阿毛 -- d/acc retweeted
This is the least surprising graph in the world if you understand how gradient descent works.
Roadster xitter.cf/i/broadcasts/1yKAPwNgg…
0:00
Tesla
Roadster
阿毛 -- d/acc retweeted
果然搞黄色才是第一生产力,
纪念我的第一个一千star仓库。
很黄很暴力,
这是Opus5.5给我留下的唯一遗产了,
给Qwen image 2.1插上翅膀,全套NSFW配件,lora加系统性提示词,
我觉得效果强于黄果短剧。
github.com/Arekejoker/qwen21…
阿毛 -- d/acc retweeted
He creado PokéBonk, un survivors-like 3D de Pokémon (si, otro mas xD)
Inspirando fuertemente en Megabonk (a nivel mecánicas de mundo y sobre todo verticalidad del mundo 3D) mezcladas tematicamente con mecanicas de siempre que ofrece Pokémon.
Aquí os dejo el tráiler.
阿毛 -- d/acc retweeted
Hey Japanese learners! I built a font that renders Furigana or Romaji above Kanji, with ligatures to support words. You can apply it anywhere to get furigana notations for free.
Give it a try, and I hope it will be helpful :)
furifont.antfu.me/
阿毛 -- d/acc retweeted
THIS IS A PURE F*CKING GOLDMINE
Anthropic just shared one of the best tricks for Claude Code.
The idea: let Opus 5.5 write the plan as a script, and let a swarm of cheaper agents run it.
Bigger jobs and way fewer Opus tokens, because the script holds the plan and Opus only sees the final answer.
You just launch Claude Code like this:
claude --model opus --effort ultracode
here's the split:
→ Opus 5.5 turns your task into a workflow script
→ Haiku 5.5 agents scan every file in parallel
→ Sonnet 5.5 agents patch each fix in its own isolated copy
→ you stop burning Opus context on a hundred agent transcripts
Opus only shows up at three moments:
→ writing the workflow before the run
→ reading the verified results after it
→ signing off before anything ships
Opus plans. Haiku scans. Sonnet ships.
JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split
the full tree
> Opus 5.5 runs the main session and writes the workflow
> scan audits every file on Haiku 5.5
> edit patches each fix on Sonnet 5.5 in an isolated copy
> verify cross-checks every finding on Sonnet 5.5
> Opus 5.5 reads only what survived verification
> JEV routes micro-forks in milliseconds
paste the tree and this prompt into Claude Code ↓
"Rebuild my Claude Code setup around this tree:
1. Run the main session on Opus 5.5 with ultracode on.
2. In every workflow, run read-only scan stages on Haiku 5.5 and edit stages on Sonnet 5.5, each edit in its own isolated copy.
3. Add one rule to CLAUDE.md: every finding gets an independent verifier agent before it's reported, and fix loops stop after two rounds with no progress.
4. Route fast deterministic forks (tool choice, path selection, retries) through JEV.
Show me every change as a diff first. No edits until I say go."
↳ code.claude.com/docs/en/work…
save it, it'll come in handy.
阿毛 -- d/acc retweeted
Introducing Grok Voice 3.0, the world’s most intelligent speech-to-speech model.
It excels at resolving customer calls, responds in under a second, and costs less than other frontier models.
阿毛 -- d/acc retweeted
Local Laya moggs Jev at @grok 4.7-built Tetris 🧩
An open-weights System One model called Laya, beat cloud-based Jev at playing Tetris by making decisions 11 times faster, running locally on a 16GB MacBook Air!
Run AI models locally -> atomic.chat
介绍比Jev快50倍,在你设备上跑的laya-mlx!
只在你的设备上占用最高1G内存
Laya是一个开源的类似于Jev的,基于文本输出概率的分类系统
我将其移植到MLX,并且做了一些性能优化!
视频中就是这个模型在我的本地M3Max上玩贪吃蛇
这个模型能够以每秒决策60次的速度玩贪吃蛇!
github.com/mizorewww/laya-ml…
Introducing Project Redwood 🚀🚀
@architectlabs is a frontier AI lab bringing together talent from Anthropic, xAI, Google DeepMind, and seasoned leaders across the hardware industry. We've raised a $24M seed round to build AI systems for chip design.
我們 Architect Labs 是一個 frontier AI lab ,由各家 Anthropic, xAI, Google DeepMind 還有各種硬體專家們組成,我們在做的是 ai system for chip design,目前募資 seed round $24M
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Today, every major hardware company has its own chip design workflow—but these organizations and processes have become so large and entrenched that truly revolutionary change is difficult. We’re starting from first principles to create an AI-native chip design workflow—one that enables chip development to finally move at the speed of AI.
