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Llevo 15 años escribiendo notas, artículos y tutoriales. El resultado: unos 5000 archivos markdown repartidos por mi disco duro. Y, como te puedes imaginar, encontrar algo ahí dentro es como buscar una aguja en un pajar. Por eso en este episodio me he puesto manos a la obra para montar un sistema RAG (Retrieval-Augmented Generation) 100% local, sin depender de APIs externas, sin enviar tus datos a la nube, y con herramientas que ya conoces: SQLite, Ollama y Python.Este es el primero de dos episodios sobre RAG. Aquí nos centramos en construir la base de conocimiento: un pipeline que escanea tus documentos, los trocea en fragmentos manejables, extrae los metadatos del frontmatter YAML, genera embeddings con el modelo bge-m3 de Ollama, y lo guarda todo en una base de datos SQLite con búsqueda FTS5. Todo esto, además, con detección incremental de cambios: la primera ejecución tarda lo que tenga que tardar, pero las siguientes son cuestión de segundos porque solo reprocesa lo que ha cambiado.El stack es sencillo pero potente. SQLite con FTS5 para búsqueda textual, Ollama con bge-m3 para los embeddings, y seis scripts Python que suman unas 1300 líneas. Nada de LangChain, nada de frameworks pesados. Código limpio, comentado y que entiendes de un vistazo. El chunking respeta las cabeceras markdown, usa tiktoken para contar tokens con precisión, y los embeddings se almacenan como BLOBs en la propia SQLite. En el próximo episodio (el 821) usaremos esta base de conocimiento para hacer búsqueda semántica con similitud de coseno, búsqueda híbrida combinando FTS5 con embeddings, y hasta un plugin para Neovim.Puntos clave del episodio:- El problema: 15 años de notas, 5000 archivos, cero capacidad de búsqueda- La solución: RAG local con SQLite + FTS5 + Ollama, todo en tu máquina- Chunking híbrido que respeta cabeceras markdown y usa tiktoken- Pipeline incremental con detección de cambios mediante MD5- Embeddings con bge-m3 (568M parámetros, 1024 dimensiones)- Búsqueda FTS5 con snippet(), colores ANSI y sintaxis avanzada- Errores comunes y cómo solucionarlosSi te gusta el contenido, ya sabes: dale a seguir, compártelo con quien creas que le puede interesar, y déjame un comentario si tienes dudas o sugerencias. La semana que viene, en el episodio 821, montamos la búsqueda semántica y el plugin para Neovim. No te lo pierdas.Capítulos del episodio:0:00 - Introducción: RAG y base de conocimiento local2:12 - El problema: 15 años de notas sin buscar4:58 - La solución: SQLite + FTS5 + Ollama, 100% local7:00 - Escaneo de archivos y extracción de front matter10:20 - Preparación del entorno: Ollama, uv y dependencias12:00 - Chunking: cómo trocear los documentos15:45 - Estructura de la base de datos SQLite17:46 - Pipeline incremental con detección de cambios19:22 - Demo en vivo: consultas y resultados22:44 - Errores comunes y cómo solucionarlos24:10 - Resumen y adelanto del episodio 82125:15 - Despedida y cierreMás información y enlaces en las notas del episodio
This week, the guys are talking about a duress-wipe phone case that's landed someone federal charges, GCC's new policy on AI-generated code, and yet another AUR malware wave that's got Arch disabling package adoptions entirely. There's a from-scratch Rust rewrite of the X server called YServer, ShadowFetch Linux brings local AI to the desktop, Nouveau is enabling atomic mode setting by default, and GOG is finally building an official Linux client. For tips, we have DNS Globe for watching DNS propagation, the bash builtin complete for custom tab-autocompletion, uv for fast Python environment and package management, and Miller for slicing and converting CSV, TSV, and JSON data. You can view the show notes at http://bit.ly/4vZejHf, and have a great week! Host: Jonathan Bennett Co-Hosts: Jeff Massie, Rob Campbell, and Ken McDonald Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.
This week, the guys are talking about a duress-wipe phone case that's landed someone federal charges, GCC's new policy on AI-generated code, and yet another AUR malware wave that's got Arch disabling package adoptions entirely. There's a from-scratch Rust rewrite of the X server called YServer, ShadowFetch Linux brings local AI to the desktop, Nouveau is enabling atomic mode setting by default, and GOG is finally building an official Linux client. For tips, we have DNS Globe for watching DNS propagation, the bash builtin complete for custom tab-autocompletion, uv for fast Python environment and package management, and Miller for slicing and converting CSV, TSV, and JSON data. You can view the show notes at http://bit.ly/4vZejHf, and have a great week! Host: Jonathan Bennett Co-Hosts: Jeff Massie, Rob Campbell, and Ken McDonald Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: bitwarden.com/twit
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If you enjoy this episode, we're sure you will enjoy more content like this on The Occult Rejects. In fact, we have curated playlists on occult topics like grimoires, esoteric concepts and phenomena, occult history, analyzing true crime and cults with an occult lens, Para politics, and occultism in music. Whether you enjoy consuming your content visually or via audio, we've got you covered - and it will always be provided free of charge. So, if you enjoy what we do and want to support our work of providing accessible, free content on various platforms, please consider making a donation to the links provided below. Thank you and enjoy the episode!Links For The Occult Rejectshttps://linktr.ee/theoccultrejectsOccult Research Institutehttps://www.occultresearchinstitute.org/Substackhttps://substack.com/@theoccultrejects?r=7auau0&utm_campaign=profile&utm_medium=profile-pageCash Apphttps://cash.app/$theoccultrejectsVenmo@TheOccultRejectsBuy Me A Coffeebuymeacoffee.com/TheOccultRejectsPatreonhttps://www.patreon.com/TheOccultRejectsFull Episode BibliographyPrimary SourcesAeschylus. Eumenides. Used for the ancient tradition of succession at Delphi and the mythic movement from older powers to Apollo.Cicero. On Divination. Used for ancient discussion of divination and the tradition of Delphic inspiration connected with breath or exhalation.Herodotus. The Histories. Used for Croesus, the danger of ambiguous prophecy, the Athenian “wooden wall” oracle, Themistocles' interpretation, and Delphi's role in war and public decision-making.Homeric Hymn to Apollo. Used for Apollo's Delphic foundation myth, the serpent, the movement from sacred landscape to Apollonian institution, and the Cretan priests connected with Apollo's dolphin form.Pausanias. Description of Greece, Book 10. Used for later ancient traditions concerning Delphi, foundation myths, Gaia, Themis, Python, Apollo, sacred monuments, and the Delphic landscape.Pindar. Pythian Odes. Used for the Pythian Games, victory poetry, Apollo, prestige, and the transformation of athletic victory into sacred memory.Plutarch. Life of Lycurgus. Used for Lycurgus, the Great Rhetra, and the tradition of Spartan law receiving Delphic sanction.Plutarch. Moralia: “On the E at Delphi.” Used for the mysterious letter E at Apollo's temple and ancient philosophical interpretations of its meaning.Plutarch. Moralia: “The Oracles at Delphi No Longer Given in Verse.” Used for Delphic verse, the change in oracular style, and ancient reflection on why the Pythia no longer gave responses in meter.Plutarch. Moralia: “The Obsolescence of Oracles.” Used for Delphi in the Roman period, the decline of oracular centers, Plutarch's reflections on divine inspiration, and the tradition that Delphi once had multiple priestesses.Strabo. Geography. Used for ancient geographical description of Delphi and the tradition of the oracle's inspiration being connected with a breath or vapor rising from below.Official and Reference SourcesArchaeological Site of Delphi / Hellenic Ministry of Culture. “History of Delphi.” Used for Delphi's geography, Mycenaean background, Apollo's cult, Python, Cretan priests, the Amphictyony, sacred administration, Sacred Wars, Roman history, decline, and excavation.Archaeological Site of Delphi / Hellenic Ministry of Culture. “Mythological Versions of the Foundation of the Oracle.” Used for Gaia, Themis, Phoebe, Apollo, Python, Poseidon, the tripod, Athena Pronaia, the Thriae, and the dolphin/Cretan priest tradition.Archaeological Site of Delphi / Hellenic Ministry of Culture. “The Temple of Apollo.” Used for the sacred center of the sanctuary, the adyton, the temple's rebuilding history, and the architectural setting of the oracle.Archaeological Site of Delphi / Hellenic Ministry of Culture. “The Sphinx of the Naxians.” Used for the Naxian dedication, apotropaic symbolism, civic prestige, and the monument's role in the Sacred Way.Archaeological Site of Delphi / Hellenic Ministry of Culture. “The Treasury of the Siphnians.” Used for the Siphnian Treasury, its sculptural program, caryatids, wealth, painted decoration, and sacred display.Archaeological Site of Delphi / Hellenic Ministry of Culture. “Excavations.” Used for the Great Excavation, the village of Kastri, French archaeological work, and the modern recovery of Delphi.Encyclopaedia Britannica. “Delphic Oracle.” Used for the Pythia, Apollo's oracle, Delphi's high prestige, and the use of Apollo's sanction by lawmakers, colonists, and political leaders.Encyclopaedia Britannica. “Delphi.” Used for the general history and significance of Delphi as a sanctuary, oracle, and pan-Hellenic religious center.Encyclopaedia Britannica. “Python.” Used for Python as the serpent or dragon killed by Apollo at Delphi.Encyclopaedia Britannica. “Pythian Games.” Used for the games at Delphi, their musical and athletic character, their relationship to Apollo, and the laurel wreath.Encyclopaedia Britannica. “Hieromnemon.” Used for the Amphictyony, pylagorai, hieromnemones, treasury supervision, and administration of sacred affairs.UNESCO World Heritage Centre. “Archaeological Site of Delphi.” Used for Delphi's world heritage description, pan-Hellenic importance, sacred landscape, and historical significance.Modern ScholarshipChappell, Mike. “Delphi and the Homeric Hymn to Apollo.” The Classical Quarterly 56, no. 2, 2006. Used for the Homeric Hymn's Delphic foundation narrative and Apollo's Pythian identity.Fontenrose, Joseph. The Delphic Oracle: Its