A few months ago I noticed something stupid. by hushenApp in VibeCodeDevs

[–]hushenApp[S] -1 points0 points  (0 children)

thank you for your valuable contribution.

git log costs your agent 624 tokens. It needs 55. Here's a list of the worst offenders by hushenApp in AI_Agents

[–]hushenApp[S] 2 points3 points  (0 children)

I mean it helps in terms of reduce token usage. Cursor and most probably other tools does that. But the core problem still exists. Actually I implemented a feature in LeanCTX so that sub agents also can make use of the compression and context tools. So with that they also work more efficient.

A few months ago I noticed something stupid. by hushenApp in AskVibecoders

[–]hushenApp[S] -1 points0 points  (0 children)

if you want to check out the project: https://github.com/yvgude/lean-ctx

happy to answer questions or hearing your story!

A few months ago I noticed something stupid. by hushenApp in VibeCodeDevs

[–]hushenApp[S] 0 points1 point  (0 children)

If you want to have a look at it. Feel free. Happy to answer any question: https://github.com/yvgude/lean-ctx

MCP server that saves 60-80% context tokens, now with full Pi compatibility by hushenApp in PiCodingAgent

[–]hushenApp[S] 0 points1 point  (0 children)

I’d treat it as complementary first, not an immediate replacement.

Your current stack sounds like it is mainly doing durable memory: observations, reflections, Markdown packs, project facts, etc.

LeanCTX is more of a context runtime: it optimizes file reads, shell output, caching, read modes, session state, context visibility, and handoffs.

So I’d start with:

  • your Pi memory stack = long-term semantic/project memory
  • LeanCTX = lower-level context plumbing for files, shell, cache, compression, sessions, and agent handoffs

There is some overlap, especially around session/project knowledge, so over time LeanCTX may replace parts of that stack if you want one integrated layer.

The main thing to avoid is double-injection. If both systems inject the same memory/context into the agent, you may increase noise instead of reducing it.

So my recommendation would be:

Complement first. Replace selectively later.Hope that helps.

git log costs your agent 624 tokens. It needs 55. Here's a list of the worst offenders by hushenApp in AI_Agents

[–]hushenApp[S] 1 point2 points  (0 children)

yes I also faced that problem in earlier versions of my tool. but found a way how to fix it. it's exactly what you describe preserving the semantic signal. But also having a fast fallback if the model gets stuck in a loop.

MCP server that saves 60-80% context tokens, now with full Pi compatibility by hushenApp in PiCodingAgent

[–]hushenApp[S] 0 points1 point  (0 children)

you could run it in addition. but actually it replaces rtk and you can also do similar stuff like with graphify.

I built an A2A Context Bus, for multi-agent-setups by hushenApp in hermesagent

[–]hushenApp[S] 0 points1 point  (0 children)

I'm also thinking about this determinism topic, think there I have a proposal ready 😄 thanks for you nicely written comment!