An independent think tank on artificial intelligence, society, and the future of thought.
We're a collective of computer science friends from the late '90s who turned a WhatsApp group into a laboratory for exploring what AI is doing to how we work, build, and think.
๐ aipster.com
This Hugging Face organization is where we publish the artifacts of our exploration โ models, datasets, and tools that come out of the experiments we write about on our blog.
We're not a company. We don't sell anything. We build things to understand them, then share what we learned.
DevRouter-1.5B โ our first release. A tiny prompt router that reads a raw developer prompt and returns a single JSON decision: a cleaned-up
rewrite, an intent / complexity classification, a suggested model-tier route, and the context the prompt forgot to include. Built on Qwen2.5-Coder-1.5B (Apache 2.0) and distilled from a
stronger teacher, it holds ~96% valid-JSON and runs at ~280 tokens/s on a single RTX 3090 โ small enough to sit in front of your real models and triage every prompt in 1โ3 seconds.
And one honest caveat, because we ship those too: Q6 and below quantizations break its JSON. A small model doing strict structured output is far more fragile than the "Q4 is fine" rule of thumb suggests โ ship Q8_0 or F16.
Phi-4-mini-instruct abliterated ยท MLC q4f16_1 โ an uncensored Phi-4-mini (3.8B), abliterated to remove refusal behavior and quantized to 4-bit MLC format for in-browser inference via WebGPU. This is the model that powers the top-tier slot in our Local AI Playground โ a browser tool where LLMs run entirely on your GPU, no server involved. Built from huihui-ai's abliteration of Microsoft's Phi-4-mini (MIT), converted with MLC-LLM's source-built toolchain after the pip nightly broke on linux. ~2.1 GB download, ~3.4 GB VRAM. Sovereignty as a feature, not a slogan.
Coming soon โ curated and synthetic datasets from our distillation experiments, released alongside the models that use them.
Coming soon โ interactive demos of our experiments.
Every day, we provide a summary of what is shaking the AI ecossystem up. It include.
This series deal with running LLMs locally and some theory behind LLMs.
We build to understand. We share to learn together.
Everything we publish here is open. Code, weights, datasets, methodology โ including the failures. Especially the failures.
Independent. Curious. Slightly skeptical.