u/pavel6490

I'm pretty passionate about local LLMs and self-learning AI. I've always wondered: why can't an AI agent work like a human? Have a local brain; when asked, think first; if unsure, ask someone smarter (a cloud model, or search); then learn from the answer so next time you don't need to ask.

That's why I have been trying to build autodidact, an open-source AI agent that learns from its cloud queries - the local model handles what it knows, escalates to a cloud model when uncertain, then distills the response into permanent local memory. Next similar query gets answered locally, for free. And the local brain is default to Qwen 3.5 8B.

In a 30-query session on my dev workload: 67% local-or-memory, $0.70 saved vs an all-cloud baseline. The more you use it, the cheaper and faster it gets.

This is just v1.x, which supports documents and codes ingestion through "autodidact learn <path to documents>", and let you chat with both local and cloud models, with a confidence evaluation and routing mechanism to decide the request should be handled by local or cloud, and learning mechanism for the local model to learn from every cloud escalation. I planned a lot for v2, which includes tool usage, skills and tools learning etc.

https://reddit.com/link/1ti6s6h/video/vbcuw5xi272h1/player

Please try and let me know if the idea makes sense:

Repo: https://github.com/BuffaloTechRider/Autodidact

Install: pip install autodidact

Quickstart: autodidact init && autodidact learn <code or document path> && autodidact chat

Happy to answer questions.

https://reddit.com/link/1ti6qj1/video/2rlq3jd3272h1/player

Hi all,
I'm pretty passionate about local LLMs and self-learning AI. I've always wondered: why can't an AI agent work like a human? Have a local brain; when asked, think first; if unsure, ask someone smarter (a cloud model, or search); then learn from the answer so next time you don't need to ask.

That's why I have been trying to build autodidact, an open-source AI agent that learns from its cloud queries - the local model handles what it knows, escalates to a cloud model when uncertain, then distills the response into permanent local memory. Next similar query gets answered locally, for free. And the local brain is default to Qwen 3.5 8B.

In a 30-query session on my dev workload: 67% local-or-memory, $0.70 saved vs an all-cloud baseline. The more you use it, the cheaper and faster it gets.

This is just v1.x, which supports documents and codes ingestion through "autodidact learn <path to documents>", and let you chat with both local and cloud models, with a confidence evaluation and routing mechanism to decide the request should be handled by local or cloud, and a learning mechanism for the local model to learn from every cloud escalation. I planned a lot for v2, which includes tool usage, skills and tools learning etc.

Please try and let me know if the idea makes sense:

Repo: https://github.com/BuffaloTechRider/Autodidact

Install: pip install autodidact

Quickstart: autodidact init && autodidact learn <code or document path> && autodidact chat

Happy to answer questions.

I'm passionate about local LLMs and self-learning AI. I've always wondered: why can't an AI agent work like a human? Have a local brain; when asked, think first; if unsure, ask someone smarter (a cloud model, or search); then learn from the answer so next time you don't need to ask.

I have been trying to build autodidact, an open-source AI agent that learns from its cloud queries - the local model handles what it knows, escalates to a cloud model when uncertain, then distills the response into permanent local memory. Next similar query gets answered locally, for free.

In a 30-query session on my dev workload: 67% local-or-memory, $0.70 saved vs an all-cloud baseline. The more you use it, the cheaper and faster it gets.

What's in v1.0:

• Confidence-based routing (logprob_uncertainty + GSA pre-screen + refusal detection). Validated AUROC 0.65–0.83 across 3 model families × 2 datasets.

• Hybrid retrieval: BM25 (FTS5) + vector (FAISS), fused via Reciprocal Rank Fusion.

• Document synthesis - `autodidact learn <path>` extracts key facts in the background, not just chunks.

• Five setup modes: Local+Cloud (default), Cloud+Cloud (no GPU), Local+Local (offline learning), custom OpenAI-compatible server, Local-only.

• All state in one portable SQLite file.

The routing layer is grounded in a paper I published recently (https://arxiv.org/abs/2605.02241) - average token log-probability matches or beats trained routing classifiers (RouteLLM-style) at zero per-model training cost, and transfers across query distributions where supervised baselines collapse.

What's NOT in v1.0 (designed and scoped):

• Tool execution / ReAct loop (v2.0)

• Skill extraction — only fact extraction so far (v2.0)

• MCP server for Claude Desktop / Cursor / Gemini CLI (v2.0)

• OpenAI-compatible proxy mode (`autodidact serve` — v1.5)

• Topic-based knowledge pages instead of flat facts (v1.5)

Looking for early adopters and contributors - especially anyone with opinions on local LLM routing, RAG retrieval pipelines, or the v2.0 agent surface. What I'd love feedback on:
• Is the routing decision (logprob + GSA + refusal detection) the
right combo, or am I overweighting one signal?
• How would you structure the skill-extraction step in v2.0 - extract
procedures from cloud responses, or learn from observed task
completions?
• What's missing from the "good first issues" list for someone wanting
to contribute?

Repo: https://github.com/BuffaloTechRider/Autodidact

Install: pip install autodidact

Quickstart: autodidact init && autodidact learn <code or document path> && autodidact chat

Happy to answer questions.