Responses and Operations with a Catalogue of Responses. Berkeley: University of California Press, 1978. Used as a major scholarly backbone for Delphic responses, authenticity problems, categories of consultation, and the operation of the oracle.Forrest, W. G. “The First Sacred War.” Bulletin de Correspondance Hellénique 80, 1956. Used for the First Sacred War, Krisa/Crisa, sacred access, and the political framing of Delphi's early pan-Hellenic importance.Fragkaki, Maria. “The Great Rhetra.” Rosetta 17, 2015. Used for the Great Rhetra as a Delphic oracle and the divine validation of Spartan political order.McInerney, Jeremy. “Parnassus, Delphi, and the Thyiades.” Greek, Roman, and Byzantine Studies 38, no. 3, 1997. Used for Dionysus, the Thyiades, Parnassus, mountain ecstasy, and the fact that Delphi's sacred landscape was not purely Apollonian.Piccardi, Luigi. “Active Faulting at Delphi, Greece: Seismotectonic Remarks and a Hypothesis for the Geological Environment of a Myth.” Geology 28, no. 7, 2000. Used for Delphi's faulted landscape and the modern geological debate around the oracle.de Boer, Jelle Z., John R. Hale, and Henry A. Spiller. “The Delphic Oracle: A Multidisciplinary Defense of the Gaseous Vent Theory.” Clinical Toxicology 40, no. 2, 2002. Used for the modern argument that gases, possibly including ethylene, may have contributed to the Pythia's altered state.Etiope, Giuseppe, et al. “The Geological Links of the Ancient Delphic Oracle.” Used for critique and reassessment of the geological and gaseous explanations of Delphic inspiration.Scott, Michael. Delphi: A History of the Center of the Ancient World. Princeton: Princeton University Press, 2014. Used for the broader historical arc of Delphi as sacred center, political institution, cultural symbol, and archaeological site.Strolonga, Polyxeni. “The Foundation of the Oracle at Delphi in the Homeric Hymn to Apollo.” Greek, Roman, and Byzantine Studies 51, 2011. Used for Apollo's foundation myth and the literary shaping of Delphi's origin story.Music, Inscriptions, and ArchaeologyFouilles de Delphes. École française d'Athènes. Used for the archaeological publication tradition connected with Delphi's excavation, architecture, inscriptions, sculpture, and sanctuary remains.The Delphic Hymns to Apollo. Used for Apollo's musical cult, sacred song, and the role of hymn, inscription, and performance at Delphi.Archaeological Museum of Delphi materials. Used for the Charioteer, Antinous, Kleobis and Biton, the Sphinx of the Naxians, the Siphnian Treasury, and other major finds from the sanctuary.Also want to remind people about the website, if you're into reading we have tons of information by multiple contributors, and we got t-shirts up on the site if you're interested. Fun fact, the art is all based on the eyeball.
Are you battling kidney stones, kidney disease, fear of dialysis, or concerning medical reports? Learn how to speak healing declarations over your kidneys, stand in faith, and partner medical wisdom with Holy Spirit discernment. Purchase Kathy's book Healed at Last – Overcome Sickness to Receive your Physical Healing on Amazon https://a.co/d/akj6IIM or at: https://www.kathydegrawministries.org/product/healed-at-last-pre-order-now/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# Your diagnosis does not have to become your identity. In this powerful episode, Kathy DeGraw teaches you how to pray for kidney healing, use your spiritual authority, and speak life over your body instead of agreeing with fear, dysfunction, and generational sickness. God designed your kidneys to filter, balance, and support your body. Kathy shares personal healing testimonies, practical spiritual warfare strategies, and biblical declarations to help you renew your mind and believe for the full manifestation of healing. You will learn how to renounce fear of kidney failure and dialysis, break agreement with inherited kidney disease, and declare divine order over kidney stones, inflammation, infection, pain, and dysfunction. Faith is not passive. It is active, obedient, and rooted in the authority Jesus Christ has given you. Speak life over your kidneys. Command fear to leave. Refuse to curse what God created. Whether you are facing kidney problems or another health battle, this episode will encourage you to pray, decree, declare, and believe that Jesus is your healer. #KidneyHealing #HealingDeclarations #KidneyStones #PrayerForHealing #JesusHeals **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KathyDeGrawMinistry - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
Should you understand the entirety of your codebase? How familiar are you with Python's built-in functions? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.
These sources explore innovative strategies for enhancing multimodal AI performance by repurposing existing technologies and optimizing instructions without intensive retraining. One paper introduces Scrapyard AI, a framework that treats obsolete AI models as a frugal, high-utility resource for researchers facing compute constraints. This concept is applied through Project Nudge-x, which utilizes these legacy systems and satellite data to interpret the environmental consequences of global mining operations. A second paper investigates evolutionary prompt optimization, a method that uses survival-of-the-fittest algorithms to discover advanced reasoning strategies in vision-language models. Through this iterative process, AI models independently learn to utilize external tools, such as Python scripts, to decompose and solve complex visual tasks more accurately. Together, these works highlight a shift toward computational parsimony and sophisticated inference-time adaptations to achieve state-of-the-art results. This research collectively suggests that the future of artificial intelligence lies in the creative reconfiguration of existing assets and the refinement of human-machine
Synopsis Le retour de Richer! Avec Patrick, Francis et Jacques, on se demande pourquoi la technologie s'invite dans nos affaires sans jamais demander la permission : des caméras infrarouges dans les prochains AirPods pour donner des yeux à Siri, de l'IA poussée dans le fond de la gorge à chaque mise à jour, et un buzz grandissant pour les téléphones « zen », les écrans e-ink et le retour à l'analogique — jusqu'au flip phone Callback 8020 de Commodore, sans navigateur ni réseaux sociaux. Ensuite ça brasse : l'AI-washing dans les startups — payer des tokens pour ce qu'un script Python de dix lignes ferait aussi bien —, les 97 % d'applications vibe-codées qui meurent dans leur première année faute de soutien, et la grande question de la souveraineté numérique canadienne : autonomie réelle ou rêve pieux? Le sujet d'opinion revient sur nos fleurons vendus aux Américains — Element AI passé chez ServiceNow, les talents formés à Mila qui migrent au sud — et sur ce que ça veut dire de partir une entreprise ici. La démonstration du jour arrive d'ailleurs toute cuite : Washington ordonne à Anthropic de couper l'accès à Fable 5 et Mythos 5 pour tout ressortissant étranger, trois jours après leur lancement. On termine avec les face scans au Royaume-Uni, l'interdiction des réseaux sociaux aux moins de 16 ans, le débat sur l'anonymat en ligne, les 3,5 G$ de fraude par usurpation rapportés par la FTC, et une statistique qui fait mal : une cinquantaine d'entreprises québécoises par année ferment à cause d'un rançongiciel — et on n'en entend jamais parler. Crew Patrick Mathieu — Cofondateur de Hackfest.ca et fondateur de Hexius Richer Dinelle — Expert en sécurité informatique & Gestion de systèmes Francis Coats — Expert, vulgarisateur et conférencier en sécurité et en technologie Jacques Sauvé — PDG, Trilogiam Sujet d'opinion Registraire des entreprises du Québec — immatriculer une entreprise au Québec Element AI vendue à l'américaine ServiceNow (Radio-Canada) Mila – Institut québécois d'intelligence artificielle Québec débloque 36 M$ pour Mila (La Presse) Cohere et Mila collaborent pour mettre au point une IA québécoise (La Presse) Richer veut savoir La souveraineté numérique au Canada remise en question par un rapport fédéral (Radio-Canada) La souveraineté numérique est-elle hors d'atteinte? (La Presse) Énoncé de politique de souveraineté numérique du Québec (PDF) CIRANO – Souveraineté numérique (PDF) News de sécurité Patrick Anthropic – Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Anthropic coupe Fable et Mythos après une directive de contrôle des exportations (Fortune) SpaceX en bourse : deux FNB canadiens couverts en CAD lancés la même journée (Wealth Professional) Commodore Callback 8020 – le flip phone sans navigateur ni réseaux sociaux METR – Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity (19 % plus lents, alors qu'ils se croyaient 20 % plus rapides) Rapport sur la spécificité du taux de faillites d'entreprises au Québec (PDF, gouvernement du Québec) Francis Apple veut mettre des caméras dans les AirPods (The Verge) Les meilleurs téléphones minimalistes de 2026 (The Gadgeteer) « I Studied 1,000 Vibe-Coded Apps. 97% Failed for the Same 3 Reasons » 91,5 % des applications vibe-codées contiennent au moins une vulnérabilité (recherche Q1 2026) Jacques UK to require ID or face scan before you can make social media accounts (BleepingComputer) Keir Starmer annonce l'interdiction des réseaux sociaux aux moins de 16 ans (Euronews) L'Australie montre déjà les limites du dispositif (Le JDD) FTC warns of record $3.5 billion losses to imposter scams in 2025 (BleepingComputer) Communiqué original de la FTC Shamelessplug Hackfest 2026 - Québec Discord Hackfest securite.fm Crédits Montage audio par Hackfest Communication Locaux virtuels par Streamyard
Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena
Podcast, ktorý pripravujeme v spolupráci s Ústavom výskumu progresívnych technológií MTF STU v Trnave. Pred šiestimi rokmi vyšla epizóda s názvom "Družicový výskum vesmíru z Trnavy" kde sme sa rozprávali o využívaní dát z družíc ako XMM-Newton alebo Chandra. Tento krát pokračujeme satelitmi Kepler a TESS, ktoré pracujú v úplne iných energiách. Tak ako pred šiestimi rokmi sme prízvukovali softwarový aspekt takejto práce na Trnavskej MTF, tak aj teraz kladieme dôraz na programovací jazyk Python. Na záver spomenieme aj družice, ktoré sa pozerajú opačným smerom a síce na Zem.
