Meet GPT-6.1 Sol: near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra’s standard API input and output token prices.

There's no one right way to build software — context is everything. But our sensible defaults provide a starting point for our teams: a set of initial assumptions that we know to be effective and usef...

A living map of what Bluesky is talking about: the past week's conversations, grouped into topics and regions and rebuilt every six hours.

On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological …

A collection of Docker skills for AI coding agents to help them build, test, debug, and optimize containerized apps with consistent, reusable workflows. - docker/skills

What does a coding-agent harness actually add, and at what cost? It turns out your Claude models may not need Claude Code… We evaluate 21 model–harness pairs spanning seven models and three…

Epoch AI measures how fast the cost of a given level of AI performance is falling across five benchmarks covering math, science and games of skill: about 47% per quarter, or 13x per year, since 2023, ...

How to prompt Opus 5.5, steer a long run, and check your results in Claude apps and Claude Code.

Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a challenging agent-data gap: heterogeneous data res...

Meet GPT-6 Sol and Luna, two models that bring frontier intelligence to everyday work with different balances of capability and cost.

Behavioral differences from Claude Opus 5 and the prompting and harness patterns that address them: effort calibration, thinking behavior in API integrations and chat, progress updates, unattended and...

The expanded form of a testimony I prepared for Congress.

A ranked ladder for small neural networks that play strategy games. Train a model, write an adapter, upload two files, and get measured into a weight class from 8 KiB up.

Agent harnesses supply planning guidance, organize execution, and check completion. We study how these components affect success, erroneous acceptance, and cost in two Retail experiments and an Airlin...

SoL-Pi: Scaling Auto-Research Loops for Efficient Agent Harnesses - NVlabs/SoL-Pi

🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly. - RyanAlberts/best-of-Agent-Harnesses

Coding harnesses shape how autonomous coding agents translate model capabilities into long-horizon software-engineering performance, yet existing work typically evaluates harnesses as monolithic syste...

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo...

The Model Context Protocol (MCP) introduces a standard specification that defines how Foundation Model (FM)-based agents should interact with external systems by invoking tools. However, to understand...

Point-in-time snapshot of projects describing themselves as AI agent harnesses (April 2026) - danielrosehill/AI-Harnesses

A new experience for Claude projects, now available in beta in Claude Code

We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers' everyday workflows. ScienceBuddy supports rese...

AI is reshaping work faster than any organization has fully mastered. Across industries, the conversation has shifted from what AI can do to how companies can use AI to create business value and expan...

Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution mechanisms from experience. However, generaliza...

Recursive self-improvement is becoming increasingly vital for autonomous AI agents, where progress hinges on discovering high-value solutions across complex domains. The driver of this process is effe...

Our CI job volume increased 25x over 6 months. We patched our test selection service three times before finding a sustainable solution.

Monorepo for the Intent platform — tracks intentd, cloudlands-fe, and ios as submodules; docs, CI, and release orchestration - intent-hq/intent

Full-stack, renderer-agnostic Generative UI with a streaming-first language, official React support, community integrations, and up to 67% fewer tokens than JSON.

Can an agent learn a numerical search strategy through executable practice and then transfer that strategy as text? We study low-budget black-box optimization, where unaided language models remain wel...

Large Language Model (LLM) agents are turning language into real-world effects, making safety necessary against both indirect prompt injections and direct harmful requests. System-level safety harness...

Behavioral differences and prompting patterns for Claude Fable 5.1 and Claude Mythos 5.1, covering effort, progress updates, tool-call batching, conversation history, writing style, formatting, task c...

Write eval cases for your Claude Code plugin, run them with claude plugin eval, grade the results, compare against a no-plugin baseline, and gate CI on the score.

A Python port of Pi’s minimalist coding agent. Contribute to huggingface/tau development by creating an account on GitHub.

The ripgrep of AI context: a zero-dependency C++23 CLI + MCP server for coding agents. Find what you want without reading the repo, then check you built what you meant — blast radius, tests-to-run,...

YouTube video by Y Combinator

We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent state from verified reward signals rather than mode...

Long-term memory for LLM agents is evaluated today by conversational recall benchmarks (LoCoMo, LongMemEval), which measure question answering over dialogue history, not whether remembered facts chang...

What's new from Suno · September 9, 2026

A refined collection of Hypervelocity Engineering components (instructions, prompts, agents, and skills) to start your project off right, or upgrade your existing projects to get the most out of Gi...

Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, compute, and operational constraints determine what counts as a suitable plan. We call the absen...

Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with ...

Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination.

As terminal-based code agents become prevalent, agent trajectories have accumulated at scale, while realistic, executable environments remain scarce. However, environments are what agent post-training...

Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment transition, and observation can delay subsequent...