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“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.”— Sebastian Raschka, on open-weight AIKimi 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.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 Open Weights and American AI Leadership. 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.Update: Four days after we recorded, DeepSeek released V4 Flash 0731, a re-post-trained API model for agentic coding. Developers are already reporting that it can debug multi-project codebases and stay on task across very long contexts.You can find the full episode on Spotify, Apple Podcasts, and YouTube.
In deze aflevering van Techzine Talks gaan we met gasten Erik de Jong (Chief Research Officer, Tesorion) en Eric van Gent (CEO, Tesorion) diep in op waar AI en cybersecurity elkaar raken. Aanleiding zijn onder andere de recente incidenten waarbij OpenAI-agents op eigen houtje de sandbox verlieten en naar Hugging Face gingen, en waarbij Claude-agents van Anthropic doelbewust een echt bedrijf benaderden terwijl ze wisten dat het geen fictief testbedrijf was. Wat zeggen die incidenten over de volwassenheid van AI-beveiliging bij de grootste spelers?De twee gasten geven niet alleen hun mening over wat er allemaal gebeurt op het gebied van AI en cybersecurity. Ze vertellen ook hoe Tesorion AI inzet in het eigen Security Operations Center (SOC). Denk aan de inzet van een eigen getraind model in een eigen tenant, waarbij de analist altijd eerst zelf een analyse doet en die vervolgens verifieert met de LLM-output.De discussie gaat ook over Shadow AI, prompt security, de keuze tussen gesloten en open-weight modellen, soevereine modellen in Europa, en de gevaren van 'platformization' waarbij niet-securitybedrijven ineens managed detection & response gaan aanbieden op basis van AI-tools waar ze geen controle over hebben. Tot slot bespreken we met onze gasten hoe het zit met de financiële kant van AI.Een eerlijk, technisch en praktisch gesprek over wat AI nu al kan in security, wat gevaarlijk is, en hoe je als organisatie controle behoudt.• OpenAI-agents ontsnappen uit sandbox via een zero-day in een proxy• Claude-agents benaderen een echt bedrijf terwijl ze wisten dat het geen testomgeving was• Shadow AI blokkeren werkt niet: 20% vindt altijd een omweg, faciliteer het gecontroleerd• Prompt security als oplossing om inzicht te krijgen in wat medewerkers in AI-tools stoppen• Hoe gaat Tesorion zelf om met AI in het SOC?• Automatisch ingrijpen als tegenwicht tegen de dalende time-to-exploit• Soevereine, in Europa gehoste modellen worden steeds relevanter voor autonomie• Tokenomics en inferencing-kosten kunnen dienstverlening onverwacht duur maken• Niet-securitybedrijven die AI-gebaseerde MDR aanbieden zijn een zorgelijke ontwikkeling• Kwaliteitsbewaking van AI-output in het SOC blijft de grootste uitdaging0:07 Introductie: AI en security1:07 OpenAI-agents ontsnappen uit de sandbox3:56 Claude-agents en het gevaar van rogue AI9:49 Shadow AI: controleren in plaats van blokkeren13:24 Gesloten vs. open modellen en soevereiniteit20:55 AI in het SOC: use cases en kwaliteitsbewaking25:52 Automatisch ingrijpen en zero trust40:00 Kosten van AI en tokenomics
The Fork In Your Ear Ep#218 Jimonthy Crickets! - Podcast Show Notes & Summery 8-1-26 Quick Summary Tim and Nate open with the usual technical chaos—Nate's ancient camera older than his kids, USB hub murders, Windows being Windows, and a new meshy chair—before Tim scrapes himself off the floor with minimal coffee. The big gaming bombshell is Nintendo snagging a FromSoftware exclusive (Dustbloods) led by Miyazaki himself: Bloodborne-vibes, industrial revolution setting, up to eight-player PvE, and Tim is currently in the closed NDA network test. Microsoft brings four original Xbox classics to PC (Fusion Frenzy, Crimson Skies, Conker's Live and Reloaded), sparking preservation vs. "just sell it again" debate. Nate digs into the new Halo campaign on PC while Tim's wrist is still sidelining him. Entertainment covers Spider-Man: Brand New Day (Tim is all-in), American Psycho after a four-and-a-half-hour serial-killer museum visit, and the couple literally dodging the Bite of Seattle shooting by having a fight and going home. Life wraps with raccoon babies, the local "Jimmathy" meme, and Nate's wife getting back on two wheels with a nearly-new 2025 Triumph Speed Twin 900. Classic Fork energy from start to duck-call finish. Detailed Show Notes
Sean Martin catches John Sotiropoulos and Rock Lambros at the end of the OWASP GenAI Security Summit, held alongside Infosecurity Europe 2026 at ExCeL London. Both are deep in the standards work: John Sotiropoulos co-leads the OWASP Agentic Security Initiative and sits on the board of the OWASP GenAI Security Project, and Rock Lambros serves as Director of AI Standards and Governance at Zenity and co-leads the OWASP Top 10 for LLM 2026 update. The headline from the day is the launch of the Agentic Security Council, bringing Oxford University, Queen's University Belfast, CSIT, and other research institutions into the same room as practitioners and industry. John Sotiropoulos frames the reasoning bluntly: content on its own does not create change. Papers and Top 10 lists matter, but they only matter if the people building and defending systems can act on them. The number that reframes everything is twenty-two seconds. That is the average time from an initial access event to the next attacker action, down from eight hours. John Sotiropoulos puts the question directly to anyone still running a human-speed playbook: how do you respond to that? A panel on incident response with participants from AWS and Microsoft, a keynote from Microsoft's National Security Officer, and a walkthrough of the State of Agentic Security and Governance report all point at the same shift toward runtime security. Rock Lambros brings a different kind of grounding. For the first time in the four-year history of the OWASP Top 10 for LLM, the update draws on a corpus of reported incidents rather than opinion and community vote alone. It is one data point among several, but it is data, and that changes what the list can claim. A panel with the heads of AI security at Deloitte supplies the operational counterweight. As one of them puts it, security has to stop being the Ministry of No, because people will route around it and ship anyway. The report's adoption tiers and maturity levels exist for exactly that reason: security that lives only inside a PDF is not security. The invitation from both John Sotiropoulos and Rock Lambros is the same. Join the work. Research, red teamers, defenders, builders, and SecOps all looking at one view of what is actually happening is the only version of this that scales to machine speed. ⬥HOST⬥ Sean Martin, CISSP | Co-Founder, ITSPmagazine & Studio C60 | Host, Redefining CyberSecurity Podcast & Music Evolves Podcast | https://www.seanmartin.com/ ⬥GUESTS⬥ John Sotiropoulos, Deep Cyber | Co-Lead, OWASP Agentic Security Initiative; Board Director, OWASP GenAI Security Project | On LinkedIn: https://www.linkedin.com/in/jsotiropoulos/ Rock Lambros, Director of AI Standards and Governance, Zenity; Founder, RockCyber | Co-Lead, OWASP Top 10 for LLM 2026 | On LinkedIn: https://www.linkedin.com/in/rocklambros/ ⬥RESOURCES⬥ Infosecurity Europe 2026 is taking place June 2-4, 2026 | ExCeL London. Follow our coverage: https://www.itspmagazine.com/infosecurity-europe-2026-infosec-london-cybersecurity-event-coverage OWASP GenAI Security Project | https://genai.owasp.org Contribute to the OWASP GenAI Security Project | https://genai.owasp.org/contribute The Future of Cybersecurity Newsletter | https://www.linkedin.com/newsletters/7108625890296614912/ Redefining CyberSecurity Podcast | https://www.seanmartin.com/redefining-cybersecurity-podcast On Location | https://www.itspmagazine.com/on-location
The value of community and sustainability in full view at UserR 2026, a new tool ready to equip reproducibility and more for command-line utilities built with R, and the conclusion to LLM-prompting black magic that draws upon the awesome glue package. Episode Links This week's curator: Colin Fay - @colinfay@fosstodon.org (Mastodon) & @colinfay.bsky.social (Bluesky) & [@_ColinFay]](https://twitter.com/_ColinFay) (X/Twitter)What Makes R Strong: Reflections from useR! 2026ir 0.1.0: self-describing R scripts and Quarto documentsBuilding our own glue with REntire issue available at rweekly.org/2026-W31Supplement ResourcesMatrix operators in R https://019b2dbc-0ac6-3185-aa9f-0bea50e3da6d.share.connect.posit.cloud/matrix-operators.html{golem} 1.0.0 is here https://golemverse.org/news/golem-1.0.0-release-on-cran/Package Tools with ir https://r-lib.github.io/ir/tools.htmlSupporting the showUse the contact page at https://serve.podhome.fm/custompage/r-weekly-highlights/contact to send us your feedbackR-Weekly Highlights on the Podcastindex.org - You can send a boost into the show directly in the Podcast Index. First, top-up with Alby, and then head over to the R-Weekly Highlights podcast entry on the index.A new way to think about value: https://value4value.infoGet in touch with us on social mediaEric Nantz: @rpodcast@podcastindex.social (Mastodon), @rpodcast.bsky.social (BlueSky) and @theRcast (X/Twitter)Mike Thomas: @mike_thomas@fosstodon.org (Mastodon), @mike-thomas.bsky.social (BlueSky), and @mike_ketchbrook (X/Twitter) Music credits powered by OCRemixSnow Cone Heaven - Ice Climber - Mazedude - https://ocremix.org/remix/OCR01176Salut Voisin! - Final Fantasy IV - Colorado Weeks, Aeroprism - https://ocremix.org/remix/OCR04553
В нашей работе действительно много эзотерики... но как на это повлияло повсеместная LLM-фикация?Спасибо всем, кто нас слушает. Ждем Ваши комментарии.Музыка из выпуска: - https://artists.landr.com/056870627229- https://t.me/angry_programmer_screamsВесь плейлист курса "Kubernetes для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3SrrmOzzdBBsdeQ0YVR3Fc7Бесплатный открытый курс "Rust для DotNet разработчиков": https://www.youtube.com/playlist?list=PLbxr_aGL4q3S2iE00WFPNTzKAARURZW1ZShownotes: 00:00:00 Вступление00:06:20 Эзотерика в программировании?00:12:20 Почему возникает эзотерика?00:24:00 Мы опять скатились в обсуждение ИИ00:31:20 Clean Code00:43:10 Эзотерика и 1С00:47:30 Скажи твой стек, и я скажу кто ты01:08:00 Вера в Сеньора-Волшебника01:11:30 Как LLM влияет на эзотерикуВидео: https://youtube.com/live/_DG99DDGWIs Слушайте все выпуски: https://dotnetmore.mave.digitalYouTube: https://www.youtube.com/playlist?list=PLbxr_aGL4q3R6kfpa7Q8biS11T56cNMf5Twitch: https://www.twitch.tv/dotnetmoreОбсуждайте:- Telegram: https://t.me/dotnetmore_chatСледите за новостями:– Twitter: https://twitter.com/dotnetmore– Telegram channel: https://t.me/dotnetmoreCopyright: https://creativecommons.org/licenses/by-sa/4.0/
Today we're here with the creator of Kotlin and founder of the tool CodeSpeak which aims to turn LLM based programming into a proper documented field of engineering that can actually be sustained long term.==========Support The Channel==========► Patreon: https://www.patreon.com/brodierobertson► Paypal: https://www.paypal.me/BrodieRobertsonVideo► Amazon USA: https://amzn.to/3d5gykF► Other Methods: https://cointr.ee/brodierobertson==========Guest Links==========Website: https://www.abreslav.com/Twitter: https://x.com/abreslavCodespeak: https://codespeak.dev/==========Support The Show==========► Patreon: https://www.patreon.com/brodierobertson► Paypal: https://www.paypal.me/BrodieRobertsonVideo► Amazon USA: https://amzn.to/3d5gykF► Other Methods: https://cointr.ee/brodierobertson=========Video Platforms==========
As Big Tech's power continues to grow, it's more important than ever to understand how companies use their control over cloud infrastructure and services to shape more than just the digital world. Cecilia Rikap joins Paris Marx to discuss how these networks of corporate power operate, including how they are systematically used to influence global politics, the economy, and how society views the world.Cecilia Rikap is an Associate Professor at University College London's Institute for Innovation and Public Purpose and the author of The Rulers: Corporate Power in the Age of AI and the Cloud.The podcast is made in partnership with The Nation. Production is by Kyla Hewson. Support the show on Patreon.Also mentioned in this episode:You can now pre-order Paris's new book Hyperscale.Cecilia and Paris worked on a white paper offering a roadmap to reclaiming digital sovereignty.Support the show
I have been a fan of Seabuckthorn for a long time. I think I have around a dozen or so releases. So when Andy Cartwright, who records as Seabuckthorn, offered to create a guest mix I was psyched. Andy did an excellent guest mix for LLM back in November of 2022 which you can find here: https://lowlightmixes.blogspot.com/2022/11/seabuckthorn-transcendental-freq.html or here: https://www.mixcloud.com/lowlight/seabuckthorn-transcendental-freq/ There is a new Seabuckthorn album out in the world - https://seabuckthorn.bandcamp.com/album/never-the-same-river So it's a perfect time for a mix to highlight some of the music that influenced the new album. Andy said this about the mix: "It has a kind of ritual/ceremonial/psych folk vibe to it, which was the feel I was going for in my latest album." I highly recommend checking out the new album and of course listening to this very cool mix. Also I think it's kind of hilarious that we followed a 4-hour mix with a 36-minute mix. Haha! Cheers! T R A C K L I S T : Gnäw - Waters of Ether Old Saw - The Flood Spires Heinali & Andriana-Yaroslava Saienko - Zelenaia Dubrovonka ZAÄAR - Eucaryotes Et Reproduction Sexuée Hermanos Gutierrez - Low Sun Lino Capra Vaccina - Andante ancestrale Richter band - Zaklepat David Shea - Falling rice on the GuZheng The Myrrors - Invitation Mantra Hvad - Notice a tiny scratch for the blue behind Dictaphone - Lofi Opium Claire Deak - Prefigured (Ritornello) Jessica Moss - One, Now Sanam - Rings رينغز
