Podcasts about langchain

Topics covered in this episode: Backup Docker volumes locally or to any S3 Pyodide 314.0 Release nb-cli: A Command-Line Interface for AI Agents and Notebook Automation Hindsight Agent Memory That Learns Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python AWS Community Day Midwest tomorrow Wednesday the 24th in downtown Indianapolis, Six Feet Up is sponsoring and there are 2 Sixies presenting 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 bonus 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. Michael #1: Backup Docker volumes locally or to any S3 Via Bryan Weber (thanks Bryan!), who spotted it over on Virtualization HowTo. Find Bryan at bryanwweber.com. offen/docker-volume-backup is a lightweight companion container that backs up the volumes your apps actually depend on, then ships them somewhere safe. It's tiny: written in Go and about 25MB compressed, roughly 1/20th the size of the shell-based image (jareware/docker-volume-backup) that inspired it. Drop it into your docker compose file as a backup service, mount the volumes you care about as read-only, and you're off. Push backups to a pile of destinations: a local directory, plus any S3, WebDAV, Azure Blob Storage, Dropbox, Google Drive, or SSH-compatible target. Mix and match as many as you want in one run. Recurring cron-style backups in a Compose setup, or one-off backups straight from the Docker CLI. Production-friendly touches worth calling out: Rotates away old backups so you don't quietly fill the disk. GPG encryption for your archives. Notifications on finished and failed runs (so you find out about failures before you need the backup). Stop a container during backup for a consistent snapshot using a simple docker-volume-backup.stop-during-backup=true label, then auto-restart it. Run custom commands during the backup lifecycle (great for a database dump before the file copy). Docker Swarm support, plus arm64 and arm/v7 builds. Hello, Raspberry Pi homelab. Fun aside from Bryan: he searched our back catalog for this tool and the search came back so fast he thought it hadn't run. Love to hear it. Calvin #2: Pyodide 314.0 Release PEP 783 is the real news — Pyodide maintainers used to hand-build 300+ packages. Now anyone can publish Pyodide wheels to PyPI with cibuildwheel. The version jump from 0.29 to 314.0 is intentional — it now tracks the Python version, so 314.x = Python 3.14. Binary compatibility is locked per Python cycle, meaning packages you build today won't break on the next Pyodide release. sqlite3, ssl, and lzma are back in the default stdlib — no more await pyodide.loadPackage("sqlite3"). Bigger download, but a much smoother experience for newcomers. bigint precision bug is fixed — values above 2^53 were silently losing precision when crossing the Python/JS boundary. The new JsBigInt type makes the roundtrip correct. Worth flagging if anyone is doing numeric work in a browser app. Experimental TCP sockets in Node.js — you can now connect Pyodide to a real database (MySQL, PostgreSQL, Redis tested) when running server-side. Blurs the line between "Python in the browser" and "Python runtime anywhere Wasm runs." Michael #3: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation From Piyush Jain (Jupyter and LangChain maintainer) on the Jupyter blog: nb-cli: A Command-Line Interface for AI Agents and Notebook Automation. nb-cli is an experimental, Rust-based CLI to read, write, execute, and search Jupyter notebooks. The premise: agents are great at CLIs but terrible at hand-editing the nested JSON in an .ipynb, so let them operate on the notebook from the outside instead of running inside it. Works with or without a Jupyter server. No server? It reads/writes .ipynb files directly and talks to kernels over ZeroMQ. Connected to a live JupyterLab, your edits show up instantly via Y.js (the same CRDT Jupyter uses). Smart output format: instead of token-heavy JSON or ambiguous plain markdown, it uses @@cell / @@output sentinels with inline metadata. Less wasted context, unambiguous structure, and it degrades gracefully on truncation. The payoff is composability. "Add a summary section and run it" becomes one shell pipeline instead of six agent tool calls. And nb search notebook.ipynb --with-errors returns only the failing cells, so the agent skips the cells that worked. Claude Code tie-in: it ships as an agent skill. npx skills install jupyter-ai-contrib/nb-cli and your agent can drive notebooks via nb. Out of jupyter-ai-contrib, which aims to become an official Jupyter AI subproject. Still early (crates.io is at v0.0.5), so kick the tires before anything load-bearing. See also marimo-pair. Calvin #4: Hindsight Agent Memory That Learns AI agents forget everything between sessions — Hindsight gives them persistent memory that learns over time Simple three-method API: retain(), recall(), reflect() — store, retrieve, and reason over memories TEMPR retrieval runs semantic, keyword, graph, and temporal search in parallel for accurate results Automatically consolidates related facts into durable observations instead of piling up duplicates pip install hindsight-all runs the entire server in-process; integrates with LangChain, LlamaIndex, Pydantic AI, CrewAI, and more Extras Calvin: Clanker: A Word For The Machine **Ponytail — You know him. Long ponytail. Oval glasses. Has been at the company longer than the version control** **Klangk: Multi-User AI Sandboxing, Collaboration and Coding Platform** Cursor announces Origin performative-ui to quick start your new idea Michael: Astral Joins OpenAI: The Interview SpaceX to acquire Cursor And OpenAI renews Open Source support Portuguese subtitles are now available for Talk Python courses DSF is hiring including Six Feet Up support Joke: Oh Babe…

I've decided that the biggest challenge we have in AI is now keeping track of the new words being created. Words like harness, layer, mesh, vector, orchestrator, tools, surface, memory – they all mean very special things. And engineers and marketing people keep dreaming up new ones (spine? pattern? control plane? MCP? LangChain? headless? MCP? mesh? ontology?). In this podcast I do my best to explain what these words mean, and give you a non-technical understanding of how all this stuff works. If people like this I'll keep you up to date on all these new words. Additional Information (Note that all our research and podcasts are in Galileo) AI Prices Are Going Up, Up, Up – And What This Means For Enterprise AI The Reinvention of Workday: From System of Record to Platform of Agents Could Microsoft Win The War For Enterprise AI? The AI vs. Labor Economy, Why Benefits Are Being Cut, The Role of Legacy Systems The Context Layer (Semantic Layer) In Enterprise AI (And Where Business Rules Go) Jensen Huang's Taipei Speech (filled with this jargon) The Superagent for HR: Galileo Mars Release Chapters (00:00:00) - The Trouble With Words in the AI Era(00:07:28) - Three Words of the Real-World Model (RAG, M(00:10:16) - Hiring with a Neural Network(00:16:27) - What is the Microsoft SQL Server Fabric or Mesh?(00:18:07) - The issue of governance in the HCM(00:19:36) - A Little More About Machine Learning

Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect) of RocketRide join the MLOps Community to walk through AIDE — the AI Integrated Development Environment. RocketRide is an open-source AI pipeline platform that lets developers build, debug, and run production-grade agentic AI workflows directly from their IDE, with support for 13+ LLM providers, 8+ vector databases, and full multi-agent orchestration.AI Is Fast. AI Projects Are Slow. Let's Fix That. // MLOps Podcast #378 with JRocketRide's Joe Maionchi (Co-founder & COO) and Rod Christensen (Co-founder & Chief Architect)A huge shout-out to  ⁨RocketRide⁩  for this collaboration!

Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system. Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.What we cover:- Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow- Decentralized agent ownership: how any squad in the company can build a flow with central supervision- Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise- The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip- KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now- The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task- Group trip planning in WhatsApp groups: the next frontier for travel agents- Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip- Why Despegar held off on giving Sofia the ability to bargain with customers, for now. Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting.Links and Resources:- Despegar: https://www.despegar.com- Sofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-Assistant- Sofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactions- MLOps Community: https://mlops.community- Subscribe for more agent and AI infra deep divesTimestamps 00:00 - Intro: Nicolas, Sofia, and Despegar in LatAm01:30 - Chappi as the brain of Sofia and the squad model04:00 - Anyone in the company can build a flow07:00 - Why airline check-in still exists and what agents could replace09:30 - The flight-delay refund story and the chatbot gap13:00 - File-the-claim-for-me as a perfect agent use case16:00 - The dreaming phase: helping users who do not know where to go yet19:00 - In-scope conversation KPI hitting 96 percent and what comes next22:00 - Beating the traditional flight search UI with conversation25:00 - WhatsApp group trip planning and the ski trip example28:00 - Personalization at scale and the new gateway to the internet31:00 - WhatsApp as the consumer control center, like WeChat in China34:00 - Sofia as gateway: complaints, customer service, and verticalized agents37:00 - Building MCP before MCP and the custom orchestration layer40:00 - Why Sofia does not negotiate prices, yet#AIAgents #MCP #AgenticAI

