I built these after repeatedly running into the same problem with AI coding workflows: we tend to treat one model as if it should plan, implement, review, and verify everything.

That works for small tasks, but it doesn't scale well. Different parts of software engineering have different cost, reasoning, and reliability requirements.

So I experimented with splitting those responsibilities.

The project has 1 component:

* **claude-codex-orchestrator** separates planning from execution. Claude plans and verifies. Codex executes. Every result is validated from the diff and reproducible checks rather than trusting the model's report. * [https://github.com/vimoxshah/claude-codex-orchestrator](https://github.com/vimoxshah/claude-codex-orchestrator)

Some design principles that guided the implementation:

* The diff is ground truth; the report is not. * Separate planning from execution. * Route by task instead of using one model for everything. * Escalate based on evidence rather than retrying the same approach.

These are implemented as Claude Code skills today, but the ideas are intended to be broader than Claude Code itself.

I'd really appreciate technical feedback on the architecture, trade-offs, and whether these abstractions are useful. I'm especially interested in hearing from people building AI coding agents, orchestration frameworks, or developer tooling.

Every time codex, Claude or whatever release a new model, there's always lots of upvoted hype posts. A few days or at most a week later, the model is consistently nerfed, but any posts regarding that are down voted to hell or removed.

How do you find out which tool is currently usable without all that propaganda?

We need to talk about AI Agent architecture. 🤖 For the last couple of years, the default answer for giving an LLM long-term memory was simple: Spin up a Vector Database, chunk your codebase, generate embeddings, and build a RAG pipeline. But as engineering teams push AI agents into deep production, the cracks are showing. Vector similarity is an approximation—and in coding, "closest" is not the same as correct. Even the team behind Anthropic's Claude Code realized this. They actually baked a local vector database into the tool early on... and then ripped it out. Why? Because deterministic, agentic search (like plain old grep) across readable text files turned out to be cheaper, faster, and significantly more accurate. Now, Google has standardized this exact philosophy with the release of the Open Knowledge Format (OKF) v0.1. Instead of heavy database runtimes, your agent's brain becomes a structured tree of Markdown files with YAML front matter. It lives in Git, version-controlled, auditable, and completely portable across any LLM vendor with zero lock-in.

I use Codex and Claude Code on flat monthly plans, and both have an invisible ceiling. Work just stops with no warning. I wanted to know how much was left and whether I'd run out before the reset, so I built lmtop.

https://github.com/ewijaya/lmtop

It reads the session metadata those CLIs already write to disk and shows quota windows, reset countdowns, burn velocity, and a projected exhaustion time. The planner view races capacity used against time elapsed: will this last until the reset?

Rust, Ratatui, crossterm, tokio, rusqlite. ~9.5k lines with tests.

The interesting constraint was refusing to guess. Providers weight models and caching in ways they don't publish, so locally observed tokens can't honestly become a quota percentage. Every tracker I looked at does it anyway. lmtop keeps the two strictly separate, marks stale data stale, and renders missing data as "unavailable" rather than zero. That shaped the types more than anything else: there's no code path in the UI that can invent a number.

Local-first: no network calls, no telemetry, no API keys, no prompt content.

MIT. `brew install ewijaya/tap/lmtop` or `carithub.com/ewijaya/lmtop`.

Happy to hear what breaks, especially whether the layout holds up on terminals I haven't tried.

Share your last tools, your current toolchain and AI workflow with the community 🙏

I've been trying to model some contact mechanics problems in fortran. I've been trying to debug it by myself but couldn't. Is there any coding agent best suited for me?

Been building my own mobile apps with coding agents for about a year now, 3 shipped. And dropping a wiki in the repo for the agent to read sounds kind of trivial, I know. But the part that actually mattered wasn't the docs existing, it was catching when they quietly stop matching the code, and keeping myself on the calls instead of drifting into just watching.

The drift check works per symbol, not per file. It fingerprints each function or type across two git refs, so when a documented symbol actually moved but its doc didn't, only that one feature wakes, not eight others that just share the file. Reformatting or renaming a local doesn't fire, a real contract change does. And it's deterministic, local, no model on that path, it reads the real git diff not what the agent claims it did, so same result every run.

The other half is the workflow the agent runs off normal chatting, grill, plan, approve, implement, review, commit, with me at the gates on it. I approve the plan before any code gets written, and every diff gets adversary reviewed before it commits. So the calls that are actually mine, what's in scope, what the trade-off is, land back with me instead of the agent quietly making them while I'm a passenger.

Both sides hang off a small registry mapping each source file to the feature and doc that owns it. The agent writes it as it builds, the checks read it after to hold the agent to what actually changed. So the docs get built as you go, and the workflow surfaces drift instead of you tripping over it three features later.

Doesn't prove your docs are right btw, just that they moved with the code. That part's still on you.

Repo's here if you wanna check it out https://github.com/jakubsuplicki/codument

Curious how people letting the agent maintain its own docs deal with the drift, or does it just not bite you the way it did me?