SimplyLiz

The architecture separates tool availability from result depth, which addresses exactly that concern.

Presets control tool availability, not output truncation. The core preset exposes 19 tools (~12k tokens for definitions) vs full with 50+ tools. This affects what the AI can ask for, not what it gets back. The AI can dynamically call expandToolset mid-session to unlock additional tools when needed.

Depth parameters control which analyses run, not result pruning. For compound tools like explore: - shallow: 5 key symbols, skips dependency/change/hotspot analysis entirely - standard: 10 key symbols, includes deps + recent changes, parallel execution - deep: 20 key symbols, full analysis including hotspots and coupling

This is additive query selection. The call graph depth (1-4 levels) is passed through unchanged to the underlying traversal—if you ask for depth 3, you get full depth 3, not a truncated version.

On token optimization specifically: CKB tracks token usage at the response level using WideResultMetrics (measures JSON size, estimates tokens at ~4 bytes/token for structured data). When truncation does occur (explicit limits like maxReferences), responses include transparent TruncationInfo metadata with reason, originalCount, returnedCount, and droppedCount. The AI sees exactly what was cut and why.

The compound tools (explore, understand, prepareChange) reduce tool calls by 60-70% by aggregating what would be sequential queries into parallel internal execution. This preserves reasoning depth while cutting round-trip overhead. The AI can always fall back to granular tools (getCallGraph, findReferences) when it needs explicit control over traversal parameters.

This is really well thought out. The git-like versioning approach for memory artifacts is something I’ve been advocating for after spending way too much time debugging agent state issues.

I’ve been working on AI memory backends and context management myself and the core insight here — that context needs to be versionable and inspectable, not just a growing blob — is spot on.

Tried UltraContext in my project TruthKeeper and it clicked immediately. Being able to trace back why an agent “remembered” something wrong is a game changer for production debugging.

One thing I’d love to see: any thoughts on compression strategies for long-running agents? I’ve been experimenting with semantic compression to keep context windows manageable without losing critical information. Great work, will be following this closely.

Idk, I got this link from someone from our team and felt like signing up here and commenting:)

Cytopia will be available for mobile devices too, when it makes sense. Right now there’s an internal build for Android on our GitHub, but that’s considered a proof of concept at this time :)

Can you submit a bug report on our GitHub page? I’ll gladly take a look at this later.

Oh, sorry, older version of OS X. We can take a look at supporting older platforms later when the project is more matured. Meanwhile you could compile it Yourself.

Quite the opposite. We have quite a big community on discord and most devs including me are online a lot. I’m not on Reddit or twitter but there are team members posting stuff from time to time.

Visit our itch.io page. It’s linked on GitHub.

We use Conan as package manager

I chose this artstyle for Cytopia because I wanted to created a game in the Spirit of SC2000. This is still my favorite city builder but it’s way outdated in terms of gameplay. And I love pixelart.

If you’re talking about the release on GitHub, it’s well outdated. We always push the latest version if Cytopia to itch.io and this works fine. I’m Cytopias lead dev and I’m on a MacBook M1 with latest OSX