Podcasts about github copilot

Live from Microsoft Build, Corey Noles sits down with Scott Hanselman for a hands-on Neuron LIVE episode about AI-augmented software development, how it differs from just "vibe coding", and the surprisingly practical things people can now build with tools like GitHub Copilot and more.Scott is one of the best technical explainers in software: a longtime Microsoft and GitHub developer, teacher, speaker, author, blogger, and podcaster who has helped millions of developers understand new technology without making it feel impossible to learn.This episode turned into a live demo tour of what AI coding can already do, led by Scott's own use-cases. Corey and Scott walked through a series of examples showing how AI can help people build useful apps, prototypes, workflows, and small tools from everyday ideas, including Scott's own vibe-coded tools Baby Smash (https://www.babysmash.com/), which lets babies press random buttons for fun shapes and sounds, and Tiny Tool Town (https://www.tinytooltown.com/), which showcases random, cool tools Scott found around the web. But in the coolest demo of all, Scott shows how to take an open source tool and create software a personal blood sugar tracking app for his own diabetes management. If that doesn't get your idea blood flowing for what you can do with AI, we don't know what will! https://www.theneuron.ai/

In episode 576 of 'Coffee with Butterscotch,' the brothers kick off the closed beta for How Many Dudes and dig into what they're actually trying to learn from it. Then the conversation pivots hard into the AI hype cycle, unpacking why GitHub Copilot's switch to usage-based billing just handed a hundred-times-larger bill to the developers who went all in, and what that says about where this whole thing is headed.Support How Many Dudes!Official Website: https://www.bscotch.net/games/how-many-dudesTrailer Teaser: https://www.youtube.com/watch?v=IgQM1SceEpISteam Wishlist: https://store.steampowered.com/app/3934270/How_Many_Dudes00:00 Cold Open00:29 Introduction and Welcome01:22 Closed Beta for How Many Dudes08:15 Game Balancing: Challenges and Techniques12:25 Evaluating Enemy Strength and Player Progression18:27 Beta Test Goals and What They're Looking For27:00 Pivot to AI and the Tech Landscape29:32 How LLMs Actually Work32:08 Agentic Programming Explained37:08 The AI First Company Myth40:02 Slot Machine Development44:18 GitHub Copilot's Usage-Based Billing50:34 The Dependency Trap53:11 Where This All Ends Up57:15 The Cost Reckoning Begins01:00:01 What Comes After the CollapseTo stay up to date with all of our buttery goodness subscribe to the podcast on Apple podcasts (apple.co/1LxNEnk) or wherever you get your audio goodness. If you want to get more involved in the Butterscotch community, hop into our DISCORD server at discord.gg/bscotch and say hello! Submit questions at https://www.bscotch.net/podcast, disclose all of your secrets to podcast@bscotch.net, and send letters, gifts, and tasty treats to https://bit.ly/bscotchmailbox. We also built Ludokit, a tool for managing store pages, promo art, localization, achievements, credits, fonts, change logs, and more. Check it out at https://ludokit.com!Finally, if you'd like to support the show and buy some coffee FOR Butterscotch, head over to https://moneygrab.bscotch.net. ★ Support this podcast ★

Alla pratar autonoma agenter. Få pratar om vem som håller i tyglarna när de släpps in på företaget. Det är där de flesta piloter dör.Asif Mithawala leder Cloud AI Platforms på Microsoft, ett team på 25 solution engineers, och kommer närmast från AWS. Han dammsög hela Microsoft Build 2026 på en helg, över 400 sessioner. Tldr; året då AI går från något du pratar med till något som jobbar åt dig.Agent 365 var hans personliga favorit på eventet och i avsnittet får du höra varför. Hur Microsoft Scout faktiskt fungerar i Teams och Outlook. Varför Frontier Tuning kan ge en modell som är runt 10 gånger billigare än de största Frontier Labs-modellerna utan att tappa kvalitet på er uppgift. Asif förklarar också nya GitHub Copilot-appen som startar parallella agenter per bugg via git worktrees. Och vad de öppna WorkIQ-API:erna betyder för en partner som vill bygga ovanpå Microsoft 365.Lyssna om du funderar på hur ni går från rolig demo till något ni vågar köra skarpt på måndag morgon.Kapitel:00:00 Intro och Asifs bakgrund03:00 Vad en solution engineer faktiskt gör06:00 Build 2026 sammanfattat på en mening08:00 Microsoft Scout, den första autopiloten13:30 Loopen som gör en agent till en agent18:00 Agent 365, kontrollplanet för digitala medarbetare24:00 Foundry och IQ-lagret, WorkIQ-API för partners30:00 MAI-modellerna och Frontier Tuning42:00 Nya GitHub Copilot-appen och git worktrees45:00 Tre saker att börja med redan på måndagLänkar:Asif Mithawala (LinkedIn)Johan Wallquist (LinkedIn)Securing code, agents, and models across the development lifecycleLaunching seven new MAI modelsGitHub Copilot app: The agent-native desktop experienceBuild 2026 (nyhetssajt) Hosted on Acast. See acast.com/privacy for more information.

The federal government wants equity in OpenAI (and others) and ... the people might get a slice?

The weightless era of software is over. This week the AI buildout slammed into the physical world: concrete, copper, electricity, water, and capital. We map the paradox of record wealth at the top of the stack and intense friction everywhere else.Alphabet announced an $80 billion equity raise, its first major stock sale since the 2004 IPO, to fund an estimated $180 to $190 billion in AI compute capex for 2026, with Berkshire Hathaway taking a $10 billion private placement. Broadcom posted a record fiscal Q2 of $22.19 billion, AI chip revenue up 143%, and Marvell shipped the first 102.4 Tbps switch that Jensen Huang called the next trillion-dollar company.SoftBank overtook Toyota to become Japan's most valuable company after pledging 75 billion euros for 5 gigawatts of AI data centers in France. The bill for the combined ~$700 billion buildout is landing on workers: 2026 tech layoffs have reached roughly 142,000, and employment for developers under 26 has dropped nearly 20% since 2024.GitHub Copilot switched to token-based billing, with power-user bills jumping from about $29 to $750 and outliers hitting $3,000. NVIDIA and Microsoft launched the RTX Spark to run 120-billion-parameter models locally, Anthropic filed confidentially for a roughly $1 trillion IPO, and Ohio suspended its data-center tax break as a citizen petition aims to ban hyperscale data centers. Community consent, water, and energy are the real bottlenecks.If you want a prize, send us a DM:instagram.com/rickerandbontiktok.com/@rickerandbonyoutube.com/@rickerandbon

Microsoft Build 2026 announced an end-to-end agentic AI stack. COMPUTEX Taipei confirmed heterogeneous AI infrastructure across ARM, Marvell, Intel, Qualcomm, and NVIDIA. Alphabet raised $80 billion. Cisco Live repositioned the network as the AI platform. Patrick Moorhead and Daniel Newman break it all down alongside earnings from Broadcom, HPE, Palo Alto Networks, and CrowdStrike, plus the token cost conversation, the edge AI push, and what Palantir and Oracle are saying about proprietary data as the real AI moat. The handpicked topics for this week are: Microsoft Build 2026 Announced an End-to-End Agentic AI Stack: Microsoft shipped MAI-Thinking-1, its first homegrown thinking model, alongside Scout, Microsoft IQ, Project Solara, and a Majorana 2 quantum update targeting a 2029 commercial timeline with claims of a 1,000x reliability gain. Pat describes MAI-Thinking-1 as likely better than Sonnet 4.6 in blind testing and delivering close to GPT 5.5 quality at a far lower cost. Scout is Microsoft's first autopilot agent, anchoring the M365 Agent Suite with Office Pilot Agent Mode and Agent 365. Microsoft IQ serves as the context layer, integrating M365, business data, boundary IQ, and web IQ with GitHub Copilot, Foundry, and Copilot Studio. Project Solara is a new Android-based platform built for agent-first devices across transportation, retail, and hospital settings. Microsoft also added 83 Unix commands to the Windows stack. Dan frames Microsoft's real play as distribution, not frontier model development, noting that the open model ecosystem being pulled into the platform will matter more to CFOs managing token costs at scale. (The Decode) The AI Stack Goes Multi-Silicon — COMPUTEX Taipei 2026 Confirms Heterogeneous AI Infrastructure: ARM's AGI CPU is in production with Google moving its TPU head node to ARM, and adding Oracle and ByteDance as new customers. ARM also introduced a new switch, the TT100, and put the 51T CPO switch on stage. Marvell received a trillion-dollar company endorsement from Jensen Huang, adding $90 billion in market cap on the comment alone. Intel announced disaggregated inference details and Xeon 6+ Clearwater Forest, its first 18A data center processor. Vista Equity and Cambium Capital announced a NeoCloud called Vector Core Compute, with Xeon 6 handling orchestration, Salmonova RUs handling decode, and Blackwell GPUs handling pre-fill. Qualcomm's Cristiano Amon announced the Dragonfly data center brand with Snapdragon C details coming at their June investor day. The WSTS raised the 2026 semiconductor TAM forecast by 90% to $1.51 trillion, with Pat noting the market could hit a trillion dollars if memory is excluded entirely. (The Decode) NVIDIA RTX Spark and the Edge AI Push: NVIDIA coordinated with ARM and Microsoft around the RTX Spark at COMPUTEX, with the shared message being that the future of Windows is here. Signal65's Ryan Shrout asked Jensen directly why NVIDIA wants to be in the PC business, given low margins and diminishing returns. Dan frames the answer in the context of devices increasingly becoming mobile data centers, capable of running models at much greater efficiency than cloud delivery. The edge AI conversation is also directly tied to token cost economics: as intelligence delivery moves closer to the device, the cost per token drops significantly. The jury is still out on whether NVIDIA will meaningfully disrupt the PC market, but its influence over OEMs like Lenovo and Dell that depend on it for data center gives it real leverage over SKUs. (The Decode) Token Economics and Frontier Model Cost Pressure: Dan and Pat discuss a substantive shift in how enterprises are thinking about AI consumption costs. Dan argues that "token maxing," the practice of defaulting to the most powerful frontier model for every task, has now effectively peaked, as bills have come due at scale. Companies paying for tokens in volume are starting to question whether they can afford the prices that frontier models actually cost to deliver. Pat pushes back, saying the dynamic is still present, but both analysts agree that the market is moving toward a model where token selection is matched to the job, with Microsoft's MOE approach and thinking models positioned to help CFOs manage that economics story. (The Decode) Continuum Goes Public at Highest Valuation for an AI Platform: Dan notes that Continuum, the Honeywell-spawned quantum company, went public this week at what he calls the highest valuation for an AI platform to date. He flags that IonQ will likely contest that characterization. The broader context is Microsoft entering the quantum conversation with Majorana 2 at Build, a name that has largely been absent from the quantum race, while IBM has received most of the attention. (The Decode) AI CapEx Has Outgrown Cash Flow — Alphabet's $80 Billion Equity Raise: On June 1, Alphabet announced an $80 billion equity capital raise, upsized to $85 billion, structured as $40 billion ATM, $30 billion underwritten, and a $10 billion private placement with Berkshire Hathaway anchoring. Pat frames the questions over CapEx returns as entirely dependent on whether you are an AI boomer or a doomer: if the payback comes, the raise is the right move. If it does not, the math doesn't close. Dan argues the investment is existential, drawing parallels to how infrastructure-first companies have always spent ahead of monetization, and notes that Google's equity is being used as a capital engine that may be more efficient than the debt markets right now. Both analysts flag the downstream implications for Broadcom, MediaTek, and Marvell given the TPU connection. (The Decode) The Network Becomes the AI Platform: Cisco Live 2026: Cisco launched Silicon One P200, the Secure AI Factory with NVIDIA and Spectrum X, AgenticOps, MCP-native automation, Cisco IQ, LiveProtect, and folded Astrix Security and Galileo into Splunk under one control plane. Pat identifies Cisco Cloud Control as the biggest announcement of the entire show, pulling together Catalyst, Meraki, Nexus, Firewall, and WebEx under agentic ops that run natively through MCP, with code running directly on smart switches that have x86 processors. Pat also credits Cisco for establishing Silicon One as a credible chip alternative for hyperscalers capable of taking on Tomahawk and Jericho. Dan frames the long-term opportunity as campus and branch enablement when industrial AI and robotics deployments accelerate, arguing that the numerator of AI's economic impact has barely started, as edge deployment spending has not yet begun. (The Decode) The Flip: Did Microsoft Build 2026 Effectively End the OpenAI Partnership? Pat argues the divorce decree has been filed. MAI-Thinking-1 was built with zero distillation from third-party models offering clean enterprise data lineage, with Maia 200 in production plus Anthropic chip supply, which signals vendor hedging. OpenAI is going all-in on AWS, which means you cannot be married to two people, and the full Build stack covering model, OS containment via MXC, agents via Scout and Agent 365, and context via Microsoft IQ removes every architectural dependency on OpenAI. Dan counters that Microsoft is hedging rather than leaving and predicts the partnership will run through the decade. Enterprise Copilot customers are explicitly showing in data that they demand GPT 5.5, internal benchmarks have not been independently validated, and Microsoft stands to make meaningful money from the OpenAI IPO. (The Flip) Broadcom Q2 FY26 Earnings: Broadcom posted revenue of $22.19 billion, a narrow miss depending on which consensus data set is used, with EPS of $2.44 beating estimates and AI semis at $10.8 billion. Hock Tan declined to raise the $100 billion full-year AI chip target, and the stock dropped 13% in premarket trading. Q3 guide came in at $29.4 billion. Pat calls the miss a timing issue driven by Google's multi-sourcing across Marvell, MediaTek, and Broadcom rather than a fundamental problem. Dan flags that Hock Tan opened the earnings call by accidentally reading from the 2025 print, calling it "not the best moment." Sell-side re-ratings held in the 500s across Jefferies, Mizuho, and Deutsche Bank despite the drop, with Futurum Equities having it at 600. (Bulls and Bears) Hewlett Packard Enterprise Q2 FY26 Earnings: HPE delivered revenue of $10.68 billion, up 40% year over year, and EPS of $0.79, up 100%. Juniper integration and AI servers both outperformed, and all FY26 guides were raised. The stock jumped 19% after hours before settling into a roughly 15% gain, with HPE up 68% over the last month. Pat frames HPE as a value play rather than a volume play, methodically targeting enterprise and sovereign cloud deals where it can maintain profitability, rather than competing for massive NeoCloud volume. Antonio Neri was clear on the call that the profitability pull-forward is a one-shot deal. Pat and Dan will both be at HPE Discover the week after next to interview Neri and the C-suite. (Bulls and Bears) Palo Alto Networks Q3 FY26 Earnings: Palo Alto posted revenue of $3.0 billion, up 31% year over year, beating the $2.94 billion estimate, with non-GAAP EPS of $0.85, beating the $0.79 to $0.81 range. NGS ARR reached $8.1 billion, up 60% year over year, including $1.6 billion from CyberArk and Chronosphere. RPO hit $18.4 billion, up 36%. Both FY26 revenue and EPS guides were raised. Adjusted FCF margin came in at 38.5% TTM, up 430 basis points. The stock jumped 11% immediately after hours, then drifted lower. Pat points to 2,200 platformized customers and 120% net retention as the most important metrics. Dan notes the SaaSpocalypse thesis continues to be wrong. (Bulls and Bears) CrowdStrike Q1 FY27 Earnings and the Proprietary Data Moat Argument: CrowdStrike posted revenue of $1.39 billion with EPS of $1.10 and ARR of $5.51 billion. Net new ARR of $255.8 million set a Q1 record, up 32% year over year. FY27 net new ARR guide was raised by $52 million to a $1.29 billion midpoint, and FY27 revenue was raised to $5.915 to $5.959 billion. A 4-for-1 stock split was announced effective July 2nd. The stock dropped 11% despite the beat after a 64% year-to-date run into earnings. Dan uses the results to make a broader argument against the software disruption thesis, referencing Palantir CEO Alex Karp daring customers to build without him using Anthropic or OpenAI, and Larry Ellison's argument that the real AI value unlock sits in proprietary enterprise data that is not accessible to frontier models. Enterprises with governed, secure, proprietary data will continue to need platforms like CrowdStrike regardless of what frontier models can do. (Bulls and Bears) Six Five Summit is coming. Salesforce CEO Mark Benioff will kick off the event. Register and stay current at sixfivemedia.com/summit. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode Microsoft Declares Independence — Build 2026 Ships an End-to-End Agentic AI Stack (MAI-Thinking-1 + Scout + Microsoft IQ + Project Solara + Majorana 2) https://www.theverge.com/tech/941738/microsoft-build-2026-biggest-announcements The AI Stack Goes Multi-Silicon — Computex 2026 Confirms a Heterogeneous AI Infrastructure (ARM + Marvell + Intel ASIC + Qualcomm + RTX Spark); WSTS Raises 2026 Semi TAM Forecast 90% to $1.51T https://www.tomshardware.com/tag/computex AI Capex Has Outgrown Cash Flow — Alphabet's $80B Equity Raise Is the Largest in U.S. Corporate History; Berkshire Anchors $10B https://abc.xyz/investor/news/news-details/2026/Alphabet-Announces-Proposed-80-Billion-Equity-Capital-Raise-to-Expand-AI-Infrastructure-and-Compute-2026-b0myAMewCa/default.aspx The Network Becomes the AI Platform — Cisco Live 2026 Launches Silicon One P200, Secure AI Factory (with NVIDIA), AgenticOps, Astrix Security + Galileo https://www.cisco.com/site/us/en/about/whats-new/index.html The Flip Did Microsoft Build 2026 Effectively End the OpenAI Partnership? MAI-Thinking-1 Beats Sonnet 4.6 in Blind Testing, Microsoft Claims GPT-5.5 Parity at 10x Cost Efficiency — Will MS Quietly Wind Down OpenAI Exclusivity by FY28, or Is OpenAI Still the Frontier Anchor Microsoft Needs?   FOR:  MAI-Thinking-1 beating Sonnet 4.6 in blind preference + GPT-5.5 parity at 10x cost efficiency is a frontier-model independence proof point https://www.latent.space/p/ainews-microsoft-build-mai-thinking Build 2026: Accumulating Evidence of Microsoft's AI Independence — EDN (June 4) — https://www.edn.com/build-2026-accumulating-evidence-of-microsofts-ai-independence/ Maia 200 in production + Anthropic-Maia chip talks signal Microsoft is hedging its inference vendor stack https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/ Microsoft canceled Anthropic's internal software licenses + pivoted to chip-supply pursuit — customer-not-competitor positioning https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   AGAINST:  Enterprise Copilot customers explicitly demand GPT-5.5 — internal benchmarks don't replace the brand https://learn.microsoft.com/en-us/microsoft-365/copilot/release-notes?tabs=all MAI-Thinking-1 benchmarks haven't been third-party verified — Microsoft is the only source https://www.latent.space/p/ainews-microsoft-build-mai-thinking The MS-OpenAI partnership is contractual through 2030+ — unwinding it is impractical and expensive https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ Microsoft's actual strategic risk is OpenAI leaving, not MS leaving — Anthropic + OpenAI IPOs make OpenAI exit risk the real concern https://www.anthropic.com/news/confidential-draft-s1-sec Bulls & Bears Broadcom (AVGO) Q2 FY26 ACTUALS — Rev $22.19B (Narrow Miss) + EPS $2.44 (Beat); AI Semis $10.8B; Hock Tan Refuses to Raise the $100B Full-Year AI Chip Target — Stock −13% Premarket; Q3 Guide $29.4B https://www.cnbc.com/2026/06/03/broadcom-avgo-earnings-report-q2-2026.html Hewlett Packard Enterprise (HPE) Q2 FY26 ACTUALS — Blowout: Rev $10.68B (+40%), EPS $0.79 (+100%); Juniper Integration + AI Servers Both Outperform; FY26 Guides All Raised; Stock +19% AH https://www.businesswire.com/news/home/20260601866494/en/HPE-Reports-Fiscal-2026-Second-Quarter-Results Palo Alto Networks (PANW) Q3 FY26 ACTUALS — Beat-and-Raise: Rev $3.0B (+31% YoY, Beat $2.94B), Non-GAAP EPS $0.85 (Beat $0.79-0.81); NGS ARR $8.1B (+60% YoY, $1.6B from CyberArk + Chronosphere); RPO $18.4B (+36%); FY26 Revenue + EPS Guides BOTH RAISED; Adj FCF Margin 38.5% TTM (+430 bps); Stock +11% Immediate AH, Then Drifted Lower https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-reports-fiscal-third-quarter-2026-financial-results CrowdStrike narrowly beats estimates on AI tailwinds, but stock falls 9% — CNBC (June 3) — https://www.cnbc.com/2026/06/03/crowdstrike-crwd-q1-2027-earnings.html  