Nik Seetharaman has watched the offensive side of cybersecurity adopt AI tools at a pace defenders can't match. So, he built a company to close that gap.  Before founding Wraithwatch, he was the first CIO of Anduril, a defense tech company he joined when it was 100 people and helped scale through hypergrowth. His path (Palantir to SpaceX to building AI-native cyber defense platforms for over a dozen federal agencies) gives him a view most CIOs and CISOs don't have. In this episode, Nik makes the case that AI has created a forcing function for CIOs and CISOs to finally work together, plus, shares how to run a pilot that actually moves the needle, why blocking AI tools backfires, and what the OpenClaw security drama gets right and wrong. 3 Takeaways: The CIO-CISO turf war is now a competitive disadvantage. Whoever figures out AI collaboration first wins. Treat every internal AI rollout like a startup: small cohort, parallel security review, measure the outcome in revenue. The best investment a new CIO can make isn't a security stack—it's coffee with people. Chapters: [01:46] What Wraithwatch Builds [05:25] AI Forces CIO / CISO Alignment [07:55] OpenClaw Security Tradeoffs [09:21] SecOps at Developer Speed [12:49] Rolling Out Risky Tools [14:44] Why Leaders Must Use AI [19:01] How to Reorg for the AI Era [22:30] Vendor Hunting for Grand Slams [25:49] $30M Contract and What's Next About Nik: Nik Seetharaman is the Founder and CEO of Wraithwatch, a next-generation cyber defense company building AI-native command-and-control platforms for the United States and its allies. He co-founded the company alongside fellow SpaceX alumni Grace Clemente and Carlos Moss. Before starting Wraithwatch, Nik served as the first CIO of Anduril, a defense technology company he joined when it was approximately 100 people, where he built the cybersecurity function from the ground up and saw the company through hypergrowth. His career spans cybersecurity leadership roles at Palantir, SpaceX, and Andel, giving him a rare vantage point across the offense-defense asymmetry that defines today's threat landscape.  Guest Highlights:"You can't have the attackers operating at the speed of a machine out here, and the defenders operating at the speed of a human in here." "Any business that's able to figure out this equation and execute it successfully is going to just crush their competition—because they're gonna move at warp speed and their competition will still be moving at human speed." "If I could rewind my first days at Andel, it's not to deploy more controls, it's not to learn about some new security stack or software—I would've just had more coffee with people. The human capital you build doing that pays off in spades. Because that's the thing that's rare in an age where the machines are taking over." Get Connected: Ian Faison: https://www.linkedin.com/in/ianfaison Nik Seetharaman: https://www.linkedin.com/in/nikseetharaman/ Resources: OpenClaw: https://openclaw.ai LangChain: https://www.langchain.com Cursor: https://cursor.com Our Sponsor:  Want to accelerate software development by 500%? Meet Blitzy, the only autonomous code generation platform with infinite code context, purpose-built for large, complex enterprise-scale codebases. While other AI coding tools provide snippets of code and struggle with context, Blitzy ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line-level dependency. With a complete contextual understanding of your codebase, Blitzy is ready to be deployed at the beginning of every sprint. Blitzy handles the heavy lifting, delivering over 80% of the work autonomously. The platform plans, builds, and validates premium-quality code at the speed of compute, turning months of engineering into a matter of days. It's the secret weapon for Fortune 500 companies globally. To hear how engineering leaders are transforming the way they deliver software, visit blitzy.com. Schedule a meeting with their consultants to enable an AI-Native SDLC in your organization today. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dans ce 155 ème épisode de DigitalFeeling, je vous partage ce qu'il faut savoir sur les agents IA avant de les déployer en entreprise. Les agents IA sont sur toutes les lèvres. Tout le monde s'interroge sur le sujet. Comment déployer concrètement un agent IA ? A quoi ça sert ? Quelles sont les précautions à prendre ?C'est quoi un agent IA ?Commençons par la définition parce que la confusion est fréquente.Un LLM (comme ChatGPT ou Claude), c'est un modèle qui répond à vos questions. Vous entrez votre commande et il répond. Son intervention s'arrête là.Un agent IA, c'est un système capable de percevoir son environnement, de “raisonner”, de décider et d'agir de manière autonome pour atteindre un objectif (celui que vous lui avez fixer) sans que vous ayez besoin d'intervenir à chaque étape.Imaginez que vous lui demandiez :“Analyse les avis clients de notre dernière campagne et envoie un rapport hebdomadaire au directeur marketing.”Un agent va : extraire les avis → les analyser → identifier les tendances → rédiger le rapport → l'envoyer automatiquement. Sans que vous touchiez quoi que ce soit entre chaque étape.Les 4 composantes d'un agent IA :Perception : ce qu'il reçoit en entrée (texte, données, résultats d'outils)Mémoire : à court terme (la conversation) et long terme (base de données externe)Raisonnement : le modèle qui décide quoi faireAction : les outils qu'il peut déclencher (web, code, emails, CRM…)Comment développer un agent IA ?Il n'existe pas une seule façon de créer un agent. Tout dépend de votre niveau technique, de vos besoins et de vos contraintes. Voici 5 grandes approches pour développer votre agent IA, de la plus simple à la plus complexe.1. Environnements agents clé en mainDes solutions packagées comme Claude Cowork, Claude Projects, les GPTs d'OpenAI ou Microsoft Copilot embarquent déjà la logique agentique, prêtes à l'emploi, sans aucune compétence technique requise.✅ Idéal pour : des cas d'usage rapides sans infrastructure à déployerDélai de mise en œuvre : quelques heures2. No-code / Low-code (pour les profils métier)Au-delà de Make, Zapier et n8n, Google Workspace Studio, intégré dans Gmail, Drive, Calendar et Sheets, permet de créer des workflows automatisés en langage naturel grâce à Gemini.Côté choix, une distinction importante à connaître pour la sécurité :n8n — open source, peut être auto-hébergé gratuitement sur vos propres serveurs (option la plus souveraine), désormais enrichi de 70 nœuds IA natifs connectés aux grands LLMMake — hébergé en Europe, conforme RGPD, propose une option on-premise (hébergé en local)Zapier — 100% cloud hébergé aux États-Unis, sans option self-hosted (à éviter pour les données sensibles)✅ Idéal pour automatiser des tâches marketing répétitives (lead nurturing, reporting, publication réseaux sociaux).Délai de mise en œuvre : quelques heures3. Frameworks d'orchestration (pour les profils techniques)LangChain a sorti sa version 1.0, et son successeur LangGraph est désormais le framework recommandé pour les agents en production : il gère des workflows complexes et des agents multi-étapes de manière bien plus robuste. LangChain a également lancé Deep Agents, une librairie inspirée de Claude Code pour les tâches longues et complexes.Le reste de l'écosystème reste solide : LlamaIndex pour la gestion documentaire et le RAG, CrewAI pour les équipes d'agents multi-rôles en production, AutoGen (Microsoft) pour les conversations entre agents.✅ Idéal pour des agents multi-rôles qui collaborent entre eux, ou des besoins très spécifiques.Délai de mise en œuvre : quelques jours.4. API des grands modèles (pour les développeurs)Appeler directement l'API d'Anthropic (Claude), d'OpenAI (GPT-4) ou de Google (Gemini) et construire la logique autour. Le function calling et le tool use permettent à l'agent d'appeler des outils externes de façon structurée.✅ Idéal pour : intégrer un agent dans un produit ou un SI existantDélai de mise en œuvre : 1 à 2 semaines5. Développement from scratch (pour les équipes engineering)Construire un agent en Python pur, en gérant soi-même la boucle de raisonnement, la mémoire, les outils et la gestion des erreurs. La solution la plus exigeante techniquement, mais aussi la plus souveraine.✅ Idéal pour : des besoins très spécifiques ou des contraintes fortes de souveraineté des donnéesDélai de mise en œuvre : plusieurs semainesEt la sécurité ?C'est souvent l'angle mort des déploiements d'agents IA en entreprise. Et c'est pourtant le plus critique.Le risque : ne pas savoir où partent vos données !Quand votre agent IA traite des informations : données clients, données RH, données financières, ces informations transitent quelque part. La question est : où ?Quelle que soit la solution choisie :Anonymiser ou pseudonymiser les données avant de les injecter dans un promptCloisonner les accès : l'agent ne doit accéder qu'aux données strictement nécessairesLogger et auditer toutes les interactions de l'agentFormer les collaborateurs à ne pas copier-coller des données sensibles dans des interfaces grand publicLa sécurité d'un agent IA n'est pas une question de modèle, c'est une question d'architecture et de gouvernance.Le DPA : le document sous-estiméLe DPA (Data Processing Agreement) est un contrat juridique obligatoire dès lors que vous confiez le traitement de données personnelles à un prestataire externe. C'est l'article 28 du RGPD qui l'impose.Ce qu'il doit obligatoirement contenir :La nature et la finalité du traitementLe type de données et les catégories de personnes concernéesLa durée du traitementLes obligations du sous-traitantLes mesures de sécurité mises en placeLes conditions de sous-traitance ultérieureLes modalités de suppression des données en fin de contratSans DPA, vous êtes en infraction RGPD et exposé à une amende pouvant atteindre 4% de votre chiffre d'affaires mondial.Bonne nouvelle : Anthropic, OpenAI, Microsoft Azure et Google Workspace Enterprise proposent des DPA standards téléchargeables et signables en ligne. Google allant même jusqu'à fournir un guide DPIA (Data Protection Impact Assessment) pour accompagner les entreprises dans leur analyse d'impact. Mistral AI propose quant à lui un DPA de droit français.Le DPA est le document qui prouve que votre entreprise est en conformité quand elle utilise un agent IA avec des données personnelles. Faites-le valider par votre DPO avant tout déploiement.Ce qu'il faut retenirLa technologie sécurise l'infrastructure. Le contrat sécurise le cadre juridique. Mais c'est la formation qui sécurise l'usage au quotidien.Adopter un agent IA en entreprise n'est jamais une décision purement technique. C'est une décision stratégique qui engage la direction juridique, le DPO, la DSI et qui nécessite que les équipes comprennent ce qu'elles utilisent réellement.

