Opus

⏭️ Forward Deployed: Voice AI on what works in 2026

Aug 25, 2026 · 36:31

Basil Chatha hosts a fireside chat with voice-AI leaders Basia Sudol (Decagon), Varun Singh (Daily), Steven Diaz (Vapi), Tyler D'Silva (Retell AI), and Sudarshan Kamath (Smallest AI) arguing that production voice agents still run on cascaded STT→LLM→TTS pipelines, not end-to-end speech-to-speech. Varun explains why outbound calls are easier than inbound. Steven and Tyler defend cascaded for guardrails and context optimization; Sudarshan says Smallest's Hydra is multimodal but enterprises still prefer cascaded hybrids. On latency, Tyler says fillers make waits natural, Steven warns giant prompts hurt cost, and Varun suggests specialized sub-agents. They also cover multilingual TTS swapping, model choices, and open-source evals.

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.

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

Jun 6, 2026 · 40:41

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

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

Jun 4, 2026 · 1:17:57

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

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

May 28, 2026 · 1:09:33

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

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

May 24, 2026 · 14:47

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

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

Apr 22, 2026 · 1:14:30

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

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

Apr 18, 2026 · 48:53

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

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

Apr 15, 2026 · 1:25:37

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

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

Mar 6, 2026 · 1:11:18

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

Why Every Agent Needs a Box — Aaron Levie, Box

Mar 5, 2026 · 1:16:58

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

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

Mar 1, 2026 · 41:56

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

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.

⚡️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.

⚡️Launching AI Diplomacy: the hardest LLM Game Benchmark yet - Alex Duffy

Jun 11, 2025 · 34:32

Alex Duffy from Every launches AI Diplomacy, a benchmark where LLMs play the board game Diplomacy, revealing distinct model personalities and arguing that benchmarks function as memes that spread and saturate. He and Tyler Marquez built the front end and harness using Open Router, finding that Claude refuses to lie and never wins, o3 schemes in its diary, and DeepSeek is flowery or aggressive. His talk 'Benchmarks are Memes' highlights Simon Wilson's pelican-on-a-bicycle test as an example of an idea that gets adopted and then saturated by models. Duffy also shares his creative writing workflow at Every: dictation, prompts with style guides and editor notes, then heavy editing to reflect on AI output. Future plans include a data viewer, front-end improvements, and a human vs AI Diplomacy tournament to test jailbreaks.

⚡️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.

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.

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

Apr 27, 2024 · 1:56:11

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

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

Apr 24, 2024 · 1:02:59

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