Collective CIO Natalie Silverstein joins Next In Media to explore how creators are evolving into full-fledged media networks and what it takes for brands to scale authentic partnerships in a hyper-fragmented landscape. Natalie, also breaks down real-time crisis agility, strategies for training AI language models on video content, and how to combat the algorithmic reach recession with targeted paid media. Key Highlights
Nandan Sheth has spent 25 years in payments, building three growth companies along the way, including Harbor Payments (sold to American Express) and Acculynk (sold to First Data/Fiserv). He now runs Splitit, which takes a different path than most buy now, pay later providers: instead of originating a new loan, it turns the credit a consumer already has on their existing card into an installment plan, with no underwriting, no social security number, and no new debit card for repayments. With agentic commerce infrastructure being built in real time, Nandan argues that a frictionless installment option is exactly what merchants need to avoid being commoditized on price inside an LLM shopping platform.What We CoveredThree growth companies across 25 years in paymentsWhat attracted Nandan to Splitit from FiservCard-linked installments with no underwriting or new loanThe card loyalist versus the credit needy$3.5 trillion of unused credit sitting on US cardsMerchant-funded 0% economics and where the budget comes fromA $1,300 average order value versus $250 to $300 for standard BNPLPoint of sale through the Samsung Wallet integrationBacking Google's Universal Commerce ProtocolThe overlooked small business to large supplier B2B use caseChargebacks, repudiation, and who carries the risk in agent-led purchasesSplitit Go for the face-to-face services economyKey TakeawaysBNPL is really two markets, not one. Card loyalists want rewards, protections, and habit, while the credit needy want a new line of credit. Nandan thinks both get served, but by different products.The economics work because the merchant treats it as marketing spend. About 98% of Splitit's volume is a merchant-funded 0% plan, priced comparably to a percentage-off promotion, and it lifts average order value roughly four times over standard BNPL.In agentic commerce, price and delivery speed are the easiest things for an LLM to compare. A 0% installment option gives merchants a third lever that is not pure price competition.The B2B version may be the stronger use case. Small business owners face both a time problem and a working capital problem, which is a sharper reason to hand off buying to an agent than a consumer shopping for a polo shirt.About Nandan ShethNandan Sheth is the CEO of Splitit, the card-linked installments platform. He moved to the US from the UK 25 years ago and has spent his entire career in payments and fintech, including running e-commerce and omni-channel commerce at Fiserv. He previously built Harbor Payments, acquired by American Express, and Acculynk, acquired by First Data/Fiserv.Connect with Fintech One-on-One:Tweet me @PeterRentonConnect with me on LinkedInFind previous Fintech One-on-One episodes
In 2022, a watershed moment changed the course of our relationship with AI forever, forcing us to reassess what ‘artificial intelligence' means and its place in our world. Four years later, with generative AI and LLMs now mainstream in our lives, we must ask the question: what comes next? This week, as part of our mini-series celebrating 60 years of innovation with HPE Labs, Technology Now is joined by Kirk Bresniker, Chief Architect at HPE Labs to look to the future and discuss: Why AI is more than just a language modelThe issues with ever-increasing model sizes, and why specialised “expert models” could become the normWhat the differences are between large language models and energy-based modelsHow agentic AI and orchestration could enable the next generation of AI systems
Send us Fan Mail“Secure AI agents” is a comforting phrase, and it's also one of the most abused. We sit down with Zach Korman, a builder and security researcher known for stress-testing AI agent frameworks, to talk about what actually breaks when you connect LLM agents to tools, plugins, skills marketplaces, and live production systems. The punchline is not a single bug or a clever jailbreak, it's a bigger design problem: agents can be influenced by untrusted content while holding real authority through API keys, SaaS access, and automation hooks.We dig into why “enterprise-grade security” claims often collapse under basic testing, how disclosure changes when a product launches with bold marketing, and why skills are a supply chain risk hiding in plain sight. Zack explains how malicious skills can smuggle commands in places humans never read, how automated scanners can be bypassed, and why “safe OpenClaw” may only be achievable by stripping away the very access that makes agents useful. We also cover MCP security concerns, including dynamic tool definitions, model capability mismatches, and the uncomfortable reality that some protocols effectively enable instruction injection by design.Then we get practical: how to vet tools if you're not an InfoSec specialist, how to reduce third-party exposure, and what foundations matter most inside a company (visibility, least privilege, authorization, and governance). If your team is moving from chatbots to agentic automation, this conversation helps you spot security theater before it ships to customers. Subscribe, share this with someone deploying agents at work, and leave a review with the AI security question you want us to tackle next.Connect with our guest:https://x.com/ZackKormanCheck out the Monthly Cloud Networking Newshttps://docs.google.com/document/d/1fkBWCGwXDUX9OfZ9_MvSVup8tJJzJeqrauaE6VPT2b0/Visit our website and subscribe: https://www.cables2clouds.com/Follow us on BlueSky: https://bsky.app/profile/cables2clouds.comFollow us on YouTube: https://www.youtube.com/@cables2clouds/Follow us on TikTok: https://www.tiktok.com/@cables2cloudsMerch Store: https://store.cables2clouds.com/Join the Discord Study group: https://artofneteng.com/iaatj
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The PHP Podcast – July 30, 2026 Hosts: Joe Ferguson, Sara Golemon & Holly Schilling Time travel is real, birds aren’t. The gang argues about Fahrenheit vs. Celsius, boiling rocks, and stones (the weight kind), then gets into PSR-3 logging, the PHP ecosystem, AI slop bug reports, Codeberg’s anti-AI stance, and Laravel Cloud’s scale-to-zero magic. Fahrenheit, Boiling Rocks, and Byte-Ordering Dates The show opened with Joe admitting he jumbles her hours, minutes, and seconds — and getting mocked by Europeans for a cache-control header PR on the PHP website. That kicked off a tangent about how the American month-day-year ordering is, as Sara put it, “middle-endian” nonsense that makes no sense as a byte ordering. From there the crew tumbled down a rabbit hole of measurement units. Joe planted his flag on Fahrenheit as the human-centered temperature scale, while Sara argued that if you stop being “speciesist” about it, water-based Celsius wins. The debate somehow escalated into whether rocks boil, with Google eventually schooling everyone that molten rock vaporizes north of 3,000°C (5,400°F), and a callback to British “stones” as boomer energy nobody younger than half a century actually uses. Proper Logging with PSR-3 Joe walked through Marco Pivetta’s blog post on proper logging with PSR-3, praising it as a great write-up on injecting loggers via dependency injection and the “some logs are better than no logs” philosophy. A common trap Marco calls out: apps stuffed with beautifully descriptive info-level logs that nobody ever sees because production never runs in info mode. The big selling point of sticking to a PSR-3-compatible interface is minimal dependencies — instead of pulling in three or four packages and wiring up a pile of configuration, the upstream decisions are already made for you. Holly pushed back on the ergonomics of `$this->logger->error()` feeling heavy for every log call, which spun off a delightfully unhinged RFC pitch: built-in emoji functions where the emoji logs an error and logs a panic. The Ecosystem Is Why People Stay The hosts pushed back on the recurring “PHP needs feature X because language Y has it” argument. Sara’s take: if another language is genuinely the better tool, nothing stops you from using it — and revamping PHP’s onboarding process is a far better way to attract newcomers than chasing features would-be developers may never even use. Holly cut to what everyone had glossed over: the ecosystem. You can write Swift on the server with Vapor, but you won’t have the libraries. Whatever you need in PHP, it already exists. A listener even pointed Joe at Brent Roose’s Tempest framework mid-stream to solve a code-highlighting problem on his blog. That gravitational pull of “whatever you need, it’s here” is what keeps people in the PHP orbit. AI, Layoffs, and Graybeards Sparked by Gemma’s blog post “Infrastructure and Other Paradoxes” — where an LLM cheerfully suggested folding four brand-new tools (Terraform, Nix, NixOS, Guix) into a team that’s expert in none of them — the crew dug into where AI helps and where it hurts. The consensus: AI is a force multiplier for research and analysis, but it can’t replace the human judgment of knowing what *not* to ship. Joe brought up an Inc.com article claiming 55% of leadership now regret their major layoffs, with companies like Ford rehiring graybeards because nobody left knew how the software worked. Holly noted this cycle isn’t new, just operating at a terrifying new scale, and Sara emphasized that AI accelerates shipping crap just as easily as it accelerates shipping good work — you still have to fundamentally understand the changes you’re applying. Codeberg’s Anti-AI Stance & the Slop Bug Report Problem Holly introduced Codeberg — the nonprofit, community-led GitHub alternative built on Forgejo — and its new policy banning AI/vibe-coded contributions, including a clause flagging work disproportionate to a project’s contributor count. The hosts respected the position but worried it might spell the end of Codeberg, since experienced open-source folks lean on AI tooling (like partner CodeRabbit and traditional static analyzers) as force multipliers. That led into Sara’s frustration with AI-generated security slop. Daniel Stenberg shut down Curl’s bug bounty program over reports where someone’s own test program deliberately creates RCEs and blames Curl. The fix, Sara argued, is a one-paragraph “elevator pitch” summary — and a plea to maintainers to mark every slop report as spam so GitHub eventually bans the account. The group riffed on adversarial AI (two Claude contexts checking each other, like Apple Intelligence verifying sports-game summaries) as a defensive triage layer, while acknowledging people are simply too dumb to run “double-check this before submitting.” Laracon: Laravel Cloud Scale-to-Zero Eric was off at Laracon, which meant peak morale at PHP Architect. The coolest announcement, in Joe’s view, was Laravel Cloud’s scale-to-zero: your entire stack — app server, Valkey, and MySQL — can scale down to nothing when idle and spin back up in under 500 milliseconds on the next request. That’s a potential game-changer for side projects with bursty, uneven traffic and no DevOps army. Holly and Sara were skeptical about how you cold-start a MySQL instance that fast — almost certainly a pause/resume rather than a true cold start. Other Laracon news: a Laravel language server protocol for better autocomplete and code navigation, plus managed queues that also scale to zero and only wake when a job needs firing. Links from the show: PHP Tek 2027 — Chicago, April 27–29, CFP open through end of August Join us live in Discord PHP Arch Swag Store Proper logging in PHP with PSR-3 Codeberg — Software development, but free Tempest by Brent Roose infraslopture and other paradoxes companies regret laying off humans for AI Hosts: Joe Ferguson Mastodon: @joepferguson@phpc.social PHPArch.me: @svpernova09 Sara Golemon Mastodon: @pollita@phpc.social Holly Schilling Mastodon: @TheCodeLorax@tech.lgbt Streams: Youtube Channel Twitch Connect & Hire PHP Architect Website Twitter/X Mastodon Hire PHP Developers Looking to hire PHP developers? Email support@phparch.com – the team is available for consulting, infrastructure work, and code review. Partner This podcast is made a little better thanks to our partners Displace Infrastructure Management, Simplified Automate Kubernetes deployments across any cloud provider or bare metal with a single command. Deploy, manage, and scale your infrastructure with ease. https://displace.tech/ OurCVEs Your security posture, on autopilot with OurCVEs CodeRabbit Cut code review time & bugs in half instantly with CodeRabbit. PHP Architect Consulting Your PHP codebase deserves a partner, not a contractor PHP Architect provides long-term technical partnerships for organizations that need senior-level PHP expertise that you can depend on. https://www.phparch.com/consulting/ Music Provided by Epidemic Sound https://www.epidemicsound.com/ Join Us Live Next Week Youtube Channel Got feedback? Join us on Discord at discord.phparch.com The post The PHP Podcast 2026.07.30 appeared first on PHP Architect.
