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eightman999/README.md
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🏠 eightman999

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🧑‍💻 About Me

I'm an information engineering student in Japan building local AI systems, LLM inference tools, coding-agent workflows, and apps.

My projects usually start with a question: Can this actually work? I enjoy testing ideas on everything from Apple Silicon to aging GPUs, then publishing implementations, measurements, and their limitations.

  • 🧠 Persistent/distributed cognition, memory, and AI agent architectures
  • ⚡ Local LLM inference, speculative decoding, and multi-GPU performance
  • ♻️ Legacy-GPU software, OpenCL 1.1, and salvaged computing
  • 🇯🇵 Japanese tokenizers, fiction-oriented language models, and long-context evaluation
  • 🧪 Reproducible benchmarks, regression tests, experiment records, and honest failure analysis
  • 🛠️ macOS / Android / web apps, creative software, and developer tools

Languages: Rust · Python · Swift · Kotlin / Java · C / C++ · C# · TypeScript / JavaScript

日本語でも活動しています。**「動くところまで作り、実測し、再現できる形で公開する」**ことを大切にしています。


🔬 Current Focus

October 2026 · public projects and experiments

Area Project / work
🧬 Kamimusuhi / かみむすび Experimental architecture for a persistent artificial cognitive individual: continuity, provenance, memory, and delegation across models and machines.
♻️ SalvagedComputing / ScrapGpuLibrary Finding useful roles for obsolete GPUs and constrained machines. Working on legacy OpenCL 1.1 inference through CL11 and portable runtime components.
⚡ Strata experiments / Qwen3.8 MTP Practical local inference with limited hardware: decoding speed, memory, quantization, and model/runtime tuning.
📏 YLSB / llama-split-bench Reproducible benchmarking of complete local-LLM configurations, including GPUs, split mode, context, KV cache, quality, and speed.
🇯🇵 J-series Tokenizers v2 / novllm Japanese tokenizer design and model training/evaluation for long-form and fiction generation, with public research tooling.
🤖 rustdsh / rdsh Contributing to Rust tooling for AI coding agents: provider integrations, execution workflows, developer experience, and safety.
🍽️ OISINT An evidence-backed restaurant research and comparison app, including source links, uncertainty, and collaborative decisions.

Additional experiments: NavSpec (structured naval-spec generation and validation) and Open Jev Core ML (experimental Apple Silicon inference, with documented limitations).

🤝 Community & Open Source

I work with the 地元のLLM community on local-AI tooling, including ScrapGpuLibrary, rustdsh, and the community Bench of Us reports.

I also contribute to J-LLM on Hugging Face, sharing Japanese models, tokenizers, and other experimental artifacts. The J-series v2 collection covers multiple tokenizer vocabulary sizes.

Projects vary in maturity. A prototype, a benchmark, or a successful test is not a production guarantee.


🚀 Selected Software

  • 🌸 Ourin / 桜鈴 — macOS-native Ukagaka-compatible baseware, built with Swift and native SHIORI runtimes.
  • 🔍 DiffLoupe — a macOS-native folder/file diff tool inspired by WinMerge.
  • ✒️ Singleline JP Font — Japanese centerline fonts and 1-bit assets; the expanded v2 family is still a release candidate with proofreading in progress.
  • ⚓ NavSpec — structured fictional naval specification generation, constraints, and regression evaluation.
  • 🧠 Open Jev Core ML — experimental Core ML / Apple Silicon conversion and benchmark results, including negative findings.
  • 🛠️ HOI4 Modding AI Agent Skills — reusable references and agent skills for game modding.
  • 📱 WattTime — a published Android microwave heating-time converter built with Kotlin.

See more in my repositories and on Furin Lab.


🧭 How I Work

idea / problem
    ↓
survey & design
    ↓
prototype
    ↓
measurement / benchmark
    ↓
failure analysis
    ↓
reproducible artifact
    ↓
next experiment

I use AI coding agents heavily, but try to keep claims separate from measurements: CI, test results, raw outputs, hashes, experiment manifests, and failure records matter more than "it seems to work".

I also run local experiments across Apple Silicon, CUDA, and legacy OpenCL systems, including mixed-generation NVIDIA GPUs, so hardware constraints are treated as part of the research rather than an afterthought.


💻 Tech & Research Stack

Languages

Languages

Development / ML

Development and ML

PyTorch · Transformers · MLX / MPS · llama.cpp / ggml · CUDA · OpenCL · Core ML · Metal

GitHub Actions · SQLite · Supabase · Cloudflare

AI-assisted development

ChatGPT Claude Gemini Codex

🔗 Links

🏠 Personal site eightman999.com — works and notes
🎐 Furin Lab furinlab.com — apps and public releases
🤗 Hugging Face Furinlab · J-LLM
🇯🇵 Japanese tokenizers J-series v2 collection
🤝 Local LLM community jimoto-no-llm · Bench of Us
🍽️ OISINT oisint.com
🐦 X @eightman999
💬 Discord _eightman_
🍳 Off-keyboard Cooking and eating good food

📊 GitHub Analytics



💬 力は「出るもの出せるもの」
💬 「やってみよう」と「どうせ駄目だ」
— 羽仁もと子『羽仁もと子著作集』

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© 2026 eightman999 · Furin Lab · build, measure, iterate.

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  1. Ourin Ourin Public

    Mac-native Ukagaka-compatible baseware for modern macOS.

    Swift 9

  2. hoi4-modding-skills hoi4-modding-skills Public

    HTML 3

  3. YLSB YLSB Public

    Your Local LLM 標準試験 YLSB v0.3 — 松竹梅

    Python 2

  4. kamimusuhi kamimusuhi Public

    Python 1

  5. open-jev-coreml open-jev-coreml Public

    Open Jev Core ML / ANE implementation, benchmarks, and research records; weights on Hugging Face, no datasets

    Python

  6. tb250-distill tb250-distill Public

    Distilling an LLM teacher into tiny GRU decision models trained on old GPUs (GT430/GT710/GT730/WX2100) via OpenCL+CLBlast

    Python