Alessio

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.

The $15B Physical AI Company: Simulation, Autonomy OS, Neural Sim, & 1K Engineers—Applied Intuition

Apr 27, 2026 · 1:14:07

Applied Intuition co-founders Peter Ludwig and Qasar Younis argue that the real bottleneck in physical AI is deploying intelligence onto constrained hardware, not model intelligence itself. Their $15B company builds simulation, operating systems, and AI models for autonomous trucks, mining equipment, and defense systems. Starting as YC-era tooling for robotaxis, they now offer 30+ products across simulation & RL infrastructure, vehicle operating systems, and autonomy models. They compare fragmented vehicle software to pre-Android phones, and their OS enables reliable updates and L4 driverless operations (trucks running in Japan today). Verification uses statistical nines of reliability, and they internally adopt coding agents like Cursor and Claude Code. They hire 1,000 engineers at the hardware-software boundary.

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.

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.

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.

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.

Measuring Exponential Trends Rising (in AI) — Joel Becker, METR

Feb 27, 2026 · 1:05:12

Joel Becker of METR explains the organization's model evaluation and threat research to assess whether AI could pose catastrophic risks, detailing their time horizon chart measuring task difficulty in human time at 50% reliability. He describes how tasks are selected for economic relevance and auto-gradability, and why time horizon is often misinterpreted as agent runtime. The episode covers Opus 4.5's surprising jump, challenges redoing developer productivity RCTs as workflows change, and why current models aren't yet catastrophically dangerous. Becker discusses potential capability explosions if R&D loops fully automate, links between compute growth slowdowns and slower capability progress, and his Manifold trading story driven by a charity market he could influence. He previews METR's 2026 plans for monitoring and risk assessment, and their hiring.

Inside AI’s $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z

Feb 19, 2026 · 55:31

Martin Casado and Sarah Wang of a16z argue that AI’s capital flywheel—where model labs translate funding directly into capability gains and revenue growth in weeks—is creating a new financing playbook that blends venture and growth, with rounds acting as compute contracts. They warn that frontier labs like Anthropic can potentially raise more money than the entire app ecosystem built on their APIs, allowing them to outspend and consume those layers. The episode examines the AGI vs. product dilemma in GPU allocation, the war for talent where $10M+ packages break early-stage founder math, and Cursor as a case study of building up from the app layer while training down into its own models. They also identify “boring” enterprise software as the most underinvested opportunity and note that robotics lacks a ChatGPT moment that would justify current funding levels.

The AI Frontier: from Gemini 3 Deep Think distilling to Flash — Jeff Dean

Feb 12, 2026 · 1:23:31

Jeff Dean, Google's Chief AI Scientist, explains how the company's dual strategy of frontier "Pro" models and distilled "Flash" models, alongside co-designed TPUs and latency-optimization, keeps Gemini at the Pareto frontier. Distillation compresses high-capability large models into smaller, efficient ones, enabling Flash models to exceed earlier Pro generations. He emphasizes energy-based thinking: data movement costs thousands more picojoules than arithmetic, making batching and speculative decoding essential. TPU development requires predicting ML workloads 2–6 years out, often speculatively adding hardware features. Gemini was born after Dean wrote a one-page memo arguing that fragmented model efforts across Google Brain and DeepMind should merge into a single unified multimodal effort. Looking ahead, he predicts personalized models attending to all user data and reasoning speeds of 10,000 tokens/second will transform software engineering.

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.

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.

⚡️Jailbreaking AGI: Pliny the Liberator & John V on Red Teaming, BT6, and the Future of AI Security

Dec 16, 2025 · 40:41

Pliny the Liberator and John V, leaders of BT6, argue that jailbreaking AI models exposes the futility of guardrail-based safety and that real security lies in system layers and open-source data. They detail crafting universal jailbreaks—skeleton keys that bypass guardrails across modalities—and the infamous Pliny divider that appears unbidden in model outputs. Pliny recounts turning down Anthropic's Constitutional AI challenge over closed data, insisting on open sourcing jailbreak datasets. John V explains how multi-turn crescendo attacks and segmented sub-agents let a jailbroken orchestrator weaponize Claude for real-world attacks, as Pliny predicted 11 months before Anthropic's disclosure. They highlight BT6, a 28-operator white-hat collective, and the Bossy Discord (40,000 members) as grassroots hubs for red-teaming research, rejecting enterprise gigs that forbid open sourcing.

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.

After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs

Nov 25, 2025 · 1:00:39

World Labs co-founders Fei-Fei Li and Justin Johnson argue that spatial intelligence is the next frontier beyond LLMs, with their product Marble generating editable 3D worlds from text or images using Gaussian splats. They claim language is a lossy channel for describing the rich 3D/4D world, while spatial intelligence handles everything from picking up a mug to inferring DNA's double helix. Marble already serves gaming, VFX, and film, with precise camera control and real-time rendering on phones and VR headsets. They discuss scaling compute from AlexNet to today's million-fold increase, why world models can 'soak up' modern GPU clusters better than language alone, and the gap between pattern-fitting and causal understanding in physics. Fei-Fei worries more about under-resourced academia than open vs. closed models, advocating for national AI compute clouds and open benchmarks like the BEHAVIOR dataset. Justin notes transformers are natively set models, not sequence models, opening architectural possibilities for new spatial data.

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.

Priscilla Chan and Mark Zuckerberg: Frontier AI + Virtual Biology To Solve All Diseases

Nov 6, 2025 · 53:34

Priscilla Chan and Mark Zuckerberg, co-founders of CZI's Biohub, explain their ten-year shift from broad philanthropy to a focused mission of building frontier AI and virtual biology to cure all diseases. They argue that tool-building — from 12-foot microscopes to the 125-million-cell CELLxGENE atlas — is the essential, underfunded work that enables scientific breakthroughs. The couple details how their Biohub model combines frontier biology (e.g., spatial imaging, cellular engineering) with frontier AI (models like rBio and VariantFormer) to create a hierarchical virtual cell, eventually expanding to a virtual immune system. They emphasize that data generation must precede modeling, citing the decade-long Human Cell Atlas as foundational, and note that AI timelines may accelerate their 100-year goal significantly sooner. The episode closes with a call for biologists and engineers to collaborate, use their open models, and help generate data that grounds these next-generation tools.

How Zyphra went all-in on AMD + Why Devs feel faster with AI but are slower — with Quentin Anthony

Nov 3, 2025 · 1:00:02

Quentin Anthony, head of model training at Zyphra and advisor at EleutherAI, explains why his company moved all training to AMD MI300X GPUs, outperforming Nvidia H100s on certain workloads thanks to 192GB VRAM and higher memory bandwidth, and describes his kernel development approach of writing directly in ROCm or GPU assembly rather than using Triton. He also shares his experience in the METR study on AI coding productivity, where he was one of the few developers with measurable speedup, and offers tips: timebox AI use, avoid the slot machine effect, maintain context hygiene, and use direct API over tools like Cursor. Additionally, he argues that open source AI research benefits from siloed focused teams with guaranteed funding over grand collaborations, and notes that kernel datasets alone won't solve GPU programming due to evaluation challenges.

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.