When you type a question into ChatGPT, the model only has what you typed to work with. But tools like Claude Code can plan, iterate, test, and recover from mistakes. They work more like we do. The difference is the agent harness: Planning tools, file system access, sub-agents, and carefully crafted system prompts that turn a raw LLM into something genuinely capable. Sydney Runkle is back on Talk Python representing LangChain and their new open source library, Deep Agents: A framework for building your own deep agents with plain Python functions, middleware hooks, and MCP support. This is how the magic works under the hood. Episode sponsors Sentry Error Monitoring, Code talkpython26 Agentic AI Course Talk Python Courses Links from the show Guest Sydney Runkle: github.com Claude Code uses: x.com Deep Research: openai.com Manus: manus.im Blog post announcement: blog.langchain.com Claudes system prompt: github.com sub agents: docs.anthropic.com the quick start: docs.langchain.com CLIs: github.com Talk Python's CLI: talkpython.fm custom tools: docs.langchain.com DeepAgents Examples: github.com Custom Middleware: docs.langchain.com Built in middleware: docs.langchain.com Improving Deep Agents with harness engineering: blog.langchain.com Prebuilt middleware: docs.langchain.com Watch this episode on YouTube: youtube.com Episode #543 deep-dive: talkpython.fm/543 Episode transcripts: talkpython.fm Theme Song: Developer Rap

AI is changing the way we all build software — that much seems clear. But the landscape is moving so fast that even the people paid to keep up are struggling. MCP or skills? Fine-tune or just prompt? LangChain or let a thousand agents loose? With almost 70 competing technologies and a shelf life of maybe six months on any advice, how do you figure out what's actually worth your time?Henry Garner is CTO of JUXT, a consultancy with about 150 senior engineers working at the coalface of AI-assisted development, including building AI platforms for tier-one banks. JUXT publishes a quarterly AI Radar — 68 technologies rated and reviewed — and Henry's been watching his own team go through the full adoption arc, from "spicy autocomplete" skepticism through to building Byzantine-fault-tolerant distributed systems over a weekend with Claude. Along the way we cover MCP vs skills, Conway's Law for LLMs, neurosymbolic AI and the unexpected return of Prolog, the "Ralph Wiggum loop" for getting agents to converge on correct implementations, and Allium — a new behavioral specification language Henry's co-authored that sits between human prose and TLA+, aiming to give LLMs just enough structure to pin down what a system should do without falling into waterfall thinking.If you're trying to make sense of the AI tooling landscape, or you've hit that wall where your agents keep drifting away from what you actually wanted, Henry's thesis — velocity through clarity of intent — might well help out yours.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinJUXT: https://www.juxt.pro/JUXT AI Radar: https://www.juxt.pro/ai-radar/Allium on GitHub: https://github.com/juxt/alliumAllium Documentation: https://juxt.github.io/allium/Composition at a Distance (Henry's blog post): https://www.juxt.pro/blog/composition-at-a-distance/A New Vocabulary for an Old Problem (Henry's blog post): https://www.juxt.pro/blog/new-vocabulary-for-an-old-problem/Model Context Protocol (MCP): https://modelcontextprotocol.io/LangChain: https://www.langchain.com/LangGraph: https://www.langchain.com/langgraphGas Town (Steve Yegge): https://github.com/steveyegge/gastownKiro (spec-driven AI IDE): https://kiro.dev/Phoenix (LLM observability): https://github.com/Arize-ai/phoenixTemporal: https://temporal.io/Taalas (LLM-on-a-chip): https://taalas.com/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

In this episode Revathy Ramalingam, Senior Software Engineer and AI Engineer at a healthcare startup, shares her inspiring personal journey from over nine years in telecom software architecture to successfully transitioning back into the industry after a seven-year career break. We explore the evolution of the AI engineer role, the practical application of RAG pipelines, and the strategic use of AI tools to rebuild a technical career.You'll learn about:- AI Career Mapping: Using LLMs to design an upskilling roadmap.- Vibe Coding: Leveraging AI tools for rapid prototyping.- RAG Implementation: Building retrieval systems with LangChain.- Interview Strategy: Proving technical skills after a career gap.- Learning in Public: Building a network through community projects.TIMECODES:00:00 Why Move to AI? Using ChatGPT to Plan a Career Pivot11:00 Learning in Public: The Power of Community Support15:35 Telecom Capstone: Predicting Network Slices with ML22:15 "Vibe Coding" & Building Prototypes with AI Dev Tools28:00 The Interview Process: Navigating a 7-Year Career Break33:45 Practical Interview Tasks: Building a PDF Q&A Assistant39:40 Career Advice: Clear Plans, AI Mentors, and Hard Work44:30 Closing Thoughts: Scaling the Learning LadderThis talk is for developers and career-changers looking for a blueprint to enter the AI engineering space. It is ideal for those interested in RAG, healthcare tech, and practical career resets.Connect with Revathy- Github - https://github.com/RevathyRamalingam- Linkedin - https://www.linkedin.com/in/revathy-ramalingam/ Connect with DataTalks.Club:- Join the community - https://datatalks.club/slack.html- Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ- Check other upcoming events - https://lu.ma/dtc-events- GitHub: https://github.com/DataTalksClub- LinkedIn - https://www.linkedin.com/company/datatalks-club/ - Twitter - https://twitter.com/DataTalksClub - Website - https://datatalks.club/

The era of the simple AI wrapper is officially dead, and the entire software infrastructure layer is being completely rebuilt. Live from the Daytona COMPUTE Conference in San Francisco, Harrison Chase, co-founder and CEO of LangChain, joins the MAD Podcast to explain why this massive shift is happening. As agents evolve from simple prompt-based systems into software that can plan, use tools, write code, manage files, and remember things over time, the real frontier is shifting from the model itself to the stack around the model. In this conversation, we go deep under the hood of this new, post-cloud architecture to deconstruct harnesses, sub-agents, context compaction, observability, memory, and the critical need for secure compute sandboxes. For anyone building in AI today, this episode cuts through the noise to reveal the new infrastructure required to make autonomous agents work in the real world.(00:00) Intro - meet Harrison Chase(01:32) What changed in agents over the last year(03:57) Why coding agents are ahead(06:26) Do models commoditize the framework layer?(08:27) Harnesses, in plain English(10:11) Why system prompts matter so much(13:11) The upside — and downside — of subagents(15:31) Why a useful agent needs a filesystem(18:13) The core primitives of modern agents(19:12) Skills: the new primitive(20:19) What context compaction actually means(23:02) How memory works in agents(25:16) One mega-agent or many specialized agents?(27:46) Has MCP won?(29:38) Why agents need sandboxes(32:35) How sandboxes help with security(33:32) How Harrison Chase started LangChain(37:24) LangChain vs LangGraph vs Deep Agents(40:17) Why observability matters more for agents(41:48) Evals, no-code, and continuous improvement(44:41) What LangChain is building next(45:29) Where the real moat in AI lives

LangChain introduces framework for observing and debugging AI agentsBitcoin's "Digital Gold" Status Challenged in Bloomberg Report, Sparking DebateInsider Selling Reaches $25 Million, Signaling Potential Market FloorInvestors Broaden Crypto Interest Amid Market Dip, Executive SaysOpenClaw Discord Bans Users Discussing Bitcoin or Cryptocurrency

פרק מספר 511 של רברס עם פלטפורמה, שהוקלט ב-18 בינואר 2026. אורי ורן מקליטים בכרכור (הגשומה והקרה) ומארחים את נמרוד וקס - CPO ו-Co-Founder של BigID - שחצה את כביש 6 בגשם זלעפות כדי לדבר על אתגרים טכנולוגיים בעולם המופלא של Data Production ו-Security.

