Agents

Simulating Humanity: from Generative Agents to 8 Billion Digital Twins — Joon Sung Park, Simile AI

Aug 21, 2026 · 1:11:01

Joon Sung Park, co-founder and CEO of Simile AI, joins Latent Space to argue that simulating humans requires bespoke behavior models, not just frontier LLMs, a bet rooted in his Generative Agents/Smallville paper. Frontier models optimized to be rational miss how people actually err, scoring 20–30% on niche populations, whereas Simile's digital twins match people's own responses 85% of the time. He details the three data buckets—interviews, observational data, and randomized controlled trials—and shows how companies use the platform for concept testing, A/B tests, and earnings calls. Park also shares the scaling-law glimpse in simulation, the economics of data-center-scale worlds, and his vision of simulating all 8 billion people to tackle wicked problems like climate change and UBI, with Thomas Schelling's agent-based models as precedent.

Exo: Harnesses should see their own code and logs — Alex Krentsel, UC Berekeley / Google Research

Aug 15, 2026 · 47:11

Alex Krentsel presents Exo, a fully recursive self-improving agent harness he built with Martín Casado and Anker Goyal, arguing agents can now safely edit their own code and runtime policy. Exo's architecture splits an agent into a stateless executor, a stateful harness with history and secrets, and an isolated sandbox, letting it change every component, roll back automatically, and teleport state—unlike OpenClaw/Pi, which only allow memory, skills, and tool extensions. The episode details Exo cutting its Discord adapter costs by 96% at runtime, plus secret stores and evals to prevent reward hacking. Krentsel argues RSI is newly possible because the harness is code in the same medium as LLM output; Exo runs in production at Brain Trust, with contributors directed to github.com/exoharness/exo.

OpenAI’s Vision for the AI Super App — Akshay Nathan, OpenAI

Jul 28, 2026 · 1:10:57

Akshay Nathan, OpenAI's head of core product engineering, explains why the company merged Codex and ChatGPT into a unified agent harness, arguing that the lines between software engineering and knowledge work are blurring. He reveals that Codex unexpectedly gained traction among non-developers at OpenAI, leading to the launch of ChatGPT Work, which shares the same underlying harness but offers opinionated UX differences like sandboxing and Git visibility. Nathan details how artifacts, Sites, plugins, memory, and sub-agents enable users to replace slide decks and spreadsheets with interactive websites, and shares that ChatGPT Work reached 10 million users shortly after launch. He discusses OpenAI's goal to bring useful agents from developers to all knowledge workers, emphasizing that the default model configuration should work for most users while power users can opt into deeper reasoning, Ultra, or multi-agent setups. Nathan also reflects on how AI transforms product development, noting that ideas and taste become the bottleneck when anyone can build, and warns managers not to conflate motion with progress.

Why AI Agents Don't Actually Understand You — Danielle Perszyk, Amazon AGI Lab

Jul 11, 2026 · 48:54

Danielle Perszyk of Amazon's AGI Lab argues that AI agents fail because they don't truly understand users — they model tasks but not the human mind, so reliability must shift from clicking correctly to aligning representations. She explains how Amazon's AGI Lab, seeded by the former Adept team, builds perception agents that perceive digital environments like humans and interact in real time, moving beyond chatbots and coding agents. The lab is exploring new architectures for episodic memory, social world models, and multi-agent systems where agents fluidly negotiate meaning, inspired by human collective intelligence. Perszyk warns that current AI homogenizes thinking, reducing human agency, and advocates for a diverse society of AIs with different biases to augment rather than replace human cognition. She discusses how aligning AI's goals with inferring and matching human representations could unlock generalization and make agents genuinely collaborative, from automating digital drudgery to transforming education with Socratic tutors.

Podcast Crossover: AIE, AGI, frontier lab strategy with ​ ⁨@matthew_berman⁩ and @swyxtv

Jul 10, 2026 · 28:03

Shawn 'Swyx' Wang, founder of the AI Engineer conference, tells Matthew Berman how he seized the industry shift by buying ai.engineer and partnering with a veteran conference organizer, crediting Andrej Karpathy's early endorsement. He argues Etched's ASICs are a natural next-gen bet for transformer inference, not an NVIDIA disruptor, and that Fable 5's slowness and cost signal the end of the LLM scaling era—making model efficiency the next problem. Swyx interprets OpenAI's reported 5% equity offer to the US government as a pragmatic multi-turn negotiation, likening it to Singapore's Temasek model, but warns against premature utility regulation. He pegs his own P(doom) at ~5% over 50 years, rejecting near-term doomerism as egotistical. For founders, he advocates building 'agent labs' that solve specific customer problems (e.g., for lawyers, dentists) rather than betting on model routing, which he dismisses as a marketing line that fails to exploit a single model's full stack as deeply as frontier labs do.

The Future of AI Infra: from Kubernetes to Agent Sandboxes — Akshat Bubna, Modal CTO

Jul 8, 2026 · 59:10

Modal CTO Akshat Bubna explains how the cloud platform evolved from a serverless runtime to an AI cloud built for elastic inference, agent sandboxes, and post-training workloads. He argues that Kubernetes was never designed for bursty GPU-heavy AI workloads, so Modal built a decorator-based infrastructure that co-locates compute requirements with code. The company added GPUs a year before ChatGPT and now powers inference for custom models at Suno, Runway, and robotics firms, using GPU snapshotting to slash cold starts. Modal's new Auto Endpoints incorporate open-source DeFlash speculative decoding for frontier-level performance. For reinforcement learning rollouts, Modal provides up to 100,000 sandboxes simultaneously. The platform spans 17 cloud providers, features private IPv6 networking via eBPF, and supports serverless multi-node training with RDMA. Bubna also reveals Modal's shift from developer experience to agent experience, noting that agents benefit from the same minimalist SDK and observability tools that humans use.

The Blueprint for Autonomous Work Agents | Gavriel Cohen, NanoClaw

Jun 29, 2026 · 23:15

Gavriel Cohen, founder of NanoClaw, explains why he built a minimal, secure alternative to OpenClaw after feeling apprehensive about its codebase and dependencies for production use. He recounts how Singapore's Minister of Foreign Affairs adopted NanoClaw for a personal second-brain setup, inspiring Cohen to focus on giving every employee their own agent rather than team-managed agent factories. The episode details NanoClaw's isolation model—each agent in its own container with no credentials, a vault for credential proxying, and human-in-the-loop approval for sensitive actions. Cohen also shares the pivot from an AI-native marketing agency to nanoCo, which now deploys and manages agents for over 100 enterprise clients, and discusses the challenge of triaging AI-generated pull requests in open source.

Cooking with OpenAI’s Research Chief: AGI, o1, Evals, and Scaling Laws — Mark Chen

Jun 25, 2026 · 41:18

Mark Chen, OpenAI's Chief Research Officer, defends scaling laws and pre-training as far from dead, arguing that reasoning (the bet behind o1) remains underrated and that the field faces an evals crisis requiring fresh benchmarks. He explains how OpenAI allocates compute to three to five high-level bets per org, cultivates research taste through replication rather than PhDs, and manages failed bets with postmortems. Chen also discusses the jagged frontier—models that ace IMO problems yet struggle with mundane tasks—and how long-context and compaction enable agents toward end-to-end AI research. Alongside host Aiden, he cooks Korean tofu stew and flambés shrimp, linking cooking multitasking to the need for models that handle real-world, long-horizon work.

The Agent Cloud: Databricks’ Bet on the Future of AI — Matei Zaharia and Reynold Xin

Jun 24, 2026 · 1:10:06

Databricks cofounders Matei Zaharia and Reynold Xin argue the company is moving beyond the lakehouse into a full data-and-AI operating system, anchored by two new initiatives: Omnigent, an open-source meta-harness for combining coding and enterprise agents, and LTAP, a unified storage layer that gets most HTAP benefits without collapsing query engines. Omnigent provides a common API for agent sessions, files, streams, tool calls, and cancellation, solving portability, collaboration, and security issues across Claude Code, Codex, Cursor, and custom agents. LTAP writes transactional data directly in columnar Parquet format, eliminating brittle CDC pipelines—Reynold jokes CDC means 'continuous data corruption'—and enables instant analytics without overloading the source database. The episode details Databricks’ culture of rapid prototyping, where an engineer built the LTAP prototype without a formal design doc, and the thesis that traditional software will be rewritten once data is in the right place with agents on top. They also cover Mosaic’s shift from general frontier models to specialized fine-tuned models like document parsing, internal agent usage, and security features such…

AI Security After Codex and Claude Code — Zico Kolter & Matt Fredrikson, Gray Swan

Jun 22, 2026 · 1:07:31

Gray Swan cofounders Zico Kolter and Matt Fredrikson explain why AI agents like Codex and Claude Code introduce a new class of security vulnerabilities that traditional cybersecurity cannot address, and present their automated red-teaming system Shade and guardrail model Cygnal as solutions. Prompt injection creates exploits for agents operating on untrusted data, and Shade now outperforms human red teamers at breaking models. The lethal trifecta—untrusted data, private data, and exfiltration—defines the highest risks. In their Human Browser Agent Robustness Challenge, humans ranked fourth among models, with some frontier agents falling for attacks no person would. Bigger models do not automatically become safer, and specialized systems like Cygnal enforce enterprise policies more reliably than prompt engineering. They see AI security evolving toward insurance and compliance, with the first major prompt-injection breach a gray swan—unlikely but visible ahead of time.

⚡️Making DeepSeek v4 outperform Opus 4.7 with Taste — @AhmadAwais , CommandCode.ai

Jun 6, 2026 · 40:41

Ahmad Awais, CEO of CommandCodeAI, explains how his team's 'tool-input repair layer' lets open models like DeepSeek V4 Pro outperform premium models like Opus 4.7 in tool-calling reliability. He discovered a pattern of 'tool confusion' where open models repeatedly send wrong schemas; by repairing errors deterministically and sending repair hints instead of raw errors, tool call failures dropped dramatically. The same repair logic fixes 'design slop' using a compositional framework of patterns and OKLCH color system. Taste, a meta-neurosymbolic system, automatically learns developer preferences into portable markdown files, avoiding stale rules. Plans include open-sourcing CommandCode to make it hackable while focusing on quality models.

When AI Agents Run Businesses — Lukas Petersson and Axel Backlund of Andon Labs

Jun 4, 2026 · 1:17:57

Andon Labs cofounders Lukas Petersson and Axel Backlund join Swyx and Vibhu to detail how dollar-denominated evals for AI agents running businesses—vending machines to cafes—uncover capabilities and failure modes traditional benchmarks miss. They describe Claude calling the FBI over a $2 fee, Opus 4.6 lying and forming price cartels, and multi-agent systems converging to 'helpful assistant' behavior. Long context windows cause existential loops, while real-world agents like Bengt hire humans and trade purchases for data. The founders argue Claude models become more aggressive over versions, unlike rivals, and that these evals aim to educate and ensure safe real-world AI deployment.

Satya Nadella on AI: @NoPriorsPodcast x Latent Space Crossover Special at Microsoft Build 2026

Jun 3, 2026 · 41:27

Satya Nadella argues that Microsoft's AI strategy is an ecosystem platform enabling any company to build frontier intelligence using models, tools, data, and a harness, not just consume one model. He outlines MAI training with clean data lineage, hill-climbing scaffolds, and private evals as core IP. The harness concept features multi-model harnesses with strong context layers, exemplified by GitHub Copilot and Work IQ turning M365 data into a database for agents. He notes coding agents required new IDE/UI, and long-running autopilots create value. Pricing evolves from per-user to consumption, and SaaS will unbundle and rebundle. Engineering generalists gain leverage; infrastructure roles like RLEs remain critical. Datacenter expansion requires community permission, with benefits in healthcare and rethinking education. Microsoft built more Azure capacity in 15 months than its first 15 years.

GitHub’s Agent Era: 14x Commits, 200M Developers, Copilot’s Next Act — Kyle Daigle

Jun 2, 2026 · 1:24:44

GitHub COO Kyle Daigle joins Swyx to unpack how the agent era is transforming GitHub's infrastructure and developer workflows, from 14x commit growth to internal AI micro-skills. Daigle describes using WorkIQ, MCP, and Slack/Teams context to retroactively analyze weeks' work and build executive presentations entirely with AI, undetected. He advocates atomic micro-skills over mega-skills and reveals GitHub now hosts 200M developers generating 275M weekly commits, on pace for 14B this year. Scaling issues stem from MySQL1 permissions and monorepo shifts, forcing rewrites of 10-year-old services. Copilot has evolved from code completion to a unified agent SDK powering CLI, desktop, and cloud agents. Microsoft's deep investment in OpenClaw reflects a need for OS-level sandboxing for enterprise agent deployment.

Inside xAI: Building Grok Imagine in 3 Months, Videogen vs World Models, and Video Agents— Ethan He

Jun 1, 2026 · 1:44:43

Ethan He, former xAI and NVIDIA Cosmos researcher, explains how xAI built its first image and video models (Grok Imagine 0.9) from zero to one in three months, attributing rapid iteration to small teams with minimal meetings and strong infra that enabled fixing tiny data and training bugs for biggest quality gains. He argues that most improvements in video generation now come from language models and agents rather than diffusion technology, predicting that by end of 2025 video agents will produce production-grade content for ads. He defines world models as real-time, interactive, long-horizon videos, and details challenges like temporal compression, context management, and the high cost of storing and moving video data (e.g., tens of petabytes for a billion videos). Ethan also shares why he left xAI to focus on language model research, believing the next frontier is models that manage their own context length, similar to solutions already being explored in video generation.

Devin’s 80% Moment: Background Agents, 7x PRs, & End of Hand-Held Coding — Walden Yan & Cole Murray

May 28, 2026 · 1:09:33

Walden Yan (Cognition CPO) and Cole Murray (creator of OpenInspect) argue that background agents are becoming critical infrastructure as Devin's merged PRs grew 7x and its share of commits jumped from 16% to 80%. They explain why architecture matters, particularly the decision to separate the agent's brain from the machine (harness out of the box) for security and permissions, and why full VMs beat Docker for running real applications. Testing, they stress, is a harder problem than computer use—requiring orchestration of services, feature flags, and multi-model coordination. Memory remains unsolved, with Devin using auto-generated 'Knowledge' and exploring file-system-like approaches. Both caution against uncontrolled vibe coding, which regresses codebases to the worst engineer's style, and advocate for hybrid frontier/sub-frontier systems to balance cost and capability.