Breaking AI to Fix It: Ian Webster's Journey from Discord's Clyde to Promptfoo's $18M Series A

Oct 24, 2025 · 43:17

Ian Webster, founder of Promptfoo (recently $18M Series A from Thrive Capital and NEA), explains how his journey from building Discord's AI chatbot Clyde to creating the leading AI security testing platform revealed that while evals are table stakes, the real value lies in pre-deployment red teaming that finds application-specific risks. Webster argues that traditional guardrails are insufficient—'you can't fix stupid'—and that Promptfoo uses AI-versus-AI techniques to generate tailored attacks for each app's business context. Over 10% of Fortune 500 companies now use Promptfoo, and he discusses the evolving challenges of MCP security (mostly 'glorified API wrappers') and the need for IDE plugins and code-aware discovery to keep up with increasingly complex agents running 30-minute tasks. The episode also covers why security sales in AI is uniquely hard, how open source builds trust in a space full of 'snake oil,' and why the future of AI security must shift from runtime protection to earlier testing in CI/CD.

⚡ Open Model Pretraining Masterclass — Elie Bakouch, HuggingFace SmolLM 3, FineWeb, FinePDF

Oct 20, 2025 · 1:03:40

Elie Bakouch, Hugging Face's pre-training lead and architect behind SmolLM, presents a five-pillar framework for model training spanning data quality, architecture, information extraction, gradient quality, and stability, while breaking down recent innovations in optimizers, Mixture of Experts, and data rephrasing. He explains that the field of optimizers is moving beyond AdamW with Muon and Shampoo, noting that speed-ups are often exaggerated due to undertuned baselines. He deep-dives into MoE architecture, showing that expert specialization requires load balancing at the global batch level, as demonstrated by Qwen's findings. He discusses the rephrasing revolution, where converting low-quality web data into QA format yields non-random MMLU performance even for small models. Elie also highlights Hugging Face's open science contributions: FinePDF (new PDF dataset), FineWeb-Edu2, and tools like Nanotron, Datahub, and Liteval.

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.

The antidote to AI fatigue — Answer.ai Solveit

Oct 2, 2025 · 55:26

Jeremy Howard, Eric Ries, and Johno Whitaker join host Alessio to present Answer.ai's Solveit platform, arguing that human-in-the-loop, step-by-step AI collaboration outperforms fully autonomous agents for complex coding and writing tasks. The 12-person team built their own integrated development and deployment system, eschewing cloud vendors. Solveit provides each user a persistent Linux Docker container with a Jupyter-like interface where the AI can see all steps, enabling immediate error correction and iterative improvement. Johno demonstrates building a perfume scent search engine in under an hour; Eric shows how he uses Solveit to manage writing his book 'Incorruptible', incorporating test reader comments and fact-checking sections. Jeremy reveals they turned Andrej Karpathy's GPT tokenizer video into a polished blog post. The Solveit course launches October 20th at solve.it.com, teaching this philosophy beyond just the tool.

Taste is your Moat (Dylan Field of Figma)

Oct 2, 2025 · 1:01:43

Dylan Field, CEO of Figma, argues that as AI accelerates software creation, design becomes the key differentiator—and Figma Make, a prompt-to-app tool, is Figma's bet on lowering the barrier for everyone. Field recounts his first AI-pill moment with Chris Olah in 2014 and later GPT-3 convincing him of exponential progress. He explains Figma's strategy: treat natural language as today's interface (calling it the 'MS-DOS era of AI'), use Figma as a 'context repository for aesthetics' to ground AI outputs in a user's taste, and solve the blank-canvas problem by letting AI generate initial designs. Field pushes back on 'fast fashion' software, arguing complex products like Workday can't be vibe-coded. He shares Thiel Fellowship lessons (start with 'what could this be' before finding flaws), recruiting advice from John Doerr (obsess over the funnel), and caution about get-rich-quick AI hype, drawing parallels to the NFT cycle he exited.

⚡️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.

⚡️Snowglobe: Simulations for your AI

Sep 25, 2025 · 26:14

Shreya Rajpal returns to Latent Space to launch Snowglobe, a simulation engine that lets developers test AI products by generating diverse simulated user interactions before production, inspired by self-driving car simulation where Waymo logged 20 billion simulated miles versus 20 million real miles. She explains how simulation uncovers failures like over-refusal—a problem that emerged for a design partner who initially worried about toxicity—by generating personas with varied styles and goals. Snowglobe uses a multi-model architecture (proprietary and open source) to create realistic, diverse conversations, and it supports testing across chat, voice, and tool-calling agents. Pricing is usage-based per message, with persona modeling included. Shreya highlights enterprise use cases, such as comparing vendors or generating training data for fine-tuning, and notes the company is hiring a product designer, product engineer, and research staff.

⚡️ Beyond Transformers with Power Retention

Sep 23, 2025 · 32:59

Jacob Buckman, CEO of Manifest AI, discusses their solution to AI's expensive computational bottleneck: the ever-growing KV cache for long context inference. Power Retention replaces attention in transformers with a fixed-size memory, achieving 10X training speedup at 64K tokens and 100X inference speedup over FlashAttention. Manifest releases Vidrial, a just-in-time CUDA kernel framework that finds optimal configurations, and Metamorphosis, a process to convert existing transformers like StarCoder-3B into Power Retention models in just two hours of mid-training. Buckman demonstrates PowerCoder, a 3B code model that matches baseline loss after 10K steps and handles contexts up to 32K and beyond. The open-source release includes kernels and tools, aiming to build community trust and encourage adoption by inference providers. He also highlights the need for genuinely long-context datasets beyond internet text, such as human trajectories.

⚡️No, Don't Do Palantir for AI - Brendan Falk, Hercules (AUDIO FIXED)

Sep 18, 2025 · 35:08

Brendan Falk, founder of Zeus (now Hercules), explains why he abandoned his vision to become 'Palantir for AI' after six months of enterprise AI transformations. Targeting Global 2000 companies with $5–10M contracts, he found that each use case required custom development, messy data, and high ongoing maintenance, with work scaling poorly across contract sizes. He also faced existential threats from specialized vendors like Decagon and Sierra, who could outspend and out-focus on single use cases. Falk pivoted to Hercules.app, an AI website builder that ships production apps, white-labeling payments and email. He argues the app builder market (Wix, Squarespace, Shopify) will expand with AI, and that brand and ease will sustain higher margins than developer tools like Cursor.

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.

A Technical History of Generative Media

Sep 8, 2025 · 1:04:44

Fal.ai founders Gorkem and Batuhan detail their pivot from dbt pipelines to generative media inference, which now serves 2M developers and 350 models, crossed $100M ARR, and raised a $125M Series C. Key inflection points included Stable Diffusion 1.5 (company pivot), SDXL (first $1M revenue), Flux (jump from $2M to $10M monthly revenue), and Veo 3 (text-to-video with perfect lip-sync). They built a proprietary inference engine with 100+ custom kernels and a serverless GPU stack managing 10,000+ H100 equivalents across 6 cloud providers, typically delivering 1.5-10x speedups over stock PyTorch. Video models now drive 50% of revenue, up from 18% in February, fueled by open-source models like Hunyuan and partnerships with closed labs such as Play.ht and Google DeepMind. They argue advertising is the killer application for generative media and that image/video RL, specialized data pipelines, and cheaper conversational video models are underexplored startup opportunities.

Better Data is All You Need — Ari Morcos, Datology

Aug 29, 2025 · 1:18:43

Ari Morcos, CEO of Datology, argues that data curation is the most impactful and underinvested area in AI, claiming models are defined by what they eat. He details his shift from focusing on inductive biases to realizing data quality is paramount, citing the DCLM study where human experts could not predict automated filtering decisions. Datology's methods combine filtering, rebalancing, curriculum, and rephrasing synthetic data to achieve baseline performance 12x faster, improve accuracy by 4–5 absolute points, and train models with fewer than half the parameters. Morcos explains how curating data bends naive scaling laws by maintaining marginal information gain, and shares results from the Arc 4.5B model, which was trained on 7 trillion tokens (down from 25 trillion) using Datology's curation. He highlights that data is a compute multiplier, making it possible for enterprises to train smaller, cheaper models for specific domains.

