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

Jul 10, 2026 · 28:03

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

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

Jun 24, 2026 · 1:10:06

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

⚡️Every product of the future will be a living system — Ronak Malde, Trajectory.ai

Jun 21, 2026 · 33:58

Ronak Malde, CEO of Trajectory.ai, recounts his journey from building AI coding agents at Windsurf (acquired by Google DeepMind in a deal involving Demis Hassabis and Sergey Brin) to launching a platform for continual learning in enterprise AI. He argues that every future product must be a 'living system' that learns from real-world user interactions, not static models. The episode details Trajectory's technical innovations, including Self-Distillation Policy Optimization (SDPO) for learning from corrections, continuous LoRA for parallel training, and open-sourcing a training stack with SkyRL. It covers partnerships with Harvey and NVIDIA to train NeMoTron 3 Super for legal workflows, improving metrics like issue spotting and citation accuracy while cutting costs. Malde explains data curation strategies that capture nuanced user edits beyond binary signals, and outlines Trajectory's roadmap from AI-native companies like Clay and Decagon to Fortune 500 enterprises. He also reveals that the idea for Trajectory emerged after giving up his acquisition equity to pursue this vision.

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

May 28, 2026 · 1:09:33

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

Inside Abridge: The AI Listening to 100 Million Doctor Visits — Abridge's Janie Lee & Chai Asawa

May 14, 2026 · 1:06:38

Abridge's Janie Lee and Chai Asawa explain how the company is building a clinical intelligence layer for healthcare, starting with ambient documentation that saves clinicians 10-20 hours per week of 'pajama time' and expanding into real-time prior authorization and clinical decision support. They argue that context is everything—integrating EHR data, payer policies, and medical literature to make AI proactive rather than reactive, exemplified by guiding a doctor to ask two extra questions during a visit to guarantee an MRI approval before the patient leaves. The hardest AI problem is delivering high-quality, low-latency, low-cost real-time guidance in a high-stakes setting, which Abridge tackles using a constellation of models, efficient post-training on its proprietary dataset of over 100 million medical conversations, and progressive rollout with rigorous specialty-specific evaluations. They emphasize personalization at three levels—individual style, specialty (e.g., cardiology vs. dermatology), and health system guidelines—and see clinicians embedded as 'clinician scientists' on engineering teams as a key competitive advantage. Looking ahead, they envision the same conversation…

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

May 2, 2026 · 1:00:26

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

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.

The Stove Guy: Sam D'Amico Shows New AI Cooking Features on America's Most Powerful Stove at Impulse

Mar 31, 2026 · 37:03

Sam D'Amico, founder of Impulse, demonstrates the company's induction cooktop that uses a three-kilowatt-hour LFP battery to deliver 10,000 watts—boiling a liter of cold water in 40 seconds and holding a precise temperature for searing scallops without burning. The stove is rebuilt from first principles with custom power electronics and firmware, enabling OTA updates and software-defined features; a planned update will triple temperature-control speed from one minute 50 seconds to 40 seconds. D'Amico shows AI features powered by Claude that set burner temperatures (e.g., 446°F for scallops) and fetch recipes like Korean fried chicken or egg fried rice with char siu. He explains the origin story from a pizza in Japan, the decision to enter home appliances to avoid app-store dependencies, and the Impulse Core modular platform sold to OEMs. The cooktop launches through Zephyr in hundreds of showrooms nationwide and globally this year.

Dreamer: the Agent OS for Everyone — David Singleton

Mar 20, 2026 · 1:04:23

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

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

Mar 17, 2026 · 1:27:34

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

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

Mar 9, 2026 · 27:10

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

Why Every Agent Needs a Box — Aaron Levie, Box

Mar 5, 2026 · 1:16:58

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

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.

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

Jan 17, 2026 · 1:13:27

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

[State of AI Papers 2025] Fixing Research with Social Signals, OCR & Implementation — Team AlphaXiv

Dec 31, 2025 · 34:45

AlphaXiv founders Raj and Rayhahn describe building the go-to platform for navigating AI research by obsessing over UI and getting authors like the Laura team to comment directly on papers, then evolving from comments to feeds to benchmarks to AI assistants. They detail how they beat Hugging Face Papers by enabling direct paragraph-level commenting and early traction with high-signal authors, and how they use social signals (views, comments, Twitter) to filter the archive firehose. They discuss the broken academic review process—20% AI-generated reviews at ICLR, quality collapsing under exponential submission growth—and argue papers are becoming less important than implementations, Docker containers, and real-world usability. Their vision: making it trivial to spin up any trending paper's code directly in browser, focusing on the top 0.1% of implementable papers that applied researchers actually care about.

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

Dec 31, 2025 · 26:48

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

[State of AI Startups] Memory/Learning, RL Envs & DBT-Fivetran — Sarah Catanzaro, Amplify

Dec 30, 2025 · 28:43

Sarah Catanzaro of Amplify Partners argues that the DBT-Fivetran merger was not the death of the modern data stack but a path to IPO targeting $600M+ combined revenue, that data catalogs failed because they were built for humans rather than machines, and that RL environments are a fad. She notes that frontier labs use dbt and Fivetran for training data curation and agent analytics, and criticizes $100M+ seed rounds raised without near-term roadmaps. For 2026, she identifies personalization via memory and continual learning as the key retention unlock, observing that AI founders are unfamiliar with growth concepts like k-factor. She prefers real-world logs over synthetic RL environments, citing Cursor's use of user activity, and says the most exciting startups combine hard research problems (RAG, rule-following, continual learning) with applications that were previously impossible.

