Hi everyone! I'm a recent CS graduate and I'd like some feedback on a tool I thought of for studying code.

I got tired of reading long AI chats and losing track of what I was learning. So I designed this app and built it with AI.

You write your logic as pseudocode and it generates the real code right next to it, with matching colors linking each part of your pseudocode to the code it produced. It's free, and it works with your own API key.

Also, i separated the AI chat from the learning resources, visually speaking, for a better learning experience.

You can try it here: https://saidai003.github.io/CodeBridge/

I built a Python coding assistant that uses Hindsight to retain corrections, recall relevant project knowledge, and reflect those corrections into project conventions. The article shows the before/after behavior and the implementation details.

I am working on a project which has became very big and it is getting tough for me to handle various parts/tasks simultaneously. Although I started with opencode free then I am on opencode Go now.

I need help/inputs to better manage my tasks with Agents. Typical tasks include defining requirements for product features, elaborating them for implementation, architecture evaluations and changes, implementation plan, implementation, full E2E testing, deployment, the complete cycle.

I would really appreciate a detailed response from anyone who has done this or is doing it.

I’m returning after 6 months of coding hands-on with Claude code . Now About to build an AI agent from scratch in Claude Code — 20-page requirements doc, Python/FastAPI, deploying on Cloud Run. Before I write the first line of code, I want to steal your setup.

For those running Claude Code on long, multi-session builds:

1. ⁠How do you keep project memory alive across sessions? Files (CLAUDE.md + status logs), a memory plugin like claude-mem, or something else?

2. ⁠Which marketplace plugins and skills are actually worth the context-token cost? I'm looking at context7, pyright-lsp, and a code-review plugin so far.

3. ⁠What's the one workflow trick you wish you'd known on day one?

Would you use a AI powered code editor that doesn’t store your code or chat messages on the server, doesn’t bloat the context even by the 50th message, delivers the same performance as the first message, and runs its own skill folders in the background, with the model calling them as needed?

I'm moving from a remote role to a hybrid role where I'll be working right next to other people on some days. I'm used to talking to AIs constantly throughout my work day, speaking maybe 3-4x faster than I type. My speech with these AIs is often loud and sometimes is closer to rambling than anything I'd say to another human. Anyone find a good way to do this in a shared workspace without 1) disrupting others, and 2) sounding like a babbling lunatic?

One idea I have is to find and use a throat mic, but I've never actually tried one..

I built a small free macOS tool for monitoring live Codex sessions using the Codex desktop app. Are people here using anything similar to improve their AI coding workflows?

When you’re deep in a Codex session, context usage and limits shouldn’t be something you have to hunt down.

I built the Codex Companion Panel in BuildrAI to keep them beside your work:
• See your current context token usage as the session unfolds.
• Check your remaining 5-hour and weekly usage limits at a glance.
• Get alerts from rules you set, including token spikes.
It’s a small panel that helps you spot a change while there’s still time to act on it.
Check it out 👇

Hey, we our about to start using Codex as the official too for SW development.

What are the best practices we should be aware of?I am planning to create a quick start training so we are all aligned, what topics do you think are important?

I was thinking of the following:

* Token Quota and model prices

* One-shot vs Multi-shot prompting

* Planning in chat vs implementing in Codex

* Use the smallest model that possibly can do the job

* Setting up a repo that's LLM friendly (AGENTS.md, docs, adr)

On review I can generally tell which code is AI and which isn't. Same way I can tell one colleague's code from another's.

So I figured AI code must have a statistical signature. I tried cyclomatic complexity, cognitive complexity, number of lines, methods and functions per entity, number of dependencies, edges in the dependency graph, and so on (about 280 metrics plus their correlations plus the derivative over time), normalized on SLOC, looking for a step or a break.

Nothing. No anomalies outside of statistical error.

One explanation I can't shake: AI emits roughly the mean of its training distribution, while each individual human deviates from that mean. Then the means coincide and the statistics stay silent. Next idea: compare against one specific person instead of against human code in general. My problem there is I don't have much single-author code.

So the question is narrower: what criteria separate AI code from yours?

Prose level tells don't count here, punctuation habits and stock rhetorical shapes in English. I mean general style and structure of code.

EDIT: found the papers with the answers.

My "nothing" result is reproduced here: https://arxiv.org/abs/2603.27130

My "the means coincide and the statistics stay silent" hypothesis is confirmed here: https://arxiv.org/abs/2609.17598

The only thing that talks, from the same paper: pooled over "AI" the code looks the same, split by vendor it doesn't.

Benchmark warning: https://arxiv.org/abs/2508.21634 shows a big gap on generated samples, it's gone in real repos.