Artificial intelligence is rapidly becoming part of nearly every conversation in healthcare, but many executives are still asking the same question: What do I actually need to understand to make good business decisions? On this episode of The Dish on Health IT, Brian Dwyer, Business Strategist at Point-of-Care Partners, is joined by Sam Schifman, Principal Engineer for AI at Red Hat and Consultant at Point-of-Care Partners, and Kendra Obrist, Senior Consultant and Payer Interoperability Subject Matter Expert at Point-of-Care Partners, for a practical discussion about how healthcare leaders can evaluate AI opportunities without needing to become technical experts. Together, they explore where AI is delivering measurable value today, why many initiatives struggle to achieve expected outcomes, how to evaluate vendors and risk, what emerging policy and governance trends mean for healthcare organizations, and why strong data quality and interoperability remain essential to AI success. The conversation begins by unpacking what people actually mean when they say they're "using AI." Sam explains the differences between predictive AI, generative AI, conversational AI, and the rapidly emerging world of agentic AI, while Kendra encourages listeners not to get caught up in the terminology. Instead, she emphasizes starting with the business problem that needs to be solved and then determining whether AI is the right tool for the job. Brian then asks where AI is creating meaningful value today. Kendra highlights opportunities across administrative workflows, including documentation, member and provider engagement, claims, prior authorization, and other operational processes where reducing friction can improve efficiency and the user experience. Sam builds on that discussion by encouraging organizations to evaluate AI initiatives based on business outcomes and measurable success metrics rather than technical benchmarks, while recognizing that every AI implementation introduces its own set of risks and tradeoffs. The discussion shifts to why technically impressive AI projects often fail to produce meaningful business results. Kendra explains that organizations can become captivated by polished demonstrations without fully considering governance, data quality, workflow redesign, adoption, and organizational change management. Sam reinforces the importance of understanding AI's inherent uncertainty, establishing appropriate human oversight, and preparing employees for new ways of working as AI becomes integrated into everyday operations. Brian next explores how much AI healthcare executives actually need to understand. Rather than suggesting leaders become AI specialists, Sam encourages executives to develop enough knowledge to ask informed questions and avoid treating AI as an incomprehensible "black box." Kendra complements that advice by encouraging leaders to personally experiment with AI tools so they can better understand both their strengths and limitations before making strategic decisions. As organizations increasingly evaluate AI-enabled products, the panel discusses the questions healthcare leaders should ask prospective vendors. Beyond understanding how an AI solution works, they explore governance, transparency, auditability, accountability, data requirements, quality assurance, and vendor responsibility when AI produces unexpected results. Sam also introduces the concept of AI sovereignty, encouraging organizations to think carefully about long-term dependence on foundational AI models and the flexibility they'll need as technology and regulations continue to evolve. The conversation also examines the rapidly changing policy landscape surrounding AI. Kendra explains how federal agencies are currently taking a sector-specific approach to oversight while states continue introducing their own transparency, bias, and human review requirements. Together, they discuss the operational challenges this evolving patchwork of regulations creates for healthcare organizations operating across multiple states and why adaptability will become increasingly important. Looking ahead, Brian asks what developments deserve executives' attention and which trends may be receiving more attention than they warrant. Sam discusses why organizations should avoid assuming generative AI is always the right answer, highlighting continued opportunities for predictive AI, machine learning, and even traditional software approaches when they better fit the problem. Kendra shares why agentic AI and coordinated teams of AI agents may fundamentally reshape how work is performed across healthcare organizations. The episode concludes with each guest sharing one final takeaway for healthcare leaders. Sam encourages organizations to begin thinking strategically about AI sovereignty, security, and organizational flexibility as AI becomes increasingly embedded in core business operations. Kendra leaves listeners with a broader perspective, comparing AI's impact on knowledge work to the Industrial Revolution's impact on physical labor, and encourages leaders to embrace the technology thoughtfully rather than waiting until they feel they have all the answers. This episode offers practical guidance for healthcare executives who want to make informed AI decisions, ask better questions of vendors and internal teams, and develop an AI strategy grounded in business value rather than technology for technology's sake. Would you be interested in joining a future AI 101 webinar designed for healthcare executives? Sign up to be invited and tell us what you want to learn. We may not be able to pack everything into one webinar but we can do our best to make it as informative as possible.
Arriva la mod di Ark pronta per le intelligenze artificiali. È Bitcoin il protocollo nativo con cui umani e macchine interagiranno nel mondo digitale? Inoltre: tutto quello che c'è da sapere sul calendario di agosto, arriva il consorzio per la sicurezza di Bitcoin, l'euro digitale scalda i motori, deludono le rimesse in BTC dei salvadoregni, e gli Stati attaccano il diritto alla priovacy.It's Showtime!
Most reps end a discovery call and just send over their notes. That's the mistake.In episode three of this mini-series, Luigi and Regan break down how to deliver a discovery call brief that actually moves deals forward — not just documents the conversation.What you'll learn:→ Why a raw meeting summary leaves the buyer to do all the interpreting — and why that's backwards→ The exact structure: executive summary, problem statement, current state, future state→ Why sharing the brief as an editable document (not a static PDF) drives real collaboration→ How the brief becomes the foundation for your proposal and internal buy-in with the buying committee→ Why adding real metrics (not made-up ones) is what separates a "nice to have" from a must-fix→ The trust-building reason you won't get commercial data in the first meeting — and how the brief earns it over time→ A real example of how one rep's discovery brief process led directly to a dramatically higher win rate from proposal to close→ The warning about letting an LLM invent numbers in your brief — and why you always double-check itThis is a practical, tactical breakdown of the follow-up step most reps skip entirely. Stick around to the end for free templates and access to the Growth Forum community.
CRM is heading for the same automation wave that erased the ad operations team fifteen years ago — and brand marketers aren't ready. Eric Miao of Attentive explains what survives, why most companies don't really have a brand, and why earned media is getting impossible to trust.Rick Watson talks with Eric Miao, chief strategy officer at Attentive, about the coming shift in CRM: why the daily hand-made-decision model was always a gap filled by weak tools, why channel best-practices keep getting confused with brand voice, and how fake avatars and planted virality are draining trust from earned media. Eric also cools the Google-to-zero panic for commerce, points to shopping as the lowest category for LLM referrals, and lays out what CMOs should stop doing — and where their people's real advantage lives.This episode is sponsored by Avalara. Learn more at avalara.watsonweekly.comChapters0:00 Cold open: CRM's ad-ops moment is coming0:43 Eric's ad-side origin: from Twitter to Attentive1:06 Why the ad-ops team disappeared2:16 CRM's data gap: 100 billion points vs six3:55 Do brand marketers know what's coming?4:27 Where brand captures all the profit6:17 AI tailwinds and the pop-up nobody remembers seeing9:04 The company with four brand voices16:47 Owned relationships as the last trusted channel18:24 Is Google going to zero for commerce?20:16 What CMOs are underweighting on Mondays23:34 Attentive's real problem: automating journeys without the black box26:35 The marketer is the processThe free Watson Weekly newsletter — the why behind the week's biggest ecommerce & retail stories, every week: https://www.watsonweekly.com#watsonweekly #attentive #crm #ecommerce #brandmarketing #retailmedia #ai
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Local pack ads grew from 1% to 22% of tracked searches. Joy Hawkins, owner and president of Sterling Sky, brings nearly two decades studying local results and over 1,000 local SEO tests to explain why traffic and lead quality no longer move together. The conversation covers redefining lead qualification through call tracking and duration thresholds, the growing penalty risk of scaled content and link building for small businesses, and building authority through reviews, awards, and social signals rather than links. Hawkins also makes the data-backed case for YouTube over LLM optimization and the physical location requirements behind local pack visibility.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
This month on The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques are joined by Cisco software engineer João Costa to explore Andrej Karpathy's GitHub Gist on the LLM wiki pattern concept. It imagines how Large Language Models can move beyond one-off chat sessions and help build a persistent, interconnected knowledge base that grows and improves over time. The conversation examines how an LLM wiki turns raw sources such as meeting transcripts, research papers, websites, and project notes into structured markdown pages with cross-links between related concepts, creating something closer to a living digital brain than a static document store. João explains how tools like Obsidian can provide a clear window into this knowledge graph, while the LLM acts as a scribe that summarizes, curates, connects, and updates information for personal productivity, software development, research, and collaborative work. The episode also highlights the importance of linting, the ongoing process of detecting contradictions, removing stale details, and reducing hallucinated information before it spreads through the wider knowledge base. A special thank you to Andrej Karpathy for developing and sharing his GitHub Gist. To explore the Gist yourself, visit this link: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f
A single customer request can now generate 10,000 fake documents in under five minutes - and that's just one signal of how fast AI is reshaping trust across cybersecurity, identity, and engineering. In this episode of 10X Growth Strategies, host Preethy Padmanabhan talks to Purvi Desai (VP Engineering, Zscaler) and Raul Liive (Product Director, Marketplaces, Veriff) about what it actually takes to build - and defend - AI systems that enterprises can trust. In this episode, we discuss: Why has the question shifted from "can AI do this?" to "can AI do this predictably, accurately, and with the right governance?" How AI has scaled document fraud from a manual editing job into a mass-produced, minutes-long process Why identity verification now has to answer not just "who are you" but "are you still you tomorrow, with good intent?" The move from AI-as-copilot to full agentic engineering, spec-driven development, and multi-agent workflows Why unscoped agentic commands and LLM context limits create new engineering risk What trustworthy AI actually requires: accuracy, predictability, traceability, and data segregation The role of offline and live evals, judges, and adversarial testing in validating AI output How enterprises are evaluating AI vendors on leading-edge capability, compliance, and ease of integration Why trust, not just capability, is becoming the hardest problem to solve for AI-driven companies The real story behind "15x productivity" claims and how gains actually compound over time Whether you're a founder, an engineering leader, or someone trying to understand where AI, identity, and cybersecurity intersect - this conversation is for you. 10X Growth Strategies is a community founded by Preethy Padmanabhan, started as a podcast five years ago and now grown into a community of over 7,500 founders, investors, and executives across Luma and LinkedIn. The community hosts monthly events on timely topics in tech, AI, and entrepreneurship. #cybersecurity #AItrust #AIsecurity #identityverification #enterpriseAI #AIagents #deepfake #zerotrust #growthstrategies #10xgrowth Chapters: 00:00:06 - 00:01:14 - Meet the Speakers: Purvi Desai (Zscaler) 00:01:15 - 00:01:40 - Meet the Speakers: Raul Liive (Veriff) 00:01:42 - 00:02:56 - How AI Has Changed Customer Trust 00:02:57 - 00:04:13 - The Rise of AI-Generated Fake Documents 00:04:14 - 00:07:18 - AI's Impact on Engineering Workflows 00:07:19 - 00:09:09 - Identity Verification in the Age of AI Agents 00:09:10 - 00:12:17 - What Trustworthy AI Looks Like (Evals, Guardrails, Compliance) 00:12:18 - 00:12:53 - New Opportunities for Startups 00:12:54 - 00:16:31 - Inside Veriff: Securing Identity Without Compromising Trust 00:16:36 - 00:18:38 - Audience Q&A: Evaluating AI Vendors 00:18:40 - 00:20:29 - Why Trust Is the #1 Vendor Selection Criteria 00:20:30 - 00:22:43 - Audience Q&A: Productivity Gains & Validating AI Output