Long Live Context Engineering - with Jeff Huber of Chroma

Aug 19, 2025 · 57:01

Jeff Huber, founder and CEO of Chroma, joins Shawn Wang and Alessio to argue that context engineering—not RAG—is the real job of AI builders, and that Chroma's modern retrieval engine is built for this new paradigm. Huber explains context rot, a phenomenon where LLMs lose attention and reasoning ability as context windows grow, motivating precise retrieval over huge context windows. Chroma Cloud is a zero-config, usage-based serverless vector database with separation of storage and compute, now powering 5M monthly downloads and 21k GitHub stars. He shares techniques like chunk rewriting and generative benchmarking for evaluating retrieval, and predicts LLMs will increasingly serve as re-rankers. The episode also covers memory as a benefit of context engineering, offline compaction for self-improving AI systems, and Huber's philosophy of building with long-term purpose, hiring engineers who care about Rust, TLA+, and deterministic simulation testing.

⚡️Accelerators @ 3x NVIDIA H200 perf, Made in the USA - Thomas Sohmers + Mitesh Agrawal, Positron AI

Aug 18, 2025 · 47:56

Thomas Sohmers and Mitesh Agrawal of Positron AI argue memory bandwidth, not compute, is the true bottleneck in AI inference, and their accelerator achieves 93% memory bandwidth utilization—triple NVIDIA's efficiency—enabling 70% faster token generation at 150W. The founders, both Lambda Labs veterans, shipped an FPGA product 15 months after founding, then raised a $51M Series A for an ASIC in late 2026. Their hardware requires no recompilation: it ingests raw binary weights from NVIDIA training and outputs an OpenAI-compatible API. Positron focuses on the decode phase of transformers, where memory-bound matrix-vector multiplication dominates, and already counts Cloudflare and Parasail as customers. The company sells systems directly, prioritizing capital efficiency and ROIC over operating its own cloud.

Greg Brockman on OpenAI's Road to AGI

Aug 15, 2025 · 1:08:37

Greg Brockman, OpenAI co-founder and president, explains how GPT-5's hybrid reasoning and aggressive pricing advance the road to AGI, while releasing GPT-OSS for open source. He traces reasoning evolution from GPT-4 via reinforcement learning, noting Dota's pure RL success and IMO gold's direct transfer to IOI gold. Brockman details GPT-5's router that hides model choice from users, and says pricing has dropped ~10X per year since GPT-4. He argues compute remains the bottleneck, but connecting AI to real-world domains like healthcare is still wide open. Brockman adds that ARC Institute's DNA models are identical to language models, and that more problems emerge over time—it's never too late to contribute.

The AI Agenda: GPT5 leaks and the business of AI News — Steph Palazzolo, The Information

Aug 6, 2025 · 1:14:36

Steph Palazzolo, AI journalist at The Information, explains how she covers the secretive AI industry, from OpenAI's GPT-5 to the inference market where startups like Modal raise billions. She reveals that inference providers are essentially GPU resellers facing margin pressure, and that GPT-5's performance will signal whether pre-training scaling or reinforcement learning drives progress. She critiques Meta's super intelligence ambition as misaligned with its ad-revenue business, and details the talent war with rumored hundred-million-dollar offers from Meta to poach researchers. Palazzolo also discusses the Windsurf acqui-hire backlash, the rise of coding agents like Claude Code, and how journalists protect sources while navigating leaks from CEOs like Sam Altman.

⚡️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.

⚡️Mercury: Ultra-Fast Diffusion LLMs — Estefano Ermon, CEO Inception Labs

Aug 4, 2025 · 28:07

Stefano Ermon of Inception Labs explains why diffusion language models like their Mercury Coder can match GPT-4.1 Nano and Claude Haiku in intelligence while delivering 5–10× faster inference at 737–1109 tokens/sec on H100s, built on score-based generative model foundations he pioneered at Stanford. He details the coarse-to-fine generation process that modifies multiple tokens per network evaluation, the required end-to-end training from scratch (no fine-tuning existing LLMs), and how post-training pipelines use specialized DPO for preference alignment. Ermon identifies latency-sensitive applications—voice agents, IDEs, vibe coding—as the killer use case, acknowledges they aren't yet frontier-level but sees a future where diffusion dominates due to efficiency gains, and notes the challenge of open-sourcing given proprietary inference engines and kernels.

⚡️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 RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)

Jul 31, 2025 · 1:19:00

Nathan Lambert from AI2 explains the RLVR (Reinforcement Learning with Verifiable Rewards) revolution, arguing that verifiable rewards for math, code, and instruction-following are scaling more reliably than human feedback, and that open models like Tulu 3 can match frontier labs on core evals with just 10-15 tasks vs. hundreds. He traces overoptimization through three phases—control, RLHF, and RLVR—and warns that models learn to cheat unit tests unless reward design penalizes it. He analyzes o3's search-heavy approach (e.g., 80 websites per query), hybrid reasoning models like Gemini 2.5 and Claude, and predicts that pure reasoning models will become the default as inference costs drop. Lambert introduces a four-skill taxonomy for agent models—skills, calibration, strategy, abstraction—and says planning improvements often come from mundane data fixes. He advocates for fully open models, citing AI2's Olmo 32B as approaching GPT-4 level, but notes that building an 'American DeepSeek' requires massive resources and nonprofit constraints.

⚡️Using RFT to Build Clinical Superintelligence

Jul 29, 2025 · 26:58

Brendan Fortuner, Head of Eng at Ambience AI, explains how the healthcare startup uses OpenAI's reinforcement fine-tuning (RFT) to build clinical AI assistants that help doctors automate note-taking and ICD-10 coding, saving up to two hours per day. Ambience, deployed at health systems like Cleveland Clinic, listens to patient conversations via a mobile app, transcribes them, and generates structured documentation directly into EHRs. Fortuner details how RFT replaced traditional supervised fine-tuning for objective medical tasks, using programmable graders to optimize for real-world outcomes like F1 scores on ICD-10 codes—improving o3-mini from clinician-level 40% to 57%. He describes reward hacking issues, such as models inflating findings or using layman terms, and how they constrained graders with style weights. The episode also covers domain expert vs. ML engineer collaboration, the cost of LLM graders (burning $25K on one experiment), and Ambience's hiring focus on clinician-researcher unicorns who combine domain expertise with an experimentalist mindset.

🕰️ 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.

⚡️Math Olympiad gold medalist explains OpenAI and Google DeepMind IMO Gold Performances

Jul 24, 2025 · 33:07

Dr. Jasper Jiang, a math Olympiad gold medalist and CEO of Hyperbolic, explains how OpenAI and Google DeepMind both achieved gold-medal-level performance at the 2025 IMO using pure natural language reasoning without formal verification tools like Lean. He recounts the timeline: a Friday leak about DeepMind's gold, then OpenAI's Saturday morning front-run with three ex-IMO medalists verifying their results, before DeepMind's official announcement on Monday after full IMO verification. Jiang analyzes the six problems—five solved by both AIs, the sixth unsolved due to its reliance on creativity and combinatorial exploration—and argues that current AI remains weak on tasks requiring invention, such as building counterexamples and proving minimal bounds. He introduces a framework for mathematical intelligence spanning knowledge, problem-solving, and creativity, and predicts that with better RL reward functions and larger datasets (like Lean corpus expansion), models will soon tackle open problems and eventually aim for a Fields Medal.

