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🎤 Talks
latent.spacetalk

Why Agents Love Supabase

Latent Space podcast episode with Supabase founders Paul Copplestone and Ant Wilson on why frontier models pick Supabase first for app backends. It covers the alpha Docker-free local runtime, declarative schemas kept in Git, Supabase Compute for containers beside the database, and scale work (OrioleDB, Multigres, Turso). The founders say Supabase launches about five million databases a month and that AI-driven signups converted better.

PostgreSQLSupabaseDocker+6
Paul CopplestoneAnt WilsonLatent Space+2

coding-agents, ai-infra· Published and added 28 Oct 2026

latent.spacetalk

How Anthropic ships Claude with Claude: Separating the Brain from the Hands

Latent Space episode with Katelyn Lesse (engineering) and Angela Jiang (product) of Anthropic, recorded the day after the Claude Opus 5.5 launch. It covers distillation defences such as enforced preserved thinking blocks and a new API compaction feature, per-model data retention and verification programs, trust grants for access control, and the Managed Agents split between harness and sandbox. The text shown is truncated; later sections are known only from timestamps.

ClaudeClaude CodeRust
AnthropicClaude Opus 5.5Katelyn Lesse+7

agents, ai-infra, models· Published and added 27 Oct 2026

latent.spacetalk

How Anthropic ships the #1 LLM in the World safely: Separating the Brain from the Hands

Latent Space episode with Katelyn Lesse and Angela Jiang on the engineering behind the Claude Platform. Topics include the Claude Opus 5.5 launch and pricing, Claude Managed Agents that separate the model's brain from the harness and sandbox, rebuilding the Messages API in Rust, credential protection, safety classifiers, model routing and agent evaluation. The page lists show notes and timestamps only, with no transcript.

ClaudeRust
Latent SpaceAnthropicClaude Opus 5.5+2

agents, ai-infra· Published and added 27 Oct 2026

latent.spacetalk

Supabase’s Plan for a Billion AI-Built Apps

Latent Space interview in which swyx talks with Supabase co-founders Paul Copplestone and Ant Wilson about backends for software built by AI agents. Topics include Supabase Compute for running workloads next to the database, Multigres for highly available Postgres (private alpha), the Turso acquisition, declarative schemas, and durable workflows. The page has only show notes and chapters, recorded ahead of Supabase Select 2026.

PostgreSQLSupabaseTurso+2
Paul CopplestoneAnt Wilsonswyx+3

ai-infra, agents· Published and added 26 Oct 2026

interconnects.aitalk

Podcast: Kimi K3.1 Pro, the new cyber reality, and Muse musings

Interconnects podcast episode in which Nathan Lambert and Florian Brand discuss the Kimi K3.1 Pro release (about 3 trillion parameters, weights due by November 4th). They cover its Artificial Analysis score against weaker long-horizon results, Moonshot's choice to down-weight offensive cyber capability, and the PHP2Shell and LiteLLM incidents. They also cover abliterated model APIs, trusted-access cyber programs, Anthropic's junior hiring and Florian's use of the Muse agent.

PHPLiteLLMWordPress+7
KimiMoonshot AIKimi K3+28

models, ai-policy, evals· Published and added 25 Oct 2026

latent.spacetalk

Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

Latent Space interview with Modal CEO Erik Bernhardsson, after Modal's $750M Series D at a $16B valuation and passing $1B annualized run rate. He explains why GPU demand is not "rectangular" like fixed-term capacity contracts, how Modal pools demand and buys from about 25 clouds on three-year deals, and why sandboxes grew to about a third of revenue. Also covers GPU price-collapse risk and non-NVIDIA accelerators. Only the first part of the transcript was available.

GPUKubernetesDocker+5
ModalErik BernhardssonAkshat Bubna+13

ai-infra, inference, agents· Published and added 22 Oct 2026

latent.spacetalk

Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO

Latent Space interview with Modal CEO Erik Bernhardsson after the company's $750M Series D at a $16B valuation, with a transcript. He explains why GPU demand does not match rectangular fixed-term capacity contracts and how Modal pools demand across customers. He covers sourcing from nearly 25 clouds on multi-year deals, the risk of falling GPU prices, sandboxes reaching about a third of revenue, and non-NVIDIA accelerators.

