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Hacker News
I tried this a couple months back. While I found it useful, I also found on my MacBook M1, the fact it was recording all the time, made my CPU melt at high temps all the time. I eventually had to shut it off and stop using because of that.
Curious how it runs on more modern hardware? Is it pretty lightweight these days?
by kerv
The 'record everything on your screen' pitch keeps hitting a privacy wall for teams — even local-only storage triggers a brutal compliance conversation. What's the story for an engineer whose company blocks unaudited desktop recorders by policy? Curious if there's a scoped mode that only captures within a designated app boundary."
by russelmelroy
How do you distinguish durable user preferences from transient screen context before an agent turns recorded activity into an automation? I'm especially curious whether each inferred memory keeps provenance and an expiry or confidence signal so stale behavior does not become a permanent rule.
by xms17189
Been a user of Screenpipe to build some "Ai-buddies" for me since last year. Core of that is giving AI a look at what is happening in time space. Without Screenpipe this was rather hard to do at the performance screenpipe gives.
by fillskills
Congrats on the launch. The arc you describe — fine-tuning, then tool-calling, then MCP, each one still "too static" — tracks with what's happened across the whole PKM space, not just agent tooling. But I'd push on one thing: MCP didn't fail because the protocol is weak, it's that access to context and surfacing of context are two different problems. An agent with MCP still has to decide to ask, at the right moment, in the right words. That's the same gap that's plagued Obsidian/Notion-style PKM forever — the archive is complete, but retrieval is still pull-based, so it only helps you when you remember to go looking.
The systems that actually feel like a second brain flip that: they push relevant history, decisions, and prior context at you before you ask, and — this part matters more than people give it credit for — they show their work. Cite where a given answer came from (which note, which meeting, which thread), not just serve up a synthesized blob. Looking at the pushback in this thread (the harvesting claim, the "zero chance I trust cloud SaaS" comments), I don't think that's really an objection to always-on capture — it's an objection to opacity. People will tolerate a lot of passive collection if they can see the receipts on what it's doing with it and where an answer traces back to. Local-only storage helps with the trust problem, but provenance is what actually earns it over time.
by jpardilla
Once Screenpipe has months of screen and audio indexed locally, what stops a compromised or over-permissioned agent from querying the whole history through the port 3030 API instead of just the current task's context? Continuous recording solves the memory problem, but it turns "give the agent access to my second brain" into a much bigger blast radius than giving it access to one document. Is there per-query scoping or a time-window restriction on what an agent can pull back, or is authentication the only gate?
by rahulladumor
How are you planing to segregate between professional use and personal use, I dont want any agent or any llm to know all the time what i have been doing on my system, it would be privacy nightmare and most of the time the screencapture is not meaningful. People may use their work laptop or devices to checkout reddit or hackernews occasionally.
Funny timing. I've been building something similar in my spare time called Daydream. There’s a lot of overlap: local screen/audio capture, OCR and transcription, window and activity context, SQLite, and a searchable memory of what happened.
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
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- Hacker News
- I tried this a couple months back. While I found it useful, I also found on my MacBook M1, the fact it was recording all the time, made my CPU melt at high temps all the time. I eventually had to shut it off and stop using because of that.
Curious how it runs on more modern hardware? Is it pretty lightweight these days?
by kerv - The 'record everything on your screen' pitch keeps hitting a privacy wall for teams — even local-only storage triggers a brutal compliance conversation. What's the story for an engineer whose company blocks unaudited desktop recorders by policy? Curious if there's a scoped mode that only captures within a designated app boundary."by russelmelroy
- How do you distinguish durable user preferences from transient screen context before an agent turns recorded activity into an automation? I'm especially curious whether each inferred memory keeps provenance and an expiry or confidence signal so stale behavior does not become a permanent rule.by xms17189
- Been a user of Screenpipe to build some "Ai-buddies" for me since last year. Core of that is giving AI a look at what is happening in time space. Without Screenpipe this was rather hard to do at the performance screenpipe gives.by fillskills
- Congrats on the launch. The arc you describe — fine-tuning, then tool-calling, then MCP, each one still "too static" — tracks with what's happened across the whole PKM space, not just agent tooling. But I'd push on one thing: MCP didn't fail because the protocol is weak, it's that access to context and surfacing of context are two different problems. An agent with MCP still has to decide to ask, at the right moment, in the right words. That's the same gap that's plagued Obsidian/Notion-style PKM forever — the archive is complete, but retrieval is still pull-based, so it only helps you when you remember to go looking.
The systems that actually feel like a second brain flip that: they push relevant history, decisions, and prior context at you before you ask, and — this part matters more than people give it credit for — they show their work. Cite where a given answer came from (which note, which meeting, which thread), not just serve up a synthesized blob. Looking at the pushback in this thread (the harvesting claim, the "zero chance I trust cloud SaaS" comments), I don't think that's really an objection to always-on capture — it's an objection to opacity. People will tolerate a lot of passive collection if they can see the receipts on what it's doing with it and where an answer traces back to. Local-only storage helps with the trust problem, but provenance is what actually earns it over time.
by jpardilla - Once Screenpipe has months of screen and audio indexed locally, what stops a compromised or over-permissioned agent from querying the whole history through the port 3030 API instead of just the current task's context? Continuous recording solves the memory problem, but it turns "give the agent access to my second brain" into a much bigger blast radius than giving it access to one document. Is there per-query scoping or a time-window restriction on what an agent can pull back, or is authentication the only gate?by rahulladumor
- How are you planing to segregate between professional use and personal use, I dont want any agent or any llm to know all the time what i have been doing on my system, it would be privacy nightmare and most of the time the screencapture is not meaningful. People may use their work laptop or devices to checkout reddit or hackernews occasionally.
- Funny timing. I've been building something similar in my spare time called Daydream. There’s a lot of overlap: local screen/audio capture, OCR and transcription, window and activity context, SQLite, and a searchable memory of what happened.
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