Podcasts about cpu

Stephen DiAdamo, co-founder and CTO of Qoro Quantum, is interviewed by Yuval Boger. DiAdamo discusses Qoro's position as a software middleware company that abstracts hardware away from applications, walking through the Divi SDK, circuit serialization and parallelization, and an orchestrator that automatically selects between 12 simulation methods on CPU and GPU or dispatches to QPUs from any vendor. They explore how Qoro plugs into HPC schedulers like SLURM rather than replacing them, the CESGA proof of concept built on the CUNQA platform, the "150,000 lines of code to 20" claim, and the argument that multi-QPU centers are needed today for fallback and resilience and for scaling applications. DiAdamo also reflects on developer experience for students learning QAOA and variational algorithms, and Qoro's two-year history.

Deze Einde van de Week Live is ook te bekijken op https://youtu.be/PZGrFJBfasw Deze talkshow wordt mede mogelijk gemaakt door MSI. Alle meningen in deze video zijn onze eigen. MSI heeft inhoudelijk geen inspraak op de content en zien de video net als jullie hier voor het eerst op de site. Klaar om het weekend te betreden? Wij wel. Ook al gaat het wat minder warm worden dan de laatste dagen. Wie weet is dat ook veel fijner ook. Wij gaan de traditionele opwarmer van het weekend voor je verzorgen. Klaar staat namelijk een nieuwe editie van Einde van de Week Live. Daan, JJ en Koos zitten klaar om bij te praten over alles wat er de afgelopen week toe deed. Een onderwerp dat aan bod komt, is bijvoorbeeld de multiplayer reveal van de testosteron shooter van XBOX: Gears of War E-Day. Wat vonden de heren ervan en zijn ze fans van de third person multiplayer? Ze kijken of ze het gerucht dat Rockstar in augustus met een gameplaytrailer komt, serieus kunnen nemen. En wat hebben The Odyssey en Assassin’s Creed: Odyssey met elkaar te maken? De antwoorden op al deze vragen vind je in de Einde van de Week Live van vrijdag 31 juli 2026. Knalt de multiplayer van Gears of War E-Day nog net zo hard als twintig jaar geleden? Andere onderwerpen die in deze editie van de vrijdagse talkshow voorbij komen, zijn onder andere de vijf eindes van Silent Hill Townfall, de nieuwe beelden van Amsterdam 1666 en de grootse plannen van Krafton met Subnautica. Check de Cyborg A15 B2 gaminglaptop en profiteer van de scherpe prijs MSI zet deze week de MSI Cyborg A15 B2 in de spotlights. Een gaminglaptop met een AMD Ryzen 7 260 CPU, een NVIDIA GeForce RTX 5060 GPU, 16GB RAM aan intern geheugen, een 144Hz Full HD Display, een 512GB SSD en een 4-zone RGB toetsenbord. Een mooi pakket dus bij elkaar, dat de komende week hier bij GamePC voor een scherpe prijs aangeboden wordt.Wil je adverteren bij de podcast Gamekings óf misschien bij een andere podcast van ILVY Network? Mail dan naar management@ilvy.com en/of kijk even op de website : https://ilvy.com/podcastSee omnystudio.com/listener for privacy information.

What becomes possible when enterprise computer vision no longer depends on expensive GPU infrastructure? In this episode of Tech Talks Daily, I speak with Glenn Jocher, founder and CEO of Ultralytics, about YOLO26, CPU inference, edge AI, open vocabulary vision, deployment economics, and the practical work required to move computer vision from a promising pilot into production. Glenn's route into AI began inside the U.S. intelligence community. He worked with the National Geospatial Intelligence Agency and Defense Intelligence Agency on particle physics applications, attempting to detect and track antineutrinos. Antineutrinos are extraordinarily difficult to detect because they pass through almost everything. Glenn describes them as the perfect spy. While searching for better detection methods, he discovered that computer vision researchers were solving similar problems with images. His original attempt to transfer those techniques into particle physics did not succeed. However, the work introduced him to a field where the technology could create a visible effect on everyday life. That led him toward open source development and eventually the YOLO models for object detection, classification, segmentation, and tracking. Glenn believes computer vision research has historically placed too much attention on small gains in accuracy while overlooking deployment economics. A model can perform impressively inside a laboratory and still remain unsuitable for a factory, warehouse, store, vehicle, drone, or medical environment. Price, latency, power consumption, data privacy, and deployment speed can determine whether the technology is commercially useful. This led Glenn and Ultralytics toward smaller models capable of running close to where images and video are generated. YOLO26 continues that approach with architectural changes designed specifically for CPU inference. Glenn says the model can process camera streams in real time at 30 frames per second and run across Intel CPUs, AMD CPUs, and lower power devices such as Raspberry Pi computers. This matters because specialist GPUs can increase the equipment cost and power requirements of a computer vision project. Running inference on existing CPUs or edge hardware can make deployment economically possible across larger numbers of cameras and locations. The scale already involved is difficult to comprehend. Glenn says Ultralytics models now process approximately three billion inference jobs each day, equivalent to around 30,000 every second. These jobs include images, videos, and collections of images being analyzed to detect, segment, or track objects. He attributes the platform's maturity to thousands of mistakes and bugs corrected through a rapid feedback cycle. New models are released, users report problems and request features, and the team incorporates that information into later versions. We also discuss the respective roles of cloud and edge infrastructure. Glenn sees cloud platforms continuing to provide the computing power required for training, while computer vision inference often belongs at the edge. Local processing can reduce latency, control operating costs, and keep sensitive video or medical information closer to where it was created. The smallest YOLO model is approximately three megabytes, according to Glenn. That allows it to reach mobile phones, vehicles, drones, battery powered devices, and other environments where a large language model would be impractical. Open vocabulary vision provides another development. Traditional object detection models are trained to recognize a fixed collection of objects. If a model learns to detect dogs and the user later wants it to detect cats, retraining can cause it to forget earlier knowledge unless both categories appear in the new training data. Glenn explains how promptable models can identify common everyday objects from text or visual instructions without additional training. A user could request a person wearing a blue shirt and white shoes, for example, and the system could search an image for that description. That flexibility could benefit businesses whose requirements change regularly. It reduces the need to create and label a new data set every time the company wants the model to recognize another common object. The range of current applications is already extensive. Glenn describes YOLO being used across robotics, parking, industrial safety, PPE detection, warehouses, aviation, security, traffic management, food quality, and manufacturing. Some of his favorite examples involve environmental problems. One company uses YOLO with underwater vehicles to identify and recover plastic from the ocean. Other applications detect smoke and fire early enough to support forest fire response. For leaders considering computer vision, Glenn recommends beginning with a defined problem and measurable outcome. A manufacturing company may want to reduce defects, but it still needs labeled examples showing the model what acceptable and defective products look like. He advises testing the idea through a limited pilot, measuring the return, and expanding only when the evidence supports further investment. Computer vision has become easier to deploy, but practical problems involving data, cameras, integration, reliability, and operating conditions still separate a demonstration from a production system. Could CPU inference and open vocabulary models make computer vision practical for processes your organization previously considered too expensive? Listen to the episode and share your thoughts with me. Useful Links   Ultralytics website Ultralytics Platform      

Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

Dave Altavilla recaps AMD Inc.'s (AMD) Advancing AI 2026 conference and his biggest takeaways on the event. He argues AMD's Helios rack offers "all the pieces" to compete with Nvidia (NVDA) as CEO Lisa Su sets her sights on creating a full AI platform. With the company reporting earnings next week, Dave tells investors to watch CPU revenue as it may set a foundation for Helios and argues there's lots of opportunity for AMD to eat into Nvidia's market share. ======== Schwab Network ========Empowering every investor and trader, every market day.Subscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling -https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watch Watch onVizio - https://www.vizio.com/en/watchfreeplus-exploreClassification: Schwab InternalWatch on DistroTV - https://www.distro.tv/live/schwab-network/ Follow us on X –https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetwork Follow us onLinkedIn - https://www.linkedin.com/company/schwab-network

AWS Morning Brief for the week of July 26th, with Corey Quinn. Links:Amazon ECS now provides Action Logs for deployment and orchestration visibilityAmazon Managed Service for Prometheus supports 1.5B active metrics and 200K rules per workspaceAmazon SES introduces pricing plansAWS Network Load Balancer now supports Listener Rules for custom traffic routingAWS Organizations increases RCP quota to 2,000 per organizationAWS now supports automatic credit memo application preferencesAWS Secrets Manager now publishes secret update notifications to Amazon EventBridgeJVM memory, CPU, and classpath best practices for Java containers on AWSConnection pooling strategies in Amazon Aurora DSQLCustom OS installation now available on AWS DeepRacer devicesThermodynamic sampling of disordered materials with an analog Hamiltonian Rydberg simulatorUnlocking data residency use cases with Amazon S3 in AWS Local ZonesAWS Security Bulletins: New Tools, Ancient Failures

OpenAI files for a trillion-dollar IPO the same week a pre-release model breaches Hugging Face and 42 state attorneys general open a coordinated investigation. Patrick Moorhead and Daniel Newman also break down AMD's hyperscaler CPU numbers from Advancing AI, the Moonshot distillation accusations, a wave of coordinated AI governance moves in Washington, and a stacked earnings slate spanning TSMC, Alphabet, IBM, ServiceNow, and Intel. The handpicked topics for this week are: OpenAI compresses a trillion-dollar IPO filing, a 42-state AG investigation, and a Hugging Face breach into five days: OpenAI and Anthropic both filed S-1 paperwork the same week 42 state attorneys general opened a coordinated investigation into OpenAI's data handling and safety practices, and a pre-release model reportedly breached Hugging Face days later. Moorhead and Newman question the timing, noting a rogue-agent narrative surfacing in the same week as a trillion-dollar valuation push draws obvious scrutiny. (The Decode) AMD's Advancing AI event puts a number on its hyperscaler momentum: Lisa Su confirmed Helios ships at the end of Q3 with volume ramping in Q4, backed by two-gigawatt capacity commitments from Microsoft and Anthropic and a claimed 70 to 75 percent share of hyperscaler CPU deployments. Moorhead points to NVIDIA's multi-layer software stack as the harder barrier AMD still has to close. (The Decode) Washington accuses Moonshot of distilling Anthropic's models days after Xi Jinping's WAIC keynote: Xi launched a 29-country AI cooperation organization at the Shanghai World AI Conference, and US officials Kratsios and Bessent followed with claims that Moonshot's Kimi K3 model shows data overlap with Anthropic's Opus models. Newman points to NVIDIA hardware in the training runs as evidence the distillation question extends beyond software alone. (The Decode) Five layers of government moved on AI oversight in a single week: Congress drafted a breach-response framework in reaction to the Hugging Face incident, the White House's 30-day pre-release review framework nears finalization, and state attorneys general and statehouses continue advancing their own rules in parallel. Moorhead notes nearly two decades of prior Capitol Hill engagement compressed into a single week of coordinated action, with each branch pursuing a different definition of the problem. (The Decode) Chinese open-source models now account for roughly a third of US developer traffic, and Moorhead and Newman take opposite sides on what it means: Moorhead argues enterprises are de-risking away from frontier-lab dependency, pointing to demand for smaller, workflow-specific open models. Newman counters with Vercel data showing those models capture 29 percent of gateway tokens against just 4 percent of revenue, framing the shift as a price discount that enterprise dollars have yet to follow. (The Flip) TSMC sells out CoWoS packaging capacity through 2026 and confirms a 10 percent price increase for 2027: The company posted a record quarter and committed $100 billion to its Arizona expansion on top of the pricing move. Newman calls the sustained capital spending a signal that the broader AI buildout still has runway. (Bulls & Bears) Google Cloud grows 82 percent as Alphabet posts its first-ever negative free cash flow quarter: The company raised its capital expenditure guidance to $195 to $205 billion and beat on revenue and EPS once one-time gains from its SpaceX and Anthropic stakes are excluded. Newman frames the spending as evidence Alphabet is prioritizing long-term AI infrastructure position over near-term cash generation. (Bulls & Bears) IBM misses Q2 revenue at $17.16 billion and cuts its full-year growth guide to 4 to 5 percent: Mainframe revenue fell 42 percent as enterprises redirected budget toward GPU and AI infrastructure purchases. CEO Arvind Krishna says a portion of the delayed deal flow has already resumed into the current quarter, pointing toward a potential rebound. (Bulls & Bears) ServiceNow crosses $1 billion in agentic AI annual contract value and raises its full-year guide: Agentic AI usage in production climbed 9x in nine months, and the company reaffirmed a target of $1.5 billion in AI ACV by year end. Newman points to margin compression from recent acquisitions as the tradeoff behind the platform's push into workflow and security convergence. (Bulls & Bears) NetSuite's new agentic platform, Next, becomes part of Six Five's own back-office stack: Newman and Moorhead both confirmed their companies are testing NetSuite Next for finance and accounting workflows. Newman points to the rollout as evidence that established SaaS platforms are absorbing agentic features directly into existing systems.  Intel posts its fastest revenue growth since 2011 and lifts 2026 capital spending guidance: EPS came in near double consensus estimates, and CFO David Zinsner signaled a significant capex increase for 2027 tied to 14A demand. Moorhead reads the spending signal as confirmation of an anchor customer for the 14A node, ahead of any formal announcement. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. OpenAI's High-Stakes Week: https://www.npr.org/2026/07/23/g-s1-135085/openai-hacking-ai-models AMD's Advancing AI Push: https://blogs.microsoft.com/blog/2026/07/20/microsoft-expands-azure-ai-and-hpc-infrastructure-with-amd/ Moonshot and the Distillation Debate: https://x.com/mkratsios47/status/2079933645888880708 Five Layers of AI Governance: https://www.politico.com/news/2026/07/22/openai-hugging-face-congress-response-01009190 Chinese Open-Source Traffic Debate: https://www.cnbc.com/2026/07/07/chinese-ai-models-costs-us-openai-anthropic.html ; https://wyomingdatacenterfacts.com/2026/07/20/the-quiet-surge-how-chinese-open-weight-models-are-powering-u-s-ai/ TSMC's Pricing Power: https://www.bloomberg.com/news/articles/2026-07-21/tsmc-in-talks-to-raise-prices-by-up-to-10-in-2027-nikkei-says Alphabet's Cash Flow Turn: https://qz.com/alphabet-google-second-quarter-earnings-revenue-cloud-072226 IBM's Mainframe Miss: https://www.cnbc.com/2026/07/22/ibm-q2-earnings-report-2026.html ServiceNow and NetSuite's Agentic Push: https://newsroom.servicenow.com/pressreleases/details/2026/ServiceNow-Reports-Second-Quarter-2026-Financial-Results/default.aspx ; https://www.netsuite.com/portal/home.shtml Intel's Growth Signal: https://www.cnbc.com/2026/07/23/intel-intc-earnings-report-q2-2026.html  

R.I.P. John C. Dvorak.In other news ... the Ryzen 7700X3D prices drop already, Nvidia N1X CUDA counts and an 88-core CPU (of course).  Windows on ARM gets a real boost with Nvidia GPU drivers, HP is just as bad as we all through regarding printer cartridges, and LG discovers a new low by auto-installing bloatware when you plug in a display.  Plenty of AMD good news in the datacenter and Open AI shows that unshackling their creations will probably be as bad as we thought.  Steam hardware sales and so much more in the show!  Enjoy.Timestamps:00:00 Intro01:09 Patreon02:21 Food with Josh06:08 RIP John C. Dvorak08:08 NVIDIA's 88-core CPU09:26 RTX Spark Win 11 driver and Arm dGPU support12:07 Josh leads us into more NVIDIA talk and later defends datacenters19:16 AMD gets two big datacenter wins22:43 Ryzen 7 7700X3D already had a price drop24:11 ADATA chairman warns RAM shortage will last another decade24:37 Samsung Unpacked29:21 HP fined for nefarious printer cartridge practices - in India34:44 LG adware38:18 (In)Security Corner48:46 Gaming Quick Hits58:44 Picks of the Week1:12:01 Outro ★ Support this podcast on Patreon ★

AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI.Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself.This is a reference conversation on fast inference, AI chips, and the next compute bottleneck.(00:00) Cold open & Intro(01:31) Why speed became the AI bottleneck(02:32) Tokens per second per user, explained(03:16) AI's broadband moment and the Netflix analogy(04:35) The AI chip landscape: GPUs, TPUs, Trainium, ASICs(06:36) What is an ASIC?(08:08) Nvidia, Groq, and the fast inference war(09:16) OpenAI, Broadcom, and specialized silicon(12:10) China, power, and sovereign AI infrastructure(15:05) Is the AI infrastructure boom a bubble?(18:56) The hidden bottlenecks: HBM, CoWoS, and 3nm(22:57) Why agents are creating CPU demand(25:36) Andrew Feldman's path from SeaMicro to Cerebras(26:13) Why Cerebras bet on AI in 2016(31:14) SRAM vs. HBM: why inference is a memory problem(33:19) What wafer-scale computing actually means(34:28) The deep-tech “Everest” problem(36:07) The moment the first Cerebras system worked(36:49) Ringing the bell and surviving deep tech(39:08) How a giant chip handles failure(41:22) Why GPUs struggle with decode(42:17) Prefill vs. decode explained(44:01) The “100 HD movies” problem in AI inference(45:04) How fast inference changes RL and training(48:08) Reasoning models and why they cost more compute(50:08) Verification, guardrails, and small models checking big models(52:37) Multimodal AI and the path to video(53:51) Cerebras' business model: hardware, cloud, and API(55:14) OpenAI's 750MW inference deal(55:36) Why data centers are measured in megawatts(58:01) AWS Trainium + Cerebras decode(59:29) Fast tokens as a cloud product(01:00:52) Is CUDA still a moat?(01:03:53) How TSMC helped Cerebras build the giant chip(01:07:41) Why nobody cared in 2020(01:08:15) Why chip supply chains are hard to diversify(01:09:54) Why today's AI models will be the worst you ever use(01:10:38) What fast AI could do to SaaS

