GitHub turned on the most expensive model in Copilot for you and didn't ask by default, GPT-6 Astra switched on automatically on 4 September unless an admin had already disabled the global default. so here're two rates worth knowing before someone runs a repo-wide agent task: > $10 in, $50 out per million, double above 272K input > cache write is a separate $12.50, which older OpenAI models never had check out the full guide on Astra in the article below

OpenAI engineer: Forget about prompt engineering. With the launch of GPT-6 Astra, typing out complex prompts is officially obsolete. We are moving past the era of standard LLMs that just predict text. Astra introduces a massive leap in AI performance: computing pure logic and mapping complex, real-time relationships instantly. It doesn't just guess what to say next; it actually thinks through the reasoning at a level we haven't seen before. He breaks down this insane performance boost in a quick 30-minute lecture, proving why this new architecture is leaving older models far behind. 👇 Watch the video and read the full Astra guide below

OpenAI Engineer: "Prompting is dead. With the new GPT-6 Astra, you no longer need it." Most people miss that standard LLMs and text prompts are just steps towards the dynamic graph. This shift is what powers GPT-6 Astra. Instead of guessing the next word based on a text prompt, Astra navigates knowledge graphs to compute logic and real-time relationships. In just 20 minutes, he demonstrates how to use Astra and why it is the only architecture that will survive the AI evolution. Watch the video, then check out the full guide on Astra below

OpenAI Engineer: "Prompting is dead. With the new GPT-6 Astra, you no longer need it." Most people miss that standard LLMs and text prompts are just steps towards the dynamic graph. This shift is what powers GPT-6 Astra. Instead of guessing the next word based on a text prompt, Astra navigates knowledge graphs to compute logic and real-time relationships. In just 20 minutes, he demonstrates how to use Astra and why it is the only architecture that will survive the AI evolution. Watch the video, then check out the full guide on Astra below

Someone already shared Fable 5.1 optimizations you can start using right NOW. Here's how to use them: 1. Set effort to 'low' 2. Run '/claude-api cost-optimize' 3. Run 'claude-api prompt-audit' 4. Change effort mid-conversation w/o cache hit 5. Update Fable 5.1 API config w/ 'claude-api migrate' Bookmark this, you'll need these tips

Someone already shared Fable 5.1 optimizations you can start using right NOW. Here's how to use them: 1. Set effort to 'low' 2. Run '/claude-api cost-optimize' 3. Run 'claude-api prompt-audit' 4. Change effort mid-conversation w/o cache hit 5. Update Fable 5.1 API config w/ 'claude-api migrate' Bookmark this, you'll need these tips

this is f**king insane someone found a way to use fable 5.1 with free gpt 5.6 luna subagents and never hit usage limits [here is how to set it up in 3 mins] 1. install 'fable-advisor' repo 2. type /model fable 3. done save this and give it to your agent

this is f**king insane someone found a way to use fable 5.1 with free gpt 5.6 luna subagents and never hit usage limits [here is how to set it up in 3 mins] 1. install 'fable-advisor' repo 2. type /model fable 3. done save this and give it to your agent

SpaceXAI team just dropped a 3-page operator’s manual for turning Grok Bot into a full multi-agent system that runs entire workflows 24/7 the shift: instead of prompting one Bot task by task, you build a Chief + specialist teams that own entire workflows here's the 7-step Grok Bot playbook: step 1 - mental model → one persistent cloud computer, multiple Bot screens, shared files, browser sessions and credentials underneath - screens are work surfaces, not security boundaries step 2 - setup order → create the Chief first - it doesn't do the work, it routes → delegates → watches handoffs → collects outputs → escalates step 3 - internal bus → stop passing important context through chat - research, drafts, evidence, decisions and handoffs live inside /workspace so every Bot can continue exactly where another stopped step 4 - specialist teams → split content, intelligence, ops, research and coding across different Bots while one Chief coordinates everything step 5 - automation → every serious workflow gets 3 gates: source gate → evidence gate → action gate - Bots keep working until something actually needs human approval step 6 - trust layer → let Bots research, draft, summarize, reconcile, verify and queue autonomously - humans approve only money, publishing, deletion, signatures and irreversible external actions step 7 - expensive lessons → constant polling burns quota, syncing everything wastes usage, bad retries duplicate actions, undocumented internals break, and too many Bots can actually make the system slower the result: Grok Bot stops being a chatbot and becomes a 24/7 AI team - every workflow has an owner, every handoff leaves evidence, and humans only step in when judgment or approval is required read this before building another Grok Bot - it breaks down the 18 rules, multi-agent architecture, handoff system, approval layer and automation model most agent tutorials never show bookmark it and build your own Grok Bot team from the full article below

SpaceXAI team just dropped a 3-page operator’s manual for turning Grok Bot into a full multi-agent system that runs entire workflows 24/7 the shift: instead of prompting one Bot task by task, you build a Chief + specialist teams that own entire workflows here's the 7-step Grok Bot playbook: step 1 - mental model → one persistent cloud computer, multiple Bot screens, shared files, browser sessions and credentials underneath - screens are work surfaces, not security boundaries step 2 - setup order → create the Chief first - it doesn't do the work, it routes → delegates → watches handoffs → collects outputs → escalates step 3 - internal bus → stop passing important context through chat - research, drafts, evidence, decisions and handoffs live inside /workspace so every Bot can continue exactly where another stopped step 4 - specialist teams → split content, intelligence, ops, research and coding across different Bots while one Chief coordinates everything step 5 - automation → every serious workflow gets 3 gates: source gate → evidence gate → action gate - Bots keep working until something actually needs human approval step 6 - trust layer → let Bots research, draft, summarize, reconcile, verify and queue autonomously - humans approve only money, publishing, deletion, signatures and irreversible external actions step 7 - expensive lessons → constant polling burns quota, syncing everything wastes usage, bad retries duplicate actions, undocumented internals break, and too many Bots can actually make the system slower the result: Grok Bot stops being a chatbot and becomes a 24/7 AI team - every workflow has an owner, every handoff leaves evidence, and humans only step in when judgment or approval is required read this before building another Grok Bot - it breaks down the 18 rules, multi-agent architecture, handoff system, approval layer and automation model most agent tutorials never show bookmark it and build your own Grok Bot team from the full article below

Everyone built a multi-agent research system this year. Nobody said why it should work. This paper does. It grounds the whole thing in Assembly Theory: a complex object is defined by the history that built it out of reusable parts. Which means throwing away failed branches isn’t wasteful. It’s deleting the thing you’re trying to build.

Nobel Prize winner and Google DeepMind CEO Demis Hassabis just revealed the real AI skill gap: “The next generation will do things that used to take teams of 10, 20, 30, 50 people” But AI does not just make people faster. It changes what one person can become. The next 5 years will not belong to people who write better prompts. They will belong to people who build better workflows. Watch the full interview. Bookmark this. It is one of the most important ideas of the decade.

Nobel Prize winner and Google DeepMind CEO Demis Hassabis just revealed the real AI skill gap: “The next generation will do things that used to take teams of 10, 20, 30, 50 people” But AI does not just make people faster. It changes what one person can become. The next 5 years will not belong to people who write better prompts. They will belong to people who build better workflows. Watch the full interview. Bookmark this. It is one of the most important ideas of the decade.

Anthropic's main manager: "Nobody types prompts from scratch. The commands should be live in the project." In 26 minutes, she walks through how Anthropic runs Claude Code, including the command library every new dev inherits on day one. Watch the full talk, then save the config below👇