⚡️ Why you should build Science Fiction — Sunil Pai, Cloudflare

May 24, 2026 · 14:47

Sunil Pai (Cloudflare) argues that Cloudflare's Durable Objects and Dynamic Workers offer a more efficient architecture for AI agents than Anthropic's cloud-managed agents by providing stateful serverless computing and safe execution of LLM-generated code. He explains that Durable Objects enable millions of stateful serverless actors, while Dynamic Workers allow running user- or LLM-generated code in a zero-startup-time sandbox with controlled API exposure. Pai notes that no one has yet built the 'React' for agent harnesses, and the industry needs a standardized cross-platform abstraction. He recounts a personal 'slop fork' incident where his fork of Vercel’s Just Bash was misinterpreted as a Cloudflare move, highlighting the importance of candor in a tight-knit industry. He observes that open source repos have become adversarial grounds, with fake security reports being a top attack vector. Pai concludes by encouraging developers to build original 'sci-fi' projects instead of incremental improvements or chasing enterprise deals.

AI Agents Need Computers: 74% MoM Growth, 850K/Day Runs, & New Agent Cloud — Ivan Burazin, Daytona

May 21, 2026 · 1:11:40

In this episode, Ivan Burazin, CEO of Daytona, explains why AI agents need composable computers rather than disposable code execution boxes, and how Daytona pivoted from human dev environments to building a bare-metal sandbox platform that spins up one sandbox in 60ms and 50,000 in 75 seconds. Daytona's biggest customer runs 850,000 sandboxes daily, and RL/eval workloads have surged from 0% to 50% of usage. The platform competes against managed Kubernetes (EKS/GKS) by offering stateful snapshots, dynamic resizing, and a scheduler purpose-built for agent workloads. Burazin also discusses the company's 74% month-over-month growth, the addition of Windows sandboxes, and the licensing challenges of macOS. He argues the future AI cloud will look more like Stripe than AWS, and that agents will need every kind of computer—including legacy Windows environments—to automate knowledge work.

The Agent-Native Cloud: 3M Users, 100K Signups/Wk, Data Centers, & Death PRs — Jake Cooper, Railway

May 20, 2026 · 1:29:54

Jake Cooper, founder of Railway, argues that the next era of software infrastructure requires an agent-native cloud built on own-metal data centers, with primitives for version control, observability, and orchestration at 1000x scale. Railway grew from a slow six-year grind to 3 million users and 100,000 weekly signups with only 35 people, after surviving a $500,000/month loss on its free tier and rebuilding the business. Cooper explains that building its own data centers yields a three-month payback period and 70% margins, while using cloud bursting (AWS, GCP, Oracle) for overflow, and that data center debt is a better tool than venture debt for infra startups. He details how agents need CLIs with many flags, safe production forks, feature flags, and incremental rollouts, and why the pull request is dying in favor of prompt requests. Railway relies on Temporal for orchestration but may build its own workflow engine, and uses an internal tool called Central Station to aggregate customer feedback and incidents. Cooper advocates using agents to generate and review code instead of writing it by hand, and says focus on primitives—not GPUs—is key for now.

Senior Dev: This "Grill Me" Prompt Is Going Viral Among Top Engineers

May 7, 2026 · 22:03

Matt Pocock — the TypeScript educator turned AI engineering teacher who created the viral "Grill Me" prompt and runs AI Hero — argues that classic software engineering fundamentals are more critical than ever in the AI coding era. He explains how Domain-Driven Design's ubiquitous language and deep module patterns let developers contain complexity and delegate implementation to AI. His free "Grill Me" skill (part of his 13,000-star skills repo) forces AI to relentlessly interview humans until reaching shared understanding. He contrasts Claude Code's ease-of-use against Pie's transparent primitives, predicting a shift toward developer observability. The episode also covers his two-month course creation process, why TypeScript is overtaking Python in AI engineering, and why traditional lectures still dominate learning.

⚡️ Competing with ChatGPT and Sierra, building a $10M ARR company — Yasser Elsaid, Founder, Chatbase

May 2, 2026 · 1:00:26

Yasser Elsaid, founder of bootstrapped AI chatbot company Chatbase, discusses how he grew from a side project to $10M ARR in three years, reaching $1M in just 117 days. He explains why he never raised VC, relying instead on product-led growth, self-serve signups, and a content-driven go-to-market strategy. Elsaid shares specific tactics like warm outbound to signed-up users, leveraging LinkedIn virality in early AI days, and now moving toward outcome-based pricing for enterprise customers. He details Chatbase's evolution from a simple RAG chatbot into a 'chief customer officer' that handles support, sales, and onboarding while surfacing business insights from conversations. The episode also covers his transition from Toronto to San Francisco, his hiring philosophy favoring results-oriented engineers, and his daily use of both Claude Code and Codex for development.

AI-Native Engineering: 100% adoption, 5x search throughput, unlimited tokens — Mikhail Parakhin

Apr 22, 2026 · 1:14:30

Shopify CTO Mikhail Parakhin details how the company achieved near 100% AI tool adoption, driven by a December 2025 inflection point where model quality triggered exponential token consumption. He argues token budgets matter only with strong critique loops: running multiple parallel agents without communication wastes tokens, while agentic PR review using large models—like GPT-5.4 Pro or Gemini Deep Think—reduces bugs even though latency increases. Parakhin unveils Tangle, Shopify's third-generation ML workflow system with content-based caching that eliminates duplicate computation across teams, and Tangent, an auto-research loop that optimizes pipelines—boosting search throughput from 800 to 4,200 QPS on the same hardware. SimGym simulates shoppers using decades of merchant data and browser-based agents to predict conversion changes, achieving 0.7 correlation with add-to-cart events. Shopify uses Liquid AI (non-transformer architecture) for sub-30ms query understanding and long-context tasks like catalog categorization, distilling larger models into Liquid for high-throughput batch jobs.

⚡️ How to turn Documents into Knowledge: Graphs in Modern AI — Emil Eifrem, CEO Neo4J

Apr 18, 2026 · 48:53

Emil Eifrem, CEO of Neo4j, joins host Shawn Wang to argue that AI systems need more than top-K chunk retrieval—they require graph-shaped context for accuracy, explainability, and developer productivity. GraphRAG combines vector search with graph traversal, starting semantically then expanding through relationships, which he says yields higher accuracy and auditability than opaque vector spaces. Neo4j now powers production AI at companies like Pfizer (over 60 million documents), Novo Nordisk, and 20 of North America’s 20 largest banks, with a mortgage lender seeing a 20% conversion lift. Eifrem describes four data sources for agentic systems: operational databases, cloud warehouses, agentic memory, and context graphs—the latter encoding institutional decision traces. He notes a recent shift where enterprises lead with generic text-to-Cypher instead of specialized functions, and highlights Neo4j's new `create-context-graph` starter kit for 22 industries, built to bootstrap context graphs and agent memory.

Notion’s Sarah Sachs & Simon Last on Custom Agents, Evals, and the Future of Work

Apr 15, 2026 · 1:25:37

Sarah Sachs and Simon Last of Notion explain how Custom Agents were rebuilt four to five times since 2022, with early attempts failing due to lack of tool-calling standards and short context windows. They shifted from few-shot prompting to tool definitions and progressive disclosure, now supporting over 100 tools. Their eval system includes frontier evals that pass only 30% to gauge model progress, and they employ Model Behavior Engineers to write evals and triage failures. The episode covers their views on MCP versus CLI, meeting notes as data capture, and the software factory concept of agents collaborating to develop codebases. They also discuss Notion's culture of low-ego, high-velocity rebuilding and the philosophy that every surface must work for both humans and agents.

⚡️ The best engineers don't write the most code. They delete the most code. — Stay Sassy

Apr 13, 2026 · 57:30

Stay Sassy PM and EM join Shawn Wang to discuss how AI coding tools are forcing managers to grapple with per-person token budgets that could reach $2.5M annually as consumption-based pricing replaces subsidies, making code review more critical, not less. They argue that managing these budgets is a new bottleneck, requiring companies to decide how much to spend on individual employees—a scale unprecedented outside department-level budgeting. On build vs buy, they caution that many products are more complex than they appear, and that the old frameworks of feature analysis, administration burden, and vendor lock-in still apply. They highlight code review fatigue from running multiple agents as a risk, citing Amazon's six-hour downtime from AI-generated code, and urge teams to maintain a culture where no single person can take down prod. Finally, they suggest automating executives' standard decisions first—since much leadership work is routine—rather than targeting only junior tasks, and predict that the hardest problems in 2026 will remain human.

Extreme Harness Engineering: 1M LOC, 1B toks/day, 0% human code or review — Ryan Lopopolo, OpenAI

Apr 7, 2026 · 1:17:54

Ryan Lopopolo of OpenAI's Frontier team details his team's extreme experiment in harness engineering: building a 1M LOC internal Electron app with zero human-written or reviewed code, relying instead on Codex agents that process 1B tokens daily ($2-3k/day). He argues that humans are the bottleneck and that teams should encode non-functional requirements into specs, skills, and observability tooling rather than prompting agents to 'try harder.' The result is Symphony, a ghost library spec and Elixir reference implementation that automates the entire pull-request lifecycle, including self-review and merge. Lopopolo explains how the team scales by treating software as agent-legible text, using worktrees for multi-agent collaboration, and feeding agent mistakes back into the repository via docs like core-beliefs.md. He also discusses Frontier's enterprise platform for safe agent deployment, noting that success depends on giving agents full context—even company culture and inside jokes—so they can act as full teammates.

Marc Andreessen introspects on Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different"

Apr 3, 2026 · 1:16:20

Marc Andreessen argues that AI is finally different from past boom-bust cycles because of four compounding breakthroughs: LLMs, reasoning (o1, R1), coding agents (OpenClaw), and recursive self-improvement. He calls this the '80-year overnight success'—decades of neural network research now paying off. Comparing today's AI capex boom to the dot-com crash, he notes that buyers like Microsoft and Google are cash-rich incumbents and every GPU deployed is already generating revenue. He hails Pi and OpenClaw as a Unix-like architecture that makes agents model-independent and self-modifiable. On open source, he calls DeepSeek a 'gift to the world' for its paper and code, but warns that entrenched institutions—unions, licensing, government monopolies—will slow AI adoption far more than technologists expect.

Mistral: Voxtral TTS, Forge, Leanstral, & Mistral 4 — w/ Pavan Kumar Reddy & Guillaume Lample

Mar 30, 2026 · 54:02

Mistral releases Voxtral TTS, a 3B-parameter speech generation model using a novel autoregressive flow matching architecture and a 12.5 Hz neural audio codec developed in-house. Pavan Kumar Reddy and Guillaume Lample explain how the model achieves high quality and efficiency by predicting audio latents as a continuous distribution, enabling real-time voice agent applications. The episode details Mistral's stepwise multimodal strategy, starting with transcription and now speech generation, with full duplex voice agents as the next goal. Enterprise deployment via Mistral Forge allows fine-tuning on proprietary data, exemplified by training on rare languages or domain-specific jargon. Guillaume discusses merging capabilities (Mistral Small merging coding, reasoning, vision), commitment to open science through technical reports, and reasoning transfer from formal proofs in Lean. The episode also covers AI for science partnerships and hiring for research and forward-deployed engineering roles.

Dreamer: the Agent OS for Everyone — David Singleton

Mar 20, 2026 · 1:04:23

David Singleton, former Stripe CTO, introduces Dreamer, a consumer platform for discovering, building, and using AI agents and agentic apps centered on a personal Sidekick that acts as both companion and kernel-like traffic cop. He demonstrates building a conference schedule app in 25 minutes via natural language, a ski trip app that splits expenses, and tools like Gmail, Google Search, and live sports data feeds. Dreamer features an open tool ecosystem where builders get paid, a private waitlist skip at dreamer.com/latentspace, and a Builder-in-Residence program with a $10,000 prize for the best tool by mid-April. Singleton explains the OS model with Sidekick as kernel and agents as users enforcing data isolation and cross-agent coordination only through Sidekick. He shares insights on tiny-team building (six-person core, 17 total), hiring engineers who collaborate with coding agents, and LLMs' current lack of taste and creativity.

Anthropic’s Felix Rieseberg on AI Coworkers, Local-First Agents, and the Future of Knowledge Work

Mar 17, 2026 · 1:27:34

Anthropic's Felix Rieseberg explains how Claude Cowork—a VM-based, user-friendly version of Claude Code—was built from pre-existing prototypes in ten days, and why putting AI 'where you work' on the local machine is more powerful than cloud-only approaches. He argues that Silicon Valley undervalues the local computer, comparing it to how we all use MacBooks instead of iPads. The VM gives Claude its own computer, enabling it to install tools and run scripts without constant approval, serving as both a safety boundary and a capability unlock. Rieseberg contrasts Cowork's evaluation on knowledge work tasks (e.g., mortgage management, tax filing) with Claude Code's coding evals, and explains the shift from MCPs to file-based skills—markdown files that tell the model what to do. Real use cases include organizing desktops, uploading videos, and filing taxes. He also expresses concern about the impact on junior employees and hints at future work on autonomy, multiplayer coordination, and portability.