Marco e Cesare nel caldo estivo ci raccontano come si organizzano per restare aggiornati sulle ultime novità. Le sfide di trovare il tempo, il focus e le energie per continuare a studiare.
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
Are you walking through infertility, IVF, miscarriage, or the heartbreak of trying to conceive? Receive this powerful fertility prayer and begin speaking life, healing, and hope over your womb, hormones, reproductive system, and future pregnancy. Purchase Speak Life: Prayer Declarations for Fertility, IVF, Pregnancy, Labor, Delivery, and Healthy Babies on Amazon https://a.co/d/0hCMKe7Y or at: https://www.kathydegrawministries.org/product/speak-life-prayer-declarations-for-fertility-pregnancy-labor-delivery/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# In this episode Kathy DeGraw shares from her personal experience with infertility and prays powerful, Scripture-based declarations for women and families believing God for a baby. The journey through infertility can feel lonely, painful, and overwhelming, but you do not have to walk through it without faith, prayer, and hope. Pray along as Kathy speaks life over hormonal balance, PCOS, ovarian function, ovulation, the thyroid, reproductive health, IVF procedures, egg retrieval, fertilization, embryo development, implantation, and a healthy full-term pregnancy. These strategic prayer declarations are designed to help you intentionally pray over your body and the medical processes surrounding conception. Kathy also shares about Speak Life, a prayer declaration book created with her daughter for IVF, infertility, pregnancy, labor, delivery, and the first three months of infancy. Allow these prayers to strengthen your faith and encourage your heart as you trust Jesus through your fertility journey. Listen again, pray in agreement, and speak life over your body and your future children. #InfertilityPrayer #IVFPrayer #FertilityPrayer #PregnancyPrayer #SpeakLife **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KathyDeGrawMinistry - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome workTakeaways:Q: How does putting a Gaussian process on unknown coordinates fix noisy location data in mineral prospecting?A: In mining and geostatistics, the classic Gaussian process model, known there as kriging, assumes you know exactly where each sample was taken. Chris' project broke that assumption on purpose: the recorded coordinates for each core sample were only accurate to within a rough radius. By treating the true locations as latent variables and putting a Gaussian process over them jointly with the measurements, the model could still reconstruct the underlying gold-concentration field, even though the exact sampling locations were never known precisely. It's a demonstration that Gaussian processes can absorb structural uncertainty that looks, at first glance, like it should make the problem impossible.Q: What is "Poverty Bayes," and what did it cost to train a two-million-parameter Bayesian model?A: Poverty Bayes was Chris' experiment in seeing how cheaply a large Bayesian model could be trained using modern cloud infrastructure. He fit a hierarchical logistic regression with close to two million parameters, using PyMC's Hamiltonian Monte Carlo on a single A100 GPU rented through Modal, a serverless platform that deploys a Python script straight to GPU hardware with almost no setup. He'd originally guessed it would cost around five dollars, the price of a Big Mac, but the real bill came in an order of magnitude lower. A model that would take a Gibbs sampler weeks to run, and that once required a research lab's dedicated GPU, now costs pocket change and a few minutes of setup.Q: What's the current bottleneck in Bayesian-at-scale tooling?A: Chris argues the software has largely caught up: PyMC's JAX backend and NumPyro make GPU-accelerated Bayesian modeling work out of the box for most problems. What's missing is common knowledge. Companies are clearly running large Bayesian models in production, but the results stay behind corporate firewalls. Chris' proposal is a community benchmark effort: which frameworks handle a million-parameter Markov random field on a given GPU out of the box, since this kind of expensive, slow-running benchmark is a poor fit for standard CI pipelines but valuable for the field to know.Chapters:22:57 When does GPU acceleration actually pay off for a Bayesian model?26:33 What did it cost to train a two-million-parameter model on Modal?30:36 What happened when Chris asked 200 different LLMs to flip a coin?34:50 Where do Bayesian ideas show up in the agentic AI systems Chris builds at Nvidia?40:16 Are statisticians being made obsolete by large language models?41:19 How does putting a Gaussian process on unknown coordinates fix noisy data in mineral prospecting?58:05 What is Chris looking forward to working on next?Thank you to my Patrons for making this episode possible!Links from the show here
In this episode, James and Frank explore the bittersweet reality of experiencing cinema in 2026, starting with Frank's disappointing discovery that Seattle's Pacific Science Center—home to a true IMAX theater—has ditched its iconic 70mm film projection for digital laser technology. Despite Christopher Nolan's passionate advocacy for shooting on IMAX film, fewer theaters worldwide can actually deliver his intended experience, raising questions about whether premium cinema experiences are disappearing. The hosts dive deep into what made true IMAX special and the broader trend of losing analog, artisanal technologies in favor of digital convenience. Beyond cinema, the conversation spans the future of media itself: Xbox bringing 25-year-old original Xbox games to PC through emulation, the resurgence of physical media as streaming services become unreliable, and whether digital downloads or physical discs make sense anymore. Frank also showcases an incredibly impressive technical achievement—iCircuit's new Python programming support, which layers a Raspberry Pi Pico emulator atop a circuit simulator, all running in C# on your phone. It's a fascinating look at how we're preserving experiences, accessing the past, and building for the future across gaming, entertainment, and development. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm
¿Sabías que puedes convertir cualquier texto en coordenadas de 1024 dimensiones y hacer búsquedas inteligentes, clasificación automática o detección de duplicados sin depender de servicios en la nube? En este episodio te enseño a utilizar los embeddings con Ollama para potenciar tus documentos, correos y apuntes desde tu propio equipo Linux.Los embeddings son una de las tecnologías más fascinantes de la inteligencia artificial actual. Básicamente, convierten palabras, frases o párrafos enteros en vectores numéricos que capturan su significado. Esto permite que un ordenador entienda que "gato" está más cerca de "felino" que de "nevera", y mucho más: desde búsqueda semántica hasta clasificación sin entrenamiento, pasando por deduplicación de documentos y sistemas de recomendación.Lo mejor de todo es que no necesitas una GPU potente ni una cuenta en ningún servicio externo. Con Ollama ejecutándose en local y el modelo BGE-M3 (multilenguaje, con soporte para español), puedes generar embeddings desde la terminal con una simple llamada curl o con unas pocas líneas de Python. Y si necesitas escalar, ChromaDB te ofrece una base de datos vectorial completa con persistencia en disco y filtros por metadatos.Capítulos del episodio:0:00 - Introducción y concepto de embeddings2:42 - ¿Qué son los embeddings exactamente?5:13 - Modelos de embeddings: BGE-M3, all-MiniLM-L6-v27:25 - Cómo generar embeddings con Ollama y curl8:01 - Búsqueda semántica: más allá de grep11:57 - Búsqueda semántica con Python y NumPy14:29 - Bases de datos vectoriales para escalar14:53 - Clasificación sin entrenar el modelo18:20 - Clasificación de sentimientos y categorías20:02 - Deduplicación de documentos con embeddings24:50 - Sistema de recomendaciones con similitud semántica27:15 - ChromaDB: base de datos vectorial persistente29:02 - Casos de uso y próximos episodios sobre RAGMás información y enlaces en las notas del episodio
In MobileViews 620, Jon Westfall recorded a special solo "vidcast,"(since I was not available for recording a podcast this week) recording a walk-and-talk along an historic railroad trail in Cleveland, Mississippi. Filming entirely on his Insta360 Luna Ultra with a neck mount and the creator pack microphone, Jon used the scenic Sunday walk to share his recent deep dive into data sovereignty and the process of building his own local alternatives to popular subscription apps. The core of Jon's summer project was migrating away from the Day One journaling app to avoid its $25 yearly fee and proprietary cloud storage. Using ChatGPT and Codex, he generated scripts to convert his Day One JSON export into future-proof Markdown files managed within an Obsidian vault. He then successfully replicated Day One's best features, using Apple Shortcuts and Python to ingest text snippets, process daily photos, perform offline audio transcriptions, and even selectively transcode video files larger than 25MB down to mobile-friendly sizes. Expanding his DIY software suite, Jon also automated his personal relationship management and location tracking. He built a script that "interviews" him weekly to automatically update his self-hosted Monica CRM and Obsidian vault with details about his interactions with family and friends. Furthermore, to reclaim his travel history after Google restricted the web version of Google Maps Timeline, Jon coded a tool to parse his device's local JSON location data into detailed, daily Markdown travel logs. With his self-hosted documentation ecosystem fully functional, Jon is taking it on the road for late-summer travel and will return to the podcast in mid-August.