Harrison Chase, cofounder of LangChain and pioneer of AI agent frameworks, discusses the emergence of long-horizon agents that can work autonomously for extended periods. Harrison breaks down the evolution from early scaffolding approaches to today's harness-based architectures, explaining why context engineering - not just better models - has become fundamental to agent development. He shares insights on why coding agents are leading the way, the role of file systems in agent workflows, and how building agents differs from traditional software development - from the importance of traces as the new source of truth to memory systems that enable agents to improve themselves over time. Hosted by Sonya Huang and Pat Grady

In this episode, we talk with Aaron Jorgensen about how JobHive came to life - starting as a small résumé-parsing experiment and gradually growing into a structured, AI-supported interview workflow. Aaron explains how the system handles voice capture, transcription, prompts, and AI avatars, and why he moved toward a multi-agent approach instead of relying on one model to do everything.We dig into what “fair scoring” actually means, how cross-checking evaluators and confidence levels work, and why it's important to keep the reasoning behind decisions visible to both employers and candidates.From the builder's perspective, Aaron walks through the practical side of developing the platform: shaping an MVP, working with LangChain, choosing AWS tools that reduce overhead, and dealing with the usual setbacks—broken features, unreliable external services, and the moments that test your patience. He also talks about the routines and habits that helped him stay consistent during the harder stretches.If you're interested in hiring workflows, AI tooling, or the reality of turning a rough prototype into a functioning product, this conversation covers it all.To learn more about Aaron's work, check out his websites or reach out to him on socials:JobHive: https://jobhive.aiAaron's Website: https://ajeema.com/LinkedIn: https://www.linkedin.com/in/mraaronjorgensen/Circle: https://pybites.circle.so/u/22287446___Book mentioned in ep: https://pybitesbooks.com/books/P3EFa-WuMMkC___

This week we want to introduce you to a new podcast from Intercom - Fin Meetups: Conversations with AI Leaders.Explore the promise and practice of AI, from the leaders building it. Hear conversations from founders, engineering, product and customer service leaders, as they discuss the key opportunities and challenges of the AI era.Episode 1 is our Fin x LangChain meetup in Amsterdam, where Fergal Reid, Chief AI Officer, Intercom, building Fin and Marco Perrini, Deployed Engineer, LangChain, shared how Fin evolved from hand-coded experiments to a complex, multi-model system powered by LangGraph.Make sure to follow Fin Meetups: Conversations with AI Leaders:Apple Podcasts: https://podcasts.apple.com/us/podcast/fin-meetups-conversations-with-ai-leaders/id1852609029Spotify: https://open.spotify.com/show/2Ae5VqOQ0ftClgEI8ltqOqOr wherever you listen to podcasts.If you'd like to join future Fin meetups, subscribe on Luma → https://luma.com/FINSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Gemini 3 is a few days old and the massive leap in performance and model reasoning has big implications for builders: as models begin to self-heal, builders are literally tearing out the functionality they built just months ago... ripping out the defensive coding and reshipping their agent harnesses entirely. Ravin Kumar (Google DeepMind) joins Hugo to breaks down exactly why the rapid evolution of models like Gemini 3 is changing how we build software. They detail the shift from simple tool calling to building reliable "Agent Harnesses", explore the architectural tradeoffs between deterministic workflows and high-agency systems, the nuance of preventing context rot in massive windows, and why proper evaluation infrastructure is the only way to manage the chaos of autonomous loops. They talk through: - The implications of models that can "self-heal" and fix their own code - The two cultures of agents: LLM workflows with a few tools versus when you should unleash high-agency, autonomous systems. - Inside NotebookLM: moving from prototypes to viral production features like Audio Overviews - Why Needle in a Haystack benchmarks often fail to predict real-world performance - How to build agent harnesses that turn model capabilities into product velocity - The shift from measuring latency to managing time-to-compute for reasoning tasks LINKS From Context Engineering to AI Agent Harnesses: The New Software Discipline, a podcast Hugo did with Lance Martin, LangChain (https://high-signal.delphina.ai/episode/context-engineering-to-ai-agent-harnesses-the-new-software-discipline) Context Rot: How Increasing Input Tokens Impacts LLM Performance (https://research.trychroma.com/context-rot) Effective context engineering for AI agents by Anthropic (https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) Upcoming Events on Luma (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) Watch the podcast video on YouTube (https://youtu.be/CloimQsQuJM) Join the final cohort of our Building AI Applications course starting Jan 12, 2026 (https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav): https://maven.com/hugo-stefan/building-ai-apps-ds-and-swe-from-first-principles?promoCode=vgrav

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

How do you build systems with AI? Not code-generating assistants, but production systems that use LLMs as part of their processing pipeline. When should you chain multiple agent calls together versus just making one LLM request? And how do you debug, test, and deploy these things? The industry is clearly in exploration mode—we're seeing good ideas implemented badly and expensive mistakes made at scale. But Google needs to get this right more than most companies, because AI is both their biggest opportunity and an existential threat to their search-based business model.Christina Lin from Google joins us to discuss Agent Development Kit (ADK), Google's open-source Python framework for building agentic pipelines. We dig into the fundamental question of when agent pipelines make sense versus traditional code, exploring concepts like separation of concerns for agents, tool calling versus MCP servers, Google's grounding feature for citation-backed responses, and agent memory management. Christina explains A2A (Agent-to-Agent), Google's protocol for distributed agent communication that could replace both LangChain and MCP. We also cover practical concerns like debugging agent workflows, evaluation strategies, and how to think about deploying agents to production.If you're trying to figure out when AI belongs in your processing pipeline, how to structure agent systems, or whether frameworks like ADK solve real problems versus creating new complexity, this episode breaks down Google's approach to making agentic systems practical for production use.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinGoogle Agent Development Kit Announcement: https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/ADK on GitHub: https://google.github.io/adk-docs/Google Gemini: https://ai.google.dev/gemini-apiGoogle Vertex AI: https://cloud.google.com/vertex-aiGoogle AI Studio: https://aistudio.google.com/Google Grounding with Google Search: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overviewModel Context Protocol (MCP): https://modelcontextprotocol.io/Anthropic MCP Servers: https://github.com/modelcontextprotocol/serversLangChain: https://www.langchain.com/Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

Ev Randle is a General Partner @ Benchmark, one of the best funds in venture capital. In their latest fund, they have Mercor ($10BN valuation), Sierra ($10BN valuation), Firework ($4BN valuation), Legora ($2Bn valuation) and Langchain ($1.4Bn valuation). To put this in multiples on invested capital, that is a 60x, two 30x and two 20x. Before Benchmark, Ev was a Partner @ Kleiner Perkins and before Kleiner, Ev was an investor at Founders Fund and Bond.  AGENDA: 05:25 Biggest Investing Lessons from Peter Thiel, Mary Meeker and Mamoon Hamid 14:36 OpenAI Will Be a $TRN Company & OpenAI or Anthropic: Who Wins Coding? 22:27 Why We Should Not Focus on Margin But Gross Dollar Per Customer 30:25 Why AI Labs are the Biggest Threat to AI App Companies 44:26 Do Benchmark Fire Founders? If so… Truly the Best Partner? 54:38 People, Product, Market: Rank 1-3 and Why? 57:36 Why the Mega Funds Have Just Replaced Tiger 01:04:08 GC, Lightspeed and a16z Cannot Do 5x on Their Funds…  01:14:09 Single Biggest Threat to Benchmark  

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer unpack the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, they cover the $1.7B acquisition of Security AI, LangChain's massive valuation, and the surprise $300M funding” round for Periodic Labs. They The post SED News: AMD's Big OpenAI Deal, Intel's Struggles, and Apple's AI Long Game appeared first on Software Engineering Daily.