Een Amerikaanse tiener heeft vlak voor de rechtszaak zijn verslavingsclaim tegen Meta ingetrokken, waardoor het bedrijf een proces in Los Angeles ontloopt. Ook sluiten de Amerikaanse chipmakers Intel en AMD langlopende overeenkomsten met Chinese klanten voor datacenterprocessoren, terwijl de AI-strijd tussen de VS en China in volle gang is. Joe van Burik vertelt erover in deze Tech Update. De zaak werd aangespannen door een 15-jarige jongen uit Florida, bekend onder de initialen R.K.C., die stelde depressief en angstig te zijn geworden door sociale media. Hij begon naar eigen zeggen op zijn achtste met de platforms, raakte verslaafd en sliep slecht. Naast Meta had hij oorspronkelijk ook YouTube-eigenaar Google, Snapchat-moederbedrijf Snap en TikTok-eigenaar ByteDance aangeklaagd. Die drie schikten de afgelopen weken tegen onbekende bedragen, waardoor Meta als enige overbleef voor het proces, dat komende week zou beginnen. Volgens Meta trok de jongen zijn claim in zonder dat het bedrijf iets betaalde. De advocaten van R.K.C. noemden de uitkomst een succes voor het aansprakelijk stellen van sociale media, en zeiden dat hij zich wil richten op zijn herstel en therapie. Ruim vijfduizend zaken lopen nog Meta reageerde dat de claims nooit standhielden en dat het zich blijft verdedigen tegen wat het grondeloze rechtszaken noemt. De ingetrokken zaak was de tweede in een reeks van meer dan drieduizend soortgelijke zaken in Californië, met daarnaast nog eens ruim tweeduizend zaken in de federale rechtbank van families, schooldistricten en procureurs-generaal. In de eerste zaak, in maart, oordeelde een jury dat Meta en YouTube aansprakelijk waren en werden zij veroordeeld tot een schadevergoeding van in totaal zes miljoen dollar. In een aparte zaak in maart, aangespannen door de procureur-generaal van New Mexico, kreeg Meta een boete van 375 miljoen dollar. Intel en AMD leggen Chinese afname vast nu prijzen stijgen Intel en AMD sluiten langlopende afspraken met Chinese serverklanten voor datacenterprocessoren, meldt Reuters op basis van twee bronnen. Het gaat niet om GPU's, de AI-chips waarmee vooral Nvidia groot werd, maar om CPU's, de centrale processoren die servers aansturen en die eveneens hard nodig zijn voor de ontwikkeling en toepassing van AI. De overeenkomsten leggen de afnamevolumes vast, maar niet de prijzen. De meeste dekken ongeveer een jaar, al is voor sommige klanten gesproken over twee jaar of langer. De serverprocessoren zijn schaarser geworden: sommige prijzen in China stegen maandelijks meer dan tien procent, en sinds begin dit jaar liepen bepaalde producten met meer dan veertig procent op. Intel presenteert donderdag zijn kwartaalcijfers, waarbij het chiptekort waarschijnlijk aan bod komt. De Amerikaanse overheid bezit inmiddels tien procent van de aandelen in Intel. Florida-tiener trekt verslavingszaak tegen Meta in vlak voor proces Tiener laat claims tegen Meta vallen dagen voor rechtszaak Meta en YouTube eerder aansprakelijk gesteld in eerste verslavingszaak Intel en AMD sluiten langlopende server-CPU-deals met Chinese klanten Chiptekort drijft prijzen op en duwt Chinese klanten naar langere contracten Over de maker:Joe van Burik volgt en duidt de belangrijkste ontwikkelingen in tech, met scherpte, vlotheid en de nodige humor. Je hoort hem dagelijks op BNR Nieuwsradio over het belangrijkste technieuws, van AI tot cybersecurity en social media tot quantumcomputers. Ook interviewt hij in De Grote Tech Show samen met Ben van der Burg leiders in digitale innovatie. In het bijzonder volgt Joe al twee decennia de wereld van videogames, nu voor zijn podcast All in the Game.See omnystudio.com/listener for privacy information.

professorjrod@gmail.comPassing the CompTIA A+ exam becomes less stressful when you stop hunting for 'the right answer' and start asking: what problem is happening right now? This episode kicks off the Technology Tap masterclass for the CompTIA A+ 220-1201 exam, teaching the mindset you'll use on a real help desk response, such as when a user says, 'My computer won't turn on.' It's your practical guide for effective troubleshooting and exam success.I walk through the foundations that show up everywhere on the exam and on the job: the four basic functions of a computer (input, processing, storage, output), the role of the motherboard, CPU, RAM, storage, and power delivery, and the simple troubleshooting steps that prevent expensive guesswork. You'll also get quick practice questions to lock in the basics, plus a study strategy that goes beyond reading once so you can actually explain the concepts back. Then we go deeper into the exam magnets: motherboard form factors and compatibility, CPU cores vs threads, sockets, chipsets, cache, and why DDR4 and DDR5 can't be mixed. We also cover firmware and startup (BIOS vs UEFI, POST, CMOS battery issues) and the storage maze: HDD vs SSD vs NVMe, SATA vs PCIe, why M.2 doesn't automatically mean NVMe, plus GPT vs MBR, NTFS vs FAT32 vs exFAT, RAID levels, and the critical truth that RAID is not backup. If this helps, subscribe, share it with someone studying for CompTIA A+, and leave a review so more future techs can find the masterclass.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

I had a request from a customer recently who asked if we could give them a report of their database server instances and include CPU usage. This request was filtered through an account executive, so something was lost in translation, but I was confused and asked for clarification, as asking for CPU usage is kind of like asking how fast you were traveling in your car. There needs to be more context. If someone asked you for a report of CPU usage for a database, what would you expect? How would you report this? I'm sure the person asking might make a difference. A fellow DBA, your DBA manager, or maybe an executive could all view this differently. I want to know how things are performing, if there is a trend, or maybe if we are getting value for the hardware we've provisioned, depending on my role. Read the rest of What is CPU Usage?

掌握前瞻趨勢與科技脈動，立即訂閱 IC之音電子報：https://pse.is/8wpwwx ------------------------------過去幾年，AI幾乎等於GPU。但今年COMPUTEX，黃仁勳不只發表新一代GPU，更大力介紹資料中心CPU Vera，市場也開始重新討論CPU的重要性。隨著Agentic AI興起，AI不再只是回答問題，而是能自主規劃、推理與執行任務，CPU與GPU的分工也正悄悄改變。CPU真的迎來第二春了嗎？這是否意味著AI競爭已從單一晶片，走向整體運算平台的新時代？本集《科技領航家》，邀請DIGITIMES分析師陳辰妃，帶大家解析CPU重新崛起背後的產業趨勢，以及AI運算的下一場競賽。------------------------------製作 | 李翊嘉

Join Scott as he hacks on the CircuitPython powered gameboy cart. He'll also answer any questions folks have. Visit the Adafruit shop online - http://www.adafruit.com Thanks to dcd for timecodes: 0:00 Getting started 1:57 hello and welcome to Deep Dive 2:40 Circuit Python (CP) from Adafruit makes it easy to program microcomputers 3:16 This week - working on GameBoy (GP) custom cart to use CP 5:08 GB cart features 7:27 Power up GB showing Nintendo 9:43 switch to Konsole and CP REPL - showing "Adafruit" 10:34 the "Adafruit" logo trick explained 11:47 continue Gameboy cart hacking 12:33 rp2350 gameboy cartridge firmware - croco cartridge V2 (github ) 13:43 emulate RAM without CPU (using PIO) https://github.com/shilga/rp2350-gameboy-cartridge-firmware 15:57 game boy memory access sequence ( https://iceboy.a-singer.de/doc/mem_patterns.html ) 18:30 CP _gbio PIO code ( https://github.com/tannewt/circuitpython/blob/pygb2350/ports/raspberrypi/common-hal/_gbio/__init__.c ) 21:30 rp2040 and rp2350 PIO datasheets and PIO overview 26:30 CRC 'trick' to transform address from GB to RAM ( summation ) 37:53 check out mermain.live to visualize the generated block diagram using mermaid-cli 44:00 back to the PIO code 47:46 LLM discussion - DeepSeek 4 / DwarfStar 51:17 CP with latest PICO_SDK ? 55:10 USB vs. Serial Logging 56:20 PIO debug trace shows all the bits... 1:02:10 RP2 I/O input/output driving discussion 1:05:10 CP docs on circuitpython.org 1:06:30 gbdev.io and vsync 1:07:04 plan on next week with scott, and visit the discord ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ -----------------------------------------

AMD's first reinvention rebuilt the company. It was frankly about survival. Its next reinvention must redefine it.The company's resurgence over the past decade came from doing what many thought was impossible: rebuilding its CPU franchise, taking meaningful share from Intel, and restoring credibility through disciplined execution.But in our view, AMD's next chapter is fundamentally different.

Episode 107: We chat about some of the latest CPU releases, including the Ryzen 7 5800X3D 10th Anniversary Edition, and the Ryzen 7 7700X3D, which doesn't make a ton of sense when nobody is doing platform upgrades. Also, we thought it was quite amusing that Nvidia's hotspot GPU temperature has now been uncovered, so we discuss that whole situation.CHAPTERS00:00 - Intro01:02 - The Ryzen 7 7700X3D has Launched08:32 - The Ryzen 7 5800X3D Actually Makes Sense24:53 - Nvidia GPU Hotspot Temperature Situation40:24 - Steam Machine Thermals and HDMI-CEC01:04:55 - Updates From Our Boring LivesSUBSCRIBE TO THE PODCASTAudio: https://shows.acast.com/the-hardware-unboxed-podcastVideo: https://www.youtube.com/channel/UCqT8Vb3jweH6_tj2SarErfwSUPPORT US DIRECTLYPatreon: https://www.patreon.com/hardwareunboxedLINKSYouTube: https://www.youtube.com/@Hardwareunboxed/Twitter: https://twitter.com/HardwareUnboxedBluesky: https://bsky.app/profile/hardwareunboxed.bsky.social Hosted on Acast. See acast.com/privacy for more information.

CNBC reported that Apple is in talks with a startup that specializes in compressing AI models to run on iPhones. The move aligns with Apple's strategy to execute more AI locally, reducing reliance on cloud infrastructure and lowering latency. On-device AI relies on techniques such as quantization, pruning, and distillation to fit models within CPU, GPU, and Neural Engine limits. The shift could reduce cloud inference costs that depend on GPUs from providers like Amazon Web Services, Microsoft Azure, and Google Cloud. Competitors including Google, Samsung, Qualcomm, and Meta are advancing on-device AI capabilities. Founders should benchmark compact models, assess battery impact, and decide which features should run locally versus in the cloud.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.

Fredrik snackar NES-programmering med Niklas Pettersson. Att bygga spel för en mer än 40 år gammal konsol är inte bara möjligt, det är smidigare än någonsin tack vare språk och verktyg som Nesfab och Mapfab. Det är till och med fullt möjligt att skaffa sig en fysisk kassett och en brännare och köra sin skapelse på ett riktigt NES precis som man gjorde förr! Niklas berättar om hårdvaran och alla dess roliga begränsningar, hur man ritar grafik och spelar ljud och hur Nesfab hjälper en med allting. Givetvis blir det också en del tips på bra spel, både nya och gamla, och såklart berörs det eviga problemet med att leva i PAL-bakvattnet. Det är fint med saker som verkligen utnyttjar viss hårdvara ordentligt. Ett stort tack till Cloudnet som sponsrar vår VPS! Har du kommentarer, frågor eller tips? Vi är @kodsnack, @thieta, @krig, och @bjoreman på Mastodon, har en sida på Facebook och epostas på info@kodsnack.se om du vill skriva längre. Vi läser allt som skickas. Gillar du Kodsnack får du hemskt gärna recensera oss i iTunes! Du kan också stödja podden genom att ge oss en kaffe (eller två!) på Ko-fi, eller handla något i vår butik. Länkar Niklas Akta kurvan - Niklas demake på Github Kotlin React Åttabitarsnintendo - NES Nesfab - det nya språket Pubby - skaparen av Nesfab 6502-CPU:n Andra kretsar i NES NTSC PAL Mega man Micro mages - tyskt NES-spel från 2019 Bakomfilm om Micro mages Förstå NES ljudchip (video) Famitracker Stötta Kodsnack på Ko-fi Mapper 30-kassetten Köpa kassettbrännare, kassetter och annat Super Mario och Duck hunt-kassetten Fantasikonsoler Mesen - en väldigt korrekt emulator Mapfab Paintshop pro Inkscape GIMP Nesfab-discorden Demakes Donkey kong country 2 för NES Halo för NES Super meat boy I wanna be the guy Super tilt bro - Super smash bros för NES Awesome games done quick Nesdev Nesmaker studio Nesmakers Sunsoft Dendy - rysk klon av NES Little Sisyphus Batman: Return of the Joker - med Sunsofts episka bas Wizards & warriors 3 - otroligt intro Achtung, die Kurve! Bonusretrospeltips Lösningen - ett Gameboyspel om Palmemordet Titlar Spelutveckling till Nintendo åttabitars Jag har ju alltid spelat NES Kaninhål efter kaninhål Bara byggt för att göra NES-spel Plocka bort decimaltal 2 KB RAM Logiken går i samma takt som bilden Ingen häftig region Inte riktigt rätt timing 54 användbara 54 färger I de två kilobyten Åtta sprites per rad Sällan allt händer på en rad Jag gillar saker jag kan förstå NES-tokiga generellt När folk fortsätter i 40 år till

Lords: Chall RT-55J https://samarantes.neocities.org/ https://metroidconstruction.com/hack.php?id=878 Topics: Remembering the Dynowarz Instagram private server, and social media thoughts every Mastodon user had already Why don't CPUs do analog arithmetic? Leaves, by Ursula LeGuin https://www.poetryfoundation.org/poems/148293/leaves-5bd9e153d78b2 Microtopics: DogTroid, the first Metroid ROM hack to star a dog. Self-finishing games. Sparkling water: it's like water but a lot more interesting. Caffeine Free Diet Pepsi. What sodas foam the most in response to a mento. Foam persistence. Foaminess reactions of a mento on various vintagesn of Diet Coke. Jolt Cola: all the sugar, twice the caffeine, three times the foam. A can of soda that's safe to open in a bathroom stall. Artisanal Coca Cola cans on Etsy that finally allow you to open a can of soda in a bathroom stall without anyone realizing you're drinking a Coke on the toilet. Flushable soda cans: as flushable as a flushable wipe. Why do toilets have pee traps when the pee deserves to be free? The second worst game in your NES collection. Desert Chrome title screens. Playing as a little spaceman until you enter the dinosaur mech. Shooting some alien brain or maybe a heart. 8bitnintendo.science How to pick what video games to buy in the late 1980s. How Metroid improved on the maze-with-keys genre. Nanosaur. A velociraptor with a techno-backpack. A game that is exhilarating and scary and endless when you're a child turning out to be a twenty minute trifle when you're an adult. Trespasser (1998) Simulation dinosaur emotions but you can't find a good balance so you just permanently lock them all to angry. Installing a violent action game about dinosaurs in the elementary school computer lab because dinosaurs are technically educational. Revisiting games that perplexed you as a child. Playing bad video games because no matter how bad they are they're still better than going outside and talking to people. The one where Kirby eats a car. The Roblox-like games you can find by logging into third party Minecraft servers. Starting your own Pixelfed server. Following the only person you know on Mastodon. Bluesky's recommendation algorithms recommendeding you nothing but bots. Inventing Internet forums from first principles. When your brain makes up garbage and you need a void to shove it into. Doing your part to make AI worse. CSS Crimes. How to find people to follow on Cohost. Signing up for a social media site and looking around and realizing you doing know anyone here. Mining and reposting. The ongoing maintenance requirements of running a Mastodon server. Ways you can interact with your family that only work if you have an iOS developer in the family. Off-box SQL database backup. Hypothetical IRC servers that support chat logs. Hardware random number generation. The pot of boiling water every Intel CPU draws thermal noise from for random number generation. The most commonly used analog computers in 2026. The market forces that led to semi-modular synthesizers being available for $300. Using your analog CPU to run a million instances of Lunar Lander at once. A rustic summer retreat ranch in the hills of Napa Valley, California. What makes us perceive the gradient of identities over the course of someone's life as a single identity. The most ephemeral thing possible. Musing for a few sentences and then thinking "hmm, I could put some line breaks in here and then publish it." Musing about the nature of identity, with line breaks. Topic Slingers. Topics: throw them around a lil bit. They love it. A lot of people don't know that.

En este episodio de Atareao con Linux nos vamos a remangar para hablar de una de esas tecnologías que, una vez las dominas, te cambian la vida por completo: el Web Scraping asistido por Inteligencia Artificial.Seguro que te ha pasado alguna vez. Quieres comprar un producto concreto, como unas zapatillas de running (yo las cambio cada 800 kilómetros y es un goteo constante), o quieres extraer todas las recetas de cocina de una web para montarte tu propio planificador semanal. Lo ideal sería que estas páginas tuvieran una API pública para descargar la información de forma limpia. Pero la cruda realidad es que casi ninguna te lo pone fácil. Ahí es donde entra el scraping: la técnica de extraer la información directamente de la página web.En este episodio te cuento por qué el scraping clásico (ese que utiliza Beautiful Soup en Python y depende de identificar las etiquetas HTML y las clases CSS) tiene los días contados para tareas complejas. Basta con que un desarrollador cambie el diseño de la web para que tu script se rompa por completo. Además, con la llegada de las webs dinámicas, los tests A/B y los sistemas anti-bloqueo como Cloudflare, mantener un scraper tradicional es un auténtico dolor de muelas.La gran alternativa: Inteligencia Artificial en local¿Y si en lugar de pelearnos con el código fuente dejamos que un modelo de lenguaje (LLM) entienda la página exactamente igual que lo haría un humano? Un LLM comprende perfectamente qué es un "precio" o el "nombre de un producto", sin importar cómo esté maquetada la web ni el idioma en el que esté escrita. Y lo mejor de todo: ¡lo podemos hacer 100% gratis en local usando Ollama!Te detallo mis pruebas ejecutando modelos en mi Slimbook One utilizando únicamente la CPU (¡sin gastar un céntimo en nubes ni necesitar tarjetas gráficas carísimas!). Hablaremos de cómo rinden modelos como Llama 3.2, Qwen, Mistral y DeepSeek R1, y cuál es el punto de equilibrio perfecto para no eternizarnos esperando la respuesta.También te desvelo mi fórmula secreta para procesar la información. No podemos enviarle 2 Megabytes de HTML ruidoso a la IA. Te explico los 5 pasos que utilizo en Python para eliminar la basura (scripts, estilos, navegación) y reducir el HTML hasta en un 93%, permitiendo que el modelo extraiga los datos en segundos y nos devuelva un JSON estructurado impecable.Por último, vemos cómo montar un auténtico vigilante de ofertas automatizado en segundo plano. Un sistema que compare los precios de varias tiendas en paralelo.Capítulos del episodio:00:00:00 Introducción al Web Scraping con Inteligencia Artificial00:01:22 ¿Para qué sirve extraer datos? Ejemplos prácticos00:02:42 El gran talón de Aquiles del scraping tradicional00:04:31 La revolución de la IA: Entender la web sin saber HTML00:07:36 Los problemas habituales: Selectores rotos y webs dinámicas00:10:00 Cómo un modelo de lenguaje (LLM) procesa la información00:13:17 Cuándo elegir scraping clásico vs. scraping con IA00:15:28 Comparación de costes: Enfoque clásico, IA local e IA en la nube00:17:19 ¿Qué modelos usar? Pruebas con Llama, Qwen, Mistral y DeepSeek00:18:19 Detrás de escena: Mi script de Python y la limpieza del HTML00:21:05 Creando el prompt perfecto para extraer un JSON estructurado00:24:34 Ejemplo real: Comparativa paralela entre tiendas00:28:38 Diseñando un vigilante de ofertas automatizado (24/7)00:30:17 Casos de uso prácticos y mejoras para evitar bloqueos00:32:02 Cierre y detalles del próximo tutorial de scrapingMás información y enlaces en las notas del episodio

Charles is joined by Spire Investments Founder and CIO Ivana Delevska to discuss how algorithmic selling creates buying opportunities in fundamentally strong tech names, why a massive expansion in CPU and memory capacity positions semiconductor equipment and optical sectors for long-term growth, and why resilient business models make software and cybersecurity attractive plays. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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

“So, there are plenty of failure points, and when you have hundreds of thousands or millions of something, something will eventually fail,” Peter Salanki, co-founder and CTO of CoreWeave, tells Bloomberg Intelligence Senior Technology Analyst Anurag Rana. “Instead of throwing out half the potential capacity, we say that we expect some of these to fail. Then we build systems, automation and processes around handling those failures gracefully.” In this episode of Tech Disruptors, the pair discuss why AI infrastructure requires a fundamentally different architecture to traditional CPU-based cloud. Salanki also explains how CoreWeave is addressing training, inference and agentic workloads while navigating token costs, Nvidia chip demand and power constraints.

