HRH bc238dev @bc238dev
CEO of JS/TS. Non-Angel investor! Hodlr. ¯\_(ツ)_/¯ California, USA Joined December 2018-
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Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs. Google's new model, Gemma 4 12B Unified supports image, audio and 256K context. You can run and train the model via Unsloth Studio. GGUF: huggingface.co/unsloth/gemma-… Guide: unsloth.ai/docs/models/ge…
Meet Gemma 4 12B! A unified, encoder-free multimodal model designed to bring high-performance intelligence directly to your laptop, and released under an Apache 2.0 license. Bridging the gap between edge efficiency and advanced reasoning. Here is what’s new with Gemma 4 12B: 👇
@jarredsumner Let’s re-write it for Go! Rust is too rusty 😅
Kokoro's price/performance is amazing! Great quality for little price.🥰
Universal anxiety is real in the modern AI era.
Chuck Norris can delete the recursion from a function without even calling it. Yeah, let that sink in.
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.
@jarredsumner @thdxr Bun should have been written in Bunx.
@TechByTaraa Kali and/or Mint. I use both in my VMs.
@jarredsumner @mitchellh Next step, code bun in Bunx 😅
@mitchellh @matteocollina I expect them to write a new programming language 😅
Frankly, I’m surprised, but hey whatever is good for the Bun ecosystem I’m fine.
Bun v1.3.14 releases tomorrow. If we do merge the Rust rewrite, this would be the last version in Zig
@jarredsumner Maybe I missed the news but why will you do the Rust rewrite?🤔
Simply Useful Apps @simplyuseful
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JSWORLD Conference @JSworldconf
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23K Followers 1K Following The Global @sveltejs Community! 🌍🌎🌏 🗣️ Discord: https://t.co/bDvUqAxQmS 📺 YouTube: https://t.co/YXWzye3Mpy
Expo @expo
85K Followers 2K Following 𝝠 Expo is everything you need to build apps. ⭐️ Star the repo: https://t.co/lqFx3kgxyo 🤗 Signup for free!: https://t.co/NFVyuqAVFE
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Simply Useful Apps @simplyuseful
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Armin Ronacher ⇌ @mitsuhiko
79K Followers 896 Following Creator of Flask. Building at https://t.co/uGuzfu0LKT. Bypassing Permissions. Can hand crank. Husband and father of 3 — “more nuanced in person”
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OpenClaw🦞 @openclaw
539K Followers 24 Following The AI that does things. Emails, calendar, home automation, from your favorite chat app. Your machine, your rules. New shell, same lobster soul. 🦞
Stitch by Google @stitchbygoogle
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159K Followers 3 Following Welcome to the Official Chuck Norris Twitter page. Chuck is an international television and film star. His TV series “Walker, Texas Ranger”ran for 81/2 years.
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Moinul Moin @moinulmoin
1K Followers 542 Following Founder & AI Engineer @ideaplexa Shipping @voicetypr Soon https://t.co/uyl3msR0Fn Ambassador of @Zai_org Member of @ampcode Build Crew.
VoiceTypr @voicetypr
81 Followers 12 Following Best AI powered offline voice to text dictation tool. Available on mac and windows by @moinulmoin Open Source - Full Privacy - Pay once, Use forever.
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225K Followers 402 Following I own a computer / Working on https://t.co/HJcOr0AUAr & https://t.co/5FRvxukoY5.
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42K Followers 1K Following 👨💻 Working on llama models @AIatMeta | Previously: @h2oai, @weights_biases 🎙 Podcast @ctdsshow 👨🎓 Fellow @fastdotai 🎲 Grandmaster @Kaggle
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503K Followers 3K Following AI research paper tweets, ML @Gradio (acq. by @HuggingFace 🤗) dm for promo ,submit papers here: https://t.co/UzmYN5XOCiYou might like
