GPT-6 Astra

$10 per million input tokens, $50 per million output tokens. Rolling out across ChatGPT Plus, Pro, Business, Enterprise and the OpenAI API.

What it's for Programming, cybersecurity, scientific research, computer control. It is the first model to cross the "critical" threshold of OpenAI's preparedness framework in cybersecurity, meaning it can attack as well as defend. A fast mode doubles the speed, and doubles the price.

The limit The rollout is not finished, not everyone has it yet. And it is a model you cannot host yourself: output runs at $50 per million tokens, where Google's Flash range sits at $3.75.

openai.com/​index/​gpt-6-astra

msitarzewski/agency-agents · 150,281 stars · MIT · last commit 5 September 2026

A library of role cards for coding assistants. Each card is a text file describing a specialist (security, testing, SEO, finance) that you drop into your tool. The repo announces more than 230 agents. Our own count after cloning, on 6 September 2026, gives 273 cards. What it does not do: no code runs, these are written instructions only. The cost is whatever the model reading them charges. The project itself documents that some tools only register part of the cards and silently drop the rest.

github.com/​msitarzewski/​…

trailhq/Graft · 5,609 stars · MIT · last commit 2 September 2026

It maps your code, in text, and gives the map to your coding assistant so it stops re-reading the entire repo on every question. Its standard protocol (SWE-bench Verified, 50 cases, same model on both sides) shows 19% less cost and 12 more accuracy points. What it does not do: it understands only 23 languages and ignores the rest, and it does not confine itself to the current repo unless you ask it to. The plain-language summaries require an API key and cost money, only the structural map is free. Its install command writes settings for all your repos, unless you pass --no-global.

github.com/​trailhq/​Graft

Tcotl/AgentCapture · 35 stars · AGPL-3.0 · last commit 4 September 2026

A honeypot for agents: 23 techniques to identify an agent visiting your site, trap it, and replay its session. Tested against five mainstream agents, all trapped. What it does not do: does not detect agents that only read without interacting. It does not block anything either, it watches and records, the decision stays yours.

github.com/​Tcotl/​AgentCapture

advaitpaliwal/feynman · 8,871 stars · MIT · last commit 6 September 2026

A command-line assistant that fetches scientific papers (arXiv, PubMed, OpenAlex, ClinicalTrials.gov), reads them, ranks them, produces a synthesis, and checks that every cited source actually exists. What it does not do: its ground is scientific literature, not current events. It does not verify a news claim. You must plug in your own model, and running heavy experiments means installing Docker, Modal or RunPod yourself.

github.com/​advaitpaliwal/​feynman

NVIDIA acquires Hugging Face for $12.9 billion. The main repository for open models passes under the control of the leading GPU supplier. Jensen Huang pledges to keep the platform open: neutrality will depend on what comes next. blogs.nvidia.com/​blog/​…

OpenAI commits one billion dollars to cyberdefence for critical services. The Daybreak programme opens subsidised access to its cyber models and products for operators of critical infrastructure, local government and open-source maintainers, with training and support, starting with the United States. openai.com/​index/​…

Building a newsroom run by machines

You want a continuous information flow: collect sources, check what is true, summarise, turn it into video, and coordinate the whole thing. Five layers. Here is what exists at each one, what it costs, and where it breaks.

Layer 1: collect. ScrapeGraphAI (30,601 stars, MIT) reads a web page and hands back structured data. You give it an address and a sentence, it figures it out. But it does not read RSS feeds or X: for those two sources, you have to write or plug in your own collection. The hosted service costs from $20 a month, the library alone is free but the model calls are on you.

github.com/​ScrapeGraphAI/​…

GPT-6 Astra: an automated AI engineer for under $6 an hour · Latent Space spent over 20 billion tokens exploring Astra in depth. The article details the real gains per task, the cases where the model fails, and why it costs more per token but less per result. latent.space/​p/​astra

Give your coding agents a memory you own · Hugging Face introduces Funes, a persistent memory system for coding agents that you host and control. huggingface.co/​blog/​funes

Project HydraFusion: frontier quality through multi-model orchestration · GitHub shows how to have several models work on the same agent to reach big-model quality while cutting cost. github.blog/​ai-and-ml/​…

LiteLLM is a proxy that many teams place between their agents and their model providers, to route requests to the right place. An authentication flaw (CVE-2026-59822, severity 8.8 out of 10) lets a stranger present a token made up out of thin air: the check fails, and instead of refusing, the proxy lets the request through with an empty identity. The attacker then reaches the tools your agents expose, with no key at all. The flaw is actively exploited, CISA added it to its catalogue on 2 September. The technical detail, the severity and the fixing version come from the project's own security advisory: github.com/​BerriAI/​litellm/​…

The right move. Move LiteLLM to version 1.84.0 or later today, that is the version that fixes the flaw. If you cannot update immediately, cut inbound network access to the proxy and rotate every API key registered there.

cisa.gov/​news-events/​alerts/​2026/​…

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