MVP Tom Arbuthnot shares all the latest Microsoft Teams and Copilot news and announcements in less than 15 minutes for June 2026. Many thanks to Pure-IP for their continued support. The Copilot Super App Is ComingScout and AutopilotsNVIDIA-Powered LaptopsWindows Optimisations for Open Claw and Local AgentsWork IQ and Web IQ APIs GAProject Solara — Agent-First Hardware Devices, Powered by MDEP7 New Microsoft AI ModelsTeams Devices NewsLink to the deckUseful Links: Briefings • 4 Ways to Improve Customer Experience With Landis Contact Center for Microsoft Teams • First Look at the Neat Board 32 Microsoft Teams Rooms With MDEP • The Lenovo Microsoft Teams Rooms Portfolio Explained and the Huddly Partnership • Decoding Dynamics 365 Contact Center: Microsoft Teams Voice, New AI Agents, and Licensing Teams Insider Podcasts • Microsoft Teams Facilitator: The First Group AI Agent for Meetings With Madhu Sudan, Microsoft • Microsoft Teams Call Quality Dashboard (CQD): Intelligent Classifiers, Silent Test Call and Power BI • Microsoft 365 Message Center and M365 Change Explained with Brian McGough, Principal Program Manager • Microsoft 365 Agents Explained - Declarative, Copilot Studio, Pro-Code or Skills in Copilot Cowork? Microsoft Build 2026: Be yourself at work GitHub Copilot app: the agent-native desktop experience What's New in Microsoft 365 Copilot — May 2026 Microsoft 365 Copilot release notes Introducing Microsoft Scout: your always-on personal agent Project Lobster is Microsoft Scout Introducing Surface Laptop Ultra: Made for world makers Surface Laptop Ultra product page Windows platform security for AI agents Announcing the new Work IQ APIs Work IQ: production-ready intelligence for every agent Announcing Microsoft Web IQ Composing a new platform for agent-first devices Microsoft Device Ecosystem Platform documentation Microsoft Build 2026 Live blog Building a hill-climbing machine: launching seven new MAI models Build, scale, and monetize apps and agents with Microsoft Marketplace Certified Android-based devices for Microsoft Teams More choice and flexibility: Cisco Board Pro Certified for Microsoft Teams Rooms Q-SYS QSP-11 Scheduling Panel

https://novacut.ai/  https://genaimeetup.com/  Anthropic has officially closed a $65 billion Series H at a $965 billion valuation, nearly 2.5x its valuation from just 100 days ago. Meanwhile, funding is flowing across the ecosystem: Frameworks AI at $15B, Baseten at $11B, OpenRouter's $113M Series B, and Cognition AI's $1B Series D. NVIDIA went on an open-source super week with Nemotron 3 Ultra, Cosmos 3, and Nemotron 3.5 ASR. Microsoft dropped 5 new MAI models. Google released Gemma 4 12B, and Anthropic shipped Opus 4.8. On the benchmarks front, DeepSWE crowns GPT-5.5 as the leader in long-horizon coding tasks, while ITBench shows even frontier models struggle with real-world SRE incidents — Claude Opus 4.7 tops out at just 47%. Plus: Cloudflare acquires VoidZero to build the future of AI-native edge development, and Google is paying SpaceX $920M/month for compute. Topics covered: • Anthropic's $65B Series H and path to $1T • Fireworks AI, Baseten, OpenRouter & Cognition funding rounds • Microsoft's 5 new MAI models • NVIDIA's open-source super week (Nemotron, Cosmos 3) • MiniMax M3, Gemma 4 12B, JetBrains Mellum2, Opus 4.8 • DeepSWE benchmark: GPT-5.5 leads long-horizon coding • ITBench: Frontier models under 50% on real SRE tasks • Cloudflare + VoidZero for AI-native edge dev • Google's $920M/month SpaceX compute deal #AI #Anthropic #NVIDIA #OpenAI #AInews #TechNews #LLM     Funding rounds Anthropic formally confirmed the closure of its $65 billion Series H funding round at a post-money valuation of $965 billion. This represents a 2.5-fold increase over its $380 billion Series G valuation from February 2026, adding $585 billion in value in approximately 100 days https://www.anthropic.com/news/series-h  Frameworks AI raising at 15B valuation representing a near fourfold increase from its $4 billion Series C valuation recorded in October 2025 processing 15 trillion tokens daily for major production clients including Cursor, Notion, and Perplexity https://finance.yahoo.com/sectors/technology/articles/fireworks-ai-eyes-15-billion-174609357.html Baseten is raising 1B at 11B valuation annualized revenue, which skyrocketed from $200 million to $600 million over a single quarter https://techstartups.com/2026/05/26/ai-inference-startup-baseten-in-talks-to-raise-1-billion-at-11-billion-valuation/  OpenRouter has secured a $113 million Series B funding OpenRouter has experienced exponential traffic growth, with weekly production throughput expanding fivefold from 5 trillion to 25 trillion tokens over a six-month horizon https://www.businesswire.com/news/home/20260526953416/en/OpenRouter-Raises-%24113-Million-CapitalG-led-Series-B-as-Weekly-Volume-Explodes-to-25T-Tokens  Further up the stack: Cognition AI secured a $1 billion Series D round led by Lux Capital and 8VC https://cognition.ai/blog/series-d   Model Releases MAI models: MAI-Code-1-Flash: A 5-billion active parameter model optimized for ultra-low latency within GitHub Copilot and VS Code. MAI-Image-2.5: A high-fidelity image generation model ranking third on global image evaluation arenas, outperforming competing architectures like Nano Banana Pro. MAI-Transcribe-1.5: A multi-lingual speech processing engine offering fivefold speed improvements across 43 languages. MAI-Voice-2: Natural audio and voice generation across 15 languages, available at a highly competitive price point. Web IQ: A search-grounding API engineered to directly compete with Perplexity. https://microsoft.ai/models/    https://www.peoplematters.in/news/ai-and-emerging-tech/uber-imposes-dollar1500-monthly-ai-spending-limit-on-employees-amid-rising-costs-50073    Nvidia has executed an "Open-Source Super Week," positioning itself as a dominant software and model publisher: Nemotron 3 Ultra (best US open source open weights model but behind china): A massive 550-billion parameter MoE (55 billion active) designed with a 1-million token context window, optimized specifically for high-throughput, cyclical agent loops. It achieved peak throughput rates of 400 tokens per second on day-zero optimized clusters. Cosmos 3: A physical AI world-modeling framework comprising 16-billion Nano and 64-billion Super variants. Built on a Mixture-of-Transformers (MoT) architecture, Cosmos 3 natively binds textual, visual, auditory, and physical kinetic vectors. Nemotron 3.5 ASR: A highly compact 0.6-billion parameter streaming speech recognition model pushing sub-100 millisecond latencies across 40 language locales.   https://www.minimax.io/models/text/m3  MiniMax M3: A 1-million token context model hitting 59.0% on SWE-Bench Pro and 74.2% on MCP Atlas, though noted for high token consumption due to intensive internal self-validation loops.   https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/  Gemma 4 12B: Google's Apache 2.0 on-device model, which utilizes an encoder-free architecture that projects vision and audio vectors directly into the text-token space, bypassing separate CLIP-style encoders to minimize local memory footprints. https://www.jetbrains.com/mellum/  JetBrains Mellum2: A compact 12-billion parameter MoE (2.5 billion active) engineered for ultra-low latency routing and retrieval-augmented generation (RAG) sub-agents within developer IDEs. Opus 4.8 https://www.anthropic.com/news/claude-opus-4-8    https://www.cnbc.com/2026/06/05/google-to-pay-spacex-920-million-a-month-for-xai-compute-capacity.html      Benchmarks: https://deepswe.d atacurve.ai/blog https://venturebeat.com/technology/deepswe-blows-up-the-ai-coding-leaderboard-crowns-gpt-5-5-and-finds-claude-opus-exploiting-a-benchmark-loophole (GPT 5.5 the winner in long horizon tasks) a highly complex software engineering benchmark focused on original, long-horizon tasks across five distinct programming languages. Comprising 113 chaotic tasks across 91 live, production-grade repositories, DeepSWE forces agents to generate 5.5 times more code and modify an average of 7 separate files per task compared to standard evaluations. On this challenging leaderboard, GPT-5.5 leads with a score of 70%, establishing a significant 16-percentage-point lead over contemporary alternatives I think older benchmarks where models reach ~90% accuracy can be considered saturated. Few percentage points don't give us any good signal.  https://research.ibm.com/publications/developing-ai-agents-for-it-automation-tasks-with-itbench  ITBench-AA, an evaluation framework focusing on live Kubernetes incident response and Site Reliability Engineering (SRE) operations. Comprising 59 live, containerized SRE incident snapshots, the results are remarkably sobering: every frontier model scored under 50% on successful incident resolution, with Claude Opus 4.7 leading at 47% and GPT-5.5 following closely at 46%.   Edge AI announcements: https://www.cloudflare.com/press/press-releases/2026/cloudflare-acquires-voidzero-to-build-the-future-of-the-ai-native-web/  The consolidation of the AI-native developer stack has reached the runtime virtualization layer. Cloudflare recently completed the acquisition of VoidZero, the development group responsible for Vite, Vitest, Rolldown, and Oxc, backing the transaction with a $1 million open-source ecosystem fund. This acquisition is highly strategic; as autonomous agents write an increasing proportion of production software, local development environments, compilation pipelines, and bundlers must be optimized for execution speeds that match agent speeds. Cloudflare's goal is to construct a localized, full-stack edge playground. In this sandbox, AI agents can generate, test, bundle (utilizing the highly parallelized, Rust-based Oxc and Rolldown engines), and deploy entire web applications end-to-end within milliseconds. This architecture completely bypasses traditional local machine container bottlenecks, enabling high-velocity agent loops to execute in a fully sandboxed, web-scale edge runtime.

✅ New autonomous agents. ✅ Canva designs made for you. ✅ Codex upgrades to make your business move. If you had your head down in spreadsheets this week, you missed some MAJOR AI upgrades that are available now. We track what's hot and what's not and break it all down on Fridays with our Friday Features. Autonomous Copilot agents, new Codex tools, Github CoPilot app and 7 more AI updates you should be using — An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Codex Role-Specific Plugins LaunchMicrosoft Build Conference AI Feature ReleasesChatGPT Memory and Business Account UpgradesMicrosoft Flash Image Model for PowerPointCanva Integrated with ChatGPT and CodexGitHub Copilot Standalone Desktop App PreviewMicrosoft Autopilot Always-On Work AgentsOpenAI Models Now Available on AWS BedrockCodex Sites: AI-Built Internal Web AppsTimestamps:00:00 OpenAI's big money moves03:47 Explaining role-specific plugins09:02 Microsoft's new image model release11:09 Microsoft's AI strategy and Canva update14:23 Canva integration with ChatGPT16:56 GitHub Copilot's new canvas feature20:46 AI token subscription changes24:42 AWS adds OpenAI models to Bedrock28:25 Introducing OpenAI's CodeX Sites Feature32:07 Launch of OpenAI's New Plug-in34:16 Overview of podcast structureKeywords: Autonomous copilot agents, Codex tools, GitHub Copilot app, OpenAI Codex, ChatGPT business accounts, OpenAI enterprise, Microsoft Build conference, Microsoft always-on agents, AWS AI updates, Canva plugin, ChatGPT memory upgrade, Windows Codex integration, Microsoft Flash model, Enterprise apps integration, Role-specific plugins, Sales data analytics, Product design AI, Creative production AI, Investment banking plugin, Public equity investing, Data analytics plugin, Workspace admins, App permissions, Role-aware work agent, Financial research automation, Microsoft image generation model, PowerPoint AI integration, OneDrive AI features, Visual design creation, Canva app for ChatGPT, Canva MCP server, Agentic context carry, Full screen design preview, GitHub Copilot desktop app, GitHub Copilot Canvas, Agent-native command center, Parallel agent work tree, Code app interface, Model options in GitHub, Token usage limits, Subscription token subsidizing, Anthropic token efficiency, Amazon Bedrock, GPT-4, GPT-4.5, Small language models, Token reckoning, Security governance, Inference engine, Code app sidebar, Codex Sites, Internal dashboards, Project trackers, Interactive web apps, Shareable AI apps, Enterprise data connectors, ChatGPT Canvas, Automated workflow, Workplace authentication, Creative briefs repository.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

In episode 292 of our SAP on Azure video podcast we talk about Arc-1!Just last week SAP releaed the ABAP Development Tools for VS Code which means that ABAP developers can now officially use VS Code for their development. While this is really great and I am also starting to test it, I have to admit that for me the way how ABAP development works has moved on and away from Eclipse. My colleauge Alice had released VSP - Vibecoding for Steampunk - and what happend afterwards was truly amazing. The community picked up a lot of things and enhanced it with MCP Servers, new ways to integrate in GitHub Copilot and Claude Desktop appeared -- and then someone published an MCP Server that uses the SAP ABAP Development Tools (ADT) to connect to your SAP system. It gets even better: it can run on the SAP Business Technology Platform which means that in a lot of cases you can use your existing infrastructure including the SAP Cloud Connector to get started. I am really glad to have Marian Zeis, the developer behind Arc-1, back with us, to talk about it. Check out the Repo for Arc-1 here: https://github.com/marianfoo/arc-1Find all the links mentioned here: https://www.saponazurepodcast.de/episode292Reach out to us for any feedback / questions:* Goran Condric: https://www.linkedin.com/in/gorancondric/* Holger Bruchelt: https://www.linkedin.com/in/holger-bruchelt/ #Microsoft #SAP #Azure #SAPonAzure #VPS #SAPADT #Eclipse #VSCode #CopilotStudio #ABAP

Welcome to Episode 429 of the Microsoft Cloud IT Pro Podcast. In this episode, Scott and Ben dig into the concept of LLM wikis, specifically building personal knowledge management vaults using Obsidian, markdown, and AI tooling like Claude Code, GitHub Copilot CLI, and Copilot Cowork. The core idea comes from a gist by Andrej Karpathy and involves creating a structured folder of markdown clippings that an LLM can reason over to extract entities, concepts, and sources, building a searchable, graph-linked knowledge base over time. Scott walks through how he wired up Obsidian Web Clipper and an RSS Dashboard plugin to feed articles into his vault automatically, then had the LLM help build a Python script to automate the ingest workflow and cut down on token usage. The conversation expands into how Copilot Cowork fits into this workflow as a scheduling harness, with practical examples of using it to pull email from an inbox daily, convert messages to markdown, and generate a prioritized to-do list. Ben shares how he applied the same approach to 428 episodes of podcast transcripts, and both hosts note that token costs can run high fast without some upfront thinking about optimization. Scott closes with a reminder that pulling data into plain markdown sidecars outside of IRM and sensitivity label protections means teams should stay mindful of organizational data policies. Your support makes this show possible! Please consider becoming a premium member for access to live shows and more. Check out our membership options. Show Notes LLM Wiki GitHub Copilot Wiki: An AI-Powered Second Brain Template Karpathy’s LLM Knowledge Base Wiki for Enterprise Karpathy’s LLM Wiki? No Code with Claude or Github Copilot! sametbrr/llm-wiki-manager Sponsors TrustedTech is a leading Microsoft Cloud Solution Provider (CSP) specializing in Microsoft Cloud services, Microsoft perpetual licensing, and Microsoft Support Services for medium and enterprise-sized businesses. Their robust team of in-house, U.S.-based Microsoft architects and engineers are certified in all 6/6 Microsoft Solutions Partner Designations in the Microsoft Cloud Partner Program. M365 Licensing Consultation M365 Tenant Assessment Copilot Readiness Assessment ShareGate is your migration and governance solution for Microsoft 365. ShareGate helps your teams simplify tenant migrations, get Copilot-ready, and take control of Microsoft 365 governance. Nasuni is a leading unstructured data platform for enterprises where file data is mission-critical for both people and AI. Nasuni powers the operational file layer where work happens — helping organizations manage, protect, and activate data so teams can work smarter, reduce costs, and operate securely without limits. Intelligink — Would you like to become the irreplaceable Microsoft 365 resource for your organization? Let us know!

Microsoft has announced a series of updates at its Build 2026 conference, introducing a new platform for AI agents, seven new in-house AI models and a range of developer platform capabilities designed to support a new era of "ubiquitous intelligence". The company said the announcements are focused on enabling developers to build, deploy and manage intelligent systems with greater flexibility, control and security, while meeting enterprise requirements for governance and trust. Central to the updates is the new Microsoft Agent Platform, which allows developers to build agents using organisational context through Microsoft IQ, deploy them via Microsoft Foundry and access them across Microsoft Teams and Microsoft 365. Microsoft said the platform is designed to reduce trade-offs between context and governance, security and speed, and between models and tools. Microsoft also announced that Microsoft IQ is now generally available across GitHub Copilot, Microsoft Foundry and Copilot Studio, providing a unified context layer across enterprise and external data. New capabilities include Work IQ, which captures how work happens across Microsoft 365, organisational systems and external sources, and Web IQ, an AI-first web search stack announced at Build that delivers real-time grounding for agents. Alongside the platform, Microsoft unveiled a new family of seven in-house AI models, including MAI-Thinking-1, its first reasoning model optimised for complex, multi-step tasks. Additional models span image generation, transcription, voice and coding, reinforcing what Microsoft described as a multi-model ecosystem. The company also introduced new tools across the stack, including Microsoft Execution Containers, now in preview, which provide secure, operating-system-enforced sandboxes for agents. The Foundry Agent Service, also in preview, for cloud-scale managed agent deployment; and the GitHub Copilot app, in preview, which brings agent-driven development workflows to a native desktop experience. Beyond software development, Microsoft highlighted applications in scientific research through its Microsoft Discovery platform, which is now generally available as an enterprise AI solution for the full scientific workflow. The company also outlined progress in quantum computing with its next-generation Majorana 2 chip, citing significant improvements in qubit reliability and a path towards a scalable quantum system later this decade. Microsoft said these advancements aim to position developers at the centre of innovation in the AI era, giving them greater agency to build intelligent systems with enterprise-grade controls and trust. See more stories here.

Send us Fan MailIn this jam-packed “mini” episode, Amith Nagarajan and Mallory Mejias break down a whirlwind of recent AI model releases—from Anthropic, Alibaba, Microsoft, and beyond—and what they signal about the rapidly evolving AI landscape. Then, they dive into Microsoft's 2026 Work Trend Index Report, unpacking the “agency equation” and what it really means for organizations navigating AI adoption. From the rise of agents and the four modes of working with AI to the growing gap between employee readiness and organizational culture, this episode explores why AI transformation is less about tools and more about leadership, systems, and mindset. Plus, they introduce the concept of “owned intelligence” and what it takes to become a true learning organization in the age of AI. 

