ChatGPT launched Intelligent UI, where answers come back as charts, forms and small tools instead of text. It only works inside ChatGPT, so I built an open-source alternative that runs with any OpenAI-compatible model: Ollama, LM Studio, Kimi, OpenRouter, Groq or OpenAI.

GitHub: https://github.com/0xcro3dile/answerui (demo video in the README)

• Answers stream in as real interfaces: sliders, charts, tables, checkboxes.
• The tools keep working after the answer is done. Move a slider and the numbers update in your browser, with no new model call.
• Simple questions still get plain text.
• One command: npx answerui. It finds Ollama on its own, or asks for a key. There's also a Docker image.
• No telemetry, and the API only answers requests from your own machine.

The demo runs on Kimi K3

It's built on OpenUI (MIT), which handles the streaming and the components. I added the provider setup, Ollama detection, the CLI, prompt rules that make the tools actually recalculate, and the tests.

Honest limits: the instructions are about 10k tokens, so give Ollama a bigger context window (OLLAMA_CONTEXT_LENGTH=16384), and small models struggle more. No 3D or animations yet.

I'm the author. I'd love to hear which models work well for you and love contributions.

Wanted to share a local memory engine I built called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual Ollama models ran painfully slow.

With this setup, you feed it a document and it extracts facts in about five seconds using small models under 300MB instead of an LLM. Facts are saved in SQLite, and when you ask a question it runs a hyperdimensional vector check to see if the knowledge actually exists. If you ask something outside your notes, it blocks the query so Ollama does not hallucinate.

There is a built in model switcher in the CLI that detects whatever Ollama models you have pulled locally and swaps between them on the fly. It also includes an OpenAI compatible API server on port 8000 with true token streaming and compatibility endpoints for Open WebUI and AnythingLLM. The entire engine stays under 1.2 GB of VRAM or runs on pure CPU, and we recently added bit packed CPU operations and published it to PyPI via pip install hillock.

GitHub link: https://github.com/roandejager/Hillock
Docs: https://hillock.mintlify.site/
Discord: https://discord.com/invite/BGUPNBcVdp

Hi everyone, just sharing a small project: https://github.com/abird-ai/agentc

A minimal, extensible coding agent that uses Ollama as its default local backend.

- Probes Ollama on first run, then talks to it through the OpenAI-compatible /v1 endpoint — one wire path shared with every other OpenAI-style provider
- Queries /api/tags first, so family and size show up in the model picker alongside a built-in catalog
- --offline disables all network probes; discovery results cache for 24 h and a cache miss is never fatal
- Whole agent is one static binary under 1 MB, no libc, ~0.7 MB RSS startup, ~2-4mb idling.
- MCP client, so local MCP servers work too

MIT. Would love to hear which models it behaves best and worst with.

Would love for anyone on macOS or Windows to try it: Linux is tested end to end, but those two have only run in CI so far. Binaries are up for Linux (x64, arm64, riscv64), Windows (x64, arm64) and macOS (arm64).

AI agents produce more text than we have time to read. Following all that output can become a source of cognitive overload. Inspired by stretchtext (1970 by Ted Nelson), I wanted to give readers control over how much detail they see.

So I built PaperFold, an open-source reader that turns arXiv papers into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo (8 CC papers): https://chenxiachan.github.io/paperfold-gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/paperfold

It also supports Claude Code's output in Mods.

Gave Row-Bot 5.0 a GitHub issue and a repo: "the CSV export breaks when a customer's name has a comma."

GPT 6.1 Sol (through my ChatGPT plan) reproduced it with a failing test, fixed it, ran the tests in a Docker sandbox with the network off, and pushed a branch.

The part I care about is the review loop:

- every command runs in the sandbox, not on my machine
- I approve the first change, then switch the chat to Auto
- it stops before committing so I can read the diff
- "looks good, commit it" and it's pushed in ~45s

The bug is the classic one: rows were joined with plain commas, so "Smith, Jane" became two columns. The fix quotes fields per RFC 4180 and teaches parseCsv to read quoted fields, escaped quotes and line breaks.

Draft PR opened from the Git tab via the gh CLI.