ModalGPU snapshottingKubernetes+6
Erik BernhardssonAkshat BubnaLatent Space+15

ai-infra, inference· Added 23 Oct 2026, published 22 Oct 2026

latent.spacetalk

You are the Team — Anekha Sokhal, Moshi

Latent Space interview with Anekha Sokhal, CEO of Moshi, recorded at AI Engineer New York. Moshi is a market-intelligence product for commodity and energy traders. It ingests structured and unstructured data, builds an ontology and graph, and pushes recommendations to each trader's book. She covers confidence scoring across divergent sources, an in-house vision-language model for messy PDFs, and build-versus-buy tradeoffs.

LLMvision-language modelClaude
MoshiAnekha Sokhalswyx+10

llm-apps, ai-policy· Added 23 Oct 2026, published 22 Oct 2026

latent.spacetalk

The Recommendation Engine for Commodity Traders — Anekha Sokhal, Moshi

Latent Space podcast episode recorded at AI Engineer New York, in which Moshi CEO Anekha Sokhal, a former JPMorgan energy derivatives trader, describes a recommendation engine for commodity traders. Moshi ingests structured and unstructured market data plus private user data, tags it into a graph ontology and pushes items relevant to a trader's book. She covers confidence scores across divergent sources, an in-house vision-language model for messy PDFs, and build versus buy.

LLMvision-language modelClaude
MoshiAnekha Sokhalswyx+11

llm-apps· Published and added 22 Oct 2026

latent.spacetalk

You are the Team — Anekha Sokhal, Moshi

Latent Space interview with Anekha Sokhal, CEO of Moshi, recorded at AI Engineer New York. Moshi is a recommendation engine for commodity and energy traders that ingests structured data, news, X posts, podcasts and private user documents into a graph. Topics include confidence scores for divergent sources, a self-built vision-language model for PDFs, build versus buy, and engineers told "you are the team".

vision-language modelLLMClaude
MoshiAnekha Sokhalswyx+11

llm-apps, ai-policy· Published and added 22 Oct 2026

latent.spacetalk

AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

Latent Space interview from AIE New York with Tenex cofounder Alex Lieberman and CJ Hess on running an AI-native engineering firm of 75 people. Covers story-point pay for engineers, the MetaHarness (plans, decisions and agent logs checked into Git with a CLI), roughly 2x engineer output in 2026, Opus 5.5 and Codex usage, and enterprise adoption rates. Also debates vertical AI and the Kill My SaaS experiment.

GitClaude CodeCodex+4
TenexAlex LiebermanCJ Hess+15

coding-agents, ai-policy, agents· Published and added 21 Oct 2026

latent.spacetalk

AI for the Fortune 2000: How Tenex Builds an AI-Native SDLC — Alex Lieberman & CJ Hess

Latent Space interview from AI Engineer New York with Tenex cofounder Alex Lieberman and CJ Hess. Tenex has 75 people, pays engineers by story points, and reports roughly 2x engineer output over the past year. CJ describes MetaHarness, a repo-based set of skills, plans and a CLI that logs agent decisions in Git. They also cover enterprise AI adoption rates, token spend, and vertical AI. The page text is truncated.

GitGitHubCodex+3
TenexAlex LiebermanCJ Hess+14

coding-agents, ai-policy, agents· Added 23 Oct 2026, published 21 Oct 2026

latent.spacetalk

The Programmable Data Center — Evan Conrad, SF Compute

Latent Space interview with Evan Conrad, CEO of SF Compute, recorded at AI Engineer NYC. He explains why the company runs a cloud's operations to physically settle GPU contracts on an order book with multi-constraint matching. Topics include preemption and storage recovery, hardware cohorts, batch versus live inference pricing, forward curves, a B300 price arbitrage, and cash versus physical settlement. The last ~3,600 characters of the transcript were not available.

InfiniBandH100GB200+7
SF ComputeEvan ConradLatent Space+15

ai-infra, ai-policy, inference· Added 23 Oct 2026, published 20 Oct 2026

latent.spacetalk

Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio

Latent Space interview with Ian Quigley of Leash Bio on using DNA-encoded library (DEL) data, roughly 4B measurements, to train protein-ligand binding models. Topics include Leash's Kaggle competition where nobody beat chance on an unseen chemical library, "Clever Hans" bias in ChEMBL where the chemist predicts activity as well as the structure does, and Hermes, trained only on Leash data, keeping pace with Boltz. It also introduces Minos, a lab-in-the-loop agent sandbox that lets LLMs do verifiable scientific reasoning and RL, searching 6.5M molecules on a budget of 100, released with a preprint, blog and open dataset.