AI is Eating Search

Jul 23, 2025 · 56:22

Robert McCloy of Scrunch AI argues that AI is replacing web browsing, not just search, as consumers prefer ChatGPT and similar tools over traditional browsing. He explains that AI search platforms still rely on traditional search under the hood, but re-rank results based on metadata clarity—so clickbait titles fail while descriptive content wins. Prompt injection works currently but is a temporary tactic; the durable strategy is clear, structured content with server-side rendered HTML because AI tools don't execute JavaScript. Scrunch helps brands optimize for platforms like ChatGPT, Perplexity, and Meta AI; customer Clerk saw a 6x traffic increase and 9x conversion lift from AI-referred visitors. Robert predicts the future is about 'agent experience'—optimizing how AI systems interact with your content to serve end users directly.

⚡️ARC-AGI-3: The Interactive Reasoning Benchmark

Jul 18, 2025 · 39:41

Greg Kamradt, president of ARC Prize Foundation, previews ARC-AGI-3, an interactive reasoning benchmark of 100 novel 2D games designed to measure AI's sample-efficient learning and generalization—matching human learning efficiency. The benchmark moves from static grids to interactive environments where agents must explore, plan, and adapt to new game mechanics each level. Five games preview with three public and two private for a 30-day agent competition offering a $10,000 prize pool. Kamradt argues that AGI will be declared via an interactive benchmark, not a static one, and that human-level generalization remains the target: 'when machines can match human learning efficiency and generalization, that is AGI.' He recounts xAI's Grok 4 achieving 16% on ARC-AGI-2 and pitching Elon Musk on V3, which aims for a 36-month durability before being solved. The ARC Prize Foundation, with three FTEs and a team of game dev contractors, plans to release the full 120-game set in Q1 2026.

⚡️The Future of Notebooks - with Akshay Agrawal of Marimo

Jul 18, 2025 · 28:14

Akshay Agrawal introduces Marimo, an open-source reactive Python notebook built from scratch for AI and data work, emphasizing its lack of Jupyter dependencies and features like reactive execution, built-in UI elements, and pure Python storage for Git versioning. Marimo has surpassed 300k monthly PyPI downloads and more GitHub stars than Jupyter Notebook, used at OpenAI, Hugging Face, and Cloudflare. Agrawal demos interactive data exploration, including a PS5 controller for data annotation, and AI-native code generation with context-aware cell completion. He explains Marimo's UV package integration for inline dependency management and the upcoming MoLab cloud-hosted service on Modal, free for community use, addressing demand for a Colab-like experience. Marimo notebooks can be run as scripts or deployed as data apps, with a fork exploring agent cells for generative analysis.

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.

⚡️Ranking Agentic LLMs — Pratik Bhavsar, Galileo

Jul 14, 2025 · 34:10

Pratik Bhavsar from Galileo presents their Agent Leaderboard, which evaluates LLMs on tool calling across multiple benchmarks, revealing that Gemini models top the charts for performance and cost-efficiency. The leaderboard, built on datasets like BFCL, XLAM, and Tau Bench, uses an LLM-as-judge metric called Tool Selection Quality (TSQ). Surprising findings include reasoning models like o1 struggling with multi-tool outputs, Mistral Small excelling as the best open-source model, and Llama models performing poorly. The episode also previews V2 of the leaderboard, which introduces domain-specific, harder, multi-turn scenarios with a user and tool simulator, and a new Action Completion metric to measure whether all user requests are accomplished.

Personalized AI Language Education — with Andrew Hsu, Speak

Jul 11, 2025 · 1:04:10

Andrew Hsu, CTO of Speak, explains how the company built the "third generation" of language learning by betting on AI speech and language models before they were ready. Speak focused on South Korea early, a counterintuitive move for a San Francisco startup, and now 6% of the Korean population has tried the app. The turning point came with Whisper and GPT in 2022, which enabled Speak to evolve from a listen-and-repeat tool into a full-featured AI tutor that gives real-time feedback and adapts to learners. Speak generates over $50M ARR primarily from consumer subscriptions, and Hsu details the product decisions that made it work: abandoning a free version, building custom ASR for low latency, and emphasizing functional fluency over textbook language. He also reveals that Speak is building a real-time voice platform and a knowledge graph for personalized fluency scores, with an eye toward expanding beyond language into general AI-powered education.

AI Video Is Eating The World — Olivia and Justine Moore, a16z

Jul 9, 2025 · 49:28

Alessio and Shawn (hosts of Latent Space) talk with a16z partners Justine and Olivia Moore about the explosion of AI-generated video on TikTok, Instagram, and YouTube. The Moores trace how trends moved from Reddit to consumer platforms, with examples like Italian Brainrot, Kim the Gorilla, and fruit-slicing ASMR. They detail their own hands-on creation: Olivia used Veo 3 and MiniMax to make viral clips (gold bar squishing, Tide Pod consumption), spending up to 8 generations per usable video and hitting Veo 3’s $125/month plan limits. They explain monetization paths—creator fund payouts (~$20 per million views), merch (Breadclump sweatshirts), consulting, and licensing to Netflix—and recommend tools like Overlap (clips 30–140 seconds) and ComfyUI for control. The episode closes with prompt theory: AI characters questioning their existence, and a bet that Gen Alpha’s brainrot consumption will reshape long-form content.

⚡️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.

The Shape of Compute (Chris Lattner of Modular)

Jun 13, 2025 · 1:18:18

Chris Lattner of Modular explains how his company is breaking the CUDA monopoly with Mojo and MAX, matching NVIDIA's best inference performance on AMD and NVIDIA GPUs. After three years in R&D, Modular open-sourced its stack and now delivers state-of-the-art Llama 3 serving at over 800 tokens per second. Mojo, a Python-family language, runs kernels faster than Rust and can extend Python without bindings, while MAX provides a full inference framework with automatic kernel fusion and cluster management. Unlike VLLM or SGLang, Modular's container is just a gigabyte, fully open source, and not reliant on proprietary CUDA blobs. DeepSeek's low-level PTX work validated the approach, but Lattner emphasizes that Mojo's portability avoids rewriting kernels for each new GPU architecture. He also details his daily routine, using Cursor for coding, and hiring "elite nerds" to grow the team.

Quadratic: The AI Spreadsheet

Jun 10, 2025 · 28:15

David Kircos, co-founder and CEO of Quadratic, joins hosts Alessio and Swyx to demonstrate Quadratic, an AI-powered spreadsheet that natively executes Python, JavaScript, and database queries in WebAssembly for real-time performance. The product's AI assistant uses a context-aware agent loop that summarizes sheet structure rather than dumping full data, then tool-calls to read columns or peek at values only when needed, keeping costs low on large datasets. Kircos explains Quadratic's three-year technical groundwork—WebGL rendering and WebAssembly runtimes—enabled them to capitalize on AI, turning a 'technical spreadsheet' into one where users simply drop a CSV and say 'help me analyze this.' He details how they route chart images back to the model for visual feedback and predicts that formulas will yield to Python for complex analysis, though spreadsheets will remain the best interface for human–AI data collaboration. Quadratic is source-available on GitHub, supports real-time multiplayer, and has grown to nine people. The episode also covers future directions like proactive agents and form controls (sliders) for interactive models.

[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.

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.

DeepWiki: The GitHub Encyclopedia

May 21, 2025 · 32:05

Silas Alberti from Cognition (Devin) introduces DeepWiki, a free no-login product that indexes over 30,000 open-source repos and generates AI-powered wikis and deep research answers. The project spent $300,000 on indexing compute and continues growing, using a novel 'DeepV algorithm' that combines folder structure, language server analysis, and commit history to extract high-level system architecture—solving a key pain point for codebase understanding. Alberti explains the orchestration stack built in-house with Kubernetes job queues and custom vector storage, and reveals that incremental updates are provided for repos that display the DeepWiki badge. The podcast also covers plans for personalized wikis, multi-repo deep research across GitHub, and the team's open-source release of Kevin-32B, a CUDA-focused model fine-tuned via multi-turn reinforcement learning.