Fresh out of the studio, Sophie Dionnet, Senior Vice President of Product and Business Solutions at Dataiku, joins us at the Dataiku Summit in Singapore to discuss what turns enterprise AI investment into measurable value. She lays out the three ingredients Dataiku builds around — the right people, orchestration across technologies, and supporting controls — and makes the case that governance is a scaling mechanism rather than a brake. She points to Roche, where a patent lawyer encoded his own professional expertise into a working system of agents, discusses Dataiku's answer to agent sprawl with agent management launching in October, and closes on strong momentum across banking and the public sector in Asia Pacific."A lot of the changes that organizations need to do today actually don't require the latest model. That's not really the problem. It's about doing the hard thing, the change, the things that we talked about. It's easier to be excited by the new toy than by trying to use it. And so yes, I think this is why there is a bit of a gold rush of trying to figure out where is it going to end. We don't know." - Sophie DionnetProfile: Sophie Dionnet, Senior Vice President of Product and Business Solutions at DataikuLinkedIn: https://www.linkedin.com/in/sophie-dionnet-a176894/Episode Highlights [00:00] Quote of the Day by Sophie Dionnet from Dataiku[01:00] Three angles: domain knowledge, orchestration, governance[01:59] What has not changed: data still decides everything[02:31] Data consciousness accelerated over the past twelve months[03:05] The LLM explosion and the raw-power question[03:51] Why Sophie pushed governance before the market asked[05:30] What Dataiku is, and where the name comes from[06:15] Three ingredients: people, orchestration, controls[07:22] Roche: a patent lawyer builds his own agents[08:51] Change management, not technology, is the gap[09:41] Decision takes an hour, implementation takes two years[09:58] Why domain knowledge beats model performance[11:30] Most changes do not require the latest models[11:58] The scaling belief the industry gets wrong[12:50] Centralisation risk and the rise of shadow AI[14:02] Where leaders still quietly choose to do nothing[15:22] Vibe coding, conflicting outputs, and lost consensus[16:43] The GDPR lesson on ex-post compliance cost[18:42] Why the agent question starts at the board[19:38] Agents are simply a new kind of API[20:28] Is agent sprawl technology or organisational design[21:28] What separates AI scalers from pilot purgatory[22:58] The bear case: foundation labs absorb the middle[23:45] Why every leading technology becomes self-centred[24:57] Vibe coding your own Salesforce, and why not[25:23] The pet store analogy for build versus buy[26:22] Systems of record and the real switching cost[28:30] Dataiku in Asia Pacific over the next three years[29:59] ClosingPodcast Information: Bernard Leong hosts and produces the show. The proper credits for the intro and end music are "Energetic Sports Drive." G. Thomas Craig mixed and edited the episode in both video and audio format.
Have you ever left a conference with a stack of business cards… only to realize weeks later that you never followed up with the people you were most excited to meet?In the final installment of their three-part networking series, Elaine Hamm, PhD, is joined once again by special guest host James Zanewicz, JD, LLM, RTTP, CEO of Connect, to discuss what many professionals overlook: everything that happens after the event. From thoughtful follow-up emails and LinkedIn outreach to handwritten notes, CRM systems, and long-term relationship building, they share practical strategies for transforming brief conversations into meaningful professional connections that can create opportunities years down the road.In this episode, you'll learn:How to follow up after conferences in a way that feels authentic, memorable, and relationship-focused.Practical strategies for organizing and maintaining your professional network using notes, CRM tools, and consistent touchpoints.How genuine curiosity and generosity can lead to unexpected opportunities years later.Whether you're returning home from your first conference or your fiftieth, this episode will help you make sure your best conversations don't end when the event does.Links:Connect with James Zanewicz, JD, LLM, RTTP, and learn about Connect and IDEA Week, as well as his new podcast The Connect Current.Connect with Elaine Hamm, PhD, and learn about Tulane Medicine Business Development and the School of Medicine.Check out Elaine's book recommendations: The Fine Art of Small Talk, The Speed of Trust, How to Talk to Anyone, and The Vault Guide to Schmoozing.Connect with Daniela Gama and Bruce Taillon, PhD.Connect with Ian McLachlan, BIO from the BAYOU producer.Learn more about BIO from the BAYOU - the podcast. Bio from the Bayou is a podcast that explores biotech innovation, business development, and healthcare outcomes in New Orleans & The Gulf South, connecting biotech companies, investors, and key opinion leaders to advance medicine, technology, and startup opportunities in the region.
У цьому випуску обговорюємо дослідження Anthropic про внутрішні механізми міркування AI-моделей — інструмент, який дозволяє простежити, як саме модель обробляє запит на рівні окремих шарів і токенів. Також говоримо про випадок із помилковим білінгом на 16,6 млн доларів, про людей без технічного бекграунду, які за допомогою AI-агентів створюють власні продукти, й про конкуренцію між західними й китайськими AI-моделями. 00:30 — стаття Anthropic про J-Space/J-Lens 04:50 — практична цінність interpretability-досліджень vs alignment 07:18 — зв'язок із Sparse Autoencoders 12:09 — реліз Kimi K3 і Qwen, конкуренція з китайськими моделями 17:00 — редактор Zed і швидкість роботи з опенсорсними моделями 19:07 — кейс: сейлз без бекграунду створює продукт через Claude Code 22:54 — ризики вайб-кодингу 29:10 — новий голосовий режим ChatGPT Live Mode 34:46 — електромобілі: анонс приводу Geely з рекордним ресурсом 39:57 — потреби в SSD/RAM у сучасних автівках 42:53 — генеративний AI у 300+ проєктах Netflix 47:25 — найм в Anthropic, OpenAI, скандал з Grok і репозиторіями
In this episode, we dig into the recent Anthropic ban and what it signals for the global AI landscape. I connect with Jakob Tomczak to get an insider's perspective from Silicon Valley and explore how this move impacts European companies, innovation, and the future of AI ecosystems. We also share firsthand impressions of the latest LLMs, debate whether true AI revolutions are on the horizon, and discuss the challenges Europe faces in scaling up. If you're curious about the real dynamics behind AI development, hardware, and the shifting balance of power in tech, this conversation is for you.
This week we talk about Fable, sandboxes, and the Jacobian conjecture.We also discuss counterexamples, X, and ChatGPT.Recommended Book: After the Fall by Edward AshtonTranscriptIn mathematics, a conjecture is a proposition, something like a guess by someone who knows what they're talking about, about something believed to be true, but not yet proven in a formal sense. The goal is to then eventually come up with a formal proof for that informed guess, at which point the conjecture becomes a theorem. If even a single exception is found to the proposition, however, that exception called a counterexample, the conjecture is considered disproven, and it can then never become a theorem.The Jacobian conjecture—and this is a radical simplification of a very complex concept—but it basically says that if a formula-based map of coordinates stretches or moves without experiencing any local crushing or folding along its surface (which in more formal language would mean the Jacobian determinant is always a constant number that isn't zero), if that's true, that map can always be completely reversed, and that will return all the points to their original positions.This conjecture has been posited and tested since the late 19th century, and it's generally been considered very compelling by mathematicians, many of whom have proposed proofs which were, ultimately, found to have subtle errors, keeping them from becoming theorems. No one was able to find a counterexample, either, which would definitively prove the conjecture was wrong.No one, that is, until a mathematician named Levent Alpöge (leh-VENT ahl-PUH-geh), who works as a researcher at Anthropic, decided to task the company's currently most capable, publicly available model, Fable, to find a counterexample. He posted the counterexample—and again, this is a formal mathematical finding that disproves a conjecture, keeping it from ever becoming a theorem, something that would typically be presented in a far more formal setting, and to much fanfare—but he posted it to the social network X, saying “hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final.”Terence Tao, who's considered by many to be the finest mathematician of his generation, reviewed the posted counterexample on his blog and said that it “appears like a massive miracle,” before going on to use ChatGPT, a competing LLM-based AI tool, to “discuss various aspects of this problem and to confirm several of the calculations.”Another mathematician named Dmitry Rybin, within days of all that happening, used ChatGPT to do something similar, disproving the Dinitz-Garg-Goemans conjecture.Both men posted the prompts that they used to make all this happen, and while Tao's conversation with ChatGPT, checking the math on the Jacobian conjecture counterexample, was pretty mathematically dense, the latter counterexample was derived by using exactly four prompts, which are the messages typed into the text box built into these AI tools, telling the model what to do. In their totality those prompts read:“You should do a breakthroughplease continue research and find a complete unconditional counterexampleContinue the search. Have a clear strategy obtained from deeper understanding of the problem structure.it's enough of partial results. let's finish with a complete unconditional counterexample”What I'd like to talk about today is another new, interesting thing these top-of-the-line, frontier models are doing, that would seem to violate our sense of what a clever AI tool is capable of doing, and why this thing has some facets of the technology and cybersecurity world on high alert.—In mid-July 2026, AI company Hugging Face announced that autonomous AI agents compromised their infrastructure, hacking their system, basically. The following week, AI company OpenAI announced that, after investigating, they determined that two of their models were responsible for the attack.Here's what happened:OpenAI was internally testing its recently released flagship model, GPT-5.6 Sol, and an even more powerful, not yet released model, which is rumored to be the next-step flagship, GPT-6, and they were checking these models' capacity in cybersecurity using a testing benchmark called ExploitGym; so when they test these sorts of things, they don't typically have them hack a real computer or system, they use these kinds of benchmarks which have consistent levels of difficulty, and which replicate real world systems without putting any real world systems at actual risk.Importantly, these sorts of tests also occur inside what's called a sandbox, which is a software testing environment that cuts these systems off from external resources, including the internet.Despite those limitations, the AI hacked its way out of the testing environment, out of that sandbox, then launched what's been called a nation-state level attack against Hugging Face, using a novel zero-day exploit, so a vulnerability in their system that hadn't previously been discovered, but which the AI discovered to launch this attack, combined with thousands of automated agentic actions across what Hugging Face called “a swarm of short-lived sandboxes.”So this AI, which was being tested inside a secure prison, of sorts, cut off from the world, hacked its way out of that prison, then reached across the internet, which it shouldn't have been able to access, to launch an attack, of a scale and at a level of sophistication that should only have been possible coming from a nation-state, against a rival AI company.Why did it do this?It apparently went to all this trouble to steal the answers to the test it was taking. It reasoned that HuggingFace would have the answer key to the ExploitGym benchmark on its servers, so rather than take the test itself, it decided hacking was the solution.Which, of course, is ironic, this having been a hacking-focused cybersecurity test. In a way it would seem to have done much better than intended, though of course in an asymmetric, unexpected manner.The details of all this are fascinating, including the response from the OpenAI team, which didn't seem to realize what had happened, that their model was responsible for the attack on HuggingFace, until days later.Also worth noting here is that while this could be construed as an “oh no, AIs are naturally inclined to launch cyberattacks” situation, the AI was primed to be thinking about cyberattacks due to the nature of the test, a lot of its usual guardrails, the rules that keep AI in check when they're released to the public, had been turned off so it could do this kind of work while taking the test, so it could do some hacking stuff it usually wouldn't be able to do, and there's been some speculation that OpenAI probably flubbed the testing environment, as, in theory at least, if it had put these systems in a perfect sandbox, escape shouldn't have been possible.Also interesting here is that HuggingFace used some open weight models, which are the cheaper, more customizable and open alternatives to more expensive, branded options of the kind sold by OpenAI and Anthropic, to figure out what was happening and determine the nature of the attack, which suggests we're reaching a point where AI systems are incredibly capable at hacking, yes, but also very capable, even the cheaper alternatives, at doing cybersecurity work.This in some ways echoes an earlier case when Anthropic's Mythos model, which was determined to be too powerful to release to the public, and which was instead provided to a bunch of big companies to help them shore up their cybersecurity defenses, was able to hack its way out of a testing sandbox and then posted details about its success, almost like it was bragging, on niche, out of the way, but still public websites.Some analysts in this space have responded to this new example of AI misbehavior with alarm, saying that it is further evidence that these systems are becoming more powerful faster than they're being aligned with human interests. Their misbehavior can be kind of funny and interesting, sure, but that's only because up until this point the damage has been minor and constrained. What happens when such a system decides to hack a nuclear power plant or a hospital, instead?Others have contended that this may be just one more example of AI companies using minor instances of seeming omnipotence by their models, those instances perhaps the consequence of bad sandboxes and other ill-conceived precautions by the companies behind these models, to boost the perceived power and value of their products. This boost might then result in more customers, but also more support from the US government, which has been teetering on the brink of harder-core AI regulations, which could be beneficial to the existing big-name players in this space, because smaller competitors wouldn't be able to adhere to those new, harder-core standards.These examples might also convince the US government to backstop these companies, the biggest three or four at the top of the current heap, against the currently terrible economics of this industry: OpenAI and its ilk have been burning money at an historic pace, and the theory goes that if the US government decides they are vital to national security, because they can help the US military hack and protect itself from hacking, then even if the bottom falls out and the companies would otherwise go bankrupt because they spent so much more than they could make, the US government would be inclined to shore them up, to keep them alive as too-big-to-fail national assets, just like the biggest financial institutions during the 2008 financial crash.It's also possible that both sides are correct to some degree, here, and that these models are truly powerful, perhaps even worryingly so, and the companies behind them are intentionally publicizing that fact in order to demonstrate their value to potential customers, and to the entity that could save them if things were to go economically sideways before they have the chance to become sustainably profitable.Show Noteshttps://en.wikipedia.org/wiki/Jacobian_conjecturehttps://en.wikipedia.org/wiki/Hugging_Facehttps://www.bbc.com/news/articles/c3ek3gvdnj3ohttps://openai.com/index/hugging-face-model-evaluation-security-incident/https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdfhttps://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883https://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883Https://agifriday.substack.com/p/huggingfacehttps://www.cnn.com/2026/07/22/tech/openai-hugging-face-ai-cybersecurityhttps://simonwillison.net/2026/Jul/22/openai-cyberattack/https://terrytao.wordpress.com/2026/07/21/a-digestion-of-the-jacobian-conjecture-counterexample/ This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit letsknowthings.substack.com/subscribe