現在每家大硬體巨頭都有自己的晶片設計流程,但很多都大到不能做革命性的流程改動;我們在做的是從零思考,創造 ai native 的晶片設計流程,讓晶片設計真正跟上 AI 的速度。
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Project Redwood - from a single specification, our AI system designed, verified, and deployed a chip in under two weeks—delivering 3.4× better performance per watt than NVIDIA Jetson on billion-parameter models including Llama, Qwen, and Kimi. It autonomously generated the RTL, verification, firmware, drivers, and kernels, co-designing the model, software, and silicon in one optimization loop. We acknowledge that silicon is the ultimate ground-truth. We’re taking our approach all the way to GDS. We intend to tape-out multiple improved families of Redwood co-designed for various use-cases, on TSMC.
Project Redwood - 一份規格書,我們的 AI 系統在兩週內完成晶片的設計、驗證與部署。在 Llama、Qwen 和 Kimi 等模型,其 performance per watt 比 NVIDIA Jetson 高出 3.4 倍。從 RTL、驗證、韌體、驅動到核心 kernels,全部由 AI 自己寫,並在同一個 loop 中自主設計模型、軟體與晶片。當然,流片才是最終的驗證標準。因此,我們會將這套方法一路推進至 GDS,並計畫採用台積電製程,針對不同應用場景協同設計多個持續改良的 Redwood 晶片系列並完成流片。
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Full report on Redwood architecture and its autonomous design. Follow @architectlabs on X
我們有公開 Redwood 架構及其自主設計流程,歡迎去看論文~
We gave our AI system a spec, and in under 2 weeks, it designed, verified, and deployed a chip that beats NVIDIA.
It’s built for low-power physical AI workloads. We’re running live inference on >B+ parameter models like Llama, Qwen, and Kimi, serving at 3.4x better perf/watt than NVIDIA Jetson.
From just a specification, our AI system autonomously generated all of the RTL Design, UVM verification, formal proofs, firmware, drivers, and kernels, co-designing the model, software and silicon as one optimization loop.
Better AI can now design better chips to run AI, leading to a loop of recursive-self improvement towards our path to abundant intelligence.
Readers added context they thought people might want to know
Architect Labs did not manufacture a physical chip; the design was tested on a commercial AMD Versal FPGA board. Furthermore, the 3.4x perf/watt gain over NVIDIA Jetson is a simulation-based projection for a future 8nm ASIC, not a physical benchmark. businessinsider.com/architect-labs… architectlabs.com/architect-labs…
阿毛 -- d/acc retweeted
In the latest Codex CLI release, I redid the lifecycle to make `codex` startup instant.
It's now ~25x faster and immediately responsive.
Ex-Google engineer just dropped 1-hour course: loops, self-improving AI, memory systems - from scratch:
00:00 - the self-building agent
03:01 - soul.md runs everything
30:16 - RAG memory: pull 20 messages, not 2,000
31:48 - the loop that knows when to stop
35:14 - find the bug, fix the prompt
50:22 - how Claude compresses your memory
1 hour of his guide beats any paid agent course
watch & bookmark - then read Karpathy's loop method below
阿毛 -- d/acc retweeted
Un desarrollador ucraniano creó un agujero negro en su terminal para obligarse a tomar descansos.
Cuanto más trabajas sin parar, más crece y deforma tu código con su lente gravitacional. Descansas y se encoge.
阿毛 -- d/acc retweeted
At our latest YC Paper Club, researchers and builders presented on self-play for LLMs, AI for biology, formal verification, and agentic coding in production.
Thank you to our presenters:
00:00 — Francois Chaubard (@FrancoisChauba1) | Introduction & Call for Presentations
05:47 — Yasa Baig (@BaigYasa) | A World Model of Protein Biology (biohub.ai/esm/protein/about)
25:38 — Luke Bailey (@LukeBailey181) | Scaling Self-Play with Self-Guidance (arxiv.org/pdf/2604.20209)
37:51 — Arnab Maiti | Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage (arxiv.org/pdf/2510.02044)
47:40 — Robert Joseph George (@Robertljg) | Lean and the New Era of Verified Intelligence (arxiv.org/abs/2602.22631)
58:52 — Lukens Orthwein (@lukensort) | Founder AI Hacks: Programming is an RTS Game Now
1:16:07 — Closing Remarks
GPT Image 2 is insanely good...I generated a 360° equirectangular panorama in Happycapy with just a skill + prompt.
Step 1: Select the generate-image skill
Step 2: Enter a prompt like: “Use a frontend 360 viewer to display an equirectangular image of […] using the GPT-Image-2 model.”
Wanna see how you all get creative with this
Video Game Devs Explain How Pausing Works And Sometimes It Gets Weird kotaku.com/video-game-devs-e… via @Kotaku




























