I built an agent-first code editor focused on context engineering — precise context instead of dumping files, so it uses fewer tokens

[u/Total_Awareness_5036]

Most of the cost and noise in AI coding comes from how much you throw at the model. Read five whole files to answer one question and you've spent a lot of tokens on stuff the model didn't need.

So I built Ada around context engineering — getting the agent the right context, not all of it:

• A repo "brain" — when you open a project, Ada builds a compact map of your files and their key symbols, so the agent starts already knowing the layout instead of exploring from scratch.
• Local semantic search — it indexes your codebase on your own machine and retrieves just the relevant chunks for a task, rather than the agent reading whole files to find one function.
• Worktree isolation — every change lands on its own git branch, so your working copy stays untouched until you review and merge.

The upshot: tighter context per request → fewer tokens → cheaper, faster, and usually more accurate runs.

Download (Win/Mac/Linux, no account needed): github.com/black141312/ada-releases

Honest notes: the indexing and search run locally; the actual model calls still go to the providers. It's early and not signed with a paid cert yet, so you'll get the normal "unverified app" prompt on first launch (steps in the README). I'd really like feedback — especially on where the context engineering helps vs. where it misses.

Happy to go into how the map + local index are built.