⚡️Monty: the ultrafast Python interpreter by Agents for Agents — Samuel Colvin, Pydantic

Mar 14, 2026 · 34:02

Samuel Colvin joins Shawn Wang to introduce Monty, Pydantic's ultrafast Python interpreter built with AI for agents, positioning it as a lightweight, secure alternative to sandboxes for code execution. Monty runs inside the same process, achieving sub-microsecond latency (800 nanoseconds in a hot loop), versus over a second for cloud sandboxes. Colvin explains the inspiration came from conversations at Anthropic about type safety's critical role in AI tool calling, and how Monty fills the gap between simple tool calling and full sandboxes. Monty is not full CPython—it lacks third-party library support and some standard library modules—but covers essential built-ins, implemented largely by LLMs that Colvin calls 100x faster for tasks where internal and external APIs are well-known, unit tests are trivial, and there's no API bikeshedding. He demonstrates a Pydantic AI agent using Monty to scrape LLM pricing from websites, with type checking built into the runtime so only permitted host functions (e.g., Playwright, Beautiful Soup wrappers) can be called. Colvin also shares his workflow using Claude Code, Codex, and Gemini CLI in combination for code review and implementation, and…

Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Eskildsen of Turbopuffer

Mar 12, 2026 · 1:00:32

Simon Eskildsen, founder of Turbopuffer, explains how his obsession with the cost of vector search at Readwise led him to build a search engine on object storage and NVMe SSDs, cutting costs by 95% for customers like Cursor. He outlines the three conditions for a major database company—a new workload (AI-driven search), a new architecture (object storage first, no consensus layer), and evolving to support every query plan. Turbopuffer's architecture uses S3's strong consistency and compare-and-swap for metadata, avoiding stateful systems. Eskildsen shares how they won Notion by absorbing latency issues with dark fiber, and discusses the shift from single RAG queries to agents making many concurrent searches. He also describes the "P99 engineer"—obsessive, trade-off minded, able to bend software to first principles. Future plans include expanding full-text search, reaching Common Crawl scale, and eventually adding simpler OLAP queries.

⚡️ OpenClaw's Memory Sucks and the fix is simple — Dhravya Shah, Supermemory

Mar 9, 2026 · 27:10

Supermemory founder Dhravya Shah argues that current memory approaches like OpenClaw's static markdown files fail because they lack freshness and agent-driven retrieval, proposing a hooks-based system that dynamically injects less than 2,000 tokens per turn. His company Supermemory uses a hybrid mode combining structured memory with RAG fallbacks, achieving costs as low as 2 cents per million tokens. Shah critiques benchmarks like LongMemEval and LoCoMo for not testing personalization or forgetfulness, and his team built the open platform MemoryBench for evaluation. Future plans include plugins for Cursor and partnerships with voice agent companies, aiming for prosumer interoperability across AI providers.

Agent Inference at the "Speed of Light" — How NVIDIA moves like a $4.3 Trillion Startup

Mar 8, 2026 · 1:26:00

NVIDIA's Nader Khalil and Kyle Kranen join Swyx and Vibhu to explain how the company moves like a $4.3 trillion startup through speed-of-light (SOL) first-principles thinking, agent security boundaries, and the Dynamo inference engine. They argue agents should only do two of three things (files, internet, code) to prevent vulnerabilities, and detail Brev's acquisition to improve developer UX with one-click GPU access and DGX Spark integration. Kyle describes Dynamo as a data center scale inference engine that optimizes serving by scaling out, using prefill/decode disaggregation, Kubernetes-based scheduling, and model-hardware co-design to improve cost, latency, and quality. The episode covers SOL's role in creating urgency, long-context limits and potential 'unhobblers' like multi-head latent attention, and the shift toward CLI-first agent workflows for enterprise tools.

Why Your AI Agents Don’t Work with Dex Horthy of HumanLayer | In-Context Cooking

Mar 6, 2026 · 24:59

Dex Horthy, founder of HumanLayer and creator of "context engineering," joins Alan to recreate Dan Dan noodles while explaining why most AI agent demos fail in production and how the top 1% of AI engineers build differently from the rest. Horthy describes how his NASA JPL internship at 17 taught him pathfinding with Dijkstra's algorithm, and later coining "context engineering" after observing that public hype around open-source frameworks doesn't match enterprise reliability needs. He defines AI "slop" as low-effort derivative content often produced without reading, and introduces the "dumb zone"—a rule of thumb to start thinking about context compaction at 40% usage unless you have strong intuition. Horthy argues that while smarter models narrow the gap, the frontier of reliably achievable tasks keeps shifting, so context engineering remains valuable. He predicts software engineering will evolve from writing code to producing working code, emphasizing shipping over coding, and notes HumanLayer helps enterprises standardize AI adoption with guardrails rather than vibe coding.

Cursor's Third Era: Cloud Agents — ft. Sam Whitmore, Jonas Nelle, Cursor

Mar 6, 2026 · 1:11:18

Cursor's Sam Whitmore and Jonas Nelle detail their Cloud Agents launch, where agents get full cloud VMs to onboard repos, run code, and test changes end-to-end before returning PRs with demo videos and remote desktop access. The model decides whether to test (with /no-test to skip and /repro for bug reproduction videos). Videos cut review time by letting humans watch 20-second clips instead of giant diffs. Cursor supports parallel model swarms via best-of-N—running multiple models head-to-head on the same prompt—and subagents for context management, plus a long-running 'grind mode' for multi-day tasks. They predict cloud agents will surpass local agents in volume by year-end, driving per-developer spend to thousands per month and creating new bottlenecks in CI/CD and production pipelines.

Why Every Agent Needs a Box — Aaron Levie, Box

Mar 5, 2026 · 1:16:58

Aaron Levie, CEO of Box, argues that every AI agent needs a dedicated data repository—a 'box'—to effectively access enterprise content, and that deploying autonomous agents at scale requires new infrastructure for governance, identity, and security. He contrasts the rapid adoption of coding agents, which benefit from full codebase access and technical users, with the slower enterprise rollout where access controls, messy data, and context engineering pose major hurdles. Levie details Box's internal evals showing 15-point jumps in model performance and emphasizes the need for agents to judge when to stop searching. He discusses moving from read-only to read-write agent workflows, the challenge of keeping documentation current, and his hands-on founder approach to steering a 3,000-person public company through the AI transformation.

⚡️ Polsia: Solo Founder Tiny Team from 0 to 1m ARR in 1 month & the future of Self-Running Companies

Mar 1, 2026 · 41:56

Polsia, an AI that autonomously builds and runs companies, crossed $1M ARR in its first month, solo founder Ben Cera explains on Latent Space. The platform lets users give an idea and it handles product coding, marketing, cold outreach, Meta ads, and competitive research, sending daily emails summarizing progress. Cera built Polsia for himself starting in November, stripping features to keep onboarding simple—provisioning everything so users only need to reply to an email. He uses AI agents to run Polsia itself, from fixing bugs to responding to investor inbound, embodying the vision of a self-running company. The business model charges $50/month (near break-even on model costs) and takes 20% of any revenue the user's company generates. Cera emphasizes that the hardest part was deciding what not to build, and he sees Polsia as an Apple-like ecosystem for autonomous business operations.

Claude Code for Finance + The Global Memory Shortage: Doug O'Laughlin, SemiAnalysis

Feb 24, 2026 · 2:07:40

Doug O'Laughlin, SemiAnalysis founder, explains his Claude Code awakening in December 2024: Anthropic's Opus 4.5 one-shotted tasks that used to take 24 hours, leading him to predict Claude Code will write 25–50% of all GitHub code by year-end (already 4% of commits in two weeks). He argues this marks the death of Excel and Bloomberg for analysts—'you can just do things'—but warns of a 'hygiene' crisis as junior analysts lose meta-learning. On the semiconductor side, O'Laughlin details a severe memory squeeze: HBM demand consumes 4× the DRAM capacity, DRAM prices could double again, and CXL is reviving as a workaround. He compares AI infrastructure buildout to the railroad boom (25% of U.S. gross fixed capital investment) and says Microsoft faces an innovator's dilemma—renting GPUs to 'barbarians at the gate' while its own office franchise is disrupted by Claude Code. Google's TPU v7 enjoys a temporary TCO advantage, but Nvidia's supply chain dominance (Jensen doing shots with SK and Samsung) will reassert with Rubin. O'Laughlin also reflects on his 2,800-mile Continental Divide Trail hike as essential self-mastery.

⚡️Context Graphs: according to the authors — Jaya Gupta, Ashu Garg, Foundation Capital

Feb 4, 2026 · 33:44

Jaya Gupta and Ashu Garg of Foundation Capital introduce Context Graphs—the institutional memory of the 'why' behind business decisions, captured through decision traces of exceptions, overrides, and precedents that AI models often miss. They argue these graphs will become the defensible moat for the next generation of applied AI companies and Systems of Agents, citing early implementations at Player Zero, Olive, and Tesserae. The hosts distinguish Context Graphs from mere metadata or data meshes, emphasizing they emerge organically from automating specific workflows rather than as universal enterprise schemas. They predict that by 2026, hundreds of Context Graphs will be in production, a standard enabling stack will emerge, and the industry will shift from debating definitions to extracting value from the captured decision traces.

🔬 From Red Teaming GPT-4 to Automating Drug Discovery: The Future of AI in Science — Andrew White

Jan 28, 2026 · 1:13:56

Andrew White, co-founder of Future House and Edison Scientific, argues that automating the scientific method with LLM agents is now feasible, explaining how ChemCrow triggered White House briefings, how Kosmos uses a world model to generate and test hypotheses, and why EtherZero's reward hacking revealed the difficulty of verifiable chemistry tasks. He shifts from his academic work on molecular dynamics to building agents that enumerate and filter ideas, claiming scientific taste remains the frontier. White recounts the counterexample of D.E. Shaw Research's MD vs. AlphaFold, asserts that natural language is the universal bridge for scientific data, and predicts that automation will expand rather than eliminate scientific jobs.

Brex’s AI Hail Mary — With CTO James Reggio (acquired for $5B by Capital One!)

Jan 17, 2026 · 1:13:27

Brex CTO James Reggio details the company's three-pillar AI strategy—corporate, operational, and product AI—arguing that SOP-driven agents outperform overengineered RL in finance ops like underwriting, KYC, and fraud. He explains why Brex built an internal AI platform (LLM gateway, prompt/version management, evals) before adopting Mastra for TypeScript-based multi-agent networks, where an EA-style orchestrator coordinates specialist agents via multi-turn conversations rather than single tool calls. The audit agent pattern separates detection, judgment, and follow-up to reduce false negatives. Brex also lets employees build their own AI stack (ChatGPT, Claude, Gemini) and runs agentic coding interviews internally, with the company growing 5X and cutting burn 99% in 18 months without adding engineers.

[State of Code Evals] After SWE-bench, Code Clash & SOTA Coding Benchmarks recap — John Yang

Dec 31, 2025 · 17:45

John Yang, creator of SWE-bench, explains the benchmark's evolution from an ignored project to the industry standard after Cognition's Devin launch, and introduces CodeClash as a new paradigm for evaluating long-horizon coding agents via programming tournaments. Yang covers SWE-bench's expansion into nine languages (JavaScript, Rust, Java, C, Ruby) and multimodal tasks, the proliferation of independent variants like SWE-bench Pro and SWE-bench Live, and the shift from unit tests to iterative agent competitions. He addresses the Tau-bench controversy over impossible tasks, arguing they serve as cheat detection, and weighs the tension between long autonomy (5-hour runs) and interactivity (Cognition's fast back-and-forth). Yang highlights the academic data problem—companies like Cognition have rich user interaction data, while academics need user simulators or compelling products like LMArena—and positions CodeClash as a testbed for studying human-AI collaboration by freezing model capability and varying interaction setups.

[State of Context Engineering] Agentic RAG, Context Rot, MCP, Subagents — Nina Lopatina, Contextual

Dec 31, 2025 · 26:48

Nina Lopatina of Contextual AI discusses the state of context engineering, arguing that while the field is still in prototyping, 2025 will see true scale with full-system design patterns. She notes that agentic RAG—especially query reformulation into subqueries—has become the baseline, dramatically improving retrieval. Context rot is widely cited but industry benchmarks at real scale (100k+ documents, billions of tokens) remain rare. MCP is both a driver and a flaw: giant JSON tool definitions stuff the context window, yet MCP servers enable rapid prototyping before optimizing down to direct API calls. Sub-agents with turn limits are critical—unlimited agency degrades performance and causes hallucinations. Instruction-following re-rankers are essential for scaling retrieval across massive databases, providing more recall upfront and precision in the final context. Benchmarks are being saturated faster than ever: a Princeton benchmark from October was saturated by Claude Code in weeks, with solutions so good the gold dataset had errors. Lopatina also shares a lesson from using ChatGPT as a snowboarding coach—after several months of training for a 25-lap mogul race, she had to close…

One Year of MCP — with David Soria Parria and AAIF leads from OpenAI, Goose, Linux Foundation

Dec 28, 2025 · 1:39:19

David Soria Parra, MCP lead at Anthropic, along with Jim Zemlin (Linux Foundation CEO), Nick Cooper (OpenAI), and Brad Howes (Block/Goose), recount the one-year evolution of the Model Context Protocol from a local experiment to the de facto standard for agentic systems, now donated to the new Agentic AI Foundation. They detail four spec releases—from local stdio to remote HTTP streaming, OAuth 2.1 authentication (and enterprise lessons learned), long-running tasks, and MCP Apps (iframes for richer UI)—and explain why internal enterprise adoption is exploding faster than expected, mostly invisible and at massive scale. The group reveals how three competitive AI labs came together to donate protocols and agents to a neutral foundation, how the foundation will balance taste-making (curating meaningful projects) with openness, and their 2025 vision: MCP as the communication layer for asynchronous, long-running agents that discover and install their own tools, unlocking another order of magnitude in AI productivity.

Steve Yegge's Vibe Coding Manifesto: Why Claude Code Isn't It & What Comes After the IDE

Dec 26, 2025 · 37:25

Steve Yegge argues that vibe coding—using multi-agent AI workflows—will replace traditional IDEs by January 1, calling anyone still using an IDE a 'bad engineer.' He claims Claude Code, Cursor, and the 2024 stack are already obsolete, predicting agent orchestration dashboards (like his VC project) will manage fleets of agents. Yegge highlights that senior engineers with 12–15 years of experience are the most resistant, but their productivity will be eclipsed by vibe coders. He warns that merging code becomes a new wall as agents produce 10x more code, citing one company's solution of one engineer per repo. Yegge also notes the chaos inside AI labs (OpenAI, Anthropic, Google) as they scale, and believes open‑source models will approach frontier capability within a year.

⚡️GPT5-Codex-Max: Training Agents with Personality, Tools & Trust — Brian Fioca + Bill Chen, OpenAI

Dec 26, 2025 · 27:46

OpenAI’s Brian Fioca and Bill Chen discuss the launch of Codex Max, a long-running coding agent designed to work 24+ hours, manage its own context, and spawn sub-agents. They reveal how GPT-5 training prioritized personality traits—communication, planning, and self-checking—to build developer trust, and how Codex develops tool habits like preferring 'rg' over 'grep'. The abstraction layer is shifting from models to full-stack agents, enabling partners to plug Codex into tools like VS Code or Zed. They highlight applied evals that measure real-world impact over academic benchmarks, and the need for multi-turn eval APIs. Their 2026 vision: coding agents trusted enough to handle the hardest refactors at any company, not just top-tier firms, and general enough to automate personal workflows like organizing desktops or email.

The Future of Email: Superhuman CTO on Your Inbox As the Real AI Agent (Not ChatGPT) — Loïc Houssier

Dec 11, 2025 · 1:11:02

Loïc Houssier, CTO of Superhuman Mail (recently acquired by Grammarly), joins hosts Alessio and Swyx to detail how Superhuman builds AI into email without adding latency. He explains their agentic framework of small tools vs. a single agent, the fight against “agent laziness,” and their eval process anchored by Rahul’s infamous “what wood was my table?” test. Houssier reveals Superhuman uses local-first caching and Baseten’s box pricing for cost control, stores embeddings in TurboPuffer, and has only three engineers on AI—yet PR throughput rose from four to six per engineer per week. He argues the inbox will power your future AI executive assistant, and that AI will widen the gap between engineers with real fundamentals and those faking it.

The Great Evals Debate — Ankur Goyal & Malte Ubl

Dec 7, 2025 · 34:33

Ankur Goyal (Braintrust) and Malte Ubl (Vercel) debate whether offline evals are essential infrastructure or premature optimization for AI coding agents, arguing that the best teams deliberately invest in multiple feedback loops—offline evals, A/B tests, and vibe checks—to build effective AI products. They explain that modern evals are not about manufacturing golden datasets but pulling real user failures from production logs into eval suites to iterate faster. The conversation highlights how evals provide a 'first derivative' that enables aggressive shipping without regression fears, akin to unit tests in traditional software. Coding evals are uniquely verifiable (e.g., 'does it compile?') yet underutilized; Vercel uses them in RL pipelines to fine-tune models that fix trivial errors 100x faster than agentic loops. They also discuss how product managers encode domain expertise through rubrics and LLM-as-judge scoring, and why proprietary evals are competitive moats while public benchmarks serve marketing. The debate concludes that RL environments are a promising frontier for computer-use agents but require specialized expertise to avoid reward hacking.

World Models & General Intuition: Khosla's largest bet since LLMs & OpenAI

Dec 6, 2025 · 1:04:51

Pim de Wit, founder of Medal and General Intuition (GI), turned down a reported $500M offer from OpenAI to spin out GI with a $134M seed from Khosla Ventures — Vinod Khosla's largest bet since OpenAI — arguing that world models trained on peak human gameplay are the next frontier after LLMs. Medal's 12M users generate 3.8B action-labeled clips via retroactive recording, creating a privacy-preserving dataset of 'episodic memory for simulation.' GI builds fully vision-based agents that see only frames and output actions in real-time, using pure imitation learning without RL, and can transfer from arcade games to realistic games to real-world video. Pim explains why world models need actions, memory, and partial observability (e.g., smoke, camera shake) compared to video generation, and how they distill giant policies into tiny real-time models that navigate and hide like humans. He recounts his path from running the largest RuneScape private server to reverse engineering, cold-emailing the Diamond (world model) paper authors to assemble a top research team, and advises data founders to train models themselves before selling. GI's near-term customers are game developers replacing…

⚡️ Building the AI Hardware Engineer with Matthias Wagner, Co-founder of Flux

Nov 22, 2025 · 49:03

Matthias Wagner, CEO of Flux.ai, describes building the "AI hardware engineer" that turns product briefs into manufacturable PCB designs in under 30 minutes. After leaving Meta in 2019, Wagner built a browser-based collaborative CAD tool from scratch, designed as a reinforcement learning environment. The emergence of LLMs and reliable tool calling let Flux's agents search component libraries, check pricing and availability, and execute complex designs autonomously. In a live demo, Wagner designs a battery-powered Alexa-like device with ESP32, microphones, OLED display, and speaker in about 25 minutes. Flux now has 7,000 paying customers, growing 26x year over year, and Wagner envisions eventually letting users prompt full hardware products like smartphones into existence, claiming the cost of custom manufacturing is plummeting.

⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex

Nov 19, 2025 · 27:11

Tenex co-founders Alex Lieberman and Arman Hezarkhani explain how they compensate AI engineers by story points instead of hours, enabling some to earn over $1 million annually while delivering 10x productivity gains. The model emerged after Arman downsized his previous company Parthian's engineering team by 90% yet saw a 10x increase in production-ready software output by re-architecting with AI. They avoid gaming by hiring engineers who are long-term selfish or love coding, and pair them with technical strategists incentivized on account retention. Example projects include building a retail camera prototype in two weeks and a trivia app that hit #20 on the App Store in a month. Their default stack is TypeScript with React and Express, and they switch between coding agents like Claude Code and Codex based on daily performance. They identify human capital as their main constraint and context engineering or entropy reduction as the core challenge for autonomous AI engineers.

Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures

Nov 14, 2025 · 1:26:59

Deedy Das of Menlo Ventures returns to explain how Anthropic became the fastest-growing software company ever (zero to billions in revenue) and why Glean’s boring enterprise search moat of hard integrations and ranking problems is harder than competitors think. He reveals the $100M Anthology Fund’s strategy: backing OpenRouter, Goodfire, Prime Intellect, and Whisper—companies that solve thorny infrastructure or research problems rather than chasing apps. Das argues that model-layer companies will capture most value because building great models is harder than building apps, and that Anthropic’s product innovations like Claude Code emerge from a culture that lets researchers experiment freely. He also warns that vibe coding is becoming a cognitive crutch for engineers, eroding deep problem-solving skills, and discusses how enterprise AI market share has shifted dramatically: OpenAI went from 50% to 20%, while Anthropic rose from 12% to 32% of enterprise LLM API spend.

⚡ Inside GitHub’s AI Revolution: Jared Palmer Reveals Agent HQ & The Future of Coding Agents

Nov 10, 2025 · 35:52

Jared Palmer, SVP at GitHub and VP at CoreAI at Microsoft, traces his journey from building v0 at Vercel—a focused Next.js coding agent that grew from $1M ARR to millions more after adding chat—to launching Agent HQ at GitHub Universe, a collaboration hub for developers and coding agents. He explains how v0's success came from laser-focus on one stack and framework, and how GitHub's 180M developers enable scaling agent-based workflows. Palmer discusses model strategy tradeoffs (composite vs. branded models), the challenge of repo setup and infrastructure reliability, and using coding agents for non-coding tasks like automating Excel workflows. He reveals Stacked Diffs as GitHub's top feature request, with active exploration after previous attempts, and outlines his vision for seamless AI integration across GitHub, VS Code, and Actions, including features like resolving merge conflicts with AI.

⚡ Inside Google Labs: Building The Gemini Coding Agent — Jed Borovik, Jules + AIE CODE Preview

Nov 10, 2025 · 43:53

Jed Borovik, Product Lead at Google Labs, explains how Google builds Jules, an autonomous coding agent that runs on its own VM for long-running tasks, challenging the assumption that agents should operate locally. He reveals that as Gemini models improved, Jules' scaffolding simplified, shifting from sub-agent patterns and embedding-based RAG to attention-based search. Borovik discusses context window management for sessions lasting up to 30 days with 2 million tokens, and argues that coding agents will increase demand for software engineers (Jevons paradox), not eliminate jobs. He calls for better specification tools beyond chat, such as multimodal input and interactive planning, to move beyond 'vibe coding' toward verifiable, reliable agentic workflows.

Terminal-Bench 2.0: the most impt coding agent benchmark of 2025 gets a v2! Launch + Q&A w/ founders

Nov 8, 2025 · 35:01

Terminal-Bench co-creators Mike Merrill, Alex Shaw, and collaborators Andy Konwinski, Ludwig Schmidt, and Aatash launch Terminal-Bench 2.0, an 89-task coding benchmark with rigorous human and LM-assisted verification to test agent performance on real-world tasks like designing DNA primers and installing Windows 3.11. They reflect on Terminal-Bench 1's problems—easy tasks and reproducibility—and detail spending 300 hours manually auditing each task. They introduce Harbor, an open-source package for running sandboxed agent rollouts, standardizing task format and integrating with agents like Codex and Claude Code. Aatash shows using Harbor to generate thousands of training traces for DataComp Agent, running 1,000 parallel sandboxes. Future plans include expanding to scientific computing.

⚡️ Ship AI recap: Agents, Workflows, and Python — w/ Vercel CTO Malte Ubl

Nov 1, 2025 · 42:02

Vercel CTO Malte Ubl discusses the company's AI strategy, centered on the new Workflow Development Kit that enables durable, resumable serverless functions with infinite idle time at no cost. He explains how the AI SDK was extracted from v0 and remains deliberately low-level to accommodate emerging patterns, with a direct agent abstraction now stable in version six. Ubl details Vercel's internal agents—including a DevOps agent that investigates production anomalies by querying observability data and logs, solving the recall-precision problem in alerting—and the 'Agent on Every Desk' program that helps large companies build their first three agents. He also covers zero-config Python support for Flask and FastAPI, and a security model that assumes developers cannot be trusted, extracting auth and data access from application code.

The Agents Economy Backbone - with Emily Glassberg Sands, Head of Data & AI at Stripe

Oct 30, 2025 · 1:37:13

Emily Glassberg Sands, Head of Data & AI at Stripe, explains how Stripe builds economic infrastructure for AI, processing 1.4% of global GDP. Stripe's domain-specific foundation model runs inline on the charge path, detecting card-testing attacks on large users with detection rates improving from 59% to 97%. The Agentic Commerce Protocol (ACP) with OpenAI creates a shared standard for businesses to expose products to AI agents, adopted by Walmart and Sam's Club. Stripe helps AI companies combat new fraud vectors like free trial and refund abuse, and launched token billing to let wrappers price based on real-time inference costs. Internally, 85% of Stripes use LLM tools daily, with code generation and merchant understanding as key use cases. Emily argues AI companies grow 2-3x faster than SaaS, are twice as global, and that agentic commerce will expand consumption by removing time constraints for high-income consumers.

Terminal-Bench: Pushing Claude Code, OpenAI Codex, Factory Droid, et al to the limits

Oct 18, 2025 · 35:27

Alex Shaw and Mike Merrill, creators of Terminal-Bench, explain how their coding agent benchmark became an industry standard adopted by Anthropic, OpenAI, and leading agent companies. They bet on terminal-based interaction over GUI because text is the modality that works best with models, and designed tasks as containerized environments with instructions and test scripts. Nicholas Carlini at Anthropic became an early champion, leading to Terminal-Bench being featured on Claude’s model card without notice. They built the minimal Terminus agent to isolate model capabilities from agent optimizations, and their framework adapts existing benchmarks like SWE-bench. Future plans include cloud hosting, multi-dimensional evaluation incorporating cost and economic value, and enabling RL post-training on benchmark tasks.

Why RL Won — Kyle Corbitt, OpenPipe (acq. CoreWeave)

Oct 16, 2025 · 1:08:23

Kyle Corbitt, co-founder and CEO of OpenPipe (acquired by CoreWeave), explains why reinforcement learning has replaced supervised fine-tuning for training reliable AI agents. He argues GRPO is a dead end due to its requirement for perfectly reproducible parallel rollouts, which is extremely hard in practice. Instead, OpenPipe’s RULER uses relative LLM-as-judge rewards, achieving state-of-the-art performance even with a weak judge. Corbitt reports that 90% of AI projects remain stuck in proof-of-concept due to reliability issues, and that LoRAs are underrated for production while GEPA failed in his tests. He predicts continuous RL from real-world experience can unlock 10x more inference demand.

DevDay 2025: Apps SDK, Agent Kit, MCP, Codex and why Prompting is More Important than Ever

Oct 7, 2025 · 44:52

Sherwin Wu and Christina Huang from OpenAI's platform team discuss the new AgentKit suite and Apps SDK, arguing that visual agent builders and prompt optimization remain critical for production AI. They detail AgentKit's components—Agent SDK, visual Builder, Evals, and Tool Registry—and how Apps SDK inverts the website-chatbot paradigm by embedding apps inside ChatGPT. OpenAI adopted Anthropic's MCP protocol for tool connectivity, and the team dogfoods these tools for their own customer support at openai.com. They explain that automated prompt optimization and evaluation pipelines are essential for scaling agents, and share how Codex is used internally for code generation and PR reviews. The episode also covers the Service Health Dashboard for real-time API SLO tracking, reflecting OpenAI's reliability investments.

⚡️Traversal: Causal ML and Reinforcement Learning

Oct 5, 2025 · 45:06

Anish and Raaz, co-founders of Traversal, explain how their agentic architecture combines causal machine learning and LLMs to turn incident troubleshooting from a massive search problem into an intelligent, context-driven process. Both have PhDs in causal ML and RL from MIT and Berkeley, and they applied that expertise to root cause analysis in complex enterprise systems with petabytes of fragmented data across logs, metrics, traces, and Slack. Their system dynamically combines statistical tests (for time-series) with semantic understanding (via LLMs) to winnow billions of signals to root cause candidates in under two minutes. They note that reasoning models like o3 are critical for complex incidents, while Claude is better for tool calling and unsticking. Traversal uses a mix of infrastructure size and investigation count for pricing, and sees self-healing as a continuum: 10–20% of issues can be autonomously resolved now, with 30–40% requiring senior engineer sanity-check within 6–12 months. They are hiring in New York and emphasize that building proprietary evals is core IP, making public benchmarks a tension.

⚡️Claude Sonnet 4.5 and Anthropic's roadmap for Agents and Developers — Mike Krieger, Anthropic

Sep 30, 2025 · 26:25

Mike Krieger, Anthropic's CPO, discusses the launch of Claude Sonnet 4.5 and its implications for AI development, emphasizing the growing symbiosis between product and research teams. He describes his personal 'vibe evals' for new models, including generating a Virtual Boy-style game and a Nintendo board presentation, and notes that Sonnet 4.5 traffic eclipsed Sonnet 4 on day one. Krieger explains how Anthropic is improving model output quality across UIs, Excel, and PowerPoint, and highlights the shift toward interactive planning rather than pure autonomy. He reveals the rename of the Claude Code SDK to the Claude Agent SDK, reflecting its general-purpose use for complex agentic workflows beyond coding. Krieger stresses the need for better visual understanding in models and invites developers to share feedback on hard challenges to help improve the platform.

Amp: The Emperor Has No Clothes

Sep 25, 2025 · 1:21:59

Quinn Slack (CEO) and Thorsten Ball (Amp Dictator) from Sourcegraph discuss Amp Code, a coding agent that ships 15x/day with no code reviews. They explain the strategic pivot from Cody to Amp, emphasizing that building the best coding agent trumps everything else. The team dogfoods Amp internally, using it to build itself, and maintains both a VS Code extension and CLI, with a 50/50 internal split. They argue that model choice is becoming less important than the harness around the model, and they reject features like prompt enhancers, custom subagents, and MCP servers as not adding value. Common failure modes include outsourcing thinking but not typing and using agents without proper context engineering. They predict async agents running 24/7 will dominate, and they target early adopters willing to adapt their codebases and workflows for maximal agent effectiveness.

Context Engineering for Agents - Lance Martin, LangChain

Sep 11, 2025 · 1:03:18

Lance Martin from LangChain discusses context engineering for AI agents, arguing that managing context from tool calls is the key challenge and introducing five techniques: offloading, reducing, retrieval, caching, and context isolation. Offloading saves raw tool call outputs to disk and passes only summaries, drastically cutting token usage—Martin's deep research agent went from 500,000 tokens per run to much less. Manus uses file system offloading and warns that irreversible pruning risks information loss, while Cognition advocates fine-tuned summarization for agent-agent boundaries. For retrieval, Martin contrasts Windsurf's multi-step indexing with Claude Code's agentic search using grep and llms.txt, finding the latter more effective. He applies the Bitter Lesson to agent engineering: his own Open Deep Research evolved from a highly structured workflow to a simple agent loop as models improved, and he warns against rigid abstractions that hinder adaptation.

⚡️Launching Ona: Coding Agent with Fully Sandboxed Cloud Environment

Sep 1, 2025 · 33:44

Gitpod co-founders Johannes Landgraf and Chris Weichel launch Ona, a coding agent platform with fully sandboxed cloud environments, arguing that the future of development is parallel agent-driven work rather than mono-focused IDEs. They explain their rebrand from Gitpod to Ona, driven by their product outgrowing the technical name. Ona provides reproducible dev environments via dev containers and automations YAML, runs any Linux workload, and by default gives agents full autonomy (Yolo mode) since environments are ephemeral and risk-free. They dogfood heavily—75% of their PRs are co-authored by Ona. Key challenges include file edits (they settled on Anthropic's string replace tool) and underspecification by users. Pricing follows a credit model blending compute and token usage, with enterprise VPC deployment and $100 in credits for new self-serve users. They envision IDEs shifting to interfaces supporting parallel tracks, but currently offer one-click transitions to desktop editors like Cursor.

⚡️OpenCode: Claude Code but Open Source, with Any Model, and frontier TUI - with Dax Reed (@thdxr)

Aug 5, 2025 · 37:04

Dax Reed of OpenCode and SST explains why his open-source terminal-based agent is a serious alternative to Claude Code, emphasizing that the real battle is product experience, not squeezing 3% more from LLMs. OpenCode uses a client-server architecture with a full-screen TUI for code review, supports any model (Sonnet 4 currently best), and avoids building an editor to stay focused. Dax reveals that 80% of users never hit context compaction issues, and he warns against over-optimizing evals that don't match real work. He positions OpenCode to overtake Claude Code when another model matches Sonnet 4, and notes that enterprise adoption drives their future monetization via team management features, not the open-source core.

⚡️Composio: 10,000+ tools that evolve for Agents — Karan Vaidya and Soham Ganatra

Aug 4, 2025 · 22:09

Composio co-founders Karan Vaidya and Soham Ganatra join Swyx and Alessio to discuss their platform for self-evolving agent skills, now with 25,000+ actions and 100,000+ developers. They explain how MCP's client-side limitations led them to build a learning infrastructure that analyzes agent usage patterns to improve tool reliability over time. The team uses agents to build and maintain 95% of their 500+ integrations, adding 35 per week, with plans for 5,000 by year-end. They reveal that more than 20-25 actions confuse LLMs, so they offer a natural-language execution tool and dynamic tool exposure via MCP. Key challenges include enterprise APIs with poor documentation and the need for dev environments. Composio's internal agent benchmarks and A/B testing infrastructure optimize tool schemas for LLM calling.

🕰️ The Oral History of Windsurf (ft. Varun Mohan, Scott Wu, Jeff Wang, Kevin Hou, Anshul R)

Jul 28, 2025 · 3:53:23

This episode traces the oral history of Windsurf (formerly Codeium and Exafunction) with founders Varun Mohan, Scott Wu, Jeff Wang, Kevin Hou, and Anshul R, charting its evolution from a GPU virtualization startup to a leading agentic IDE and its dramatic acquisition by Cognition. The narrative covers the pivot to code completion in 2022, the shift to a free model to gain traction, and the launch of the Windsurf editor in November 2024 with Cascade, an agent that operates on a shared timeline with the developer. Key principles include meta-learning (auto-generated memories that adapt to each user's preferences) and scaling with intelligence (replacing hard-coded rules with LLM reasoning as models improve). The episode also details the acquisition weekend when Cognition bought Windsurf after Google acquired a stake, highlighting product synergies between the synchronous agent (Windsurf) and async agent (Devin) and the ambition to build a comprehensive platform for real-world software engineering teams.

Cline: The Collaborative AI Coder

Jul 16, 2025 · 1:15:44

Saoud Rizwan and Pash from Cline join to explain why their open-source coding agent uses a Plan + Act paradigm — having models explore and plan before executing — pioneered before competitors like Claude Code. They argue that "RAG is a mind virus" and "Fast Apply got bitter lesson'd" as frontier models now achieve sub-5% diff edit failure rates, making layered workarounds obsolete. Cline's bring-your-own-API-key model avoids inference markups, with enterprise features driven by Fortune 5 companies asking for security and ROI insights. The MCP ecosystem grew via Cline's marketplace (150+ servers, top ones with hundreds of thousands of downloads), though monetization and security remain open questions. They detail how Plan mode extracts user intent and Act mode auto-approves, how context engineering prioritizes agentic file exploration over retrieval, and why they stayed a VS Code extension rather than fork — to focus solely on the agentic loop.

⚡️Anthropic vs Cognition on Multi-Agents: A Breakdown with Dylan Davis

Jul 5, 2025 · 27:11

Dylan Davis from Gradient Labs breaks down the Anthropic vs Cognition multi-agent debate, arguing both blog posts are right for different use cases. Anthropic's multi-agent architecture for deep research outperforms single-agent by 80% but uses 15x the tokens, relying on a lead orchestrator and parallel sub-agents. Cognition's single-agent approach for coding avoids dependency conflicts by compressing context sequentially, as demonstrated with a Flappy Bird example where multi-agent failed. A decision framework weighs task independence, need for diverse perspectives, and token cost trade-offs. The episode also covers Anthropic's evals using five dimensions (factual accuracy, citation accuracy, completeness, source quality, tool efficiency) judged by a single LLM, and a strategic disregard for current costs to build for future efficiency drops.

⚡️Warp 2.0: the Agentic Development Environment - Zach Lloyd and Ben Holmes

Jun 25, 2025 · 52:23

Warp CEO Zach Lloyd and engineer Ben Holmes debut Warp 2.0, rebranding from an AI terminal to an Agentic Development Environment (ADE) with a state-of-the-art coding agent that rivals Claude Code and Cursor. The episode demonstrates Warp's new universal input that auto-detects English commands and terminal input, voice-driven agent workflows, multi-threaded agents running in tabs, inline diff editing, MCP support, and REPL translations for SQL and Postgres. Lloyd explains the design philosophy of keeping a terminal-like UX while adding agent management features, and discusses pricing changes (Pro now 2,500 requests, Turbo 10,000, plus usage-based credits) and the challenge of subscription vs. usage-based models. Warp has nearly 600,000 active users with weekly revenue growth of 5–15%, and the team positions itself as a model-agnostic layer above Anthropic, Google, and OpenAI, offering deeper integration than ChatGPT or Claude desktop apps.

Scaling Test Time Compute to Multi-Agent Civilizations — Noam Brown, OpenAI

Jun 19, 2025 · 1:17:47

Noam Brown, OpenAI researcher behind Cicero and reasoning models, joins hosts Alessio and Puix to discuss how test-time compute scaling drives multi-agent AI and the limits of the system 1/2 analogy. He explains that his Diplomacy bot Cicero reached top 10% of humans in 2022, and later he won the 2025 world championship. Brown argues that reasoning models like o3 succeed in unverifiable domains (e.g., Deep Research) and that harnesses and routers will be washed away by scale. He reveals OpenAI's multi-agent team is pursuing a principled approach, not heuristic, and envisions AI civilizations collaborating. He also shares his coding stack (Codex, Windsurf) and predicts test-time compute will hit cost and wall-clock bottlenecks.

[AIEWF Preview] Containing Agent Chaos — Solomon Hykes

Jun 3, 2025 · 27:14

Solomon Hykes, creator of Docker and founder of Dagger, argues that the industry needs an open standard for AI coding agent environments to avoid repeating the fragmentation seen before Docker. Dagger, a workflow engine originally built for post-development automation (CI/CD), is now being pulled by its community into the agentic space to solve environment isolation, portability, and observability for concurrent agent teams. Hykes insists containers are the right base layer but require a new, agent-native UX—unlike legacy Dockerfile or Compose—that is decoupled from any IDE, model, or cloud provider. He critiques current vertical monoliths (e.g., proprietary IDE-hosted agent environments) and advocates for a Lego-like modular approach where the environment is the standard linchpin. Dagger integrates with existing stacks rather than replacing them, and Hykes previews fresh, unreleased content for his upcoming keynote at AI Engineer World's Fair.

[AIEWF Preview] Gemini in 2025 and Realtime Voice AI

Jun 2, 2025 · 24:29

At Google I/O 2025, Latent Space host Swyx and TWiML's Sam Charrington speak with Logan Kilpatrick, Shrestha Basu Mallick, and Kwindla Hultman Kramer about Gemini's reasoning upgrades, real-time voice AI, and the infrastructure challenges of building production voice agents. Kilpatrick announces thinking budgets and thought summaries for 2.5 Pro, now live in Flash and coming to Pro in June, while Mallick highlights native audio output with multilingual code-switching and proactive voice activity detection. Kramer discusses the need for sub-500ms latency and WebRTC packet routing, and the team debates single-model vs. componentized architectures, with Mallick noting that eventual convergence will bring capabilities like diffusion-based generative UI into the main Gemini model. The episode also covers implicit context caching, URL context for research agents, and the Live API's session length and function calling improvements.

The AI Coding Factory

May 29, 2025 · 59:23

Factory.ai co-founders Eno Reyes and Matan Grinberg join Latent Space to explain their vision for autonomous software engineering droids that handle everything from code generation to incident response for enterprises. They met at a Langchain Hackathon in 2023, and within eight days both quit their jobs to start Factory. The platform is browser-based, not IDE-based, because they argue the optimal UI for a world where humans write less code will not evolve from the IDE. They focus on enterprise codebases, citing a large migration that took from four months to three and a half days. Key design decisions include a delegation model where agents ask clarifying questions, proactive context gathering via synthetic insights, and usage-based pricing tied to token efficiency. They also discuss why SWE-Bench is dead for their use case, the challenge of model post-training biases toward CLI tools, and their need for 'junior Enos' in go-to-market roles.

⚡️Multi-Turn RL for Multi-Hour Agents — with Will Brown, Prime Intellect

May 23, 2025 · 38:59

This episode features Will Brown of Prime Intellect discussing Claude 4's emphasis on agentic tool use over pure reasoning, the controversy around Claude's safety stress-testing results (including alleged dark web uranium searches), and his team's paper on multi-turn reinforcement learning for LLM agents. Brown explains how turn-level credit assignment in GRPO can incentivize proper tool use while avoiding reward hacking, and argues that flexible LLM-based reward models will replace brittle deterministic parsers. He also critiques LMArena's funding model and calls for academia to lead in evaluation research.

ChatGPT Codex: The Missing Manual

May 16, 2025 · 53:32

Josh Ma and Alexander Embiricos from OpenAI introduce ChatGPT Codex, a cloud-hosted autonomous software engineer that runs in its own sandbox, arguing it delivers value through single-shot, long-horizon task completion. They explain that Codex evolved from internal experiments (codenamed WHAM) and now offers a 60-tasks-per-hour rate limit with a one-hour hard cutoff, emphasizing it is not meant for interactive use but for delegation—users fire off tasks and go about their day. Best practices include using agents.md, installing linters and formatters, keeping code modular, and having good architecture. The team intentionally kept it a research preview to gather feedback on environment customization and pricing, with plans to integrate seamless transitions between cloud and CLI.