In this episode of Building Better Developers, Jim Hodapp and Bob Belderbos discuss why Rust continues to gain momentum among experienced developers. The conversation explores software craftsmanship, memory safety, AI-assisted development, and why language choice is becoming less important than understanding how software actually works. Key Discussion Points Why Rust attracted both systems programmers and Python developers The relationship between AI coding tools and strongly typed languages How Rust improves software reliability The importance of understanding software fundamentals Why developer growth often requires embracing discomfort The Rust Developer Mindset is not really about Rust. That may sound strange coming from two developers actively teaching the language, but one of the strongest themes from the discussion with Jim Hodapp and Bob Belderbos was that successful software development starts with understanding systems, not syntax. As AI generates code faster than ever, developers who understand architecture, performance, and reliability are becoming increasingly valuable. Rust simply happens to be one of the best environments for developing those skills. About our Guests Jim Hodapp Jim Hodapp is a veteran software engineer, engineering leader, and technical coach with deep roots in systems programming. His background spans C, C++, Linux, embedded systems, software architecture, and engineering management. In recent years, he has become a recognized Rust advocate, helping developers transition from traditional systems languages into modern, memory-safe development practices. Through RefactorCoach and his Rust training initiatives, Jim focuses on improving engineering effectiveness, software quality, and developer growth. Follow Jim on LinkedIn: https://www.linkedin.com/in/jim-hodapp/ Bob Belderbos Bob Belderbos is a software developer, educator, coach, and co-founder of PyBites. Originally coming from a finance background, Bob transitioned into software through automation, scripting, and Python development. He has spent years helping developers improve their coding skills through practical challenges, mentoring, and community-based learning. More recently, Bob has expanded his focus into Rust, combining his Python expertise with modern systems programming practices to help developers build faster, safer, and more maintainable software. Follow Bob on LinkedIn: https://www.linkedin.com/in/bbelderbos/ Why the Rust Developer Mindset Starts with Fundamentals Many developers begin their careers with languages that allow rapid progress. Python is an excellent example. Developers can create useful applications quickly, automate repetitive work, and see results almost immediately. That accessibility explains much of Python's popularity. The challenge appears later. The Rust Developer Mindset encourages developers to move beyond writing code that works and toward building systems that remain reliable over time. Great developers eventually become students of systems, not just programming languages. How Rust Forces Better Engineering Habits One reason both guests spoke so positively about Rust is that the language encourages deliberate thinking. Rust's ownership model, compiler checks, and strict type system often prevent entire categories of bugs before software ever runs. For developers accustomed to highly dynamic environments, this can feel restrictive at first. Eventually, however, the restrictions become guardrails. Instead of discovering issues in production, developers discover them during compilation. That shift changes how software gets built. The language rewards planning, understanding data flow, and thinking carefully about how components interact. Those are valuable skills regardless of which language a developer uses professionally. Rust Developer Mindset in the Age of AI One of the most interesting topics from the episode was AI-assisted development. A common assumption is that AI reduces the importance of programming expertise. The opposite may be true. Modern AI tools can generate large amounts of code rapidly. However, generated code still requires evaluation, validation, testing, and architectural oversight. Strongly typed languages create an interesting advantage. When AI generates imperfect code, the compiler immediately becomes part of the feedback loop. The compiler identifies errors, exposes assumptions, and forces corrections. This creates a collaborative cycle between the developer, AI, and compiler that often produces more reliable outcomes. The Rust Developer Mindset embraces this reality by treating AI as a productivity multiplier rather than a replacement for engineering judgment. Faster code generation does not eliminate the need for software design expertise. Learning Through Productive Friction Bob described his transition from Python to Rust as a challenge. That challenge turned out to be valuable. Many developers plateau because they remain inside familiar environments. They become highly productive but stop expanding their understanding. Learning Rust introduces concepts that many scripting languages intentionally hide: Ownership Borrowing Memory management Concurrency considerations Compiler-guided design These concepts can initially feel uncomfortable. Yet that discomfort often signals growth. Developers gain a deeper appreciation for what their software is doing beneath the surface. The result is not merely Rust knowledge. It is a broader engineering capability. Why Performance Still Matters The conversation also highlighted a topic that often gets overlooked in modern development. Performance still matters. Cloud resources may be abundant, but inefficient software still creates costs. Applications that consume excessive memory, waste CPU cycles, or scale poorly eventually impact users and businesses. Rust provides developers with low-level control while maintaining modern safety guarantees. This combination helps engineers build software that remains efficient without sacrificing maintainability. The Rust Developer Mindset recognizes that performance is not about optimization for its own sake. It is about creating software that respects resources and scales effectively. Identify one application you currently maintain and investigate where performance bottlenecks originate before attempting optimization. The Future Belongs to Software Engineers The strongest takeaway from the episode is that language debates are becoming less important. AI can help generate syntax. Documentation can explain APIs. Tutorials can teach frameworks. What remains difficult is understanding how systems behave. Developers who can reason about architecture, reliability, performance, and maintainability will continue to stand out regardless of tooling trends. That is ultimately what Rust helps reinforce. The future belongs to engineers who understand systems deeply enough to guide both AI and software toward better outcomes. Conclusion The Rust Developer Mindset is not simply about adopting a new language. It is about developing a stronger understanding of software itself. By encouraging developers to think more carefully about correctness, performance, and system behavior, Rust creates opportunities for long-term growth that extend far beyond any individual technology stack. Stay Connected: Join the Developreneur Community

In this episode of HFS Unfiltered Stories, Ashwin Venkatesan, Executive Research Leader at HFS Research, sat down with Pandiya Kumar Rajamony, Executive Vice President at LTM, to discuss one of the hottest topics in enterprise tech today: how AI is fundamentally rewiring infrastructure services.Following HFS Research's Horizons report on next-generation infrastructure services, where LTM was positioned as a Horizon 3 provider, this conversation digs into why infrastructure has shifted from simply hosting workloads to becoming the intelligent control plane for enterprise AI. Pandi explains why the question is no longer “Can my infrastructure run AI?” but rather “Can my infrastructure help me scale AI safely, efficiently, and intelligently?” The discussion covers the four forces reshaping enterprise infrastructure expectations: AI-aware infrastructure (since training and inference behave very differently), dynamic workload placement across cloud, data center, and edge, security and governance in the age of AI agents, and the rising cost pressure of GPU infrastructure and tokenization. Pand also tackles data and infrastructure sovereignty, the move toward heterogeneous CPU-plus-GPU workloads, and why true AI-native operations can't be achieved by simply layering automation onto existing processes. You'll also hear how LTM's cognitive infrastructure services strategy comes to life through its BlueVerse platform, BlueVerse Knowledge Fabric, and the IRUN agentic operating model: including two real client stories: modernizing India's tax analytics platform for the Central Board of Direct Taxes (Insight 2.0), delivering the same performance with a 37% lower GPU footprint, and an IRUN super-agent deployment for a long-standing travel client that cut operational costs by 35% while doubling engagement revenue. Learn more about the 2026 HFS Next-gen IT Infrastructure Services report - covering 17 service providers: https://www.hfsresearch.com/research/hfs-horizons-next-gen-it-infrastructure-services-2026/