In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of “dogfooding” - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification.The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications.We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the “sweet spot” by asking employees what they hate most about their jobs.The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers “cannot be trusted.” Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development.What was launched at Ship AI 2025:AI SDK 6.0 & Agent Architecture* Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can “define once, deploy everywhere”. How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design?* Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy?* Type Safety Across Models: AI SDK 6 promises “end-to-end type safety across models and UI”. Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral?Workflow Development Kit (WDK)* Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern?* Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)?Vercel Agent (beta)* Code Review Validation: The Agent reviews code and proposes “validated patches”. What does “validated” mean in this context? Are you running automated tests, static analysis, or something more sophisticated?* AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies?Python Support (For the first time, Vercel now supports Python backends natively.)Marketplace & Agent Ecosystem* Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model?“An Agent on Every Desk” Program* Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agentFull Video EpisodeTimestamps00:00 Introduction and Malte's Background at Google01:16 Vercel's AI Engineering Philosophy and Ship AI Recap03:19 Deep Dive: Workflows vs Agents Architecture09:33 AI SDK Success Story: Staying Low-Level and Humble16:35 Framework Design Principles and Open Source Strategy19:20 Vercel Agent: AI-Powered DevOps and Anomaly Detection27:06 Internal Agent Use Cases: Lead Qualification and Abuse Analysis29:49 Agent on Every Desk Program and Enterprise Adoption32:13 Python Support and Multi-Language Infrastructure39:42 The Future of AI-Native Security and Development Get full access to Latent.Space at www.latent.space/subscribe

In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: AI SDK 6.0 & Agent Architecture Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? Type Safety Across Models: AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? Workflow Development Kit (WDK) Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? Vercel Agent (beta) Code Review Validation: The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? Python Support (For the first time, Vercel now supports Python backends natively.) Marketplace & Agent Ecosystem Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? "An Agent on Every Desk" Program Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent

George Werbacher, Head of Security Operations at Live Oak Bank, reviews the practical realities of implementing AI agents in security operations, sharing his journey from exploring tools like Cursor and Claude Code to building custom agents in-house. He also reflects on the challenges of moving from local development to production-ready systems with proper durability and retry logic. The conversation explores how AI is changing the security analyst role from alert analysis to deeper investigation work, why SOAR platforms face significant disruption, and how MCP servers enable natural language interactions across security tools. George offers pragmatic advice on cutting through AI hype, emphasizing that agents augment rather than replace human expertise while dramatically lowering barriers to automation and query language mastery. Through technical insights and leadership perspective, George illuminates how security teams can embrace AI to improve operational efficiency and mean time to detect without inflating budgets, while maintaining the critical human judgment that effective security demands. Topics discussed: Understanding AI's role in augmenting security analysts rather than replacing them, shifting roles toward investigation and threat hunting. Building custom AI agents using Python and exploring frameworks like LangChain to solve specific SecOps use cases. Managing moving agents from local development to production, including retry logic, failbacks, and durability requirements. Implementing MCP servers to enable natural language interactions with security tools, eliminating the need to learn multiple query languages. Navigating AI hype by focusing on solving specific problems and understanding what agents can realistically accomplish. Predicting SOAR platform disruption as agents take over enrichment, orchestration, and response with simpler automation approaches. Removing platform barriers by enabling analysts to use natural language rather than mastering specific tools or query languages. Exploring context management, prompt engineering, and conversation history techniques essential for building effective agentic systems. Adopting tools like Cursor and Claude Code to empower technical security professionals without deep coding backgrounds.  Listen to more episodes:  Apple  Spotify  YouTube Website

Dell Technologies has announced Dell AI Data Platform advancements designed to help enterprises turn distributed, siloed data into faster, more reliable AI outcomes. Why it matters As enterprise AI adoption surges and data grows, organisations need a platform that can securely transform distributed, siloed data into actionable insights. The Dell AI Data Platform, a critical component of the Dell AI Factory, delivers an open, modular foundation to create value from scattered data silos. By decoupling data storage from processing, it eliminates bottlenecks and provides the flexibility needed for AI workloads like training, fine-tuning, retrieval-augmented generation (RAG) or inferencing. The platform, integrated with the NVIDIA AI Data Platform reference design, is powered by four core building blocks: Storage engines for smart data placement and seamless data movement Data engines to turn data into actionable insights Built-in cyber resiliency Data management services Together, they create a scalable, flexible foundation for customers to realise AI's full potential. Dell AI Data Platform storage engines deliver peak AI performance Dell PowerScale and Dell ObjectScale, the Dell AI Data Platform's storage engines, offer the performance, security and multi-protocol access essential for AI data. Dell PowerScale delivers NAS (network-attached storage) simplicity and parallel performance for AI workloads like training, fine-tuning, inferencing and retrieval-augmented generation (RAG) pipelines. With new integration of NVIDIA GB200 and GB300 NVL72 and ongoing software updates, Dell PowerScale delivers reliable performance, simplified management at scale and seamless compatibility with applications and solution stacks. PowerScale F710, which has achieved NVIDIA Cloud Partner (NCP) certification for high-performance storage, delivers 16k+ GPU-scale with up to 5X less rack space, 88% fewer network switches and up to 72% lower power consumption compared to competitors. Dell ObjectScale, the industry's highest-performing object platform, provides extremely performant, scalable S3-native object storage for massive AI workloads. ObjectScale is available as an appliance or through a new software-defined option on Dell PowerEdge servers that is up to 8 times faster than previous-generation all-flash object storage. New advancements improve ObjectScale's speed, scalability and efficiency. S3 over RDMA support will soon enter tech preview. It will offer up to 230% higher throughput, 80% lower latency and 98% lower CPU usage compared to traditional S3. Small object performance and efficiency improvements for large deployments deliver up to 19% higher throughput and up to 18% lower latency for 10KB objects. Deeper AWS S3 integration and bucket-level compression give developers and data scientists better tools to store, move and use large amounts of data. Dell AI Data Platform data engines power real-time AI Dell is also expanding its data engines, the specialised tools in the Dell AI Data Platform that organise, query and activate AI data. Dell's data engines are built in collaboration with trusted AI leaders like NVIDIA, Elastic and Starburst. The new Data Search Engine, developed in collaboration with Elastic, speeds decision-making by allowing customers to interact with data as naturally as asking a question. Designed for tasks like RAG, semantic search and generative AI pipelines, it integrates with MetadataIQ data discovery software to search billions of files on PowerScale and ObjectScale using granular metadata. Developers can build smarter RAG applications in tools like LangChain with the engine, ingesting only updated files to save compute time and keep vector databases current. The Data Analytics Engine, developed in collaboration with Starburst, enables seamless data querying across spreadsheets, databases, cloud warehouses and lakehouses. The new Data Analytics Engine Agentic Layer transforms raw data into business-ready products in...

welcome to wall-e's tech briefing for wednesday, october 22! explore today's key topics in tech: openai atlas launch: openai introduces the atlas web browser, leveraging chatgpt for conversational search, challenging google's dominance in online search and advertising. netflix & generative ai: ceo ted sarandos discusses generative ai as a tool for enhancing, not replacing, creative storytelling, already utilizing it for special effects. langchain's unicorn status: with a $1.25 billion valuation, langchain garners significant investment, cementing its role in aiding ai agent development through its open source framework. call for google regulation: cloudflare's ceo matthew prince advocates for tighter regulations on google's search and ai practices in the u.k., citing competitive concerns. aws outage resolution: amazon resolves a dns issue causing significant internet disruptions, underscoring the dependency on aws infrastructure by major websites and services. stay tuned for more tech insights tomorrow!

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Discover ACA Dynamic Sessions, a versatile, container-based, low-latency compute platform that allows you to execute LLM-generated code securely and with low cold-start latency. Chapters 00:00 - Introduction 00:44 - Background 02:03 - Introduction to Dynamic Sessions 04:15 - Demo: using LangChain with Dynamic Sessions 07:30 - Demo: using Dynamic Sessions with Small Language Models 10:35 - Demo: bring your own container to Dynamic Sessions 14:55 - Demo: MCP 17:26 - Wrap up Recommended resources Learn Docs Azure Product page Connect Scott Hanselman | @SHanselman Nir Mashkowski | @nirmsk | LinkedIn Azure Friday | Twitter/X: @AzureFriday Azure | Twitter/X: @Azure

Amey Desai, the Chief Technology Officer at Nexla, speaks with host Sriram Panyam about the Model Context Protocol (MCP) and its role in enabling agentic AI systems. The conversation begins with the fundamental challenge that led to MCP's creation: the proliferation of "spaghetti code" and custom integrations as developers tried to connect LLMs to various data sources and APIs. Before MCP, engineers were writing extensive scaffolding code using frameworks such as LangChain and Haystack, spending more time on integration challenges than solving actual business problems. Desai illustrates this with concrete examples, such as building GitHub analytics to track engineering team performance. Previously, this required custom code for multiple API calls, error handling, and orchestration. With MCP, these operations can be defined as simple tool calls, allowing the LLM to handle sequencing and error management in a structured, reasonable manner. The episode explores emerging patterns in MCP development, including auction bidding patterns for multi-agent coordination and orchestration strategies. Desai shares detailed examples from Nexla's work, including a PDF processing system that intelligently routes documents to appropriate tools based on content type, and a data labeling system that coordinates multiple specialized agents. The conversation also touches on Google's competing A2A (Agent-to-Agent) protocol, which Desai positions as solving horizontal agent coordination versus MCP's vertical tool integration approach. He expresses skepticism about A2A's reliability in production environments, comparing it to peer-to-peer systems where failure rates compound across distributed components. Desai concludes with practical advice for enterprises and engineers, emphasizing the importance of embracing AI experimentation while focusing on governance and security rather than getting paralyzed by concerns about hallucination. He recommends starting with simple, high-value use cases like automated deployment pipelines and gradually building expertise with MCP-based solutions. Brought to you by IEEE Computer Society and IEEE Software magazine.

