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CodeAF is an open-source Go coding harness built for open models like DeepSeek, Qwen, and GLM

AgentField's Apache-2.0 CodeAF ships as one 53 MB binary (curl -fsSL https://agentfield.ai/get/codeaf | bash) that turns each request into its own task on its own branch, runs it on a per-task crew of open models via OpenRouter, provider keys, or local Ollama, and claims first place among ten harnesses on DeepSWE with DeepSeek V4 Flash. It's an early preview with anonymous telemetry on by default, which CODEAF_TELEMETRY=off or DO_NOT_TRACK=1 turns off.

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OpenAI adds opt-in textGrain watermarking to its API and will watermark ChatGPT and Codex text in the EU

Starting Oct 5, API customers anywhere can turn on invisible text watermarking per project or org for select models (off by default), and eligible ChatGPT and Codex users in the EU get it in the coming weeks to meet the EU AI Act. The signal lives in word choice, outputs under 200 tokens and code snippets aren't required to carry it, and the detector is limited to approved researchers for now…

OpenAI

Hugging Face's OpenEnv proxy turns Claude Code, Codex, and OpenCode into RL training environments

An open source capture proxy sits between an unmodified harness and vLLM, speaks the OpenAI, Anthropic, and Gemini API formats, and records exact token IDs and logprobs so TRL can train on real agent runs; 10 harnesses work today. Training Liquid AI's LFM2.5 2.6B across four harnesses lifted its average solve rate from 42% to 54% (Claude Code 33% to 49%), and the proxy, trainer, tasks, and all seven checkpoints are open…

Cursor SDK 1.0.31 lets you steer a running agent mid-turn and swap its system prompt

In @cursor/sdk 1.0.31, run.steer(text) injects a message into the turn in flight (a running foreground subagent moves to the background and keeps going), custom tools can carry MCP annotations like readOnlyHint and destructiveHint , and systemPrompt on Agent.create() replaces Cursor's built in prompt while rules and skills still load. Steering and the prompt swap are TypeScript local agents only, cloud runs return revert to followup , and systemPrompt is enabled per account, so it fails on first send() until yours is on…

AWS ships an aws-ai-ml skill that lets Claude Code, Codex, and Kiro benchmark and size SageMaker endpoints

Install it with npx skills add aws/agent toolkit for aws/skills/aws ai ml (after aws configure agent toolkit , which needs AWS CLI 2.35+ and uv), and your agent can load test a live endpoint, rank instance types for a model from S3, JumpStart, or Hugging Face, and diff two benchmark runs, all as SageMaker Python SDK v3 code you review and run. It runs under your own AWS credentials and asks before driving real traffic, so clean up the endpoints and S3 objects it creates to avoid charges…

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