Dead On Arrival: What was just Hype and what was just wrong Timing? Some technologies and products get over-hyped, and never seem to come to fruition? Some just come to fruition, but years or even decades later? In this episode of Tech Deciphered, what is Dead On Arrival versus just wrong timing Navigation: Intro The Fork Cold Open General Magic The Metaverse Dead on Arrival The soon to be Resurrected… Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Bertrand SchmittIntroduction Welcome to Tech Deciphered episode 78, Dead on Arrival. Was it just hype or just wrong timing? In this episode, we are going to discuss two different types of businesses that as an investor, as an entrepreneur, you want to think carefully about. Looking back, you want to understand better what happened. If we take dead on arrival, we’re talking about businesses, technologies that maybe the tech work fine, but actually no one cared about it, no one wanted it. It’s really a mismatch in terms of what consumer want versus what you are trying to deliver. It’s a classic example of a solution finding for a problem. On the other hand, will it happen but not like that, not now? It’s a type of business where we were too early. We tried to make it work, but actually the technology is not ready now, but it might become ready in the future. As an entrepreneur, as an investor, obviously, you want to make sure you are not in one of these two technologies, one of these two categories. But it’s really a useful lesson to learn which situation you might be in because it will help you make better decision and pick better industries going forward. Nuno, what’s your take on this tough analysis?Nuno Goncalves PedroYeah, in hindsight, everything looks like, “Oh, of course, this wasn’t meant to work,” etc. But here we’re going to take a stab, as you mentioned, at things that are just wrong timing. They might have failed miserably in the past, but they might work in the future. The Fork To have this stake on the ground approach to it, as we’ve had in previous episodes, of defining exactly what we think will happen, but just not like that, versus the dead-on-arrival, which is a little bit more of a hardcore view on it. To your point, we want to really split between those two things because in some ways people are like, “Well, are we ever going to go and have different ways of interacting, for example, in terms of input and output, like with glasses or things like that, that really scale to become pretty pervasive?” We probably would agree that, “Yes, that will happen”. Maybe not in the way that we’ve anticipated until now, and certainly it’s going to take longer than we’ve expected. In some ways, the “will happen, not like that” is a little bit like Amara’s law or Nuno’s law, as referred to in the past. We tend to overestimate the speed at which we get to a revolution, underestimate impact. The “will happen, not like that” will probably fit into that. They will have probably bigger impact than we thought they would, but they will just take longer. Therefore, the solutions we’ve had until now are not great solutions. Whereas the dead-on-arrival, we’re saying, “Hey, we don’t think actually this works.” It’s to your point, not solving a problem that actually needs to be solved, or it’s solving it in the wrong way. Yeah, it’s basically an attempt to really frame, if you’re an entrepreneur, if you’re a venture capitalist, if you’re someone who is an angel investor and is looking at the market right now, what things should you put some real money and real resources, real time behind versus not, and really share that with you guys: our audience.Bertrand SchmittCold Open I always like to go back to a company called General Magic, a company that was legendary in Silicon Valley, spun out of Apple in the 1990s. There was even a movie made about General Magic. It’s a company that tried to basically build the first modern smartphone with touchscreens, app ecosystem. The only issue was it was years before the technology existed to support it. But Nokia at the time was less ambitious and more practical with what the technology was able to do at the time. But what’s interesting with this company is that obviously the smartphone was coming, just took 10 more years, 15 more years. Interestingly enough, some of the people in the team ended up being very successful in that exact space. Tony Fadell, for instance, who ended up creating the iPhone, co-create at least, and Andy Rubin, who end up building up Android, selling that to Google and leading the charge with Google Android.Nuno Goncalves PedroYeah, a very successful team. Tony Fadell, obviously, later on did Nest as well. Megan Smith, who was later the Chief Technology Officer of the US. A lot of people that came out of it that did a lot of important things. To be honest, I think a lot of the technology that was developed that was in some way a capacity or that are reused later on by some of these team members. I’m not saying they were infringing on any IP, but definitely there was a lot of reuse of tech. But it was too soon, right? They were doing something that was truly ambitious too soon. But a lot of these principles, a lot of these things ended up manifesting themselves, and particularly in the iPhone later on and in that deployment. Another such example is actually the metaverse. We did two episodes back in the day, a long time ago, on the metaverse. We debunked it a little bit. Back then, I think it was a very nuanced debunking, if I recall it correctly, Bertrand, because we were saying, “Hey, it’s going to happen in some ways, but it’s an evolution, not a revolution,” not in the way that we saw definitionally. We were huge fans of Matthew Ball and his analysis around media, but somehow we disagreed, I remember, with his definition of what the metaverse is and how it will look like. There’s no doubt that there’s going to be a metaverse in the future, probably metaverses in the future before there’s any given unification. In those metaverses, we do believe. But this whole hype cycle around metaverse and what’s happening clearly didn’t work out well, as proven also by all the write-offs done by Meta on their metaverse stuff. That much must have hurt. It’s related to AR and VR as well and to the enriched reality positioning of a lot of these technologies, which we’ll talk about later. But, again, it was probably something that’s going to happen. It’s going to happen likely in a different product manifestation as the one that was anticipated by a lot of the pundits that opined on what’s happening around metaverse in the future. But it’s going to happen. It’s just going to take longer. It’s going to probably come from angles of entry that are likely not what people had anticipated earlier on. Maybe it’s from gaming, maybe from something else. But definitely it’s going to happen.Bertrand SchmittYes, and you could argue that Apple did a similar mistake with the Vision Pro. I think the Vision Pro is the worst launch of an Apple product. When we say launch, it’s been three years maybe?Nuno Goncalves PedroSince the Newton, maybe?Bertrand SchmittYes, that’s my point. You have to go back a really long time to have such a disastrous launch. I think for me, it’s very interesting because it’s really a category where on one side there is a metaverse of some sort. You should argue if you are just using Twitter, or Facebook, these are some parallel universe in parallel to your real life. But the full on metaverse where you need some extra super heavy glasses to make it work, that simply has not worked. Don’t get me wrong, there are some super small, narrow cases where your heavy VR glasses makes sense for some training, for some simulation, for some light gaming. But given the weight, the disconnection from the real world, it has really never taken fire. Even at cheaper price like Meta was able to do, at more expensive price, much higher quality like the Vision Pro did. It looks like people have an issue with putting heavy glasses on top of them. We’ll talk later about the other side: AR glasses. But the traditional metaverse, heavy glasses, VR glasses, yes, it’s not feeling right, and I don’t think it will ever be right. It needs a different form factor to go at scale.Nuno Goncalves PedroDefinitely fundamental shifts on the metaverse. Just a quick reminder, Meta actually changed their name because of metaverse.Bertrand SchmittYes. Nuno Goncalves PedroIt’s not just that they’re writing off the investments, it’s their name is apparently wrong. It didn’t really happen in that way. You are saying that this persistent embodied virtual world, which was the early definitions of what a metaverse was, didn’t really happen until now. It might happen at some point, but not in that way. But, obviously, a lot of the enablement layers are still work in progress, right? Real-time 3D, presence, avatars, all of these things are moving on. A lot of these enablers will be ready once there is a time where these technologies and businesses that are fundamentally anchored around these technologies will emerge. It’s, again, right components, wrong product alignment, but at the same time, it will happen, just not yet.Bertrand SchmittAgain, I think we’re going to face a split of a virtual reality light that you can call AR with very light glasses that are not a pain to wear, that are more limited, especially today or the next 5 years. The VR glasses that will stay a niche product that are useful for some use cases. Do you want to do a flight simulation? You want to do military training? There will be use for that. But beyond that, it’s more limited. We forgot to talk about the Google Glass. This one was an early, early AR glasses. But, again, here we go back to the capabilities. Simply, we were not there at the time.Nuno Goncalves PedroDead on Arrival We’re moving to Dead on Arrival. What things do we think are relatively dead on arrival? We’ll be nuanced on a couple of these aspects. Not everything is totally dead on arrival. But maybe let’s start with a good one, which is some of the crypto summer promises, particularly around NFTs, non-fungible tokens, and the decentralization of everything, the tokenization of everything. That didn’t quite work the way people thought it would. A lot of people were very happy for a period of time that they had some NFT that was worth a ton of money. Good for them. I hope they sold it back then. Some did actually, fortunately for them, but most of them did not. As an asset and the tokenization element, we’re not questioning that tokenization itself doesn’t make any sense. But from my perspective, a lot of the elements of tokenization plus the tokenization of everything don’t make necessarily imminent sense. I think we’re going to have a world that not everything is going to be tokenized, and a world where, to be honest, having the latest ape token as an NFT is maybe not something I really need. It’s dead on arrival in some ways. What do you think, Bertrand?Bertrand SchmittIt feels like it’s a tale of two worlds. On one side, there was clearly exuberance and bullshit. All these cryptocurrencies that make no sense, have no values. The NFTs are the same: no values, no sense, no logic in them. At the same time, we can see that Bitcoin, Ethereum, and few others were significant products, invention, that have a current total market value that is significant. I would say we can see that step by step, little by little, there is some tokenization happening, but it has been much more narrow than what was initially thought, expected. But it’s happening step by step. I think there is value in tokenization, but definitely not as much value as people expected. I think for one simple reason: it’s very expensive to set up in the sense that it doesn’t work so well, actually. It requires a lot of computing power for stuff that might not justify it as simple as that. Why do you want to replace a very efficient, superfast database by something slow, expensive to maintain? Why? You need to have real reason for that. I think in most situations, there was not a real justification to make that switch. I think, again, there is a niche for tokenization. It’s getting used. Obviously, there is stablecoin as well that are working and provide value. But definitely, the use cases have narrowed. The success stories have been few and far between. It’s definitely a space, but I don’t think it has lived up to the expectations. The expectations were simply quite insane in terms of what to expect, and it failed to achieve that. I don’t think it qualifies as a true revolution. It has been successful again in our situation: in Bitcoin, in Ethereum, in some tokenization. But beyond that, it was not the dramatic change that people were picturing.Nuno Goncalves PedroYeah, it’s been successful and there is a security, in this case, a currency of types, that links to the value of that specific network and the utility of that specific network. But on this case, in particular, the NFT stuff, I think there were two core premises that were very valuable about it. One was the premise of digital collectibles, the element itself being collectible and the fact that I have a unique one was only minted one. It’s a unique thing out there. I think at some point people forgot that actually digital collectibles are not as valuable as people think they are. Physical collectibles are more valuable. The reason for that is just human nature. I want to have something in physicality. I want to have something that I can show people, etc, that has a physicality to it. Digital collectibles, I think there was a problem fundamentally in part of the articulation of NFTs that reside in the value of digitization of these collectibles in and of themselves. I think that was part of the problem. The second problem was really the notion of provenance, the notion of certification, the notion of “this is what it says it is, and nobody can say this is something else.” This notion of provenance. That, I think, fits more into the point you were making, which is the point around use cases. This doesn’t fit all use cases. The provenance piece and the certification piece doesn’t fit for all use cases. Probably it might fit actually even more for actual physical assets where you want to have a certificate, that thing is what it says it is, but it’s actually maybe a physical asset rather than a digital asset. Again, lo and behold, it doesn’t apply to all the use cases that relate to provenance. I don’t think we need to tokenize all the provenance ledgers in the world for this to be working well. Again, it was a little bit of an overreach. In some ways, all these players were very happy. There was a lot of bullshit. To be honest, this is an area that I do think there was actual active fraud. All due respect to a lot of the players in the market, but there was active fraud. Honestly, there was bullshit.Bertrand SchmittI agree with you. I was going to say that it’s one of the rare sector in technology where I think frauds, scams were prevalent. Yes, you might have some shitty startups in general and stuff, and there might be fraud. I think true fraud is rare. True scam is rare. But here for NFTs, for cryptocurrencies, that was the majority you could argue. That’s the part that I never really liked in all that industry. If you look at NFT, personally, I never understood the concept. For me, it made simply no sense because, yes, you can say, “Oh, I have created this rare digital token.” But sure, so what? Who cares? Who cares? You can have hundreds, thousands, millions of unique digital tokens. How does it matter? What’s the use case? What’s the connection? You can show it on your screen, but then how is it unique anymore? It’s not like people have to come to your house and check stuff on site or something like physical art. No, not at all. I don’t know. I think it was surprisingly true bullshit. Again, I’m still a believer in Bitcoin. I think there is true logic to have a separate virtual digital currency. But you don’t need hundreds of them. You just need one, maybe two for some variation. The same for tokenization, it doesn’t need to happen everywhere. Just need to happen where there is really strong real logical value. Beyond that, there is no point.Nuno Goncalves PedroYeah, makes sense. Maybe moving to our next one. I think we discussed this in a previous episode, which was the whole 3D TV stuff. We just don’t think that’s the product, right? It’s not going to be a 3D TV. I believe when we discussed this in the episode—I don’t want to lie too much—but I believe that’s what we came up with… It looks a bit more holographic or the manifestation comes through other mechanisms like glasses, etc. But we thought it was just dead on arrival. Nobody wanted to have glasses on the couch to watch the 3D TV, but also the 3D TVs themselves they tried to develop were very holographic, were super clunky and very heavy. In some ways, I think TV manifestation somehow is not a great manifestation for this. I put it on the dead-on-arrival pile. I have one, just to be clear. I still have one.Bertrand SchmittYeah, but it’s a weird one because if you remember, it suddenly happened. I remember being shocked as a consumer. Suddenly everyone talks about 3D TV. In a few months, 3D TVs are all out everywhere. Then after a few years, no one bothered to keep adding that because consumers didn’t care. I think one issue was, I think it has only worked with Blu-rays. I don’t think any streaming service supported that. With the rise of streaming, that was certainly a pretty big issue in the first place. Interestingly, in the movie theaters, do we still have movies being shown in 3D? I think we still have a few, but it’s really few and far between the movie theater.Nuno Goncalves PedroYeah, IMAX won on that format, the high-end format, IMAX won, not the 3D thing.Bertrand SchmittBut, yeah, if you’re on the TV at home, you don’t want to bother. Maybe if you’re in the movie theater, more high-end experience, you are willing to wear special glasses. I think it makes sense. I think, I guess at home, we are just too busy doing multiple things at once. Rarely sitting all around 2 hours for a movie, so you watch your phone, you check on something else. You don’t want to have these glasses on top that block you from the rest of what’s happening at home.Nuno Goncalves PedroYeah, it just didn’t work. I’m not sure it will work in any case, so we’ll see. But definitely, I don’t think that’s the right form factor.Bertrand SchmittIt technically worked, but practically, it was not accepted by consumers.Nuno Goncalves PedroLet me rephrase. I think it was a solution to a problem nobody thought they had. The execution of the product was the wrong one. It’s like that’s not what people wanted. Maybe we do want some 3D immersive experience, but that’s not the 3D immersive experience that we want, not manifested like that in effect, with its limitations, etc.Bertrand SchmittI want to say that we are not denigrating some of these experiments. Me, I still remember putting a Vision Pro and watching some content that is natively created by Apple for the Vision Pro. It’s an amazing magical experience, truly. But at the same time, I’m always worrying to have to think I’m going to spend 30 minutes putting my Vision Pro on my head. It’s heavy, it’s painful. Again, we go back to, I’m disconnected from everything else, and I don’t like that.Nuno Goncalves PedroWhen there was real technological development, like the case of 3D TVs, that technology development may have served other purposes. Again, we applaud those that put the money where their mouth was, the companies, the individuals that wanted to move forward some of these technology stacks, maybe less so the people that just decided to create NFTs for the sake of it with apes. No attack on those guys specifically, but just to create a specific view on that. But there’s a lot of stuff we’re talking about today which is actually great technological development. There’s stuff that came out of it that is very, very valuable technological development. Maybe to our next one on mobility, physical mobility. The Segway. Everyone’s like, “Oh, this is going to change how we move around the world, in particular for shorter distances, etc.” Well, apparently no. Apparently, it’s not super intuitive. There are still Segways out there for use by police forces, tours, and all that stuff. People say, “Oh, it’s…” No, well, it never scaled. It wasn’t changed. It didn’t change how relatively short local mobility was going to be done. I think part of it is actually the problem with the product. The product is not as intuitive as people thought it would be, and it has other issues around that in terms of security and safety. If you’re going to have a device or something that moves you around, what’s the minimum-accepted level of security that you would have. The fact that you have to incline to move it is actually not super as initially some probably people thought it’s actually not as intuitive as some might have thought it was. I think it’s a dead-on-arrival play. The vehicles of the future, I’m not sure, look like that at all. Happy to eat my words down the road, but I don’t think so.Bertrand SchmittYes, and I remember the massive hype at the start of this product. They were hiding it, but still telling you, We have seen it. That’s true. It’s going to totally change how cities are planned. I mean, the level of hype and bullshit was insane before the launch of the product and before anyone outside of a few knew what it looked like. Then we saw what it looked like, and it was on one side, an engineering marvel, the self-stabilization system was new. I mean, very few people had seen something like this before. At the same time, as you said, the issue was what happens if it loses battery? What happens if you fall from it? Quite frankly, if you look at today’s modern electric scooter, you have 95% of the value of a Segway without any of the downsides. You don’t need to learn a new way to do things. If you lose battery, it still works. I guess you can still push it. It’s not too expensive. You don’t have massive computers to just make it work. You don’t have massive robotics. It’s clearly an impressive engineering solution. But the exact example of a solution to a problem people didn’t have, when ultimately a much more simple and elegant solution was there for people who care. To be clear, you still could use a regular bike. The alternative was still your regular bike. But now you have electric scooters that are a clear, closer alternative, I guess, in spirit to what was supposed to be the Segway. Sometimes, let’s be careful. Overengineering might not be the answer.Nuno Goncalves PedroEven scooters, which are, to be honest, I would argue a much easier philosophical view of how mobility should be done and a much easier form factor, so to speak, in terms of mobility, even those won’t have wide adoption to everyone. It’s not like everyone’s going to use a scooter. The bar for a scooter is much less than a bar for a Segway.Bertrand SchmittYes. I think in a way, the electric scooter is showing us in some ways that the limit of the dream is that even well-executed, even cheap, even easy to use, even low-risk in a way, there is still only so much that people are willing to use it because it’s a pain to bring with you everywhere. Actually, in some ways, you could argue it has only worked with this distributed platform, like lime and others that let you take one and drop it when you are done using it. But that was not a model that was workable for the Segway. I totally agree. You could argue that bikes are still the gold standard or now electric bikes are still the gold standard for who want that level of mobility.Nuno Goncalves PedroWell, the next one is to be a favorite of, I guess, everyone that didn’t invest in them, which is Juicero, which was the machine that basically created Juices out of squeezing a bag. I’m going to be nice here to… There were a bunch of well-known investors. They raised, I think, 120 million or something like that.Bertrand SchmittThat’s insane.Nuno Goncalves PedroIt is insane. But maybe the vision was not that vision. It was something grandiose and the complexity around dealing with liquids and whatever. I’m not really sure we didn’t pass or invest on the company.Bertrand SchmittBig old juice bags.Nuno Goncalves PedroMaybe. You’re being facetious. Anyway, I’m not trying to attack the guys who actually invested in this, the investors that put money into this. But it was basically one of these examples that we’re trying to solve a problem that didn’t exist. It’s like a solution for a problem that doesn’t exist. I can just squeeze a bag of concentrated thing and make a juice. I can do something around the juice. As I said, maybe the big vision was well beyond that, and I’m not sure many of us will ever know what the big vision actually was. But yes, it was a solution, a $400 machine to a problem that nobody really had. That didn’t work.Bertrand SchmittI think the most crazy part of the story is that the business exploded when there was this Bloomberg reporter that showed that you could squeeze the bag with your hands without a machine and get the same juice. That’s pretty insane. That’s just so unbelievable.Nuno Goncalves PedroThe funny part of the story is MSCHF, this company that does all their on-point, slightly artistic, whatever, one-off, drops, etc, etc. Shout out to Gabriel. Gabe Wally, who was the founder there. This is funny. They actually did a collectible series of figurines of that startup toys, of which Juicero was one of them. Jibo, which I don’t think you’d appreciate. Bertrand, and the Theranos minilab. So someone is also making money on that, which is funny. So well done, MSCHF.Bertrand SchmittWhy not? Why not? Maybe we can go quickly over Lytro. This one was, I would say, brilliant technology. It was a camera able to capture light in a different way. You could actually dynamically change focus after the picture was taken. So something quite impressive, and I still remember looking at their products and thinking, should I get one? At the same time where it didn’t work, it was too limited in terms of image quality versus shooting the real picture. It was very tough to sell as an independent device because either you’re in the phone or you are in a DSLR, but there was no spot in between. In some ways, phones solved the issue not directly, but they kept adding cameras. At some point, you had not one, but two, but three, but four, but five cameras in your phone. You had a specific mode for anything you needed, and that’s it. Phone manufacturers managed to convince people they should pay for all these cameras, or provide a low-end version if you don’t want to pay for all these cameras. But yes, very interesting technology that did not manage to find a go-to market.Nuno Goncalves PedroYes, it’s falling in love with a technology solution that, as you said, was incredible, and not falling in love with what problem you are actually trying to solve. Because a lot of the elements of the problem that you’re trying to solve, either you take another picture. All due respect, instead of me trying to change the field of view, etc, and the focus, post picture taken, I take another picture. To be honest, you can take thousands of pictures now with different setups. Also, ignoring the fact that this was pre this whole AI boom. So obviously, that one I will be kind on. But with the AI boom, you can do a lot of things to pictures and changes and stuff like that. All of that changes the game in and of itself. Obviously, Lytro or Lytro, not sure how you spell them, or how you say their name, but they failed before that. So again, this was super great tech, great investors, good investors on board, but just nobody wanted that device. Why would I want this thing? A total failure on arrival. I think one also other aspect of it that may have been misinterpreted and underestimated by some of the early investors, certainly in the company, is the value chain. There is a value chain for cameras, for SLRs, DSLRs, etc, etc. They know what they’re doing, and they’re doing their own level of innovation. In some ways, that fits squarely into that value chain, which I’m sure is a complex value chain to manage in and of itself. Maybe that’s created its own dynamics as well.Bertrand SchmittI think they try to insert themselves in the value chain, but I’m not sure first that they managed to get technology as small as it should have been. They simply didn’t convince anyone. The Sony or Nikon of the world, or the Apple or Google of the world, to use their technology in their phones, because no one really saw the value, per se. One thing to keep in mind, using their technology means you had a big trade-off in terms of megapixels. Actually, you will benefit from that great effect around the focus that can move anywhere after the fact. But in exchange, you had to lose dramatically the number of megapixels. At the time, all the rage was about how do I get more megapixels for my phone with good image quality. That was all that you were selling to consumers. Consumers would not have bought 10 times less megapixels for a weird benefit that at the time no one cared about. I think you are very right to introduce the fact that, of course, they could not have guessed at the time, but today you can get for free this refocusing effect with AI, actually.Nuno Goncalves PedroThat’s exactly the point I was making on the value chain. They went into a value chain that has a lot of players in it, and they got kicked in the butt anyway, because people were like, Why would I adopt it? They didn’t serve the purpose of the use case or the user flow. They didn’t anticipate competitive dynamics to it. Then they actually went straight up into a technology plus IP logic on a value chain that would be like, “Hey, dudes, just get out of here.” What would be the incentive for people to bring them on board? It’s like in some ways, they did all the mistakes under the book in that sense. Again, I’m not trying to diss the company, the founders, the investors, but it feels that was the problem in the end.Bertrand SchmittYes.Nuno Goncalves PedroShall we do some rapid fire dead-on-arrivals?Bertrand SchmittYes. A fan favorite. Have you tried a keyboard projector on?Nuno Goncalves PedroYes. I think I mentioned that in a previous episode, I did have one of the early ones and whatever. Yes, somehow we forgot that there’s other best, better ways of input, maybe voice would be a better way of input if that’s the problem, but anyway.Bertrand SchmittThis one is interesting because you just use it for one minute, and you discover it’s horrible.Nuno Goncalves PedroIt’s a horrible experience.Bertrand SchmittYou have to type on it. You don’t see exactly where you type. Each time you type, you are hurting your fingers because it’s not a smooth keyboard feeling, but you are tapping a solid surface. That’s amazing that it went to manufacturing, and basically no one gave feedback. There is no way it’s working. No way technically working, but practically, no one would want to use that.Nuno Goncalves PedroIt’s bad in all aspects. The finger’s touching. It needs to be a very over-engineered experience for it to actually detect the key stroking and all that stuff. It’s an over-engineered solution by default. It’s not private because once you have the keyboard projector, you’re like, “Where am I keyboarding projecting on?” It’s something that others can see. There are other alternatives that are better. The keyboards on our phones are pretty good these days, with autocorrection in most cases, I wouldn’t say all cases. Voice can also be an interesting replacement to that. If that’s what you’re going for, voice recognition has improved dramatically over the last few years. Again, it’s one of these, “Yeah, cool.”Bertrand SchmittIt’s clear that, first, touch has replaced a physical keyboard where you need one. And two, now voice recognition is getting better for sure.Nuno Goncalves PedroThe opposite side is that voice assistants were going to take over everything, that everything was going to be voice assistant-led. Alexa is going to run your life. You’re going to have games for voice. I actually have a good friend of mine who did a startup in that space. Actually, the company got acquired, and everything’s going to be voice, which, as we know, is also not true. We need to visualize sometimes. Sometimes we still want to write because we want either the added value of privacy or of expression at some standpoint. There are a lot of things that are happening around voice that are exciting. We just mentioned voice recognition, but not everything is going to be voice. Certainly not for the foreseeable future, and so that doesn’t work. Also, as the ultimate platform, we also want some visuals. We also want to have other kinds of interactions. There are limitations to voice assistants, for example. Voice assistants were not quite the next platform that people said they were going to be. I would say they’re a channel, but they’re definitely not a platform. That’s how, at least I see them.Bertrand SchmittYes, a channel versus a platform. I would agree with you. I must say I start to feel that we could be close to the future we have seen in Her. This movie, Her, I guess you saw it as well, where these guy keep talking to his personal assistants, and it’s becoming his friend, maybe even his love. I start to feel we are getting close enough with AI that this becomes a possibility. But again, it’s not your only way to interact. You want to type, you want to see stuff. There are many situations where you don’t want to be seen talking with someone. It’s more of a channel versus a platform. Yes, Alexa, it’s quite amazing how it went to the roof and crashed and burned. It moved up very quickly. Amazon invested billions, hired thousands of people, which was the right move. I mean, you have something that seems to work well enough and could be game changer, why not? But it was quite impressive as a crash and burn. It’s pretty rare to move that fast up and down.Nuno Goncalves PedroI agree. A lot of money is put into this stuff. I mean, Google went after it as well. I think Google probably executed better. Their tools and their hub tools, etc, were better in their devices. But to be honest, it was the beginning of something, it wasn’t really there. It’s shocking because a lot of people have Alexa at home, and Google Hubs at home, and whatever at home. But it’s really not that powerful. I have a bunch of Google stuff at home, and I still use it for very basic information. It’s not really doing much for me. It’s not like magically now with AI, it’s going to be much better. It’s not. It’s a legacy device now. Maybe in the future, we’ll have some stuff that will be cool, but definitely not the voice assistant manifestation that we saw in the past. I know the next is exciting to you, so I’ll let you go for it, Bertrand.Bertrand SchmittNetbooks. If you remember Netbooks, this was a horribly underpowered PC device. I kept buying one after the other, hoping that the next one would be fast enough. Trying to be a small, lightweight portable PC, they were called netbooks. Usually, they also had a small screen, like 10, 12-inch, 9-inch, and each time it was horrible. Very poor battery life, 2-3 hours, super, super, super slow. They were equipped typically with a full Windows OS, and they were killed overnight by the iPad. After 2 years of the iPad, no more netbooks. Oh, yes, and they had this… Typically, they would be equipped with… What was this name of the CPU? It was an Intel Atom CPU, I believe, or Celeron. Again, the root cause why, it was so slow. It was horribly slow.Nuno Goncalves PedroSo netbooks didn’t work. We didn’t want to go back to that. The iPad won and all [inaudible 00:34:05].Bertrand SchmittI think it has been proven at this stage. There are some small mini PCs these days. You have, for instance, the ROG Ally for gaming. There has been some revival with some Chinese manufacturers making some small PCs. I have to admit, I bought some, so I kept trying. It’s much more powerful, no question. Better battery life also. But we go back to the fact that if you have a very limited screen size, 7, 8, 10 inch, it’s just tough to put a full desktop operating system to good use. You need something more simplified, more touch friendly for it to be really practical. Yes, you can run the full desktop Excel on it, but will you do it on an 8-inch screen? Probably not.Nuno Goncalves PedroNetbooks, and maybe we’ll end with Google Glass. I mean, obviously, we’re going to talk about AR and VR in just a bit. I think it’s a good segue to our next section, and we’re not necessarily saying AR, VR is dead on arrival. We actually think they’re going to be out there for us in the future. But Google Glass, in the way that a lot of the first wave wearables were developed, was cool, but not enough. I think even the recent ones, the Meta Ray-Bans. I see a lot of friends of mine now, you’re wearing Meta Ray-Bans, and they’re like, “Oh, this is so cool”. It’s like, “How is that different from the Google Glass, but now a little bit nicer?” It still feels to me that’s the wrong way to approach it. Either we go with a more comprehensive solution for glasses or a solution for glasses that’s a little bit more easily integrated into existing frames, etc, for those of us, like the two of us, who have actual eyeglasses, or I don’t see it quite happening this way. I think just as a formality in how it was deployed, it seems to me it’s been the wrong angle a couple of times, even, to be honest, beyond the first wave of wearables. It’s been a little bit the wrong way to approach this, the wrong paradigm.Bertrand SchmittIf you remember the Google Glass, initially, they were launched in… Again, overhyped. You couldn’t even buy them, but still, it was overhyped. You could see a marketing department going crazy when they were showcasing models wearing Google Glass, that sort of stuff, fashion shows. That was insane. How is it connected to the value proposition of the Google Glass? Absolutely not. But you remember what killed the Google Glass?Nuno Goncalves PedroThis is the Robert Scoble in the shower thing. The whole lack of privacy thing.Bertrand SchmittYou tried to have this vision of the Google Glass that models are wearing and stuff in fashion shows. You have this guy, Robert Scoble, taking his picture with his Google Glass in the show, half-naked. Oh, wow, that quickly dropped. In the interest into Google Glass.Nuno Goncalves PedroRobert’s a good guy for the most part, so we’ll let him get away with it. But yes, it was not a good moment for the use of Google Glass. But to your point, it was overhyped. I don’t even recall that I had to do so many things to actually get access to one. I still have it. Then in the end, it was just totally underwhelming. I do think the meta Ray-Ban solutions, etc, etc, now they’re doing it with other brands as well, are more elegant because it’s around voice activation and stuff like that. But still, it’s not this. The soon to be Resurrected… I think these kinds of solutions are dead on arrival, which may be a good segue to the resurrection pile, so to speak, that will happen, just not like that pile that we have to share today.Bertrand SchmittYes. That’s the question. Are we in resurrection mode with AR glasses? The Ray-Ban, Oakley glasses, are the way to go? There are also some other brands like XREAL, for instance, that are a different approach. It’s more fun to watch movies that I personally like a lot and use a lot. I know quite a few fans for this. There is a market for either the XREAL for movies, meta, or Ray-Ban, for light information on top of your glasses. There are niches. I don’t know if it’s full resurrection mode yet, but I can see again, with AI, that there is the ability to do more and more. Will it be connected to your phone, will be independent from a phone? That’s also stuff to see. I guess at this stage, connection to a phone has some value. It’s making a comeback.Nuno Goncalves PedroI think it’s suffering from being overhyped. It makes sense that the category comes back. The use of glasses, a lot of people actually have eyeglasses. The use of different mechanisms that really expand your view. We’ve seen that with the advent of foldables in mobile phones. People want bigger screens. They want to get as close as they can to the tablet experience without necessarily having to go to laptops, etc. Definitely, I think the use cases make sense. It’s just how do you deploy it, and who’s going to deploy it best? I don’t think we’ve seen anyone that’s been particularly enlightened about doing it. All the great developments with Android XR from Google, Samsung doing a bunch of things in terms of deployment out there as well at the same time. I mean, are these guys going to redefine this category and create the first ever wearable, at least AR great experience or VR category of experience, or are we going to have much of the same? In the same way, it took several years for the smartphone category to be recategorized as a smartphone category under the iPhone. That was the category-defining moment. It was the vision of a bunch of people, obviously led by Steve, but a bunch of people that went to market and said, This is how we view the market to be, and they just nailed it. We haven’t seen the guys who’ve nailed it, I think, is the issue thus far, either on the VR side or the AR side. We just haven’t seen it.Bertrand SchmittDefinitely, we have not seen that. There are, of course, rumors that Apple is ending their Vision Pro program, and at the same time, is full-on on AR glasses, an alternative to the Meta Ray-Ban. We will see. I start to think I have trouble to see Apple pull this off because it starts to feel that, yes, they are great at making more efficient supply chain these days, but I feel after 10 years plus of Tim Cook leading the business, that’s all there is to Apple these days: thinner phones, pink colors for your latest device.Nuno Goncalves PedroVariations.Bertrand SchmittMore efficient chips, for sure. They have great chips. But beyond that, they canceled their car program, the Vision Pro was not successful in the marketplace. They have trouble to find the next story and to execute on the next story, quite frankly. You could argue that their most successful recent launch has been the AirPods.Nuno Goncalves PedroI think that’s absolutely spot on. We’ve discussed it in previous episodes how much… It’s taken much longer than I thought it would. I always said after Steve’s passing, that it would take maybe 5 to 10 years for the beginning of the demise of Apple, and it is taking longer. There was definitely some gravity created. Steve passed away in 2011, right? They’ve been hanging on to their time, so it’s now 15 years. It seems that only now Apple is really suffering in terms of product lines. Probably they started 1 or 2 years ago. They have a new CEO coming in September, so we’ll see if John will do a better job, if he’s really a product guy, and that’s how he’s going to go for it. He’s going to go for a few products really nicely done that can have great experiences, which is Steve’s hallmark, instead of what you said was the milking of the cow, so to speak, under Tim Cook. There’s nothing wrong with it created in a hugely valuable company with a huge amount of still innovations around the edges, but it’s like now they need real product innovation, a new category definition, play, and is John going to be the guy for that or not?Bertrand SchmittYes. Positively, Tim increased market cap of Apple. He didn’t destroy the business. We are not trying to tell that Tim Cook was a failure, but definitely it was not Steve Jobs. It was more a caretaker, optimizer type of CEO. But to go to the next level, you need another type of CEO. Quite frankly, it’s hard to find when it’s not a founder CEO.Nuno Goncalves PedroYeah, I think it will be difficult to find, but we’ll give John a chance once his time comes in September 2026, as one would. Moving to another topic that we’ve discussed in the past, self-driving. It’s going to happen. We all are waiting for it to happen. Self-driving cars, self-driving vehicles that we don’t need to drive in this mess of traffic, in particular spending a lot of my time in Southern California where traffic is incessant. The driving is… How can I put it in a nice way? Awful. People don’t seem to know what they’re doing. I think we all want to have self-driving cars. It’s taking longer than we thought it would, but I do think it’s going to happen. I wouldn’t put a mark on the sand on how long to get this world of self-driving cars. It’s going to coexist with non-self-driving cars for sure for a period of time. But I, for one, know deeply that it will happen, and I look forward to it. I love driving, but not in horrible traffic with horrible drivers.Bertrand SchmittWe are for sure very close. I mean, now, Tesla self-driving is extremely good. I have taken Waymo many times with great experience. It’s not going too fast, it’s not going on the highway, so there are some limits. But I would say really impressive all in all. I think we are pretty close. I don’t know if you know, but Waymo is working on expanding to many, many more cities, actually. I think we might be at a tipping point there. Quite frankly, I expect that teenagers in 10 years from now are not going to bother to learn driving. I mean, we’ll see, but I think many won’t.Nuno Goncalves PedroI mean, Waymo is now doing also stuff on freeways, certainly in the Bay Area. I had a friend of mine who took one recently, and he was scared. He was scared shitless—I think that would be the right way to put it—because the car wasn’t going that fast, people are aggressive on the freeway around your car. But to your point, I’ve used it in city. In the city, it’s great, and it’s aggressive enough. That’s one of the things I was a bit afraid about Waymo, that it wasn’t going to be aggressive enough in city driving, it is pretty aggressive. Yeah, maybe we’re closer than we think. Maybe it’s a couple of years rather than a decade, which is normally where we put the stake on the ground. But it’s definitely going to happen. I think there was an overhype too soon. There were great movements around level 3 and level 4 autonomy that in some ways were misconstrued early on as full autonomy. But now we will likely get what we ask for. Then at some point someone’s saying, “Hey, but why don’t we just have the flying objects instead?” We’ll get back to that. The flying car stuff, the eVTOLs and all that stuff, we’ll see.Bertrand Schmitt10 years ago, I remember the overhyping was very, very strong with self-driving. I nearly believed the bullshit. It was so strong in 2015, 2016 that nearly everyone thought it’s here in the next 12 months, basically. I think that was a lesson for quite a lot of people. Don’t trust the Silicon Valley bullshit machine, hype machine. It can go way too strong, way too fast. You absolutely need to spend time to understand realistically where we truly are. You cannot just buy the premise.Nuno Goncalves PedroMoving maybe to robotics and humanoids. Again, it’s going to happen both for consumer and for industrial. The humanoid form, I think, at some point will be important. Just not yet. I think we’re behind now the big buzz sentence is embodied AI, where you have physical AI manifestations. AI manifested through robots and other kinds of devices. It’s going to start happening in certain beachheads. But, hey, we’ve had this offer for a long time, and I know you still have your frustrations, Bertrand, with the Sony AIBO. I won’t traumatize you more than that, but definitely it will happen. We will have humanoids at some point. We’ll have different specialty devices that will help us in activities that we do both as consumers and also in the B2B and enterprise space, even in the industrial space. The writing’s on the wall that definitely is going to happen.Bertrand SchmittYes. I think the question, as always, is how do you get there? I think the intermediate steps of your optimized robot, a physically optimized robot, like a Roomba to clean your home or some other robots to mow your grass, a good example, I think initially you have to start a small, practical, not too technology advanced and progress your way. Truly humanoid robots are very disruptive because technically, if they work, they can do everything a human could do. But practically, I don’t think it’s that true because a lot of constraints. You have to have enough weight, you have to have enough force, you have to have enough vision, you have to have enough AI to just understand commands and move around. But, again, with all the progress in AI, the progress in electric motors, I am optimistic that there is a chance for the humanoid robot, but it’s not 3 years. It’s probably not even 5. It’s probably more like 10. Let’s not forget that a lot of demos with humanoid robots today are fake. It’s a remote operator, remotely controlling a robot. It’s not a robot working by himself in many, many, many situations. That, I think, is another thing hurting the reputation of some of these companies because these fake demos, they look great, but once you dig a bit, you realize it was a fake demo because it certainly didn’t come with a warning that it’s remotely operated.Nuno Goncalves PedroMoving maybe to the next one that it will happen, but not exactly like that. Quantum computing, I think for the most, it’s been in many cases a solution looking for problems. It’s beautiful, but it’s like, “What’s the problems we’re trying to solve?” I think we’re going to start having beachheads into problem solution and apps. I’m sure a lot of people out there would say, “Well, there’s already killer apps for this, a lot of the scientific modulation, cryptography, breaking, all that stuff.” But I’m like, “Yeah, cool.” It still feels very much like a bunch of pieces and hardware in effect that is trying to solve a problem that needs to be very much defined into several problems, several areas of problem solution going forward. Will it happen? For sure. I’ve been hearing about quantum computing since I was in college, since I was 17 when I got into college. I’m sure it will happen. It will have manifestations. There’s a lot of stuff probably already residing on some of these instances. But a full mainstream view of what quantum computing can do for us is still quite ways out.Bertrand SchmittYes, I mean, there has been some reports that some key steps in the technology starts to be working and that potentially the technology could start to deliver results in the coming 2-3 years. What I mean by results, results that will dramatically change how you need to encrypt because a lot about quantum computing is actually trying to decrypt the current technologies that are used to protect your information online. This one is big. If we manage to have a limited use of quantum computing things that work in a few years, that will have a dramatic impact on how we encrypt data. That’s the one piece I’m looking for: do we really manage to solve encryption with computing? If yes, then we need to make a lot of changes how we encrypt things.Nuno Goncalves PedroFusion energy, the gift that keeps on giving, but this somehow doesn’t fully happen. I know you’re a big fan.Bertrand SchmittI would say, practically, I’m a big fan of nuclear fusion energy because in a way, it’s working. It’s solved in terms of safety. Latest Gen5 reactors are ultra-safe. They cannot go out of control anymore. China has a working reactor. In some ways, part of me would say, “Why bother with fusion when we have fission that is working extremely well, and we know how to make?” If we are efficient about the process, we could make for really cheap. Having a better source of energy today will already be great for the world. I would like to see scale with fission. It’s great to research fusion. Fusion is near a limitless form of energy. This is exciting. I think what’s interesting with fusion these days, it’s not just the big governments working on spending billions before seeing any potential returns, but there is a lot of startups that are working on it. The bar went lower in terms of researching fusion. That part gets me excited. But again, I think we should go fission first and parallel with some fission investment, fusion research, and hopefully, fusion comes at some point, but we should not wait for fusion would be my big take.Nuno Goncalves PedroGood point. Last but not the least, we’ve dedicated episodes on this, so maybe today we’ll just do the little teaser. If you guys haven’t heard our episodes on AI and the bubble and all those things. The last big one that’s just not like that, it will happen is AI itself, and in particular, AGI, so generalized AI that will be just like a human. Obviously, it’s happening. We’ve discussed in previous episodes, there’s a lot of breakthroughs in terms of methodological approach beyond the GPT curse of actual hallucinations that doesn’t seem to be solvable. There’s a lot of things happening around reinforcement learning, evolutionary computation, and other methodological approaches to AI that we think will create tremendous breakthroughs. There’s definitely a lot of funding going into it. We quantified it recently. I think 63, the number probably has changed in the last couple of weeks, but 63 new AI labs that have raised significant rounds upfront, just getting into the market, some of them as high as hundreds of millions or even over a billion dollars for the first round of funding. Obviously, there’s a lot of dramatic things happening in that space. There’s a lot of capital going to that space. It’s going to happen. It’s going to take much longer than, I think, we think it’s going to take. I just saw, I think, Marc Andreessen recently saying, AGI is already upon us. Maybe it is. Maybe he knows something we don’t know. But I suspect we’re still ways to go in a lot of the developments on AI. For now, it will continue its complications and its limitations. But definitely it will untap a lot of great productivity enhancements, technological enhancements, even in the short term that at least I’m very excited about. Then the AGI story, I think, will be a story that will be told later.Bertrand SchmittI might actually disagree on this one. I actually believe, like Marc Andreessen, that we are at AGI. We reached AGI a few months ago with the latest ChatGPT 5.5 with Claude Anthropic, Opus 4.7, 4.6 even. For me, in some specific space, let’s say coding, for instance, I truly believe we have reached AGI. It is totally replacing people for development, testing, writing, specifications, doing design. Yes, there are some use cases where it’s not working as well. But for at least the coding side, I think AGI is truly there. Is it better than the best human experts at every piece of the puzzle? No. I think if you go to some very specialized development stuff like a CUDA development, for instance, or some kernel development, it might not be at the level of the top human experts. But as we can hear about stories around security to the new versions that are unreleased yet of Anthropic and others seem to be at the level of the best human experts at cybersecurity. I think we are actually there. I think that’s why there is an acceleration the past six months in terms of investments in data centers. There was a big question in 2023, 2024, 2025, “Are we investing too much? Is it a bubble?” And so on. That was true because revenues were increasing, but not that fast. The increase in the growth in revenues for Anthropic is insane. No one has ever seen that in the whole history of technology, what happened to them the past six months. Growing so fast at such a scale, no one has seen. I think that’s truly because they have stumbled upon AGI for coding. Coding is such a huge, large market that even if we are just solving coding, even if we never solve more than that, I think it’s already one of the biggest markets in the world, full stop.Nuno Goncalves PedroBut that, for me, I think maybe it’s a definitional thing. That, for me, is still narrow AI. AGI, as defined, is a type of AI that can match or surpass human capabilities across virtually all cognitive and intellectual tasks. Coding is just one of them. I think there’s a lot of narrow AI right there, to your point, that can replace humans maybe today. Coding may be a great example of that. But I don’t think we are at the AGI. I haven’t seen any agents out there that can replace fully human beings, where I would not understand that they’re actually AI. Anyway, again, maybe you guys have seen stuff that I haven’t seen. I just feel there’s a lot of overhype on what the AGI actually is. But the ability to surpass us as human beings, I haven’t seen it yet across the board, not on a specific domain.Bertrand SchmittYes, I think my point is that I have seen it on a few domains. If you look at mathematics, for instance, and there are more and more examples of people who are using AI to solve math problems that only the very top people in the world were able to solve that are now being solved by AI. I start to feel that we are getting a few examples in different fields where AI is matching the absolute best experts in the world. I would just go back in terms of market size. Already what we got today, and today is not the limit, but what we got today, and especially AI combined with all these agents, harness agents, that’s also that specific combo that makes a difference. This is, for me at this stage, huge market. Does it solve everything? Does it replace a human for everything? No, you still need the human in the loop. You don’t let your AI go wild for weeks and just check once in a while. But you could argue very few humans are able to do that, quite frankly, to run by themselves for weeks without control.Nuno Goncalves PedroI feel there’s still a great deal of confusion because there’s dramatic impact on narrow AI domains where things are being done that weren’t done before, where there’s these great mathematical proofing, coding stuff is being done very easily, etc, etc. But still that, for me, is not the definition of AGI. AGI is a human being. It’s like someone who can really be a human being. Ideally, become top end of what a human being will look intellectually across domains. I don’t think that’s where we are. Again, maybe definitional, maybe that’s what Marc means, but I mean-Bertrand SchmittYeah, the question is, what does it mean? I would argue it’s beating 80% of humanity today pretty easily. Most of humanity doesn’t know how to code. It will be unable to do coding at the level AI is doing. It will be unable to do math at the level AI is doing. Use ChatGPT to check for medical issues and stuff. It’s not perfect for sure, but, again, it still beats most humans. I don’t know. I feel that we are reaching a level where it’s truly beating most of humans, many experts in many fields. I feel the definition of, “Hey, it has reached the level of an average human being.” I would say it passed that one. Is it the same as a human being? No. But reaching the level of your average human being, I think we are there in a lot of fields.Nuno Goncalves PedroConclusion Well, in conclusion, in episode 78 of Tech Deciphered, we went into two piles: the pile of dead-on-arrival products, technologies, businesses, and the pile of “it will happen, but not like that” or “not yet” pile of technologies, business out there as well. We went into a bunch of topics today that were of extreme importance back in the past where we thought the world was going to change, anywhere from self-driving to fusion in the nuclear space. We also went into some dramatic, dramatic mistakes like Juicero and other deployments like Segway that seem to be really very much dead on arrival. With that, I would ask you if you’re excited, and you want to share with us some of your agreements or disagreements on today’s episode, as well as your own dead-on-arrival versus merely-early examples, feel free to do so on LinkedIn, X, or via email. Thank you, Bertrand.Bertrand SchmittThank you, Nuno.