Alex Merced discusses the rising standards within the Agentic AI Space. Buy Alex Merced’s latest book “Architecting an Apache Iceberg Lakehouse” use discount code mercedconf25 for a 40% discount. https://www.manning.com/books/architecting-an-apache-iceberg-lakehouse Follow Alex on Social at AlexMerced.com

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/ How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html How New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.html Content Engineering: https://x.com/RLanceMartin/status/1948441848978309358 https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharing Manus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus Cognition Post: https://cognition.ai/blog/dont-build-multi-agents Multi-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-system Human-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch - Bitter Lesson in AI Engineering - Hyung Won Chung on the Bitter Lesson in AI Research: https://www.youtube.com/watch?v=orDKvo8h71o Bitter Lesson w/ Claude Code: https://www.youtube.com/watch?v=Lue8K2jqfKk&t=1s Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/ Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraph Scaling and building things that "don't yet work": https://www.youtube.com/watch?v=p8Jx4qvDoSo - Frameworks - Roast framework at Shopify / standardization of orchestration tools: https://www.youtube.com/watch?v=0NHCyq8bBcM MCP adoption within Anthropic / standardization of protocols: https://www.youtube.com/watch?v=xlEQ6Y3WNNI How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/ RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/ Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/

Lance: https://www.linkedin.com/in/lance-martin-64a33b5/How Context Fails: https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.htmlHow New Buzzwords Get Created: https://www.dbreunig.com/2025/07/24/why-the-term-context-engineering-matters.htmlContent Engineering: https://rlancemartin.github.io/2025/06/23/context_engineering/ https://docs.google.com/presentation/d/16aaXLu40GugY-kOpqDU4e-S0hD1FmHcNyF0rRRnb1OU/edit?usp=sharingManus Post: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-ManusCognition Post: https://cognition.ai/blog/dont-build-multi-agentsMulti-Agent Researcher: https://www.anthropic.com/engineering/multi-agent-research-systemHuman-in-the-loop + Memory: https://github.com/langchain-ai/agents-from-scratch- Bitter Lesson in AI Engineering -Hyung Won Chung on the Bitter Lesson in AI Research: Bitter Lesson w/ Claude Code: Learning the Bitter Lesson in AI Engineering: https://rlancemartin.github.io/2025/07/30/bitter_lesson/Open Deep Research: https://github.com/langchain-ai/open_deep_research https://academy.langchain.com/courses/deep-research-with-langgraphScaling and building things that “don't yet work”: - Frameworks -Roast framework at Shopify / standardization of orchestration tools: MCP adoption within Anthropic / standardization of protocols: How to think about frameworks: https://blog.langchain.com/how-to-think-about-agent-frameworks/RAG benchmarking: https://rlancemartin.github.io/2025/04/03/vibe-code/Simon's talk with memory-gone-wrong: https://simonwillison.net/2025/Jun/6/six-months-in-llms/Full Video EpisodeTimestamps00:00 Introduction and Background00:53 The Rise of Context Engineering01:57 Context Engineering vs Prompt Engineering05:56 The Five Categories of Context Engineering10:02 Multi-Agent Systems and Context Isolation14:48 Classical Retrieval vs Agentic Search17:12 LLMs.txt and MCP Servers24:51 Context Pruning and Memory Management37:25 Memory Systems and Human-in-the-Loop42:55 The Bitter Lesson Applied to AI Engineering51:21 Frameworks, Abstractions, and Building for the Future Get full access to Latent.Space at www.latent.space/subscribe

Miles Grimshaw is a Partner at Thrive Capital, an investment firm that builds and invests in internet, software, and technology-enabled companies. Thrive recently closed on $5BN in new funds and also announced Thrive Holdings, a permanent capital vehicle to invest in, acquire, and operate businesses for the long term with the strategic application of technology.During his time at Thrive, Miles has led investments in companies like Airtable, Monzo, Benchling, Lattice, and more recently Cursor, a code editor built for programming with AI, which you'll hear us chat about. That team raised a $900 million round at a $9.9B valuation in June.Prior to Thrive, Miles was a General Partner at Benchmark, where he led seed investments, most notably in LangChain.We spoke about trillion dollar companies, silicon valley as an idea, business genetics, practicing scales, and Swedish House Mafia.0:00 - Intro2:14 – “The Era of Doing”6:15 – Startup Capital Intensity in the Age of AI9:14 – The Rise of Trillion Dollar Outcomes15:11 – Silicon Valley as an Idea21:04 – Physics vs Biology-Style Investing25:41 – Business Genetics and Compounding33:04 – Dying of Indigestion and Going Multi-Product35:55 – Co-Pilots, Command Centers, and Defensibility40:07 – Investing Stage Agnostically44:29 – When is VC a Good Capital Instrument?49:18 – Thrive's Core Beliefs53:57 – A Bet vs a Commitment57:49 – The Few Ideas Miles Takes Seriously59:47 – Doing a Few Big Things vs a Million Little Things1:03:54 – Practicing Scales1:06:22 – What Should More People Be Thinking About?

Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain actually behave inside an established codebase. Our guest, Matt Makai, now VP of Developer Relations at DigitalOcean, creator of Full Stack Python and Plushcap, shares hard-won tactics. We unpack what breaks, from brittle “generate a bunch of tests” requests to agents amplifying technical debt and uneven design patterns. Plus, we also discuss a sane git workflow for AI-sized diffs. You'll hear practical Claude tips, why developers write more bugs when typing less, and where open source agents are headed. Hint: The destination is humans as editors of systems, not just typists of code. Episode sponsors Posit Talk Python Courses Links from the show Matt Makai: linkedin.com Plushcap Developer Content Analytics: plushcap.com DigitalOcean Gradient AI Platform: digitalocean.com DigitalOcean YouTube Channel: youtube.com Why Generative AI Coding Tools and Agents Do Not Work for Me: blog.miguelgrinberg.com AI Changes Everything: lucumr.pocoo.org Claude Code - 47 Pro Tips in 9 Minutes: youtube.com Cursor AI Code Editor: cursor.com JetBrains Junie: jetbrains.com Claude Code by Anthropic: anthropic.com Full Stack Python: fullstackpython.com Watch this episode on YouTube: youtube.com Episode #517 deep-dive: talkpython.fm/517 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

MariaDB is a name with deep roots in the open-source database world, but in 2025 it is showing the energy and ambition of a company on the rise. Taken private in 2022 and backed by K1 Investment Management, MariaDB is doubling down on innovation while positioning itself as a strong alternative to MySQL and Oracle. At a time when many organisations are frustrated with Oracle's pricing and MySQL's cloud-first pivot, MariaDB is finding new opportunities by combining open-source freedom with enterprise-grade reliability. In this conversation, I sit down with Vikas Mathur, Chief Product Officer at MariaDB, to explore how the company is capitalising on these market shifts. Vikas shares the thinking behind MariaDB's renewed focus, explains how the platform delivers similar features to Oracle at up to 80 percent lower total cost of ownership, and details how recent innovations are opening the door to new workloads and use cases. One of the most significant developments is the launch of Vector Search in January 2023. This feature is built directly into InnoDB, eliminating the need for separate vector databases and delivering two to three times the performance of PG Vector. With hardware acceleration on both x86 and IBM Power architectures, and native connectors for leading AI frameworks such as LlamaIndex, LangChain and Spring AI, MariaDB is making it easier for developers to integrate AI capabilities without complex custom work. Vikas explains how MariaDB's pluggable storage engine architecture allows users to match the right engine to the right workload. InnoDB handles balanced transactional workloads, MyRocks is optimised for heavy writes, ColumnStore supports analytical queries, and Moroonga enables text search. With native JSON support and more than forty functions for manipulating semi-structured data, MariaDB can also remove the need for separate document databases. This flexibility underpins the company's vision of one database for infinite possibilities. The discussion also examines how MariaDB manages the balance between its open-source community and enterprise customers. Community adoption provides early feedback on new features and helps drive rapid improvement, while enterprise customers benefit from production support, advanced security, high availability and disaster recovery capabilities such as Galera-based synchronous replication and the MacScale proxy. We look ahead to how MariaDB plans to expand its managed cloud services, including DBaaS and serverless options, and how the company is working on a “RAG in a box” approach to simplify retrieval-augmented generation for DBAs. Vikas also shares his perspective on market trends, from the shift away from embedded AI and traditional machine learning features toward LLM-powered applications, to the growing number of companies moving from NoSQL back to SQL for scalability and long-term maintainability. This is a deep dive into the strategy, technology and market forces shaping MariaDB's next chapter. It will be of interest to database architects, AI engineers, and technology leaders looking for insight into how an open-source veteran is reinventing itself for the AI era while challenging the biggest names in the industry.

