ctx

a pretty cool set of performance optimizations if you care to look into what makes it so fast. I built it for myself and didn't look at my own usage levels until this week and realized the search and blame commands are being used over 1k times per day (yes it's a lot, but against 5b tokens per day it's reasonable).

ta is deleted during indexing and checked again before exporting. My setup is a laptop and a mac mini without an interface. The agent can work on the mini all night, and in the morning I extract its memory. Then the agent on my laptop will know what the mini tried, what broke, and what eventually worked. There are other projects (cass, ctx, claude-mem): https://github.com/Dicklesworthstone/coding_agent_session_se... https://github.com/ctxrs/ctx https://github.com/thedotmack/claude-mem I wanted deja to intentionally use a small approach: one binary code with zero dependency, no LLM calls, searchable verbatim text instead of generated summaries, and synchronization via your own SSH. It does not implement its own network transport; it just connects to the system ssh/scp. You can install it via curl, brew, npx or go install, then run: deja install --all

the cleanup runbook, but the root cause of the failure was not clear to the agents, so they would think it was a test regression and go down the wrong rabbit hole debugging. When the agent searched history, it realized this failure had been encountered before and found the right workaround immediately. That got the agent onto the right cleanup path, and later we improved the log output so the same failure would be clearer next time. It's a boring story, but it's real agent productivity. Another nice use case is quickly generating session transcripts for sharing. You can exclude the noisy intermediate messages, so the transcript shows the important parts of the session more cleanly. Try attaching a session transcript to your next PR so your teammate and their agent can review the provenance and prompting behind the change. If you're up for an additional challenge, ask your agent to "exhaustively review all agent history in this repo and find where the SDLC is struggling or isn't agent-native". Using past sessions to recursively improve the agentic SDLC is a loop that we're using a lot today. If you try it out, please let us know what you think!

ed our ADE to work like that too. You should be able to have a sensible default desktop app, and then powerful customization options to change how it looks and works. - The AI ecosystem is rapidly coalescing into a state-adjacent oligopoly. Today, SpaceX announced it will acquire Cursor, and last week the US government shut down Fable/Mythos. This trend is not new, but it has been accelerating lately. It makes the case for an open agent tooling ecosystem much more compelling. - We realized we cannot build the one perfect ADE for everyone. No amount of predetermined customization options can support the near-infinite creativity that is possible through open source code. This is especially true when everyone has a coding agent that can quickly implement customization on the fly. Maybe this is "Software 3.0" finally starting to show itself. From here, the roadmap is to make ctx more hackable. Right now it is a fairly involved Rust daemon plus desktop app. It's performant, but customization still requires cloning the repo, changing code, and rebuilding. We want to move toward a Pi-like model with extensions, plugins, and hot-reloading, while keeping the core runtime in Rust. We'd also like to accelerate development of the mobile app (via a bring-your-own tunnel like ngrok, or hosted tunnel) as well as support for Windows (it is Mac and Linux only right now). Happy to answer any questions about it.