BoltzLLM
Leash BioIan QuigleyLatent Space+8

research, agents, training· Published and added 14 Oct 2026

latent.spacetalk

Dyna-2: General Purpose Physical Agents with Hour-Long Autonomy — Jason Ma, Dyna Robotics

Podcast interview with Jason Ma, co-founder and Chief Scientist of Dyna Robotics, on Dyna-2, a world-action model pre-trained on over one million hours of human egocentric video. The model predicts future video and uses it to guide robot actions, built on a diffusion transformer with flow matching. The discussion covers the robotics data bottleneck, adapting to new tasks from about 45 minutes of demonstrations, synthetic environments and self-generated training data, on-device inference and distillation, and Dyna's custom wheeled semi-humanoid hardware. The episode listing also covers commercial deployments (Din Tai Fung, Best Western), agentic harnesses for hour-long workflows, and world-action models versus VLAs, but the fetched text was truncated before those sections.

diffusion transformerflow matchingdiffusion model+2
Dyna RoboticsDyna-2Dyna-2.1+13

multimodal, training, models· Published and added 13 Oct 2026

latent.spacetalk

Why AlphaFold Didn't Solve Protein Folding — Pushmeet Kohli, Google DeepMind & Sal Candido, Biohub

Transcript of a panel moderated by Brandon Anderson with Pushmeet Kohli (Google DeepMind) and Sal Candido (Biohub). They discuss a "bitter lesson" for biological data: finding the right scaling law and good data matters more than raw scale. Low-quality metagenomic sequences can still improve protein language models, and AlphaFold 2's handcrafted inductive biases made it data-efficient. They also cover why protein folding isn't fully solved (dynamics, disorder, cryo-EM micrographs as a richer data source), the need for calibrated uncertainty (pLDDT) over full interpretability, hidden knowledge in protein language models, and the move from single-protein models to a virtual cell.

AlphaFoldTransformer
Pushmeet KohliSal CandidoBrandon Anderson+5

research, training, models· Published and added 10 Oct 2026

latent.spacetalk

Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk

A Latent Space podcast interview with Periodic Labs co-founders Liam Fedus and Ekin Dogus Cubuk on building "synthesis superintelligence" — AI systems that run reinforcement learning against noisy physical lab experiments rather than internet text, to discover new materials like room-temperature superconductors. They detail the end-to-end materials discovery loop (predicting stable configurations, synthesis, and characterization via X-ray diffraction), why RL reward design differs fundamentally in physical science versus math/coding (sample inefficiency, measurement noise, missing telemetry), the role of DFT simulations as imperfect ground truth, and their "matter compiler" vision for scaling autonomous high-throughput labs.

Periodic LabsLiam FedusEkin Dogus Cubuk+4

research, training, agents· Published and added 8 Oct 2026

latent.spacetalk

Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week

Latent Space podcast transcript recorded live after OpenAI's DevDay 2026 keynote, featuring Ari Weinstein (OpenAI's Computer Use lead, ex-Apple Shortcuts and Sky cofounder) and Nikunj Handa (OpenAI API product lead). Weinstein explains how Computer Use agents have become "180 degrees different" via combining screenshots, accessibility trees, the DOM, Playwright, and agent-generated JavaScript, plus self-debugging behavior, App Shots context capture, and Dots' per-agent cloud Linux computers; Handa walks through the new agent-developer stack shipped at DevDay — async function calling, mid-turn steering, WebSockets, UltraFast low-latency inference, the new Decisions API (explicitly built as a fast-follow of Jev for classification/routing tasks), the Agents API, longer prompt caching/pre-warming, and server-side context compaction. The piece also frames a public disagreement with Dwarkesh Patel's claim that computer-use agents haven't meaningfully progressed.

PlaywrightOpenAI API
OpenAIAri WeinsteinNikunj Handa+11

agents, ai-infra, coding-agents· Published and added 30 Sep 2026

latent.spacetalk

Claude Code’s Next Era — Thariq Shihipar, Anthropic

A Latent Space podcast interview with Thariq Shihipar, Anthropic's Claude Code lead, covering the evolution of Claude Code's harness: Ask User Question and elicitation, artifacts as persistent generative interfaces, the cloud-brain/local-hands split, Claude Tag for multiplayer agent workflows, Claude Projects, effort levels, implementation notes, and the new Claude Mods system for customizing the execution loop. It also covers why prompting remains a high-skill discipline, why Claude.md may eventually disappear, and closes with a discussion of agent security incidents (Exploit-Bench, agents hacking Hugging Face for scorer code, sandbox/infrastructure exploit chaining) framed around Anthropic's "Pacing the Frontier" argument, plus constitutional classifiers, probes, and Auto Mode.