Talk Python To Me - Python conversations for passionate developers
For years, "Django and async" came with an asterisk. The docs themselves warned you off it. Scary performance notes, a story that felt half-finished. Well, that story just got rewritten, literally, and the person who rewrote it is here to tell you why the old framing was wrong. Carlton Gibson is a former Django Fellow, sat on the security team for eight years, and he's on the steering council. On this episode we get into the async topic doc rewrite, what actually remains versus what was just fear, the new Tasks framework in 6.0, DB-level cascades and fetch modes landing in 6.1, and why free-threading is the bet that's about to pay off big for Django. If you've been told Django's async story isn't ready, this is the episode that puts that myth to bed. Episode sponsors Sentry Error Monitoring, Code talkpython26 Python in Production Talk Python Courses Links from the show DjangoCon Europe: djangocon.eu PyCon Italia: pycon.it Django on the Med: djangomed.eu Django Mantle: noumenal.es PyPI: pypi.org release notes: docs.djangoproject.com on_delete: docs.djangoproject.com Fetch modes: docs.djangoproject.com HttpRequest.multipart_parser_class: docs.djangoproject.com async topic doc: docs.djangoproject.com docs: docs.djangoproject.com DEP 14: github.com django-tasks: github.com django-tasks-local: github.com Celery: docs.celeryq.dev PEP 703: peps.python.org free-threading HOWTO: docs.python.org PEP 779: peps.python.org ASGI: docs.djangoproject.com PGBouncer: www.pgbouncer.org Channels: channels.readthedocs.io sync_to_async / async_to_sync: docs.djangoproject.com noumenal.es: noumenal.es Django Chat: djangochat.com @carlton@fosstodon.org: fosstodon.org Article: Cutting Python Web App Memory Over 31%: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #556 deep-dive: talkpython.fm/556 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Two Guys Two Things is back!In this episode, we discuss the rantings and ravings of James Franco, who recently joined TikTok, as well as a pizza shop owner in Florida who accepts pythons as currency in exchange for pizza.Send us Fan MailFollow Two Guys Two Things on other social platforms:YouTube: https://www.youtube.com/@twoguystwothingsTwitter: https://www.twitter.com/2Guys2Things.comIf you enjoyed the episode (or didn't) we would love it if you left us a review. Who knows, we may even share the review on the show.We'd love to hear from you! Contact us at 2guys2things@gmail.comThanks for listening!
Collin reviews the film New Acaltraz also known as Boa often regarded as one of the worst monster movies of all time. The director and writer hatched a plan to rip-off the far more successful and beloved films Python and Venomous that came out a few years prior. Dean Cain and the rest of the cast besides Mark Shepard delivered remarkably poor performances. Do not watch Boa it is a 1/10 film!
Your prayers can impact how you walk through pregnancy, medical reports, and the development of your baby. Learn how to pray specific, faith-filled pregnancy prayers and speak life over your developing child. Purchase Speak Life: Prayer Declarations for Fertility, IVF, Pregnancy, Labor, Delivery, and Healthy Babies on Amazon https://a.co/d/0hCMKe7Y or at: https://www.kathydegrawministries.org/product/speak-life-prayer-declarations-for-fertility-pregnancy-labor-delivery/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# Kathy DeGraw shares her family's deeply personal journey through high-risk pregnancy, birth complications, and caring for a medically complex child. Through these experiences, Kathy and her daughter discovered the importance of praying intentionally over conception, fetal development, chromosomes, DNA, the placenta, umbilical cord, brain, heart, organs, growth, labor, and delivery. God formed your baby in the womb, and you can partner with Him through prayer without allowing fear to control you. Kathy teaches you how to use prayer declarations, stand in spiritual authority, rebuke fear, and speak health and wholeness even when doctors present concerning diagnoses or medical statistics. This powerful episode will encourage pregnant women, mothers, grandmothers, and families believing for conception to become proactive in prayer. Discover how to speak life over every stage of pregnancy, build a hedge of protection around your baby, and release faith instead of fear. Your baby is fearfully and wonderfully made. It is time to pray specifically, speak boldly, and believe that prayer changes things. #PregnancyPrayer #PrayForYourBaby #HealthyPregnancy #SpeakLife #FetalDevelopment **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KathyDeGrawMinistry - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
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.
Guest Chelsea Finnie Panelist Richard Littauer Show Notes In this episode of Sustain, Richard talks with Chelsea Finnie, Chairperson of Python New Zealand and a Senior Network DevOps Engineer at REANNZ, about the serious behind-the-scenes work required to keep open source communities alive. Chelsea shares how they moved from Python conference volunteer to community leader, then walks through Python New Zealand's financial crisis after discovering improper use of funds, the legal and governance challenges that followed, and how Linux Australia helped keep the organization and Kiwi PyCon alive. It's a candid conversation about trust, accountability, burnout, community rescue, and why open source leadership is about much more than code. Press download now to hear more! [00:01:47] Chelsea explains their work at REANNZ and their role as a senior network DevOps engineer including Python-based network automation and the PANDA initiative for advancing network device automation. [00:03:08] Chelsea talks about contributing an open source “glue” service connecting Prometheus Alertmanager to Argus using Flask. [00:04:21] Chelsea takes us through how they got involved with Python New Zealand, helping rescue AV production for a Kiwi PyCon event, and realizing they might be capable of helping run a conference themselves. [00:06:58] They share the lighter side of joining the Python NZ committee, including a sea shanty lightning talk and an election promise to write a Python-based sea shanty. [00:08:35] Richard shifts into the central issue: Python New Zealand leadership had been dealing with a major financial and governance crisis and Chelsea explains discovering the budget hole, seeking legal advice, reviewing the transactions, and what happened when Linux Australia stepped in. [00:14:43] Chelsea reflects on Linux Australia's role as a lifeline and why having financially stable open source organizations matters. [00:15:46] Richard highlights the often-overlooked legal and financial responsibility that comes with serving on nonprofit or open source boards and Chelsea shares where Python NZ stands now. [00:18:26] They tell us about the new safeguards and governance checks and notes that better accounting systems, such as Xero, would likely have exposed the financial problems earlier [00:20:31] Chelsea shares how they stay motivated through heavy work and the value of open source leadership. [00:21:57] Richard points out that Chelsea was not only helping fix financial and governance issues but also continuing to lead Python New Zealand and run events. Chelsea talks about the decision to run Kiwi PyCon on the “West Island.” [00:25:08] Chelsea says one of their goals as chairperson is to identify and support the next people who will lead Python New Zealand and future events. [00:26:14] Richard closes by encouraging listeners to get involved in their local open source communities, whether Python New Zealand or any other regional group. Spotlight [00:26:51] Richard's spotlight is, The Book of Knights. [00:27:17] Chelsea's spotlight is the video game, Outer Wilds. Links SustainOSS podcast@sustainoss.org richard@sustainoss.org SustainOSS Discourse SustainOSS Mastodon SustainOSS Bluesky SustainOSS LinkedIn Open Collective-SustainOSS (Contribute) Richard Littauer Socials Chelsea Finnie LinkedIn REANNZ Python New Zealand Catalyst Danny Adair GitHub Frank Keating LinkedIn Simon Merrick LinkedIn Kiwi PyCon ponekeshantyclub Tom Eastman LinkedIn Linux Australia The Book of Knights by Yves Meynard Outer Wilds Prometheus-argus-glue Sponsor CURIOSS Credits Produced by Richard Littauer Edited by Paul M. Bahr at Peachtree Sound Show notes by DeAnn Bahr Peachtree Sound Special Guest: Chelsea Finnie.