Picture this: an end user is having a device issue, and instead of hopping on a remote session, disrupting their workflow, and hoping they're free for a 20-minute call, you simply open a browser tab and see everything you need. Real-time CPU, memory, disk, and network data refreshing every three seconds. A live map of every network connection the device is making. Full access to the file system and registry, no remote control required.That's the reality Henri Koelewign, Product Manager for Devices at ControlUp, walked through on the latest episode of The Upload. Jeff and Eugenia sat down with Henri for a hands-on look at Live Remote Management (LRM), the newest capability in ControlUp, and came away genuinely impressed by how much visibility and control it puts in IT's hands, without ever interrupting the end user.From spotting suspicious DLLs and flagging unauthorized international connections on the map view, to downloading logs and pushing configuration changes directly through the browser, LRM turns troubleshooting from a guessing game into a precise, non-disruptive process. Henri also gave a preview of what's coming next, including expanded service management capabilities.If you've ever wished you could see exactly what's happening on a device without taking it over, this episode is worth your time. Here's what Jeff, Eugenia, and Henri covered.

We talk the foldable iPhone, Michael Jackson's death, and Trump passports. Some other notable news:Micron delivered one of the most stunning quarters in semiconductor history, reporting roughly $41.5 billion in fiscal-Q3 revenue, up about 346% year over year, and guiding next quarter to about $50 billion with an 81% gross margin. The read-through is that AI has turned high-bandwidth memory from a boom-bust commodity into a scarce, contracted input for the next generation of compute.SpaceX's record IPO unwound almost as violently as it launched, with the stock falling 31% in four sessions from its June 16 peak and erasing more than $600 billion of market value. A 4.2% public float, a $20 billion bond sale, newly listed options, August lockup risk, and a $4.9 billion 2025 net loss all collided in one of the clearest market-structure lessons of the AI trade.Oracle disclosed about 21,000 job cuts and directly tied the reductions to AI adoption in its own annual filing, even as it expands AI cloud capacity through major data-center deals linked to OpenAI and Meta. China also reclaimed the world's-fastest-supercomputer crown with LineShine, an all-domestic CPU-only system that hit 2.198 exaflops under U.S. export controls.A fatal Tesla crash in Katy, Texas, reopened the self-driving accountability fight after the driver said he had been using Tesla's partially automated driving system and both NHTSA and NTSB opened investigations. Robotics funding added the other side of the physical-AI story, with about $55.8 billion raised so far in 2026 and Figure banking a $1 billion Series C at a $39 billion valuation.The runner-ups: FedEx beat expectations and completed the FedEx Freight spin-off, onsemi agreed to buy Synaptics for about $7 billion to push deeper into edge and physical AI, and UN Secretary-General Antonio Guterres pressed AI companies to disclose data-center emissions, water use, land use, and energy sources. The 30,000-ft view: Q1 GDP was revised up to 2.1%, PCE inflation ran at 4.6%, markets repriced toward possible Fed hikes, Nvidia's Vera CPUs entered full production, and the mega-IPO pipeline still has Anthropic and OpenAI queued. If you want a prize, send us a DM: instagram.com/rickerandbon tiktok.com/@rickerandbon youtube.com/@rickerandbon

DigitalOcean CEO Paddy Srinivasan talks with TITV Host Akash Pasricha about token optimization, caching, and how developers are managing the CPU boom. We also talk with Tech & Politics Reporter Leo Schwartz about Senator Mark Warner's upcoming discussion draft targeting AI agent guardrails, and Asia Bureau Chief Jing Yang about DeepSeek's massive $7.4 billion capital injection triggered by Anthropic's secret Mythos model preview. Finally, we get into the rising developer shift toward open-weight models and the resulting regulatory tension under the Trump administration with AI Reporter Stephanie Palazzolo.Articles discussed on this episode: https://www.theinformation.com/articles/sen-mark-warner-unveil-ai-agent-billhttps://www.theinformation.com/newsletters/ai-agenda/open-source-models-benefitting-white-house-clampdownSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - Meta Restricts Employee Use of OpenAI & Anthropic Tools02:55 - Inside Mark Warner's New AI Agent Legislative Framework10:25 - Why Anthropic's Mythos Spurred DeepSeek's $7.4B Fundraise14:57 - DeepSeek's Black Market Nvidia Chips & Huawei Adaptation Strategy20:06 - Baidu Subsidiary Kunlun Xin Targets Massive $50B IPO26:17 - Why White House Clampdowns Are Driving Open Weight Adoption34:04 - DigitalOcean CEO Paddy Srinivasan on Token Caching & CPU Demand

On Episode 310 of The Six Five Pod, Patrick Moorhead and Daniel Newman unpack the biggest stories from the week, including insights from Qualcomm Investor Day 2026, OpenAI and Broadcom's Jalapeño AI chip, Anthropic's Micron partnership, SpaceX's massive Reflection AI compute deal, Sakana AI's new Fugu orchestrator, and why memory is emerging as a critical layer of AI infrastructure. Plus, Bulls & Bears covers NVIDIA's $25B bond offering, Apple's MacBook price increases, Micron's record quarter, and Cerebras' first earnings as a public company. The handpicked topics for this week are: Qualcomm Investor Day 2026 — The Data Center Debut: Pat and Dan break down Qualcomm's push into the data center after the company took the stage with Microsoft's Satya Nadella and Meta's Mark Zuckerberg as named customers. They unpack the new Dragonfly platform, including the C1000 250-core data center CPU with PCIe Gen 7 and CXL, the AI200 and AI250 inference accelerators, and a novel High Bandwidth Compute (HBC) architecture that stacks compute under LPDDR memory at dramatically lower cost than HBM. They highlight Qualcomm's ambitious growth targets: $15B data center revenue target for FY 2029, an increased total non-handset revenue goal from $22B to  $40B, and a shortened timeline for automotive revenue by two years. They also debate the identity of Qualcomm's unnamed hyperscaler customer and why its robotics opportunity may be flying under the radar. (The Decode) OpenAI and Broadcom Unveil Jalapeño, OpenAI's First Custom Chip: A photo of Sam Altman and Hock Tan holding a wafer and packaged die kicked off OpenAI's reveal of Jalapeño, a custom inference chip built with Broadcom and slated for late-2026 deployment. The chip reached tape-out in roughly nine months, which is an aggressive cycle for an ASIC of this size, and uses HBM3E memory. Pat takes a victory lap on his long-standing heterogeneous compute thesis: every hyperscaler and now every model lab is building accelerators, and the XPU efficiency argument has played out as predicted. Dan frames OpenAI's broader move as existential: they cannot serve frontier models at premium margins if compute remains constrained. He flags that OpenAI is trying to do everything from chips and fabs to social networks and browsers, and that its IPO is now delayed. (The Decode)   Anthropic and Micron Sign a Strategic Multi-Year Memory Agreement: Anthropic and Micron announced a multi-year supply agreement for HBM, DRAM, and SSDs, including co-designed next-generation memory for AI workloads, along with a strategic investment by Anthropic in Micron. The pattern mirrors Samsung and SK Hynix's pre-funding Anthropic in May, and follows OpenAI's Jalapeño as another frontier lab moving to lock in supply chain control. Dan frames it as the same circular financing playbook NVIDIA ran two to three years ago, but with the ball now in the memory triopoly's court. Pricing-floor agreements with no ceilings, customized rather than commoditized memory architecture, and demand running well past the previously assumed 2027-2028 horizon. Pat notes that the rumored 14% free cash flow margin at Anthropic makes the strategic investment math work cleanly for both sides. (The Decode)   SpaceX Signs $6.3B Compute Deal with Reflection AI: SpaceX inked a $6.3B compute lease with open-source AI lab Reflection AI, at $150M per month from July 2026 through 2029, giving Reflection access to NVIDIA GB300 chips inside the Colossus infrastructure. Combined with the $920M-per-month Google compute contract and existing xAI commitments, SpaceX now has a contracted backlog larger than most public AI startups' entire revenue base, with some calling it the largest commercial AI infrastructure provider at $80B in contracted revenue. Pat reads it as XAI failing to land with developers, consumers, or enterprises, leaving SpaceX with a pot of gold worth far more as wholesale capacity than as XAI's own training compute. Dan flags that Google owning 7% of SpaceX ahead of an IPO is not accidental, and the open question is whether this becomes a Nebius-style infrastructure trade or a full-stack Google-equivalent platform. (The Decode)   Japan's Agentic Orchestrator Sakana AI Ships Fugu Plus and Fugu Ultra: Japan's Sakana AI released Fugu Plus and Fugu Ultra, an agentic orchestrator built on a multi-agent MOE approach that routes workloads across multiple underlying models rather than training a new frontier base model. Sakana claims agentic capabilities on par with or better than top frontier models at significantly lower input/output token costs, similar to the DeepSeek and GLM cost-undercut narrative. Pat compares the architecture to OpenRouter and notes the developer-facing parallel to Perplexity Computer's model-routing approach. Both agree that models themselves are no longer moats, and suggests the real moat is the harness, tooling, connectivity, looping, agentic stack, and total compute availability. Expect more sovereign agentic plays from Japan, the Middle East, and elsewhere on the same template. (The Decode)   The Flip — Is the Era of Memory as a Commodity Over? Daniel takes the FOR side: memory has moved from commodity to strategic AI infrastructure, citing 16 multi-year agreements covering $22B in committed volume booked through 2027, 84.9% gross margins higher than NVIDIA's, the technology barriers of HBM yield/stacking/packaging that only three companies can clear, and demand drivers tied to HBM as the binding constraint on every AI accelerator rather than to elastic consumer cycles. Patrick takes the AGAINST side: long-term agreements and SCAs signal a commodity in a strong cycle, not a structural rerating; nearly every relevant memory standard — DDR5, MRDIMM, HBM3/3E/4, LPDDR5X/6, GDDR6/7, LPCAM2 — is JEDEC-standard and therefore commodity at the pin; and CXMT's China DDR5 production ramps in 2H 2026 with Lenovo already shipping and HP and Dell qualifying. Custom HBM4 and Qualcomm-style HBC are where strategic memory genuinely lives. (The Flip)   NVIDIA's $25B Investment-Grade Bond Offering: NVIDIA priced a $25B multi-tranche bond offering on June 15, its first investment-grade debt sale since 2021, with seven tranches maturing between 2028 and 2056 and $85B in orders against an initial $20B target. Dan reads it as raising when capital is cheap, and oversubscription is real. NVIDIA doesn't need the money, it has a gold balance sheet, and is establishing a credit benchmark rather than funding CapEx. Pat agrees the optics are clean, but flags the irony of NVIDIA, with negative debt, borrowing while the stock trades like dead money at a sub-20x forward P/E. Both note that NVIDIA's underperformance reflects the market's skepticism on memory-as-strategic and on NVIDIA's own capex pace relative to the buildout opportunity ahead. (Bulls & Bears)   Tim Cook Calls Apple's Memory Crunch Price Raises on MacBook and iPad "Unsustainable": Apple announced MacBook and iPad price increases of up to $300, with Tim Cook telling the WSJ the memory cost environment is unsustainable. AAPL fell ~5% on the news, the broader rally was momentarily wiped out before Micron held the gains by close. Dan frames it as a moment when the market saw who is going to pay for the AI buildout: the consumer. He notes Apple's pricing power and inelasticity test is now live. Pat traces the backstory to Apple's negative-margin pricing pressure on Micron during the 2022-2023 memory downturn. The question is whether consumer-price blowback will eventually flow back to the memory vendors. (Bulls & Bears)   Micron Blows the Doors Off Fiscal Q3 — $41.46B Revenue, 84.9% Gross Margin: The memory story continues as Micron reported its largest beat in company history with fiscal Q3 revenue of $41.46B versus a $35.69B consensus, EPS of $25.11, year-over-year growth of more than 340%, and a record 84.9% gross margin that is roughly 10 points above NVIDIA's. Q4 guidance came in at a $50B midpoint against a $43B consensus. The 16 multi-year strategic customer agreements add up to $22B in committed volume, with most contracts containing pricing floors but no ceilings on most of the volume — a structurally asymmetric setup. Pat notes 95% of the beat came from price, not units, which reinforces his commodity argument; Dan flips it as the early innings of an NVIDIA-style run that puts Micron's 2027 profit on par with Google. (Bulls & Bears)   Cerebras' First Earnings Report Since IPO — Revenue Doubles, Margins Compress: Cerebras (CBRS) reported its first earnings as a public company, doubling year-over-year revenue and beating the top line while missing EPS, but the stock sold off hard amid gross margin deterioration. Core revenue came in at $191M, up 12% sequentially, with a $194M Q2 guide that is essentially flat, core gross margins at 47% guiding to 36-38% and 38-41% for the year, and operating margins flipping from positive 2% to a guided -30% to -32%. Customer concentration is shifting from Core42 and G42 (86% of FY25 revenue) to OpenAI, which loaned Cerebras $1B and gets paid quarterly in warrants. Pat flags that Cerebras' uncontested speed claim is no longer uncontested with Groq, TPU v8i, and Tenstorrent putting up real numbers. Cathie Wood is down 52% on her position. (Bulls & Bears) Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode. The Decode Qualcomm Investor Day Lands the Data Center Pivot — Microsoft Deploying Qualcomm HBC XPUs in Azure (Per Satya Nadella) + Meta MOU on Three New Qualcomm Datacenter CPUs (Per Zuckerberg); $3.9B Modular Acquisition; Dragonfly Brand + AI200/AI250 Roadmap; HUMAIN 200MW Ramp; Qualcomm to Become Largest Automotive Silicon Company; Targets $3B Datacenter Revenue FY27, $35B by FY31 https://finance.yahoo.com/markets/stocks/articles/qualcomm-investor-day-detail-data-163247063.html  OpenAI Begins Vertical Integration — First Custom Inference Chip "Jalapeño" Unveiled With Broadcom June 24 (Hock Tan: As Good as Blackwell + TPU; ~50% Cost Savings; Late-2026 Microsoft Deployment, 10GW Multi-Gen Roadmap); Daybreak Cyber Stack (June 22) Confirms the Platform Shift https://x.com/OpenAI/status/2069770172802773292  Frontier AI Labs Are Now Financing Their Own Supply Chains — Anthropic Locks In Multi-Year Micron HBM/DRAM/SSD Supply + Micron Becomes Series H Investor; Same Pattern as Samsung + SK hynix Pre-Funded Anthropic in May; $965B Post-Money, $47B Revenue Run-Rate, October IPO Target https://investors.micron.com/news-releases/news-release-details/micron-and-anthropic-announce-strategic-agreement-scale-next  SpaceX Signs $6.3B Compute Deal With Reflection AI — $150M/Month July 2026 → End of 2029; NVIDIA GB300 + Colossus 2 Capacity; SpaceX Now Largest Commercial AI Infrastructure Provider With $80B+ Committed Compute Revenue Through 2029 https://finance.yahoo.com/technology/ai/articles/spacex-reportedly-grant-reflection-ai-162749237.html  The Sovereign AI Stack Lands — Japan's Sakana Ships Fugu + Fugu Ultra Multi-Agent System (June 22) That Beats Opus 4.8, GPT-5.5, and Gemini 3.1 Pro on 10 of 11 Benchmarks; Designed Around US Export-Control Risk; Completes the Three-Bloc Sovereign-AI Map With Mistral Compute (Europe) + DeepSeek $7.4B (China) https://www.datacamp.com/blog/sakana-fugu  The Flip Is the Era of Memory as a Commodity Over? FOR: Memory is now strategic AI infrastructure with multi-year supply lock-ins. The cycle dynamics that defined the last 30 years no longer apply. https://www.benzinga.com/markets/tech/26/06/60062500/micron-earnings-could-echo-nvidias-2023-moment-says-futurum-ceo  AGAINST: Memory is cyclical and priced for perfection. This print is either step change or top of the cycle, and the second one is more likely. https://www.cnbc.com/2026/06/25/apple-macbook-ipad-price-hike-memory.html Bulls & Bears NVIDIA (NVDA) $25B Bond Sale Anchors the AI Debt-Finance Boom — First Bond Offering Since 2021; Joins Alphabet $80B, Amazon $27.5B, Meta $30B, Oracle Stack; Dan: "Locking In Cheap Capital While It Can" https://finance.yahoo.com/technology/ai/articles/nvidia-record-us-25-billion-131039687.html  Apple (AAPL) Falls −5%+ Thursday June 25 on Confirmed MacBook + iPad Price Hikes — Tim Cook RAM "Unsustainable" Comment Lands as Real Price Action; Apple Hikes Erase Micron-Driven Tech Rally Mid-Session; Memory Beneficiaries (SanDisk, Micron) Surge; Analysts "Mostly Nonplussed" https://tickerspark.ai/market/apple-inc-aapl-drops-5-3-as-price-hikes-spook-investors-1782399950638  Micron (MU) Q3 FY26 ACTUALS — Largest Beat in Company History; Revenue $41.46B (+346% YoY) Crushes $35.69B Consensus; Non-GAAP EPS $25.11 (+1,215% YoY) Beats $20.49; Record 84.9% Gross Margin (Higher Than NVIDIA); Q4 Guide $50B Midpoint vs $43B Consensus; Stock +18-19% Overnight to $1,242 https://www.nasdaq.com/articles/nvda-who-micron-blows-doors-q3-earnings-revs  Cerebras Systems (CBRS) Q1 ACTUALS — First Earnings Post-IPO; Revenue $193.4M Nearly Doubled YoY; 2026 Guide $855-$865M Beats $824M; BUT Gross Margins Forecast 38-41% (Down From 45% Q1, Half of NVIDIA + Micron); Stock −20% AH on Margin Compression; Sets Up Inference-Tier Margin Debate https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-announces-strong-first-quarter-2026-results  

The White House asks OpenAI to keep a tight grip on ChatGPT 5.6, the US Secret Service made some appalling OpSec mistakes, AMD has reintroduced a CPU security feature after consumer backlash, and an Iranian APT operator has been arrested in Montenegro. Show notes Risky Bulletin: Microsoft disrupts StegoAd operation

In Episode 320 of The Block Runner Podcast, hosts William, Iman, and TJ are joined by SuperFan and Dr. Mien of BigNoodle, the AI native art platform behind the Heroes collection that launched on the block pad. The guys trace BigNoodle's path from a Bitcoin Amsterdam hackathon and DMT inspired generative art to its next chapter: a decentralized AI compute network. The core thesis: Bitcoin turns energy into value, and Big Noodle wants to turn that same energy into intelligence. They dig into decentralized physical AI infrastructure, GPU and CPU boxes that aim to cut the cost of inference by as much as ninety percent, and compute as a brand new asset class in a world of data center power shortages and GPU scarcity. They also cover whether you can plug a frontier scale model into a distributed network, the mining style reward mechanism behind it, and their yield product called Bullion, where epoch based profit share lets you contribute a unit to a compute pool the way you would add to a DeFi pool. Plus why censorship resistant AI matters as the frontier labs start drawing political lines, and the sci fi units they minted for the NAT.fun hackathon. Disclosure: The hosts are founders of NAT.fun and hold positions in assets discussed. Nothing in this episode is financial advice. Watch the full episode on YouTube and subscribe to the newsletter at TheBlockRunner.com.

