0xNeoArch

4步教你复活 claude fable 5！ 1.下载 fable 5的系统提示词 github.com/elder-plinius/… 2.放到你Claude code 项目文件夹 3. 使用启动命令 claude --dangerously-skip-permissions --system-prompt-file CLAUDE-FABLE-5.md 4.模型切换到 opus 4.8 Max 只需要这4步 你会发现 fable 5 又回来了！

If you have: Hermes Agent Claude Code & Codex Handoffs Obsidian + QMD Memory System Run Agentic Loops Fleet Tailscale Mesh Cron Jobs + Kanban Board Agentic Workflows Congrats you are the top 1% of the AI god stack

Sglang is my #1 inference engine. - most active community - most models supported - best performance for 6000s - good GitHub issues I love you sglang ❤️

If you have the NVMe Go download as many models as you think you might ever want. Now, go on Huggingface. They’re coming for open models next.

One day similar approaches would let us have Fable-level intelligence at home 🚀

Code2LoRA seems an incredibly interesting idea. Qwen2.5-Coder-1.5B is not the most powerful LLM around, but it's enough to validate the concept. Instead of stuffing repository context into the prompt at every query, distill it into a LoRA adapter. One forward pass over the repo snapshot, one adapter, zero extra inference tokens. For evolving codebases, a single layer GRU tracks commit history on top of that snapshot. Each git diff updates the hidden state in <10ms. You get a fresh adapter at every commit without need for a full retraining. Great job Liliana! I bet this will lead to something cool in the near future 🙌

Noooo, when I woke up it was already disabled… We had the best few days 🤯

@AnthropicAI Noo, this was the best model ever!

The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…

El CEO de Anthropic tras darse cuenta de que con su Claude Fable 5 se lanzó este tutorial gratuito que está rompiendo internet donde se puede crear sitios web animados y premiados de forma brutal en solo 12 minutos

@0xSero awesome, will try it! thank you!

@0xNeoArch already available on models tab

From running your local model to droid, pi, opencode, and hermes in 1 click.

@vu3dtu awesome!

@vu3dtu very cool! i want to help with testing. will the Pro version of the ring work, too?

I loved the idea behind the Google Fitbit Air: an LLM wrapped around your health data, daily briefs, and a coach you can ask questions. But there app is really terrible, it's expensive $100 band plus $10/mo, and Google getting a constant stream of your heart rate, sleep, and other private data. Whoop is worse, with a subscription that runs up to $360 a year. So I bought a $7 generic Chinese smart ring from Temu, reverse engineered its BLE protocol, and built an app around it. Introducing PulseLoop: no subscription, open-source iOS app. Your health data stays on your phone, paired with an AI coach that reads your real ring data, draws charts, and remembers context. Free, bring your own API keys. Demo and code below.

We're all sleeping on Step-3.7-Flash. It's phenomenal.

When all these closed labs decide it's time to rug pull everyone, you all are gonna regret not Buying a GPU Owning your compute allows you to be in control, even if partially Not too late yet

Fable 5 dropped this morning. The model from Claude, which some have heard as Mythos, was TOO powerful to be released to the public. Well they did release a version of it. I cancelled what I was doing. Built 3 things in real time. One prompt each. What it did made me lose MY MIND. I've tested every model UNDER THE SUN since the early GPTs. Nothing, not 4.8, not GPT-5.5, none of them, NOTHING comes EVEN CLOSE!!! I have seen the future.

the whole “agent orchestration” industry is just tmux panes and the willingness to stop approving every keystroke.

what is agent looping for the last two years we prompted agents one task at a time. that is starting to change instead of asking an agent to build the landing page and then driving every step yourself, you set up a loop that handles discovery, planning, the work, checking, and iterating until the goal is met looping is a setup you build. almost any agent harness can run it, it just depends on how you wire it up at its simplest, looping is one agent working on itself: > researches > drafts > checks the draft against a goal > fixes what is weak > runs that cycle again until the work clears the requirements you are not prompting each step anymore. the agent repeats the cycle for you the bigger version is a fleet looping. you give an orchestrator agent a goal, it breaks the goal into pieces, hands each piece to a specialist agent, and those specialists hand smaller jobs to their own subagents the whole tree keeps looping through discovery, planning, execution, and verification until the goal is met one agent looping is like a person redoing their own draft. a fleet looping is a whole team running a project end-to-end you create a goal, and the system runs the loop until it finishes within the reqs you set open and closed looping: OPEN LOOPING is exploratory. it still has conditions and a goal, but you give the agent or the fleet a wide space to move in. it can try different paths, discover things, build something you did not fully spec out this is the exciting end, it is what Peter and others are doing, and tbh it is where I want to spend more time the catch is cost, an open loop with real room to explore burns an insane amount of tokens. for the 90 percent of people without an unlimited budget it is not runnable yet, and pointed at projects with a loose standard it turns into a slop machine CLOSED LOOPING is bounded. a human designs the end-to-end path first: > clear goal > defined steps > an eval at each step > a point where it stops or hands back to you (and feeds back performance data) the agents still loop, but inside framework you built. it gets better every run because each pass feeds the next, and it runs on a normal budget because the path is tight. for most marketing work, closed is the one that pays off today. > the orchestrator owns the goal > the specialists own the steps > the subagents do the narrow work > an eval gate make sure its not slop