English UTXO segment, which pairs with the Wasabi and Ashigaru releases and gives newer listeners the vocabulary before the coinjoin talk.Bitcoin Optech Newsletter #415 - 2026-07-24Two items worth surfacing. Fabian Jahr's draft BIP459 proposes full aggregation of BIP340 schnorr signatures using DahLIAS, combining multiple signatures into a single 64-byte aggregate, with cross-input signature aggregation as a downstream possibility. And libsecp256k1 #1765 adds an optional BIP352 silent-payments module supporting receiver scanning from only the scan secret and spend pubkey, so the spend private key stays offline. Silent payments quietly becoming infrastructure is a good recurring beat.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 Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @

This week, The Gay Mix comes in hot with the most glamorous smart-light failure imaginable before Daniel drops a proper newsroom bombshell: he quit his job. After weeks of executive chaos, mystery PowerPoint math, disappearing developers, and enough stress to qualify as cardio, he finally chose peace—and perhaps a future taking happy vacation photos at Disney. Adam, already between jobs himself, naturally suggests they simply make a living podcasting. What could possibly go wrong?From there, the newly unemployed besties get entrepreneurial as Adam unveils his all-in-one podcast production playground, complete with show planning, contact wrangling, soundboards, trivia, and a UI only a developer could love. The contact segment brings Kathy Bacon's allegedly murdered backyard, suspicious celebrity-death arithmetic, Lamont Cranston's deep-cut Miss America reference, and a bold invitation to text the show pictures best left undescribed here. There are also elaborate funeral plans, a possible AI afterlife, a spicy detour through Broadway ticket prices and living wages, and a News Game performance that proves quitting your job does not automatically improve your knowledge of rivers.Birthdays bring Lynda Carter, Jennifer Lopez, and Elisabeth Moss, while Adam closes out the evening with a 3D-printing saga involving scratched light boxes, rogue nozzles, missing connectors, and emergency Walmart LEDs. Daniel teases an equally maddening mystery for the after show, the new production tool gets one last live shakedown, and everyone somehow reaches the closing music with dignity mostly intact. We hope you enjoy this week's blend of liberation, innovation, funeral catering, and technical support with just a whisper of gay panic.Email: Contact@MixMinusPodcast.comVoice/SMS: 707-613-3284

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

In this episode of The GaryVee Audio Experience, I sit down with Naveen, founder of Glance, at Cannes 2026 to talk about agentic commerce — the first time in 30 years the user interface of how people buy things is going to change. We discuss why agents will quietly handle the categories you don't care about so you have more time for the ones you love. I also explain why brands that aren't structuring their websites and content to be read by agents will have no opportunity of being bought.You'll learn about:• What Agentic Commerce Really Is• Why the UI of Shopping Is Changing• How Brands Get Found by Agents• The Late-90s Google Moment Again• Why You Should Participate Early