OpenAI's Thibault Sottiaux posted that for the next 28 days the Codex and ChatGPT Work team will each day either ship one improvement relevant to most users or hand out a full usage reset. It's an either/or rather than 28 guaranteed resets, and he hasn't said how a reset would work or which plans it covers.
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…
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…
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…
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…
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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