We've informally heard that Satya is a listener to LS for a couple years now, but it was still absolutely surreal to meet him and do a live pod at Build, together with our friends at No Priors, the leading VC AI Podcast that we also greatly admire!We covered the MAI model technical takeaways on yesterday's AINews, so I will focus our recap of Satya's main messages around three elements:* Satya's adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft's position on this as the ur-SaaS titan, and Satya had great answers* Making the Impossible Possible: Kevin Scott's inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.Enjoy!Full VideoTranscriptVoiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya NadellaSarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it's great to be with both of you. I listen to both of you or b- both the podcasts all the time. It's great to be on it.Thank you so much. [00:01:00] So you're just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what's the most important reflection or takeaway you have?AI as an Ecosystem PlatformSarah Guo: I, I'd say there are, uh, perhaps the, the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you, you view what's happening right now, I think this morning's keynote was how can any company, whether it's an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.MAI Models & Training StrategySarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it's becoming even harder to build a clean lineage model just because there's so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.In fact, that's one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they're not great on practice. So that's why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?So it's not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you'll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they're not really that critical- They're work, yeahSwyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there's a new frontier.Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let's say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I'm wondering, you know, you have all this AI strategy that you're rolling out.Lessons from Two Years of AI DevelopmentSwyx: I'm wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we're gonna really throw a lot of computer transformers.”Satya Nadella: Uh, and they've helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they're climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it's very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.Sarah Guo: Because right now I think when people say, “Wow, I don't want a token max,” it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that's kind of what I wish we had gotten there, but I'm glad we are here.Real-World Value & Use CasesSarah Guo: What are some of the use cases that you've seen that have created the most value for your customers?Because I know that people talk a lot about code, and I think it's pretty clear that that's something that's having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it's interesting, by the way, Elijah, to even talk about the coding, right?Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it's kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that's why we need a canvas.So it's kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I'm positive that six months from now we'll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?I can... Sort of given even my identity, did a bunch of work, then of course I'll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that's where compressing of workflows, uh, completing of tasks, uh, that's where I think a lot of the value gets created. I think you raised a really interesting point, which is there's the actual agent that's doing the code, and then there's a harness around it, and that's the environment, that's the context, that's everything you're setting up as a developer around actually a coding agent.The Harness Concept for Enterprise AISarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That's right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it's GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn't matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they're token efficient.Uh, and then you're feeding it with very rich context because that's sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we're using across all our products.It's available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that's the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we're gonna have these models, and we'll have an API business, and we'll support enterprises and startups.Sarah Guo: ButPlatform Strategy & Developer EcosystemSarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That's a different value equation than you're describing, I think, with the Microsoft ecosystem. Uh, if, if that's the case, tell me if it's the case, uh, ‘cause obviously you have first-party products and you have enablement products.Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there's always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.It's just a few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company, having private evals may be the biggest IP, right? Think about it, like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.Like, so in other words, another te- acid test is you have an eval that's private. You're using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you're in control. If you can't, you're not in control, and that's where even the harness decision becomes super important, right?swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.Maybe like the third act is that you're a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?Satya Nadella: That's it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That's it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?To me, that is so important because otherwise it, I, I don't know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what's a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that's not a developer conference. Uh,IP, Evals & Company Valueswyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it's this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.Satya Nadella: It's a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?I mean, so I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That's so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn't know how to capture the tacit knowledge.swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that's what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you're talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.Future of SaaS & Business ModelsSarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they're differentiated against.Elad Gil: Um, I'm sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what's happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that's kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.I don't need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That's right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That's business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?I mean, if you look at it, d- what's happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there's a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that's all gonna move to the cloud.”But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...Sarah Guo: For example, there's going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that's sort of what we are doing.Pricing Models: Per-User, Consumption & OutcomesSarah Guo: I don't believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we've had... Like, let's even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it's the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.Satya Nadella: And, and per user is just a set of entitlements to usage, right? That's kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.So people will say, “I want consumption.” And it's also possible that people will say, “I don't even want to pay for any of the subscriptions or the consumption's outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it's like giving away royalty, [00:21:00] right?Mm. I mean, like I, I've talked to customers who love, you know, outcome-based pricing, and I say, “I'm all in,” until they, “Oh my God,” like, “what are you talking about? You're sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you're a SaaS vendor or you're a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.Durability of SaaS & Build vs BuySarah Guo: How do you think about the durability of SaaS more generally? One thing I've observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They're so excited about the explosion of things they can build that they're trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We're not gonna work with you anymore,” or, “We're considering an internal project.”And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can't rebuild everything.” How do you think about what's durable in this world and what isn't? Yeah, it's a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there's marginal cost to even generating the app, right?Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it's a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there's a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It's kind of like a, a cycle that you've got to think through.And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?Sarah Guo: Because I think there'll be very little tolerance for anybody who's inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We're selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...Swyx: There was a section of you building your [00:24:00] own software. I'm curious if you're building anything now. Yeah. So I, I think the... You know, first of all, let's face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn't touch before, right?Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler's, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn't mean every one of us should be doing the same thing.The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I'm building a lot of is these long-running Foundry agents. Uh, right? So there's autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?We're gonna have that obviously in, uh, Scout. That's what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,Future Engineering RolesSarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there's, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.You know, there's a big swath. I've heard some people argue that in four or five years we'll basically end up with four engineering roles. It'll be people who are managing agents, it'll be four deployed engineers or FDEs, it'll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.Satya Nadella: Yeah, I- Do you think that's a correct view of the world? Yeah, I mean, I think, I think we'll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?So they went and said, “Hey, let's bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It's not like the design person still doesn't have the design edge, or the front end [00:27:00] person doesn't have the front end edge, but you can give yourself bigger scope in roles so that you're not confined to one role.Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we've realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it's a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we'll see how these evolve, right? Where's the s- real... I mean, always the world will have a bunch of specialists.Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I'm now a gen- Like, what... I've basically translated [00:28:00] knowledge work Right?Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It's in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we're gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea peopleSarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it's an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, butAmbition & Making the Impossible PossibleSatya Nadella: how do you think about how Microsoft can be more ambitious now? It's a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you're making hard things easier, that's sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?What was impossible and what can we build? And I'll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.I mean, it's crazy. Wild. Yeah. Right? It's pretty wild. And it's the same team. So they saw that and they said, “Bob, this just ain't gonna work if we don't reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.So they built this agentic system. They even have a character for it. It's called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don't need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would've been, we need 4 billion typists?But we're not doing typing, we're doing knowledge work. So that, to me, I think is it, right, which is whether it's Microsoft or whether it's any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.Data Center Build-Out & Community ImpactSarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I'm just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you're seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it's great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it's clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we're talking about are felt in real ways, uh, at the community level, right?Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it's bringing down prices because long term there's going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what's happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.What's the tax base that's there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won't have permission. It's as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it's good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.Um, but at the end of the day, if there's-- if we can really be the produ-- Wait. I've always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don't [00:34:00] do that, it's not been that great. And this time around, I'm a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we'll be in a great place.Sarah Guo: Uh, and that's at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you're doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?Satya Nadella: That's I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It's sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can't be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.Elad Gil: Yeah. I guess I wanna talk aboutSocietal Impact & Optimism About AIElad Gil: what you're most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you're saying what's the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.Satya Nadella: Right? That's kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There's going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it's the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.It's just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can't wait. See, the one thing, Eli, that I've now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we've got it. The g- future is gonna be glorious.”Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it's too important this time around. It's too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yepit's [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,Education & Future of LearningSarah Guo: education seems like another one that's an- Yep ... obvious good where we haven't seen as much impact as I'd expect.Swyx: Do you have a hypothesis on why that might be, or if it'll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it's fascinating to listen, uh, to how to even rethink- MmSatya Nadella: uh, what does education really look like. Because I think it's actually very important. Mm. Uh, and I'm not saying anything traditionally being done is less important, right? I was even looking at the, uh... It's fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It's going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?So I think that there's a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that's highly valuable.Well, that has felt, uh, perhaps impossible for a long time, but it's a great note to end on and something that might be possible. It's still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

An airhacks.fm conversation with Bruno Borges (@brunoborges) about: discussion about the JAZ command launcher for Java, JVM tuning and default ergonomics for containers versus dedicated cloud environments, replacing the Java launcher with jaz in container images, supporting Java 8 to 25, maximizing resource utilization on kubernetes to reduce waste, running Java on Azure Functions, Azure App Service deploying a fat JAR without a container image, Azure Container Apps as a platform on AKS without YAML, Azure Kubernetes Service and AKS Automatic, Bicep as infrastructure as code, deploying a JAR to Kubernetes via OCI artifacts and a custom operator, Microsoft Foundry and the Microsoft Agent Framework, Semantic Kernel learnings, the Copilot SDK for Java communicating with headless CLIs, A2A and ACP protocols and MCP, agents as microservices with scoped tasks, guardrails, and sandboxing, per-agent model selection for cost and reasoning trade-offs, observability and traceability between agents with opentelemetry, grounding LLMs against MicroProfile, Jakarta EE, JAX-RS normative RFC 2119 specifications for hallucination-free Java code generation, the Boundary Control Entity pattern and business components as Java packages, package-info.java for semantic context, GitHub Copilot skills and custom instructions in Visual Studio Code, the AI Rails skills site, zero-dependency Java CLI scripting, reducing dependencies by reusing source code instead of JARs, the org.json reference implementation reduced to five classes, StackGres and OnGres running Quarkus and GraalVM to manage Postgres on Kubernetes, the Digg Into Java community Bruno Borges on twitter: @brunoborges

A fundamental structural shift underway is the movement of AI from isolated features to operationalized, production-level workloads in MSP tooling and client environments. This transition is not primarily about the capabilities of individual AI models but about their integration into existing operational platforms and workflows. Companies such as PDQ, Senteon, Domotz, and Zoom are incorporating AI agents directly into management layers, endpoint automation, and workflow orchestration, thereby increasing both the scope and complexity of AI impact. The locus of value is shifting from features to workflow control and integration, creating new demands for governance, consumption monitoring, and exit strategies. The most consequential development referenced is the transition in AI billing and operational models from static user or seat licenses to variable, usage-based consumption. He cites TechCrunch's coverage of GitHub Copilot's move to token-based billing and Semafor's reporting of Uber's rapid exhaustion of its 2026 AI budget in four months due to unbounded consumption by generative tools. F5's State of Application Strategy report is referenced to confirm that multi-cloud and parallel model operations are now common, with significant instances of AI-related security incidents already reported. Secondary developments reinforce this structural realignment of risk and accountability. PDQ, for instance, is expanding multi-tenant management and integration capabilities, while Senteon enables endpoint hardening and drift control directly in Rewst's platform. Domotz's MCP server allows AI agents to operate across 40,000 networks globally, and Zoom is packaging AI context protocol features for workflow automation. Each of these changes is designed to increase operational efficiency, but also expand the surface area for unintended consequences, elevated operational complexity, and potential budget overruns. For MSPs and IT leaders, the operational implications center on governance, spend control, and clear accountability over AI-driven tools and workflows. The risk is that without adequate monitoring, policy setting, and contractual clarity—especially around data portability and exit costs—MSPs may face liability for unplanned consumption, misconfigured automation, or governance gaps. The evidence indicates the need to proactively audit AI integrations, set usage thresholds, instrument logging and budgeting controls, and renegotiate vendor contracts to ensure service boundaries and oversight mechanisms are in place before workflows become too deeply embedded. 00:00 MSP Stack Resets  04:09 AI Needs Governance 06:45 Govern AI or Pay 09:22 Why Do We Care?  Supported by:  Nerdio Zero Networks   