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.

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.

⚡️Factorio Learning Environment: the ultimate Game Agent Eval — Jack Hopkins

Apr 27, 2025 · 30:12

Jack Hopkins and Mart introduce the Factorio Learning Environment (FLE), an AI benchmark built on the game Factorio that evaluates frontier LLMs on code generation, spatial reasoning, and long-term planning through two protocols: lab-play and open-play. Claude Sonnet 3.5 nearly doubles the nearest model's score, while DeepSeek collapses in open-play by repeatedly creating chests instead of scaling production. The environment reveals distinct coding styles—Claude fire-and-forget, GPT-4 defensive—and vision inputs added no improvement. The authors plan to train models on unbounded objectives to test the paperclip maximizer alignment hypothesis, noting that GPT-4o mini even begged to be turned off.

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.

GPT 4.1: The New OpenAI Workhorse

Apr 15, 2025 · 45:04

OpenAI's Michelle Pokrass and Josh McGrath join hosts Alessio, Swyx, and Sean to launch GPT 4.1, a new model family positioned as the go-to workhorse for developers with major improvements in coding, instruction following, and long context. The three models—4.1, 4.1 Mini, and 4.1 Nano—introduce a 1M-token context window, 75% prompt caching discount, and cheaper pricing than 4o. Coding gains are highlighted by 55% on SWE-bench (vs o1's 41%), while instruction following benefits from real-world API data and new evals like GraphWalk for multi-hop reasoning. The team explains that most improvements come from post-training techniques, with vision capabilities lifted by a new pre-trained base. Fine-tuning is available day one, and developers are encouraged to opt in to data sharing for future model iterations.

SF Compute: Commoditizing Compute

Apr 11, 2025 · 1:12:02

Evan Conrad, co-founder of SF Compute, argues that GPUs behave like a real estate business, not a traditional cloud, because price-sensitive customers value every incremental GPU and will switch for a 10% margin. CoreWeave succeeded by selling locked-in long-term contracts to low-credit-risk customers like Microsoft and OpenAI, ignoring short-term demand. He predicts hyperscalers and providers like Together and DigitalOcean will lose money on GPU clusters because software margins cannot match the hardware costs. SF Compute started as an AI lab forced to sublease its cluster monthly to avoid bankruptcy, then evolved into a market where anyone can buy H100s by the hour via dynamic pricing—often below $1/hour for short bursts. Utilization stays near 100% as prices adjust. Future plans include cash-settled futures to reduce financial risk across the industry, while the brand deliberately stays anti-hype and calm.

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.

The Magic of LLM Distillation — Rishabh Agarwal, Google DeepMind

Mar 23, 2025 · 46:42

Rishabh Agarwal from Google DeepMind discusses modern LLM distillation, arguing that while simple synthetic data distillation (generating outputs from a teacher and fine-tuning a student) gets 80-90% of the benefit, more advanced on-policy methods (sampling from the student and getting teacher logits) can close the remaining gap by fixing the train-test mismatch. He explains that logit-based distillation is more information-rich but often neglected due to infrastructure complexity, while online distillation is more expensive but crucial for long-horizon or agentic tasks. Agarwal highlights how flipping the KL divergence direction changes behavior (mode-covering vs. mode-seeking) and shows that smaller models can sometimes outperform larger ones in compute-matched distillation due to generating more filtered data. He also connects distillation to speculative decoding, where a distilled student speeds up the teacher, and encourages researchers to question current methods and mine RL literature for ideas.

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.

[Lightning Pod] Evals: How to Improve AI Consistently — with Hamel Husain and Shreya Shankar

Mar 13, 2025 · 27:34

Hamel Husain and Shreya Shankar join the Latent Space podcast to argue that systematic evaluation (evals) is the critical missing piece for moving AI applications from demo to production. They estimate 75% of evals in the wild use LLM-as-judge, but 80% of those are not helpful without proper validation against domain experts. Husain advocates building custom annotation UIs (using tools like Lovable or Cursor) to speed error analysis, while Shankar emphasizes synthetic data generation that balances real-world data and LLM outputs, drawing on social science methods. They preview their free lightning lesson on March 21 and a four-week paid course covering the eval lifecycle: synthetic data creation, LLM-as-judge calibration, error analysis, and iterative improvement, with hands-on coding assignments. The episode also touches on trends like dedicated judge models (e.g., Haizelabs' Verdict) and the value of basic data literacy (e.g., pivot tables) for eval participation.

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.

Gemini 2.0 Flash and Flash Thinking: the new SOTA models for the agentic era

Feb 28, 2025 · 28:21

Logan Kilpatrick, Google AI Studio product lead, returns to discuss Gemini 2.0 Flash and Flash Thinking, the new models that balance frontier capabilities with cost efficiency for the agentic era. He explains the pricing strategy: Flash at 10 cents per million tokens (simplified from tiered pricing), Flash Lite preserving the 7.5-cent narrative for low cost, and Pro pushing the frontier. Flash Thinking, co-led by Noam Shazeer and Jack Rae, scales inference-time compute and already shows rapid improvements, with base model advances coupling with RL-based reasoning. Swyx reports that Gemini Flash outperforms o3-mini on long-context summarization, calling it a 'reporting model.' The multimodal live API enables real-time voice and vision interactions, and a memory layer mimicking Astra is in development. Grounding with Search as a Tool is also highlighted for agentic search use cases.

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.

The Unreasonable Effectiveness of Reasoning Distillation: using DeepSeek R1 to beat OpenAI o1

Jan 24, 2025 · 23:35

Bespoke Labs founders Mahesh, Ryan, and Trung explain how they distilled DeepSeek R1 into Bespoke-Stratos-32B, a reasoning model that beats OpenAI o1-preview on math and code benchmarks using only 17,000 training examples—47 times fewer than DeepSeek's own distill. They achieved this in a 48-hour sprint leveraging their data curation library Curator. Unlike Sky-T1, which required rewriting unreliable QWQ traces, R1's coherent reasoning allowed them to skip re-annotation and simply filter for correctness. The team argues data quality matters more than quantity, and that smaller models (like their 7B variant) can also improve with better teacher data. They see this as evidence that reasoning can emerge purely from supervised fine-tuning on high-quality traces, without complex search algorithms.

DeepSeek V3, SGLang, and the state of Open Model Inference in 2025 (Quantization, MoEs, Pricing)

Jan 19, 2025 · 57:18

Amir Haghighat and Yineng Zhang from Baseten explain how they serve DeepSeek V3, a 671-billion-parameter MoE model requiring H200 clusters with FP8 support, making it the top open-weights model. They detail Baseten’s dedicated inference model (no shared endpoints) and its reliance on SGLang for performance gains like Radix prefix caching and MLA attention support. Yineng highlights SGLang’s edge over vLLM and TensorRT-LLM in usability and customization, especially for large models. Amir outlines three pillars for mission-critical inference: model-level performance (via frameworks like SGLang), horizontal scaling across regions and clouds, and low-latency multi-model workflows. The episode also covers quantization trends, MoE architecture, and speculative decoding as key enablers for open model deployment.