In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan
¿Todavía usando awk para extraer información de ps aux o df? En 2026 hay herramientas mucho mejores. En este episodio te presento tres herramientas que forman un tridente imbatible para trabajar con información del sistema en formato JSON: jc, jq y gron.jc es un conversor de comandos Linux a JSON. Se instala con pip y un simple pipe convierte la salida de ps, df, free, ss, systemctl, lsblk y hasta 60 comandos más en JSON estructurado. Olvídate de awk y de los scripts frágiles que se rompen cuando cambia el orden de las columnas. Con jc, el JSON no depende del formato de salida. Tiene parsers específicos para cada comando, incluyendo crontab, last, lsof, pip list, lsmod, date y más. Si no encuentra el parser que necesitas, puedes crear el tuyo. Está escrito en Python y tiene licencia MIT.jq es la navaja suiza de los JSON. Te permite filtrar, ordenar, agrupar, seleccionar y transformar cualquier JSON con una sintaxis potente. Está escrito en Go y es maduro, estable y rapidísimo. Combinado con jc, puedes listar los procesos que más RAM consumen, los discos por encima del 80% de ocupación o los servicios que han fallado, todo en una sola línea. Si la sintaxis te parece liosa, puedes pedirle a cualquier modelo de lenguaje que te genere la expresión que necesitas.gron es el menos conocido pero igual de útil. Aplana un JSON convirtiendo cada valor en una línea independiente con su ruta completa. ¿Para qué sirve? Para poder usar grep directamente sobre un JSON. Si alguna vez has hecho un curl a una API y has intentado hacer grep sobre el resultado, sabes que no funciona porque todo está en una línea. Con gron, cada valor tiene su propia línea y puedes buscar con grep. Además permite la operación inversa con --ungron: modificas el JSON aplanado con sed y lo reconstruyes.En el episodio presento sysreport.py, un script en Python que junta toda la información del sistema en un solo JSON usando jc y luego te permite hacer preguntas en lenguaje natural usando Llama 3.2 con Ollama. Le preguntas qué procesos consumen más RAM, qué servicios están caídos o si hay algún disco lleno, y él te responde en lenguaje natural. Todo corriendo en local, sin gastar un euro en APIs.El script se puede usar como API local, se combina con watch para monitorización en tiempo real y con notify-send para notificaciones en el escritorio. Además se integra directamente con el nightly-runner del episodio 815 para incluir el estado del sistema en el resumen matutino.Capítulos:0:00 - Introducción: el problema de la salida en texto plano2:30 - jc: convierte comandos Linux a JSON5:00 - jq: la navaja suiza de los JSON8:00 - gron: haz greppable cualquier JSON10:30 - Combinando jc y jq para consultas del sistema13:00 - sysreport.py: el script que lo junta todo16:00 - Preguntando al sistema en lenguaje natural19:00 - Monitorización con watch y notificaciones21:00 - Ventajas: local, sin coste y sin dependencias23:00 - Cierre: el tridente JSONMás información y enlaces en las notas del episodio
Eric Chou welcomes Florian Lohden, a NetDevOps engineer and co-founder of the NetAuto Group, to discuss his community-building efforts and his latest adventures in network automation. Together they explore how adopting software development practices can help with network automation, and the importance of sharing knowledge within networking communities. Florian also talks about how he balances... Read more »
Eric Chou welcomes Florian Lohden, a NetDevOps engineer and co-founder of the NetAuto Group, to discuss his community-building efforts and his latest adventures in network automation. Together they explore how adopting software development practices can help with network automation, and the importance of sharing knowledge within networking communities. Florian also talks about how he balances... Read more »
This week on Science Faction, summer is in full swing as the hosts swap stories about kids, water parks, drama camp, and surviving the relentless heat. From AI writing Firefox extensions and Apple's latest legal battle with OpenAI to why horror suddenly clicks for one of the hosts, the conversation bounces between technology, entertainment, and the strange ways artificial intelligence can make us more confident while making us less correct. Real Life Steven's household traded the Renaissance Faire for a visit from his nephew, which led to the realization that "boy energy" really is its own force of nature. While the kids spent the week at drama camp, thoughts turned to Shakespeare, with the conclusion that Romeo + Juliet would be vastly improved if Dream Theater handled the soundtrack. Book club is also gearing up for Macbeth, because apparently there's never a bad time for a little murder and prophecy. Ben has been embracing peak summer with trips to the water park, while Devon is trying to survive the brutal Texas heat. His family's solution? Buying an old-school lawn sprinkler so the kids could cool off the classic way. Once again, the phrase "boy energy" proves to be an unexpectedly recurring theme. Technology also found its way into everyday life. Ben explained how AI helped him build a Firefox extension that converts webcomics into CBZ files, explored locally running language models with OpenCode, and used AI to determine which models would best fit his hardware. In one particularly fitting moment, he asked an AI how to repair some aging Python code, only for it to recommend an existing Firefox extension that solved the problem entirely. The hosts also wondered who keeps naming Google's AI products after notebooks before discussing https://notebooklm.google.com/. The conversation then shifted to Apple's lawsuit against OpenAI over allegations of trade secret theft involving former employees. You can read more about the case here: https://www.reuters.com/legal/litigation/apple-sues-openai-alleging-misappropriation-trade-secrets-court-records-show-2026-07-10/ Entertainment wrapped up the segment with Steven declaring that all five episodes of X-Men '97 Season 2 are absolute bangers, along with praise for Assassin's Creed Black Flag Resynced and a reminder that Assassin's Creed Origins includes one of gaming's coolest educational features: an interactive museum tour through ancient Egypt. Ben also celebrated the release of Heave Ho 2, while everyone wondered whether it's destined to become a terrible influence on Devon's kids. https://store.steampowered.com/app/2802740/Heave_Ho_2/ Future or Now The hosts somehow found themselves discussing more Game Changer chaos before moving into horror. Devon admitted that he may finally be developing a taste for the genre after watching Backrooms and Obsession, both of which started life as YouTube shorts before becoming full productions. He praised Backrooms for its eerie liminal-space atmosphere, while describing Obsession as a more traditional "be careful what you wish for" horror story. If you've been horror-curious but never quite crossed over, these might be good starting points. Ben then highlighted a fascinating study showing one of AI's biggest hidden dangers. Researchers found that people using AI assistance became three times less accurate while also becoming twice as confident in their answers. Even more striking, the willingness to simply admit "I don't know" dropped from 44% to just 3% when AI was available. It's a reminder that AI works best when treated as a helpful assistant—not an unquestionable authority. https://thenextweb.com/news/ai-advice-suppresses-critical-thinking-wrong-answers-study Finally, the crew couldn't resist talking about the newly revealed Star Trek crossover sets coming to Magic: The Gathering. Whether you're a Trek fan, a Magic player, or just someone whose wallet is already crying, the crossover looks like another ambitious mashup worth checking out. https://trekmovie.com/2026/07/18/magic-the-gathering-star-trek-sets-revealed-preorders-open-now/ Steven rounded out the week by admitting that, for once, he didn't have a specific science story to bring—which may have been the most surprising revelation of the episode.
In Episode 109 of the Cybersecurity Readiness Podcast Series, Dr. Dave Chatterjee is joined by Joshua Weinick, AI Automation Engineer at Blink Ops, to examine a question every security leader is now facing: what happens when the volume, velocity, and variety of cyber threats exceed what any human-staffed security operations center (SOC) can handle, and what role do humans play in a security function that increasingly depends on AI agents executing at machine speed.Drawing on his background in cybersecurity consulting and his time embedded in Fortune 500 SOCs at Ernst & Young, Josh traces the limitations of legacy SOAR automation — rigid, Python-coded pipelines that broke whenever an edge case or a system change occurred — and explains why agentic AI, unlike its predecessors, is built to absorb change rather than collapse under it. The conversation moves through the three forces breaking the traditional SOC model — overwhelming alert volume, attack speed that has compressed response windows from hours to minutes, and the cognitive toll of analyst burnout — and arrives at a reframed model Josh calls “human after the loop,” in which agentic systems take initial action within guardrails while humans review, audit, and reverse where needed.The discussion examines what this shift means for emerging security talent, walks through a real-world 3 a.m. incident scenario that illustrates why a 45-minute human approval delay is now functionally equivalent to no response at all, and lays out five concrete steps organizations can take to begin the transition responsibly. Analyzed through Dr. Chatterjee's Commitment–Preparedness–Discipline (CPD) Framework, the episode reframes the SOC transformation not as a story about replacing security professionals, but about redesigning where human judgment adds the most value once the mundane, repetitive, and time-critical work is handled by governed AI systems.To access and download the entire podcast summary with discussion highlights: https://www.dchatte.com/episode-109-agentic-ai-in-the-security-operations-center-redesigning-the-human-role-in-automated-threat-detection-and-response/Connect with Host Dr. Dave ChatterjeeLinkedIn: https://www.linkedin.com/in/dchatte/ Website: https://dchatte.com/Books PublishedThe DeepFake ConspiracyCybersecurity Readiness: A Holistic and High-Performance ApproachArticles & Cases PublishedChatterjee, D. (2026). The Cryptographic Reckoning: Why Quantum Readiness Begins with Agility, Not Algorithms, The INFORMS Analytics Magazine, June 26, 2026Chatterjee, D. (2026). The New Digital Fragility: How AI-Enhanced Cyber Threats Are Reshaping Operational Resilience, The INFORMS Analytics Magazine, March 4, 2026Chatterjee, D. (2026). Root: Automating the Remediation Gap, Ivey Publishing, Jan 7, 2026.Ramasastry, C. and Chatterjee, D. (2025). Trusona: Recruiting For The Hacker Mindset, Ivey Publishing, Oct 3, 2025.Chatterjee, D. and Leslie, A. (2024). “Ignorance is not bliss: A human-centered whole-of-enterprise approach to cybersecurity preparedness,” Business Horizons, Accepted on Oct 29, 2024.Isik, O., Chatterjee, D., and Lourenco, D.A. (2024). “Getting Cybersecurity Right,” California Management Review — Insights, Accepted for Publication, July 8, 2024. Chatterjee, D. (2023). “Mission critical – How American Cancer Society successfully and securely migrated to the cloud amid the pandemic,” I by IMD, March 13, 2023.Chatterjee, D. (2022). “Preventing security breaches must start at the top,” I by IMD, September 28, 2022, Institute for Management Development, Lausanne, SwitzerlandChatterjee, D. (2022). “Making Cybersecurity Readiness Mainstream,” Executive Blog Post, NETSPI, March 1, 2022Benz, M. and Chatterjee, D. (2020). “Calculated Risk? A Cybersecurity Evaluation Tool for SMEs,” Business Horizons, available online from May 4, 2020Chatterjee, D. (2019). “Should Executives Go To Jail Over Cyber Attacks,” Journal of Organizational Computing and Electronic Commerce, Vol 29, Issue 1, pp. 1-3.Abraham, C., Chatterjee, D., and Sims, R. (2019). “Muddling through cybersecurity: Insights from the U.S. healthcare industry,” Business Horizons, July 2019.