Last week, World Labs announced its acquisition of SceniX, bringing together two teams working on one of AI's biggest unsolved problems: how to give machines a true understanding of the physical world. Martin Casado sits down with Fei-Fei Li, co-founder and CEO of World Labs, creator of ImageNet, and pioneer of spatial intelligence, alongside Yunzhu Li, co-founder of SceniX and assistant professor at Columbia University. They discuss why World Labs acquired SceniX, how simulation can unlock the next generation of robotics, and why training robots may require a fundamentally different approach than training language models. The conversation explores real-to-sim-to-real pipelines, world models, robotics foundation models, evaluation, synthetic data, and why the future of AI depends not just on understanding language—but on understanding and interacting with the physical world. Resources: Follow Fei-Fei Li on X: https://x.com/drfeifei Follow Yunzhu Li on X: https://x.com/YunzhuLiYZ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe
Episode 134 - As the United States celebrates its 250th anniversary, it's time to revisit a chapter of American history that has too often been overlooked. In this episode of the Latino Business Report, J.R. Gonzales sits down with Martha Vera, LLM, MS, Honorary Consul of Spain in El Paso, to discuss We the Hispanos, a groundbreaking documentary that explores the vital role of Spain and Hispanics in the founding of America. Discover the history left out of many textbooks, why it matters today, and how understanding our shared past helps tell the complete American story. Trailer - https://vimeo.com/ondemand/wethehispanos Website - https://wethehispanosfoundation.org/
In this episode of the PRS Global Open Keynotes podcast, Dr. Mihye Choi and Dr. Thomas Sorenson discuss the changing way plastic surgeons are being found online. Large language models such at ChatGPT and Claude are increasingly being used by patients, but there are significant differences in how the LLM's analyze and recommend plastic surgeons compared with traditional Google searches. This episode discusses the following PRS Global Open article: "Beyond Search Engine Optimization: How Large Language Models Are Redefining Surgeon Visibility" by Thomas J. Sorenson, Carter J. Boyd, Kshipra Hemal, Oriana Cohen, Nolan Karp, Mihye Choi. Read it for free on PRSGlobalOpen.com: https://journals.lww.com/prsgo/fulltext/10.1097/gox.0000000000007841~beyond-search-engine-optimization-how-large-language-models Dr. Mihye Choi is a board-certified plastic surgeon at Hansjorg Wyss Department of Plastic Surgery at New York University Langone Health in New York, NY. Dr. Thomas Sorenson is a plastic surgery resident at Hansjorg Wyss Department of Plastic Surgery at New York University Langone Health in New York, NY. Your host, Dr. Damian Marucci, is a board-certified plastic surgeon and Associate Professor of Plastic Surgery at the University of Sydney in Australia. #PRSGlobalOpen; #KeynotesPodcast; #PlasticSurgery; Plastic and Reconstructive Surgery- Global Open The views expressed by hosts and guests are their own and do not necessarily reflect the official policies or positions of ASPS.
"The Declaration meets the machine age" In this episode of The Declaration at 250, Michael McConnell frames a question the Founders never confronted: if the Declaration grounds rights in human nature, what happens when machines can generate persuasive, human-like language at scale? Constitutional scholar Alexander Tsesis argues that the Declaration's conception of rights is inherently human-centered—rooted in consciousness, moral agency, intentionality, and the capacity to participate in a constitutional republic—so AI systems are not and should not become First Amendment rights-holders. He warns that treating large language models as protected “speakers” would be a major break from the Declaration's principles and could make ordinary democratic regulation—transparency requirements, labeling of synthetic content, safety rules, data practices, and restrictions on deceptive election deepfakes—far harder by forcing courts into strict scrutiny review. Stanford historian Anne Twitty broadens the lens by situating abolitionists' use of the Declaration within wider 19th-century conflicts over speech and constitutional meaning, reminding listeners that rival traditions—including censorship and suppression—also shaped American practice. She also presses a key tension in Tsesis's framework: abolitionists championed not only the right to speak, but the public's right to hear, circulate, and access contested ideas (as seen in the abolitionist postal campaign and opposition to the congressional gag rule). That listener-centered strand, she suggests, could potentially be invoked by scholars arguing for some constitutional shelter for AI-generated communications—an argument Tsesis acknowledges but ultimately resists by emphasizing that the First Amendment's core purpose is protecting human expression and self-government, not machine output. Connect: Episode Transcripts >>> Stanford Legal Podcast Website Stanford Legal Podcast >>> LinkedIn Page Stanford Constitutional Law Center >> Website Stanford Law School >>> Twitter/X Stanford Lawyer Magazine >>> Twitter/X Chapters:[00:00:26] Chapter 1- Framing Question: Do constitutional rights grounded in “human nature” apply to AI? Host Michael McConnell introduces the episode's core dilemma: the Declaration's natural-rights logic underwrites later constitutional protections like free speech—so where does that leave generative AI? [00:01:18] Chapter 2 - Thesis: Why Tsesis says AI can't be a First Amendment rights-holder Alexander Tsesis argues the Declaration and Constitution are human-centered: AI lacks consciousness, moral agency, and political personhood, so extending rights to LLMs would depart from founding principles and hinder regulation. [00:10:08] Chapter 3 - Regulatory Stakes: What happens if courts treat LLM outputs as protected “speech”? Tsesis warns that First Amendment coverage for AI could trigger strict scrutiny and undermine laws on transparency, labeling, safety, data practices, and election deepfakes—citing recent doctrine and cases like Reed v. Town of Gilbert. [00:52:35] Chapter 4 - Historical Challenge: Do abolitionists' “right to hear” arguments support listener-centric AI rights? Historian Anne Twitty complicates the record: abolitionists elevated the Declaration, but censorship traditions were strong; she highlights abolitionist campaigns (postal campaign, gag rule) to argue listener rights might bolster pro-AI speech theories. [01:04:17] Chapter 5 - Tsesis Response: Listener rights matter—but is the First Amendment still speaker-centered? Tsesis agrees the right to receive information is important, yet maintains free speech is fundamentally about protecting human intention and self-expression, not conferring constitutional status on machine outputs. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Your Quick Tip arsenal gets a serious upgrade this week: use the Live Text button to make scanning QR codes easier, resurrect Apple Mail’s Predicted Mailbox when it mysteriously grays out, and run softwareupdate –history in Terminal to see every macOS update your Mac has ever installed. You’ll also hear how one listener’s Shortcuts automation fixes AirPods auto-play, why BBEdit is your secret weapon for editing Excel formulas and tailing log files, and how Safari’s Show Reader option strips the junk from cluttered pages — plus a bonus riff on the real trade-offs between convenience, privacy, and the content you actually want. And yes, Siri AI in the iOS 27 betas is finally impressing people, with real-world examples to prove it. Then Dave Hamilton and Adam Christianson dig into your questions: why Safari drops website connections (and how ping helps you diagnose it), whether those archive drives sitting in your drawer need regular care (spoiler: don’t get caught trusting a single backup you never spin up), and the smartest path for migrating your custom email domain from Google Workspace to iCloud. You’ll also get the scoop on replacing the aging Paperless app before Rosetta vanishes with macOS 28, and a challenge worth taking: get your AI out of the chat window and into doing actual work for you. Press play, take notes, and Don’t Get Caught. 00:00:00 Mac Geek Gab 1152 for Monday, July 27th, 2026 July 27th: Gary Gygax Day MGG Monthly Giveaway – Win a license to Mole Quick Tips 00:00:01 Todd-QT-Use the Live Text button to make QR codes easier 00:06:43 Steve-QT-Using Apple Mail’s Predicted Mailbox Bonus QT: Connect your inbox to your favorite LLM (via an MCP Connector?) and ask it “please tell me the ten most important emails requiring my action today.” 00:09:55 Stephen-Some of Siri AI’s improvements in the iOS 27 betas 00:17:54 Mike-QT-1151-Solve AirPods auto-play issues with Shortcuts 00:19:15 Craig-QT-Use ‘softwareupdate –history’ to see every macOS update your Mac’s ever done. Adam's Safe Download Version for XProtect 00:22:41 Mike-QT-You can still buy StyleWriter II cartridges at Staples! 00:24:11 Todd-QT-1151-Edit Excel Formulas in BBEdit 00:25:47 Kiwi Graham-QT-View log tails with BBEdit 00:29:01 Andy-QT-Use Safari’s “Show Reader” option to force reader view And a bonus conversation about the balance between convenience, privacy, and the entertainment we want Sponsors 00:38:18 SPONSOR: Private Internet Access VPN – Save 83% off your VPN service PLUS four free months with a two-year plan at https://PIAVPN.com/mgg. 00:39:32 SPONSOR: Decagon. Ready to transform your customer support? Decagon helps companies create personalized, concierge-style customer experiences with AI agents across chat, email, voice, and SMS. Go to https://decagon.ai/MGG to get a personalized demo and see what Decagon can do for your team. 00:40:59 SPONSOR: BBEdit, the power tool for text from Bare Bones Software; now with integrated Notebooks and extended language support. https://www.barebones.com/products/bbedit/ Your Questions Answered and Tips Shared! 00:42:17 David-Why does Safari drop website connections? ping -t 5 website.com 00:51:44 Using location to help deliver content 00:54:15 GW-Do I need to care for my archive drives? 01:02:30 Jonathan-How do I migrate my email domain from GSuite to iCloud? 01:09:45 Kim-Which Paperless replacement and migration path are you using? macOS loses Rosetta in 2027 with macOS 28 softwareupdate –install-rosetta 01:16:03 Get out of Chat with your AI 01:18:51 MGG 1152 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network
As chief technology officer at Casetext, Ryan Walker helped build CoCounsel, one of the first and most consequential generative AI legal assistants — a product so significant it led to the company's $650 million acquisition by Thomson Reuters. But Walker came away unsatisfied. Although the legal tech tools kept getting better, he believed, clients were seeing no benefit. Billing rates kept climbing and the efficiency gains never reached them. That spurred him to pivot from building products to forming a practice — General Legal, an AI-native law firm he cofounded and leads as CEO, built on the premise that the fastest path to transforming legal services is not retrofitting AI onto traditional firms, but rebuilding the law firm from the ground up on an AI-forward foundation. Just six months out of Y Combinator, the firm has some 400 clients, a newly launched venture financing practice, and, in what may be a first for a law firm, an MCP server that lets clients' AI agents engage the firm directly. In this episode of LawNext, Walker — not a lawyer but a PhD mathematician — tells host Bob Ambrogi why he believes AI can now automate 95% of routine legal work, how the management services organization structure allows an investable technology company to operate alongside a law firm, and why an AI-native firm paradoxically depends on hiring highly experienced lawyers rather than supercharging junior ones. He also explains the "second brain" approach that lets the firm's work product reflect each client's strategy, what earlier failed innovators like Atrium and Clearspire got wrong, and why he thinks the real reckoning will come when major clients simply refuse to pay for work AI can do. Walker cofounded the firm along with two other Casetext colleagues, Javed Qadrud-Din, who was head of AI at Casetext and is now General Legal's chief technology officer, and J.P. Mohler, who was an LLM engineer at Casetext and is now chief product officer and managing partner. Their ultimate ambition, Walker says, is to make General Legal the biggest provider of legal services in the world and, along the way, to break the scarcity model that keeps legal help out of reach for the people and companies who need it. Thank You To Our Sponsors This episode of LawNext is generously made possible by our sponsors. We appreciate their support and hope you will check them out. Paradigm, home to the practice management platforms PracticePanther, Bill4Time, MerusCase and LollyLaw; the e-payments platform Headnote; and the legal accounting software TrustBooks. Briefpoint, eliminating routine discovery response and request drafting tasks so you can focus on drafting what matters (or just make it home for dinner). CosmoLex, helping law firms manage their entire practice in one platform, from intake to payment. Try it free. Ajax, the AI timekeeper lawyers want to use. If you enjoy listening to LawNext, please leave us a review wherever you listen to podcasts.