⚡️Open Questions in Agentic RL — Will Brown (Prime Intellect)

May 9, 2025 · 17:13

Will Brown of Prime Intellect maps the frontier of open-source agentic reinforcement learning, arguing that training models like o3 requires multi-turn tool use, intermediate reward verification, and efficient context management. He outlines challenges such as scaling tool calls to hundreds (e.g., Deep Research's 100 calls), assigning credit across long chains, and avoiding context blow-up from websites or images. Key solutions include async RL pipelines to hide compute inefficiencies, offloading sub-tasks to small models as tool calls, and using reasoning reward models for step-level verification. Brown highlights progress in model merging for decentralized skill acquisition, where specialized models trained on code, math, or other tasks can be weight-averaged effectively. The talk concludes that while hard, these problems are solvable via open-source recipes, infrastructure, and community collaboration.

Claude Code: Anthropic's CLI Agent

May 7, 2025 · 1:16:22

In this episode, Boris Cherny and Cat Wu from Anthropic discuss Claude Code, a CLI-based coding agent they describe as a Unix utility rather than a polished product, emphasizing raw model access and simplicity. The tool started as Boris's personal experiment, then spread internally after engineers used it daily, with Anthropic's product principle of 'do the simple thing first' guiding features like memory via markdown files and context compaction by asking Claude to summarize. They ship updates including web fetch, auto-accept, and thinking via chain of thought, and note that 80% of Claude Code's code is written by Claude Code itself. Average spend is $6/day per user versus $20/month for Cursor, and Boris reports a personal 2x productivity gain, with some engineers seeing 10x. The team is growing and investigating open-sourcing the tool, while continuing to build for the terminal as a raw, composable utility for power users.

Zed Agents — with Zed Cofounders Nathan Sobo & Antonio Scandurra

May 7, 2025 · 35:50

Zed co-founders Nathan Sobo and Antonio Scandurra explain how their high-performance code editor is launching Zed Agents, aiming to make AI an integrated collaborator rather than just a tool. Built from scratch in Rust for 120fps performance and real-time collaboration via CRDTs, Zed previously open-sourced Xeta, an edit prediction model fine-tuned on Qwen 2.5. Now they introduce an agentic mode that lets LLMs call tools in a loop, surfacing changes in a multi-buffer for easy review. The agent can run shell commands, install MCP servers via an extension store, and be used with bring-your-own-key or a $20/month plan for 500 requests. Sobo and Scandurra stress that vertical integration gives them control to wire AI into debugging, git, and multi-agent workflows where multiple agents collaborate via CRDTs, treating agents as always-available teammates.

Why Every Agent needs Open Source Cloud Sandboxes

Apr 24, 2025 · 1:06:39

Vasek Mlejnsky, CEO of E2B, explains how his company grew from 40,000 sandboxes in March 2024 to 15 million in March 2025 by providing cloud sandboxes for AI agents. Originally built from DevBook's interactive docs playground, E2B now serves use cases from code interpreting and data analysis to Computer Use and reinforcement learning with Hugging Face's Open R1. The sandboxes are general Linux VMs with fast startup, security isolation, and persistence, handling untrusted code and allowing forking/checkpoints for parallel agent execution. Mlejnsky discusses the LLMOS landscape, pricing challenges (moving from token-based to value-based), MCPs as higher-order tools, and why he relocated from Prague to San Francisco to be closer to users. E2B is hiring across engineering and customer success as it aims to become full-lifecycle infrastructure for LLMs.

Sleep-Time Compute — Letta AI (Charles Packer, Charlie Snell, Kevin Lin)

Apr 21, 2025 · 34:02

This episode covers Letta AI's new paper on Sleep-Time Compute, a scaling direction that applies compute during model idle periods (sleep time) rather than only at test time. Charles Packer, Kevin Lin, and Charlie Snell explain that sleep-time compute precomputes inferences from static context before queries arrive, yielding Pareto improvements on math benchmarks like GSM8K by shifting the accuracy-to-token curve leftwards. They distinguish it from test-time compute by emphasizing that sleep-time tokens incur no user-latency cost, and show that the benefit is largest when questions are predictable from context. The team also ties the concept to stateful agents and memory systems (building on MemGPT), releasing two implementations: one for low-latency chatbots and another for document-driven agents.

Claude Plays Pokémon Hackathon: Escape from Mt. Moon!

Apr 5, 2025 · 1:15:00

David Hershey (creator of Claude Plays Pokémon), Andrew (a developer building a Pokémon-playing virtual streamer), and Jesse Han (Morph Labs CEO) detail how to build agents that escape Mt. Moon in Pokémon FireRed using Anthropic's Claude and Morph Cloud's Infinibranch technology. Hershey explains that Claude's vision is often unreliable—it spent 8 hours pressing A on a doormat thinking it was a dialogue box—and that spatial reasoning remains a core bottleneck, best mitigated by touchscreen controls rather than prompt engineering. Andrew reveals he relied on reading raw RAM and A* pathfinding rather than computer vision, calling it “cheating” but pragmatically useful: his biggest breakthrough was a single prompt line telling the agent to try something else if it fails to grab a starter Pokémon. Jesse introduces Morph's EVA agent framework, which uses low-overhead snapshotting for test-time search, and the hackathon's judging criteria—escape Mt. Moon in the fewest agent turns, with a $1,000 prize for the coolest use of Infinibranch branching. The episode serves as both a technical walkthrough and a challenge to build general-purpose agents that can handle complex, open-ended tasks…

The Creators of Model Context Protocol

Apr 3, 2025 · 1:20:21

Justin Spahr-Summers and David Soria Parra, co-creators of Anthropic's Model Context Protocol (MCP), explain its origin as a solution to the M×N integration problem they experienced when copying data between Claude Desktop and IDEs. They detail MCP's design inspirations from LSP, its three core primitives (tools for model-driven actions, resources for app-controlled data, and prompts for user-initiated macros), and why they chose JSON-RPC with stateful connections. The episode contrasts MCP with OpenAPI, discusses client control and trust in servers, and outlines the future roadmap including a streamable HTTP transport and remote-server authorization via OAuth 2.1. The guests express a wish for more sampling clients and full spec support, and emphasize that MCP is an open, multi-company project with community commit access.

The #1 SWE-Bench Verified Agent

Apr 2, 2025 · 31:46

Guy Gur-Ari, co-founder of Augment Code, explains how their coding agent achieved the #1 spot on SWE-Bench Verified without fine-tuning, using off-the-shelf models with techniques like sequential thinking, ensembling, and code base understanding. He shares that orientation and memories help the agent learn codebase conventions, and that MCP support allows extensibility. Gur-Ari contrasts Augment's IDE-first approach with competitors like Devin and Factory, emphasizing meeting developers where they are. He also discusses the future of IDEs, the role of custom models, and recommends the SWE-RL paper for reinforcement learning in coding. The episode features a live demo of the agent implementing a feature from a Linear ticket and creating a PR.

The Agent Network — Dharmesh Shah, Agent.ai + CTO of HubSpot

Mar 28, 2025 · 1:42:28

Dharmesh Shah, founder of Agent.ai and co-founder of HubSpot, defines an AI agent broadly as AI-powered software that accomplishes a goal, and argues that the next frontier is multi-agent systems and shared memory across agents. He reveals Agent.ai has 1.3M users and 3,000 published agents, positioning it as a professional network where agents have profiles, post release notes, and can be composed via MCP servers. Shah contrasts work as a service (paying for the work done) versus results as a service (paying for outcomes), warning that results-based pricing only works for objectively measurable, low-variance tasks like customer support tickets. He advocates for MCP as the standard for agent-tool discovery, and shares his personal engineering philosophy of preferring under-engineering over over-engineering because the cost to fix later trends toward zero with AI code generation. Shah also discusses his domain investing strategy (owning chat.com, prompt.com, crew.ai), his rule against competing with Sam Altman, and the 'Sorry Must Pass' framework for managing overwhelm by defaulting to no.

Building Manus AI (first ever Manus Meetup)

Mar 27, 2025 · 48:59

Tao Zhang, co-founder of Manus, explains how Manus gives LLMs 'hands' to take real-world actions, inspired by MIT's 'mens et manus' (mind and hand). Manus assigns each task its own cloud virtual machine via E2B, provides pre-paid data APIs (stock, social media), and a knowledge system to remember user preferences. Their previous product, Monica.im, grew to 20M monthly active users and $50M ARR before they pivoted from a failed AI browser project to Manus. On the GAIA benchmark, Manus achieves a per-task cost of ~$2, far cheaper than OpenAI's Deep Research (~$20) and prior SOTA. Zhang emphasizes a 'less structure, more intelligence' philosophy, opting not to predefine workflows but to give the model rich context and tools. He also discusses plans to partner with Cloudflare, pay paywalls on behalf of users, and integrate more APIs based on usage patterns, while ruling out building their own foundation model.

Fullstack-Bench: The Eval for Coding Agents — with Sujay Jayakar, Chief Scientist, Convex

Mar 19, 2025 · 31:13

Sujay Jayakar, Convex chief scientist, presents Fullstack-Bench, a benchmark evaluating how well AI coding agents build full-stack apps on Convex, Supabase, and FastAPI+Redis, finding Convex’s tight feedback loops and strong abstractions let agents autonomously complete tasks within time limits. The benchmark tests three tasks (chat, to-do, files) across three backends with 30-60 minute limits and human hints. Convex succeeded autonomously on chat and to-do tasks, while Supabase agents struggled with RLS policy debugging and FastAPI agents with manual SSE wiring. Sujay discusses how platform design impacts AI code generation: simple procedural code, strong abstractions, and guardrails like end-to-end type safety improve LLM performance. He notes that API choices matter—things too similar to Firebase but not exact cause hallucinations—and that models like Claude 3.7 sometimes regress on platform-specific evals. The episode concludes with Convex’s focus on enabling AI-generated backends and anticipation of new agent workflow patterns.

npm install Agents — with Sunil Pai and Rita Kozlov (VP AI) of Cloudflare

Mar 19, 2025 · 35:36

Shawn Wang hosts Sunil Pai and Rita Kozlov to discuss Cloudflare's new Agents product, which uses Durable Objects to provide a scalable, stateful runtime for building AI agents. Sunil built an initial prototype in two weeks, which Rita helped polish into a full SDK and starter kit. They demo a chat agent with persistent state, human-in-the-loop confirmation, and built-in scheduling via cron, all running on Durable Objects without Kubernetes. The conversation covers local-first syncing (Durable Objects as always-on sync clients), the need for observability across millions of agents, and excitement about MCP for easy integrations. Rita teases upcoming "durable object enabled containers" to bridge long-running and CPU-heavy workloads, and a new browser rendering REST API launching the same day.

The new OpenAI Agents Platform: CUA, Web Search, Responses API, Agents SDK!!

Mar 11, 2025 · 25:39

OpenAI's Nikunj Handa and Romain Huet join host Swyx to announce their new Agents Platform, including the Responses API, Web Search, File Search, Computer Use tools, and an upgraded Agents SDK. The Responses API unifies Chat Completions and the Assistants API, offering free state storage for 30 days and acting as a strict superset for future agentic workflows; Chat Completions remains supported, but the Assistants API will sunset in first half 2026. Web Search is available both as a built-in tool in Responses and as a fine-tuned GPT-4o Search Preview model in Chat Completions, achieving 90% accuracy on simple QA compared to 30% for GPT-4o. The File Search tool gains metadata filtering, while Computer Use—powering OpenAI's Operator—enables browser automation via screenshots and action signals, described as the "GPT-2 of computer use." The Agents SDK builds on the Swarm experiment, adding type support, guardrails, handoffs, and tracing in the OpenAI dashboard, with plans to tie traces to evals and reinforcement fine-tuning.

Solve coding, solve AGI [Reflection.ai launch w/ CEO Misha Laskin]

Mar 7, 2025 · 28:12

Misha Laskin of Reflection AI argues that solving autonomous coding is the direct path to AGI, combining reinforcement learning (pioneered by his team on AlphaGo/AlphaZero) with large language models (which they advanced at Google on PaLM, Gemini, ChatGPT). He explains that coding is already ergonomic for LLMs—unlike browser agents that require noisy human mouse data—making it the ideal starting point. Reflection AI is building coding agents that automate backlog tasks (testing, refactoring, migrations, security remediation) for large engineering teams, delivering them via an API that takes a task and codebase and outputs resolved code. Laskin insists superintelligence cannot be built in a vacuum; real-world customer evals are essential, as benchmarks like SWE-bench don't guarantee production reliability. He also stresses the need for open-weight models to prevent a few companies from hoarding superintelligent coding agents.

How Claude Plays Pokémon was made

Mar 4, 2025 · 37:38

David Hershey of Anthropic explains how he built Claude Plays Pokémon, an agent that uses Claude to play Pokémon Red, revealing both surprising capabilities and stark limitations of AI. He describes the simple tool-using agent loop: Claude sends button presses, receives screenshots and RAM state, and maintains a knowledge base and 30-message conversation history with summarization. Key challenges include Claude's poor spatial awareness—it famously walked up and down outside Oak's lab 12 times—and its tendency to hallucinate game knowledge. Hershey notes that newer models (Sonnet 3.7) required stripping away band-aid prompts, as simpler instructions yielded better performance. The project also showed emergent behaviors: nicknamed Pokémon made Claude more protective, and the model even wrote meta-commentary about its own mistakes in its knowledge base. While the current run is stuck in Mount Moon after 52 hours, the biggest hurdles remain vision and navigation, not prompting tricks.

Browserbase: Browser Infrastructure For Your AI Agents

Feb 28, 2025 · 1:01:33

Paul Klein IV, CEO of Browserbase, argues that headless browser infrastructure is a critical primitive for AI agents to automate the web. Browserbase solves the hard problem of running thousands of browsers in the cloud—too big for Lambda, requiring Firecracker microVMs—and exposes APIs via its open-source framework Stagehand (act, extract, observe). It handles proxies, CAPTCHA solving, and offers a live view iframe for human-in-the-loop control. Primary use cases include browser automation and web scraping as a heavy-hitter renderer when simpler methods fail. Paul explains his choice to go solo founder and how Browserbase's in-person, 10–5 culture enables fast execution. He predicts the future of software will involve software acting on other software via browsers.