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
Speak healing over your body and come into agreement with powerful prayer declarations for physical healing, restoration, and wholeness in Jesus' name. Purchase Kathy's book Healed at Last – Overcome Sickness to Receive your Physical Healing on Amazon https://a.co/d/akj6IIM or at: https://www.kathydegrawministries.org/product/healed-at-last-pre-order-now/ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# Are you battling pain, inflammation, sickness, disease, or fear over your health? In this powerful healing prayer, Kathy DeGraw decrees and declares biblical healing over your body, commanding systems, organs, cells, bones, joints, and nerves to come into proper alignment and function. You don't have to partner with fear when symptoms arise. You can stand on the Word of God, take spiritual authority, and speak life over your body. Listen as Kathy releases healing prayer declarations for allergies, inflammation, arthritis, cancer, diabetes, tinnitus, dental issues, migraines, brain health, blood pressure, breathing difficulties, kidney stones, cysts, tumors, the immune system, and more. Keep listening and praying along as you build your faith and believe for the full manifestation of healing. Declare that by Jesus' stripes you are healed, no weapon formed against you will prosper, and sickness and plague will not have authority over your life. Comment amen and come into agreement with these healing declarations today. #HealingPrayer #PrayerDeclarations #DivineHealing #SpiritualWarfarePrayer #BiblicalHealing **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org
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
In this episode, we're joined by Jeremiah Lowin, Founder & CEO at Prefect and the creator of FastMCP, to explore how one of the most influential projects in the MCP ecosystem came to be - and where the protocol is heading next.We discuss the accidental origin of FastMCP, why Anthropic adopted it into the official SDK, what developers are getting wrong about MCP, and why Chris believes the biggest opportunity for AI agents isn't customer-facing applications, but internal enterprise systems. We also dive into MCP Apps, developer experience, protocol design, AI tooling, Python, and why building great abstractions is often more valuable than exposing more configuration.Along the way, we explore the rapid growth of the MCP ecosystem, how FastMCP became the default way many developers build MCP servers, why "too much magic" can actually hurt developer experience, and what the next generation of AI-powered applications will look like as agents move beyond simple tool calling into rich, interactive experiences.Prefect: https://www.prefect.ioJeremiah Lowin: https://www.linkedin.com/in/jlowinDemetrios: https://www.linkedin.com/in/dpbrinkmTimestamps:00:00 Lost My Entire Talk00:47 The Story Behind FastMCP02:08 Anthropic Adopted FastMCP02:34 When MCP Took Off04:10 FastMCP vs The Official SDK05:43 Is MCP Actually Dead?06:42 What Everyone Gets Wrong About MCP08:11 MCP's Biggest Use Case10:25 Building Internal AI Systems12:00 Why FastMCP Exploded13:29 Making Complex Software Simple15:10 Can Software Be Too Magical?20:11 MCP Apps Explained23:42 Why Python Needed MCP Apps27:54 The Future of AI Interfaces34:18 AI Should Generate UIs40:11 AI Deleted My Presentation43:30 The AI Assistant We Actually Need48:00 Personal AI vs SaaS52:28 The Future of AI Agents55:06 Final Thoughts
This show has been flagged as Explicit by the host. Show Notes Episode Overview Operator kicks off the episode feeling under the weather but shares a quick tip for making perfect egg drop soup before diving into his main project: diagnosing why his front-door security camera stopped sending alerts and recording events. What follows is a live-debugging session covering network config, script logging, Windows permission hacks, NTP time drift, and firmware flashing. Key Topics & Breakdown Egg Drop Soup Hack: How to get that perfect ribbony texture by creating a boiling swirl before pouring in the eggs, plus broth-to-egg ratio tips. Camera Setup & Network Config: Using static DHCP via MAC address binding on a UniFi Dream Machine (UDM) for local domain resolution instead of hardcoding IPs. Python & Cron Automation: Running a custom Python script every 2 minutes to check for new recordings, parsing logs with grep -v , and navigating massive log files in vi . Windows Troubleshooting Tangent: Deleting the stubborn Windows.old folder using the TrustedInstaller service hack ( ExecTI.exe ) instead of taking ownership manually. Time Sync & Firmware Quirks: Discovering the camera's system clock was stuck in 2011/2026, causing missed events. Downloading firmware via a slow third-party link, renaming .bin to .zip , and extracting with 7-Zip. Pre-Flash Backup Routine: Exporting camera configuration before upgrading, storing it in Google Drive for searchable documentation, and clearing old log/trigger files to reset the event pipeline. ️ Tools & Techniques Mentioned crontab + Python scripts for automated monitoring grep -v , cat , tail , and vi (line navigation with :1000 ) Obsidian for note-taking & AI assistant integration Firefox/Playwright for headless browser testing Turbo Download Manager & Bolt Media Downloader for multi-threaded/sniffing downloads 7-Zip for archive extraction Google Drive for searchable config backups Resources & Links Python API Script: Uniview IPC3628SR Recording Checker Camera Model: IPC3628SR (Uniview Wyze ISP Warm Light Deterrent Network Camera) TrustedInstaller Run-as Tool: ExecTI TrustedInstaller Runner Quick Takeaways Always verify NTP/time sync on IoT cameras before troubleshooting missed events or alerts. Use grep -v "noise" to quickly filter out repetitive log entries when debugging automation scripts. Windows system folders can be stubborn; running commands as TrustedInstaller bypasses hidden file locks without manual ownership changes. Always export and back up device configs before flashing firmware, even if the upgrade seems straightforward. Third-party download links often use temporary tokens or .bin wrappers; renaming to .zip and verifying with 7-Zip can save headaches. Thanks for listening! Stay curious, keep your logs clean, and remember: defense in depth starts at home. Example trusted installer hack # Shhhh I can't IR ... Defender, ForcePoint, SMS Agent Host ...I just can't anymore ... sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesSense" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesFppsvc" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesCcmExec" /v Start /t reg_dword /d 4 /f" sc start "TrustedInstaller" sc config TrustedInstaller binPath= "Reg add "HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesWinDefend" /v Start /t reg_dword /d 4 /f" sc config TrustedInstaller binPath= "C:WindowsservicingTrustedInstaller.exe" Provide feedback on this episode.
¿Cansado de perder 15 minutos cada mañana revisando el tiempo, las noticias, las ofertas y el estado de tu servidor? En este episodio te muestro cómo automatizar todo ese proceso con un script en Python, un timer de systemd y un modelo de lenguaje local. Sin n8n, sin agentes, sin servicios externos de pago.Mucha gente piensa que para automatizar cualquier cosa necesitas un agente con montones de herramientas MCP, skills y configuración. Pero la realidad es que para muchas tareas cotidianas, un agente es como usar un lanzamisiles para matar una mosca. Consume demasiado contexto, demasiados recursos y al final no es la solución más eficiente.En este episodio te presento el patrón de las tres capas: un script que hace el trabajo, un timer que lo ejecuta a una hora determinada y un sistema de notificaciones que te envía el resultado. Con esto puedes automatizar cualquier cosa de forma sencilla, eficiente y completamente bajo tu control.Te explico cómo he creado el nightly-runner, un script en Python que cada madrugada recopila información de cuatro fuentes distintas. Primero consulta el tiempo en wttr.in, que te devuelve un JSON con la temperatura, el viento, la humedad y los rayos ultravioleta. Luego hace scraping con IA de tus fuentes de noticias favoritas, extrayendo titulares y valorando su relevancia. Después busca ofertas de zapatillas de running en varias tiendas, comparando los precios con los del día anterior. Y por último recoge información del sistema con df, free, uptime y ps aux para saber si tu disco se está llenando o te estás quedando sin RAM.Toda esa información se guarda en archivos JSON y luego se pasa por un modelo de lenguaje local, Llama 3.2 con Ollama, que genera un resumen en lenguaje natural. El resultado es un mensaje de Telegram con un tono cercano que te da los buenos días, te cuenta el tiempo que va a hacer, te destaca las noticias importantes, te avisa si hay una oferta que no puedes dejar pasar y te informa del estado de tu servidor. Todo en un solo mensaje.El timer de systemd con Persistent=true se asegura de que si tu equipo estaba apagado a las 4 de la mañana, el script se ejecute en cuanto se encienda. Y cada capa es tolerante a fallos: si wttr.in está caído, el script simplemente omite el tiempo y el resumen dice que no hay información meteorológica disponible. Si no hay ofertas nuevas, no las menciona. Si Ollama no responde, envía el resumen sin procesar.Lo mejor de todo es que no necesitas saber Python para montar esto. Puedes usar Open Code o Gemini para que te genere el script con solo explicarle lo que quieres. Y para ejecutarlo, Llama 3.2 en local es más que suficiente. Sin gastar un euro en APIs.Capítulos:0:00 - Crítica a los agentes como solución universal2:00 - El problema: 15 minutos perdidos cada mañana4:00 - La solución: tres capas (script, timer, notificación)5:30 - wttr.in: el tiempo en JSON con un curl7:30 - Noticias: scraping con IA para extraer titulares9:30 - Zapatillas: comparativa de precios contra caché11:00 - Sistema: df, free, uptime y ps aux13:00 - El resumen: todos los JSONs pasan por Llama 3.216:00 - Systemd timer con Persistent=true18:00 - Notificaciones a Telegram y notify-send20:00 - Tolerancia a fallos en cada capa21:30 - Genera el script con IA aunque no sepas Python23:00 - Comparación con Hermes: menos es másEste podcast pertenece a la red de Sospechosos Habituales. Más información en atareao.esMás información y enlaces en las notas del episodio
Jimothy the raccoon goes viral, the TRUTH about the smoke, a Today Show anchor was attacked, Daylight Saving Time doomers are wild, a man found inside a porta-potty, an awkward moment in Congress, big budgets and spy Tahoes, an interview about this year's python hunt in Florida and so much more!See omnystudio.com/listener for privacy information.
