Readit NewsOne thing I keep coming back to though - what if the tool could actually watch how you use your CRM and then suggest automations based on what it sees you doing repeatedly?
I've been building something called MemoryLane (https://github.com/deusXmachina-dev/memorylane) that does exactly this - it captures screen activity, spots repeated workflows, and suggests automations. Works as an MCP server so you can plug it into Claude or Cursor. Instead of you having to describe what you want automated, it just watches and proposes stuff.
Have you thought about adding something like pattern detection to denchclaw? Feels like it'd fit really well with the "everything app" direction. For us the most useful engine for executing skills and automations is surprisingly cowork thus far, haha
I've been working on a slightly different angle with MemoryLane (https://github.com/deusXmachina-dev/memorylane) - instead of giving agents a place to write their own memories, it captures the user's screen activity and makes it queryable. So the agent gets context about what the human was doing, not just what the agent itself did. It plugs in via MCP so Claude Code / Cursor can just ask it stuff.
I think there's something interesting in combining both - agent-vfs for the agent's own state, and something like MemoryLane for the human side. How do you think about that boundary between what the agent remembers vs what it knows about the user?
We need to use vector DBs just because of the amount of data. But on a different layer we want to help create file-based instrucations/skills for patterns that we detect and think can be automated.
I've been building MemoryLane (https://github.com/deusXmachina-dev/memorylane) which comes at this from the other side - it records screen activity, spots repeated patterns with AI, and then tells you "hey you keep doing this, want to automate it?" Works as an MCP plugin for Claude/Cursor.
Feels like pattern detection (finding what to automate) + browser agents like yours (actually doing the automation) is the right combo. Are you thinking about the discovery side at all, or mostly focused on execution?
We added a blacklist coordinator that checks four rules on every window-focus event:
- App name (1Password, Signal) — normalized across .app, .exe, bundle IDs
- Window title wildcards (*bank statement*)
- URL patterns (*://*.bank.com/*)
- Incognito auto-detection (on by default, zero config)
When anything matches, capture pauses and queued frames get flushed. Switch away, it resumes.
Surprising part: the matching logic was trivial. The annoying work was cross-platform app name normalization and making the settings UX simple enough that people actually configure it.
Processing hundreds of screenshots/hour forced us to optimize for token costs.
The surprise: send video, not images
- Single screenshot (1698×894): 1,812 tokens
- Same frame in video: 258 tokens (Gemini 2.5) or ~70 tokens (Gemini 3)
- Full 8-hour workday: ~$1-3
Video gives you timestamps for free and compresses well since consecutive frames are nearly identical. We keep costs down by having the LLM write short summaries while running OCR locally for text extraction.
I have mostly used the entire API surface so far. Check out the usage in this github action script: https://github.com/parallax-labs/context-harness/blob/main/s...
This is used to build the search index on the website (below)
This tool is made for not only local, but embedded in a ci context
MemoryLane looks really cool — same problem, different surface. Local embeddings + SQLite + hybrid FTS/vector + MCP into Claude is basically the same stack; the screenshot-ingestion and Electron UX are a neat take for “everything I’ve seen on this machine.” I’ll definitely poke around the repo. If you want to see how we’re using custom agents on top of that pipeline, a couple of blog posts go into it: Chat with your blog
- https://parallax-labs.github.io/context-harness/blog/enginee... (persona grounded in your own writing, inline + Lua agents) this is in the same vein. Allowing agents to write into the vector store with an MCP tool is on the road map.
- https://parallax-labs.github.io/context-harness/blog/enginee... Unified context for your engineering team (shared KB, code-reviewer and on-call agents).
Did you think of building some proactive AI tools to make use of all this centralized data?