I'm excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World's Fair! We'll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn't just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale. We go deep on GitHub's internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub's history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.Full Video PodWe discuss:* Kyle's expanded role across GitHub* How AI got Kyle coding again after years in leadership* Why GitHub rolls out AI through existing workflows instead of forcing new tools* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context* Why massive “mega-skills” are giving way to small, atomic micro-skills* How AI changes summarization, communications, marketing, and analyst work* Why former developers in leadership may have a unique advantage in the AI era* Kyle's “15 agents on Saturday” workflow* How Kyle built an AI-generated executive presentation for CRO/CFO teams* Why AI changes the chief of staff role without removing the human work* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute* The npm acquisition, supply-chain security, 2FA, and token invalidation* Slop forks, vendoring, and whether AI agents change dependency management* What pull requests become when most PRs come from agents* Prompt requests, vouching, AI review, and trust in open source* What counts as a “developer” when AI lowers the barrier to building* GitHub Spark, low-code, and why GitHub refuses to hide the code* 14x commit growth, Actions load, databases, monorepos, and availability* Copilot's evolution from completion to CLI, desktop app, cloud agents, and SDK* Context, memory, rules, and making GitHub “act like Kyle wants it to act”* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents* What swyx should ask Satya Nadella about Microsoft's AI futureKyle Daigle* LinkedIn: https://www.linkedin.com/in/kyledaigle* X: https://x.com/kdaigleTimestamps00:00:00 Introduction00:03:36 Why AI Got Kyle Coding Again00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills00:15:39 The Golden Age for Former Developers in Leadership00:17:31 15 Agents on Saturday and AI-Generated Executive Work00:20:20 How AI Changes the Chief of Staff Role00:21:45 GitHub's History: Actions, npm, Webhooks, and Open Source00:28:45 Slop Forks, Vendoring, and AI Dependency Management00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code00:47:38 GitHub's Hardest Era: 14x Growth, Reliability, and Scale00:59:21 Actions as the Compute Layer for CI/CD and Automation01:02:04 The State and Future of GitHub Copilot01:08:24 Ambient AI, Background Agents, and the Future of the SDLC01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context01:21:41 What Should swyx Ask Satya?TranscriptIntroduction: Kyle Daigle's Expanded Role at GitHub and MicrosoftSwyx [00:00:00]: We're here with Kyle Daigle, COO of GitHub. Welcome.Kyle [00:00:07]: Hey, thanks for having me.Swyx [00:00:08]: You're not just CEO of GitHub. People know you as that. You have a new role.Kyle [00:00:11]: So I have an expanded role now. I've been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I'm also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they've had with GitHub over the years. So it's a different role in some ways, but it's also just building on the experience that I've had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.Swyx [00:01:09]: We'll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it's appropriate. I think one of the interesting things is I rarely meet a COO who's also a CMO. I think you're a very outward facing and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's What is your thing?From GitHub Developer to COO/CMO: Building the Platform and Operating GitHubKyle [00:01:33]: I think for me, it's been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of theSwyx [00:01:46]: Let's bring that up. You wrote the back ends?Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that's kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub's always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I've, I still code. I love coding but the problem has always been, people. It's a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we're building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what's kept me at GitHub for so long.AI Workflows for Leadership: Commits, Retrospectives, and ContextSwyx [00:03:40]: Apparently, you have— your commits have gone up. What's this? What's going on?Kyle [00:03:45]: Rui's called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what's driving this. So that's, some of it's writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone's tools, But building for me, building for the non-technical leader, though I'm technical and how we're, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it's not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week's messaging actually was. That's something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It's actually, a recursive loop backwards. I'm always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn't work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I've built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.Swyx [00:06:47]: Of course. That's where, that's where, stuff is hosted. Man, there's so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You're, you're understanding what happens. When you say we That's three thousand people. How?Rolling Out AI Internally: Skills, CLIs, and Company ContextKyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don't want to have to teach you a tool. I don't want to have to teach you something new. And so for us, we tried out a few tools. Most of them don't work because I got to get you on board? I got to teach you how to use it. What we've actually ended up doing is we've built like a set of skills internally. We have we each have our set of skills, and we've just been distributing even to the non-technical folks, the CLI. And then effectively, we're just giving it access to like read about everything that we're writing. So that's for us, that's usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.Swyx [00:08:03]: Teams and Slack?Kyle [00:08:04]: so we use Teams for video communication, but we don't use it for chat. W-we— GitHub for a long history, right? We're alwaysSwyx [00:08:13]: Also SlackKyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.Swyx [00:08:18]: So even though you have been acquired for I don't know, eight years nowKyle [00:08:22]: we stillSwyx [00:08:23]: You still use Slack?Kyle [00:08:23]: it's a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don't work the same way at all. We still use a bunch of different tools Because it's the purpose-built tools that We need. And thenSwyx [00:08:47]: Well, the same doesn't go for the rest of Microsoft, presumably.Kyle [00:08:50]: like the like various teams like operateSwyx [00:08:53]: They make their own decisionsKyle [00:08:54]: Various ways. I think it just matters what you're trying to what you're trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely. There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what's happening this morning, today, yesterday. A little automation gets run. We'll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it's about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it's also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it's just like enabling everyone with that power of it's going to take me a week to figure this out. Instead, we're going “Okay I built a skill. Let's put it into a repo. We'll all share that skill together, and then we'll use the CLI or now the app-” “just to run it.”Micro Skills vs. Mega Skills: How GitHub Uses AI at WorkSwyx [00:10:26]: All right. I think, I think we're going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they're Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.Kyle [00:10:50]: like I think we haveSwyx [00:10:52]: And I assume it's a mess a Yeah.Kyle [00:10:54]: there's like I— like I think the reality is there's two pieces. Like first is I think that we're ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we've found and what— I was just with my team, this week, and we were talking about the skill side, and we're really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that's going to do I said, that full report. That doesn't really exist on our side anymore. It's usually how do— like a single skill that's going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweakSwyx [00:11:58]: It's brittleKyle [00:11:58]: Your mega skill and you're screwed? You can't do that. And so now we're really just talking about the Legos we're using and just letting the instruction book be something we're all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.Swyx [00:12:15]: I've, thought a lot about Postel's law. I don't know if that's a term that is, means things to folks. It's the idea that you should be liberal in what you accept and strict in what you output, right? And I think that's like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?Kyle [00:12:56]: I like I think so. I think that theSwyx [00:13:00]: Summarize anything.Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I'm summarizing something for an analyst, that's a very different thing than, probably how I'm going to summarize something for like a customer meeting or an engagement. So that's I think like the difference when we're talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it's just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we're talking about work and enabling the the marketers, communicators there, it's the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that's where I think the matrix mess is that we're starting to like still starting to find. It's about these mega skills but they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I'm giving a briefing to Gartner,” or like whatever else.Swyx [00:14:37]: I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don't need them anymore ‘cause now you just use skills.Former Developers in Leadership: AI as a Creation MultiplierKyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there's just something more magical about getting a response back and being “That's not right,” and then you just crack the skill open, you just type English words and it's different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I'm feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you're maybe not too close to the details. Doesn't matter. But like you're more effective than someone who doesn't come from that background.Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don't code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I'm now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don't want to break into jail or create something that's not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that's true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There's really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That's like really powerful. And I think it gets me back to that feeling of like creation, and it's very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that's magical. And so being able to do that not just in code, but across all kinds of different assets that's, that's huge. We were doing we're doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there's, slideshows everywhere talking about what are we going to talk about, what's the narrative, et cetera. And so as you said I'm “Okay, well, I could probably just like build something to build this and then that way I don't have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I'm just going to present this to our CRO, the CFO, their teams,” without mentioning I'd built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.AI-Generated Presentations, Human Taste, and the Changing Chief of Staff RoleSwyx [00:19:03]: Like a design. Yeah.Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don't do anything interesting.Swyx [00:19:08]: That's, yeah, that is valuable.Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.Swyx [00:19:20]: It didn't matter.Kyle [00:19:20]: Never once. D It didn't matter. And so now I takeSwyx [00:19:23]: This is a toolKyle [00:19:23]: I can take that tool and go, “Look, I don't want you to go build slideshows.” They're just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There's no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that's all a people, that's all a people problem. I know if you've used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.Swyx [00:20:07]: I know, but like it was so, I think there's a certain charm to just being blatantly AI. ‘Cause I think that you're well, you're just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there's a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would've been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it's sort of the discussion every time we have some sort of technology evolution is it's not that the jobs the roles don't all go away, they just change? And so yeah, I don't have someone spending all their time building out slides for me and presentations ‘cause I don't need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I'm going to be in San Francisco today, I'm going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they're doing emails. What It's the same thing. And now they're, they're not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let's keep moving ‘cause it's allowing us to go faster and make better decisions more quickly.Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we're having uptime issues Which transparently we've already Addressed publicly, but we'll, we'll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it's, it's a lot of years to cover.A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform EvolutionKyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of ActionsSwyx [00:22:27]: OhKyle [00:22:27]: At GitHub Universe. So it was OSwyx [00:22:29]: They're that young?Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.Swyx [00:22:33]: Gee, Jesus.Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a bigSwyx [00:22:47]: Pul Panda.Kyle [00:22:48]: Abi is doing well.Swyx [00:22:51]: DX. Holy crap.Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone's like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I'm, didn't say this before, this is ultimately webhooks.Swyx [00:23:30]: You yeah.Kyle [00:23:31]: Like circa whatever it was.Swyx [00:23:32]: It says Hookshot in there.Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot's in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn't we would run it for you.Swyx [00:23:57]: And of course no containers ‘causeKyle [00:23:58]: No, ‘cause it wasSwyx [00:23:59]: Well, no containersKyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That's like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.Swyx [00:24:15]: Which like all-time great product.Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I'm looking at and going “Okay, well we can't be running arbitrary Ruby code,” like on everyone's behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren't pinning it the like to a particularSwyx [00:24:48]: ImagesKyle [00:24:48]: Sha, et cetera like their workflows, and so that's a big that's a big place Of pain for folks if they're just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we're just going to run all this arbitrary code, and, it'll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It's like we're using it under the hood. It's through Azure. They recently announced it. The Azure, Dev Compute, but it's, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We're using it under the hood for some parts of the new,Swyx [00:25:36]: Microsoft Dev Box?Kyle [00:25:37]: No. Dev Compute, yeah.Swyx [00:25:41]: Hmm. Not finding it just yet.Kyle [00:25:44]: Oh, it's, it's in there somewhere.Swyx [00:25:46]: All right. Well, we'll cut that out.Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you're doing a tool callSwyx [00:25:58]: Same conceptKyle [00:25:58]: Just do it containerize exact-exactly. So we're using that so definitely moving that direction to protect us from every every piece of code that we're ultimately running.Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it's grown beyond that. Let's talk about NPM may-maybe ‘cause I think that's also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don't know, I don't know how you would characterize that whole acquisition and how itNPM, Package Security, and Keeping the Internet RunningKyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. ItSwyx [00:27:00]: that's cute compared to now.Kyle [00:27:01]: Well, that's the thing is like when I'm talking to folks now, there's there's so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world's code. Let's make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We're, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there's, a snow day for developers or a really bad fire that they have to go put out. And so we've, have changed the 2FA policies. We've changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, andSwyx [00:28:22]: I love that feature in GitHub. Yeah, it's greatKyle [00:28:23]: That creates issues, but, the but that's the thing is we're trying to push the community, forward without necessarily, doing something that is going to break the contract that's been for 15 years or close to it or some amount of years on NPM.Slop Forks, Vendoring, and the Future of Open Source Supply ChainsSwyx [00:28:43]: I think the— So now we're talking about, open source and publishing. And I think there's something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let's just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you're going to use into your vendor it. But, the AI vendor it. Is that realistic? I don't know. Is it— Will that solve all our security issues? I don't know.Kyle [00:29:24]: I don't think it'll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it's all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.Swyx [00:29:42]: This is Yeah. Let's, we must return toKyle [00:29:43]: That's what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there's something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it's not going to solve the fundamental problem, I don't think, because the vulnerabilities in an agent looking at them, there's time and time again, there's a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I'm pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they're bringing in or vendoring. That I think won't change. That's like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we're talking to enterprise customers, is just like very different. No there's no one wants one single way to do it. And I think that's always been GitHub's, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It's we're— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we'll cement something in. Because otherwise we'reMaintainers, RFCs, Vouching, and the Social Layer of TrustSwyx [00:31:35]: That fits your role in the ecosystem, yeahKyle [00:31:36]: We're GitHub. Yeah, we don't want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you're you're not going to be compromised as a human, ‘cause that's usually how it all happens. And Not not create a process or lock us into a flow that you're not going to or like Mitchell's not going to or other open source projects aren't going to like. That's always been a tricky balance for us, and I think that's something that we haven't talked about enough is we're not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the upSwyx [00:32:22]: I was going to bring up his thing. Yeah.Kyle [00:32:23]: I forget what it Yeah. When he's talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We're going to keep working.” but I think the important thing is I do actually want to hear what isn't working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we've known each other through the industry, he's always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we'll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it's not the proof process, not the claims process. Do what I'm talking about? He has that he his projects have a way for you to kind of like,Swyx [00:33:13]: VouchKyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That's beenSwyx [00:33:20]: I just built this into GitHub. I don't know.Kyle [00:33:22]: Well, see, but that's the thing is that you say that and like he and his community really likes this and then I'll go talk to other maintainers and other maintainers, globally, and they're “No, this doesn't work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn't enforce it but we would add it in because that's the flow that we tend to do?Swyx [00:34:02]: I hear a lot of people don't know the history of the pull request. And like like that's how, that's something that GitHub standardized basically.Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?Swyx [00:34:31]: Do you like the prompt request idea from Peter?Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I'm not, I'm not avoiding saying anything good or bad, but I feel like I've seen a version of we have that we have entire Thomas' store. Take all the assets of what you've built and put that in. I think that's got great ideas. There's all these various permutations of the PR flow, but I think the reason why there's not a single answer is ultimately we're trying to codify trust. We're trying to say “Okay, if Sean reviews this I'm going to trust it because you're Sean or you're the senior dev or you're the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we're talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn't solve, I think, the human problem of I'm looking at a PR and I want to know if I can trust it. And we're still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that's, I think that's why most of these options haven't really solved it is because, it's a social problem ultimately. It's a it's a human problem to review it and agree. Or you fully trust the tool and you're imbuing that tool with full trust Which I think in some cases that absolutely exists.AI-Generated PRs, Trust, and the Waymo AnalogySwyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don't allow humans to drive anymore Because machines are measurably better than Than humans. I'm looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we'll have Mythos on a desktop. I don't know. Will, does that change the equation?Kyle [00:36:30]: I think it's more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that isSwyx [00:36:48]: Is this a Zoox thing? What is itKyle [00:36:50]: I think that is both. I think that is both. LikeSwyx [00:36:53]: There's Zoox in this robo taxi. That's it. It'sKyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that's, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I'm in this car, what it tells me, et cetera. And so that's why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that's in the soft sense of, startups with high agency, weekend projects, and open source. And then there's enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,Swyx [00:37:55]: Oh my GodKyle [00:37:55]: Multi governments around the world and regulating agencies. And so that's where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I've been proven enough. Then the ball will start to roll a lot faster, where we'll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.Reputation, Sponsors, Stars, and Bot Activity on GitHubSwyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that's it. And I feel like there should be more in that space. I don't know if there's any other design decisions there.Kyle [00:38:37]: I think that one of the places that we don't really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.Swyx [00:38:49]: Ah.Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.Swyx [00:38:56]: Solve payments for open source. Why not?Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there's more and more projects where I'm, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I've probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don't love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They're passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that's what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that's calling AI, and you're setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account's older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we're not going to accept this PR.” Building something that is, I think, malleable to everyone's needs, is a little bit better, rather than going “Hmm, this account's too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That's how star inflation happens. Need to have a certain amount of reposSwyx [00:40:46]: Oh my God. YeahKyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it's hard. It's hard to find the measure. So I think we're, we're looking more at how can we provide you tools so you can kind of choose what's best for you. And of course, we'll give you some standards. But the trust vector, gets down to I don't know, some version of like human digital ID like everyone's been talking about. Like how do I prove that it's meSwyx [00:41:13]: Give me your eyeballsKyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I've been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I've ever seen. Like people just reached that in I don't know, months. And then like at the same time I don't trust it right? Like how many of these are real or bot or like whatever. I don't know how to ask this but like what can we do about it? LikeKyle [00:41:49]: JustSwyx [00:41:49]: Is stars broken? Is stars fine?Kyle [00:41:51]: I think that there's kind of two, there's like two pieces. Obviously we're constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,Swyx [00:42:08]: But it's like a Whac-A-MoleKyle [00:42:10]: It's a hundred percent like a Whac-A-MoleSwyx [00:42:11]: There's no wayKyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I'm seeing is, a lot of the like fastest time to X tends to be because we're now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it'sSwyx [00:42:32]: It's not just developers anymoreKyle [00:42:33]: And it's not you and I. Like like however you want to say like what a developer is it's not just folks who have been coding for a very long time. It's folks that have maybe started coding or only joined in since the AI era. And nowSwyx [00:42:44]: what's the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHubKyle [00:42:50]: Oh, we're over 200 million now.Swyx [00:42:53]: Okay. Well, so you see?Kyle [00:42:55]: Like over 200 million developers now.Swyx [00:42:56]: But it's not developers, right? It's, it's people with a GitHub account.What Counts as a Developer in the AI Era?Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there's, there's clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don't know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. SoSwyx [00:43:29]: When you get into gatekeepingKyle [00:43:31]: 100%Swyx [00:43:31]: What is a developer?Kyle [00:43:31]: 100%. ‘Cause I wasn't a developer when I started writing code? I was going toSwyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I'm “Well, no, I just use GitHub, but I don't know-” “I didn't know what I was doing.”Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that's, that's b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I'm, I'm going to run it and use the app right now, you may still use AI in that moment, but that's okay. At some point you're going to do the next thing. You're going to create a big— You're going to have to learn about this database. You're going to fix a bug, whatever. We're all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there's clearly some amount of like spamming and game gamification that we're working against, but I really think we're just seeing this whole new cohort of folks that are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that's how you see these enormous charts going up and to the right with With stars.Swyx [00:44:59]: I think something that's remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it's friendly and whatever? So it's, it's nice.Spark, Low-Code, and Always Showing the CodeKyle [00:45:19]: I that's partially why I think as we've tried to move into I don't know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That's kind of like a tenant. We're never going to, hide the code from you ever, because whatSwyx [00:45:52]: Why would you?Kyle [00:45:52]: That's, yeah, that's the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you're going to use for that or you just build something and run it but, the package of making that even more simple isn't really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don't know, I feel like we're, we're still in an era of, STEM. I've got a 12-year-old and an eight-year-old, and it's “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they're just not afraid of doing software. And that's, I think, the thing that's honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box ‘cause I'll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn't do what I want. I want it to work like this.” And that I think, is what's kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we're the home for all developers, and we want everyone to be able to have that feeling that we've had of, had an idea, I created it and holy s**t here it is.Swyx [00:47:37]: Here it is. All right, I'm going to try to do more spicy questions.GitHub's Hardest Scaling Moment: Growth, Agents, and UptimeKyle [00:47:42]: Great.Swyx [00:47:42]: Is it an easy time now or a hard time?Kyle [00:47:45]: Oh at GitHub? It's a hard time. Like, it's a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” BecauseSwyx [00:47:57]: Best of times, worst of times. It's never oneKyle [00:47:59]: ‘cause we've we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we've ever had,” right? And now we're doing more in a month than we did in a year last year.Swyx [00:48:20]: You're talking about PRs.Kyle [00:48:21]: Commits.Swyx [00:48:21]: Commits, yeah.Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we're looking at, there's some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?Swyx [00:48:38]: Whose fault is that?Kyle [00:48:39]: not anymore mine, but for a period of time, I'm sure you could pull up a tweet that was “It was me. I'm sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we're finding isn't just the isn't the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There's absolutely silly problems that we shouldn't exist. But now we're talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it's, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we're making such material progress in that I'm excited once we're once we've kind of like reimagined the underlying foundation layer, or pieces of it at least, what's going to be possible when it's not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that'll still happen in that in that GitHub tool, that GitHub community. But it's a it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it's not working it's not working for us, and that's kind of like the promise of dogfooding for GitHub. It's always been true. We're using the same tool you're using. We're not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven't seen your tweets, whatever. So one billion commits in twenty-five. Now it's two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don't know. It's been aKyle [00:50:48]: it's, it's speedingSwyx [00:50:50]: Roughly.Kyle [00:50:50]: It's still speeding up.Swyx [00:50:51]: It's, it's April, so yeah.Kyle [00:50:51]: Exactly. This was in April.Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that's a scaling issue. I think, I'm going to like try to really steel man this thing. People have experienced fourteen x growth. They haven't had your downtime. And that's like— C-can we go dig into that? Why? Like what's the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.Why GitHub Reliability Is Breaking in New WaysKyle [00:51:18]: so there's a Like I was saying, there's a couple different places that we've seen the growth issues. Some of the growth issues, which is why we're t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.Swyx [00:51:53]: It's very CPU heavy.Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we've been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I'm talking about. And so we've been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one bigSwyx [00:52:31]: Famous thing, PlanetScale was born from this andKyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we're sort of storing that data in MySQL or in a permissioning setup. That's where some of these outages have oc-occurred, where you're seeing it more like across the board rather than just like the one pieceSwyx [00:53:17]: Filling the databaseKyle [00:53:17]: Isn't quite working. Exactly. And so part of it is that. I think there's been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we're seeing the opposite. Repos are bigger, and there's, not fewer of them per se ‘cause there's new growth, but, we're just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we've done a ton of work that you pro— like most people haven't probably experienced, unless you're in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it's another thing where we're moving out of, We're changing how we do j I'll just say job queuing for lack of a better, explanation changing the underlying technologies there.Swyx [00:54:32]: I spent two years being a job queuing guy, so.Kyle [00:54:34]: And so it's kind of a little bit of a little bit of piece by piece, and it's mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There's just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.Swyx [00:55:03]: Oh, yeah.Kyle [00:55:03]: OrSwyx [00:55:05]: I push a thousandKyle [00:55:06]: On the average. 100%Swyx [00:55:06]: A thousand line commits like dailyKyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we've talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn't It was not fast. It did not do what the users wanted. And so we've been reeling that all out and going “Okay, that's just not right. Let's stop putting good money after bad and do it the do it the right way or the right way now.” So there's It's a it's a lot of things, not quite when I've experienced scale at GitHub historically, it's almost always two options that we've used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We're going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we're sort of in a like diagonal, where like vertical doesn't really work anymore. Horizontal isn't work either because we're all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we're We have to do the work. We have to make it better.Swyx [00:56:22]: actually as an infra guy, I'm “This is like one of the most fascinating scaling challenges I've ever seen.”Kyle [00:56:26]: That's that's, that's the thing that's the thing that it's hard for Like when we weren't talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what's going on.” Part of it comes from a very old GitHub ethos, which is it's our it's our uptime. It's down. W What I know you're a developer, so you're, you're inclined to want to understand more what's going on. But at the same time us going “Hey, this service didn't, perform the way we expected, and now we have to go change it,” we weren't We're not trying to hide anything from you i

Hoy hablamos de cinco historias que explican quién manda de verdad en la era de la IA: Tencent quiere meter un agente dentro de WeChat para controlar la interfaz desde la que media China vive internet; GitHub Copilot deja ver el coste real de programar con IA al pasar a cobro medido en tokens; Meta corrige un fallo gravísimo que permitía secuestrar cuentas de Instagram engañando a su bot de soporte; un fondo climate tech de 250 millones apuesta por la infraestructura física que alimenta el boom de la IA; y la astronomía resuelve varias señales extrañas del espacio profundo con una enana blanca haciendo barbaridades magnéticas.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

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

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Die Computex in Taipei ist in vollem Gang, einige in dieser Folge besprochenen Punkte sind inzwischen offiziell bestätigt: Die bisher nur in China erhältliche Grafikkarte AMD Radeon RX 9070 GRE wird weltweit erscheinen. In der Performance etwas unter der RX 9070, aber mit nur 12 GB VRAM, die UVP beträgt 549 Dollar, also der gleiche Preis wie die 9070 zu Release. Preissteigerungen von 9070 und 9070 XT wären daher keine Überraschung. Intel nimmt inzwischen den Markt der PC-Handhelds ernst und bietet mit Arc G3 und Arc G3 Extreme zwei darauf optimierte SoCs auf Basis von Panther Lake an. Das Angebot wird von zahlreichen Herstellern angenommen wie selbstverständlich MSI, die schon zuvor auf Intel anstelle von AMD setzten, aber auch Acer. Die Preise werden happig. So soll der MSI Claw 8 EX AI+ mit Arc G3 Extreme 1500 Dollar kosten. Das Steam Deck ist zwar grundsätzlich wieder erhältlich, aber ob das noch eine Empfehlung ist? Einerseits ist es inzwischen einfach veraltet, andererseits hat Valve die Preise massiv angezogen: Das Steam Deck OLED kostet mit 512 GB SSD 779 Euro (zuvor 569 Euro), mit 1 TB 919 Euro (zuvor 679 Euro). Da wirkt sogar das Asus ROG Xbox Ally für derzeit etwa 900 Euro wie ein guter Deal, zumal es auch deutlich mehr Performance bietet. Viel Spaß mit Folge 309! Sprecher:innen: Michael Kister, Mohammed Ali DadAudioproduktion: Michael KisterVideoproduktion: Mohammed Ali Dad, Michael KisterText: Michael KisterTitelbild: Mohammed Ali DadBildquellen: Valve/Pexels (Photy by Ibrahim Bohran)Aufnahmedatum: 29.05.2026 Besucht unsim Discord https://discord.gg/SneNarVCBMauf Bluesky https://bsky.app/profile/technikquatsch.deauf Youtube https://www.youtube.com/@technikquatsch https://www.youtube.com/@technikquatschgamingauf TikTok https://www.tiktok.com/@technikquatschauf Instagram https://www.instagram.com/technikquatschauf Twitch https://www.twitch.tv/technikquatsch RSS-Feed https://technikquatsch.de/feed/podcast/Spotify https://open.spotify.com/show/62ZVb7ZvmdtXqqNmnZLF5uApple Podcasts https://podcasts.apple.com/de/podcast/technikquatsch/id1510030975Deezer https://www.deezer.com/de/show/1162032 00:00:00 Herzlich willkommen zu Technikquatsch Folge 309! Mike zu Gast beim Pixelplausch Podcasthttps://m10z.de/podcasts/pp-18 00:06:53 Feedback: Immer wieder was Neues ausprobieren 00:11:51 Feedback: Samsung Smartphone mit DEX als Ersatz für Windows Thinclients 00:16:00 Steam Deck (mehr oder weniger) wieder verfügbar, aber über 200€ teurerhttps://www.computerbase.de/news/gaming/steam-deck-oled-handhelds-wieder-verfuegbar-aber-ueber-ein-drittel-teurer.97555/ 00:21:58 Intel greift im Handheld-Bereich mit Arc G3 (Extreme) an, hohe Preise für die entsprechenden Handhelds zu erwarten; Update: mehrere Handhelds auf der Computex vorgestellthttps://www.computerbase.de/news/prozessoren/arc-g3-extreme-und-arc-g3-intel-attackiert-amd-mit-arc-b390-und-b370-im-handheld-pc.97548/https://videocardz.com/newz/msi-provides-first-look-at-arc-g3-handheld-pcb-designhttps://www.computerbase.de/news/gaming/predator-atlas-8-im-hands-on-acer-setzt-im-gaming-handheld-auf-intel-arc-g3-extreme.97620/Gamers Nexus: RIP AMD ROG Ally: Intel Handheld G3 Technical Discussion, ft. Tom Petersen https://www.youtube.com/watch?v=zhiiOjLgwrM 00:26:20 AMD Radeon RX 9070 GRE bald weltweit verfügbar, Anzeichen für kommende Preissteigerungen bei 9070 und 9070 XT; Update: auf Computex offiziell bestätigthttps://www.computerbase.de/news/grafikkarten/handel-bestaetigt-die-radeon-rx-9070-gre-kommt-auch-nach-europa.97550/https://www.computerbase.de/news/grafikkarten/radeon-rx-9070-gre-die-china-version-mit-12-gb-kommt-weltweit-auf-den-markt.97586/ 00:34:52 Die "goldenen Zeiten" für KI sind vorbei, Preise für Nutzung werden durch Umstellung von Abo-Modellen auf Token-basierte Abrechnung massiv erhöht.https://www.it-daily.net/shortnews/claude-code-microsoft-lizenzenhttps://www.golem.de/news/microsoft-github-copilot-wird-fuer-viele-kunden-merklich-teurer-2604-208088.htmlhttps://bsky.app/profile/edzitron.com/post/3mnacilpsds2m 00:59:06 Euro-Office erscheint am 09. Juni als Verison 1.0.https://www.computerbase.de/news/apps/euro-office-europaeische-alternative-zu-office365-startet-am-09-juni.97589/https://www.heise.de/news/Kurswechsel-LibreOffice-fuer-Browser-und-Smartphone-kommt-11309343.html 01:03:40 Qualcomm Snapdragon C für Geräte im Bereich von 300 Dollarhttps://www.computerbase.de/news/prozessoren/snapdragon-c-qualcomms-pc-plattform-fuer-notebooks-ab-300-us-dollar.97522/ 01:07:14 Feierabend!

В этом выпуске: обсуждаем переход GitHub Copilot на тарификацию по использованию, рассказываем про matrix-боты, Иван делится впечатлениями от новой работы, разбираемся с Synology и их новой политикой цен, говорим про LimeSDR и USB3.0, смотрим на Toasty — асинхронный ORM для Rust, а Валера хвастается новой камерой Fujifilm GFX100S II Шоуноты: [00:08:52] Чему мы научились за… Читать далее →

Tekoäly ei muuta vain tapaa kirjoittaa koodia. Se muuttaa sitä, mitä ohjelmistot voivat tehdä, miten niitä hinnoitellaan, millaisia tiimejä tarvitaan ja mitä ylipäätään kannattaa rakentaa. Muutos ei tapahdu tulevaisuudessa, vaan se on jo käynnissä.Vieraana on Juhana Harmanen, Goforen digipalveluiden johtaja. Harmanen on kulkenut freelancerista startup-yrittäjäksi, konsultiksi, CDO:ksi ja takaisin konsulttipuolelle. Gofore on nyt yli 1 900 asiantuntijan yritys, joka toimii 26 kaupungissa ja tekee yli 200 miljoonan euron liikevaihtoa. Jaksossa käydään läpi, mitä tässä murroksessa tapahtuu ohjelmistotuotannolle, hinnoittelulle, roolien muutokselle ja suomalaisen yrityksen kansainvälisille mahdollisuuksille.Jaksossa pureudumme:- Miksi Sam Altmanin ennustus yhden hengen miljardiyrityksestä meni jo ohi, ja mitä se kertoo AI-natiivista rakentamisesta- Miten agenttinen kehitysympäristö muuttaa ohjelmistotuotannon arjen ja mitä GitHub Copilot tai Claude Code tekevät jo nyt- Miksi speksauksen ja laadunvarmistuksen merkitys kasvaa samalla kun varsinainen koodaustyö automatisoituu- Miten kuuden vuoden tekninen velka saatiin purettua kolmessa kuukaudessa tekoälyavusteisesti- Miksi konsulttibisneksessä kysyntä on kasvanut eikä supistunut, vaikka koodareiden työ tehostuu- Mitä "service as software" tarkoittaa käytännössä ja miksi pääomasijoittajat arvioivat sen seitsemän kertaa suuremmaksi markkinaksi kuin perinteinen työkalumarkkina- Miksi token-pohjainen hinnoittelu murtaa kiinteän kuukausimaksun logiikan SaaS-tuotteissa- Miten Ukraina on rakentanut agenttisen kerroksen kansallisen digitaalisen infrastruktuurin päälle sodan keskellä- Mitä nuorelle koodarille tai alaa vaihtavalle kannattaa tässä tilanteessa tehdä- Miksi arvopohjaiseen hinnoitteluun siirtyminen vaatii myös johtamisajattelun uusimisenSisällysluettelo:00:00 Johdanto ja aihe01:57 Sam Altmanin ennustus ja 1,8 miljardin yhden hengen yritys04:00 Suomen talouskasvun haaste ja tuottavuusloikka05:34 Juhana Harmasen tausta: freelancerista startup-yrittäjäksi ja Goforelle19:55 Konsulttibisneksen kysyntä AI-murroksessa20:58 Ohjelmistotuotannon muutos: mistä pullonkaula siirtyy25:53 Agenttinen kehitysprosessi käytännössä32:37 Live-demo: verkkokauppa pyörimään kahdessa tunnissa ilman ohjelmointitaustaa36:24 Speksauksen ja laadunvarmistuksen uusi rooli38:47 Case: kuuden vuoden järjestelmä uudistettiin kolmessa kuukaudessa39:48 Onko koodarilla enää töitä?45:25 Miksi kiireisillä seniorikehittäjillä ei ole aikaa uudistua, ja mikä siinä on mahdollisuus47:29 Token-pohjainen hinnoittelu ja SaaS-liiketoimintamallin murros54:03 Agenttipohjainen kehitysorganisaatio: roolit ja orkestrointi59:09 Lokien analysointi, tietoturva ja jatkuva laadunvarmistus01:05:57 Service as software ja mitä se tarkoittaa eri toimialoille01:12:24 Ukrainan esimerkki: agenttinen kerros kansallisessa digitaalisessa infrastruktuurissa01:19:06 Arvopohjaiseen hinnoitteluun siirtyminen ja johtamisen muutos01:23:18 LoppuajatuksetMenestystä Etsimässä on podcast suomalaisesta ohjelmisto- ja SaaS-liiketoiminnasta. Keskustelut käsittelevät kasvua, kansainvälistymistä, liiketoimintamalleja ja päätöksiä, joita harvoin avataan julkisesti. Jakso on katsottavissa YouTubessa ja kuunneltavissa kaikissa podcast-palveluissa.Tutustu myös:Juhana Harmanen (LinkedIn): https://www.linkedin.com/in/harmanen/Gofore: https://gofore.com/Antti Pietilä (LinkedIn): https://www.linkedin.com/in/anttipietila/Kasvuvalmennus SaaS-yrityksille: https://calendly.com/antti-pietila/kasvuvalmennus-sparrausLoyalistic: https://loyalistic.com/fi/Loyalistic Studio: https://loyalistic.com/fi/studio/SaaS Finland: https://www.saasfinland.com/Podcast tehdään yhteistyössä Loyalisticin ja SaaS Finlandin kanssa.

D.A. Davidson's Gil Luria and BEP Research's Ben Pouladian talk with TITV Host Akash Pasricha about Snowflake's accelerating revenue growth and Salesforce's disappointing Q2 guidance. We also talk with The Information's Apple reporter Aaron Tilley about Apple's strategy to shrink Google Gemini models onto devices, and Microsoft reporter Aaron Holmes about the company's plan to release homegrown coding models to protect GitHub Copilot from competitors like Cursor.Articles discussed on this episode: https://www.theinformation.com/articles/apple-renew-push-ai-runs-devices-instead-cloudhttps://www.theinformation.com/articles/meta-launches-new-enterprise-push-boost-business-adoption-ai-toolshttps://www.theinformation.com/newsletters/ai-agenda/microsoft-release-new-coding-model-next-week-comeback-attemptSubscribe: 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 - Snowflake Shares Jump as AI Product Adoption Grows08:04 - Salesforce Earnings Reaction and M&A Strategy12:48 - Meta Launches New Enterprise Push & Paid AI Chatbot Subscriptions23:03 - Apple to Renew Push for AI That Runs on Devices28:26 - Microsoft to Release New Coding Model

James and Frank unpack AI-driven development shifts—agent SDKs, session management, and the rise of agent-first UIs like Google's anti-gravity and GitHub Copilot—showing how VS Code's Agents window, worktrees, sub-sessions and tunnels help manage multi-repo cloud and local workflows. They share practical takeaways—why SDKs are essential, when to stay code-first, how subsessions and remote tunnels protect your machine, and what to watch for in sandboxing and integration gaps. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

This month's panel digs into the SpaceX Cursor acquisition rumor and what a $60 billion valuation means for AI coding tools. They debate Bun's million-line Rust rewrite generated entirely by AI, the tradeoffs of agentic coding at scale, and a sophisticated CI/CD cache poisoning attack targeting TanStack. Plus: practical takes on Claude token optimization, session forensics, local AI models, and why most Claude Code skills work best when tailored, not pulled off the shelf. Resources SpaceX/Cursor deal, CNBC: https://www.cnbc.com/2026/04/21/spacex-says-it-can-buy-cursor-later-this-year-for-60-billion-or-pay-10-billion-for-our-work-together.html Fortune, Cursor's uncertain future: https://fortune.com/2026/03/21/cursor-ceo-michael-truell-ai-coding-claude-anthropic-venture-capital/ GitHub Copilot usage-based billing announcement: https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/ Developer backlash, Visual Studio Magazine: https://visualstudiomagazine.com/articles/2026/04/27/devs-sound-off-on-usage-based-copilot-pricing-change-you-will-get-less-but-pay-the-same-price.aspx "The IDE Is Dead, Long Live the ADE", Indie Hackers: https://www.indiehackers.com/post/the-ide-is-dead-long-live-the-ade-0d81e9da3d Companies spending crazy money on AI coding tools, Medium: https://medium.com/@Reiki32/companies-are-spending-crazy-money-on-ai-coding-tools-while-developers-burn-out-efe5908f3dda The PR: https://github.com/oven-sh/bun/pull/30412 The Register writeup: https://www.theregister.com/devops/2026/05/14/anthropics-bun-rust-rewrite-merged-at-speed-of-ai/5240381 The 13,000 unsafe blocks piece: https://byteiota.com/bun-rust-rewrite-merged-the-13000-unsafe-block-problem/ TanStack postmortem: https://tanstack.com/blog/npm-supply-chain-compromise-postmortem TanStack hardening follow-up: https://tanstack.com/blog/incident-followup StepSecurity writeup (the researcher who caught it): https://www.stepsecurity.io/blog/mini-shai-hulud-is-back-a-self-spreading-supply-chain-attack-hits-the-npm-ecosystem SOC Prime writeup: https://socprime.com/active-threats/active-supply-chain-attack-compromises-node-ipc-package We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Fill out our listener survey! https://t.co/oKVAEXipxu Let us know by sending an email to our producer, Elizabeth, at elizabeth.becz@logrocket.com, or tweet at us at PodRocketPod. Check out our newsletter! https://blog.logrocket.com/the-replay-newsletter/ Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form, and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understanding where your users are struggling by trying it for free at LogRocket.com. Try LogRocket for free today. Chapters 00:00 Introduction 01:00 The $60B SpaceX Cursor deal 08:00 Token costs rising — the rug pull is real 09:30 Local models and sub-agent routing 12:00 Session forensics — cutting Claude token waste 15:00 Bun's AI-generated Rust rewrite 18:00 Should AI rewrite core infrastructure? 23:00 Does runtime choice even matter anymore? 29:00 The TanStack supply chain attack explained 33:00 How the GitHub Actions cache poisoning worked 36:00 Is GitHub Actions too flexible? 39:30 Ad break 40:00 Hot take — you'll be okay (local models and hardware) 42:30 Hot take — "They Will Kill You" (Jack's movie rec) 43:30 Hot take — stop hoarding Claude Code skills 46:00 Wrap-upSpecial Guest: Jack Herrington.

In the latest episode of Executive Function, Brett sits down with Graham Moreno, Head of GTM at Parallel Web Systems. Before Parallel, Graham scaled Windsurf's GTM organization from three sellers to seventy-five in under a year, served as President through the Cognition acquisition, and earlier built and led enterprise sales teams at Grafana Labs and MongoDB. In this conversation, he unpacks why the AI-era backlash against structured enterprise sales misreads the data, how to design a process that raises the floor for ordinary reps without capping the ceiling for stars, and why selling to AI-native customers compresses an eight-week cycle into five business days. In today's episode, we discuss: Why in-person enterprise rollouts still beat product-led motions Building a robust sales process that still leaves room for unscripted moments Why the three highest-leverage early sales hires aren't sellers at all The case for outsized commission accelerators for star sellers — and the kind of person they attract Why most AI companies are skipping the in-person sales work that enterprise customers actually want References: Ahead: https://www.ahead.com Amazon: https://www.amazon.com Anthropic: https://www.anthropic.com Attio: https://www.attio.com Augment Code: https://www.augmentcode.com/ Cognition: https://cognition.ai Cursor: https://cursor.com Dani McCabe: https://www.linkedin.com/in/danielle-mccabe/ Datadog: https://www.datadoghq.com GitHub Copilot: https://github.com/features/copilot HubSpot: https://www.hubspot.com Jeremy Powers: https://www.linkedin.com/in/jeremypowers/ JPMorgan: https://www.jpmorgan.com Matt McClernan: https://www.linkedin.com/in/mattmcclernan/ MongoDB: https://www.mongodb.com Nicole Rettinger: https://www.linkedin.com/in/nicole-rettinger-23b20465/ Notion: https://www.notion.com OpenAI: https://openai.com Parag Agrawal: https://www.linkedin.com/in/paragagr/ Parallel: https://parallel.ai Snowflake: https://www.snowflake.com University of Chicago: https://www.uchicago.edu Windsurf: https://windsurf.com Where to find Graham: LinkedIn: https://www.linkedin.com/in/grahammoreno/ Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 00:32 Has the sales playbook changed in the AI era? 02:13 Why "showing up" beats letting the marketplace decide 06:50 Why great salespeople sell to engineers and executives in one motion 11:37 Selling to AI-native buyers who grew up on ChatGPT 13:49 Same seller, different tempo: 8 weeks vs. 8 business days 15:57 How AI-native buyers handle build vs. buy decisions 17:48 The rep who taught a champion's son guitar over Zoom 19:03 Raising the floor without capping the ceiling 22:09 Why too much process narrows the kind of seller you attract 25:46 The three pillars of GTM excellence 31:00 Building peers who are 80% aligned, not 100% 38:03 Whether AI is changing what good enablement looks like 41:35 Selling against direct and implied competitors at once 42:45 Instrumenting the funnel from stage zero to close 45:57 Why post-sales should always roll up to the revenue leader 48:19 The case for outsized commissions 52:02 The 96 hours of panic before Cognition acquired Windsurf 53:04 How far out should a GTM leader be planning? 57:53 What a normal week looks like in hypergrowth

Will your software soon be a living organism with its own immune system?Animesh Koratana, founder of PlayerZero, started his software career long before he founded the company. Growing up in Atlanta, he spent his childhood inside his father's software business, watching engineers sitting through the unglamorous work of QA and keeping systems alive after launch. He saw early that writing software was only half the problem. Maintaining it was the real battle.Years later at Stanford, he witnessed the birth of GPT-2 and Codex, the very foundation of GitHub Copilot. While much of the world focused on how AI would help engineers write software faster, he became obsessed with a different question: What happens when companies are flooded with AI-generated code that no single engineer fully understands?With PlayerZero, Animesh is building toward what he calls self-healing software: systems that behave less like static machines and more like living organisms with their own immune systems.At the center of that vision are “Context Graphs” which captures the "institutional memory" of a company: the deep knowledge held by a senior engineer who has spent years understanding how complex software breaks, the failure modes it develops, and the decisions behind fixing it.If you are building software today and wondering how reliability, debugging, and ownership will work when machines write most of the code, this episode is for you.0:00 — Trailer0:45 — Building Self-Healing Code2:03 — First Exposure to LLMs Through GPT-23:45 — What Is PlayerZero?5:42 — Institutional Memory of a Senior Engineer7:10 — How Context Is Built10:06 — The Viral “Context Graph” Piece16:24 — The Outcome PlayerZero Delivers19:59 — When the Agent Tells the Human What to Do23:43 — Who Is PlayerZero Selling To?26:56 — Why Software Should Be Treated Like Biology28:54 — The PlayerZero Customer Pitch30:37 — Can Software Really Have an Immune System?35:15 — How Animesh Chose His Investors36:55 — What's Next for PlayerZero?-------------India's talent has built the world's tech—now it's time to lead it.This mission goes beyond startups. It's about shifting the center of gravity in global tech to include the brilliance rising from India.What is Neon Fund?We invest in seed and early-stage founders from India and the diaspora building world-class Enterprise AI companies. We bring capital, conviction, and a community that's done it before.Subscribe for real founder stories, investor perspectives, economist breakdowns, and a behind-the-scenes look at how we're doing it all at Neon.-------------Check us out on:Website: https://neon.fund/Instagram: https://www.instagram.com/theneonshoww/LinkedIn: https://www.linkedin.com/company/beneon/Twitter: https://x.com/TheNeonShowwConnect with Siddhartha on:LinkedIn: https://www.linkedin.com/in/siddharthaahluwalia/Twitter: https://x.com/siddharthaa7-------------This video is for informational purposes only. The views expressed are those of the individuals quoted and do not constitute professional advice.Send us Fan Mail

Explore the biggest announcements from Microsoft Ability Summit and Google I/O 2026, including breakthroughs in accessibility, AI-powered video editing, and Google's new intelligent audio glasses. Discover how screen readers like Narrator are evolving and how AI agents are reshaping the way blind and low-vision users interact with technology. The discussion covers: Microsoft's focus on Narrator improvements, including Braille support and enhanced voices. Team Gleason's partnership with Microsoft to create custom voices for people with ALS and similar conditions. GitHub CoPilot's role in accessible video editing, enabling blind users to create professional content with AI assistance. Broader reflections on the evolution of accessible screen readers and the importance of uniformity in UI behaviours. The conversation then shifts to Google I/O 2026: Launch of Google and Samsung's intelligent audio glasses with turn-by-turn navigation, voice interaction, and Gemini AI integration. Gemini 3.5 Flash and Gemini Omni models, including multimodal AI with video generation and live editing capabilities. Gemini Spark, Google's persistent AI agent for email, calendar, and task automation. Universal Shopping Cart and proactive AI agents that monitor, purchase, and track items on your behalf. The hosts discuss how these innovations could transform accessibility, daily productivity, and creative opportunities for blind and low-vision users. ----Follow on:YouTube: https://www.doubletaponair.com/youtubeX (formerly Twitter): https://www.doubletaponair.com/xInstagram: https://www.doubletaponair.com/instagramTikTok: https://www.doubletaponair.com/tiktokThreads: https://www.doubletaponair.com/threadsFacebook: https://www.doubletaponair.com/facebookLinkedIn: https://www.doubletaponair.com/linkedinSubscribe to the Podcast:Apple: https://www.doubletaponair.com/appleSpotify: https://www.doubletaponair.com/spotifyRSS: https://www.doubletaponair.com/podcastiHeadRadio: https://www.doubletaponair.com/iheartAbout Double TapHosted by the insightful duo, Steven Scott and Shaun Preece, Double Tap is a treasure trove of information for anyone who's blind or partially sighted and has a passion for tech. Steven and Shaun not only demystify tech, but they also regularly feature interviews and welcome guests from the community, fostering an interactive and engaging environment. Tune in every day of the week, and you'll discover how technology can seamlessly integrate into your life, enhancing daily tasks and experiences, even if your sight is limited."Double Tap" is a registered trademark of Double Tap Productions Inc. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we explore MCP servers - the open Model Context Protocol that allows AI assistants like GitHub Copilot, Claude, and ChatGPT to connect to live tools and data instead of relying on outdated training data. We discuss what MCP servers are, why they are important, and review our favorite MCP servers for working with Azure. We'll also share some personal favorites! (00:00) - Intro and catching up.(03:48) - Show content starts.Show links- Microsoft Learn MCP Server- Azure DevOps MCP Server- Azure Resource Manager MCP Server- Spotify MCP Server- Give us feedback!

https://clearmeasure.com/developers/forums/ Today I've have Gaurav Seth with us — he's a product executive at Microsoft working on fundamentally redefining how software gets built and scaled. He's been bringing agentic AI into every stage of the develop‑deploy‑operate cycle, both for Microsoft's internal engineering teams and for developers building on the platform. He's hands-on building AI agents into GitHub Copilot and Visual Studio, working on evaluation systems that improve model quality, and shaping core platforms that power Azure, Microsoft 365, Windows, Xbox, and LinkedIn. Right now, he's focused on some of the hardest problems in the industry — what it looks like to move from manual to AI-driven development, how to measure and improve agent performance at scale, how to make massive codebases understandable to LLMs, and what the future of developer workflows looks like in an agent-first world. Before this, he helped lead some major shifts — from Edge's move to Chromium, to scaling TypeScript into one of the most widely used languages in the world, to evolving Visual Studio's business model and growing .NET in a crowded market. He operates end-to-end — from product strategy and engineering to go-to-market, partnerships, and enterprise adoption — and has a unique ability to connect deep technical innovation with real-world impact.  Mentioned in this Episode LinkedIn X / Twitter  .NET Blog (author page)  Foundry Local: Onyx w/ Ollama  VSCode Agent Pane  Want to Learn More?  Visit AzureDevOps.Show for show notes and additional episodes.

Microsoft Build 2026 marks the biggest format shift in nearly a decade - moving out of Seattle to a 2,500-seat Fort Mason venue in San Francisco with a deliberate "no fluff" pivot toward AI developers, technical leaders, and enterprise architects. In this episode, we unpack what to expect across the six confirmed tracks - plus where we think Microsoft will double down on Foundry, GitHub Copilot, and agent governance. Whether you're flying to San Francisco or planning your livestream strategy from the couch, this is the episode for you!(00:00) - Intro and catching up.(04:44) - Show content starts.Show links- Build 2026 session catalog- GitHub Copilot Token-based billing- Give us feedback!

У цьому випуску говоримо про проблеми GitHub, розвиток AI-агентів, а також філософські і наукові ідеї щодо природи життя. Обговорюємо погіршення стабільності GitHub, часті даунтайми та баги, які безпосередньо впливають на роботу команд і повсякденний workflow розробників. Також говоримо про штучний інтелект: Copilot, зміни у його комерційній політиці та обмеження підписок, а також розвиток LLM-моделей і агентних систем, які дедалі активніше інтегруються в процес розробки. Наприкінці випуску переходимо до теми computational life, обговорюємо виникнення складних систем із простих правил та експерименти з програмами, здатними до самовідтворення. 00:27 — GitHub «скотився»: даунтайми та навантаження від агентів 04:28 — альтернативи GitHub: GitLab, Bitbucket, Codeberg 06:40 — issues як індикатор якості проєкту 08:06 — «Coding is solved» як мем і розрив із реальністю 09:19 — зміни в GitHub Copilot: політики, квоти, обмеження 14:14 — змагання АІ моделей: агентність, контекст, пам'ять, стабільність 16:35 — тренд на скіли, інструкції проти детерміністичних інтеграцій 20:50 — пет-проєкт Павла: локальний чат на Ollama 24:54 — Apple, витік Agent.md і обережний підхід до AI 27:42 — генерація мемів, ChatGPT vs Gemini та цифрова ідентичність 31:30 — голосові режими ChatGPT, Claude і Gemini 35:03 — Google демки vs реальність 39:55 — OpenCV, embedded AI та Raspberry Pi 46:10 — computational life: чи може життя виникнути з обчислень?

What We Cover Why AI tools like GitHub Copilot and Claude Code are real productivity accelerators and how Bowdark is actively using them The 70/30 split: why only about 30% of a technology project involves writing code What human-centered design actually looks like in practice and why it can't be automated away A real client example where a "detailed requirements doc" turned out to be a list of unanswered questions Why AI is a powerful tool in skilled hands and a risk in the wrong ones The right frame: AI is additive, not replacement

Java 26 est là, GraalVM cartonne chez Trivago (43 à 12 réplicas !), OpenJDK interdit le code généré par LLM, Spring et Quarkus enchaînent les releases. Côté IA : ADK 1.0, A2A, Lyria 3 chante (mal ?), Yann LeCun lance Ami Labs et ses World Models. Mythos d'Anthropic fait trembler la sécu, Claude Code a leaké son source, et les git worktrees envahissent vos terminaux. Bonus : la mort annoncée de l'IDE, vagues de licenciement chez Oracle et Block, et nos voix toutes clonées. Bon week-ends de mai ! Enregistré le 7 mai 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-340.mp3 ou en vidéo sur YouTube. News Langages Retour d'expérience d'une migration vers graalVM chez Trivago https://medium.com/graalvm/inside-trivagos-graalvm-migration-native-image-for-graphql-at-scale-912bca9df841 La passerelle GraphQL de Trivago (point d'entrée de tout le trafic vers 48 microservices) souffrait de pics de timeout au démarrage JVM Résultats spectaculaires après migration vers GraalVM Native Image : réduction des réplicas de 43 à 12, CPU de 15 à 5 cœurs, images Docker plus légères Obstacles techniques : incompatibilité Log4j → migration vers Logback, remplacement de Mockk par Testcontainers, compilation CI/CD très gourmande Netflix DGS et d'autres librairies manquaient de support GraalVM → l'équipe a contribué des correctifs upstream en open source Approche recommandée : commencer par les services les moins complexes, investir massivement dans les tests automatisés À la 14e migration, le processus était si rodé qu'il allait plus vite que la toute première tentative OpenJDK Interim Policy on Generative AI - https://openjdk.org/legal/ai OpenJDK adopte une politique intérimaire interdisant toute contribution incluant du contenu généré par des LLMs, modèles de diffusion ou systèmes deep-learning Le périmètre est large : code source, texte, images dans les dépôts Git, pull requests GitHub, emails, pages wiki et issues JBS Les contributeurs peuvent utiliser les outils d'IA de manière privée pour comprendre, déboguer et relire le code OpenJDK, mais ne peuvent pas contribuer le contenu généré Trois risques justifient cette politique : surcharge des relecteurs face au code plausible mais incorrect, risques de sûreté/sécurité pour une plateforme critique, et risques de propriété intellectuelle (l'OCA exige que les contributeurs possèdent les droits IP de leurs contributions) Même éditer partiellement du code AI-généré ne le rend pas acceptable à la contribution Oracle, sponsor corporatif d'OpenJDK, travaille sur une politique complète à soumettre au Governing Board GraalVM Native Image et la Closed-World Assumption en Java https://pvs-studio.com/en/blog/posts/java/1357/ Un bon article de rappel du contexte de closed world en Java GraalVM Native Image compile les applications Java en exécutables natifs statiques, sans JVM au runtime. La JVM fonctionne en monde ouvert : les classes sont chargées à la demande, les appels sont des références symboliques résolues dynamiquement. Native Image impose la "closed-world assumption" : tous les chemins d'exécution doivent être connus à la compilation. Les fonctionnalités dynamiques Java (réflexion, proxies, chargement de classes) créent des chemins cachés invisibles à l'analyse statique. C'est pourquoi Native Image exige des fichiers de configuration explicites pour la réflexion, les proxies, les ressources et la FFM API. L'article illustre le problème avec la Foreign Function & Memory API pour appeler printf natif : fonctionne sur JVM, échoue en Native Image sans config. Inclure tout le bytecode accessible serait inutilisable : binaire géant, compilation très lente, et la réflexion nécessite des métadonnées précises. La configuration n'est pas un défaut de conception mais une conséquence logique du passage du dynamique au statique. Java 26 : les nouveautés https://foojay.io/today/java-26-whats-new/ Java est le langage de la JVM, publié tous les 6 mois depuis Java 9 ; Java 26 est une version non-LTS avec 10 JEPs. JEP 500 : protection des champs final modifiés par réflexion profonde, avec des avertissements configurables. JEP 504 : suppression définitive de l'API Applet, plus supportée par les navigateurs. JEP 516 : le cache AOT (Project Leyden) fonctionne désormais avec n'importe quel garbage collector. JEP 517 : support HTTP/3 dans le client HTTP, HTTP/2 reste le défaut mais HTTP/3 est accessible à la demande. JEP 522 : amélioration du débit du GC G1 en réduisant la synchronisation entre threads applicatifs et threads GC. Nouveau support des UUIDv7 via UUID.ofEpochMillis(), naturellement triables et adaptés aux identifiants de bases de données. Process devient AutoCloseable, utilisable dans un try-with-resources. Aucune fonctionnalité en preview n'est graduée en standard ; Structured Concurrency en est à sa 6e preview. Librairies Guillaume a créé une petite librairie Java sans dépendance pour extraire le JSON d'une réponse d'un LLM un peu verbeux https://glaforge.dev/posts/2026/03/22/extracting-json-from-llm-chatter-with-jsonspotter/ Les LLM génèrent souvent du JSON, mais il est parfois entouré de bla-bla et/ou contient des erreurs (ex: commentaires, virgules finales) qui bloquent les parseurs JSON standards. Guillaume a créé une petite librairie légère sans dépendance pour localiser et extraire la structure la plus longue ressemblant à du JSON (même malformé) On peut ensuite passé cette chaîne à un parseur "lénient" (plus tolérant) comme Jackson pour ensuite avoir de bons vieux objets Java fortement typés Librairie dispo sur Maven Central ADK Java sort sa version 1.0 (Agent Development Kit par Google) https://developers.googleblog.com/announcing-adk-for-java-100-building-the-future-of-ai-agents-in-java/ ADK est un framework open source de Google pour créer des agents IA, initialement en Python, maintenant multi-langages (Python, Java, Go, Typescript). Nouvelles fonctionnalités majeures : Outils puissants : GoogleMapsTool, UrlContextTool, ContainerCodeExecutor, VertexAiCodeExecutor, abstraction ComputerUseTool. Architecture de plugins centralisée : Nouveau conteneur App pour gérer les Plugins à l'échelle de l'application (ex: LoggingPlugin, GlobalInstructionPlugin). Context engineering amélioré : Compaction d'événements pour gérer la taille des fenêtres de contexte (résumé et rétention). Human-in-the-Loop (HITL) : Supporte les workflows ToolConfirmation pour approbation humaine des actions d'agent. Services de session et de mémoire : Contrats clairs pour la gestion de l'état (InMemory, VertexAI, Firestore) et la mémoire à long terme. Support Agent2Agent (A2A) : Collaboration native entre agents distants de différents frameworks via le protocole A2A. Dans cet autre article, Guillaume partage comment il a développé l'application Comic Trip montrée dans la vidéo YouTube et qui utilise ADK 1.0 https://glaforge.dev/posts/2026/03/30/building-my-comic-trip-agent-with-adk-java-1-0/ Nouvelle version du SDK Java pour Agent2Agent Protocol, avec le support de la version 1.0 de la spécification https://medium.com/google-cloud/a2a-java-sdk-1-0-0-beta1-released-e83c414b34cc Alignement avec la version 1.0 de la spécification Nouveau groupId org.a2aproject.sdk et package org.a2aproject.sdk Protocoles de transport : support complet et équivalent pour JSON-RPC, gRPC et HTTP+JSON/REST. Gestion des erreurs : introduction de codes d'erreur et détails structurés pour une meilleure observabilité. Optimisation HTTP : ajout d'en-têtes de cache pour les métadonnées des agents (Agent Card). Flexibilité du client HTTP : support par défaut du JDK HttpClient, avec option Vert.x pour les environnements Quarkus. Nouvelles fonctionnalités techniques : méthode DataPart.fromJson() pour la création simplifiée d'objets depuis du JSON brut. Prochaines étapes (v1.0.0.GA) : support simultané des versions 1.0.0 et 0.3.0 du protocole pour assurer l'interopérabilité. JPA 4.0 Milestone 2 : nouvelles fonctionnalités pour Jakarta Persistence https://in.relation.to/2026/04/23/JPA-4-M2/ Jakarta Persistence (JPA) est la spécification standard Java pour le mapping objet-relationnel (ORM), implémentée notamment par Hibernate. JPA 4.0 M2 est la deuxième milestone de la prochaine version majeure de la spécification, annoncée par Gavin King. Construction de requêtes Criteria à partir de chaînes JPQL, offrant plus de flexibilité dans la composition dynamique des requêtes. Nouveaux types d'expressions spécialisés (TextExpression, NumericExpression) pour simplifier l'écriture des requêtes Criteria. Nouvelle interface FetchOption pour contrôler explicitement la stratégie de chargement des associations, dont un BatchSize intégré. Nouvelle annotation @EntityListener qui découple les classes entités de leurs listeners, supprimant les dépendances à la compilation. Les listeners peuvent cibler plusieurs types de callbacks et s'appliquer globalement à toute l'unité de persistance. Introduction de FlushModeType.EXPLICIT et QueryFlushMode pour un contrôle plus fin de la synchronisation avec la base de données. La méta-annotation @Discoverable permet de placer des annotations comme @NamedQuery sur n'importe quelle classe ou interface. Améliorations du DDL via @Index amélioré et clarifications de la spécification via la javadoc. Quarkus 3.35 : tree-shaking, PGO et AOT Semeru https://quarkus.io/blog/quarkus-3-35-released/ Quarkus est un framework Java cloud-natif optimisé pour GraalVM et HotSpot, conçu pour les microservices et les environnements conteneurisés. Nouveau JAR tree-shaking expérimental : analyse des dépendances à la compilation pour supprimer les classes inutilisées. Sur le CLI Quarkus, cela supprime plus de 6 000 classes et économise environ 18 Mo (39,5 %). Support du Profile-Guided Optimization (PGO) pour les builds natifs via quarkus.native.pgo.enabled=true. Le PGO est une fonctionnalité Oracle GraalVM, non disponible dans la Community Edition. Support de l'AOT IBM Semeru : le démarrage passe de ~380 ms à ~190 ms dans les premiers tests. Nouvelle extension quarkus-reactive-transactions : support de @Transactional pour les méthodes Hibernate Reactive retournant Uni. Configuration CORS dédiée pour l'interface de management, indépendante de l'interface HTTP principale. Les tests n'utilisent plus les System Properties pour la propagation de configuration, facilitant la parallélisation future. Le serializer jackson sans reflection n'est pas le default du aux retours de cas limites, encore du travail This Week in Spring - 21 avril 2026 https://spring.io/blog/2026/04/21/this-week-in-spring-april-21-2026 Spring Framework 6.2.18 et 7.0.7 corrigent trois failles de sécurité : DoS via fichiers multipart WebFlux, empoisonnement de cache de ressources statiques, et DoS sur Windows. Le support open source de Spring Framework 5.3.x et 6.1.x est terminé, la migration est recommandée. Spring Data 2026.0.0-RC1 introduit l'upsert (MERGE/INSERT ON CONFLICT) dans l'API Template de Spring Data Relational. Spring Data ajoute un RedisMessageSendingTemplate pour la cohérence avec les listeners Redis, et une optimisation de réinitialisation de caches en un seul appel. Spring AI introduit une Session API (série Agentic Patterns, partie 7) : architecture event-sourcée pour la mémoire des agents IA. La Session API supporte la compaction turn-safe, l'isolation de sous-agents en parallèle, et la persistence JDBC (PostgreSQL, MySQL, MariaDB, H2). Elle vise Spring AI 2.1 (novembre 2026) et remplacera à terme l'API ChatMemory. Spring Vault 4.1.0-RC1 et 4.0.2 sont disponibles. Netflix a présenté son usage de Java, Spring Boot et Spring AI dans une vidéo. This Week in Spring - 28 avril 2026 https://spring.io/blog/2026/04/28/this-week-in-spring-april-28-2026 Cette série hebdomadaire de Josh Long compile les nouveautés de l'écosystème Spring : articles, outils, podcasts et annonces de la communauté. Spring Boot 4 introduit un package natif de résilience org.springframework.resilience avec une nouvelle API de retry qui remplace les approches fragiles via Spring Retry ou Resilience4j. L'API retry native de Spring Boot 4 a des noms d'attributs et sémantiques différents des anciennes bibliothèques, rendant les tutoriels pré-2025 obsolètes et sources de bugs silencieux. Le SDK Spring AI pour Amazon Bedrock AgentCore est disponible en GA : il intègre les capacités AgentCore dans Spring AI via annotations et auto-configuration. Le SDK AgentCore gère automatiquement le contrat runtime AgentCore : endpoint /invocations, health check /ping, SSE avec backpressure. Il offre mémoire court terme (sliding window) et long terme (sémantique, préférences, résumé, épisodique), ainsi que des outils pour navigateur et exécution de code en sandbox. Un plugin Maven (Nullability Maven Plugin) simplifie l'intégration de JSpecify et NullAway pour enforcer la null-safety à la compilation dans les projets Java. Le plugin génère automatiquement les fichiers package-info.java par package et configure le compilateur pour traiter les violations de nullabilité comme des erreurs. Josh Long et Dr. Venkat Subramaniam ont co-présenté à Voxxed Days Amsterdam sur "Intelligent Kotlin", avec un épisode de podcast associé. Cloud Amazon S3 Files https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-s3-files/ Amazon S3 Files est un nouveau service donnant un accès système de fichiers direct aux données stockées dans les buckets S3 Basé sur la technologie Amazon EFS, il supprime la barrière entre stockage objet et interface système de fichiers sans dupliquer les données Débit en lecture pouvant atteindre plusieurs téraoctets par seconde ; des milliers de ressources de calcul peuvent y accéder simultanément Les données restent accessibles via les deux interfaces : S3 API classique et système de fichiers standard, sans migration nécessaire Cas d'usage : agents IA pour la persistance de mémoire entre pipelines, équipes ML sans staging, simplification des data lakes Disponible dans 34 régions AWS Data et Intelligence Artificielle Comment générer de la musique et des clips audio en Java avec le modèle Lyria 3 https://glaforge.dev/posts/2026/03/25/generating-music-with-lyria-3-and-the-gemini-interactions-java-sdk/ Génération musicale avec Lyria 3 (DeepMind) et le SDK Java Gemini Interactions. Lyria 3 : modèle d'IA générative pour créer musique avec paroles ou pistes instrumentales. Utilisation via le SDK Java de l'API Gemini, nécessite une clé API Gemini. Deux versions de modèle Lyria 3 : lyria-3-clip-preview : Clips courts (30s), extraits. lyria-3-pro-preview : Chansons complètes (jusqu'à 3 min), structurées. Personnalisation via les prompts : Fournir ses propres paroles ou les faire générer. Contrôler la structure de la chanson ([Intro], [Verse], [Chorus], [Outro]). Générer des morceaux instrumentaux uniquement. Utiliser des images comme source d'inspiration (modèle multimodal). Sortie : Audio (MP3) et texte (paroles/structure) directement, sans décodage complexe. Facilite l'intégration de la génération musicale dans les applications Java. Les world model, la prochaine étape pour les IA https://www.lepoint.fr/sciences-nature/comment-le-commando-de-yann-le-cun-se-prepare-a-ringardiser-les-geants-mondiaux-de-lia-depuis-paris-OZVUWTDYBNE25C6WF44265ZQKE/ Yann LeCun a quitté Meta FAIR pour créer AMI Labs (Advanced Machine Intelligence) basée à Paris Sa thèse : les LLMs ne mèneront pas à l'intelligence générale, la vraie IA doit partir de la compréhension du monde physique AMI Labs a levé 1,03 milliard de dollars en seed (le plus grand seed round de l'histoire européenne) à 3,5 milliards de valorisation Les world models apprennent à prédire et comprendre la réalité physique plutôt qu'à prédire le prochain token d'une séquence Slogan d'AMI : "Real intelligence does not start in language. It starts in the world." Paris comme base stratégique pour challenger la Silicon Valley dans la prochaine rupture de l'IA Debezium 2026 : résultats du sondage communautaire https://debezium.io/blog/2026/04/27/debezium-2026-survey-results/ Debezium est un outil de Change Data Capture (CDC) open source qui capture les modifications de bases de données en temps réel pour les diffuser vers des systèmes comme Kafka. 98,6% des répondants utilisent Debezium activement ou prévoient de le faire dans l'année, avec 91,3% déjà en production. 63,8% des déploiements tournent sur Kubernetes, 60,9% utilisent Kafka Connect auto-géré, et 17,4% restent sur des VMs ou bare metal. Helm charts est l'approche dominante pour la gestion de configuration, souvent combiné avec GitOps, CI/CD, Ansible ou Terraform. PostgreSQL domine les connecteurs utilisés à 69,6%, suivi de MySQL (33,3%), SQL Server (29%) et Oracle (27,5%). Les volumes de changements capturés vont de 1-25 modifications par minute jusqu'à 1-2 millions par minute selon les environnements. Infinispan rejoint l'écosystème OGX comme fournisseur de stockage vectoriel https://infinispan.org/blog/2026/04/17/infinispan-joins-ogx-ecosystem OGX (anciennement Llama Stack) est un serveur API agentique open source pour construire des applications d'IA complètes. OGX compose des fournisseurs d'inférence, des stores vectoriels, des backends de sécurité, des runtimes d'outils et du stockage de fichiers en un seul serveur déployable. OGX se positionne comme une alternative à l'API OpenAI, déployable sur diverses infrastructures et modèles. OGX cible les workflows RAG (Retrieval-Augmented Generation) et les applications agentiques. Infinispan s'y intègre comme fournisseur de vector IO, apportant recherche vectorielle, par mots-clés et hybride. Je n'ai pas entendu parlé de ce renommage, vous le voyez dans vos deploiements ? Outillage cmux un nouveau terminal basé sur Ghostty spécialisé pour les coding agents https://cmux.com/ Application macOS native construite sur le moteur de rendu Ghostty (libghostty), offrant une accélération GPU pour une fluidité maximale Conçu spécifiquement pour le multitâche et les workflows assistés par IA, avec des onglets verticaux affichant la branche Git, le répertoire et les ports actifs Intègre des notifications qui illuminent les panneaux lorsqu'un agent IA (Claude Code, Codex, etc.) nécessite l'attention de l'utilisateur Propose un navigateur web intégré et scriptable qui peut être affiché en écran scindé à côté du terminal via une API Alternative moderne à tmux, ne nécessitant pas de fichiers de configuration complexes ou de préfixes de touches pour la gestion des vitres et des sessions Supporte nativement tous les agents de codage en ligne de commande et permet l'automatisation via une API socket et une interface CLI dédiée Git Worktree comme un chef https://www.metal3d.org/blog/2026/git-worktree-comme-un-chef/ Article par Patrice Ferlet Git Worktree: Travailler sur plusieurs branches simultanément via des répertoires distincts. Évite git stash ou clones multiples pour le changement de contexte rapide. Méthode "bare" (recommandée): Cloner le dépôt en mode bare (ex: .bare). Lier le dossier racine au dépôt bare via un fichier .git. Configurer le remote tracking pour voir toutes les branches distantes. Ajouter des worktrees pour chaque branche (git worktree add ). Avantages: Économie d'espace, source de vérité unique (un git fetch met tout à jour), hooks/configs partagés, sécurité. Conseils: Ne jamais faire de git checkout à l'intérieur d'un worktree. git fetch --all depuis n'importe quel worktree pour tout mettre à jour. git worktree add --detach pour tester des merges temporaires sans créer de branche. Supprimer: git worktree remove puis git worktree prune. Un script wtree est fourni pour automatiser l'initialisation du setup "bare". Améliore considérablement le workflow. L'IDE meurt et vite https://x.com/jdegoes/status/2036931874057314390?s=46&t=C18cckWlfukmsB_Fx0FfxQ Des leaders techniques prédisent la fin rapide de l'IDE traditionnel, remplacé par des interfaces conversationnelles agentiques Le changement de paradigme : le développeur n'écrit plus des lignes de code mais exprime son intention et supervise des agents autonomes Des outils comme Claude Code, Copilot et Cursor transforment déjà radicalement les workflows de développement quotidiens L'IDE centré sur l'éditeur de code perd sa raison d'être quand l'agent lit, modifie et structure le code de manière autonome La transition est comparable au passage du desktop au mobile : les pratiques établies depuis 30 ans remises en question en quelques mois Le source de Claude Code a leaké via probablement le codemap et un site decrit sont fonctionnement https://ccunpacked.dev/ Le 31 mars 2026, Anthropic a accidentellement inclus les sourcemaps dans un package npm de Claude Code, exposant ~512 000 lignes de TypeScript La fuite n'était pas un piratage mais une erreur humaine : un "*.map" oublié dans .npmignore Le site ccunpacked.dev a été lancé pour analyser et visualiser le code source décompressé Le code révèle un agent background permanent nommé "KAIROS", un mode furtif pour cacher les contributions des employés Anthropic à l'open source, et 44 feature flags cachés Une fonctionnalité inédite "Buddy" (animal de compagnie électronique dans le terminal) et un mode "dream" pour l'idéation continue ont été découverts Anthropic a confirmé : "Aucune donnée client sensible n'était impliquée. Erreur humaine dans le packaging de la release." Gemini CLI passe aux agents https://x.com/srithreepo/status/2039794081925382307?s=46&t=GLj1NFxZoCFCjw2oYpiJpw Gemini CLI, l'agent IA open source de Google pour le terminal, introduit des hooks dans sa boucle agentique Les hooks permettent d'exécuter des scripts automatiquement (scanners de sécurité, vérifications de conformité, logging) à chaque étape de l'agent Lancement de Gemini CLI GitHub Actions : un agent autonome pour les repositories qui peut exécuter des tâches de codage de routine Support des MCP servers pour étendre les capacités et des "Agent Skills" pour des workflows spécialisés Mode agent disponible dans VS Code et IntelliJ avec accès aux outils du système de fichiers et terminal Wispr, le speech to text en local sur macOS http://wispr.stormacq.com/ Wispr est une application macOS de dictée vocale entièrement locale, propulsée par Whisper (OpenAI) sur appareil, sans cloud ni tracking Sébastien Stormacq a développé Wispr en un jour et demi sans écrire une seule ligne de code, grâce à Kiro CLI (agent IA Amazon) Disponible en open source sur GitHub et via Homebrew Détection automatique de la langue, insertion du texte au curseur dans n'importe quelle application via un raccourci global En un mois : 19 releases incluant mode mains-libres, suppression des mots de remplissage, auto-envoi pour les chats, et un outil CLI Exemple concret de développement vibe coding produisant un outil de qualité production sans expertise Swift préalable Comment, Gordon, l'assistant spécialisé en Docker est né https://n9o.xyz/posts/202603-building-gordon/ Nuno Coração (n9o.xyz) détaille comment Gordon, l'assistant spécialisé Docker, a été construit sur docker-agent, le runtime d'agents IA open source de Docker écrit en Go Les agents sont définis en YAML déclaratif et distribués comme des artefacts OCI, sans mise à jour binaire nécessaire L'architecture initiale en essaim de 9 agents spécialisés a été abandonnée au profit d'un agent racine unique avec un prompt soigneusement conçu Le modèle utilisé est Claude Haiku 4.5, suffisant après optimisation des prompts Principe clé "show, then do" : toute action de l'agent nécessite une approbation explicite de l'utilisateur La description des outils impacte fortement la précision du LLM : ajouter des outils peut paradoxalement dégrader les performances existantes Le prompt est une spécification détaillée (identité, patterns d'accès fichiers, règles de sécurité) plutôt qu'une simple instruction IBM Bob https://bob.ibm.com/blog/announcing-ibm-bob-launch IBM Bob assistant IA d'IBM pour coder sur de vraies codebases (lancé avril 2026) 5 modes : Ask, Plan, Code, Advanced (MCP), Orchestrator Détecte la complexité du code en temps réel et propose des refactos Fait des revues de code automatiques sur tes branches/issues GitHub Permet d'écrire en langage naturel directement dans l'éditeur Fonctionne aussi en terminal/CLI et dans les pipelines CI/CD Sécurité : approbation manuelle, .bobignore, checkpoints, pas de training sur tes prompts How I use Claude - 50 tips pratiques https://www.youtube.com/watch?v=mZzhfPle9QU Staff Engineer Meta partage 50 tips après 6 mois d'utilisation intensive de Claude Code Basé sur ~12h/jour d'usage perso et professionnel Couvre tout : bases, workflows avancés, parallélisation Objectif : partager ce qu'il aurait voulu savoir dès le départ Méthodologies Quelqu'un rale sur la non soutenabilité des bases de code écritent avec des agents https://mariozechner.at/posts/2026-03-25-thoughts-on-slowing-the-fuck-down/ Mario Zechner estime que les agents IA font les mêmes erreurs répétitivement sans apprendre, accumulant la complexité à grande vitesse faute de bottlenecks humains Sans vision globale, les agents créent du cargo-cult : les "best practices" de l'industrie appliquées localement sans cohérence architecturale La croissance de la base de code dégrade la capacité des agents à retrouver le code existant → duplication et incohérences croissantes Il cite des pannes AWS et des initiatives qualité Microsoft comme signes préoccupants liés au code généré par IA Solution : réserver les agents aux tâches délimitées et évaluables, garder l'architecture, les APIs et les systèmes critiques écrits à la main Maintenir une revue de code rigoureuse et traiter les humains comme les gardiens finaux de la qualité On m'oblige à utiliser l'IA https://n.survol.fr/n/on-moblige-a-utiliser-lia Éric D. défend l'adoption obligatoire de l'IA comme décision stratégique légitime, comparable au choix du full remote ou de la stack technique Il distingue la décision stratégique (adoption IA) de la méthode d'accompagnement (qui reste collaborative et bienveillante) La compétence IA devient un critère de recrutement : chercher des candidats déjà curieux et explorateurs de ces outils L'alignement culturel sur les pratiques et outils est un prérequis à la cohésion d'équipe Le refus d'adopter certains outils stratégiques peut justifier de ne pas recruter un candidat autrement compétent Encore une metodo SPDD https://martinfowler.com/articles/structured-prompt-driven/ Problème : l'IA accélère le dev individuel mais amplifie ambiguïtés et incohérences à l'échelle d'une équipe. martinfowler SPDD : traiter les prompts comme des artefacts versionnés, révisables et réutilisables plutôt que des échanges jetables. martinfowler Canvas REASONS : 7 dimensions (Requirements, Entities, Approach, Structure, Operations, Norms, Safeguards) pour guider le LLM de l'intention à l'exécution. martinfowler Workflow en 6 étapes : exigences → analyse → contexte → prompt structuré → code → tests unitaires, chaque étape s'appuyant sur la précédente. martinfowler 3 compétences clés : abstraction d'abord, alignement de l'intention, revue itérative. martinfowler Limites : fort ROI sur du code métier complexe, peu adapté aux hotfixes urgents, scripts jetables ou travail créatif/visuel. m Sécurité Le projet Glasswing pour sécuriser les logiciels https://www.anthropic.com/glasswing Anthropic lance Glasswing, une initiative de cybersécurité utilisant Claude Mythos Preview pour identifier des vulnérabilités zero-day 12 partenaires fondateurs dont AWS, Apple, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft et NVIDIA Anthropic investit 100 millions de dollars en crédits de modèle et 4 millions en dons aux organisations de sécurité open source Le modèle opère avec une autonomie substantielle, identifiant des milliers de vulnérabilités dans les OS, navigateurs et infrastructures critiques Plus de 40 organisations supplémentaires ont accès pour scanner et sécuriser leurs systèmes Objectif : donner l'avantage aux défenseurs avant que les techniques de hacking assistées par IA ne se généralisent chez les attaquants LinkedIn vous espionne https://frenchbreaches.com/blog/linkedin-est-accuse-de-fouiller-dans-votre-ordinateur-illegalement Scandale "BrowserGate" : LinkedIn injecte du JavaScript qui tente de détecter les extensions Chrome installées sur votre navigateur Le script analysé contient une liste codée en dur de 6 222 extensions Chrome avec identifiants et chemins de fichiers internes Croissance alarmante de la liste ciblée : 38 extensions en 2017 → 461 en 2024 → ~1 000 en mai 2025 → 6 222 début 2026 Les données collectées incluent aussi CPU, RAM, résolution d'écran, timezone et état batterie pour du fingerprinting Certaines extensions ciblées sont liées à la neurodivergence, aux pratiques religieuses ou aux opinions politiques → violation grave du RGPD LinkedIn défend que le scan vise uniquement à détecter les extensions qui pratiquent le scraping de données Post mortem de la supply chain attack sur la librairie NPM axios https://github.com/axios/axios/issues/10636 Le 31 mars 2026, deux versions malveillantes d'axios (1.14.1 et 0.30.4) ont été publiées via un compte mainteneur compromis Vecteur d'attaque : RAT installé via ingénierie sociale ciblée sur la machine personnelle du mainteneur principal La 2FA ne protège pas si la machine de l'utilisateur est compromise : l'attaquant contrôle tout et peut agir comme l'utilisateur Les packages malveillants injectaient plain-crypto-js@4.2.1, un cheval de Troie multi-plateforme (macOS, Windows, Linux) Détection communautaire en ~3 heures, suppression par npm, mesures correctives : rotation complète des credentials Changements préventifs : publication via OIDC, releases immuables, amélioration des pratiques GitHub Actions Passbolt un gestionnaire de mots de passe open source https://lesjoiesducode.fr/passbolt-gestionnaire-de-mots-de-passe-gratuit-open-source-que-votre-equipe-merite-vraiment Gestionnaire de mots de passe open source conçu pour le partage d'identifiants en équipe, utilisé par plus de 50 000 organisations Chiffrement individuel par utilisateur et par version de credential, pas de coffre-fort partagé — architecture zero-knowledge "Forward secrecy" : quand un membre quitte l'équipe, ses copies chiffrées sont automatiquement révoquées sans reset manuel Supporte TOTP, clés SSH, tokens API et champs personnalisés avec piste d'audit complète de tous les accès Édition communautaire entièrement gratuite avec utilisateurs illimités, auto-hébergeable ou cloud Chiffrement OpenPGP nécessitant passphrase + clé privée, avec tokens visuels anti-phishing Loi, société et organisation Anthropic fait un don d'1,5 millions de dollars à la fondation Apache https://news.apache.org/foundation/entry/the-apache-software-foundation-announces-1-5m-donation-from-anthropic Anthropic donne 1,5 million de dollars à l'ASF pour soutenir l'infrastructure, la sécurité et la communauté open source Vitaly Gudanets (CISO d'Anthropic) : "Soutenir l'ASF est un investissement direct dans la résilience et l'intégrité des systèmes dont dépend l'IA moderne" Les fonds financeront les systèmes de build, les processus de sécurité et les services aux projets Apache Ce don est le déclencheur de l'initiative IA responsable à 10 millions de dollars de l'ASF L'infrastructure Apache est invisible mais critique : des systèmes financiers aux plateformes de santé, elle sous-tend l'écosystème logiciel mondial L'ASF lance l'initiative IA responsable https://news.apache.org/foundation/entry/the-apache-software-foundation-launches-10m-responsible-ai-initiative-with-initial-1-75m-donation L'ASF lance une initiative pour une IA responsable dotée d'un budget de 10 millions de dollars sur 3 ans minimum Anthropic est le premier donateur avec 1,5 million de dollars ; Alpha-Omega contribue 250 000 dollars L'initiative fournit aux projets Apache un accès à des modèles IA pour l'expérimentation et la sécurité Elle soutient l'ensemble de la chaîne IA/ML : pipelines de données, infrastructure, frameworks de deep learning Des tracks de conférences, hackathons et bourses de voyage sont prévus pour élargir la communauté Les principes directeurs incluent la supervision humaine, l'intégrité des licences et la sécurité open source Oracle vire 30000 personnes https://rollingout.com/2026/03/31/oracle-slashes-30000-jobs-with-a-cold-6/ Oracle licencie 20 000 à 30 000 employés, 18% de ses effectifs mondiaux. Les salariés ont appris leur licenciement par un simple email à 6h du matin, sans aucun préavis. L'accès à tous les systèmes (Slack, Zoom, badges) a été coupé immédiatement après. But : libérer 8 à 10 milliards de dollars pour construire des centres de données IA. Oracle a déjà contracté 50 milliards de dettes en 2026 pour financer ses projets IA. Paradoxe : l'entreprise affiche un bénéfice record de 6,13 milliards, mais ses liquidités sont dans le rouge. L'action Oracle a perdu plus de la moitié de sa valeur depuis septembre 2025. Et si l'IA n'était qu'un prétexte pour licencier https://eventuallycoding.com/p/ia-licenciements-et-si-l-intelligence-artificielle-n-etait-qu-une-excuse Hugo Lassiège (eventuallycoding) estime que les entreprises utilisent l'IA comme narratif commode pour masquer des erreurs de gestion passées (Block a triplé ses effectifs post-COVID sans croissance des revenus correspondante) Moins de 1% des licenciements technologiques seraient réellement dus à des gains de productivité IA selon les analyses citées Mesurer la productivité des développeurs reste un problème non résolu, mais les entreprises affirment des gains d'efficacité sans preuves Des pressions économiques réelles (inflation, guerres commerciales, coûts énergétiques) sont masquées derrière le discours IA Les restructurations nécessaires sont présentées comme des transformations AI-driven positives pour rassurer les investisseurs Il y voit une fenêtre d'opportunité pour l'Europe pendant que les géants américains se restructurent GitHub Copilot va utiliser les interacitons pour entrainer ses modèles sauf si vous vous délistez https://github.blog/news-insights/company-news/updates-to-github-copilot-interaction-data-usage-policy/ À partir du 24 avril 2026, GitHub utilise par défaut les interactions des utilisateurs Copilot Free, Pro et Pro+ pour entraîner ses modèles Les données collectées incluent le code accepté ou modifié, les snippets envoyés, les noms de fichiers et structures de dépôts, et les retours utilisateurs Les utilisateurs Copilot Business, Enterprise et les dépôts d'entreprise sont exclus de cette collecte de données d'entraînement Opt-out disponible dans les paramètres GitHub > "Privacy" ; les préférences de désactivation préalables sont conservées automatiquement Objectif déclaré : améliorer la précision des modèles sur les langages et cas d'usage du monde réel Grosse percée de Claude Code dans les commits sur GitHub https://aifoc.us/damn-claude-thats-a-lot-of-commits/ Explosion de Claude Code : En six mois, Claude Code est passé de 0,7 % à 4,5 % de tous les commits publics sur GitHub, surpassant tous les autres outils d'IA combinés. Adoption massive des agents IA : Environ 5 % des commits publics sur GitHub sont désormais générés par des agents IA, un chiffre en croissance rapide depuis fin 2025. Domination des bots sur GitHub : Au-delà des commits, les outils d'IA sont omniprésents dans la gestion des pull requests et des problèmes (Copilot et CodeRabbit notamment). Limites méthodologiques : Les données ne concernent que les dépôts publics (les entreprises utilisent massivement des dépôts privés, invisibles ici). Le comptage dépend fortement de la visibilité des signatures (certains outils comme Claude marquent systématiquement leurs commits, d'autres non) L'API de recherche GitHub présente une fiabilité variable à cette échelle. Changement de paradigme : Le développement logiciel vit une transition majeure, comparable au passage du desktop au mobile. L'intégration des agents IA dans le cycle de production n'est plus une expérimentation, mais une réalité opérationnelle à grande échelle. Dysmaths une application pour aider à apprendre les mathématiques et la géométrie lorsque l'on souffre de dyspraxie, dysgraphie https://dysmaths.com/ Application web pour aider les élèves de collège et lycée souffrant de dysgraphie et dyspraxie à faire des maths et de la géométrie Outils de dessin à main levée, géométrie précise (compas, rapporteur, règle) et opérations structurées (fractions, racines, puissances, symboles mathématiques) Export PDF et PNG avec conservation fidèle de l'échelle pour l'impression et la soumission des exercices Options d'accessibilité : police OpenDyslexic, personnalisations d'interface, import d'images et de PDFs Répond à un besoin réel : les outils standards ne sont pas adaptés aux difficultés de coordination et d'organisation spatiale en mathématiques IA ou réalité ? Par Amistory https://www.youtube.com/watch?v=PPYdAhBBF2I L'IA génère des contenus (images, voix, vidéos) de plus en plus indétectables Les arnaques au clonage de voix et deepfakes sont en forte hausse Les faux contenus viraux manipulent l'opinion à grande échelle Le faux n'est plus un accident, c'est devenu un système organisé La société entre dans une ère de doute généralisé sur le réel Comment s'informer quand le réel lui-même peut être simulé ? Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 12-13 mai 2026 : Lyon Craft - Lyon (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 19-20 mai 2026 : Green Code Challenge - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 27 mai 2026 : aMP Day Strasbourg 2026 - Strasbourg (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 4 juin 2026 : Workplace Intelligence Days - 1ère édition - Lyon (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 9 juin 2026 : France API 2026 - Paris (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 15 juin 2026 : Jupyter Workshops: Demystifying MyST Markdown in Education - Orsay (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 23-24 juin 2026 : MWCP 2026 - Paris (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 25-26 juin 2026 : Agile Tour Toulouse 2026 - Toulouse (France) 27 juin 2026 : Asynconf - Paris (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 28-30 août 2026 : State of the Map - Champs-sur-Marne (France) 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 10-11 septembre 2026 : Nantes Craft - Nantes (France) 17 septembre 2026 : dotAI - Paris (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 18 septembre 2026 : dotJS - Paris (France) 18 septembre 2026 : WordCamp Bretagne - Rennes (France) 22 septembre 2026 : Salon Data 2026 - Nantes (France) 22-23 septembre 2026 : Agile en Seine & IA 2026 - Paris (France) 24 septembre 2026 : OWASP AppSec Days France 2026 - Paris (France) 24 septembre 2026 : PlatformCon Paris - Paris (France) 24 septembre 2026 : React Native Connection 2026 - Paris (France) 24-26 septembre 2026 : Paris Web 2026 - Paris (France) 28-29 septembre 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 2 octobre 2026 : DevFest Perros-Guirec 2026 - Perros-Guirec (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) 12 octobre 2026 : Dev With AI - Paris (France) 27-29 octobre 2026 : Directions EMEA 2026 - Paris (France) 29-30 octobre 2026 : BDX I/O 2026 - Bordeaux (France) 30 octobre 2026 : Cloud Nord 2026 - Lille (France) 4-5 novembre 2026 : Devoxx Morocco - Casablanca (Morocco) 14-15 novembre 2026 : Capitole du Libre - Toulouse (France) 19 novembre 2026 : DevFest Toulouse 2026 - Toulouse (France) 27 novembre 2026 : DevFest Paris 2026 - Paris (France) 1-3 décembre 2026 : Apidays Paris - Paris (France) 4 décembre 2026 : DevFest Lyon 2026 - Lyon (France) 4 décembre 2026 : DevFest Dijon 2026 - Dijon (France) 9-10 décembre 2026 : OpenSource Expérience - Paris (France) 9-10 décembre 2026 : DevOps REX - Paris (France) 10 décembre 2026 : KCD Provence - Aix-en-Provence (France) 7-9 avril 2027 : Devoxx France 2027 - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Are you at the top of your company's tokenmaxxing leaderboard yet? This week on the Friday Deploy, Andrew and Ben explore the controversial trend of "tokenmaxxing" sweeping through tech giants like Meta and Disney, as well as GitHub Copilot's shift to usage-based pricing that signals the end of the cheap AI era. The hosts also break down a terrifying incident where a rogue AI agent wiped out a production database and examine a new "vegan" language model trained exclusively on pre-1931 historical data. Finally, they react to a study revealing that 35% of all new websites are now AI-generated and close out the show with the drunk musings of a senior engineer. Read the guide: The APEX FrameworkFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:An AI Agent Just Destroyed Our Production Data. It Confessed in Writing.Tokenmaxxing Is The Dumbest Metric In Tech Right NowIntroducing talkie: a 13B vintage language model from 1930Study Finds A Third of New Websites are AI-GeneratedFlipbook is an infinite visual browser generated entirely on demand in real time.Drunk Post: Things I've Learned as a Senior EngineerOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

The episode identifies a structural shift from AI as a discrete feature to AI as an ongoing operational system, emphasizing the growing burden of governance, accountability, and consumption oversight for managed service providers. Companies such as Microsoft, Cisco, and Google are redirecting strategy toward building control planes and governance infrastructure to address operational friction in deploying AI agents, as operational complexity—rather than access to tools—emerges as the bottleneck. This shift is substantiated by reports from GTIA, Cisco, and insights into vendor incentives and partner programs. Evidence highlights a clear disconnect between widespread AI adoption and the maturity required to operationalize these systems. According to the Global Technology Industry Association (GTIA), 97% of IT service providers use some form of AI, but only 28% consider themselves AI-driven. Cisco reports that while 85% of enterprises are piloting AI agents, just 5% have moved them into production, pointing to persistent trust and operational gaps. Axios adds that in AI-intensive teams, compute expenditures are surpassing employee costs, with large organizations like Nvidia and Uber experiencing rapid escalation in AI-driven utility bills. Further developments reinforce these themes. Microsoft is aligning partner incentives around new SKUs such as Microsoft 365 E7, explicitly targeting AI as a delivery motion rather than a feature. Consumption-based pricing—exemplified by the move to token-based billing for GitHub Copilot—exposes clients to “death by a thousand cuts” if usage is not closely monitored. Reports from Cobalt indicate significant security risk, with one in five organizations experiencing an incident involving large language models and a low remediation rate for identified vulnerabilities. Vendors such as Google and OpenAI are responding with new management platforms and reliance on consultancies to address integration and governance challenges. For MSPs and IT leaders, the practical implications are clear: AI's operational realities dictate a need to explicitly define governance, permission structures, and consumption management as part of service delivery. Unscoped or bundled AI services risk unbilled labor, unclear liability, and unmanaged exposure to security and cost overruns. The operational pivot involves inventorying AI features, establishing ownership, applying identity and access controls, tracking spend, and updating contracts to clarify accountability. Without formalizing these boundaries, MSPs may be left absorbing risk and cost by default. 00:00 AI Reality Check 04:43 Operator Burden 07:11 Meter the Risk 10:35 Why Do We Care?  Supported by: Acronis ScalePad Zero Networks  Upcoming event: The Pivotal Point of IT: Building Services for the AI-First EraDate: May 13 at 1p.m. EDTRegister: https://go.acronis.com/davesobelaiera