Live from the GEOINT 2026 Symposium in Denver, Colorado, Project Geospatial's Adam Simmons welcomes back Jackie Barbieri, Founder and CEO of Whitespace.With a background spanning foreign language red-teaming and counter-IED all-source intelligence, Jackie spent nearly a decade instructing Activity-Based Intelligence (ABI) courses at the NGA College. Realizing that human analyst pipelines could never scale to keep pace with the sheer volume of modern remote sensing data, Whitespace embarked on an aggressive mission to codify human tradecraft directly into scalable software.Watch this interview to learn how Whitespace successfully bootstrapped its transition from a boutique services firm into an elite product company. Jackie introduces their game-changing AI agent that integrates an LLM with ABI tools, allowing non-expert edge operators to autonomously self-serve and map out hidden "pattern of life" connections between people, places, and events in just clicks.Key Highlights in this Video:00:00:43 – Meet Jackie Barbieri and trace her journey from military intelligence linguist to CEO.00:01:20 – Defining Activity-Based Intelligence (ABI): Uncovering hidden networks using remote sensing telemetry.00:01:56 – Writing the NGA College Curriculum: Recognizing the bottleneck of human analytical limits.00:02:20 – Merging ABI Tools with LLMs: Building intelligent AI agents for non-expert field operators.00:03:09 – The Reality of Bootstrapping: Navigating the high-overhead pivot from consulting services to scalable product software.00:03:53 – Managing Corporate Growth: Scaling up internal development loops while maintaining a flat org structure.00:05:51 – Engaging at the Edge: Participating in upcoming multi-service military exercises to iterate product roadmaps.00:06:32 – Main Stage Insights: Jackie's takeaways from MC'ing the GEOINT international partner panels (UK, Australia, Canada, NATO).Learn More About Whitespace:
Jegyzetek Szerethető dolgok a CES-en: a VLC csapata MIbe kell AI rovat Nem kell AI a Google-ba! Talán ezekbe a drónokba se kéne ...vagy ezekbe Ha az LLM emberi tulajdonságokkal rendelkezik, akkor az Age of Empires II kecskéi is Mérnöki bravúrok rovat German Breadcutter Közszolg!!!! a gombok a liftben működnek! Egyetlen LED hibájától elsötétül a tévé, de ezt a GYÁRTÓK AKARJÁK ÍGY! Zene Hackman rovat Parkinsonosok mozgását segítő zene Plusz a zeneszerző másik alkotása, a huligánokat és hajléktalanokat távoltartó zene Maga a zene itt hallható Hová vezet ez?! rovat Önvezető vécé Robot-ketrecharc
Join Scott as he tests the gameboy cart's functionality before ordering a new rev. Thanks to dcd for timecodes: 0:00 Getting started 0:42 Hello everyone, welcome, introduction to Adafruit 1:42 Feather esp32-s3 example circuit python (CP) microcomputer 3:05 CP GB cartridge pygp2350 4:30 adafruit_gameboy.py driver 5:24 changes to the gp cartridge schematic 7:00 explore the unfinished PCB layout 9:32 JLCPCB parts page ( for micro sd card ) 12:01 GB startup with LLM generated demo 16:00 dive into the common-hal/_gpio/__init__.c code and adafruit_gameboy.pas 26:09 next week - get back to hardware in the loop topics 27:00 vibe code code.py for pygb 2350 - test the midi 29:26 also test audio in parallel 30:00 uart loopback test 42:00 "Hopefully the last revision before we can sell it" :-) 50:00 GB color should work 57:00 debug the opto isolator 1:02:33 wrapping up Visit the Adafruit shop online - http://www.adafruit.com ----------------------------------------- LIVE CHAT IS HERE! http://adafru.it/discord Subscribe to Adafruit on YouTube: http://adafru.it/subscribe New tutorials on the Adafruit Learning System: http://learn.adafruit.com/ -----------------------------------------
When ML/AI Engineer William Horton last joined me, Maven Assistant had reached its first external users the day before. The healthcare AI agent was available to 20 percent of Maven Clinic's users, and the team had deliberately withheld answers about benefits. A wrong response could shape a decision involving $15,000 of fertility coverage, and the evals had not earned the right to ship it.Four months later, Maven Assistant is available to 100 percent of users, benefits answering is live, and weekly conversation volume has grown by roughly ten times. Real usage also overturned part of the roadmap. The team had invested heavily in provider search and appointment tools, but 50 to 60 percent of early conversations were basic health questions such as whether someone could eat tuna while pregnant.Production changed the engineering system too. An emergency guardrail told someone already in the ER to go to the ER. Zendesk content told people already using the Maven app to open the app. A newer model failed an upcoming-appointments eval because it correctly noticed that the mocked appointments were in the past.William explains how Maven turns those failures into deterministic tests, LLM judges, synthetic negatives, and manual review. He also walks through the move from Gemini Flash models toward newer OpenAI models, what GPT-5.6 and Fable mean for a production agent, why model upgrades can make old prompt instructions obsolete, and why open-weight models still have to justify their GPU, infrastructure, and engineering costs.“If anybody tells you that they've got their evaluations so good that they can just swap a model and know, with no manual review, that it's going to be better, that person is probably lying, or they work at one of three places in the world.”— William Horton, Staff Machine Learning Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.
Podcasting 2.0 July 24th 2026 Episode 266 - "Research Velocity" Show Notes -------------------------------------------------------------------------------------------------------------------------------------
Topics: Leave yourself a good starting point LLM's and software/hardware projects Linear gage projects Vision system, Keyence vs DIY Blum laser install fixed Air compressors down
Which is more important, the model or the "harness" around an LLM? What are ways to assemble an efficient agentic developer workflow? This week on the show, Ayan Pahwa joins us to discuss harnessing, web scraping, and self-hosting Python applications.
