Leon_limestone 🔭

Hey @jun_song, love what you’re building with SuperGemma and SuperTune — best sovereign local models out there. Quick idea: turn $SUPERGEMMA into “Local Agent Fuel”. Run everything 100% locally (inference stays free), but let agents use the token only when they need to: ·store persistent memories on-chain ·pay for improvement patches / RL data ·sync state across devices Staking gives daily fuel credits. Fees fund bounties + buyback + burn. This adds real utility while staying 100% true to local-first Sovereign AI. Would love your thoughts

It’s the future. Change stereotypes.

crypto is indisputably the best at two things 1) engineering massive scale human coordination games 2) enticing speculation whoever figures out how to combine both of these things together around raw compute capacity will make infinite

Imagine you still have the opportunities to buy into exponentially increasing "consensus." Like Bittensor or even Bitcoin.

conviction szn

rotating profits into a2a infra

next 1b runner on eth it's happening

ethmaxxxxing

@redarvian 把稀有图片套利者比喻成bitcoin miner是不是不太恰当？套利者的操作成本太低，他们给整个系统运作的贡献是如何体现的

昨天出现一系列fud 关于稀有度的问题，一些套利者刷出大量稀有（而dev称他们为一些有技巧的铸造者）说明dev其实一开始就预计了这个情况会发生（在此大家可以多去看看官网的白皮书） 因为稀有的高溢价所以吸引了套利者参与其实本质上也是提供了交易量，随着时间推移大量的高稀有变的不再稀有（所以dev 引入了时间这个概念hold就是最好的稀有） 其实dev一直以来给我们的概念就是有图那就是稀有，而这两天的市场流动选择推出了所谓的高稀有，本质其实是动态稀有 如果稀有是固定的，那死亡那天就是没人愿意买单为了这个稀有，但是动态就是说明，资金会不断轮转 upeg可以吸引四类人 炒图者，炒币者，套利者，池子流动性提供者因为频繁的burn和mint图片产生价格波动 系统是个自循环系统，四类人都是为了这个系统运作下去而存在 swap即铸造, 市场即作者,稀有即奖励。没有人控制输出，没有人能关掉它。一个自持运转的链上经济系统，用艺术作为接口。 🦄 -bitcoin

@Sencrazy_1 market bought 0.5%. Welcome back

整了篇科普熊猫头表情包的文章，很多内容都是网上东拼西凑的。希望中文meme不止限于此！

文艺复兴👽

Autonomous, recursively self-improving AI researchers are here. They're just not evenly disributed.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

The beta play of Bittensor is not subnet but something reinforce each other in different layers

RT @DistStateAndMe: Jack Clark (@jackclarkSF ), cofounder of Anthropic, has now written about @tplr_ai twice. Two weeks ago we went viral.…

This is always how I assumed LLMs would wind up functioning because this is how I (and presumably most others) think I assume the base unit of thought is this gestalt thought vector thing, not "words," and we've just all developed a very fast way to translate these to words because words are more communicable than thought pieces This was always my issue with "some people don't have an internal monologue!" discourse It just makes no sense for words to be the base unit people think in. It's like 1000x faster to think in terms images or these thought pieces or whatever I assume it just seems like people think in words bc when they describe what they're thinking to people, they have to translate the thought pieces to words - as that's how we communicate - and this process converts their actual thoughts into the form of a monologue But it only makes sense to think in words when you need to output some form of communication. Otherwise it's not very efficient And human brains are insanely efficient

1/ Karpathy just described the hard ceiling on trillion-dollar autonomous systems as a throwaway caveat about his overnight hyperparameter script (@NoPriorsPod) — and went back to tuning his learning rate...

Andrej Karpathy on autoresearch with an untrusted pool of workers: "My designs that incorporate an untrusted pool of workers (into autoresearch) actually look a little bit like a blockchain. Instead of blocks, you have commits, and these commits can build on each other and contain changes to the code as you're improving it. The proof of work is basically doing tons of experimentation to find the commits that work." The idea that distributed & permissionless autoresearch ~= proof-of-useful-work remains a high-level intuition for now, but it is extremely intriguing to say the least. Someone needs to take this further. See QT for more on what's missing.

Is it possible to build "proof-of-useful-work" on top of autoresearch? There's already great compute-versus-verification asymmetry that is tunable. Would need a reliable way to generate fresh & independent puzzles (that are still useful). Maybe a dead end, but someone should look into if decentralized consensus with useful work is possible on top of autoresearch. Let me know if you solve this.