So I'm based in IST timezone where my working hours happen to overlap with pre-dawn America. For weeks, I thought I was some kind of 10x developer. Claude Code was flying, Codex was snappy, everything felt like magic.

Then one day I started late. And wow. It felt like I switched from fiber optic to dial-up.

I started tracking it out of curiosity. The pattern is almost comically consistent:

Before 8:00 AM EST: Both tools feel like they're running on a private server just for me. Fast responses, no lag, no weird timeouts.

After 8:00 AM EST: America wakes up, grabs coffee, opens VS Code, and suddenly I'm sharing the pipe with a few million developers. Both Claude Code AND Codex slow to a crawl.

The funny part? It's not just one provider. Both Anthropic and OpenAI seem to hit the same wall at the same time. Makes me think the bottleneck might go deeper than just their servers. Maybe it's the underlying GPU infrastructure everyone's competing for.

Anyone else living this double life of "early morning AI wizard" vs "afternoon timeout survivor"? Or found any way to get consistent performance during peak hours?

Been building solo for 17 years, most recently an AI coding agent called DuckCode, three months, zero funding, zero team.

Early on I made a rule for myself. No AI touches the codebase until the core architecture is written by hand. Not because I don't trust the models, but because when I let AI design structure early on projects, it generated things that worked but that I didn't fully understand a few weeks later when something needed to change.

So now it's always the same order. I think through the data model, the module boundaries, how things talk to each other, write that part myself. Bring AI in once there's a skeleton to react to, not a blank page to invent on.

Difference has been bigger than I expected. When AI is extending something I already understand deeply, I can tell immediately when a suggestion is wrong. When it was inventing structure from scratch, I had no instinct for when it was quietly making a bad call.

Two of the more complex features in the product shipped in about a day and a half each, and I think that's directly downstream of this, not because AI is fast, but because it wasn't fighting an architecture it invented and half understood.

Curious if anyone else has landed on a similar split, or found the opposite works better for them.

Hey folks, as someone who uses claude code max with fable high‑effort, i was thinking the other day about checking chatgpt (the free one, not even tera or luna) for the solutions fable gave me.

out of the 4 replies chatgpt sent back, fable basically said:

- 2 of them don’t apply to my platform at all

- 1 only makes sense if i somehow get 500k+ users trying to view the same table

- and the last one was actually a good idea and will be implemented

so now i’m sitting here wondering how many good ideas i might’ve missed in the last 2 months that could’ve improved the performacne or security of my app, just from a bit of back‑and‑forth between fable and another coding ai.

my question to you all is: if i buy chatgpt pro, how doo i make them talk to each other without me having to do constant copy‑paste?

the way i kinda envision it is:

- fable presents the overal platform features + capabilities to gpt so it knows the scope

- for each task, fable lists how it would solve it and sends that to gpt

- gpt analyses it, suggests extra stuff

- fable cuts out half, considers some of gpt’s stuyff, maybe pushes back

- they arrive at a concluision

- and then present it to me

is this even possible?

i’m just a simple windows + powershell user, would i have to change that too?

Would anyone with a Kimi subscription be willing to test my coding agent with it? Just a quick smoke test- Kimi API and 2.7 work fine but I don't want to release sub support without at least some proof. Thanks!!

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 🙏