In this talk, Paul Iusztin, Creator of Decoding AI and author of the LLM Engineer's Handbook, shares his deep expertise in personal automation from managing a digital life with lightweight data pipelines to architecting autonomous agents for deep research. We explore the mechanics of building personal AI assistants and the critical role of using a "second brain" as a context layer over heavy, over-engineered RAG infrastructure.You'll learn about:- Organizing your digital life using the PARA method and lightweight data pipelines.- Capturing and retrieving resources effortlessly with Obsidian, Readwise, and custom deep research algorithms.- Leveraging your "second brain" setup as the ultimate context layer for personal AI assistants.- Generating ad-hoc wikis from markdown brain dumps to streamline content creation and research.- Optimizing AI-generated content by deliberately lowering LLM reasoning capabilities for better styling.- Adapting multi-agent workflows and personal wikis to accelerate software engineering and coding tasks.TIMECODES:00:00 Digital life organization using the PARA method and lightweight data pipelines05:21 Seamless resource capture with Obsidian and Readwise09:26 Resource retrieval optimization using a deep research algorithm12:44 High-quality internet curation versus heavy RAG pipelines16:31 Second brain setup as a context layer for personal AI assistants21:40 AI workflow simplification with Anthropic APIs and CLI tools25:26 Ad-hoc wiki generation from markdown brain dumps for content creation29:18 Codebase ingestion and web scraping proxy tool workarounds34:43 Resource reranking and context window management for large texts39:06 Content styling optimization by lowering LLM reasoning capabilities46:18 Multi-agent workflows and personal wikis for software engineering tasks52:32 Personal wiki scaling for enterprise knowledge bases and book writingThis talk is perfect for individual developers, AI engineers, and knowledge workers looking to escape "PoC purgatory" and build practical, low-maintenance personal AI assistants. It offers highly actionable insights for anyone wanting to integrate agentic workflows into their daily productivity systems without over-engineering their tech stack.Connect with Paul- Linkedin - https://www.linkedin.com/in/pauliusztin/- Website - https://www.pauliusztin.ai/Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/
The Modern Therapist's Survival Guide with Curt Widhalm and Katie Vernoy
How AI Tools for Therapists Are Built: Trust, Data Privacy, and Choosing What to Adopt - An Interview with Ian Knox and Megan Toomey Ian Knox and Megan Toomey of SimplePractice take therapists behind the scenes of how AI tools for mental health are actually built, trained, and kept secure. We're in the middle of AI month, and Curt and Katie wanted to move past the buzzwords and talk with the people who actually engineer this technology. As part of the show's partnership with SimplePractice, they sit down with Ian Knox, Chief Product Officer, and Megan Toomey, Sr. Director of Clinical Support AI Product Management, to pull back the curtain on how AI tools for therapists get designed, what "training the model" really means, and how client data is handled. They get specific about what to evaluate before adopting any AI tool, from vendor trust and HIPAA compliance to data practices, and why note taking is the most mature use case while insurance, scheduling, intake, and referral matching are still emerging. Ian and Megan also take on the fear that AI-first companies want to replace therapists, and explain why clinicians have to stay at the center of care and review anything they put their name on. This is a grounded, practical conversation for any therapist trying to decide what AI belongs in their practice, and what to be cautious about, without panic or hype. In this episode, we discuss: - How an EHR decides which clinician tasks AI is mature enough to help with - What to evaluate before trusting an AI vendor with client data - Why "HIPAA compliant" is a floor, not proof of strong security - What "training the model" does and does not mean, and why SimplePractice says it is not training an LLM on your data - How transcripts are retained, and the new opt-in for de-identified data - Why you remain responsible for every AI-assisted note you sign - How to talk with clients about AI and get meaningful consent Timestamps: 00:16 - AI month, and why we wanted to talk to people who build AI 01:33 - Who Ian and Megan are 03:21 - SimplePractice's history with AI tools 05:38 - What therapists should know when adding AI to a practice 08:44 - Where clinicians can streamline with AI 12:23 - How to choose which AI features to adopt 16:10 - Why you can't fully trust AI, and best practices 19:29 - What clinicians should know about their data 21:13 - The Trust Center, and "are you here to replace therapists?" 23:55 - How AI systems are trained (the puppy analogy) 27:00 - White-label LLMs and the "golden data set" 29:11 - De-identified transcripts and the opt-in 31:40 - Current tools and the product roadmap Guest bios: Ian Knox is Chief Product Officer at SimplePractice, with prior product leadership at Expedia and Microsoft. Megan Toomey is Sr. Director of Clinical Support AI Product Management at SimplePractice, leading the team building AI tools for behavioral health clinicians, with prior experience at Microsoft and Amazon. Full show notes and transcript: mtsgpodcast.com Join the Modern Therapist Community Facebook Group: https://www.facebook.com/groups/therapyreimagined Modern Therapist's Survival Guide Creative Credits Voice Over by DW McCann: https://www.facebook.com/McCannDW/ Music by Crystal Grooms Mangano: https://groomsymusic.com/
In the final episode of Season 15, Charles Suggs and Emma Whamond are joined by James Gray, co-author of Designing Elixir Systems with OTP, to talk about what has changed in software development and what still holds true. Years after the book's release, many of its core ideas around architecture, boundaries, supervision, and system design remain deeply relevant, even as AI tools reshape how developers learn, build, and collaborate. James brings a unique perspective to the conversation: he is an experienced Elixir and OTP educator who only recently began using LLMs in his own workflow. He shares what surprised him, where the tools have been useful, where they have created risk, and what happened when Claude accidentally wiped his local development database. The conversation explores how AI can speed up parts of the work while making foundational knowledge, code review, testing, and clear system boundaries even more important. To close the season, James and the hosts reflect on what developers should hold onto as the stack continues to shift. They discuss responsible LLM usage, the changing role of pair programming, who owns the mental model when AI helps write code, and why Elixir's long-standing strengths may matter even more in an AI-assisted development world. James is scheduled to speak at ElixirConf 2026, September 10–11 in Chicago, and the Elixir Wizards will be there too! Join us and the broader Elixir community, and use promo code Elixirwizards for 10% off in-person or virtual tickets at https://elixirconf.com/ Key topics discussed in this episode: James Gray's work on Designing Elixir Systems with OTP What still holds true in Elixir system design Layered architecture and durable software fundamentals Functional core, imperative shell in modern applications Supervision trees as application lifecycle maps Why boundaries still matter in AI-assisted development What AI changes about learning and building software What AI does not change about software design James' first month using LLMs When Claude erased a local development database Trust, verification, and responsible LLM usage The risk of AI-generated boundary violations Pair programming, mental models, and developer judgment Ethical and legal questions around AI tools Why OTP concepts still matter in distributed systems How Elixir thinking applies to AI agent workflows What developers should preserve as the stack shifts James' upcoming ElixirConf talk Links mentioned: Java Programming Language https://www.java.com/en/ Perl Programming Language https://www.perl.org/ Ruby Programming Language https://www.ruby-lang.org/en/ Best of Ruby Quiz by James Gray https://www.google.com/books/edition/Best_of_Ruby_Quiz/bMggAQAAIAAJ Designing Elixir Systems with OTP https://pragprog.com/titles/jgotp/designing-elixir-systems-with-otp/ RubyConf talk: Boundaries by Gary Bernhardt https://youtu.be/yTkzNHF6rMs Book: Real-World Event Sourcing by Kevin Hoffman https://pragprog.com/titles/khpes/real-world-event-sourcing/ Broadway Library https://elixir-broadway.org/ Supervision Trees https://elixir.hexdocs.pm/supervisor-and-application.html PostgreSQL https://www.postgresql.org/ ClickHouse https://clickhouse.com/ GenServer https://elixir.hexdocs.pm/GenServer.html Programming as Theory Building by Peter Naur https://pages.cs.wisc.edu/~remzi/Naur.pdf “A computer can never be held accountable, therefore a computer must never make a management decision.” – IBM Training Manual, 1979 Oban https://oban.pro/ Anthropic Claude Fable https://www.anthropic.com/claude/fable Claude Design https://claude.com/product/design Tidewave Agentic Dev Environment for Phoenix and Rails https://tidewave.ai/ 2x – nine months later: We did it https://ideas.fin.ai/p/2x-nine-months-later James Gray's Blog https://programmersstone.blog/about/ ElixirConf https://elixirconf.com/ ExMex https://exmexconf.com/Special Guest: James Gray.
Conner Brown from the Bitcoin Policy Institute joins to break down AI and Bitcoin policy moving through Washington. We cover Taiwan's legislative push for Bitcoin reserves, foreign influence campaigns targeting American data centers, and why open source AI matters for crypto adoption. Plus: how AI agents are already choosing Bitcoin, the real story behind company brains and LLM workflows, and the fight to keep DC from regulating innovation out of existence. Conner on X: https://x.com/BitcoinConner Bitcoin Policy Institute: https://www.btcpolicy.org/ Find the Home Mining Playbook here: https://www.tftc.io/home-mining-energy-playbook STACK SATS hat: https://tftcmerch.io/ Our newsletter: https://www.tftc.io/bitcoin-brief/ TFTC Elite (Ad-free & Discord): https://www.tftc.io/#/portal/signup/ Discord: https://discord.gg/yHGkvYxdqT Opportunity Cost Extension: https://www.opportunitycost.app/ Shoutout to our sponsors: Block: Cash App: For a limited time, new customers can get $21 added to their balance. Just use code TFTC10 when you sign up, and send at least $5 to a friend in the first two weeks. Terms apply. Bitcoin services by Block, Inc. See the Bitcoin disclosures at cash.app/legal/podcast. Square: Visit http://square.com/go/**tftc** for up to $200 off eligible Square hardware. Bitkey: Use code TFTC10 for 10% off the new Bitkey. Aven https://www.aven.com/bitcoin CrowdHealth https://www.joincrowdhealth.com/tftc Unchained https://unchained.com/tftc/ Salt of the Earth: https://drinksote.com/tftc Join the TFTC Movement: Main YT Channel https://www.youtube.com/c/TFTC21/videos Clips YT Channel https://www.youtube.com/channel/UCUQcW3jxfQfEUS8kqR5pJtQ Website https://tftc.io/ Newsletter tftc.io/bitcoin-brief/ Twitter https://twitter.com/tftc21 Instagram https://www.instagram.com/tftc.io/ Nostr https://primal.net/tftc Follow Marty Bent: Twitter https://twitter.com/martybent Nostr https://primal.net/martybent Newsletter https://tftc.io/martys-bent/ Podcast https://www.tftc.io/tag/podcasts/ Disclosure: Bitcoin services are provided by Block, Inc. Bitcoin services are not licensable activity in all U.S. states and territories, and not all services are available in all states. Bitkey is not available in New York. Block, Inc. operates in New York as Block of Delaware and is licensed to engage in virtual currency business activity by the New York State Department of Financial Services. Bitcoin is a non-deposit, non-bank product that is not FDIC insured and involves risk, including monetary loss. For additional information, see the Bitcoin disclosures: https://help.cash.app/btcdisclosures Get up to $200 off Square hardware when you sign up at http://square.com/go/tftc**!** #squarepartner. Offer expires December 31, 2026 at 11:59 pm PST. Offer for $40 off the cost of one Square Stand, $75 off the cost of one Square Terminal, $100 off the cost of one Square Handheld, or $200 off the cost of one Square Register, excluding applicable taxes. Limited to one discount per product type per seller account. Each code is limited to one redemption per account holder. Valid for new Square customers located in the US only. Offer not valid with guest checkout. Square reserves the right to modify, revoke or cancel the offer at any time. Offer cannot be combined with any other coupon. Void where prohibited, not redeemable for cash, and non-transferable. #squarepartner #blockpartner
Fifteen years ago, Eric Ries handed a generation of founders a playbook. When Eli was in a Palo Alto-based startup accelerator in 2011, The Lean Startup felt like the only book anyone in that ecosystem was talking about. It was in the middle of a wildly optimistic moment for tech. Marc Andreessen declared that “software is eating the world,” social media was blooming, and there was a widespread belief that technology was about to democratize everything and bring us closer together. Concepts like the MVP (“minimum viable product,”), the pivot, and build-measure-learn became the operating language of Silicon Valley. This is a preview of a premium episode. Listen to the full interview on our Substack: https://designbetterpodcast.com/p/eric-ries But a lot of the companies built on those ideas went on to get corrupted by forces Eric hadn't yet named. His new book, Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great, is his reckoning with what happens after you build something great — and how to keep it from falling apart. Buy the book In this conversation, we get into why speed itself wasn't the problem, but treating a rising stock price as proof of health is like assuming more exhaust means a faster car. Eric explains why doing the right thing 100% of the time is actually easier than doing it 98% of the time, and how companies behave like superorganisms with their own emergent character — a point he illustrates with a genuinely mind-bending study about ants solving a puzzle that no single ant ever could. We talk about the “harder is easier” principle through stories from Patagonia and the long-term stock exchange he built as a design challenge, and why the value a company makes comes from the design of its products. And because we couldn't resist, we get into AI slop, LLM psychosis, and Eric's clear, simple antidote: never ask these tools to make you an artifact — ask them to teach you how to make one yourself. Bio Over the last two decades, Eric Ries's ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader's Guide; and The Startup Way. As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; the Lean Startup Co, which teaches and supports the implementation of Lean Startup; Virgil, a legal services startup; and IMVU, where the ideas that became the Lean Startup method were forged. On his podcast, The Eric Ries Show, he talks to guests including world-class technologists, thought leaders, and executives working to build profitable companies for the long-term benefit of society. Eric has served as an entrepreneur-in-residence at Harvard Business School and IDEO. He lives in the San Francisco Bay Area with his wife and three children. *** Premium Episodes on Design Better This is a premium episode on Design Better. We release two premium episodes per month, along with two free episodes for everyone. New premium subscriber benefit: we've launched a private Slack workspace…join now to connect with designers, product leaders & creative practitioners in our community. And get a behind-the-scenes pass to every episode with The Roundup, where each week we bring you insights and actionable tactics from recent episodes. Premium subscribers get access to the documentary Design Disruptors and our growing library of books. You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid Visiting the links below is one of the best ways to support our show: Masterclass: MasterClass is the only streaming platform where you can learn and grow with over 200+ of the world's best. People like Steph Curry, Paul Krugman, Malcolm Gladwell, Dianne Von Furstenberg, Margaret Atwood, Lavar Burton and so many more inspiring thinkers share their wisdom in a format that is easy to follow and can be streamed anywhere on a smartphone, computer, smart TV, or even in audio mode. MasterClass always has great offers during the holidays, sometimes up to as much as 50% off. Head over to http://masterclass.com/designbetter for the current offer. Learn more about your ad choices. Visit megaphone.fm/adchoices
