I've been building TokenMizer, a local proxy that sits between your editor/CLI and whatever model you're using (Claude, GPT, Ollama) and handles two things I kept re-solving by hand: session checkpoint/resume, and a graph-based memory instead of a flat transcript.

The problem: once a long agent session hits the context limit, the usual fix is summarization, and summaries lose the reasoning behind a decision, not just the decision itself. I'd see a summary saying "switched to Argon2" with no trace of why bcrypt was rejected, so the agent would re-litigate the same tradeoff two sessions later. Flat transcripts have the opposite problem: everything is kept, but nothing is prioritized, so retrieval is just recency-biased keyword luck.

What TokenMizer does differently: instead of one growing text blob, decisions, constraints, and open questions are stored as nodes with edges (this decision depends on that constraint, this question was resolved by that decision). Checkpointing snapshots that graph plus a resumable session state, so you can kill a session and pick it back up without replaying the whole history through the model again.

Where it's rough: there's no eval harness yet comparing retrieval quality against a naive flat-transcript baseline, so right now my evidence is anecdotal (my own sessions), not benchmarked. I also learned the hard way that benchmarking your own memory system by asking it questions only it can answer is circular, so I'm holding off on publishing numbers until I have an honest comparison.

Repo: github.com/Shweta-Mishra-ai/tokenmizer (I'm the author). It's a Python project, MIT licensed. If you've hit the same summarization-loses-reasoning problem, I'd be interested in how you're handling it, and PRs/issues on the eval-harness gap would genuinely help.

Hi everyone,

I've been working on GitHub Autopilot V4 over the last few months. It's an AI-powered GitHub App focused on pull request reviews, issue analysis, repository workflows, security checks, validation, retries, and fallback handling.

This has been one of the most challenging projects I've built so far and taught me a lot about backend systems, GitHub integrations, reliability, and software architecture. The project is open source and still evolving, but I'm happy to finally share it.

I'd genuinely appreciate any feedback or suggestions.

GitHub: https://github.com/Shweta-Mishra-ai/github-autopilot Thanks! 🚀

Hi everyone,

I've been working on a side project called GitHub Autopilot V4 over the last few months.

I originally started it to experiment with AI-powered PR reviews and repository workflows, but I ended up spending far more time on things like retries, validation, security, webhook handling, and failure recovery than on the AI features themselves.

One thing I learned is that generating AI responses is easy. Building something that behaves reliably is much harder.

For developers who have built GitHub Apps, AI agents, or developer tools:

What do you think is the biggest challenge in making AI useful inside real software development workflows?

I'd genuinely appreciate any feedback or suggestions.

GitHub: https://github.com/Shweta-Mishra-ai/github-autopilot Thanks! 🚀

Hey everyone,

I've been working on an AI GitHub App called GitHub Autopilot for the last few months.

The goal is simple: help with PR reviews, issue analysis, repository workflows, and AI-assisted automation directly inside GitHub.

A lot of the work actually went into reliability stuff like validation, retries, fallbacks, and security rather than the AI itself.

Still improving it, but I'd love feedback from people building agentic systems.

Repo: https://github.com/Shweta-Mishra-ai/github-autopilot Curious what features you'd expect from an AI agent operating inside a GitHub repository.

I started building GitHub Autopilot to reduce the repetitive work that comes with maintaining repositories.

What began as a simple PR review bot evolved into a GitHub App that can review pull requests, triage issues, scan for secrets, generate fix suggestions, explain code changes, and provide repository insights.

The project is self-hostable, open source, and built around reliability, security, and automation rather than just AI features.

Repository:

https://github.com/Shweta-Mishra-ai/github-autopilot

License: MIT

I started building GitHub Autopilot to reduce the repetitive work that comes with maintaining repositories.

What began as a simple PR review bot evolved into a GitHub App that can review pull requests, triage issues, scan for secrets, generate fix suggestions, explain code changes, and provide repository insights.

The project is self-hostable, open source, and built around reliability, security, and automation rather than just AI features.

Repository: https://github.com/Shweta-Mishra-ai/github-autopilot

License: MIT

I've been building GitHub Autopilot over the last few months and recently open-sourced it.

It's a self-hosted GitHub App that automates repository maintenance tasks such as:

• AI PR reviews

• Issue triage

• Secret scanning

• Repository health reports

• AI-powered fix suggestions

The most interesting engineering challenges weren't prompts or model selection.

Most of the work went into reliability and security:

• Multi-model routing (Groq, Gemini, OpenRouter)

• Circuit breakers per provider

• Hallucination detection and confidence scoring

• Webhook replay protection

• Permission-gated destructive actions

• Rate limiting and abuse protection

The goal was to build something that could run on free-tier infrastructure while still being reliable enough for real repositories.

Repository:

https://github.com/Shweta-Mishra-ai/github-autopilot

Happy to answer questions or discuss the architecture.

I kept running into the same problem with long AI coding sessions: once context gets large enough, important decisions and project state get lost.

So I built TokenMizer, an open-source system that treats session history as a structured graph instead of flat conversation text.

It tracks things like:

• Tasks and status changes

• Architecture decisions

• Dependencies

• Files modified

• Errors and fixes

The goal is to preserve project state in a compact resume block rather than repeatedly summarizing entire conversations.

I recently published the research paper and open-sourced the implementation.

Paper: https://arxiv.org/abs/2606.06337

GitHub: https://github.com/Shweta-Mishra-ai/tokenmizer

Would love feedback from people building AI agents, memory systems, or long-running coding workflows.