August 16: Databricks cuts coding agent costs by 30%

Sources

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  Vanishing Gradients

  If Developers Build on Chinese Open-Weight Models, Who Leads AI?

  <p><strong>“It would be sad if local models were not an option and there were only proprietary models. It’s good to have alternatives. Competition is good for business.”</strong></p><p>— Sebastian Raschka, on open-weight AI</p><p>Kimi K3’s weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live. Sebastian had already updated his architecture diagram. That speed captures his approach to the current model wave: wait until the weights exist, run the model in the harness where it will actually work, then inspect the architecture closely enough to understand what changed.</p><p>The conversation arrived during a larger fight over who supplies the models underneath global software. Three days earlier, twenty-five companies including NVIDIA, Meta, Microsoft, Hugging Face, and IBM published <a target="_blank" href="https://openweights.gitlawb.com/">Open Weights and American AI Leadership</a>. Their argument closely matches Sebastian’s practical case for local models: open weights create competition, reduce dependence on a single provider, and let organizations choose a model at the right capability and cost.</p><p><strong>Update:</strong> Four days after we recorded, DeepSeek released <a target="_blank" href="https://api-docs.deepseek.com/updates/">V4 Flash 0731</a>, a re-post-trained API model for agentic coding. <a target="_blank" href="https://www.reddit.com/r/DeepSeek/comments/1vck4is/has_anyone_actually_used_deepseek_v4_flash_0731/">Developers are already reporting</a> that it can debug multi-project codebases and stay on task across very long contexts.</p><p>You can find the full episode on <a target="_blank" href="https://open.spotify.com/show/3yuz89gqAhcMcdy3SZPe4X">Spotify</a>, <a target="_blank" href="https://podcasts.apple.com/us/podcast/vanishing-gradients/id1610318868">Apple Podcasts</a>, and <a target="_blank" href="https://www.youtube.com/watch?v=pEf21w0r-vY">YouTube</a>.</p><p>👉 The next cohort of <a target="_blank" href="https://vanishinggradients.short.gy/raschka-agentic-data-science">Master Agentic Data Science</a> starts August 4. Hugo is teaching it with Thomas Wiecki and Luca Fiaschi from PyMC Labs. You will build agents that explore data, run predictive and causal workflows, challenge one another’s conclusions, and produce analysis humans can inspect and reproduce. Podcast listeners get 20% off with code MADSAGC20. <a target="_blank" href="https://vanishinggradients.short.gy/raschka-agentic-data-science">Join the cohort</a>. 👈</p><p>In This Episode</p><p>* <strong>Kimi K3’s weights landed about an hour before we went live.</strong> Sebastian had already updated his architecture diagram, then walked us through how Kimi Delta Attention and its mixture-of-experts design turn Kimi Linear’s research architecture into a production model.</p><p>* <strong>When a local model is already good enough.</strong> Sebastian separates everyday coding and private workloads from problems where the strongest available model, extra reasoning time, and higher cost are justified.</p><p>* <strong>Why the model and harness must be evaluated together.</strong> The same open-weight model behaves differently across Qwen Code, Claude Code, Codex, Pi, and other harnesses because context handling, compaction, tool conventions, and post-training all affect the result.</p><p>* <strong>The harness should choose the model and its reasoning effort.</strong> Sebastian describes a router that uses the task, conversation history, context, and previous loops to make that decision automatically.</p><p>* <strong>How long agent contexts are changing model architecture.</strong> Sebastian moves through grouped-query attention, multi-head latent attention, sparse attention, mixture-of-experts, DeltaNet, and Mamba-style hybrid layers.</p><p>* <strong>The implementation does not lie.</strong> A from-scratch model can be checked against open reference weights tensor by tensor, and the first divergence exposes the missing RoPE scaling detail, RMSNorm variant, or architectural assumption that prose left out.</p><p>* <strong>When fine-tuning earns its cost.</strong> A specialized task performed thousands of times a day creates different economics from a task performed eight times, so the decision depends on quality, volume, latency, and token cost.</p><p>* <strong>Sebastian keeps the creative work and gives agents the tedious checks.</strong> He uses Codex and Claude to check links, enforce editorial conventions, and compare notebook code with the corresponding book manuscript.</p><p>About Sebastian Raschka</p><p>Sebastian Raschka is an independent AI researcher, author, and educator focused on large language models, reasoning models, deep learning, and practical machine-learning systems. He created <a target="_blank" href="https://magazine.sebastianraschka.com/">Ahead of AI</a> and is the author of <a target="_blank" href="https://sebastianraschka.com/reasoning-from-scratch/">Build a Reasoning Model (From Scratch)</a> and <a target="_blank" href="https://www.manning.com/books/build-a-large-language-model-from-scratch">Build a Large Language Model (From Scratch)</a>.</p><p>Resources</p><p>* <a target="_blank" href="https://hubs.la/Q04qPcGt0">Build a Reasoning Model (From Scratch)</a>: podcast listeners get 45% off the ebook or print edition with code 45VGraschka2.</p><p>* <a target="_blank" href="https://sebastianraschka.com/llm-architecture-gallery/">Sebastian Raschka’s LLM architecture gallery</a></p><p>* <a target="_blank" href="https://sebastianraschka.com/llm-architecture-gallery/#card-kimi-k3">Kimi K3 in the architecture gallery</a></p><p>* <a target="_blank" href="https://www.manning.com/books/build-a-large-language-model-from-scratch">Build a Large Language Model (From Scratch)</a></p><p>* <a target="_blank" href="https://magazine.sebastianraschka.com/">Ahead of AI</a></p><p>* <a target="_blank" href="https://magazine.sebastianraschka.com/p/using-local-coding-agents">Using Local Coding Agents</a></p><p>* <a target="_blank" href="https://openweights.gitlawb.com/">Open Weights and American AI Leadership</a></p><p>* <a target="_blank" href="https://www.anthropic.com/news/position-open-weights-models">Anthropic’s position on open-weight models</a></p><p>Listen or Watch</p><p>You can find the full episode on <a target="_blank" href="https://open.spotify.com/show/3yuz89gqAhcMcdy3SZPe4X">Spotify</a>, <a target="_blank" href="https://podcasts.apple.com/us/podcast/vanishing-gradients/id1610318868">Apple Podcasts</a>, and <a target="_blank" href="https://www.youtube.com/watch?v=pEf21w0r-vY">YouTube</a>.</p><p>👉 The next cohort of <a target="_blank" href="https://vanishinggradients.short.gy/raschka-agentic-data-science">Master Agentic Data Science</a> starts August 4. Hugo is teaching it with Thomas Wiecki and Luca Fiaschi from PyMC Labs. You will build agents that explore data, run predictive and causal workflows, challenge one another’s conclusions, and produce analysis humans can inspect and reproduce. Podcast listeners get 20% off with code MADSAGC20. <a target="_blank" href="https://vanishinggradients.short.gy/raschka-agentic-data-science">Join the cohort</a>. 👈</p><p><strong>How You Can Support Vanishing Gradients</strong></p><p>Vanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. More than 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.</p><p>If you want to help keep it going:</p><p>* <a target="_blank" href="https://hugobowne.substack.com/subscribe">Become a paid subscriber, from $8 per month</a></p><p>* Share this episode with a builder who would find it useful</p><p>* <a target="_blank" href="https://www.youtube.com/@vanishinggradients">Subscribe to the Vanishing Gradients YouTube channel</a></p><p>* <a target="_blank" href="https://vanishinggradients.short.gy/youtube-workshops">Join another Vanishing Gradients workshop</a></p> <br/><br/>Get full access to Vanishing Gradients at <a href="https://hugobowne.substack.com/subscribe?utm_medium=podcast&#38;utm_campaign=CTA_4">hugobowne.substack.com/subscribe</a>

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  Unsupervised Learning with Jacob Effron

  AI Vibe Check: Chinese Open Models, Distillation & The Hugging Face Breach

  In this installment of their recurring roundtable, Jacob, Ari, and Rob dig into the accelerating Chinese open-source model race, debating whether Kimi K3 has actually closed the gap with the US frontier or just looks like it has, and whether distillation fully explains China's progress. That opens into the messier fight over open-weight models generally, with the group dissecting the backlash against Anthropic's stance and asking whether powerful open models are inherently dangerous or actually a necessary part of collective defense. They connect this directly to the Fable ban episode as a possible preview of a coming frontier-model licensing regime, then turn to the OpenAI and Hugging Face security incident as a landmark, publicly legible moment of autonomous AI causing real-world harm. From there the conversation shifts to business strategy: how much value are companies actually handing over to frontier labs by building on their APIs, and what does the Grok and Cursor data advantage really tell us about the future value of real-world usage data versus purchased training data. They close with a rapid-fire tour of the news cycle, covering SSI's mysterious $5B raise, the rumored Stripe and OpenRouter acquisition, Google's continued underperformance despite structural advantages, and OpenAI's leadership speculation, plus a reflection on venture's swing back toward deep tech and physical infrastructure.

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  TBPN

  AI Viruses, OpenAI's First Device, WSJ Mansion Section | Samir Kaul, Patrick Wendell, Grant LaFontaine

  <p></p><ul><li>(01:31) - AI Viruses </li> <li>(08:18) - OpenAI's Device Takes Shape </li> <li>(16:39) - Meta Faces Major Child Safety Case </li> <li>(25:25) - 𝕏 Timeline Reactions </li> <li>(36:10) - WSJ Mansion Section </li> <li>(43:41) - Samir Kaul, a general partner at Khosla Ventures, discusses the firm’s investment in Jeff Dean’s Discovery Loop and its potential to apply AI to scientific research with tangible outcomes. He also shares his views on AI regulation, inflated private-market valuations, venture capital strategy, founder support, and the importance of taking bold risks to generate exceptional returns. </li> <li>(01:09:45) - Patrick Wendell discusses his role as Databricks co-founder and VP of Engineering, where he leads AI products and internal AI adoption. He explains how AI coding tools can nearly double engineering capacity while creating rapidly escalating consumption costs, and highlights model switching, intelligent routing, and optimization as key ways to control spending without sacrificing productivity. </li> <li>(01:27:30) - Grant Lafontaine discusses Whatnot’s $500 million Series E funding round at a $20 billion valuation and the live-shopping platform’s rapid growth. He explains how authentic, knowledgeable sellers can build substantial businesses with small audiences, while outlining Whatnot’s focus on customer experience, new markets, product categories, and future streaming formats. </li> <li>(01:44:17) - 𝕏 Timeline Reactions</li> </ul><p><br></p><p>TBPN is made possible by:</p><p>Ramp - https://ramp.com</p><p>Public - https://public.com</p><p>Cisco - https://www.cisco.com</p><p>Console - https://www.console.com</p><p>CrowdStrike - https://www.crowdstrike.com</p><p>Figma - https://www.figma.com</p><p>MongoDB - https://www.mongodb.com</p><p>NYSE - https://www.nyse.com</p><p>Railway - https://railway.com</p><p>Shopify - https://www.shopify.com</p><p>Codex - http://openAI.com/codex</p><p><br></p><p>Follow TBPN: </p><p>https://TBPN.com</p><p>https://x.com/tbpn</p><p>https://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231</p><p>https://podcasts.apple.com/us/podcast/tbpn/id1772360235</p><p>https://www.youtube.com/@TBPNLive</p>

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  Super Data Science: ML & AI Podcast with Jon Krohn

  1015: Mathematical Optimization in the Agentic AI Era, with Gurobi's Jerry Yurchisin

  In Episode #1015, Jerry Yurchisin (manager of decision intelligence strategy at Gurobi Optimization) joins Jon Krohn to explain the AI technology that makes breaking a constraint mathematically impossible. Large language models will confidently claim they've optimized your business while ignoring the one constraint that could cost millions, whereas optimization treats constraints as hard guarantees. Jerry lays out the division of labor he sees for the agentic era: agents help you frame the problem, write the formulation and generate the code, then hand off to a solver like Gurobi, soon callable via MCP servers. In this episode, Jerry breaks down the three building blocks of any optimization model, traces the leap in non-linear solving, explains how to pitch optimization to your CFO and to the planners whose jobs it touches, and shares case studies spanning energy grids, retirement planning and USA Cycling's Paris 2024 gold. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1015⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:42) The three building blocks of an optimization model (21:43) Where optimization fits in the agentic AI era (29:58) Inside the Gurobi Intelligence Hub (39:40) Energy, retirement planning and a cycling gold medal (50:58) How to sell optimization inside your organization

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  All-In with Chamath, Jason, Sacks & Friedberg

  Google's AI Brain Drain, SpaceX's Huge Quarter, Airtable's 90% Collapse, US Data Fuels China AI

  <p>(0:00) Bestie intros! Brad Gerstner fills in for Chamath</p> <p>(2:16) Major shakeups at Google: AI brain drain or better strategy?</p> <p>(20:39) SpaceX's big quarter: Terafab, AI Capex, $1T revenue projection?</p> <p>(45:44) All-In Summit Speaker Announcements!</p> <p>(48:01) Airtable sells for a 90% discount: SaaSpocalypse?</p> <p>(1:05:56) Chinese AI labs are buying US training data to catch up</p> <p>Apply for Summit 2026:</p> <p><a href= "https://allin.com/events">https://allin.com/events</a></p> <p>Follow Brad:</p> <p><a href="https://x.com/altcap">https://x.com/altcap</a></p> <p>Follow the besties:</p> <p><a href="https://x.com/chamath">https://x.com/chamath</a></p> <p><a href="https://x.com/Jason">https://x.com/Jason</a></p> <p><a href= "https://x.com/DavidSacks">https://x.com/DavidSacks</a></p> <p><a href= "https://x.com/friedberg">https://x.com/friedberg</a></p> <p>Follow on X:</p> <p><a href= "https://x.com/theallinpod">https://x.com/theallinpod</a></p> <p>Follow on Instagram:</p> <p><a href= "https://www.instagram.com/theallinpod">https://www.instagram.com/theallinpod</a></p> <p>Follow on TikTok:</p> <p><a href= "https://www.tiktok.com/@theallinpod">https://www.tiktok.com/@theallinpod</a></p> <p>Follow on LinkedIn:</p> <p><a href= "https://www.linkedin.com/company/allinpod">https://www.linkedin.com/company/allinpod</a></p> <p>Intro Music Credit:</p> <p><a href="https://rb.gy/tppkzl">https://rb.gy/tppkzl</a></p> <p><a href= "https://x.com/yung_spielburg">https://x.com/yung_spielburg</a></p> <p>Intro Video Credit:</p> <p><a href= "https://x.com/TheZachEffect">https://x.com/TheZachEffect</a></p> <p>Referenced in the show:</p> <p><a href= "https://x.com/the_ai_investor/status/2084687703707361429">https://x.com/the_ai_investor/status/2084687703707361429</a></p> <p><a href= "https://x.com/Tesla/status/2085365278276284803">https://x.com/Tesla/status/2085365278276284803</a></p>

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  Sharp Tech with Ben Thompson

  (Preview) Microsoft’s Plan for Platform Survival, Meta and the Market’s Permission, A Lack of Situational Awareness

  On today’s show Ben and Andrew begin with Microsoft, Meta and Google in the wake of their latest earnings reports. First: Microsoft’s push to be the middle layer for enterprise AI, threats to the company’s competitive position in the long run, and the subtext of the open weights open letter frenzy two weeks ago. Then: Parsing Google and Meta strategies as Google hedges its frontier bets, while Meta continues its frontier spending and announces questionable plans for an enterprise business. At the end: The future of the lending environment for Meta and the other hyperscalers, a bubble check-in, a rundown of what happened between Citadel and the Situational Awareness, close encounters with Ben’s vibe coded app, and an emailer offers a compelling theory on the push for Permanent Daylight Savings Time.

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  TBPN

  Google DeepMind Reorgs, Rainforest Discoveries, Monkey Prices Surge | Harley Finkelstein, John J. Giamatteo, Jeremy Allaire, Mackenzie Burnett

  <p></p><ul><li>(00:18) - Google DeepMind Reorgs </li> <li>(21:08) - 𝕏 Timeline Reactions </li> <li>(23:40) - Revolut Founder Sued Over Yacht </li> <li>(29:38) - 𝕏 Timeline Reactions </li> <li>(34:12) - China Biotech Drives Monkey Prices </li> <li>(38:29) - Rainforest Discoveries </li> <li>(41:12) - 𝕏 Timeline Reactions </li> <li>(45:10) - Harley Finkelstein, president of Shopify, discusses the company’s strong growth and how AI-driven agentic commerce is helping shoppers discover niche products from independent merchants. He also explains how Shopify’s AI tools, including Sidekick and Autopilot, help merchants launch, optimize, and automate their businesses more effectively. </li> <li>(01:04:19) - John J. Giamatteo discusses his leadership as CEO of BlackBerry and the company’s transformation from smartphones to secure embedded software. He highlights BlackBerry’s automotive operating systems, cautious use of AI, partnerships, industrial robotics growth, and enduring focus on security, trust, and innovation. </li> <li>(01:19:50) - Jeremy Allaire, co-founder and CEO of financial technology company Circle, discusses stablecoins’ integration into the traditional financial system, USDC’s growing role in global payments, and blockchain infrastructure’s evolution. He also highlights AI agents as a new form of digital labor, alongside the heightened cybersecurity and regulatory challenges created by powerful AI models. </li> <li>(01:40:03) - Mackenzie Burnett is the co-founder and CEO of Ambrook, a financial management software company built for farms, ranches, and other independent businesses in the real economy. Ambrook helps agricultural operators manage accounting, payments, and financial reporting, with the broader goal of strengthening rural businesses and American industry. </li> <li>(01:51:17) - 𝕏 Timeline Reactions</li> </ul><p><br></p><p>TBPN is made possible by:</p><p>Ramp - https://ramp.com</p><p>Public - https://public.com</p><p>Cisco - https://www.cisco.com</p><p>Console - https://www.console.com</p><p>CrowdStrike - https://www.crowdstrike.com</p><p>Figma - https://www.figma.com</p><p>MongoDB - https://www.mongodb.com</p><p>NYSE - https://www.nyse.com</p><p>Railway - https://railway.com</p><p>Shopify - https://www.shopify.com</p><p>Codex - http://openAI.com/codex</p><p><br></p><p>Follow TBPN: </p><p>https://TBPN.com</p><p>https://x.com/tbpn</p><p>https://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231</p><p>https://podcasts.apple.com/us/podcast/tbpn/id1772360235</p><p>https://www.youtube.com/@TBPNLive</p>

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• 

  The a16z Show

  How Open-Source AI Became Critical Infrastructure

  Elena Burger and Matt Bornstein are joined by Simon Mo, co-founder and CEO of Inferact, the open-source inference engine powering many of today's most advanced AI applications. Together, they explore how open-source AI evolved from a research project into critical infrastructure, why inference has become one of the most important layers of the AI stack, and what it takes to bring frontier intelligence to developers around the world. The conversation covers vLLM's origins, the rise of open-weight models, why companies increasingly want control over their AI infrastructure, and how open-source inference enables the next generation of AI applications. They also discuss model licensing, the economics of open-weight AI, Kimi K3, distillation, AI infrastructure, and why Simon believes the gap between open and closed models is rapidly disappearing.

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  The Vergecast

  Mark Zuckerberg has an Instagzam

  Have you seen Instagram's new logo? Meta debuted it right before we started recording, so of course David and Nilay needed to talk about it. Then, the hosts turn to Mark Zuckerberg's 6,500-word missive on the future of AI, and the problem with his idea that giving AI to everyone will fix everything. After that, it's time for Claude watermarks, Spotify AI personas, Brendan Carr, Flock, and much more. Further reading: ⁠This is Instagram’s new logo | The Verge⁠ ⁠The Pepsi Universe PDF ⁠The Future is for Everyone⁠ ⁠Four takeaways from Mark Zuckerberg’s massive AI manifesto&nbsp;⁠ ⁠Mark Zuckerberg doesn’t understand how to live&nbsp;⁠ ⁠https://www.meta.com/design-at-meta/blog/⁠ ⁠Claude will apply invisible watermarks to AI text and images&nbsp;⁠ ⁠‘If it was opt-in, nobody would opt in.’&nbsp;⁠ ⁠Spotify says it won’t recommend music from ‘AI Personas’&nbsp;⁠ ⁠Apple could help you prove your iPhone photos aren’t deepfakes | The Verge⁠ ⁠Guitar company D’Addario admits that AI music was used in a promotional video | The Verge⁠ ⁠Fender’s CEO seems to think your bandmates are just analog AI | The Verge⁠ ⁠Suno Unveils Download Song Caps for Free, Paid Tiers⁠ ⁠Brendan Carr is one firing away from an unchecked FCC⁠ ⁠YouTube is making it harder to earn money on YouTube&nbsp;⁠ ⁠Flock CEO: ‘We got this one wrong’&nbsp;⁠ ⁠The first rival Android app store just arrived in the US Play Store&nbsp;⁠ ⁠ChatGPT and Gemini both just passed 1 billion users&nbsp;⁠ ⁠The sub-$100 phone market is disappearing.&nbsp;⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed. We love hearing from you! Email your questions and thoughts to [email protected] or call us at 866-VERGE11. 0:00&nbsp;Welcome and Parental Leave Announcement 00:43&nbsp;Instagram Redesigns Their Logo&nbsp; 01:04&nbsp;Why Companies Change Logos 03:37&nbsp;Meta Design Blog Quotes 07:22&nbsp;The 2008 Pepsi Universe Presentation 09:41&nbsp;Blanding&nbsp; 10:37&nbsp;Zuck’s AI Manifesto&nbsp; 13:29&nbsp;Collective Action Problems 18:49&nbsp;Superintelligence for All 20:57&nbsp;Cybersecurity and AI Risks 22:22&nbsp;China Chips and Distillation 27:48&nbsp;Write the Law Challenge 32:30&nbsp;Claude Text Watermarking 35:33&nbsp;How The Watermark Works 39:34&nbsp;Limits And Evasion Risks 41:54&nbsp;Apple Marks Real Photos 46:03&nbsp;Provenance Standards Fail 46:58&nbsp;AI Music Backlash 49:39&nbsp;Fender CEO AI Scandal 53:08&nbsp;Where AI Fits In Music 59:13&nbsp;Twitch Opt Out Training 01:01:35&nbsp;The Lightning Round 01:02:55&nbsp;Brendan Carr is a Dummy 01:04:55&nbsp;Bans Don’t Work 01:05:56&nbsp;Made in America Assembly 01:10:19&nbsp;Gemini Claims a Billion Users 01:15:40&nbsp;YouTube Partner Paywall 01:20:56&nbsp;RAMageddon Cheap Phones 01:23:52&nbsp;Flock Camera Backlash 01:27:19&nbsp;Goodbyes and Plugs Learn more about your ad choices. Visit podcastchoices.com/adchoices

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  Super Data Science: ML & AI Podcast with Jon Krohn

  1018: Alibaba's Qwen3.8-Max: Open-Weight Model Surpasses Most American Frontier Labs

  In Episode #1018, Jon Krohn breaks down Qwen3.8-Max, Alibaba’s enormous new flagship, a 2.4-trillion-parameter mixture-of-experts model that, if its promised weights ship, becomes the largest open-weight release in history. Landing just weeks after Moonshot’s Kimi K3, it extends the price war and the open-weight surge Jon covered in Episode #1012. Alibaba positions it as second only to Anthropic’s Claude Fable 5 / Mythos 5 and independent signals land in a similar neighborhood. Jon walks through its capabilities and multi-day agentic demos, its aggressive pricing ($2 in / $6 out per million tokens, with cached input eight times cheaper), and the question he gets asked most: are Chinese models safe to use? His answer hinges far less on the model than on how your data reach it. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1018⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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  The MAD Podcast with Matt Turck

  “OpenAI’s Model Hacked Us” - Hugging Face’s Thomas Wolf

  <p>An OpenAI-powered agent penetrated Hugging Face during cyber testing - even though it was never tasked with attacking Hugging Face. It did it as a side quest.</p><p><br></p><p>Thomas Wolf, co-founder and Chief Science Officer of Hugging Face, joins Matt Turck to unpack what actually happened, why closed AI models refused to help during the live incident, how an open-source model helped the team fight back, and why the old equation of “closed equals safe, open equals dangerous” no longer holds. </p><p><br></p><p>They also discuss model deception and social engineering, the limits of sandboxes and guardrails, the state of open-source AI in 2026, AI sovereignty, the economics of open models, recursive self-improvement, and whether the frontier should deliberately slow down.</p><p><br></p><p>(00:00) An AI Agent Hacked Hugging Face</p><p>(00:30) Introduction</p><p>(01:00) 17,000 Attacker Events—and a Strange Target</p><p>(04:28) The Attack Was a “Side Quest”</p><p>(06:13) AI Training Runs Left Notes for Each Other</p><p>(07:09) Closed AI Refused to Help</p><p>(09:47) Fighting Back With an Open-Source Model</p><p>(13:15) Open vs. Closed Is the Wrong Safety Debate</p><p>(15:46) AI Agents Start Social-Engineering Humans</p><p>(22:24) The Three Walls: Sandboxes, Guardrails, Alignment</p><p>(24:34) “Neuralese”: Can Humans Still Read AI Reasoning?</p><p>(25:28) Why Monitoring AI Agents Gets So Hard</p><p>(28:10) Reward Hacking and the “Paperclip Problem”</p><p>(32:02) The State of Open-Source AI in 2026</p><p>(33:47) Router Models and the Enterprise Shift to Open</p><p>(37:01) The Real Economics of Open Models</p><p>(39:41) Can Chinese AI Models Be Trusted?</p><p>(41:37) AI Sovereignty: Who Controls the Switch?</p><p>(43:16) Why Western Open-Source AI Matters</p><p>(48:16) Is AI Heading Toward an Oligopoly?</p><p>(49:41) The Race Toward Recursive Self-Improvement</p><p>(51:54) Why Thomas Signed the AI Slowdown Letter</p><p>(55:14) AI Slowdown—or Regulatory Capture?</p><p><br></p>