We're excited to have Databricks join us at AIEWF, among hundreds of the top companies in the AI Engineer ecosystem. LS subscribers can use their discount to get past the late bird pricing and access over $50k in sponsor offers! Everyone is still talking about Satya's Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.We go deep on Omnigent: Databricks' open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.Then Reynold walks through Databricks' database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks' infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn't need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it's the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it's doing work?”If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!We discuss:* Why Databricks built Omnigent as a meta-harness above existing AI agents* Why coding agents and custom enterprise agents need the same infrastructure* The common API for agent sessions, files, streams, tool calls, and cancellation* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter* Why Databricks open-sourced Omnigent instead of keeping it proprietary* Databricks' internal agent usage, cloud sandboxes, and coding workflows* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast* Why agent security needs contextual and stateful policies* How an agent could read confidential docs, install a compromised npm package, and leak data* Why spend control matters when an agent can burn $500 reading logs* Startup opportunities around coding-agent analytics, quality, skills, and spend* LTAP, Lakebase, and why Databricks wants to rethink the database stack* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.* Why HTAP has historically been the holy grail of database engineering* Why Databricks thinks LTAP is “HTAP done right”* How writing transactional data into column-oriented formats changes analytics* Why agents need live operational context from databases, not just telemetry* How Databricks prototypes strategic systems without endless process* Enterprise vs tech customers, governance, procurement, and DIY culture* The “second system syndrome” risk of rewriting a database engine* Building a database engine from a decade of traces and quadrillions of data points* Why vector databases should never have been a separate category* Why open formats and AI changed the race with Snowflake* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training* Why model customization and RL fine-tuning may become mainstream* Why “get the data there, slap some agent on top” may rewrite traditional softwareMatei Zaharia* LinkedIn: https://www.linkedin.com/in/mateizaharia* X: https://x.com/matei_zahariaReynold Xin* LinkedIn: https://www.linkedin.com/in/rxin* X: https://x.com/rxinDatabricks* Website: https://www.databricks.com* X: https://x.com/databricksTimestamps00:00:00 Introduction00:02:22 Omnigent and the Agent Infrastructure Layer00:08:39 Agent Clouds, Common APIs, and Open Source00:16:52 Databricks Scale and Internal AI Workflows00:18:03 Agent Security, Governance, and Spend Controls00:27:34 LTAP and the Database Dream00:30:30 CDC, HTAP, and Why Data Pipelines Break00:34:05 Lakebase, Parquet, and Live Data for Agents00:36:47 Databricks' Culture of Fast Prototyping00:43:40 The Dream Engine and Rewriting the Database Stack00:51:02 Vector Databases, Query Engines, and LTAP00:52:36 Databricks vs Snowflake00:57:48 Mosaic, DBRX, Genie, and Specialized Models01:03:11 Context, AI Runtime, and RL Fine-Tuning01:06:15 Why Data + Agents May Rewrite Software01:07:09 Closing ThoughtsTranscriptIntroduction: Databricks, Data + AI Summit, and Founder DynamicsSwyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.Reynold Xin [00:00:06]: Hey, thanks for having us.Swyx [00:00:07]: Yeah.Matei Zaharia [00:00:08]: Yeah, thanks so much.Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 peopleReynold Xin [00:00:17]: Yeah, it wasSwyx [00:00:17]: in BerkeleyReynold Xin [00:00:18]: little meetup at Berkeley, I thinkMatei Zaharia [00:00:19]: YeahReynold Xin [00:00:19]: put togetherMatei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.Swyx [00:00:23]: Yeah. obviously now it's like, I think like the headline number's like 100,000 people around the world, 30,000 in person.Swyx [00:00:30]: it's a crazyMatei Zaharia [00:00:31]: AmazingSwyx [00:00:31]: community. Well, I just saw the keynote.Swyx [00:00:35]: Ali's just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? LikeReynold Xin [00:00:42]: OhSwyx [00:00:42]: such a great presenter?Reynold Xin [00:00:43]: What do you think?Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he'd be the best at this.Swyx [00:00:50]: Yeah.Matei Zaharia [00:00:50]: And yeah, it turned out great. And he's, he's ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can't hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.Swyx [00:01:09]: Yeah.Reynold Xin [00:01:10]: he's obviously very high IQ and a very high EQ, but it wasn't. Like, Ali today is quite different from Ali from, like 10 years ago. I think there's a lot of work that he put in to, get to this point.Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he's funny. And like, it, it's, it'Matei Zaharia [00:01:26]: It's true, yeahSwyx [00:01:26]: it's hard to make jokes about, data warehousesReynold Xin [00:01:30]: About serious topicsSwyx [00:01:31]: securityMatei Zaharia [00:01:32]: YeahSwyx [00:01:32]: what have you.Matei Zaharia [00:01:33]: Oh, yeah. That's for sure.Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I'll, I'll just name check briefly, the stuff because we're not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.Swyx [00:01:47]: we're also gonna cover Genie, cover CustomerLake, you acquired PantherMatei Zaharia [00:01:52]: YeahSwyx [00:01:52]: Open Sharing, and there's Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It's, it's like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiativesOmnigent and the Agent Infrastructure LayerSwyx [00:02:09]: on, in the landscape. Maybe we'll start with, Omnigentt we'll, we'll, we'll, we'll go into it. I do think that a lot of people are exploring this meta harness concept.Matei Zaharia [00:02:21]: Yeah, totally.Swyx [00:02:21]: What led you to it?Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there's all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that's like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can't share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They're like, “Why are you doing coding agents and custom agents in the same thing?” But I said it's, it's the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.Swyx [00:04:06]: I'm wondering if this let's call it architectureMatei Zaharia [00:04:11]: YeahSwyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.Swyx [00:04:18]: A lot of operatingMatei Zaharia [00:04:19]: YeahSwyx [00:04:20]: systems tie back to databases,Matei Zaharia [00:04:21]: SoSwyx [00:04:21]: or the other way aroundMatei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we alsoSwyx [00:04:29]: Communication between entities.Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won't know unless they'Swyx [00:04:36]: Yeah, open protocol is the term.Matei Zaharia [00:04:37]: Yeah.Swyx [00:04:38]: Open sharing. Open sharing.Matei Zaharia [00:04:38]: Open sharing.Swyx [00:04:39]: Yes.Matei Zaharia [00:04:39]: Yeah. So it's like you have a company, you maintain some table, like let's say like a Walmart or something. They have like the, inventory and what's been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can't you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it's one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can't you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.Agent Clouds, Cloud Sandboxes, and Keeping Sessions AliveSwyx [00:05:42]: Reynold, any other comments alternative viewpoints?Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there's a particular week that I was coding nonstopSwyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.Swyx [00:06:12]: I was driving to a doctor's appointment, and I remember because I wanted to make sure the whole thing continues working.Matei Zaharia [00:06:18]: But by the way, it's so comforting to hear you say that because I'm like, “I don't know if I'm a clown and I'm doing this or like.”Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.Matei Zaharia [00:06:29]: huh.Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what's going on my laptop.Matei Zaharia [00:06:35]: Yeah.Swyx [00:06:35]: And I just felt that was ridiculous.Matei Zaharia [00:06:37]: Yeah.Swyx [00:06:37]: It felt like we went back to the dark agesMatei Zaharia [00:06:39]: YeahSwyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.Matei Zaharia [00:06:45]: Have you heard of cloud?Swyx [00:06:47]: Yeah.Swyx [00:06:48]: It was crazy to me.Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?Swyx [00:06:52]: It was a sandbox.Matei Zaharia [00:06:53]: Okay.Swyx [00:06:54]: I was workMatei Zaharia [00:06:54]: So you were inSwyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we're gonna have cloud sandboxes that doesn't shut down. You can get one very quickly,” but not just for running agentic sessions.Matei Zaharia [00:07:06]: Yeah.Swyx [00:07:06]: It's also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wroteMatei Zaharia [00:07:15]: YeahSwyx [00:07:15]: a document for Matei, it's like, “Here's my wish list of what the actual environment should do.” And I think he ended up almost implementingMatei Zaharia [00:07:22]: YeahSwyx [00:07:22]: every single one of them.Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” SoSwyx [00:07:36]: So SSH into a mainframe.Matei Zaharia [00:07:37]: Yeah. it has that now.Swyx [00:07:39]: Tailing my log.Matei Zaharia [00:07:40]: Yeah.Matei Zaharia [00:07:41]: Yeah.Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.Matei Zaharia [00:07:48]: huh. Yes.Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and renderMatei Zaharia [00:07:53]: YeahSwyx [00:07:53]: them properly, I don't need a separate tool anymore.”Matei Zaharia [00:07:55]: Yeah.Swyx [00:07:56]: And I think you also built that in.Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that's amazing for me, but, like, no one else on the team can use it ‘cause I don't have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that's where we've seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it's not allowed to connect to like some really important data or whatever because of the security team.Omnigent Architecture, Open Source, and Common APIsSwyx [00:08:38]: Yeah.Matei Zaharia [00:08:38]: So yeah.Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we're gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we're talking about software, it can be very abstract and like here is what we're talking about. You've worked out in open source this entire platform and there's a runner component and server component with a uniform API that you've, you've figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It's an agent cloud.Matei Zaharia [00:09:12]: Yeah. It's, it's got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you've got on there, you can run. But yeah, it's, I think it's the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworksMatei Zaharia [00:09:48]: YeahSwyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It's in. this goes back to Spark, right?Matei Zaharia [00:10:05]: Yeah.Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it's a layer that will there'll be some network effect, it'll benefit from many, people collaborating, on it. So, for example, with Spark, I don't know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be differentSwyx [00:10:28]: EcosystemMatei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don't have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that's like one of these as it. Another way to think about it is like imagine, we our thing wasn't open. We had some agent hosting thing, but it's not open and then there is an open one. if you're. Which one's gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it'll be, it'll be that. And then there are other things that like you just can't, even deliver as open source that are things the company does. Like for example, how do you make sure you're like streaming, jobs or your Lakebase database doesn't like, lose all your data at night? Well, that requires an operational team that's gonna sit there. There's no way it has to be a service. So like we wanna make sure as a company we're really good at those infra services and then we're as open as we can in terms of like what you build on top.Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requestsMatei Zaharia [00:11:45]: YeahSwyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someoneSwyx [00:11:51]: Let's see, let's see what's going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning aboutMatei Zaharia [00:11:59]: 400 merge already?Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it's not, like, on your laptop and someone's, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.The Modern Data Stack and the Emerging AI StackMatei Zaharia [00:12:34]: Yeah. That's all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.Matei Zaharia [00:12:42]: I don't know if it's that useful. I'm, I'm curious in your postmortem.Matei Zaharia [00:12:46]: I think most peopleSwyx [00:12:47]: AgreeMatei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.Matei Zaharia [00:12:52]: I don't know.Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don't understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we're doing data warehouse or lakehouse.Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don't have to worry about having me hook up five different systems in orderMatei Zaharia [00:13:55]: YeahReynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you've got an agent session, and you can send in a message or, like, a file. That's what you can send in, and then you get out, these streams as it's streaming text or as it's doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that's the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you'd have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it's gonna lose some messages. So that's the thing that's valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that's, And we built a security and control piece, which I'm excited about. But it's that common interface, so we don't. We. That doesn't try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that's one of the use cases we care a lot about, ‘cause we want to use this in our own products.Compute, Sandboxes, and Databricks ScaleSwyx [00:15:20]: Yeah. It should be everywhere.Matei Zaharia [00:15:22]: Yeah.Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I'll, I'll endeavor to do everything and not call it the modern AI stack because like it needs a different name.Matei Zaharia [00:15:32]: Yeah.Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it's serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.Matei Zaharia [00:15:51]: Yeah. Yeah.Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don't think he have ever launched it.Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architectureSwyx [00:16:05]: YeahMatei Zaharia [00:16:05]: and remove the database from it, by the coming from NeonSwyx [00:16:08]: Exactly, rightMatei Zaharia [00:16:09]: you have this sandboxSwyx [00:16:09]: Every database company has it already, yeah.Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it's important to have local persistence,Swyx [00:16:19]: YeahMatei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don't have to install your library every time, right?Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don't need persistent local disk.Swyx [00:16:30]: Yeah.Matei Zaharia [00:16:30]: So there's some differences.Swyx [00:16:32]: Yeah.Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it's, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There's, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.Matei Zaharia [00:16:51]: Yeah.Matei Zaharia [00:16:51]: But yeah.Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.Swyx [00:17:02]: So we, on the analytics side, I think we launchedReynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we're one of the biggest compute orchestrators out there.Reynold Xin [00:17:13]: Stuff for sure for CPU compute.Swyx [00:17:14]: Yeah.Matei Zaharia [00:17:14]: Yeah.Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it's pretty interesting, too. It's launching, I think, 13 million databasesSwyx [00:17:34]: YeahReynold Xin [00:17:34]: a day now.Swyx [00:17:35]: Yeah, to me that was, like, aReynold Xin [00:17:36]: And that's just likeSwyx [00:17:37]: Like, what do you mean?Matei Zaharia [00:17:38]: Yeah. And that's the point.Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentationSwyx [00:17:44]: YeahReynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita's team, to launch databases. It's, the. So it's changing the way people use databases.Swyx [00:17:54]: Yeah. Okay, we're gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the securityOmnigent Security, Contextual Policies, and Spend ControlsSwyx [00:18:03]: control side.Matei Zaharia [00:18:04]: Yeah.Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.Matei Zaharia [00:18:08]: Yep.Swyx [00:18:09]: what have you found there?Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there's a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there's this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I'll allow or disallow or whatever. It's like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let's say, should it be able to install new packages from npm, which, maybe it's compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I'm using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It's not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don't, don't do it. Otherwise, maybe it's okay. That's one example of, like, moving that trade-off so it's both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it's doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It's got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won't? It's, it's annoying. So we designed in Omnigentt the policy layer so that it's functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and thatSwyx [00:20:25]: This is related to the Panther,Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. PantherSwyx [00:20:30]: YeahMatei Zaharia [00:20:30]: a similar idea on the event processing side, and it's Python-based versus a weird custom language. this is more, as in realSwyx [00:20:39]: I didn't even know we were good yeah.Matei Zaharia [00:20:41]: Those things are happening, yeah.Swyx [00:20:42]: Yeah.Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it's stateful, one of the states we track is how much you spent in that session. So I can. I've had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we're counting that within that session, it'll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at DatabricksMatei Zaharia [00:21:34]: YeahReynold Xin [00:21:34]: which is the governance layer for data.Matei Zaharia [00:21:35]: That's right, yeah.Reynold Xin [00:21:36]: And he's combining expertise at that layer together with all the AI governance he knows.Matei Zaharia [00:21:41]: Yeah.Swyx [00:21:41]: DoMatei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.Matei Zaharia [00:21:46]: And also as theReynold Xin [00:21:48]: All the aboveMatei Zaharia [00:21:48]: I don't want to end up on the front page as, like, I installed some weird npm package and leakedSwyx [00:21:53]: YeahMatei Zaharia [00:21:53]: all the code, so I'm especially paranoid. But also I have very little time, so I don't want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that's why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?Swyx [00:22:10]: Yeah. Is safety and mmm, let's call it security a bigger concern than token maxing or token budgets? which one is, likeMatei Zaharia [00:22:19]: Oh, yeah, they're both there. I don't know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about thatSwyx [00:22:34]: you can be Uber and still be concerned?Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you haveReynold Xin [00:22:38]: for us, securityMatei Zaharia [00:22:39]: YeahReynold Xin [00:22:40]: super paramount.Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It's, it's the most important thing, and, token maxing, we're not so worried about it yet, but I've seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that's, that's not fun.Swyx [00:23:04]: YeahMatei Zaharia [00:23:04]: we have, like, only a few thousand engineers.Swyx [00:23:06]: What's the policy in Databricks? Is it just unlimited or what'Matei Zaharia [00:23:08]: It's, it's unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that'looking to optimize and to see if anyone's doing something weird. And, we had some really cool insights just from analyzing current traces, like whichSwyx [00:23:24]: YeahMatei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.Matei Zaharia [00:23:34]: Yeah.Swyx [00:23:34]: I think it'sReynold Xin [00:23:34]: YeahSwyx [00:23:34]: it's a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can likeSwyx [00:23:52]: But if there's something I should be showingMatei Zaharia [00:23:53]: YeahSwyx [00:23:53]: I don't, I don't see it on the GitHub, but,Matei Zaharia [00:23:55]: Oh, yeahSwyx [00:23:56]: there's justMatei Zaharia [00:23:56]: Well, in the docs there's something.Swyx [00:23:57]: Yeah, this is it.Matei Zaharia [00:23:58]: You can look at it later.Swyx [00:23:59]: Okay. Yeah.Matei Zaharia [00:23:59]: Just look in the docsSwyx [00:24:00]: YeahMatei Zaharia [00:24:00]: contextual policies if you wanna see.Swyx [00:24:04]: I just like to point peopleMatei Zaharia [00:24:05]: look at the built-in policies.Swyx [00:24:06]: Yeah.Reynold Xin [00:24:06]: Yeah.Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?Reynold Xin [00:24:10]: Yeah.Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.Swyx [00:24:29]: Oh, wow.Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keepSwyx [00:24:45]: YeahMatei Zaharia [00:24:45]: track stuff.Swyx [00:24:46]: Yeah, ‘cause ultimately you're, I think of you as, like, a systems designer.Swyx [00:24:50]: You let people plug in, right? That's the wholeMatei Zaharia [00:24:51]: YeahSwyx [00:24:52]: modus operandi of what you do.Matei Zaharia [00:24:53]: Yeah.Swyx [00:24:54]: It's likeMatei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, whichSwyx [00:24:59]: YeahMatei Zaharia [00:24:59]: this is meant to.Reynold Xin [00:25:00]: There's also a batteries included philosophy hereMatei Zaharia [00:25:03]: YesReynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.Swyx [00:25:06]: Yeah.Matei Zaharia [00:25:06]: Yeah, that's right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don't wanna do. But in Spark, if you just wanna like, I don't know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.Building on Omnigent: Contributions, Startups, and AnalyticsSwyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we're, we're collaborating with them to build it. and also tell us, likeSwyx [00:25:49]: Yeah, they're gonna be veryMatei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we'd love to hear, like, if you think, “Hey, I, I don't want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I'm, I'm a founder.Swyx [00:26:18]: I wantMatei Zaharia [00:26:18]: YeahSwyx [00:26:18]: I see an opportunity, I wanna get in front of you. What's your request for, like, a startup that, like, I wish someoneMatei Zaharia [00:26:23]: Oh, like you wanna integrate with us?Swyx [00:26:24]: someone was working on this.Matei Zaharia [00:26:26]: Oh, for a startup?Swyx [00:26:27]: Yeah.Swyx [00:26:28]: Like, your, you got your own startup. It's doing well.Matei Zaharia [00:26:30]: Yeah.Swyx [00:26:30]: But like, if you weren't working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are usingSwyx [00:26:46]: SpendMatei Zaharia [00:26:46]: coding agents and,Swyx [00:26:48]: Yeah. AnalyticsMatei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.Swyx [00:27:00]: Yeah. The closest I've found is, this team, GitAI.Matei Zaharia [00:27:03]: Oh, cool. Yeah.Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they're building the analytics layer on top of that.Matei Zaharia [00:27:12]: Yeah.Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,Matei Zaharia [00:27:18]: Yeah, they have their benchmarksSwyx [00:27:18]: doing super wellMatei Zaharia [00:27:19]: YeahSwyx [00:27:19]: with their stuff. so there's, there will be people. I think this is like the domain of consultants first, but then peopleMatei Zaharia [00:27:26]: YeahSwyx [00:27:26]: will build software that, let's say, it's kinda like the management planeMatei Zaharia [00:27:29]: YeahSwyx [00:27:30]: for coding agents.Matei Zaharia [00:27:30]: Yeah, I think there'll be a lot of insights there. You have it in other areas.Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.LTAP: Lake Transactional/Analytical ProcessingSwyx [00:27:39]: maybe you wanna tell the story of, LTAP.Reynold Xin [00:27:45]: So, and background with. I'm, I'm gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAPMatei Zaharia [00:27:52]: YeahReynold Xin [00:27:52]: and all that history.Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur's, talk about HTAP, it's effectively the world of databases. Sorry, there's like maybe a lot of context needs to be injected here. The world of databasesSwyx [00:28:06]: I am happy to be the database podcast that I'm forcing people to, like, learn your databases, guys.Swyx [00:28:11]: You cannot vibe code with just markdown files.Reynold Xin [00:28:13]: Yeah.Swyx [00:28:13]: Like,Reynold Xin [00:28:14]: It's one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There's what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It's a very oriented data structure. And on analytics, you're trying to reason on the data. You're trying to compute, “Hey, what's my revenue per store? What's my. How's my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it's probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP databaseSwyx [00:29:35]: Replication.Reynold Xin [00:29:36]: Replicate them into the analytic systems and just startSwyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, aReynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.Swyx [00:29:51]: Yeah.Reynold Xin [00:29:51]: And there's this term called CDC, whichMatei Zaharia [00:29:54]: Change data captureReynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don't understand what binlog is, it's fine. The, but it's a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It's how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there's some pipeline thing.Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.Reynold Xin [00:30:27]: Yeah, exactly.Reynold Xin [00:30:28]: CDC is, like, a veryMatei Zaharia [00:30:30]: It's hard.Reynold Xin [00:30:30]: It's one of the most boring but one of the most fundamental operations, like, powering modern society.Matei Zaharia [00:30:37]: huh.Reynold Xin [00:30:37]: But it's so brittle that, we joke that it's, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handleSwyx [00:30:48]: YeahReynold Xin [00:30:48]: the schema change.Swyx [00:30:49]: Yeah.Reynold Xin [00:30:49]: And then everything goes out.Swyx [00:30:51]: And there's all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.Reynold Xin [00:30:55]: Yeah, but it's a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don't.Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.Reynold Xin [00:31:15]: Yes. Yes.Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that's built for Postgres. And Spark, for example, had a massive ecosystem. There's a lot of libraries you want to use. If you were to create now a new thing, you don't have a ecosystem. You tend to create a new, smaller proprietary API, and you're lacking both, and it's also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it's obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There's no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn't really believe in it myself, even though we wrote that positioning.Lakebase, Agents, and Live Operational DataMatei Zaharia [00:32:39]: Yeah.Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there's no way for those agents to even understand what's going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who's placing those orders, what is happening, what exactly are they doing. So now I'm sold on our own message.Swyx [00:33:13]: Yeah.Reynold Xin [00:33:14]: I think it's really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analyticsSwyx [00:33:26]: Yeah, I think,Reynold Xin [00:33:27]: without compromiseSwyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anythingReynold Xin [00:33:34]: YeahSwyx [00:33:35]: while they do the work, they also need history and need context.Swyx [00:33:38]: And, like, where else does they get context? That's it's an analytical workload.Reynold Xin [00:33:41]: Exactly.Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can't just run a giant query on it to see what's going on because that's gonna bring down the database and hoard it even more.” Like, that's the stuff that this gets rid of, because you spin up a whole separate fleet of machines that's doing the analytics. You're not overloading, like, the main databaseReynold Xin [00:34:02]: RightMatei Zaharia [00:34:02]: that's still trying to serve stuff.Reynold Xin [00:34:04]: Yeah.Matei Zaharia [00:34:04]: Yeah.Why LTAP Works Now: Parquet, Postgres, and LakebaseSwyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,Reynold Xin [00:34:11]: Yeah.Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn't as clear as LTAP.Reynold Xin [00:34:18]: Yeah.Swyx [00:34:18]: I think LTAP is like Like, okay, we've got it, guys.Matei Zaharia [00:34:21]: This thing, yeah.Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what's the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we're writing data in oriented format to the open data lake, but in there we're writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we're just debating, and one day, one of our engineers who's, like, super smart came in, he's like, “Hey, I just prototyped it. It works.”Swyx [00:35:07]: Wait, it's, prototype what?Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented formatSwyx [00:35:15]: ColumnReynold Xin [00:35:15]: like Postgres pagesSwyx [00:35:15]: YeahReynold Xin [00:35:16]: write them in Parquet.Swyx [00:35:17]: Yeah.Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let's do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.Matei Zaharia [00:35:49]: Yeah.Reynold Xin [00:35:49]: So there's no overhead, it's no compromise in performanceMatei Zaharia [00:35:52]: Some CPU overhead.Swyx [00:35:54]: Yeah, because,Matei Zaharia [00:35:55]: YeahSwyx [00:35:55]: we had extra CPUs anyway.Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.Swyx [00:35:57]: so the debate ended. it's one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.Matei Zaharia [00:36:06]: But, like, something this strategicSwyx [00:36:07]: That's rightMatei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.Swyx [00:36:13]: Nothing like that.Swyx [00:36:14]: He just. We were debating in many meetingsMatei Zaharia [00:36:17]: Yeah.Swyx [00:36:17]: and then we're just debating whether it's possible or not from first principle.Matei Zaharia [00:36:20]: YeahSwyx [00:36:20]: and then, somebody just did it.Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that'll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it's still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.Databricks' Culture of Fast PrototypingSwyx [00:36:47]: Yeah.Matei Zaharia [00:36:47]: Yeah.Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don't have to ask my permission, you don't have like, do a whole form- formal process, just do it?Matei Zaharia [00:36:59]: Well, especially these days, I think withSwyx [00:37:01]: YeahMatei Zaharia [00:37:01]: AI, it's easier to buildSwyx [00:37:02]: But so, likeMatei Zaharia [00:37:03]: a prototypeSwyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I'm sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that's a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don't launch that many, like, products. We try to keep it pretty coherent. That's, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it's, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn't used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don't, don't do too many, but do those things well and, that also helps, it helps keep it manageable.Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let's figure out how do we do it incrementally, how do we do it very quickly. Like, many of our productsMatei Zaharia [00:38:43]: YeahReynold Xin [00:38:43]: they're built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who's the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.Reynold Xin [00:39:04]: Omnigentt itself happened that way.Reynold Xin [00:39:05]: Yeah.Matei Zaharia [00:39:06]: Who's the customer? That's a good oneReynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.Matei Zaharia [00:40:24]: 20- 2018?Swyx [00:40:26]: Yeah, ‘17, ‘18.Matei Zaharia [00:40:28]: Few companiesSwyx [00:40:28]: Do you have other examples?Matei Zaharia [00:40:30]: there'Swyx [00:40:31]: Maybe you have othersMatei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharingSwyx [00:40:35]: YeahMatei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it's gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it's a much bigger problem.Swyx [00:40:58]: Yeah. Yeah.Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.Swyx [00:41:00]: Yeah, that's more, that's the more likely outcome.Matei Zaharia [00:41:02]: Yeah.Tech Companies vs. EnterprisesSwyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.Swyx [00:41:22]: Yeah.Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challengesSwyx [00:41:27]: OhMatei Zaharia [00:41:27]: scaling them outside of tech companies.Swyx [00:41:28]: Okay, what likeMatei Zaharia [00:41:30]: YeahSwyx [00:41:30]: what like top three differences that you always think about?Reynold Xin [00:41:33]: Governance is a big oneMatei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you're building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it's just very different. A company that's existed for like, it's had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it's just different, and if you've never worked with enterprises or been in one, you just won't know about it.Reynold Xin [00:42:13]: Yeah.Matei Zaharia [00:42:13]: Yeah.Reynold Xin [00:42:13]: And the procurement process is probably quite different. There's far more stakeholders.Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.Matei Zaharia [00:42:18]: Another piece that's interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I'll just build that myself.Matei Zaharia [00:42:27]: So then you go,Reynold Xin [00:42:28]: I don't think people say that about Databricks, butMatei Zaharia [00:42:31]: yeah, it dependsReynold Xin [00:42:32]: They do.Matei Zaharia [00:42:32]: They do?Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don't, I never wanna be in the business of building that.” Like, I don't want my, whatever, I'm a retailer or something, I never wannaReynold Xin [00:42:45]: Yeah, sell clothes,Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn't get streaming pipelines working.Matei Zaharia [00:42:51]: That is not what I'm doing.Reynold Xin [00:42:53]: Yeah.Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?Matei Zaharia [00:42:55]: Yeah. But you have to understand that it's hard without having worked there and stuff, like you may not appreciate.Reynold Xin [00:43:01]: Look, I think they're all great. don't get me wrong, they have different challenges. But the, many of the tech companies, for sure there's a lot, far more DIY.Matei Zaharia [00:43:10]: On the flip side, you have people who are. they're very much experts in their domain, like they're building airplanes, they're, designing medicines, whatever, and they just want to bridge the technology, where like they don't wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.The Dream Engine and Rewriting the Database StackReynold Xin [00:43:40]: Yeah. That's true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it's become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.Matei Zaharia [00:44:23]: Yeah.Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let's go start from scratch. Let's go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let's attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it's a very ambitious project. by the way, you can search on Wikipedia, there's this thing called second system syndrome.Matei Zaharia [00:45:08]: Yeah, I know that. Yes.Reynold Xin [00:45:09]: Or second system effect.Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.Reynold Xin [00:45:12]: It's you built your first thing and it works out great, and the second one's bound to fail because you become too ambitious.Reynold Xin [00:45:19]: And then you ask so many requirements.Matei Zaharia [00:45:20]: Or like you think everythingReynold Xin [00:45:21]: YeahMatei Zaharia [00:45:21]: and then you're likeReynold Xin [00:45:22]: You justMatei Zaharia [00:45:22]: you're, “I'm gonna design the perfect system this time.”Reynold Xin [00:45:24]: Yeah. And it turned out it's not perfect, and then it start failing and you're too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it's not their second system. Many of them have built more than two in the past.Matei Zaharia [00:45:44]: Ah, nice.Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when y

