NIMBL - Devlog #3: Full redesign

In this section I did a lot I used AI to port the opencode UI to mine and then did the changes so it fits mine more appropiate such as more modes instead of just build and plan and did indepth work on the backend

The UI: Port Opencode and credit them

The first NIMBL chat screen was heavily inspired by OpenCode. I realized that it didnt make sense to deign it myself if opencode is under the MIT lisence and I love their UI so I used the downloaded opencode directroy to port it into nimbl with the help of AI and routed the backend to it. Of course I didnt forget that I need to update my readme so they get all the credit they deserve

The app now has a transcript, persistent prompt, modes, autocomplete, dialogs, provider/model controls, message actions, paste handling, and token/cost status. So its fully functional for a beta and got all necessary commands

The Backend

The early version could call a model but thats it, it couldnt read files or write them. So i implemented File operations but made it they reject project or symlink escapes, .env files, Git metadata, NIMBL state, credentials, and private keys. Commands have allowlists, timeouts, bounded output, abort support, and explicit permissions. Build mode gets write tools so with that you can code but all other 3 modes do not.

Also implemented sessions so you can open old sessions just like in opencode, this project is proball going to be a more token efficent opencode but in noway an alternative

Also worked on how cost are caculated but this was quite hard and I still dont understand it I let AI do it without understanding it but I will try to learn more of it later.

Context

Dumping a whole repository into a prompt is expensive and bad. To fix this, NIMBL keeps a lightweight local index of your project. It automatically respects .gitignore rules, skips binary files and oversized logs, and watches for changes in real time so it never wastes time rescanning everything from scratch.

Finding the right files isn’t handled by rescaning the entire directory but saved in its memeory. it uses straightforward text matching based on terms, symbol names, and file paths. More importantly, NIMBL shows what it did, you can see exactly which files were picked, why others were skipped, and how many tokens the prompt actually used.

Where It Stands

The project now posses, permissions, sessions, providers, budgets, context, structural extraction, tokenizers, and much more and I tried it myself and it can be used for simple coding.

The next step is proving NIMBL saves tokens on tasks without making answers worse. Which it was orignally designed for