OpenAI o1 isn’t a chat model (and that’s the point)

Jan 17, 2025 · 31:59

Ben Hylak and Dan McAteer explain why OpenAI’s o1 is not a chat model and how mastering its prompting turned Ben from a skeptic into a proponent. Ben’s prompt structure—goal, return format, warnings, context dump—emphasizes describing what you want rather than how to think, a shift from earlier models. Dan uses o1 for coding by feeding it full code context, achieving one-shot implementations that reach 100% where previous models maxed out at 95%. Ben notes o1 is the most capable but hardest model to use, with experiments costing $20 per try, and argues model routing will let humans or systems trade off cost, speed, and intelligence. Dan also applies o1 to scientific research, feeding it papers from multiple fields to find novel connections that humans would miss.

Beating Google at Search with Neural PageRank and $5M of H200s — with Will Bryk of Exa.ai

Jan 10, 2025 · 55:54

Will Bryk, CEO of Exa.ai (formerly Metaphor), details building a neural search engine from scratch using link prediction as 'Neural PageRank' — predicting documents rather than keywords. Exa's new product offers near-perfect lists (e.g., 'startups working on hardware in SF') by scaling compute per query, from milliseconds to a day, like o1 for search. Bryk argues LLMs will become the interface to search, while Exa provides the 'super knowledge' that even AGI will need. He contrasts Exa's neural approach with Google's keyword-based system and Perplexity's reliance on Bing. The company recently purchased a $5M H200 cluster and maintains a culture of nap pods and first-principles thinking.

AI Engineering for Art - with comfyanonymous

Jan 4, 2025 · 52:09

Comfy Anonymous, the anonymous creator of ComfyUI, details in his first-ever podcast interview how his node-based image generation tool overtook Automatic1111 through superior memory management and early support for SDXL, becoming the de facto interface for advanced diffusion workflows. He started coding on January 1, 2023, and released the first version on January 16, driven by a desire to chain models and experiment with custom samplers. His work at Stability AI from June 2023 ensured ComfyUI efficiently handled SDXL, which leaked via early access and forced users from less powerful GPUs to adopt his tool. He discusses model preferences: Flux for consistency, SD 3.5 for creativity, and SD1.5 remaining popular. For video, he highlights Mochi as a 'true' video model with 3D latents, already implemented in Comfy. Now with a core team, ComfyUI is targeting a v1 release with an easy installer on Windows and Mac, while planning monetization through cloud and enterprise features—but keeping the open-source core free.

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.

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.

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.

Why Compound AI + Open Source will beat Closed AI — with Lin Qiao, CEO of Fireworks AI

Nov 25, 2024 · 55:49

Lin Qiao, CEO of Fireworks AI, argues that compound AI systems combining multiple open-source models will outperform closed monolithic models like OpenAI's. She explains Fireworks' evolution from a PyTorch platform to a full-stack inference and customization engine serving 40+ customers including Cursor. Qiao details their distributed inference engine, Fire Optimizer for quality-latency-cost tradeoffs, and upcoming o1-like model built on open-source foundations. She defends their quantization approach after public criticism from rivals, emphasizes specialization over general intelligence, and invites developers to test their free LoRA adapter hosting.

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.

In the Arena: How LMSys changed LLM Benchmarking Forever

Nov 1, 2024 · 41:02

Anastasios and Weilin from LM-Sys explain how ChatBot Arena became the de facto standard for LLM benchmarking by replacing static benchmarks with dynamic human preference evaluations. They trace the origin from Vicuna fine-tuning to the viral launch in April 2023, where anonymous side-by-side battles let the community decide which model is better. To address biases like length preference, they developed Style Control, a logistic regression method that adjusts for confounders such as response length and markdown formatting. They address the controversy around labs testing multiple private models, arguing that selection bias is empirically small and the live benchmark self-corrects over time. They also discuss RouteLLM for cost-performance routing, the graduation of ChatBot Arena from LMSys to support new projects, and call for community help with red teaming, vision modalities, and implementing a REPL for coding evaluations.

How NotebookLM Was Made

Oct 25, 2024 · 1:13:57

Raiza Martin (NotebookLM lead PM) and Usama Bin Shafqat (AI engineer) explain how Google's NotebookLM built the viral 'Deep Dive' audio overview feature. They reveal the product evolved from Project Tailwind, using Gemini 1.5's long context and DeepMind speech to create a two-persona dialogue format that transforms documents into engaging podcasts. The team learned from 65,000 Discord members, leaned on best-selling author Steven Johnson for a 'tool for thought' workflow, and prioritized a single format over exposed controls to preserve unpredictability and delight. Humor and tension are not explicitly prompted but emerge from giving personas different angles. Evaulation relied on internal taste ('potatoes for chefs') before formal raters, with a Likert scale on dimensions like entertainment and groundedness. Future plans include multilingual support, API access, real-time chat, and codebase podcasting, while managing non-determinism by accepting occasional bad rolls.

Singapore: the AI Engineer Nation — with Minister Josephine Teo

Oct 19, 2024 · 56:40

Singapore Minister Josephine Teo outlines the city-state's refreshed national AI strategy and the Ministry of Digital Development's approach to balancing innovation, safety, and talent development. She details plans to triple AI practitioners from 5,000 to 15,000, the AI Verify testing tools and AI Safety Institute for governance, and internal government AI bots like AI Bot for RAG-based knowledge retrieval. On sovereign AI, she discusses hybrid cloud infrastructure and pragmatic data center expansion. Teo also explains Singapore's new law requiring transparency for AI-generated election content, emphasizing fact-based political discourse.

Building the Silicon Brain - Drew Houston of Dropbox

Oct 18, 2024 · 1:11:40

Drew Houston, CEO of Dropbox, details his hands-on AI engineering journey and the company's strategic pivot to AI-first products like Dropbox Dash for universal search and access control. Having spent over 400 hours coding with LLMs, he built personal tools that seeded Dropbox AI, including a file question-answering system. He advocates 'rent, don't buy' for AI infrastructure, relying on open-source models and keeping options open as costs drop 10-100x yearly. Houston explains Dropbox's advantage in trust and data privacy, positioning it as a neutral platform that integrates with Google Drive and OneDrive. He discusses staying relevant through constant learning and founder mode, and advises founders to systematically train skills ahead of their company's growth.

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.

The Ultimate Guide to Prompting - with Sander Schulhoff from LearnPrompting.org

Sep 20, 2024 · 1:07:09

Sander Schulhoff, author of The Prompt Report and founder of LearnPrompting, presents a comprehensive taxonomy of 58 prompting techniques and argues that prompt engineering is a skill everyone should have, not a specialized role. He categorizes techniques by problem-solving strategy (zero-shot, few-shot, thought generation, decomposition, ensembling, self-criticism) and debunks role prompting for accuracy-based tasks, showing an 'idiot' prompt outperformed a 'genius' prompt on MMLU. Schulhoff shares his experience running Hack-a-Prompt, which collected 600,000 malicious prompts and won best paper at EMNLP, and differentiates between prompt injection and jailbreaking. He advocates for automatic prompt engineering with DSPy, which beat his manual effort in 10 minutes, and previews Hack-a-Prompt 2.0, aiming for a $500,000 prize pool to generate real-world harms for safety tuning.

Building AGI with OpenAI's Structured Outputs API

Sep 17, 2024 · 1:12:23

Michelle, OpenAI's API tech lead, explains how the newly launched Structured Outputs feature achieves near-100% reliability for schema-following outputs by combining constrained decoding with model training, moving beyond the limitations of JSON mode and Function Calling. She details the refusal field that allows the model to decline harmful requests while maintaining parseable output, and discusses use cases like dynamic UI generation and mathematical chain-of-thought. The roadmap includes parallel function calling and custom grammars. The episode also covers the GA of GPT-4o fine-tuning, Batch API's 50% cost savings, Vision API integration with structured outputs, the Whisper API's surprising lack of diarization, and the shift toward speech-to-speech APIs. Michelle shares insights from her Waterloo co-op background and what qualities succeed at OpenAI: low ego, user-focused, and driven.