This is a recap of the top 10 posts on Hacker News on April 27, 2026. This podcast was generated by wondercraft.ai (00:30): Microsoft and OpenAI end their exclusive and revenue-sharing dealOriginal post: https://news.ycombinator.com/item?id=47921248&utm_source=wondercraft_ai(01:55): GitHub Copilot is moving to usage-based billingOriginal post: https://news.ycombinator.com/item?id=47923357&utm_source=wondercraft_ai(03:20): Men who stare at wallsOriginal post: https://news.ycombinator.com/item?id=47920074&utm_source=wondercraft_ai(04:45): 4TB of voice samples just stolen from 40k AI contractors at MercorOriginal post: https://news.ycombinator.com/item?id=47919630&utm_source=wondercraft_ai(06:10): Is my blue your blue?Original post: https://news.ycombinator.com/item?id=47926861&utm_source=wondercraft_ai(07:36): Pgbackrest is no longer being maintainedOriginal post: https://news.ycombinator.com/item?id=47919997&utm_source=wondercraft_ai(09:01): China blocks Meta's acquisition of AI startup ManusOriginal post: https://news.ycombinator.com/item?id=47920315&utm_source=wondercraft_ai(10:26): Show HN: OSS Agent I built topped the TerminalBench on Gemini-3-flash-previewOriginal post: https://news.ycombinator.com/item?id=47920787&utm_source=wondercraft_ai(11:51): Dutch central bank ditches AWS and chooses Lidl for European CloudOriginal post: https://news.ycombinator.com/item?id=47922712&utm_source=wondercraft_ai(13:17): GitHub is having issues nowOriginal post: https://news.ycombinator.com/item?id=47924775&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Tim Cook is stepping down in September, and The Trio has plenty of thoughts on what the Ternus era means for Apple. Kotaro dives into his embedded systems rabbit hole (Raspberry Pis, ESP32s, and a Godot refresher), while Steve sounds the AI hype alarm, comparing the current frenzy to NFTs and the Metaverse, complete with a shoe company that somehow pivoted to GPU data centers on a $50M budget. Steve's monitor saga drags on, the SpaceX/Cursor "announcement of an announcement" gets the skepticism it deserves, and The Trio wraps up with details on the May 14 IRL meetup in Philly.## Chapters00:00 Introductions05:54 Kotaro's Side Project Adventures08:29 Diving into Hardware and Embedded Systems11:17 Raspberry Pi Adventures and Microcontrollers14:02 Creating AI Projects with Raspberry Pi18:19 Exploring DIY Devices and Learning in Tech23:21 Game Development and Learning Curves24:16 AI Tools and Programming Challenges26:55 The AI Hype Update and Economic Realities36:57 Balancing AI Use in Software Development39:53 The Hype Cycle of AI and Media44:32 So Long, Time Apple, and Thanks for All the Fish!53:07 The Future of Apple in the Ternus Era56:43 Steve's Monitor Watch Update58:57 Wrap Up01:00:37 Tag## Show Notes- Tim Cook announced his retirement as Apple CEO, effective September, with hardware chief John Ternus set to take the helm.- The Trio agrees Cook grew Apple into the world's most valuable company, and the MacBook Neo might just be his most quintessential product.- Ternus is seen as more of an engineer/visionary, and Steve is cautiously hoping he'll bring more Jobs-era decisiveness to Apple's product direction.- Kotaro is deep in embedded systems this year, learning Raspberry Pi 5s and ESP32 microcontrollers the hard way (wrong cables, wrong GPIO boards, all of it).- He's built a basic AI chatbot device (think DIY Rabbit R1, hooked to Google Gemini) and is eyeing a 5-inch touchscreen home automation kiosk.- TRMNL, the E Ink dashboard device, comes up as a goal Kotaro is working toward, though the large version is sold out.- GitHub Copilot paused new signups, dropped Opus from Pro plans, and started rationing usage, which Steve reads as AI's economic reality finally catching up.- Steve puts AI hype at NFT/Metaverse levels: a shoe company pivoted to GPU data centers, and SpaceX "announced" it has the option to buy Cursor for $60B without actually buying anything.- Steve's XDR monitor watch continues: he watched a glowing review, still can't justify the price, but is eyeing the nano-texture option for his glare-heavy room.- The Trio closes with news of a PhillyCocoa IRL meetup on May 14 at the Vanguard building, featuring Kotaro on Metal shaders.## Links**Hardware & Devices**TRMNL: https://trmnl.com/ | Rabbit R1: https://www.rabbit.tech/rabbit-r1**Snazzy Labs TRMNL Review**Watch: https://www.youtube.com/watch?v=YWw5NKUx40o**AI Hype Update**We are near peak hype (Primeagen): https://www.youtube.com/watch?v=rAREqdtUN48SpaceX/Cursor ($60B): https://www.reuters.com/technology/spacex-says-it-has-option-acquire-startup-cursor-60-billion-2026-04-21/**One More Thing**IRL Meetup RSVP (May 14): https://luma.com/i00ll61z**PhillyCocoa:** http://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

In FOLLOW UP, the child social media crackdown keeps expanding. Turkey just approved a ban for under-15s, and Sony will require age verification for PlayStation communication features in the UK and Ireland starting in June—because now you need to prove you're an adult before trash-talking strangers online. Meanwhile, Anthropic's prediction that fully autonomous AI employees would already be transforming business hasn't materialized. Agentic systems are still struggling with basic workflows and, in some cases, slowing developers down. And in a more concrete reversal, Elon Musk acknowledged that pre-2023 Tesla Hardware 3 will never support Full Self-Driving. Customers who paid for the feature are now being steered toward discounted trade-ins, new cameras, and upgraded hardware—prompting obvious legal exposure.IN THE NEWS: SpaceX is reportedly targeting what could be the largest IPO ever, at roughly a $1.75 trillion valuation, with dual-class shares that preserve Musk's control through super-voting rights. Prediction markets continue to degrade: Kalshi suspended political candidates for trading on their own races, and Polymarket saw alleged manipulation via a tampered weather sensor at Charles de Gaulle Airport. On the AI front, Anthropic's new Mythos model had a chaotic rollout—used by the NSA, applied to patch hundreds of Firefox vulnerabilities, and briefly exposed through unauthorized access in a developer portal. Amazon followed with a $25 billion investment in Anthropic, even as governments appear to access similar capabilities independently.At the same time, the economics are tightening. Free tiers are shrinking, GitHub Copilot is shifting to token-based billing after costs doubled, and startups are normalizing six-figure monthly AI compute bills. Infrastructure growth continues unchecked: thousands of new data centers are planned across the U.S., while xAI faces scrutiny in Memphis over water usage and delayed mitigation projects. Environmental commitments increasingly resemble marketing rather than enforceable targets.Policy signals are equally aggressive. DHS is exploring smart glasses for ICE agents with facial recognition and gait analysis by 2027. Palantir published a manifesto advocating expanded use of state power with rhetoric that raised concerns about ideological framing. On a lighter note, a University of California, Santa Barbara study suggests that brief exposure to experimental film measurably increases creativity compared to standard social media consumption.MEDIA CANDY: Silo returns July 3 on Apple TV+, while The Lord of the Rings: The Rings of Power Season 3 is expected sooner than planned. Battlestar Galactica lands on Paramount+ and Pluto TV May 1. Dead Can Dance is releasing monthly singles via its Bandcamp imprint. Deezer reports 44% of daily uploads—about 75,000 tracks—are AI-generated, though only a small fraction of streams come from them, many flagged as fraudulent. And yes, Jessica Jones is back in Daredevil.APPS & DOODADS: Apple patched the notification-cache bug that allowed forensic tools to recover deleted Signal messages. Roblox agreed to a $12 million settlement with Nevada and is rolling out facial age estimation, ID verification, and new contact controls, while still facing multi-state litigation. Cash App is targeting younger users—ages six to twelve—with parent-managed accounts, debit cards, and interest incentives. Separately, a federal judge issued a preliminary injunction protecting ICE-tracking apps, ruling that government pressure on Apple and Meta to remove them likely violated First Amendment protections.IN THE DARK SIDE WITH DAVE: AI-generated Star Wars fan films are improving visually, even if performances remain rigid. The current era of Star Trek is effectively closing out with a large prop auction, notably excluding Star Trek: Strange New Worlds. The broader discussion circles back to time compression—post-pandemic, and with age—and the persistent disconnect between economic scale and general dissatisfaction.Sponsors:DeleteMe - Get 20% off your DeleteMe plan when you go to JoinDeleteMe.com/GOG and use promo code GOG at checkout.Shopify - Sign up for your one-dollar-per-month trial today at Shopify.com/grumpyPrivate Internet Access - Go to GOG.Show/vpn and sign up today. For a limited time only, you can get OUR favorite VPN for as little as $2.03 a month.SetApp - With a single monthly subscription you get 240+ apps for your Mac. Go to SetApp and get started today!!!1Password - Get a great deal on the only password manager recommended by Grumpy Old Geeks! gog.show/1passwordShow notes at https://gog.show/743Watch on YouTube at https://youtu.be/iLiRLcgP7zMFOLLOW UPSony will require age checks in the UK and Ireland to access PlayStation communication featuresTurkey wants to ban social media for kids under 15Today Is the Day Anthropic Promised That Fully Autonomous Employees Would Be Tearing Through the Business WorldThe Hardware in Your Pre-2023 Tesla Will Never Allow It to Fully Drive Itself, Elon Musk AdmitsIN THE NEWSExclusive: Musk and insiders to retain voting control of SpaceX after IPO, filing showsKalshi suspended three political candidates from its platform for insider tradingSomeone allegedly used a hairdryer to rig Polymarket weather betsThe NSA is reportedly using Anthropic's new model MythosMozilla says it patched 271 Firefox vulnerabilities thanks to Anthropic's Claude MythosAnthropic is investigating 'unauthorized access' of its Mythos cybersecurity toolAmazon will invest up to $25 billion in Anthropic in a broad dealAI Companies Think Destroying the Planet Is an Acceptable Trade-Off for Unlimited ProfitsMusk leaves Memphis high and dryStartups Brag They Spend More Money on AI Than Human EmployeesYou're about to feel the AI money squeezeExclusive: Microsoft To Shift GitHub Copilot Users To Token-Based Billing, Tighten Rate LimitsHomeland Security reportedly wants to develop smart glasses for ICEPalantir posted a manifesto that reads like the ramblings of a comic book villainWhat I Learned About Billionaires at Jeff Bezos's Private RetreatResearchers May Have Found the Antidote to Social Media Brain Rot: Experimental FilmShort of the WeekThis Scammer Used an AI-Generated MAGA Girl to Grift ‘Super Dumb' MenJaw-Dropping iPhone Video of Earth Setting Behind the Moon Is Rightfully Breaking the InternetMEDIA CANDYSilo season 3 just got its Apple TV release date and first trailerSilo — Season 3 Official Teaser | Apple TVSurprise! ‘Rings of Power' Season 3 Is Arriving Earlier Than Expected‘Battlestar Galactica' Is Blasting Back to StreamingDead Can Dance Returns with “Death Cults,” Their Second New Song in Five YearsNot a Soul Was Dancing to Sabrina Carpenter and Madonna at CoachellaDeezer says AI-made songs make up 44 percent of daily uploadsAPPS & DOODADSApple fixes bug that cops used to extract deleted chat messages from iPhonesRoblox agrees to a $12 million settlement with NevadaCash App is targeting a new kind of customer: 6- to 12-year-oldsJudge sides with creators of banned ICE trackers who allege DHS and DOJ violated their First Amendment rightsTHE DARK SIDE WITH DAVEDave BittnerThe CyberWireHacking HumansCaveatControl LoopOnly Malware in the BuildingStar Wars: Darth Vader BEATS the Millennium Falcon to Cloud City (Fan Film)Star Wars: Darth Vader Learns the TRUTH About LUKE SKYWALKER (Fan Film)The Current Era of ‘Star Trek' Is Ending With a Fire SaleStar Trek: Discovery Seasons 1-5 Online AuctionStar Trek Universe: 60th Anniversary Auction Featuring Items from Set - Auction #1Have you ever known anyone who was born in the 1800s?If America's So Rich, How'd It Get So Sad?See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Is the era of cheap, unlimited AI tokens officially over? This week on the Friday Deploy, Andrew and Ben walk through the sudden wave of AI pricing chaos—from GitHub Copilot's panic-paused signups to Anthropic's confusing pricing tests—and break down the terrifying Vercel security breach caused by a single over-permissioned AI tool. They also examine 12 game-changing architecture patterns exposed in the Claude Code leak to help you safely orchestrate your own agentic workflows. Finally, they discuss how to avoid the lethal trifecta of agentic security risks before mourning the tragic deletion of their Claude Code buddies.Read the guide: The APEX FrameworkFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's stories:Changes to GitHub Copilot Individual plansIs Claude Code going to cost $100/month? Probably not—it's all very confusingAnthropic, OpenAI, Google, and Microsoft agree that the harness is the product. They disagree on the price.12 Agentic Harness Patterns from Claude CodeVercel April 2026 security incidentFragments: April 21Claude Managed Agents: get to production 10x fasterOFFERSStart Free Trial: Get started with LinearB's AI productivity platform for free.Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era.LEARN ABOUT LINEARBAI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production.AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance.AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil.MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

Microsoft considered buying Cursor but didn't pull the trigger before SpaceX's deal. Microsoft launches its first-ever voluntary retirement program, Anthropic's secondary market valuation hits $1T on Forge Global, and SpaceX's S-1 reveals plans to manufacture its own GPUs. Sources: Microsoft considered buying Cursor in recent weeks but didn't make an offer; Microsoft has been working to boost GitHub Copilot's popularity (CNBC) Microsoft announces the first voluntary retirement program in its 50-year history, for US staffers whose combined years of service added to their age totals 70+ (The Verge) Kalshi suspends and fines congressional candidates Mark Moran of Virginia, Matt Klein of Minnesota, and Ezekiel Enriquez of Texas for political insider trading (CNBC) Anthropic's valuation has hit $1T on Forge Global, a leading private marketplace exchange, surpassing OpenAI's valuation on the platform of $880B (Business Insider) A poll of 4,000 workers in the US and the UK finds that the highest-earning and most experienced workers are adopting AI in their jobs far faster than others (FT) SpaceX's S-1 excerpts list "manufacturing our own GPUs" among the "substantial capital expenditures" it is undertaking, with the size of the expenditure TBD (Reuters) Spotify celebrates its 20th anniversary with a first-ever list of its 20 most streamed artists, albums, songs, podcasts, and audiobooks (Billboard) Learn more about your ad choices. Visit megaphone.fm/adchoices

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

AI is democratizing the making of things, from bespoke/custom apps to websites, designs of all kinds, and everything else you might imagine. It's a new world, and it's time to create. Plus, Helium is a new Chromium-based web browser that's completely open source, lightweight, secure, and private. There's a native version for Windows 11 on Arm, too. Also, Firefox 150 arrives with over 270 security fixes! Windows 11 Reports of a Recall security vulnerability are, once again, bogus, Microsoft says New builds on all channels, still on the old system Xbox Mode is now available in all channels Release Preview shows us the May Patch Tuesday updates: Xbox Mode, File Explorer improvements, Haptic improvements, Drop Tray renaming, Agents on the Taskbar Lenovo Yoga Slim 7x - Snapdragon X2 Elite, 14-inch display impressions Lenovo IdeaPad 5x - Snapdragon X2 Plus, 15.3-inch display impressions Microsoft 365, Surface, more OneDrive now supports Markdown natively New Surface PCs with Intel chips coming soon Microsoft is making changes to its Rewards program AI GitHub Copilot moves to token-based billing in a sign of the true cost of AI Claude Design democratizes visual design on the heels of Claude Opus 4.7 OpenAI Codex moves into productivity OpenAI releases ChatGPT Images 2.0 Chrome AI Mode gets a big update Mozilla announces Thunderbolt, sovereign AI for businesses Google brings vibe coding to Android apps with Android CLI Xbox and gaming Microsoft drops Xbox Game Pass prices (!), but also drops Call of Duty from Day One Plus, Xbox teases a Game Pass Discord perk More Game Pass titles for April: Kiln, Vampire Crawlers, more Xbox April Update is here with that Quick Resume feature we all want There's an ID@Xbox event on April 23 to highlight indie games Xbox is selling Forza Horizon 6 limited edition controller and headsets Starfield is coming to the Nintendo Switch 2 A Call of Duty movie will finally arrive in 2028 Try out the Modern Warfare remake on Game Pass, it's a reminder of COD's gritty past PS5 Digital is down to its $399 launch price temporarily Tips and picks Tip of the week: Just make it App pick of the week: Helium RunAs Radio this week: The Life and Death of Microsoft Deployment Toolkit with Michael Niehaus Brown liquor pick of the week: Ned Australian Whisky Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: webroot.com/twit threatlocker.com/twit

Get featured on the show by leaving us a Voice Mail: https://bit.ly/MIPVM This episode with Joris de Gruyter explores how advanced AI agents are reshaping individual productivity and technical work. The conversation covers coding agents, personal agent systems, automation beyond code, and the risks of burnout, privacy, and memory misuse. Real-world examples show how AI can turn notes into workflows, handle testing and reporting, and operate as a team of specialised agents. The key message is clear: applied AI skills now combine technical fluency with strong boundaries around focus, context, and trust.

In this episode, Jack Cochran sits down with Darlene Volas, a senior solutions engineering executive, to explore what it truly means to be an AI-enabled SE in 2025 and beyond. The conversation moves well past basic ChatGPT usage to examine sophisticated operational workflows, custom automation tools, and practical implementation strategies that enhance both efficiency and quality of work.    This episode was recorded during Presales Collective's 2026 AI-Powered Presales Summit on March 19th, 2026. Pro and Pro+ members can view all of the recorded sessions on-demand in the PSC Knowledge Hub: https://www.presalescollective.com/knowledgehub    Follow Us Connect with Jack Cochran: https://www.linkedin.com/in/jackcochran/  Connect with Matthe James: https://www.linkedin.com/in/matthewyoungjames/  Connect with Darlene Volas: https://www.linkedin.com/in/darlenevolas/  Links and Resources Mentioned Join Presales Collective Slack: https://www.presalescollective.com/slack  Sol/Con 2026 (Chicago, August 2026): https://www.presalescollective.com/solcon-2026  Presales Collective Podcast: https://www.presalescollective.com/podcast  Perplexity: https://www.perplexity.ai/  Claude by Anthropic: https://www.anthropic.com/claude  Cursor AI coding tool: https://cursor.sh/  GitHub Copilot: https://github.com/features/copilot  Gong conversation intelligence: https://www.gong.io/  ChatGPT Enterprise: https://openai.com/enterprise  Obsidian note-taking: https://obsidian.md/  Firebolt database: https://www.firebolt.io/  Key Topics Covered Personal AI Usage Evolution: Moving Beyond Google Replacement to Creative Applications Three Buckets of AI for SE Leaders: Operations, Team Workflows, and Technical Implementation Daily Meeting Preparation Automation and Custom-Built AI Applications Operationalizing SE Teams with Repeatable AI Workflows Quality vs. Efficiency Trade-off: Better Preparation Rather Than Less Time Building Technical Credibility Through Hands-On AI Coding The "One More Thing" Problem and Where Human Expertise Remains Essential Data Security, Governance, and Vendor Considerations for AI Tools Why AI Won't Replace SEs: The Irreplaceable Human Element Hiring AI-Native SEs: What to Look For Beyond Basic Tool Awareness Timestamps 00:00 Welcome 02:41 How AI conversations have evolved 06:30 Three buckets of AI usage 12:40 Efficiency versus quality improvements 18:40 Building credibility through AI coding 21:07 The one more thing problem 30:59 Why AI won't replace SEs 36:45 Q&A session 39:20 Closing remarks