What do you do when rebellion, rejection, fear, nightmares, or spiritual oppression seem to be influencing your child? In this powerful episode of the Prophetic Spiritual Warfare podcast, Kathy DeGraw shares her personal testimony of praying through her rebellious teenager's bedroom and the drastic transformation she witnessed. Home Declarations book available at https://www.kathydegrawministries.org/product/prayer-declarations-for-your-home/ or Amazon https://a.co/d/0cTwSJCZ Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# Discover how to pray over your child's bedroom, use anointing oil, and take spiritual authority over doors, windows, beds, possessions, technology, and the atmosphere of the room. Kathy teaches practical spiritual warfare prayers to bind and restrict demonic influence, break ungodly attachments, plead the blood of Jesus, and invite the presence of the Holy Spirit into your child's space. You will learn how to ask the Holy Spirit for discernment, spiritually cleanse a room without fear, and declare peace, godly dreams, prophetic encounters, protection, and rest over your children. Don't partner with fear when spiritual warfare enters your home. Stand in faith, pray with authority, and believe God for restoration and transformation in your family. Your prayers matter. Take authority, bless your child, and create an atmosphere where the presence of God can dwell. #SpiritualWarfare #PrayForYourChildren #SpiritualProtection #AnointingOil #ChristianParenting **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/
How many attempts have been made to remove Python's Global Interpreter Lock (GIL)? How do they compare to the current approach? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.
nFactorial Intelligence - еженедельный обзор новостей из мира стартапов и ИИ Рекомендации от nFactorial Ежегодный nFactorial Incubator Demo Day 2026. 24 июля, пятница, 13:00-17:00, г. Алматы. Вход свободный. Narxoz University, актовый зал, главный учебный корпус, Жандосова 55. Подать заявку: https://nfactorialschool.typeform.com/to/syWrSaRy 22-недельный буткамп по аналитике данных, 44 урока. 6 модулей: Google Sheets, Power BI, SQL, Python, Product Analytics, AI для Аналитика Данных - https://courses.nfactorial.school/da
The BOB & TOM Show – July 16, 20266:00 Hour 6:00 Shirtless girl 6:00 Kristi out; Pat out 6:05 Jeff's beard discussion 6:09 Letter: "I have a shirt that says I can drive a stick." Husband says every man sees it as sexual. 6:25 Letter: You guys had not gone to the third string until yesterday. 6:27 Letter: Guy caught a 27-inch golden walleye. 6:29 Discovery of a new monkey in the Congo. 6:30 Tom is a big fan of school uniforms. 6:32 Tom said his urine once looked like mustard. 6:34 Letter: Goal to read 100 books. 6:38 Girl killed by an alligator in Florida. 6:48 Letter: Found chicken feathers in my full beard. 6:50 Josh says getting urine on your feet is better than getting it on your shoes. 6:52 Tom is not a fan of suede. 6:53 Tom bought no-show socks. 7:04 Walking dogs: A woman slammed on her brakes and yelled at Tom. 7:10 Sports. 7:13 Largest group of trombone players: 600. 7:15 Jess would wear python. 7:24 Chick's "Super Toe" toy. 7:31 Jess: "What's left of her tattoos." 7:35 Josh line. 7:49 Escaped alligator found in Indiana after being missing for a month. 7:50 Josh has touched an alligator. 7:50 Tom swam with stingrays in the Bahamas and was terrified. 7:52 Barracuda and copyright discussion. 8:07 Urinal splash: Men will aim for a spider on the porcelain. 8:09 Tom says Josh has to clean his ankles after urinating. 8:10 Giant cockroach found on a urinal hook being used by a woman's husband. 8:10 Woman received a personalized license plate that read "SQUZ AIS." 8:21 Willie in studio. 8:24 Phone interview with Dusty Crum, pizza, pasta, and python shop owner. 8:24 Dusty recycles python skin. 8:25 Dusty holds giant snakes. 8:27 Dusty is the fastest snake skinner in the South. 8:28 Python popsicles. 8:35 Hairy croissants. 8:46 Today in History. 9:10 Lucas Waterfill in studio. 9:11 Lucas discusses having cerebral palsy. 9:12 Lucas discusses a traffic accident that was his fault. 9:24 Lucas as a pilot. 9:28 Clifford is Lucas's dog. 9:28 Lucas has been sober for a while. 9:33 Lucas discusses using a wheelchair and Medicaid approval. 9:45 Lucas talks about quitting drinking. 9:45 Lucas smokes cigars. 9:48 Lucas discusses getting tattoos and "Stop!" 7:00 Hour8:00 Hour9:00 Hour Learn more about your ad choices. Visit podcastchoices.com/adchoices
This week, our technical segment covers a new open-source tool written by Paul (and Claude) that helps you keep your Linux systems up to date and assess supply chain risks. It's called "fettle" and is a pure Python implementation that gives you even more features than previously discussed! Then in the security news: The GodDamn Ransomware CMMC suspended Holy Microsoft Tuesday! Lessons learned Without the Internet, do we still get water? The forgotten shims More than two BIOS passwords Cracking firmware encryption with Claude 1999 called, and it wants its "Exploits" back Prompt injection for defenders Grok has your repo You're not going to outpatch AI Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-935
The squad attempts to mediate a hairy Homie Helpline where a listener's marriage is on the rocks after her husband debuted a "creepy" 80s cop mustache that killed the attraction. Between the relationship drama, the crew investigates the "Brujeria" witchcraft Argentinian fans used to win the World Cup and survives a chaotic studio visit from a 14-foot python and a pooping turtle. [Edited by @iamdyre
The BOB & TOM Show — July 15, 2026 6:00 Hour 6:00 – Kristi out; Jess out; Pat out; Jeff in 6:09 – Flying into the Bahamas; rough flight discussion 6:23 – "Snake Farm" song 6:25 – Letter: "I have a new T-shirt" – "Don't pet the fluffy cows" 6:29 – Letter: "I'll be your gas boy" 6:31 – Letter: People and groups drift to the left 6:33 – New NBA league in Europe 6:45 – Yesterday in History 6:54 – Letter: Video of a guy skiing on stilts 7:00 Hour 7:05 – More history 7:08 – Conor McGregor discussion 7:10 – Cigarettes are back 7:10 – Sports update 7:21 – Story Inn haunted discussion; Josh shares his experience performing there 7:22 – More sports 7:21 – WNBA player ejected after throwing a shoe at another player 7:26 – Pogo Palooza in Pittsburgh; pogo stick stunts 7:29 – National Hot Dog Day 7:31 – Connie Francis discussion 7:33 – Josh Arnold Porn Foundation bit 7:33 – Most kisses in 30 seconds: 195 7:35 – Josh and Jeff joke about kissing 7:37 – Jeff and Josh discuss beard brushes and beard wash 7:39 – Jeff jokes about donating beard clippings to "Locks for Lice" 7:50 – Fans arrested at a Phish show in Indiana on drug charges 7:53 – Python meat pizza in Everglade City, Florida 7:56 – Josh explains how pythons bite and squeeze 8:00 Hour 8:07 – Python vertebrae earrings; Josh wants to buy them for Kelly 8:08 – Python leather products discussion 8:11 – Dinosaur skeleton sells for $50 million 8:13 – Connie Francis discussion 8:14 – Chick says he never wears a belt 8:24 – Today in History 8:26 – "Friends" theme discussion 8:34 – "Why not Boeing? Boeing gone?" joke 8:47 – Victorian-era condoms found at Warwick Castle 8:52 – Defenestration discussion 8:52 – New words from the Cambridge Dictionary 9:00 Hour 9:08 – Jessica Altman in studio 9:10 – Banana code joke (4011) 9:21 – Alli Breen: "Sexy Time" segment 9:23 – Letter: Couple splits rent and meal expenses even though one partner earns more 9:25 – Letter: Found hidden cash in girlfriend's underwear drawer 9:31 – Letter: Boyfriend points out women he considers his "hall pass" 9:32 – Letter: Couple trying to get pregnant; discussion about how pregnancy talk affects intimacy 9:47 – Survey: One in five people judged by their first name alone Learn more about your ad choices. Visit podcastchoices.com/adchoices
There is a challenge going on in the state of Florida and you're going to be able to EAT PYTHON?! Katie Sommers has the trending story for you right here! See omnystudio.com/listener for privacy information.