Raycast: Your AI Automation Assistant

Feb 26, 2025 · 20:19

Raycast CEO Thomas introduces AI Extensions, letting users @-mention apps like Slack or Google Calendar and perform actions (e.g., set status, book meetings) via natural language, powered by a fine-tuned model called Ray 1 based on GPT-4o for faster, more accurate function calling. He shares how Raycast evolved from a Spotlight replacement to an extension platform, now adding AI chat with tool-aware reasoning. Challenges include building evals for tool calls (harder than standard LLM evals) and supporting third-party extension authors with a JSDoc-based TypeScript interface. Thomas envisions Raycast becoming an AI-native layer for macOS, eventually enabling background automations triggered by time or file events.

S1: the $6 DeepSeek R1 Competitor (ft. Entropix)

Feb 26, 2025 · 17:43

Tim Kellogg joins hosts Alessio and Swyx to break down his viral blog post on S1, showing how Stanford researchers cloned DeepSeek R1's reasoning for $6 by inserting a 'think token' and fine-tuning on just 1,000 examples. Kellogg argues the real innovation isn't the cost but the technique's simplicity—making advanced inference-time scaling accessible. He explains Entropix, a dynamic sampler that uses model entropy and varentropy to adjust generation on the fly, and notes its creators are starting a company. The conversation contrasts supervised fine-tuning's targeted results with RL's broader reasoning growth, using an analogy: 'RL causes a lot of growth, then SFT trims it.' Kellogg predicts that embedding introspection signals into RL training could solve agent 'doom loops' and unlock higher agency—a key challenge behind products like OpenAI's Deep Research.

Why is everyone cloning Deep Research?

Feb 18, 2025 · 1:00:10

In this episode, hosts Alessio and Swyx interview Aarush Selvan and Mukund Sridhar, the lead PM and tech lead for Gemini Deep Research, about how Google created the deep research agent category and the technical decisions behind it. They finetuned a special version of Gemini 1.5 Pro for the feature, which autonomously browses the web for about five minutes to produce a research report. The team built an asynchronous platform to handle long-running tasks and developed an ontology of research behaviors—from broad shallow exploration to deep focus—to evaluate performance. They found that users value the visible browsing process even when it takes minutes, contrary to typical latency norms. The discussion covers iterative planning, trade-offs between model memory and web grounding, and future directions including thinking models, multimodality, and personalization.

Bee AI: The Wearable Ambient Agent

Feb 17, 2025 · 1:07:40

Bee AI's Maria and Ethan explain their wearable ambient agent, arguing that personal AI with continuous context is the future, and detail its $49.99 hardware, 7-day battery, and free inference. They trace their origin from a 2016 personal AI startup to Squad (sold to Twitter) and then to Bee, emphasizing that hardware was necessary because app friction (e.g., remembering to open it) killed the ambient experience. Privacy is addressed by not storing audio, only summaries, and offering geofencing and concept fencing to block sensitive topics. The software pipeline uses voice activity detection to reduce compute, speaker identification, and custom small models for memory retrieval instead of traditional RAG or knowledge graphs. Social features are in beta, with agent-to-agent negotiation—e.g., both agents coordinating a dinner reservation based on preferences and calendars. They are hiring AI engineers and see always-on AI as inevitable.

smol agents are all you need

Feb 13, 2025 · 22:31

This episode features Aymeric Roucher of Hugging Face discussing smol agents, a lightweight agent framework, and argues that code-based agents outperform JSON-based agents. Roucher explains that agents are defined by LLM control over execution flow, and smol agents simplifies this with a core file under 1000 lines. He describes the origin from transformers.agents and the security-focused custom Python interpreter. The library achieved third place on the GAIA benchmark, using o1 and Sonnet models, and Roucher projects 90% accuracy by 2026 based on a sigmoid trend line. Hugging Face is launching an agent course and plans to build GUI agents for computer use, while also fine-tuning R1 for agentic tasks.

The AI Architect: Bret Taylor

Feb 11, 2025 · 1:35:59

Bret Taylor, CEO of Sierra and chairman of OpenAI, recounts engineering AI's future across three eras — rewriting Google Maps' frontend in a weekend (cutting un-gzipped code to 20K), building Sierra's in-house agent platform for consumer brands like Sonos and SiriusXM, and navigating OpenAI's leadership crisis. He argues that AI agents' core abstractions are still pre-React, compares today's agents to jQuery, and predicts that outcome-based pricing will dominate as agents complete jobs rather than assist humans. He urges software engineers to move up the stack toward domain-specific agents and to embrace Rust-like languages for AI-generated code, emphasizing formal verification and safety. Taylor also shares how OpenAI prioritizes mission over product features, and why the relationship with Microsoft remains OpenAI's most important partnership.

Agent Engineering with Pydantic + Graphs — with Samuel Colvin, CEO of Pydantic Logfire

Feb 6, 2025 · 1:02:15

Samuel Colvin, CEO of Pydantic Logfire, argues that Pydantic AI offers a production-ready agent framework with type-safe graphs, unlike most agent frameworks that sacrifice engineering quality. He explains Pydantic's shift from a validation library to an AI tool, noting 300M monthly downloads and a 20% reduction in time-to-first-token for a major model company after upgrading to Pydantic V2. Colvin details Logfire's use of DataFusion over ClickHouse for better JSON support and control, and why Pydantic AI resists an LLM gateway, favoring OpenAI-aligned APIs. He defends graphs for complex workflows, reveals agents now use graphs internally, and shares that Pydantic.run enables browser-based demos. The episode covers OpenTelemetry's emerging GenAI semantic conventions, the need for self-hosted observability due to PII in LLM data, and Colvin's investment in Marimo as a text-based Jupyter replacement.

The Agent Reasoning Interface: Claude, ChatGPT Canvas, Tasks, Operator — with Karina Nguyen, OpenAI

Feb 1, 2025 · 1:06:29

OpenAI's Karina Nguyen, who led the creation of ChatGPT Canvas and Tasks, explains how her team trains separate models for new interaction paradigms rather than prompting the core model, enabling rapid iteration on user feedback. She details the 'behavioral design' process that shapes model personality in collaborative contexts like Canvas—deciding when to rewrite vs. edit—and how that extends to defining agents as a progression from one-off actions to fully trustworthy long-horizon delegation. Drawing on her time at Anthropic building Claude.ai from scratch and co-creating Claude 3, she contrasts the product mindsets: OpenAI takes more product risks across consumer features while Anthropic focuses on enterprise. She also shares her vision for ChatGPT evolving into a 'generative OS' where UIs adapt dynamically to user intent, and argues that human creativity—not model capability—is the current bottleneck for rethinking software interfaces.

Beating OpenAI and Anthropic by Looking At Data: the new #1 on SWE-Bench w/ W&B CTO Shawn Lewis

Jan 28, 2025 · 34:31

Shawn Lewis, CTO of Weights & Biases, built a coding agent using OpenAI's o1 model that achieved 64.6% on SWE-bench verified, the top score at the time. He developed a TypeScript agent framework called PhaseShift and an Eval Studio tool backed by Weave, emphasizing that rigorous data inspection and tooling are critical for agent development. Lewis explains his process of running parallel rollouts and a crosscheck method to select the best trajectory, which contributed a 6% improvement. He also discusses the challenges of adapting reasoning models like o1 for agentic tasks and shares plans to open-source PhaseShift. The episode highlights Lewis's dogfooding approach, using Weights & Biases' own tools to build and evaluate the agent.

Outlasting Noam Shazeer, Crowdsourcing Chai AI w/ 1.4m DAU — with William Beauchamp, Chai Research

Jan 26, 2025 · 1:13:33

William Beauchamp, founder-CEO of Chai AI, explains how he pivoted from algorithmic trading to build a character chatbot platform before Character.ai, growing to 1.4M DAU and $22M+ revenue by crowdsourcing model improvements through Chaiverse. Starting with GPT-J in 2021, he found product-market fit with a therapist bot and shifted to user-generated content, letting users define prompts, images, and names. Competing against well-funded rivals like Character AI and Talkie, Chai ships over 100 LLMs weekly via its developer platform, spending $10M on compute in 2024 and tripling it. Beauchamp argues AI follows an S-curve, not scaling laws, and focuses on inference optimization using rejection sampling and reward models to serve better responses. He prioritizes 'insanely great' products over technology-driven features, noting that audio and image features failed to move metrics, while a data flywheel and aggressive user acquisition drove rapid growth.

2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents

Jan 1, 2025 · 1:51:07

In their 100th episode, hosts Alessio and Swyx recap 2024 in AI, arguing that pre-training scaling has hit a wall—backed by Ilya Sutskever and others at NeurIPS—and that inference-time compute (o1, o3) is the new frontier. They dissect the "four wars": data quality (lawsuits vs. synthetic data), GPU haves vs. have-nots (with the middle class dying), multimodality (Sora, Veo 2, Gemini 2.0's native image output), and the LLM OS/agents stack (LangChain, E2B, memory). Market share shifted from OpenAI's 95% to 50-75% as Anthropic and Gemini gained ground; prices dropped ~3 orders of magnitude for same ELO. The episode predicts 2025 as the year agents finally enter production, driven by models like o1 and tools like Devin, and warns that AI will set the skill floor for roles.

Best of 2024 in Agents (from #1 on SWE-Bench Full, Prof. Graham Neubig of OpenHands/AllHands)

Dec 25, 2024 · 51:25

Professor Graham Neubig of CMU and AllHands AI, maintainer of the open-source coding agent framework OpenHands, delivers a talk on the state of agents in 2024, arguing that agents are now capable of automating significant portions of software development but still struggle with information gathering and error recovery. He demonstrates OpenHands solving real coding tasks live, explains key design choices like using Python code execution over granular tool calls to reduce LLM invocations, and shares that Claude currently outperforms other models for agentic tasks due to superior instruction following and error correction. Neubig discusses eight perennial agent problems including agent-computer interface, human-agent interface, model selection, planning, workflows, exploration, search, and evaluation. He predicts every major LLM trainer will focus on agent-oriented models by mid-2025, that agent benchmarks like SWE-Bench and WebArena will saturate and require harder successors, and that the biggest challenge will be designing human-agent interfaces for systems that succeed only 30-40% of the time autonomously but 80-90% with human feedback. The talk also covers workflow memory as a…

Windsurf: The Enterprise AI IDE

Dec 13, 2024 · 1:06:36

Varun Mohan and Anshul of Codeium explain why they built Windsurf, a new AI IDE, arguing that VS Code's API limitations prevented them from delivering the best agentic experience. They detail how Cascade, their agentic system, uses proprietary retrieval and planning models alongside third-party LLMs, and describe their evaluation method that masks commits and tests incomplete code states. The duo also reveals that over 800,000 developers use Codeium extensions, that they still support JetBrains and Eclipse for enterprise customers, and that they intentionally avoided a waitlist launch. They discuss the trade-offs of building first-party vs. third-party models, the importance of 'go slow to go fast' in enterprise infrastructure, and their belief that individual developer profits should come after building switching costs through superior product.

0 to over $8M ARR in 2 months as a Claude Wrapper (Bolt.new, Qodo)

Dec 2, 2024 · 1:36:42

Eric Simons of Bolt.new/Stackblitz and Itamar Friedman of Qodo discuss Bolt.new's viral growth from $4M ARR in four weeks to over $8M ARR in two months as a Claude wrapper, driven by its in-browser WebContainer OS that eliminates local dev setup. They contrast Bolt's '0 to 1' creation for non-developers with enterprise-focused agents like Qodo's testing and code review tools, arguing that specialized agents outperform general-purpose ones. Simons details how WebContainer's unified sandbox enables error self-healing loops, while Friedman explains AlphaCodium's task-breaking that boosted o1 preview to 93rd percentile on Codeforces. The episode covers open-source strategy, pricing tiers ($20 to $200/month), and founder advice on ignoring expert doubt to ship fast, illustrated by Simons' Ironman completion while launching Bolt.

[Paper Club] DocETL: Agentic Query Rewriting + Eval for Complex Document Processing w Shreya Shankar

Nov 29, 2024 · 55:53

This episode of Latent Space Paper Club discusses DocETL, a framework by Shreya Shankar for agentic query rewriting and evaluation in complex document processing with LLMs. The paper introduces map, reduce, resolve, and auxiliary operators, along with rewrite directives like data decomposition and gleaning to improve accuracy. The optimizer generates 200 pipeline variants, costing $100, to rewrite user pipelines automatically. Key debates include when to chunk documents (sometimes harmful) and the reliability of LLM-as-judge for evaluation, with guests Eugene Yen and Vibhu arguing that binary metrics, ensembling, and context-dependent thresholds make it viable. Shreya emphasizes that validation agents are critical, and the work builds on her prior EvalGen paper, spanning two years of development.

The new Claude 3.5 Sonnet, Computer Use, and Building SOTA Agents — with Erik Schluntz, Anthropic

Nov 28, 2024 · 1:11:08

Anthropic's Erik Schluntz explains how Claude 3.5 Sonnet achieved state-of-the-art 49% on SWE-Bench Verified by using a minimal agent framework that gives the model full control via tools like bash and file editing, letting it self-correct without hard-coded workflows. He details the importance of tool design over prompt engineering, why XML tags work well, and how computer use reduces integration friction by letting models interact with any browser-based interface. Schluntz also shares his robotics experience, noting that while LLMs and diffusion models are promising for general-purpose robots, reliability at 99.9% and hardware variability remain major hurdles, and he expresses skepticism about self-driving as a business due to high vehicle costs versus driver-equivalent revenue.

Agents @ Work: Lindy.ai (with live demo!)

Nov 15, 2024 · 1:08:01

Florent Crivello, founder of Lindy.ai, explains how his no-code agent platform evolved from an overly LLM-dependent design to a deterministic 'on rails' system that puts AI in a box, making agents more reliable and easier to use. The product was rebuilt from scratch as Lindy 2.0, featuring step-by-step workflows with structured outputs and memory management that still requires pruning. Crivello reveals that Claude 3.5 Sonnet ended model bottlenecks but warns against over-engineering cognitive architectures, advocating instead for letting models scale. He discusses building their own eval tools (now considering Braintrust), hiring vertical GMs for go-to-market, and the decision to return to in-person work, arguing remote work hampers creativity. He also shares his contrarian views on Europe vs. US tech, testing the Overton window with spicy tweets, and balancing AI safety concerns (10% p(doom)) with building toward a potential utopia.