Episode 254: What if the future of AI in customer experience is built not by giant platforms but by small, reusable, open source AI agents? Gurdeep Pall, President of AI Strategy at Qualtrics, believes open, modular AI agents will outmaneuver big tech's locked-down systems. In this conversation from the X4 Summit, Gurdeep argues that “experience agents”—task-specific bots that can plug into any stack—will give companies more control, better performance, and real freedom. Closed AI platforms promise convenience, but they trap businesses in rigid walled gardens. Gurdeep argues that modular architectures unlock something better: flexibility, reuse, and evolution. “Break down the agents to very specific functionality,” he says. “And those agents can be invoked by many different agents for different types of tasks.” This isn't just a tech choice. It's a business and philosophical stance. Qualtrics is partnering with LangChain and releasing open connectors to build an ecosystem of interoperable agents. The goal? Let companies mix, match, and scale customer-facing systems without depending on any one vendor. “This is one semantic level up,” he says, comparing today's agentic architectures to the launch of the web and mobile eras. “What agents are going to do for user experience—taking our digital game to the next level—is very exciting.” Guest: Gurdeep Pall, President of AI Strategy, Qualtrics Host: Rob Markey, Partner, Bain & Company Give Us Feedback: https://bit.ly/CCPodcastFeedback Time-Stamped Topics: (00:01) Why Qualtrics is going all-in on open agentic AI (00:04) An overview of the Qualtrics and LangChain partnership (00:06) The modular architecture of “experience agents” (00:08) Why one task might require seven agents (00:09) How specialization allows reuse and scale (00:10) Rejecting the walled garden model (00:11) Making open systems friction-free (00:12) A real-time use case from the X4 stage (00:14) Plug and play simplicity for complex integrations (00:15) Why this is a new digital paradigm Time-Stamped Quotes: [7:00]  “It's about how you break up the task. Like, when you call the human, the human didn't sit there and not do anything and the password got reset. The human went to a piece of software and they went and worked on it. So, what we are talking about here is the combination of software and the human, now organized most efficiently.” [8:00] “ If you're able to break down the agents to very specific functionality, then those agents can be invoked by many different agents for different types of tasks.”  [10:00] “ There is one example of a very small, open system called the Internet, which somehow, through open standards, became one of the most incredible innovations of human beings ever. So what we are trying to do is to take a stand and say, 'We believe in open systems and we want to let our customers know that this is a choice.'”

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Why you should listenSanjeet Mahajan shares his journey building AgentForce agents and custom AI solutions, revealing the critical prompt engineering techniques that eliminate hallucinations and deliver real ROI.Learn the decision framework for when to use AgentForce versus building custom agents with LangChain and CrewAI, plus real case studies from hospitality and real estate showing measurable results.Discover how to create your own "Content Crafter" AI agent that generates marketing ideas in 10 minutes instead of 3-4 weeks, based on your unique business journey and client data.As a Salesforce consultant, you're watching competitors struggle with AgentForce implementation while others race ahead with custom AI agents. The hallucinations, pricing concerns, and lack of clear guidance on when to build versus buy has left many of you spinning your wheels. I see this frustration constantly - talented consultants who know their platforms inside-out but feel lost in the AI maze. In this episode, I talk with Sanjeet Mahajan from Kizzy Consulting, who's spent months in the trenches building both AgentForce and custom agents. We dive deep into the prompt engineering techniques that actually work, the decision framework for choosing platforms, and proven case studies that show real ROI. If you're tired of AI hype and want practical implementation strategies that work, this conversation will give you the roadmap you need.About Sanjeet MahajanSanjeet Mahajan is the Founder & CEO of Kizzy Consulting, a Salesforce Ridge and ISV Partner helping nonprofits, real estate, and homecare teams grow with clean data, smart automation, and human-first design. A seasoned Technical Architect with a deep curiosity for AI, Sanjeet is building intelligent systems that think, act, and adapt—so businesses don't just keep up, they leap ahead.Resources and LinksKizzyconsulting.comSanjeet's LinkedIn profileLangChainCrewAIN8NNotebookLMNapkin.aiPrevious episode: 624 - How to Turn Client Cloud Platform Pain Into Profitable Migration Projects with Jon TopperCheck out more episodes of the Paul Higgins PodcastSubscribe to our YouTube channel: @PaulHigginsMentoringFree Training for AI & Tech Consultants Ready to Stop Trading Time for MoneyJoin our newsletterSuggested resource

Sherwood Callaway, tech lead at 11X, joins us to talk about building digital workers—specifically Alice (an AI sales rep) and Julian (a voice agent)—that are shaking up sales outreach by automating complex, messy tasks.He looks back on his YC days at OpKit, where he first got his hands dirty with voice AI, and compares the wild ride of building voice vs. text agents. We get into the use of Langgraph Cloud, integrating observability tools like Langsmith and Arize, and keeping hallucinations in check with regular Evals.Sherwood and Demetrios wrap up with a look ahead: will today's sprawling AI agent stacks eventually simplify? // BioSherwood Callaway is an emerging leader in the world of AI startups and AI product development. He currently serves as the first engineering manager at 11x, a series B AI startup backed by Benchmark and Andreessen Horowitz, where he oversees technical work on "Alice", an AI sales rep that outperforms top human SDRs.Alice is an advanced agentic AI working in production and at scale. Under Sherwood's leadership, the system grew from initial prototype to handling over 1 million prospect interactions per month across 300+ customers, leveraging partnerships with OpenAI, Anthropic, and LangChain while maintaining consistent performance and reliability. Alice is now generating eight figures in ARR.Sherwood joined 11x in 2024 through the acquisition of his YC-backed startup, Opkit, where he built and commercialized one of the first-ever AI phone calling solutions for a specific industry vertical (healthcare). Prior to Opkit, he was the second infrastructure engineer at Brex, where he designed, built, and scaled the production infrastructure that supported Brex's application and engineering org through hypergrowth. He currently lives in San Francisco, CA.// Related Links~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Sherwood on LinkedIn: /sherwoodcallaway/ #aiengineering Timestamps:[00:00] AI Takes Over Health Calls[05:05] What Can Agents Really Do?[08:25] Who's in Charge—User or Agent?[11:20] Why Graphs Matter in Agents[15:03] How Complex Should Agents Be?[18:33] The Hidden Cost of Model Upgrades[21:57] Inside the LLM Agent Loop[25:08] Turning Agents into APIs[29:06] Scaling Agents Without Meltdowns[30:04] The Monorepo Tangle, Explained[34:01] Building Agents the Open Source Way[38:49] What Production-Ready Agents Look Like[41:23] AI That Fixes Code on Its Own[43:26] Tracking Agent Behavior with OpenTelemetry[46:43] Running Agents Locally with Phoenix[52:55] LangGraph Meets Arise for Agent Control[53:29] Hunting Hallucinations in Agent Traces[56:45] Off-Script Insights Worth Hearing

Scott and Wes break down how to code with and for AI; perfect for skeptics, beginners, and curious devs. They cover everything from Ghost Text and CLI agents to building your own AI-powered apps with embeddings, function calling, and multi-model workflows. Show Notes 00:00 Welcome to Syntax! 03:56 How to interface with AI. 04:07 IDE Ghost Text. 05:45 IDE Chat, Agents. 08:00 CLI Agents. Claude Code. Open Code. Gemini. 11:13 MCP Servers. Context7 14:47 GUI apps. v0. Bolt.new. Lovable. Windsurf. 19:07 Existing Chat app like ChatGPT. 22:37 Building things WITH AI. 23:32 Prompting. 26:53 Streaming VS not streaming. 28:14 Embeddings and Rag. 31:09 MCP Server. CJ's MCP Deep Dive. 32:36 Brought to you by Sentry.io. 33:25 Multi-model, multi-provider. 36:27 npm libs to use to code with AI. OpenAI SDK. AI SDK. Cloudflare Agents. Langchain. Local AI Tensorflow. Transformers.js. Huggingface. 44:12 Processes and exploring. Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Is building the backend for your AI application slowing you down? In this episode of the MongoDB Podcast, host Jesse Hall sits down with Srikar and Jimmy, the creators of Daemo AI, a revolutionary tool designed to eliminate the tedious "plumbing" of backend development.Discover how Daemo AI is building upon deprecated MongoDB features like Realm App Services, creating a more powerful and flexible solution for developers. We dive deep into their tech stack, including Next.js, Deno, and Express , and explore why they chose MongoDB for its speed and flexibility in AI applications. Plus, you'll see a live demo of Daemo's new SDK and CLI , learn how it can generate data migrations and dummy data on the fly , and get a real answer to the big question: Is AI going to take your job? In This Episode, You Will Learn: What Daemo AI is and how it accelerates development. * How to build AI agents and integrate them with frameworks like LangChain. Why MongoDB is the ideal database for rapid-growth startups and AI. The future of developer jobs in the age of AI.

