At this point I'd much rather see people collaborate on one of these implementations, benchmark against them, or upstream the useful bits into MLX/MLX-LM instead of producing yet another near-identical repo.

The local-LLM ecosystem really does not need every implementation idea rediscovered five times and wrapped in a new README. AI-assisted coding makes producing a new repo cheap; maintaining, benchmarking, and integrating one is the actually valuable part.

The main difference is the product direction. Screenpipe seems focused on continuously giving agents context through APIs, MCP, and skills. Daydream is more narrowly built around answering "what did I do today?" through a timeline you can inspect, replay, search, and turn into a daily digest.

I'm also treating deletion as part of the data model. If you cut a sensitive span, its frames, audio, OCR, transcripts, embeddings, and summaries should be deleted or invalidated too.

Mine is still early and Linux-first. I'm open-sourcing it in case anyone wants to contribute, poke around, or use it as a starting point. It’s built with Tauri, a Rust backend, React/TypeScript, SQLite, GStreamer, Whisper, OCR, and VLM processing.

I genuinely didn’t know you were building this when I started. Apparently personal memory capture is becoming a SaaS category too lol.

Code is here: https://github.com/snackbit/daydream

so if i were to tell an agent to move the contents of a confluence doc into a file, it would do so without even reading the confluence page - in theory.

Comments URL: https://news.ycombinator.com/item?id=48965850

Points: 371

# Comments: 166

Personally, I use gpt 5.5 high with planning every time and plan various smaller features/changes in parallel, then approve them one after another. This allows me to steer it (which I need more often than not) before approving the plan, thus reducing the otherwise accumulating slop.

Using goal doesn't work for everyone, unless you have an unreasonably strong test suite or harness that the agent can verify against.

But on the other hand, I've been using 5.5-high on a daily basis in multithreading workflows, i.e. in parallel. I'm barely exhausting my weekly limits. I can't even Human-as-a-Service fast enough to catch up and read all the plans and implementations it does. So there is that.