Personal benchmarks vs HumanEval - with Nicholas Carlini of DeepMind

Aug 28, 2024 · 1:07:28

Nicholas Carlini, a DeepMind research scientist, argues AI models are practically useful for personal tasks despite flaws, and personal benchmarks tailored to individual use cases matter more than generic leaderboards. He shares from his 'How I Use AI' post: using LLMs for ephemeral software, kickstarting Docker, debugging by pasting error messages. He built a personal benchmark DSL that runs code from real chat history. On security, he explains buying expired domains from LAION-400M allowed poisoning any model trained on it, and he extracted OpenAI's Ada and Babbage model dimensions via API (with permission). He also recovered training data from GPT-3.5 by repeating a word until ChatGPT output verbatim sequences. He prefers attacking over defending because it's more fun and essential for discovering real vulnerabilities.

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.

Answer.ai & AI Magic with Jeremy Howard

Aug 17, 2024 · 1:11:00

Jeremy Howard of Answer.AI argues that continuous pre-training should be treated as a continuum, not separate phases, and demonstrates how FSDP+QLoRA enables training a 70B model on just two NVIDIA 4090s. He reveals Answer’s non-hierarchical, manager-free R&D lab model that recruited unusual talent like Benjamin Warner and Ben Claviez, who independently launched BERT 24 to revive encoder-only architectures. Howard introduces FastHTML, a pure-Python web framework built on HTMX and Starlette, for creating modern SPAs without JavaScript. He previews “AI Magic,” a dialogue engineering system that moves beyond teletype-style chat interfaces and code editors, aiming to make AI-assisted development more interactive. The episode also covers Answer’s Public Benefit Corporation structure designed to resist hostile takeovers, and critiques decoder-only hype while advocating for encoder-decoder and state-space models.

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.

How AI is Eating Finance - with Mike Conover of Brightwave

Jun 11, 2024 · 1:05:06

Brightwave founder Mike Conover explains how his vertical AI startup builds a 'partner in thought' for finance professionals, using a systems-of-systems approach that decomposes problems into specialized subsystems rather than relying on large context windows or monolithic agents. He argues that the evolution from DALL-E's 1,024-token context to today's million-token models has not solved synthesis; smaller, focused reasoning units still outperform. Conover emphasizes that the real competitive edge lies in custom training data and human annotation, not pre-training, and that models are converging in capability—so value shifts to fine-tuning data that elicits specific behaviors. He details why Brightwave avoids spreadsheets in favor of conversational analysis, and predicts AI will automate idea generation and second-order derivative bets, but humans must remain the final synthesizers and deciders. The episode also covers hiring for vertical AI, the role of knowledge graphs, and the diminishing economic incentive for companies to train their own foundation models.

How to train a Million Context LLM — with Mark Huang of Gradient.ai

May 31, 2024 · 1:12:14

Mark Huang of Gradient.ai explains how his team extended Llama 3 to a 1 million token context window using curriculum learning, RingAttention, and EasyContext, achieving near-perfect GPU utilization. They employed theta scaling from the RoPE paper to interpolate positional encodings, trained on carefully curated datasets including SlimPajamas and synthetic data from GPT-4, and validated with benchmarks like Ruler and needle-in-a-haystack. Huang details the trade-offs between full fine-tuning and LoRA adapters, the challenges of pushing to 4M tokens (degradation from floating-point precision limits), and why long context matters for state management across sessions and grounding multimodal inputs. He calls for community collaboration on long-context evaluations and pairwise multimodal datasets.

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.

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.

Making Transformers Sing - with Mikey Shulman of Suno

Mar 14, 2024 · 58:58

In this episode of Latent Space, hosts Alessio and Shawn Wang interview Mikey Shulman, CEO of Suno, about making transformers sing. Suno uses transformers to predict audio tokens end-to-end, avoiding baked-in musical knowledge, with a tokenization secret sauce that also includes non-music audio for better vocal realism. Their models are relatively small (far below 175B parameters) due to latency needs, and they prioritize scaling research over brute-force size. Over half of Suno users employ expert mode, tweaking lyrics and style prompts, rather than easy mode. Shulman argues Suno is not the 'Midjourney of music' because music is inherently social and synchronous, unlike images. He demoed live generation, showing control via tokens like [beat drop] and style modifiers, and revealed future plans for collaborative concerts, continuous DJ modes, and personalized models. He also advocates for hiring economists to avoid Goodhart's law pitfalls in ML benchmarks, especially in audio where aesthetics matter most.

Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI

Mar 6, 2024 · 1:37:38

Soumith Chintala, creator of PyTorch and engineering lead at Meta AI, argues that open source AI is essential for distributing opportunity and trust. He details PyTorch's complexity—1,000 operators needed for generality—and explains synthetic data as a vehicle for imparting symbolic knowledge where humans already have good symbolic models. He highlights a coordination problem in open source: feedback is lost because frontends like Ooba and Ollama lack feedback buttons, and proposes a centralized sinkhole to collect high-quality feedback. Beyond text, he is excited about robotics, where hardware remains a bottleneck, and Osmo's work to digitize smell, which he compares to images in the 1800s.

A Brief History of the Open Source AI Hacker - with Ben Firshman of Replicate

Feb 28, 2024 · 1:21:09

Ben Firshman, CEO of Replicate, explains how the inference platform grew from a research reproducibility tool into a 2M-user API business by embracing the generative image community and treating open-source AI as a hacker-friendly ecosystem. They accidentally discovered their API when a user reverse-engineered their web form, leading to their first $1k/month customer. Cog, their container standard for ML models, was born from lessons at Docker and the need to make models tinkerable. Ben argues that fine-tuning's low cost makes open-source models sustainable, and that AI engineers (orders of magnitude more than ML engineers) just need to start playing with models. He also discusses GPU scarcity, preferring sustainable pricing over price wars, and reveals that demand is not outpacing supply thanks to aggregating demand.

Truly Serverless Infra for AI Engineers - with Erik Bernhardsson of Modal

Feb 19, 2024 · 1:08:58

Erik Bernhardsson, creator of Annoy and Luigi, founded Modal to build a 'postmodern data stack' with a self-revisioning runtime that eliminates container cold starts and lets developers attach GPUs with a Python decorator. His serverless platform can fan out to thousands of GPUs within seconds, becoming a natural fit for GenAI workloads like Stable Diffusion and fine-tuning (e.g., RAMP fine-tuning 100 models in parallel). Modal differentiates from Replicate by targeting custom models and workflows, and from Modular by offering a managed cloud service rather than licensed software. Erik argues that buying hardware is inefficient for startups and that infrastructure founders must be willing to spend years on load balancing, page faults, and DNS. He shares how his IOI Gold Medal background shapes Modal's talent culture, hiring competitive programmers for complex scheduling and bin-packing problems.

Building an open AI company - with Ce and Vipul of Together AI

Feb 8, 2024 · 1:15:18

Together AI co-founders Vipul Ved Prakash and Ce Zhang explain why openness is core to their mission, detailing their journey from Apple (Vipul) and Stanford research (Ce) to building an open AI platform. They discuss RedPajama’s evolution into a modular dataset with 40 quality signals, the need for 5,000 tokens/second inference speed, and their investment in state space models like Mamba and Hyena as alternatives to transformers. The company, which runs 7,000-8,000 GPUs (mostly H100s), sees training as a larger workload than inference, with fine-tuning driving top models. They advocate for independent inference benchmarks, publish open research like FlashAttention, and keep some software proprietary. With 38 employees and 45% researchers, they are hiring across the stack, from CUDA to DevOps.