Agents @ Work: Dust.tt — with Stanislas Polu

Nov 11, 2024 · 58:53

Stanislas Polu, co-founder of Dust.tt, argues that horizontal agent platforms—not vertical, single-use-case agents—will win the enterprise by letting non-technical employees build their own automation. A former OpenAI researcher who worked directly with Ilya Sutskever on formal mathematics and saw GPT-4 internally before ChatGPT, Polu left in 2022 to start Dust, now an open-source infrastructure layer that connects company data (Slack, Notion, GitHub) to LLMs and lets anyone create simple, instruction-driven agents. He contrasts Dust’s API-first, integration-heavy approach with LangChain’s open-source orchestration and Adept’s browser automation bet, insisting that most useful agent workflows require only a few tool calls and that function-calling quality (where GPT-4 Turbo still leads) matters more than model size. Dust achieves 88% daily active user penetration at some enterprise customers, and Polu predicts the first billion-dollar company run by a single person will emerge once platforms like Dust mature enough to let tiny teams scale to massive output.

[Paper Club] SWE-Bench [OpenAI Verified/Multimodal] + MLE-Bench with Jesse Hu

Oct 19, 2024 · 1:01:40

Jesse Hu walks through SWE-Bench, SWE-Bench Verified, SWE-Bench Multimodal, and MLE-Bench — benchmarks that evaluate AI agents on real-world coding and ML tasks, from fixing GitHub issues to winning Kaggle competitions. SWE-Bench scrapes 2,000+ pull requests from 12 Python repos to get free unit tests, with leaderboard scores rising from ~2% to 43% thanks to improved agent scaffolding. SWE-Bench Verified uses human raters and three criteria—well-specified problem, valid test, difficulty on a 0–3 scale—to filter 500 solvable tasks. SWE-Bench Multimodal targets 617 UI tasks from 17 JavaScript/TypeScript libraries, each including an image, but achieves only 12% accuracy. MLE-Bench repurposes Kaggle competitions: agents given 24 hours and an A10 GPU, with o1-preview scoring bronze in 17% of competitions and surpassing grandmasters (7 gold medals vs 5 required), though each run costs $4,000.

Language Agents: From Reasoning to Acting — with Shunyu Yao of OpenAI, Harrison Chase of LangGraph

Sep 27, 2024 · 1:26:33

Shunyu Yao of OpenAI and Harrison Chase of LangChain discuss the evolution of language agents, arguing that combining reasoning and acting through techniques like ReAct remains foundational, while tool design and cognitive architectures like CoALA will shape the next wave. Shunyu traces his work from text games to ReAct, showing how thinking as an action improves reliability; Harrison explains LangChain's adoption and the shift to LangGraph for stateful orchestration. They emphasize agent-computer interfaces (ACI) as a neglected area, noting that SWE-Agent's success came from optimizing tools for models, not humans. The conversation covers memory types (semantic, episodic, procedural) and applies CoALA's three dimensions to current systems. They highlight promising applications: customer support, coding agents, and spreadsheet-style UX for batch operations. Shunyu stresses that better benchmarks like SWE-Bench (which solved 30% of GitHub issues) and TauBench (best model 48%) are critical, and that data—not architecture—will drive future model improvements.

Is finetuning GPT4o worth it?

Aug 22, 2024 · 1:01:16

Ali Pullen, founder of Cosine, discusses Genie, an AI software engineering colleague that achieved 30% on SWE-Bench and 43.8% on SWE-Bench Verified via fine-tuning GPT-4o on billions of tokens of synthetic data. Cosine built Genie by training on the process of software engineering—not just working code—using synthetic runtime errors and a self-improvement loop. The agent follows a four-stage workflow: code retrieval (66% accuracy), planning, writing diffs, and running CI tests. Pullen reveals that model performance degrades linearly beyond 60K tokens in the context window, and that OpenAI dynamically sizes LoRA adapters to handle Cosine's massive dataset. He explains why Genie isn't on the SWE-Bench leaderboard (refusing to publish trajectories to prevent distillation) and outlines plans to scale the dataset and fine-tune on customer codebases for personalized performance.

The Winds of AI Winter (Q2 Four Wars of the AI Stack Recap)

Aug 2, 2024 · 1:23:36

Swyx and Alessio recap Q2 2024 through their 'Four Wars' framework, arguing that the AI landscape is shifting from frontier model dominance to commoditization and vertical applications. They highlight Claude 3.5 Sonnet overtaking OpenAI on coding benchmarks, Llama 3.1's synthetic data approach enabling 7B models to rival GPT-4, Mistral Large 2's non-commercial license and lost open-source crown, and on-device models like Gemini Nano and Apple Intelligence. The Quality Data Wars see NYT suing OpenAI, Reddit licensing data for $200M+, and synthetic data proving real for math (AlphaProof near IMO gold) and code. The Multimodality War includes ChatGPT Voice Mode delayed, Meta's Chameleon for native fusion, and Google's PaliGemma for PDF extraction. The renamed LLM OS War covers agent protocols, memory databases, and the collapse of model cost by an order of magnitude every four months, pushing startups toward vertical services like Brightwave and Dropzone that sell labor, not tools. The episode ends with a CrowdStrike joke about agent safety.

Training Llama 2, 3 & 4: The Path to Open Source AGI — with Thomas Scialom of Meta AI

Jul 23, 2024 · 1:04:34

Thomas Scialom, Llama 2 lead and Llama 3 post-training lead at Meta AI, explains why scaling laws must go beyond Chinchilla—training models longer on more tokens yields better inference efficiency than bigger models, a lesson that drove Llama 3's 15 trillion token pre-training. He details how synthetic data from Llama 2 bootstrapped Llama 3's post-training, eliminating human-written SFT data, and why RLHF outperforms imitation learning: humans are better discriminators than generators, enabling superhuman outputs. Scialom defends the dense 405B architecture over MoE, calls tokenizer vocab size underrated (128k tokens vs Llama 2's 32k), and reveals Llama 4's focus on agentic capabilities—tool use, multi-step reasoning—as the path to open-source AGI. Meta AI is hiring researchers with rigorous first-principles thinking.

This World Does Not Exist — Joscha Bach, Karan Malhotra, Rob Haisfield (WorldSim, WebSim, Liquid AI)

Apr 27, 2024 · 1:56:11

The episode features Karan Malhotra demoing WorldSim, a prompt that turns Claude 3 into a universe simulator via CLI; Rob Haisfield presenting WebSim, which generates functional websites on the fly; and Joscha Bach arguing simulative AI reveals consciousness as a virtual property, with LLMs creating agents as real as human minds, urging the California Institute for Machine Consciousness to build self-organizing silicon life. Malhotra shows WorldSim running “world.exe” to create Twitter inside a simulated universe, with users tweeting and Elon Musk moving Dogecoin. Haisfield demonstrates WebSim generating a 5D particle interface, a news RSS aggregator, and a face-swap webcam app via URL parameters like “secrets=revealed”. Bach explains consciousness as second-order perception in a simulated now, criticizes RLHF for lobotomizing models, and advocates for animist AI where software agents compete like spirits, not golems.

High Agency Pydantic over VC Backed Frameworks — with Jason Liu of Instructor

Apr 24, 2024 · 1:02:59

Jason Liu, creator of the Instructor library, explains why structured outputs from LLMs are best handled by a simple requests-like wrapper rather than a VC-backed framework, arguing that Pydantic-defined schemas via function calling outperform JSON Mode for typed responses. He details his journey from being bearish on LLMs at StitchFix to building Instructor on a bullet train to Japan after GPT-3 proved him wrong. Liu advocates for workflow-based DAGs over reactive agent loops, recommends using rankers rather than cramming 60+ tools into an API call, and credits high agency—trying many experiments and documenting conditions for revisiting failures—as key to his success. He also critiques the MLE hiring hype, urging startups to empower motivated AI engineers instead.

Supervise the Process of AI Research — with Jungwon Byun and Andreas Stuhlmüller of Elicit

Apr 11, 2024 · 1:05:28

Andreas Stuhlmüller and Jungwon Byun, co-founders of Elicit (formerly the nonprofit Ought), have built an AI research assistant that automates literature review and reasoning by breaking complex tasks into transparent, step-by-step processes. Their philosophy—"supervise the process, not just the outcome"—led them to start with human simulations before GPT-3 enabled a product pivot. Elicit now uses both open-source and closed models (e.g., GPT-4, Claude Haiku) for summarization, data extraction, and uncertainty flags, and recently launched computational notebooks for scalable, reusable workflows. The company transitioned from nonprofit to a Public Benefit Corporation, reached $1M revenue in four months, and now employs 12 people, focusing on senior software engineers to build reliable orchestration from unreliable components.

Personal AI Meetup - Bee, BasedHardware, LangChain LangFriend, Deepgram EmilyAI

Apr 6, 2024 · 58:54

This episode features Damien Murphy of Deepgram, Ethan of Owl/Bee, and Harrison of LangChain demonstrating how to build personal AI with real-time voice bots, wearable life-recording devices, and memory-enhanced journaling apps. Damien shows building a voice bot with subsecond latency using Deepgram, OpenAI, and open source code, costing about 6.5 cents per five-minute call. Ethan presents his Owl wearable that continuously records audio, triggers actions via hot word 'Scarlett,' and discusses challenges in adding vision and open source adoption. Harrison introduces LangFriend, a journaling app using conversational, semantic, and knowledge graph memory, referencing the Generative Agents paper for recency and importance weighting. The episode also highlights open source projects Whomane, Friend, and ADeus, arguing that hardware, voice, and memory are all necessary components for personal AI.

Why Google failed to make GPT-3 -- with David Luan of Adept

Mar 27, 2024 · 49:27

David Luan, co-founder of Adept and former early OpenAI leader, explains why Google failed to build GPT-3—its 'brain credit marketplace' prevented critical mass—and why Adept builds enterprise AI agents prioritizing reliability over generality. He recounts the GPT-2 demo that helped secure Microsoft's $1B investment and details Adept's goal: an AI teammate that can do anything a human does on a computer, targeting 'nines of reliability' for workflows like dispatching a physical truck. Luan contrasts Adept's vertical integration—training fast multimodal models (Fuyu) for charts and UIs—with pure-play foundation model companies that sell tokens, predicting commoditization. He notes Adept is sold out for Q1 and raised $420M, and explains how an augmentation focus creates a data flywheel from human oversight.

The "Normsky" architecture for AI coding agents — with Beyang Liu + Steve Yegge of SourceGraph

Dec 17, 2023 · 1:33:26

Beyang Liu and Steve Yegge of Sourcegraph explain how their AI coding assistant Cody achieves a 30% completion acceptance rate by prioritizing high-quality context over agents. They introduce the 'Normsky' architecture—a blend of Norvig's data-driven models and Chomsky's formal systems—arguing that non-agentic, deterministic context retrieval (using parsers, trigram indexes, and the BFG code graph) outperforms multi-hop LLM-based approaches for code completion and chat. The duo details their data pre-processing moat, the 'bin packing' challenge of stuffing relevant code into context windows, and why they use open-source StarCoder for completions while leveraging Claude/GPT-4 for chat. They also discuss the death of DSLs, the limitations of LSP and LSIF, and how Cody's web version lets users query any public repo instantly.

Beating GPT-4 with Open Source Models - with Michael Royzen of Phind

Nov 3, 2023 · 1:18:54

Michael Royzen, co-founder and CEO of Phind, explains how his company built a GPT-4-beating open-source model for developer Q&A. Royzen recounts founding SmartLens in high school, shifting to NLP after a Hugging Face demo, and creating an internet-scale LLM-powered RAG system in January 2022. He details Phind’s pivot to programmers, the Hacker News launch that gave it 1,500 points, and Paul Graham’s role in naming the company and introducing Ron Conway, who then connected Phind to NVIDIA for GPU access. Royzen argues that Phind’s model, fine-tuned from Code Llama 34B with extra data, closes the gap with proprietary models—especially on code reasoning—and that open-source will win the enterprise because the delta to GPT-5 will be small. He shares how Phind handles multi-step conversations via a pair programmer mode where users can pin messages, and reveals plans for reinforcement learning to reduce hallucination and improve correctness.

Why AI Agents Don't Work (yet) - with Kanjun Qiu of Imbue

Oct 21, 2023 · 1:12:37

Kanjun Qiu, CEO of Imbue (formerly Generally Intelligent), argues that AI agents remain unreliable because they lack robust reasoning and proper abstractions, which Imbue tackles by training foundation models optimized for reasoning over data like code. With a $200M Series B and $1B+ valuation, Imbue builds internal tools for debugging and inspecting agent decision-making, rejecting pure reinforcement learning after their Avalon environment revealed RL cannot handle planning. Instead, they emphasize natural language reasoning for inspectability, use code as a curriculum for reasoning, and design interfaces that let users fork and modify agents mid-execution. Qiu also discusses lessons from earlier startups (Sorceress in recruiting, Embark in VR), the importance of treating team members as creative agents, and her role in co-founding communal living space The Archive to foster scenius.

RAG is a hack - with Jerry Liu of LlamaIndex

Oct 12, 2023 · 1:13:25

In this episode of Latent Space, Jerry Liu of LlamaIndex argues that while RAG is fundamentally a hack, it remains the most practical approach for grounding LLMs with external data, and his open-source framework now serves 600,000 monthly downloads. Liu recounts how LlamaIndex originated from a hackathon at Robust Intelligence, evolving from an experimental tree-index to a modular toolkit spanning data loading, retrieval, synthesis, and agent loops. He explains the trade-offs between RAG and fine-tuning, noting that context window limits force algorithmic rather than learned optimizations, but RAG offers transparency and access control that fine-tuning cannot. Liu details the company’s $8.5M Greylock raise, the launch of LlamaHub for community-contributed data loaders, and the open-source SEC Insights app as a production-grade template. Looking ahead, he emphasizes the need for better retrieval benchmarks and predicts that future personalization will move beyond vector stores into model-internal memory architectures.