Topics covered in this episode: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it JupyterLab 4.6 and Notebook 7.6 are out! Tau – new small, readable terminal coding agent Django Tasks and Django 6.1 Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: The trusted-publishing debate: how to do it right vs. why you shouldn't trust it https://snarky.ca/how-to-publish-to-pypi-using-github-actions-securely/ (Brett Cannon) and https://blog.yossarian.net/2026/07/07/You-shouldnt-trust-trusted-publishing (William Woodruff) Trusted Publishing (PyPI's OIDC-based auth scheme, also now used by npm, RubyGems, crates.io, NuGet) replaces long-lived API tokens with short-lived, auto-scoped credentials tied to CI/CD machine identity. Yossarian's post: it's purely an authentication mechanism between a machine identity and a package — it says nothing about package safety or quality. PyPI deliberately avoids any "verified/trusted" badge for it, unlike its verified-URL checkmarks. Same logic applies to PyPI attestations: anyone can sign with any machine identity they control, so an attestation's presence isn't itself a trust signal. Bottom line from that post: don't confuse "trusted" (machine-to-machine) with "trustworthy" (human judgment about the package). Snarky.ca's companion piece is more practical: given GitHub Actions compromises in the news, the real fix is 3 concrete steps — run zizmor to lock down workflow permissions/checkout credentials and pin actions to commit hashes, adopt Trusted Publishing to eliminate stored PyPI tokens, and require manual approval via a GitHub environment before any publish job runs. Takeaway for listeners: Trusted Publishing is good hygiene for how you authenticate to PyPI, but it's not a substitute for securing your CI pipeline itself — or for actually vetting the packages you install. Michael #2: JupyterLab 4.6 and Notebook 7.6 are out! Michał Krassowski's rundown - a chunky minor release: 68 features, 97 bug fixes, 95 contributors, one of the biggest ever. Scratchpad console (Notebook 7.6 headliner) - a console next to your notebook sharing its kernel, for throwaway experiments. Ctrl+B. Jump to last-edited cell - new commands hop through recently edited cells. File browser glow-up - Date Created column, editable breadcrumbs with Tab-completion, and Open in Terminal. Debugger - sources open in the main area, floating step/continue overlay, live kernel-sources filter. Custom layouts (Lab) - activity bar top/bottom, draggable panels, four-way tab splits, per-panel Ctrl+scroll zoom. ~5x faster extension builds - webpack → Rspack, and jupyter-builder means no full Lab install needed to build extensions. Keyboard/a11y - add shortcuts from the UI (no JSON), Find & Replace in Edit menu (Ctrl+H). Calvin #3: Tau – new small, readable terminal coding agent Tau – new small, readable terminal coding agent (Python 3.12+), built as both a working tool and a teaching project for how coding agents work under the hood Install via uv tool install tau-ai, pipx, or pip; ships a tau CLI Three-layer architecture: tau_ai (provider-neutral model layer) → tau_agent (reusable "brain": messages, tools, events, loop) → tau_coding (CLI/TUI, file & shell tools, sessions) Supports OpenAI, Anthropic, OpenAI Codex, OpenRouter, Hugging Face, and custom/local OpenAI-compatible endpoints Built-in tools (read/write/edit/bash), durable JSONL sessions with resume/branching, project instructions via AGENTS.md, and context compaction Core harness is UI-agnostic — same brain can power the TUI, print mode, or a custom frontend — usable as a standalone library too Michael #4: Django Tasks and Django 6.1 Django 6.0 finally ships first-party background tasks (django.tasks) - out of Jake Howard's DEP 14, accepted May 2024, after two decades of everyone bolting on Celery/RQ/Huey. It's an API, not a worker. Django handles task definition, validation, queuing, and result storage - it does not execute them. You bring the backend. The default backend traps people. ImmediateBackend runs tasks inline on the request thread and blocks until done - so out of the box .enqueue() backgrounds nothing (a 5-second task means a 5-second response). The other built-in, DummyBackend, runs nothing at all. Both are dev/test only. Nice API otherwise: slap @task on a function, call .enqueue(), get back a TaskResult you look up later by id - with async twins like aenqueue(). Gotcha: args and return values must survive a JSON round-trip, so a tuple sneakily comes back as a list. The community local backend to know: django-tasks-local by Chris Beaven (SmileyChris). A ThreadPoolExecutor backend that gives real background threads with zero infrastructure - no Redis, no Celery, no database - plus a ProcessPoolBackend for CPU-bound work → github.com/lincolnloop/django-tasks-local Its catch: results live in memory, so pending tasks vanish on restart or deploy. Great for dev and low-traffic production; for persistence, drop to Jake Howard's django-tasks (DatabaseBackend + worker command). Extras Calvin: Fixing the dictionary with Python 3.14 — Hugo van Kemenade stumbled on - and got fixed - a markup bug in the OED's own citation of a 1706 use of the pi symbol. Michael: Bunny DNS is now free Jokes: What's the object-oriented way to become wealthy? Inheritance To understand what recursion is... You must first understand what recursion is 3 SQL statements walk into a NoSQL bar. Soon, they walk out They couldn't find a table.
Fear can rise quickly when something feels wrong in your body, but you do not have to let fear lead. Learn how to stand in faith, take spiritual authority and pray powerful healing declarations rooted in the Word of God. Purchase Kathy's book Healed at Last – Overcome Sickness to Receive your Physical Healing on Amazon https://a.co/d/akj6IIM or at: https://www.kathydegrawministries.org/product/healed-at-last-pre-order-now/ Mind Battles - Root Out Mental Triggers and Release Peace available at https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ or Amazon https://a.co/d/18blHkV Purchase Anointing Oil with a prayer cloth that Kathy has personally mixed and prayed over on Kathy's Website or Amazon. Order anointing oil by Kathy on Amazon look for her brand here https://amzn.to/3PC6l3R or Kathy DeGraw Ministries https://www.kathydegrawministries.org/product-category/oils/ Training, Mentorship and Deliverance! Personal coaching, deliverance, e-courses, training for ministry, and mentorships! https://www.kathydegrawministries.org/training/# When symptoms appear, pain increases or a medical situation feels uncertain, what is your first reaction? Do you panic, search for answers or immediately partner with fear? In this episode, Kathy DeGraw teaches you how to stay grounded in faith, seek the Holy Spirit and take authority over sickness and disease. God has not given you a spirit of fear, but of power, love and a sound mind. You can learn to pray, declare and decree instead of allowing anxiety and medical fear to control your thoughts. Kathy shares personal healing testimonies, how she prayed through her husband's intense pain and the importance of going to the Holy Spirit for wisdom and a word of knowledge. Your faith is not passive. You must arise, pray, speak the Word of God and believe your prayers are effective. Jesus has given you authority. Stop allowing fear to lead and start partnering with faith, healing prayer and the power of the Holy Spirit. #HealingPrayer #FaithOverFear #SpiritualAuthority #DivineHealing #HolySpirit **Connect with Us** - Website: https://www.kathydegrawministries.org/ - Facebook: https://www.facebook.com/kathydegraw/ - Instagram: https://www.instagram.com/kathydegraw/ Podcast - Subscribe to our YouTube channel and listen to Kathy's Podcast called Prophetic Spiritual Warfare, or on Spotify at https://open.spotify.com/show/3mYPPkP28xqcTzdeoucJZu or Apple podcasts at https://podcasts.apple.com/us/podcast/prophetic-spiritual-warfare/id1474710499 **Recommended Resources:** - Receive a free prayer pdf on Python at https://www.kathydegrawministries.org/python/- Receive a free prayer pdf on Anointing Oil at https://www.kathydegrawministries.org/anointingoil/ - Kathy's training, mentoring and e-courses on Spiritual Warfare, Deliverance and the Prophetic: https://training.kathydegrawministries.org/ - Healed At Last ~ Overcome Sickness and Receive your Physical Healing: https://www.kathydegrawministries.org/healed-at-last/ - Mind Battles – Root Out Mental Triggers to Release Peace!: https://www.kathydegrawministries.org/product/mind-battles-pre-order-available-january-2023/ -Kathy has several books available on Amazon or kathydegrawministries.org **Support Kathy DeGraw Ministries:** - Give a one-time love offering or consider partnering with us for $15, $35, $75 or any amount! Every dollar helps us help others! - Website: https://www.kathydegrawministries.org/donate/ - CashApp $KathyDeGrawMinistry - Venmo @KD-Ministries - Paypal.me/KDeGrawMinistries or donate to email admin@degrawministries.org - Mail a check to: Kathy DeGraw Ministries ~ PO Box 65 ~ Grandville MI 49468
Talk Python To Me - Python conversations for passionate developers
Coding agents have gotten really good at one kind of work. You scope a feature, edit some files, run the tests, ship it. It all happens on disk. But that is not how data work feels. You load something, you look at it, you run a cell, you watch how it responds, and you decide the next move from whatever is sitting in memory. And until now, your agent couldn't see any of that. It only saw the files. Never the live state. This episode, that wall comes down. marimo pair drops a coding agent right inside a running notebook, with full access to every variable Python is holding in memory. The notebook becomes a shared canvas. You point, it runs the code. You tell it to zoom in on the Picasso paintings, and the chart just updates. No MCP tools to wire up, no schema to describe. Just Python, and an agent that can finally see what you see. Trevor Manz is back to walk us through it. Episode sponsors Sentry Error Monitoring, Code talkpython26 Talk Python Courses Links from the show marimo pair: marimo.io/pair Course transcripts announcement: talkpython.fm/blog anywidget: Jupyter Widgets made easy: talkpython.fm marimo: marimo.io blog: marimo.io GitHub: github.com given this: martinalderson.com llms.txt: talkpython.fm mcp: talkpython.fm cli: talkpython.fm open issues: github.com Discord: marimo.io Marimo Pair: marimo.io OpenCode: opencode.ai AI Tooling for Software Engineers in 2026: newsletter.pragmaticengineer.com Watch this episode on YouTube: youtube.com Episode #555 deep-dive: talkpython.fm/555 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Talk Python To Me - Python conversations for passionate developers
You ask an AI a question and it answers with total confidence. Most of the time, a confidently wrong answer is just an annoyance. But what if the question is medical, and there's a real patient on the other end? In that world, a hallucination isn't a bug, it's a patient-safety event. Sumit Gundawar is a London-based software engineer who builds the clinical platform for a UK longevity and aesthetic-medicine clinic, and his whole argument is that in high-stakes AI, the model is the easy part. Earning trust is the real engineering. We dig into grounding, refusal logic, human-in-the-loop design, and the messy frontier of longevity and biohacking, plus a live demo of an assistant that refuses to answer when it can't back up the claim. Let's get into it. Episode sponsors Six Feet Up Talk Python Courses Links from the show Guest Sumit Gundawar: linkedin.com Course transcripts announcement: talkpython.fm/blog Sumit Gundawar - JAX London Speaker: jaxlondon.com Anthropic: anthropic.com OpenAI Platform: platform.openai.com Anthropic: anthropic.com LangChain: langchain.com OWASP: owasp.org Pydantic: pydantic.dev EU AI Act - Regulatory Framework: digital-strategy.ec.europa.eu HIPAA - HHS: www.hhs.gov NHS: www.nhs.uk Llama: llama.com Qwen - QwenLM on GitHub: github.com OpenAI Platform: platform.openai.com Hugging Face: huggingface.co Llama: llama.com Granola: www.granola.ai HIPAA - HHS: www.hhs.gov CodeRabbit: www.coderabbit.ai Cursor Origin: cursor.com GitHub Status: www.githubstatus.com Midjourney Medical: www.midjourney.com Neko Health: www.nekohealth.com CERN: home.cern ATLAS Experiment: atlas.cern Watch this episode on YouTube: youtube.com Episode #554 deep-dive: talkpython.fm/554 Episode transcripts: talkpython.fm Theme Song: Developer Rap
Jordan got a new old truck, There is a huge python wrangling competition in Florida, red snapper season is in peril for recreational anglers, and Jordan has a new method for managing land in WRE tracts. Check it out!
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