Mark Ericksen, creator of the Elixir LangChain framework, joins the Elixir Wizards to talk about LLM integration in Elixir apps. He explains how LangChain abstracts away the quirks of different AI providers (OpenAI, Anthropic's Claude, Google's Gemini) so you can work with any LLM in one more consistent API. We dig into core features like conversation chaining, tool execution, automatic retries, and production-grade fallback strategies. Mark shares his experiences maintaining LangChain in a fast-moving AI world: how it shields developers from API drift, manages token budgets, and handles rate limits and outages. He also reveals testing tactics for non-deterministic AI outputs, configuration tips for custom authentication, and the highlights of the new v0.4 release, including “content parts” support for thinking-style models. Key topics discussed in this episode: • Abstracting LLM APIs behind a unified Elixir interface • Building and managing conversation chains across multiple models • Exposing application functionality to LLMs through tool integrations • Automatic retries and fallback chains for production resilience • Supporting a variety of LLM providers • Tracking and optimizing token usage for cost control • Configuring API keys, authentication, and provider-specific settings • Handling rate limits and service outages with degradation • Processing multimodal inputs (text, images) in Langchain workflows • Extracting structured data from unstructured LLM responses • Leveraging “content parts” in v0.4 for advanced thinking-model support • Debugging LLM interactions using verbose logging and telemetry • Kickstarting experiments in LiveBook notebooks and demos • Comparing Elixir LangChain to the original Python implementation • Crafting human-in-the-loop workflows for interactive AI features • Integrating Langchain with the Ash framework for chat-driven interfaces • Contributing to open-source LLM adapters and staying ahead of API changes • Building fallback chains (e.g., OpenAI → Azure) for seamless continuity • Embedding business logic decisions directly into AI-powered tools • Summarization techniques for token efficiency in ongoing conversations • Batch processing tactics to leverage lower-cost API rate tiers • Real-world lessons on maintaining uptime amid LLM service disruptions Links mentioned: https://rubyonrails.org/ https://fly.io/ https://zionnationalpark.com/ https://podcast.thinkingelixir.com/ https://github.com/brainlid/langchain https://openai.com/ https://claude.ai/ https://gemini.google.com/ https://www.anthropic.com/ Vertex AI Studio https://cloud.google.com/generative-ai-studio https://www.perplexity.ai/ https://azure.microsoft.com/ https://hexdocs.pm/ecto/Ecto.html https://oban.pro/ Chris McCord's ElixirConf EU 2025 Talk https://www.youtube.com/watch?v=ojL_VHc4gLk Getting started: https://hexdocs.pm/langchain/gettingstarted.html https://ash-hq.org/ https://hex.pm/packages/langchain https://hexdocs.pm/igniter/readme.html https://www.youtube.com/watch?v=WM9iQlQSFg @brainlid on Twitter and BlueSky Special Guest: Mark Ericksen.

If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle: linkedin.com LangGraph: github.com LangChain: langchain.com LangGraph Studio: github.com LangGraph (Web): langchain.com LangGraph Tutorials Introduction: langchain-ai.github.io How to Think About Agent Frameworks: blog.langchain.dev Human in the Loop Concept: langchain-ai.github.io GPT-4 Prompting Guide: cookbook.openai.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**In this episode of MongoDB Podcast Live, host Shane McAllister is joined by Sachin Hejip from Dataworkz. Sachin will showcase “Dataworkz Agent Builder” which is built with MongoDB Atlas Vector Search, and demonstrate how it can use Natural Language to create Agents and in turn, automate and simplify the creation of Agentic RAG applications. Sachin will demo the MongoDB Leafy Portal Chatbot Agent, which combines operational data with unstructured data for personalised customer experience and support, built using Dataworkz and MongoDB.Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #Dataworkz #Observability #Developer #BigData #RAG

**(Note: Spotify listeners can also watch the screen sharing video accompanying the audio. Other podcast platforms offer the audio-only version.)**Struggling with millions of unstructured documents, legacy records, or scattered data formats? Discover how AI, Large Language Models (LLMs), and MongoDB are revolutionizing data management in this episode of the MongoDB Podcast.Join host Shane McAllister and the team as they delve into tackling complex data challenges using cutting-edge technology. Learn how MongoDB Atlas Vector Search enables powerful semantic search and Retrieval Augmented Generation (RAG) applications, transforming chaotic information into valuable insights. Explore integrations with popular frameworks like Langchain and Llama Index.Find out how to efficiently process and make sense of your unstructured data, potentially saving significant costs and unlocking new possibilities.Ready to dive deeper?#MongoDB #AI #LLM #LargeLanguageModels #VectorSearch #AtlasVectorSearch #UnstructuredData #Podcast #DataManagement #RAG #SemanticSearch #Langchain #LlamaIndex #Developer #BigData

In this episode: Jason sits down with Harrison Chase, CEO of LangChain, to explore how AI-powered agents are transforming the way startups operate. They discuss the shift from traditional entry-level roles to AI-driven automation, the importance of human-in-the-loop systems, and the future of AI-powered assistants in business. Harrison shares insights on how companies like Replit, Klarna, and GitLab are leveraging AI agents to streamline operations, plus a look ahead at what's next for AI-driven workflows. Brought to you in partnership with Google Cloud.*Timestamps:(0:00) Introduction to Startup Basics series & Importance of AI in startups(2:04) Partnership with Google Cloud & Introducing Harrison Chase from Langchain(4:38) Evolution of entry-level jobs & Examples of AI agents in startups(8:00) Challenges & Future of AI agents in startups(14:24) AI agents in collaborative spaces & Non-developers creating AI agents(18:40) Closing remarks and where to learn more*Uncover more valuable insights from AI leaders in Google Cloud's 'Future of AI: Perspectives for Startups' report. Discover what 23 AI industry leaders think about the future of AI—and how it impacts your business. Read their perspectives here: https://goo.gle/futureofai*Check out all of the Startup Basics episodes here: https://thisweekinstartups.com/basicsCheck out Google Cloud: https://cloud.google.com/Check out LangChain: https://www.langchain.com/*Follow Harrison:LinkedIn: https://www.linkedin.com/in/harrison-chase-961287118/X: https://x.com/hwchase17*Follow Jason:X: https://twitter.com/JasonLinkedIn: https://www.linkedin.com/in/jasoncalacanis*Follow TWiST:Twitter: https://twitter.com/TWiStartupsYouTube: https://www.youtube.com/thisweekinInstagram: https://www.instagram.com/thisweekinstartupsTikTok: https://www.tiktok.com/@thisweekinstartupsSubstack: https://twistartups.substack.com

LangChain is a popular open-source framework to build applications that integrate LLMs with external data sources like APIs, databases, or custom knowledge bases. It's commonly used for chatbots, question-answering systems, and workflow automation. Its flexibility and extensibility have made it something of a standard for creating sophisticated AI-driven software. Erick Friis is a Founding Engineer The post LangChain and Agentic AI Engineering with Erick Friis appeared first on Software Engineering Daily.

Episode 653: Shaan Puri ( https://x.com/ShaanVP ) talks to Furqan Rydhan ( https://x.com/FurqanR ) about the biggest opportunities in AI right now.  — Show Notes:  (0:00) Intro (4:42) Define the Job-to-be-done (8:20) How to build an AI Agent workflow (11:16) AI Tools break down (27:05) How Polymarket won (31:48) Why VR is a sleeping giant? (44:43) Be a lifelong player in tech (58:52) The unbeatable combination (1:02:27) Adam Foroughi's A+ execution (1:18:35) Betting on -1 to 0 — Links: • Furqan's site - https://furqan.sh/  • Founders, Inc - https://f.inc/  • Applovin - https://www.applovin.com/  • Claude - https://claude.ai/  • OpenAI - https://platform.openai.com/  • Langchain - https://www.langchain.com/  • AutoGen - https://autogenai.com/  • Crew - https://www.crewai.com/  • CloudSDK - https://cloud.google.com/sdk/  • Perplexity - https://www.perplexity.ai/  • “Attention is all you need” - https://typeset.io/papers/attention-is-all-you-need-1hodz0wcqb  • Anthropic - https://www.anthropic.com/  • Third Web - https://thirdweb.com/  • Luna's AI Brain - https://terminal.virtuals.io/  • Oasis - https://oasis.decart.ai/welcome  • Polymarket - https://polymarket.com/  • Gorilla Tag - https://www.gorillatagvr.com/  • Yeeps - https://tinyurl.com/59z2yrdu  — Check Out Shaan's Stuff: Need to hire? You should use the same service Shaan uses to hire developers, designers, & Virtual Assistants → it's called Shepherd (tell ‘em Shaan sent you): https://bit.ly/SupportShepherd — Check Out Sam's Stuff: • Hampton - https://www.joinhampton.com/ • Ideation Bootcamp - https://www.ideationbootcamp.co/ • Copy That - https://copythat.com • Hampton Wealth Survey - https://joinhampton.com/wealth • Sam's List - http://samslist.co/ My First Million is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Arie Desormeaux // Editing by Ezra Bakker Trupiano