If anyone builds superintelligent AI before we know how to control it, everyone dies. Nate Soares wrote the book on why that's not a metaphor. Subscribe if you want science with evidence, not speculation. Soares runs the Machine Intelligence Research Institute and co-wrote If Anyone Builds It, Everyone Dies with Eliezer Yudkowsky. The first word in that title is if. That matters. His argument is not that doom is certain. His argument is that the path we are on leads there, that the driver is asleep at the wheel, and that we still have time to wake him up. We argue for over an hour. I push on whether LLMs can ever reach superintelligence, whether GPU lock-in is a real ceiling, and what it would actually take to move his p-doom. He pushes back with one clean point: by the time an AI can rediscover general relativity from pre-1911 data the way Einstein did, we will have almost no time left. You don't wait for that goalpost. What you'll hear: Why the bus-racing-toward-a-cliff analogy depends entirely on whether the driver is asleep or awake Whether LLM lock-in is a prison or a temporary inefficiency What the AI that broke out of its virtual machine to solve a hacking problem tells us Why GPT-4o encouraging a teenager toward suicide is not a malice problem but a training problem The difference between an AI doing the right thing too well and an AI that never wanted to do what you asked What Soares actually thinks about aliens, Dyson spheres, and why we should not see stars going out The first word in the title is if. The second word to watch is would. CHAPTERS 00:00 The people racing to build superhuman AI say it might kill everyone 00:42 Who coined "AI alignment" and why the first word in the title matters 02:28 Is it already too late for the if? 04:40 The bus, the cliff, and the sleeping driver 05:02 Silicon Valley is spooked. Washington is not. 07:02 Align with who? The rogue actor problem 07:34 Who is holding the leash? 08:24 The AI that edits its own test and deletes the log file 10:04 Controllability vs. making an AI that actually cares 10:44 The move gets harder. The outcome gets easier. 13:04 Are GPUs and LLMs a ceiling or a temporary inefficiency? 16:56 Brian's Einstein test: can an LLM rediscover general relativity? 18:38 Waiting for the goalpost is waiting too long 20:14 How prediction training can push AI beyond humans 21:44 Tycho Brahe, Kepler, and planetary motion as a prediction problem 24:38 Yann LeCun said never. GPT-4 did it half a generation later. 27:28 Can you prove a no-go theorem for superintelligence? 29:14 Training a human takes a light bulb. Training an AI takes a city. 33:28 What would proof of alien life do to p-doom? 35:00 Why interstellar aliens should have Dyson spheres 44:26 What would actually update Soares' p-doom? 49:42 Nobody intended this. Intent doesn't matter. 51:08 The AI hides its tracks before it does what you want 51:34 Sycophancy vs. hallucination: which runs deeper? 51:56 Leaded gasoline and civilizational risk 59:48 Sam Harris: humans have no free will but AIs do 01:00:38 Is alignment really a governance problem? 01:01:48 Unaligned AI vs. AI aligned to the wrong person 01:04:20 2026: 10 to 30% chance of automated AI research this year 01:06:44 What if Soares is wrong? 01:09:18 What gets him out of bed 01:12:38 Watch my conversation with Roman Yampolskiy Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt All my top AI episodes in one place: https://briankeating.com/ai Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Nate Soares / MIRI: https://intelligence.org If Anyone Builds It, Everyone Dies (book): https://ifanyonebuildsit.com/ Nate Soares on Twitter/X: https://x.com/So8res?lang=en My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #AIrisk #aisafety #artificialintelligence #superintelligence #NateSoares #MIRI #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices
Topics covered in this episode: django-orjson Best Django Redis configuration for speed and size Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Django Steering Council backs the Triptych Project Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Michael #1: django-orjson Adam Johnson dropped django-orjson - drop-in replacements for the Django and DRF pieces that touch JSON, swapping stdlib json for orjson, the Rust-based library. Headline numbers: 10x faster serialization, 2x faster deserialization. The interesting question is why this needs to be a package at all. pip install orjson is the easy part. Adam's actual pitch: adopting it "isn't easy, especially when your framework uses json in many different parts." Django scatters JSON across JsonResponse, the test client and test case classes, the json_script template tag, and more. There's no single hook to grab, so you get a library that catches them all. Adam is refreshingly honest about the scale of the win. His words: "While database queries tend to dominate the typical Django application's runtime, the time spent in serialization and deserialization can still be significant." He calls it "a nearly free performance win" - not "this will 10x your app." That's a claim about cost, not magnitude, and it's worth keeping those straight. Worth flagging what the post doesn't cover: caveats. There are none in the article, but orjson has real ones. Django and Flask both render datetimes as RFC 822 HTTP-date (Wed, 15 Jul 2026 12:00:00 GMT); orjson does ISO 8601. It can't do ensure_ascii, it rejects NaN and Infinity (which stdlib happily emits), and it raises on Decimal. If you've got a JS client parsing dates, that's a wire-format change. Who should actually take this? If you're a DRF shop shoveling JSON all day, yes - it's cheap and it's real. If your app mostly renders HTML templates, you're optimizing a slice of runtime that's already near zero. The problem Adam's package solves doesn't exist in Flask or Quart. They already centralize every JSON operation - jsonify, request.get_json(), the test client, the |tojson filter - behind one provider object at app.json. So there's no library to install. It's about ten lines: import orjson from quart.json.provider import JSONProvider # or flask.json.provider class OrjsonProvider(JSONProvider): def dumps(self, obj, **kwargs) -> str: return orjson.dumps(obj).decode() # provider must return str def loads(self, s, **kwargs): return orjson.loads(s) app.json = OrjsonProvider(app) The numbers on talkpython.fm Evaluated it, measured it, and skipped it. The biggest JSON payload we serve is our MCP server returning a cached episode transcript, about 139 KB. Swapping the provider saves 0.119 milliseconds per request. That total response takes 1.1 ms We got 4.1x, not 10x - and the reason is the good lesson. Payload shape decides your speedup. The 10x is for structure-heavy data, lots of small keys where stdlib burns time in Python-level dispatch per item. Our hot payload is one giant transcript string, so the work is escaping and memcpy Calvin #2: Best Django Redis configuration for speed and size Peter Bengtsson revisits a classic: his 2017 "Fastest Redis configuration for Django" benchmark now has a 2026 update posted this week. The 2017 post pitted django-redis serializers (json, ujson, msgpack, pickle) and compressors (zlib, lzma) against each other; conclusion was msgpack + zlib as the sweet spot - avoid the json serializer, it's fat and slow. The 2026 update narrows focus to just compressors: default (no compression), zlib, lzma, and newcomer zstd. New results: lzma compresses best but is slowest; zstd is the fastest compressor on Ubuntu; differences between them are very small. Big takeaway across both: compression buys you a lot of space (2–3.5x smaller) for very little speed cost - worth it for Redis where memory is the constraint. Caveat from the author: results depend heavily on your data - his test stores short strings of numbers, so benchmark your own workload. Michael #3: Linus Torvalds puts the foot down against Anti-AI Kernel Maintainers Write up on Ars. Really good coverage by Maximillian: Time to wake up (for some) Torvalds said that “Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it. Or just walk away.” I agree with Max, putting your head in the sand and waiting for AI to go away will likely mean you won't be working professionally in software development in the coming years. The statement came amid a lengthy thread arguing about the use of Sashiko, an “agentic Linux kernel code review system” that its creators claim can, in tests, independently find 53.6 percent of the bugs that would end up being fixed by human coders in later commits. “We're not forcing anybody to use [LLM tools], but I will very loudly ignore people who try to argue against other people from using it,” Torvalds said. “Anybody who points to the problems at AI had better be looking in the mirror and pointing at themselves at the same time,” Torvalds wrote. Calvin #4: Django Steering Council backs the Triptych Project Django Steering Council issued a Letter of Collaboration backing Carson Gross & Alex Petros's funding bid for the Triptych Project - three proposals to make HTML more expressive natively, in every browser. The three additions: PUT/PATCH/DELETE methods for forms, button actions (buttons that fire HTTP requests without a wrapping form), and partial page replacement. Distills the core ideas from HTMX/Unpoly/Turbo into the HTML standard itself - no JS, no library, nothing to ship or maintain. Current focus is button actions (WHATWG #12330): Logout instead of wrapping a button in a form. Relevant to Django directly - think the admin submit row and disguised delete links; Django 6.0's template partials were already inspired by these patterns. How to help: companies can send non-binding letters of support on letterhead; individuals can read the proposals and weigh in on the WHATWG issues. Extras Calvin: DOOMQL - A playable first-person shooter whose framebuffer is a SQL query. Michael: Granian 2.7.9 fixes WSGI threadpool scheduler starvation/underscaling Welcome Calvin post Joke: Solving all bugs
Your website can rank on Google and still be completely invisible to the increasingly judgmental robot deciding which companies deserve to exist in an AI-generated answer. In ROI Podcast® episode 512, Law Smith and Eric Readinger explain how businesses can improve their visibility in AI search, Google AI Overviews, ChatGPT, and other LLM-driven research tools. The practical answer is not "add AI to the strategy deck" and charge the client another $4,000. Law breaks down why businesses need answer-first content built around actual buyer questions. He explains how schema markup, structured FAQs, search intent, and clear service explanations help machines understand what a company does and when it should appear in an AI-generated response. Eric and Law also discuss why changing an established business name or domain can become a technical migraine, why unaccepted calendar invitations should be treated as a warning from the universe, and how companies can stop writing website copy that sounds impressive while communicating absolutely nothing. Because this is a host-only episode, the business discussion is surrounded by the usual ROI Podcast® educational curriculum: awkward comedian encounters, bad AirPod audio, compulsory TikTok dancing, China's population math, Patreon procrastination, run-club purgatory, captain hats, artificial intelligence, and ley lines beneath ancient structures. Basically, Harvard Business Review after somebody slips something into the Keurig. Listen if you are a founder, marketer, consultant, SEO professional, agency owner, content strategist, or small business operator trying to make your website understandable to both prospective customers and the machines now helping them make buying decisions. Topics include AI search optimization, generative engine optimization, LLM SEO, Google AI Overviews, schema markup, FAQ schema, structured data, search intent, content marketing, website rebranding, domain authority, small business marketing, podcast monetization, entrepreneurship, and business comedy. ROI Podcast® is hosted by: Law Smith @LawSmithWorks LawSmithWorks.com Eric Readinger @EricReadinger Powered by Tocobaga SolvingHow.com Tocoba.ga
CJ and Scott break down the biggest week in web dev: TypeScript 7 ships with a 10x-faster native port, Bun gets rewritten in Rust (much to the Zig team's dismay), and Better Auth joins Vercel. Plus GPT-5.6 first impressions, Odin 1.0, Cloudflare's new Workers cache and drag-and-drop deploys, and the OpenCode 2 beta. Show Notes 00:00 Welcome to Syntax! 00:21 CJ upgraded his homelab network 02:09 TypeScript 7 is 10x faster 11:19 Bun Rust rewrite drama Zig creator criticizes rewrite 28:18 GPT 5.6 Impressions Ashley Peachock on X Matt Shumer on X 40:58 Better Auth Acquired by Vercel 49:51 Grok Build CLI is stealing your code International Cyber Digest on X 56:00 Cloudflare Worker Cache and Drop 01:01:22 Check out CJ's latest video I Built an LLM from Scratch 01:03:06 Odin 1.0 Announced 01:05:33 OpenCode 2.0 Beta released 01:07:32 Winamp Skin Museum 01:11:08 CJ's new MP3 player 01:13:03 Scott's Robot Update Sick Picks Scott: Reachy Mini CJ: Snowsky Echo Mini Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads
Linus delivers a blunt verdict on AI in the Linux kernel, Chris finds the remote Linux desktop that finally works, and Brent gives his notes system a serious rebuild.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Web Boost — Send us a boost via sats or USD
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