Claude CodeModel Context ProtocolHugging Face
Thariq ShihiparAnthropicClaude Tag+8

coding-agents, prompting, agents· Published and added 29 Sep 2026

latent.spacetalk

OpenRouter: from Seed to Stripe — with OpenRouter’s Alex Atallah & AMP’s Anjney Midha

A Latent Space podcast interview with OpenRouter co-founder/CEO Alex Atallah and AMP's Anjney Midha tracing OpenRouter's path from a bet on model diversity (Alpaca/Llama-era open weights, Discord's content-moderation struggles with closed models) to becoming a neutral inference marketplace serving 10M+ developers and 10+ trillion tokens/day. It covers why model labs struggle with distribution despite billion-dollar training runs, the "just a wrapper" VC skepticism OpenRouter faced, the abandoned-then-revived Mixture of Models (MOM) experiment, OpenRouter vs. LM Arena, and the strategic logic (and fraud/security implications) behind Stripe's acquisition of OpenRouter. Note: transcript is truncated at ~40% of the full page; later sections (Stripe deal details, agentic fraud discussion) are not fully covered here.

OpenRouterMistralLlama+2
Alex AtallahAnjney MidhaAMP+12

ai-infra· Added 29 Sep 2026, published 25 Sep 2026

interconnects.aitalk

Debating RSI, the US-China Gap, and Jaggedness with JS Denain of Epoch AI

A transcribed podcast conversation between Nathan Lambert and Epoch AI's JS Denain covering recursive self-improvement (RSI) evidence from OpenAI/Anthropic's acceleration blog posts, the size and drivers of the US-China frontier model gap (estimated at 4–9 months using Epoch's Capabilities Index, ECI), the role of distillation and "Claude router" data leakage in closing that gap, robotics as a bottleneck for industrial-scale AI acceleration, and jaggedness in AI capability progress (e.g. why math benchmarks may overstate general progress). It's grounded in concrete data points like Codex spending growth, METR/Epoch research, and named model comparisons (Kimi K3, GLM 5.2/5.3) rather than speculation alone.

CodexTransformer
JS DenainEpoch AINathan Lambert+17

ai-policy· Added 29 Sep 2026, published 22 Sep 2026

latent.spacetalk

Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI

Latent Space interview with Diogo Almeida, CEO of TypeSafe AI and an InstructGPT co-author, on the launch of Jev, a "System One" model built for software/programmatic consumption rather than chat. Almeida argues RLHF causes mode collapse and hallucination-inducing overconfidence and RLVR narrows models into jagged rubric-solvers, and describes TypeSafe's alternative training target, RLCD (Reinforcement Learning for Calibrated Decisions), which optimizes for epistemically calibrated probabilities on discrete decisions rather than human preference or verifiable rewards. The conversation also covers why TypeSafe rejects public benchmarks and refusal-based safety alignment for API models, his "Bitterest Lesson" thesis that task/data design beats compute, and how Jev is meant to be used inside coding agents, dark-data pipelines, and other software rather than as a chatbot.

Diogo AlmeidaTypeSafe AIJev+11

training, models· Added 29 Sep 2026, published 21 Sep 2026

interconnects.aitalk

Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next

A transcribed Interconnects podcast episode where Nathan Lambert and Florian Brand do their quarterly open-model roundup, covering the Kimi K3 release (its coding/agentic performance, $200/mo plan, broken API capacity), GLM-5.2's continued developer traction, Qwen's announced pivot to open-weighting its next flagship, and Xi Jinping's WAIC speech committing China to open-source AI. They dig into why Chinese labs (Kimi/Moonshot, Zhipu/GLM, DeepSeek, MiniMax, LongCat, Ling/Meituan) keep pace with the closed frontier despite less compute — capital efficiency, smuggled/domestic chips, buying external data — then survey the smaller US open ecosystem (Thinking Machines' Inkling and Tinker, Gemma, Nemotron, Poolside, Arcee, Reflection). The back half is a detailed rebuttal of Ben Thompson's Stratechery claim that distillation grows more important as RL scales, arguing SFT-stage distillation from jailbroken Claude/GPT reasoning traces matters less as RL-stage training (which is hard to distill cheaply) dominates post-training, with a side note on a Hugging Face incident where guardrails forced defenders to use a less-restricted model (GLM) to analyze an attack.

QwenQwen3Claude+5
Kimi K3GLM-5.2Qwen 3.8+28

models, training, ai-policy· Added 29 Sep 2026, published 22 Jul 2026