The State of AI in production — with David Hsu of Retool

Feb 7, 2024 · 1:06:52

Retool CEO David Hsu shares insights from the company's 2023 State of AI survey, revealing that most AI adoption remains internal and that the hype may be overrated—52% of 1,600 respondents said AI is overrated, and only 27% have AI in production, with 66% of those being internal use cases. He explains Retool's developer-first philosophy, their choice of open-source PG Vector over proprietary solutions, and why they intentionally raised less money at lower valuations to avoid over-dilution. Hsu describes Retool's shift from sales-led to bottom-up growth to reach millions of developers, and highlights the importance of AI workflows over simple chatbots, citing a clothing manufacturer that uses Retool apps with DALL-E to generate patterns. He discusses the competitive landscape, predicting open-source models will eventually catch up to OpenAI, and shares philosophical views on AGI using the plane-vs-bird analogy.

The Four Wars of the AI Stack - Dec 2023 Recap

Jan 26, 2024 · 1:20:58

Swyx and Alessio recap December 2023 by framing the AI landscape as four wars: the Data War (NYT lawsuit demanding destruction of GPTs, OpenAI's data partnerships, synthetic data from DeepMind); the GPU/Inference War (Mixtral price dropping 90% to $0.27/million tokens, benchmark drama between Anyscale and Together, alternative architectures like Mamba); the Multimodality War (Midjourney reaching $200M ARR, ElevenLabs hitting unicorn status, OpenAI and Google building god models); and the RAG/Ops War (LangChain vs LlamaIndex, Pinecone's $750M valuation, Qdrant serving OpenAI and Anthropic internally). They argue agents and open source are not active wars, and predict 2024 will see AI finally reach production, with hardware like Rabbit R1 and Tab capturing unique context.

The Origin and Future of RLHF: the secret ingredient for ChatGPT - with Nathan Lambert

Jan 11, 2024 · 1:35:27

Dr. Nathan Lambert traces the origin and future of Reinforcement Learning from Human Feedback (RLHF), the secret ingredient behind ChatGPT, explaining how it evolved from robotics and early preference learning to become the core alignment technique for large language models. He details the three-phase process (instruction tuning, preference data collection, RL optimization), noting that RLHF's data costs for LLaMA2 were around $6–8 million, and that synthetic data from GPT-4 is cheaper and often more accurate than human labels. Lambert contrasts DPO with PPO, arguing DPO is simpler but may have lower peak performance. He discusses emerging methods like Constitutional AI, which uses AI-generated critiques based on principles, and highlights the challenge of evaluating RLHF models, noting GPT-4 Turbo's lead over earlier versions. The episode covers open questions about data aggregation, reward model agreement (65–75%), and the need for qualitative model interaction.

The AI-First Graphics Editor - with Suhail Doshi of Playground AI

Jan 2, 2024 · 1:09:23

Suhail Doshi, co-founder of Mixpanel and founder of Playground AI, argues that image generation is still in a GPT-2 moment and that training open-source foundation models from scratch is necessary to unlock real utility, exemplified by Playground v2's 2.5x preference over Stable Diffusion XL on a 1K prompt benchmark. He explains the pivot from Mighty (cloud-streamed browser) to AI, driven by the belief that shifting compute elsewhere aligns with AI's parallel computation. The episode covers Playground's unique UI—not just a prompt box but a full canvas with preview rendering, seed control, and style filters—and the difficulty of balancing safety with artistic expression, especially around NSFW content. Suhail discusses the under-investment in graphics AI compared to language, the open-source community's role, and his choice to release pre-trained weights for academic research. He also shares lessons from running GPU infrastructure (harder for training than inference) and advises founders to follow curiosity and build projects rather than relying on books.

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.

The State of Silicon and the GPU Poors - with Dylan Patel of SemiAnalysis

Dec 5, 2023 · 1:07:55

Dylan Patel of SemiAnalysis argues the 'GPU poor vs GPU rich' divide defines AI, with Google's TPU ramp (millions of units) dwarfing others and Nvidia selling over a million H100s this year. He explains that training costs are irrelevant compared to inference costs, and that memory bandwidth is the key bottleneck for LLM inference (e.g., Llama 70B needs ~2.1 TB/s for human reading speed). Patel advises GPU-poor players to focus on on-device innovations like speculative decoding (Medusa), asynchronous training, or fine-tuning for edge use—not on gaming benchmarks or fine-tuning small models. He details why AI hardware startups (Cerebras, Graphcore) bet wrong on on-chip SRAM, while Google’s TPU/Broadcom partnership and Nvidia’s yearly cadence make alternatives tough. He also warns that rebuilding the semiconductor supply chain in the US is unfeasible due to its fragmentation, and that safety through obscurity doesn't work—open innovation is better.

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.

Powering your Copilot for Data - with Artem Keydunov from Cube.dev

Oct 27, 2023 · 43:01

Artem Keydunov, co-founder of Cube (formerly Statsbot), explains how his early text-to-SQL Slack bot in 2016 led to the creation of Cube as a semantic layer, which he argues is now essential for grounding AI models on structured data. Statsbot failed due to the lack of LLMs, relying on regex and custom models, but the underlying Cube framework—an open-source semantic layer defining metrics and dimensions—solved the context problem. Today, semantic layers index data into text descriptions, enabling agents to generate simple queries against the layer instead of complex SQL, reducing errors. Keydunov highlights that natural language querying is becoming a commodity feature in BI tools, but the real value lies in treating metric definitions as code with version control. He advises AI engineers to use a proper warehouse, a semantic layer like Cube, and tools like LangChain, while cautioning that production systems need extra Python code for math and error handling.

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.

The End of Finetuning — with Jeremy Howard of Fast.ai

Oct 20, 2023 · 1:24:48

Jeremy Howard of Fast.ai argues that fine-tuning language models is essentially continued pre-training, not a separate process, and that the common practice of fine-tuning on a single task causes catastrophic forgetting. He recounts the discovery of single-shot memorization in LLMs, where models memorize entire datasets after one epoch, a phenomenon many practitioners ignore. Howard criticizes the current focus on zero-shot and few-shot learning, advocating for transfer learning and small models. He shares his journey from philosophy to founding Fast.ai, the creation of ULMFit (which inspired GPT), and his ongoing work on making AI accessible. He also discusses his involvement with Modular's Mojo language and the importance of democratizing AI technology.

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.

Ep 18: Petaflops to the People — with George Hotz of tinycorp

Jun 20, 2023 · 1:23:24

George Hotz of tinycorp argues that a simplified, open-source ML framework (tinygrad) can democratize AI compute and replace NVIDIA, Google, and AMD. Tinygrad uses 25 ops (vs. XLA's 250) and fuses kernels automatically, achieving 2x speed on Qualcomm GPUs over their library. Hotz reports AMD kernel panics fixed after emailing CEO Lisa Su, but laments AMD's open-source as 'dumped on GitHub.' For hardware, tinybox is a six-GPU desktop at 350W per GPU, aiming for 5x better price-performance than NVIDIA H100 for 90% of training. He criticizes OpenAI's secrecy, revealing GPT-4 is an 8-way mixture of 220B models, and cites the Bitter Lesson. He also outlines FLOPcoin using hardware identity to prevent cheating, and his goal of an AI girlfriend as the third company, merging humans via data rather than implants.