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

Dec 28, 2025 · 1:39:19

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

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

Dec 26, 2025 · 37:25

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

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

Nov 22, 2025 · 49:03

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

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

Nov 19, 2025 · 27:11

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

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

Nov 14, 2025 · 1:26:59

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

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

Nov 8, 2025 · 35:01

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

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.

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

Nov 1, 2025 · 42:02

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

⚡️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.

Amp: The Emperor Has No Clothes

Sep 25, 2025 · 1:21:59

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

Context Engineering for Agents - Lance Martin, LangChain

Sep 11, 2025 · 1:03:18

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

⚡️Launching Ona: Coding Agent with Fully Sandboxed Cloud Environment

Sep 1, 2025 · 33:44

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

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.

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

Aug 4, 2025 · 22:09

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

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

Jul 28, 2025 · 3:53:23

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

⚡️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.

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.

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.

⚡️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 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.

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.

Zed Agents — with Zed Cofounders Nathan Sobo & Antonio Scandurra

May 7, 2025 · 35:50

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

The Creators of Model Context Protocol

Apr 3, 2025 · 1:20:21

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

The #1 SWE-Bench Verified Agent

Apr 2, 2025 · 31:46

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

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

Mar 28, 2025 · 1:42:28

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

Building Manus AI (first ever Manus Meetup)

Mar 27, 2025 · 48:59

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

[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.

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.

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.

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.

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.

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.

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

Dec 2, 2024 · 1:36:42

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

[Paper Club] Embeddings in 2024: OpenAI, Nomic Embed, Jina Embed, cde-small-v1 - with swyx

Dec 1, 2024 · 41:46

Shawn Wang (swyx) surveys four key embedding developments: OpenAI's text-embedding-3 models, Nomic Embed, Jina Embed v3, and CDE-small-v1. OpenAI's models introduce Matryoshka embeddings, compressing output from 1024 to 64 dimensions (94% storage reduction) with only an 8% performance drop. Nomic Embed is a fully open-source BERT-based model trained on English data, offering reproducible code. Jina Embed v3 supports 89 languages and uses task-specific LoRA adapters—e.g., a retrieval adapter yields a huge boost over the base model. CDE-small-v1 (143M parameters) employs a two-stage adaptation: first conditioning on the corpus, then embedding; it outperforms 7B-parameter models on many tasks, though its stateful API complicates deployment.

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.

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.

Production AI Engineering starts with Evals

Oct 11, 2024 · 1:56:16

Ankur Goyal, founder and CEO of Braintrust, argues that evaluation is the core workflow of production AI engineering and shows how his platform makes evals accessible to software engineers. Drawing from his experience at SingleStore and Impira, he details Braintrust's evolution from an eval tool into an end-to-end AI development platform used by Stripe, Zapier, Vercel, and other top AI teams. He shares market data: OpenAI handles over 95% of production workloads, fine-tuning is declining, and open-source models account for under 5% due to reliability issues. Ankur explains why he avoided building a vector database—the real challenge is permissions and joins, not vector search—and predicts o1-style reasoning will replace complex agent frameworks. He reveals Braintrust's differentiators: hybrid on-prem, TypeScript-first SDK, and declarative eval structures.

Building AGI in Real Time (OpenAI Dev Day 2024)

Oct 4, 2024 · 2:09:35

OpenAI's DevDay 2024 unveils the Realtime API with WebSocket-based voice and function calling, Vision Finetuning, Prompt Caching, and Model Distillation. Product leads Olivier Godement, Romain Huet, and Michelle Pokrass detail design choices like zero-code caching and WebSockets for human-level latency. Alistair Pullen of Cosine explains how fine-tuning GPT-4o on custom reasoning traces beat o1 on SWE-bench Verified, while Sam Altman and Kevin Weil discuss AGI levels (o1 as level 2 reasoner), the coming agent era (level 3), safety through iterative deployment, and the importance of building for the frontier. The episode also covers moderation policy relaxation, plans for o1 system prompts by year-end, and the competitive admiration for NotebookLM.

[Paper Club] Who Validates the Validators? Aligning LLM-Judges with Humans (w/ Eugene Yan)

Sep 28, 2024 · 1:00:55

Eugene Yan presents the paper 'Who Validates the Validators? Aligning LLM-Judges with Humans' by Shreya Shankar, introducing EvalGen, a framework that helps developers iteratively align LLM evaluators with human labels by grading outputs, refining criteria, and tracking coverage (recall) and false failure rate (1−precision). Yan demonstrates his own prototype 'Label', which forces users to label 20 samples before unlocking evaluation mode and 50 samples plus one evaluation run before optimization, gamifying the alignment loop. The discussion covers binary vs. pairwise evaluation, the subjectivity of criteria, and Shreya Shankar's plans to integrate natural language feedback into EvalGen's next version. Yan advocates that labeling data is essential before deploying LLM evaluators in production, turning the process into prompt fine-tuning rather than vibe checks.

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.

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.

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.