https://github.com/haystackeditor/haystack-editor
It's still probably the best tool to navigate, visualize and understand complex codebases, which is particularly important now with AI coded repos. I keep looking for alternatives but they are all notably worse.
About a month ago I spent a few frustrating hours building it from source for my system and making it work, and I've enjoyed using it as my main IDE since.
I wish I had the time to make a fork and bring in a newer version of VSCode. If anyone takes it up I might help at least.
I think there is a lot of value with "reconnecting" with your codebase, so I do have some plans to bring the core concept of Haystack back in one form or another.
Happy to chat whenever you'd like, I can stalk a contact for ya or vice versa!
RE Pricing: Because of the high volume of our customers, this is quite necessary unfortunately. We can definitely shift down costs as existing intelligence becomes cheaper though.
1. A centralized review mechanism for a team or org that operates on coding agent conversations in addition to diffs (and the codebase). It evaluates multiple different variables (e.g. how sensitive are the changes, how much did the author do to derisk, and what did the author's coding agent gloss over) and helps enforce your team's guidelines moreso than just an individual's prompt
2. Adversarial review that operates in addition to other AI review agents (e.g. BugBot, or Greptile) and filters any comments to only the things the author cares about. This helps cut down on the "AI reviewer battleground" that is present in pull requests
3. A review interface that allows human reviewers to quickly understand what the author did to verify their changes and focus on the author's design decisions
We actually jury-rigged all of this together before building Haystack, but found that it doesn't scale to the team level (since every individual has their own ideas/opinions of what constitutes a human review).
We also found that reviewing through purely Claude Code/Codex was slow and difficult because stuff like author traces are not pre-processed and you have to get your agent to specifically explore/understand them.
At the same time, AI does not write code that's easy for humans to review, as it writes very verbosely and with the goal of getting the job done vs producing readable code.
I really don't think humans should be reviewing the absolute mountains of LLM-produced code; doing so would only exhaust someone's cognitive budget.
Therefore, I think that bug finding should be left to AI to review, while architectural decisions should be reviewed by humans.
On second thought, exactly as interesting.
Everyone should have been doing XP for 30 years now: