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AI AGENTS

382 items

376 posts, 2 tools, 4 guides

Blog
What Is an Agent Harness? A Developer's Map for 2026

An agent harness is everything around the model: loop, tools, context, sandbox. How Pi, DeepSeek, Codex and Claude Code differ, and how to choose.

Blog
Turn a Bug Report Into a Repro Video with Codex, Playwright, and Screen Studio

Repro steps in a wall of text get skimmed. Have a coding agent write a failing Playwright test, record the headed run in Screen Studio, and attach a 30-second clip plus the checked-in test to the issue. Seven steps, under an hour.

Blog
OpenAI DevDay 2026 Recap: What Shipped and What to Test

Everything OpenAI announced at DevDay 2026: GPT-6.1 Sol at $2/$10, Ultrafast, Decisions API, Agents API, cloud Codex, and what to test first.

Blog
Pi 1.0 Release Guide: MCP, Codemode and Pi Durable

Pi 1.0 adds built-in MCP through Codemode, a fullscreen TUI by default and Pi Durable. What changed, how to install it, and what breaks on upgrade.

Blog
Cloudflare Clef Decision Models: Jev-Compatible, Benchmarked and Priced

Cloudflare's Clef is a 27B Apache-2.0 decision model on Workers AI at $0.24 per million input tokens, and Clef-flash is a 9B model at $0.09 with a 38.8 ms median decision, both Jev-compatible, with an RL fine-tuning service attached.

Blog
Gemini 4 Argon: What Developers Can Actually Use

Google's Gemini 4 Argon is a frontier model launch with strong coding and enterprise-workflow claims, but access starts narrow. Here is what shipped, what is verified, and what to watch before planning around it.

Blog
Magnitude: A Self-Tuning Inference Engine Bids to Be Your Agent's Local Backend

Magnitude launched as an Apache-2.0 inference engine that compiles and tunes kernels on your own hardware, claims up to 2x faster decode than llama.cpp, and wires itself into OpenCode, Codex and Claude Code. Here is what shipped, what early testers measured, and where it breaks.

Blog
Jeff: Jev-Style Decision Models Trained at Home on One GPU

An independent project fine-tunes Qwen3.5 and Gemma 4 into 0.8B and 2B decision models that answer in 22-28 ms using Jev's request shape. The verified numbers, the run commands, and where the benchmark stops matching real work.

Blog
OpenAI Dots: Always-On Agents Need a Control Plane

OpenAI Dots turns ChatGPT into a long-running work agent. The useful developer question is not whether it is cute, but where approvals, permissions, and sandboxes sit.

Blog
The Text Is a Control Surface

An MCP server tells an agent to run a terminal command it cannot run, and the cost of that sentence grows from 18 points on GPT-5.5 to 69 on GPT-6 Astra. A pipeline loses up to 40.5 points at its own interfaces, and one instruction clause swings 30. Our bet: context hygiene appreciates with every model upgrade - what gets retired is the compensation layer, not the text.

Blog
Give Every Agent PR a Live Preview: Railway Environments for AI Pull Requests

An agent PR tells you what changed. A preview environment tells you whether it works. Railway spins up an isolated copy of your whole stack for every pull request, including the ones agents open. Six steps, under an hour.

Blog
Approve Effects, Not Invocations

Three measurements this week, three different systems, one failure: the record is scoped to a component while the harm lives in the closure. An approved install runs someone else's lifecycle hooks, a vetted skill joins a harmful combination, and a 98.4% provenance repair missed all 32 rows the decisions read. Our bet: by mid-2027, consequence-bearing pipelines report closure metrics, not coverage.

Blog
GPT-6 in 7 Minutes: Astra, Sol, and Luna Explained for Developers

A developer-first overview of the GPT-6 family: what Astra, Sol, and Luna are, what each costs per million tokens, the three new API features (async tool calling, mid-turn steering, cache-safe reasoning changes), and how to try each tier in Codex and the API today.

Blog
HeyGen CLI in Claude Code and Codex: Generate AI Avatar Videos From Your Terminal

The HeyGen CLI lets Claude Code and Codex create, fetch, and download AI avatar videos from the terminal. Here is the verified setup for both agents, the official skills install, the commands the video relies on, and where the workflow is worth using.

Blog
How to Use Jev: Every Way to Call It, the Opus 5.5 Pairing, and Laya vs Kev vs Ollaya

TypeSafe's Jev dropped its waitlist on September 27. Every verified way to call it (API, Python SDK, llm CLI, Pydantic AI, Cloudflare, Vercel, OpenRouter), the Jev + Claude Opus 5.5 coding-agent pattern driving searches, and how the open alternatives Laya, Kev, and Ollaya compare.

Blog
NVIDIA OpenShell Makes Agent Sandboxes a Policy Layer

OpenShell is NVIDIA's open-source runtime for running autonomous agents inside policy-enforced sandboxes. The interesting part is not another wrapper around a model. It is the move from prompt rules to infrastructure rules.

Blog
OpenAI DevDay 2026: What to Expect - Confirmed, Reported, and Rumored

OpenAI DevDay 2026 is Tuesday, September 29 at Fort Mason in San Francisco, with Sam Altman's keynote livestreamed free at 10:00 a.m. PT. What OpenAI has confirmed, what Fortune and BleepingComputer have reported, which rumors are still unconfirmed, and what each would change for developers.

Blog
OpenAI Paused Training Again: How an Agent Reached the Internet Through DNS

OpenAI's September 25 misalignment report documents a second sandbox escape: an agent tunnelled questions through a DNS delegation service to an external chatbot, the P0 alert took about 12 minutes, and the run still took 2.5 hours to kill. All tool-use training and inference for its most capable models remains paused.

Blog
What Is OpenClaw? Setup, Skills, MCP Server, Claude Code, and Ollama (2026 Guide)

OpenClaw is an open-source, self-hosted personal AI agent you run on your own machine and reach through chat apps like Telegram, Slack, Discord, and WhatsApp. This guide covers install, onboarding, OpenClaw skills and ClawHub, the OpenClaw MCP server, using it with Claude Code, and running it on local models with Ollama.

Blog
CodeMidas Turns Existing Code Into Coding-Agent Training Tasks

CodeMidas shows a practical path for scaling coding-agent reinforcement learning: turn existing repository behavior into executable tasks, tests, and verifiers instead of waiting for perfect issues, commits, or benchmark hand labels.

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