Blog

How AI code review can make correct code worse

Blueprint: One-shot bigger coding tasks

There Will Be a Scientific Theory of Deep Learning

Keystone-Eval: Our agentic benchmark for self-configuring agents

How to usefully run 1,000 agents in parallel

Bouncer: Heal your feed

How Offload Works: Inside the Rust CLI that sped up our tests by 6x

A case study in testing with 100+ of Claude agents in parallel

Usefully run hundreds of Claudes in parallel with mngr

Introducing Keystone: Building self-configuring agents

Offload: We made agents run our test suite 6x faster

Vet: Prevent coding agents from making mistakes

Latchkey: Use plain old REST instead of MCP

LLM-based Evolution as a Universal Optimizer

Beating ARC-AGI-2 with Code Evolution

A more radical Imbue

Choices and Knives

Digital Freedom Depends on Access Rights: Matt Boulos in Lawfare

How we made sandboxed coding agents 10x faster to start

A Healthy Ecosystem for AI Agents: Introducing the AFI

Closed vs. open AI ecosystems: Kanjun on Decoder

Sculptor: the missing UI for parallel coding agents

How to build robust software systems with AI: Josh at the AI Engineer World's Fair 2025

Empowering humans in the age of AI

Sculptor: Catch and fix issues as you code

One-person enterprise: Kanjun at the World Economic Forum Annual Meeting

The future of AI is human-centered: Kanjun on the Pioneers of AI podcast

Glenn McDonald: On Dungeons & Dragons, data alchemy, and radically open software

Keeping humans in the loop: Josh at the Democratize Intelligence Summit

Human agency vs. “agentic” AI: Kanjun on the NonZero podcast

A more empowering view of general agents: Kanjun on NYSE TV

Transforming AI agents into personal collaborators: Kanjun on the NVIDIA AI Podcast

The ethical and practical future of AI: Matt Boulos at the MIT Club of Northern California

The AI paradox: Kanjun at the 2024 Progress Conference

Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas

Agency in the age of AI: Kanjun on the Minus One podcast

Reinventing AI agents: Kanjun on the Gradient Dissent podcast

Ari Morcos, DatologyAI: On leveraging data to democratize model training

From bare metal to a 70B model: infrastructure set-up and scripts

Training >70B LLMs on 10,000 H100 clusters: Josh on the Latent Space podcast

Open-sourcing CARBS: how we used our hyperparameter optimizer to scale up to a 70B-parameter language model

Training a 70B model from scratch: open-source tools, evaluation datasets, and learnings

Sanitized open-source datasets for natural language and code understanding: how we evaluated our 70B model

On the Stepwise Nature of Self-Supervised Learning

Scaling Laws For Every Hyperparameter Via Cost-Aware HPO

Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)