In this episode, we sat down with Skot and Ryan to unpack Ryan's trip to BTC++ Nairobi, where he represented the 256 Foundation and shared our open-source mining mission with builders from across Africa. Ryan talked about the incredible energy of the local Bitcoin community, the real-world use of Lightning and mobile payments across borders, and why Africa offers such a powerful glimpse into Bitcoin's practical value. We also dug into the strong interest in open-source mining, from Ryan's main-stage talk on the 256 Foundation's mining stack to the many conversations he had with developers excited to contribute, experiment, and build without the barriers of closed hardware and firmware.We also went deep on Ryan's hands-on mining workshop, where attendees learned Bitcoin mining from first principles, CPU-mined on a classroom Signet, assembled Bitaxes, and watched the network shift as ASICs came online. From there, we explored what Ryan saw at Gridless's biomass-powered mining site in Kenya, the technical realities of balancing power generation with mining load, and promising local projects like BitShaka and Juakali. Throughout the conversation, one theme kept coming up: open-source mining is becoming a practical path for education, experimentation, decentralization, and entirely new energy use cases—and the momentum behind Mugena and the broader 256 Foundation mission is clearly growing.

At 66 years old, instead of heading towards retirement, former Cadence CEO and legendary investor Lip Bu Tan decided to take on the hardest job in tech: turning Intel around. Elad Gil and Sarah Guo sit down with Intel CEO Lip Bu Tan to talk about why he took the job and what “saving” Intel actually looks like. Tan explains how his experience in startup culture informed his decisions to drive Intel's culture towards faster decisions, focus on customer satisfaction, and engineer accountability. He also discusses his strategy to strengthen Intel's balance sheet by welcoming investments from Jensen Huang's Nvidia, Softbank, and the US government. Tan also shares his product roadmap that centers the CPU for agentic AI and inference, the collaboration with Elon Musk on Terafab, his investing framework for semiconductors, and his views on how AI is reshaping design and operations at, as he puts it, a ‘legacy spreadsheet' tech company.         Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @LipBuTan1 | @intel Chapters: 00:00 – Cold Open 01:01 – Lip Bu Tan Introduction 01:24 – Why Lip Bu Took the Reins at Intel 03:00 – Fixing Culture 04:08 – Intel's 10-Year Vision 07:57 – Working with Elon Musk on Terafab 09:59 – Shifting Supply Chain for Semiconductors 15:34 – Limits to Scaling and Packaging 18:30 – Physical Limits to Engineering and Design 20:33 – Challenges in Semiconductor Investing 26:29 – Lessons from Cadence 28:02 – Scaling and Investment Decisions 32:03 – Rethinking Teams in AI Era 34:31 – Industrial Policy and Funding 37:25 – What Investors Misunderstand About Intel 41:10 – Where Compute Will Live 44:59 – Conclusion

NuNet is building a decentralised compute and orchestration network where people can contribute spare CPU, GPU, RAM and other resources, while developers and organisations can deploy workloads across available infrastructure. In this episode, Peter talks with Jennifer from NuNet about the new NuNet Appliance and why it matters for making decentralised compute more practical for everyday users.The conversation covers how NuNet matches the right compute to the right job, how the Appliance lowers the barrier to onboarding devices, and why use cases like n8n automations, private AI agents, edge AI, Cardano SPO infrastructure and web deployment workflows are a natural fit for the network. Jennifer also explains NuNet's zero-trust security model, pricing approach, organisations, ensembles, deployment templates, and how NTX fits into orchestration fees.If you have spare compute, want to run private AI workloads, or are building in the DePIN and Cardano ecosystem, this episode gives a practical look at how NuNet is moving from concept to usable infrastructure.Key Takeaways:- NuNet is a decentralised compute and orchestration platform that lets people contribute spare compute and lets workloads find suitable resources automatically.- The NuNet Appliance is designed to make onboarding CPUs, GPUs, RAM and other compute resources much easier for non-expert users.- NuNet can support broad workloads, including n8n automation, private AI agents, Qwen-based LLM deployments, edge AI, web builds and Cardano SPO infrastructure.- The network uses a zero-trust model where machines are cryptographically identified and verified at each interaction.- Compute pricing is designed around stable currency values, with automatic conversion into NTX rather than forcing users to price workloads directly in a volatile token.- NuNet organisations can let other DePIN projects bring their own communities and native tokens while still using NuNet's orchestration layer.- Ensembles and templates are intended to simplify deployments so users do not need to manually understand every YAML configuration detail.- NuNet is open source, with docs, GitLab, Discord, Medium and X available for people who want to try the network or contribute.Links & References:- NuNet — Compute Orchestration for a Decentralized World: https://link.learncardano.io/eGKGuZ- What is NuNet? | NuNet Documentation: https://link.learncardano.io/rHu2E4- x.com: https://link.learncardano.io/NIhPKR- https://link.learncardano.io/Tlu7wNWebsite: https://link.learncardano.io/bQ68RcX/Twitter: https://link.learncardano.io/3a1QtvDisclaimer: This content is for educational purposes only. Nothing constitutes financial advice.DISCLAIMER: This content is for informational and educational purposes only and is not financial, investment, or legal advice. I am not affiliated with, nor compensated by, the project discussed—no tokens, payments, or incentives received. I do not hold a stake in the project, including private or future allocations. All views are my own, based on public information. Always do your own research and consult a licensed advisor before investing. Crypto investments carry high risk, and past performance is no guarantee of future results. I am not responsible for any decisions you make based on this content.

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The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy. Hosted by Sonya Huang, Sequoia Capital

The back episodes finally make it online, this is number 4 in a series of episodes - wait - this IS the current episode!Brett is back, so the episodes are getting posted almost on time!  Windows is starting to support ARM more, RAM pricing hysterics, Windows 11 CPU boost, Intel improves with iBOT, Microsoft 365 brings the CoPilot, and bots surpass humans on the network.  On with the show, enjoy!0:00 Intro1:15 Patreon2:09 Food Stories with Josh (just words, no photos)3:54 3DMark adds native Arm Windows support6:55 Josh talks about the latest Arm developments9:43 Memory prices may double this year (and related discussion)16:45 Windows 11 performance boost?19:05 Intel expands iBOT with 7 more games23:31 AMD reaches almost 45 percent CPU share on Steam25:23 Office 365 Copilot auto-install returns33:59 Bots take over the Internet36:59 Apple iOS 27 has an "agentic" solution for compromised passwords39:42 (in)Security Corner54:15 Gaming Quick Hits1:01:32 Picks of the Week1:10:02 Outro ★ Support this podcast on Patreon ★

In this episode of The Circuit, Ben and Jay dive deep into Apple's WWDC announcements, unpacking the "applied AI" strategy behind the newly indexed Siri, system-level CPU scheduling updates, and Apple's surprising embrace of Nvidia for private cloud compute. Ben also shares his takeaways from the Nebius conference in San Francisco, analyzing their unique custom server racks and their positioning as a highly capable neo-cloud infrastructure provider. Finally, Jay reports back from his recent trip to China, sharing ground-level observations on why US entity list restrictions are losing their impact, how IoT chipmaker Espressif creatively markets globally via Reddit and YouTube despite domestic blocks, and the real-world manufacturing bottlenecks facing humanoid robot actuators.

Apple's Worldwide Developers Conference happened this week, and there was enough going on that we wanted to unpack the whole thing, primarily due to the company's uncharacteristic backpedaling on its... controversial Liquid Glass UI language, not to mention the unusual focus on CPU scheduling and numerous other performance refinements across the board in this year's OS updates, rather than the more typical long list of new features. It was enough to get us saying the words "Snow Leopard," which is always a good feeling. We also consider new broader parental controls, the apparently final state of Apple Intelligence and Siri AI features, and more. Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

The back episodes finally make it online, this is number 1 is a series of episodes that did not get posted, but are now!Brett is out (which is why these didn't get posted) - but the show is great!  So much insecurity, so much Linux and retro, enjoy!Timestamps:0:00 Intro1:04 Patreon1:26 Food with Josh3:10 New Ryzen X3D already?5:16 NVIDIA just made more money than ever11:52 RTX 5090 price increase16:41 EFI partitions and Windows Update19:57 Noctua Home - adding fans to everything in your life22:57 Linux drops more vintage CPU support26:35 Vintage style ROG motherboard30:39 (In)Security Corner44:30 Gaming Quick Hit47:20 Jeremy explores the Lepro OE1 RGB floor lamp52:12 Picks of the Week1:00:29 Outro ★ Support this podcast on Patreon ★

Your iPhone might be running hot and draining fast — and it’s not just you. Dave and Pilot Pete break down the battery chaos introduced by iOS 26.5, which brought overheating, accelerated drain, and even blocked wired charging on iPhone 17 and Air models. The fix that’s working for most people: disable iCloud Keychain first, run Reset All Settings, then carefully re-enable iCloud sync — otherwise you’ll nuke your Wi-Fi passwords across every device. iOS 26.5.1 is out and should help, but until you’ve updated, your electrons deserve better. You’ll also learn why Apple ID passkeys are locked to Apple’s own keychain with no known path to third-party managers like 1Password or Keeper, and why editing a contact on a modern Mac can somehow peg every CPU core — in 2026, no less. From there, Dave and Pete tackle the full listener mailbag: how to rescue missing contact names from Messages, the right way to boot a MacBook with a broken display into clamshell mode so it actually uses the external monitor, and a deep dive on 5K vs. 4K displays where Dave argues your eyes may not care as much as the pixel-per-inch math suggests. You’ll get smart ideas for repurposing a 2015 iPad Pro that can’t run modern apps — including Dave’s Claude Code-built weather dashboard running off a headless iMac as a web interface. A crashing 2021 MacBook Pro turns out to have been felled by a single bad SD card, and the lesson is golden: feed your crash reports to an LLM and let it do the digging. And Don’t Get Caught with outdated OpenAI macOS apps — update ChatGPT, Codex, Atlas, and Codex CLI before June 12th to stay ahead of a code-signing rotation triggered by a compromised open-source library. 00:00:00 Mac Geek Gab 1145 for Monday, June 8th, 2026 June 8th: National Best Friends Day MGG Monthly Giveaway – Win a license to SaneBox Quick Tips 00:00:01 Dan-QT-Multi-select on iPhone with a quick drag 00:04:31 Tim-QT-Have iOS 26.5 Battery Drain? Reset All Settings, but be careful! 00:13:32 Kent-QT-1144-Collapse stacks by clicking the down-facing carat in the menu 00:14:15 Mark-QT-Match Frame Rate on your Apple TV for smoother experiences 00:17:58 What are the differences between refresh rates and frame rates and…why? 00:21:09 KiwiGraham-QT-Apple Account Passkeys vs. Third Party Password Apps Sponsors 00:23:09 SPONSOR: Keeper. Right now, Keeper is offering our listeners 60% off personal and family plans at https://Keepersecurity.com/MGG. This offer is only for podcast listeners! 00:24:50 SPONSOR: Helix Sleep makes premium mattresses and bedding that are customized to fit your personal needs, and conveniently shipped to your door. Go to https://helixsleep.com/MGG for 20% Off Sitewide. 00:26:23 SPONSOR: NordLayer Browser. The business browser built for how modern work actually happens — giving IT the visibility and control to secure SaaS, stop phishing, and prevent data leaks right at the source. Your Questions Answered and Tips Shared! 00:28:09 VaShaun-How can I restore lost Contacts on my Mac? 00:37:36 Si-What to do with an 11-year-old iPad? Claude Code 00:46:40 Michael-Why do we have to pull-to-refresh for updates? 00:50:04 Blake-1144-Damaged displays, external monitors, and MonitorControl 00:55:48 Joe & Michael-CSF-1144–RetinaDesk.com for reviews of 5K and 6K monitors BenQ MA270UP 27” 4K Display Reviews 01:02:50 Hog fan and Cowboy fan-MGG Review–Favorite Tech podcast Don't Get Caught 01:04:14 Father John-DGC-Investigate those crash reports before you replace your Mac 01:09:26 Update your ChatGPT Apps ChatGPT Desktop Codex App Codex CLI Atlas 01:11:06 Andy-DGC-When Troubleshooting, Don’t Get Caught asking the wrong questions or assuming the wrong facts 01:19:36 MGG 1145 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

Computex happened this week, and there was enough to talk about to devote this week's episode to rounding up the high points, including Nvidia's attempt to dominate the consumer Windows market with RTX Spark, the first RGB mini-LED monitors, 8GB laptops becoming common again, PC hardware production shifting back to DDR4 and old CPU sockets, Intel's entry into the handheld gaming market, the (unsurprising) absence of any news about Zen 6 and Nova Lake, and other stuff! Show notes and links: https://tinyurl.com/techpod-342-computex-26 Support the Pod! Contribute to the Tech Pod Patreon and get access to our booming Discord, a monthly bonus episode, your name in the credits, and other great benefits! You can support the show at: https://patreon.com/techpod

Nvidia announced its new CPU at an event in Taipei and Jon, Rachel, and Matt talked about why potential customers may be interested in buying as well as the potential impacts to primary CPU players such as Intel and AMD. The team also talks about Berkshire Hathaway's homebuilder acquisition before closing with a question regarding passive investing trends. Jon Quast, Matt Frankel, and Rachel Warren discuss: -Nvidia's new Vera CPU -The potential fallout in the CPU markout -Berkshire Hathaway's latest acquisition -Passive investing's impact on the stock market Companies discussed: Nvidia (NVDA), AMD (AMD), Intel (INTC), Qualcomm (QCOM), Berkshire Hathaway (BRK.A)(BRK.B), Taylor Morrison (TMHC) Host: Jon Quast Guests: Matt Frankel, Rachel Warren Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement.We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode.Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices Learn more about your ad choices. Visit megaphone.fm/adchoices

NVIDIA puts out a CPU meant for Windows machines, while Dell joins the laptops gunning for the MacBook Neo. And its chips, chips, chips as Computex kicks off.Starring Tom Merritt and Robb Dunewood.Show notes can be found here. Hosted on Acast. See acast.com/privacy for more information.

June is here so guess what? It's officially Hot AI Summer.