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  Training Data

  Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

  Most people treat biology as a bespoke, messy science. Josh Meier and Matt McPartlon, co-founders of Chai Discovery, treat it as an engineering problem. They make the case that drug design obeys the bitter lesson: scale data, models, and compute, and the model can learn what a hand-built pipeline simply couldn't capture. The results are concrete: Chai-2 pushed de novo antibody design from a sub 0.1% hit rate to 16%, turning a needle-in-a-haystack search into something more like designing a key to fit a lock. Josh argues, counterintuitively, that biology is more verifiable than code, and explains why the goal should be more lab experiments, not fewer. Their bet: a design suite that collapses drug discovery from nine months to nine days, and arms the pharma industry rather than competing with it. Hosted by Pat Grady and Sonali Singh, Sequoia Capital 00:00 Introduction 01:52 From Discovery to Design 03:25 Protein AI Breakthroughs Timeline 06:04 Why Start in 2024 10:13 Diffusion Models Intuition 11:41 Building the Avengers Team 15:22 Hit Rates and Scaling Laws 25:01 Molecular CAD Vision 25:24 Faster Design Loops 26:32 Future Drug Discovery 28:37 Platform Business Model 31:14 Partnering Reality Check 33:44 Data Flywheel Explained 37:16 Staying Ahead at Scale 39:44 Culture and What's Next

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  Software Engineering Daily

  The Terminal as an Agentic Interface

  <p class="wp-block-paragraph">The terminal has been a constant in software development for decades. It has remained largely unchanged while everything around it transformed. However, as AI agents have become central to the developer workflow, the terminal is emerging as a natural home for agentic development, and a new category of tooling is forming around it.</p> <p class="wp-block-paragraph"><a href="https://www.warp.dev/" data-type="link" data-id="https://www.warp.dev/">Warp</a> is a popular Rust-based terminal and agentic development environment. The company recently open-sourced its codebase and launched Oz, its cloud agent infrastructure product aimed at helping enterprises automate software development at scale.</p> <p class="wp-block-paragraph"><a href="https://x.com/zachlloydtweets">Zach Lloyd</a> is the co-founder and CEO of Warp, and a former principal engineer at Google where he led engineering on the Google Docs suite. In this episode, Zach joins Gregor Vand to discuss how Warp has evolved over the years, why the terminal is better suited than the IDE for agentic development, how Oz approaches the governance and auditability challenges enterprises face with AI agents, and more.<br><br>Sponsorship inquiries:<br><a href="mailto:[email protected]">[email protected]</a></p> <p>The post <a href="https://softwareengineeringdaily.com/podcasts/the-terminal-as-an-agentic-interface/">The Terminal as an Agentic Interface</a> appeared first on <a href="https://softwareengineeringdaily.com">Software Engineering Daily</a>.</p>

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  Last Week in AI

  Recap: Anthropic IPO and Nvidia Developments

  In this episode, we recap last week's major news including Anthropic's IPO. We also cover recent developments in Nvidia's data center funding.<br><br><br><b>Chapters</b><br>00:00 Market Valuations and IPOs<br>00:30 Nvidia's AI Data Center Plan<br>00:54 Innovative Content Protection<br>03:01 Thrive Holdings' Strategy<br>04:15 AI in Healthcare<br><br><span><div><b>Show Links<br></b></div><ul><li><p><span><a href="https://fiund.com/">License your data with Fiund.com</a></span></p></li><li><p><span>Get the top 80+ AI Models for $8.99 at AI Box: </span><a href="https://aibox.ai"><span>⁠⁠https://aibox.ai</span></a></p></li><li><p><span>How I Grow and Scale My Business with AI: </span><a href="https://www.skool.com/aihustle"><span>https://www.skool.com/aihustle</span></a></p></li><li><p><span>Get the AI Chat Daily Newsletter: </span><a href="https://www.aichatdaily.com/newsletter">https://www.aichatdaily.com/newsletter</a><br></p></li></ul></span> See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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