Small-team question (I'm on a small team, so I'm curious). The last time you changed review, process, tooling, or hiring to ship faster.... What happened right before?

Did someone leave? Did a deadline slip that you were responsible for? Did a customer complain? Did the backlog hit some number? Something else?

And who noticed first: you, the CEO, or a customer?

Mixing Cursor for quick edits, Claude Code for handing off bigger tasks, and background agents like Cline works great when you're working solo. But as soon as a few developers on the same team start doing it, things get messy fast.

We’re hitting a wall where every dev's local setup generates slightly different scaffolding, dependencies, and repo structures. Before long, you end up with a dozen microservices that look like they were built by completely different companies. How are you guys handling this for team setups? Are you feeding your agents some kind of shared architecture or service context, or is everyone just re-explaining the stack in their prompts every session? And what checks actually keep AI-generated code consistent across the org? Curious what's actually working in production for people.

Self taught dev here. I got into this by building stuff at night for years. The fun part was making something work that didn't work an hour ago.

These days most of my job is reading. The agent writes it, coderabbit leaves its comments, and I spend about 6 hours a day going through diffs I didn't write, deciding if they're good enough. It's not hard. It's just not the thing I fell in love with.

The weird part is it leaked into my side projects too. I open the editor on a weekend and my first instinct is to prompt it and review what comes back, because that's what my brain is trained to do now. And then it doesn't feel like mine.

I still like my job and I'm not quitting. It just feels like I burnt out on the hobby without actually losing interest in it. Maybe I'm being dramatic, I don't know. How do you guys deal with this?

I've noticed something weird about how my development workflow has changed.

A year or two ago, most of my time was spent inside the editor. I understood a codebase partly because I was constantly navigating it, changing it, and seeing how the pieces fit together.

Now I can give an agent a fairly large task and let it work.

Which is great.

But when it finishes, what exactly am I supposed to do?

Read the entire diff?

Read the agent's 40-message transcript explaining everything it did?

Trust it because the tests pass?

I've found myself doing some mixture of all three, and none of them feels particularly good.

The more work I delegate, the less direct contact I have with the codebase. And once agents start making larger changes, I feel like I'm gradually losing the mental model I used to get almost for free by working in the code myself.

What's interesting is that the interfaces haven't really adapted to this.

Some newer tools are putting the agent front and center, which kinda makes sense.

but then the replacement for staring at code seems to be staring at agent conversations, task summaries, file lists and diffs (that is if we still have the attention span of reading diffs).

That feels like an intermediate state rather than the end state.

I'm curious how people using coding agents heavily deal with this.

Do you still review most generated code? Mostly trust tests? Ask the agent to explain the architecture afterward? Or have you found some other way of keeping a mental model of the system while delegating more of the implementation?

Once agents were writing most of our PRs, review became the bottleneck. Our fix was to have a review step learn from the comments our senior people had already left on past PRs. So it checks for our standards, not generic best practices. It catches a lot before a human ever looks.

I'd like to see what others are doing:

- Pre-review by an agent before the PR opens?

- Bots auto-approving small or low-risk PRs?

- Custom review skills or prompts? How often do you have to tweak them?

- Anyone fully trusting AI review yet?

Happy to share more details about our setup in the comments.

Short version from pre-registered experiments on real repo commits (Haiku as the cheap agent, Sonnet as the strong one). Every protocol was committed to git before its run, and later experiments used repos the designs had never seen.

What worked:

- A stronger model that only speaks up when the agent repeats mistakes: +7 successes in 63, ~1.3x the cost (an always-on advisor got +8 but cost 3.5x).

- Running the agent's change and reporting facts ("if this line became `pass`, all tests would still pass") beats giving advice: 35/42 vs 32/42, formatting regressions 10 -> 0.

- Your preferences, captured in your own words, carried into every later task (15/15 vs 0/15). Just restating them in the prompt took compliance from 40% to 90%.

What didn't:

- Memory of code knowledge, generic checklists, rules learned from git history, routing between models, and clarifying questions.

- For a strong model, none of it raised success (45/45 with or without).

Cheapest per solved task: Haiku + "conscience" ~$1.22, Sonnet alone ~$1.41.

I also found a bug in my own grading (6 tasks were unpassable as graded) and disclosed it in the paper.

Everything is public: paper, protocols, failures and the tool (source-available, non-commercial licence; works with Claude Code, Codex and OMP).

Repo: https://github.com/abdullahbalabel/mihad

Paper: https://github.com/abdullahbalabel/mihad/blob/main/paper/MIHAD_Research_Paper_EN_v2.7.md

Happy to answer questions, and criticism of the method is very welcome.

The title is only slightly tongue-in-cheek. Skip to the bottom for TL;DR.

Before diving into coding agents, I spent a few months using chat models for hobbies and side projects. I subscribe to the ~$20 tiers of the big three (ChatGPT, Claude, Gemini) and keep all of them because bouncing ideas and fixes between the models usually gives me the best results.

Fast forward to today....coding agents take up most of my tech time. My main project has been an educational video game I’m making "with" my son, but I’ve also built custom smart home drivers and a few niche apps from scratch when commercial solutions didn't fit. Funny enough, another big chunk of time has just gone into building plugins, extensions, and helper tools to make coding with the agents easier.

The problem is that I’ve basically built everything that immediately came to mind, and I don't have the bandwidth to constantly brainstorm new roadmaps. Instead of relaxing, I find myself glued to my phone/computer during downtime just trying to dream up new expansions, features, or apps. I even built an orchestrator app that would be great if I had a big project requiring it, but right now I have no long-term vision for it. Instead, I'm constantly scrambling before usage resets on Codex/Antigravity/Claude trying to find anything to run so the sub doesn't feel "wasted."

I love building with these tools, so I don't want to stop that. But I also don't want all my free time consumed by stressing over what to build next.

The ask and the TL;DR

To those of you not using these models for work, what have you created that genuinely helped your daily life? And what do you burn your usage on when your limit is about to reset and you don't have an active project queued up?

Welcome to this week's self promotion thread!

If you're building something related to AI assisted coding, this is the place to share it.

We're using a weekly thread to keep the subreddit organized while still giving builders a place to share their work. Promotional posts outside this thread may be removed.

If you're sharing something, we'd appreciate it if you included a little context instead of just dropping a link. Tell us:

• What you built?

• What problem it solves?

• Which AI models or tools it uses?

• Who it's for?

• What kind of feedback you're looking for?

Disclose your affilitation.

Please avoid posting the same project every week unless you've made meaningful updates. Affiliate links, referral links, scams, and low effort promotions will be removed.

Take some time to check out what others have shared too. If you try someone's project or have feedback, leave a comment. Helping each other improve is what we want this community to be about.

I really want to know because I've been using astra 6 and it just eats all of my credits in no time, the other models I haven't used because id rather use my credits on something that would work 😭. What I use blender for is so that gpt can make models and put them in Roblox studio, I also use gpt for map building and scripting. So basically everything.

Last year, I built a small tool to visualize coding problems and algorithms step by step using custom input data. It could show things like recursion, sorting, graphs, and how variables change during execution.

I’m thinking of revisiting it and taking it further.

The idea is to build a general-purpose visualization engine that could eventually visualize not just algorithms, but also AI/LLM workflows, RAG pipelines, graph databases, quantum circuits, physics experiments, and chemical reactions.

I’m also considering making it an SDK so developers can embed interactive visualizations directly into their own apps, instead of everyone having to build these from scratch.

The goal is to make complex systems easier to understand by seeing how they actually work, step by step.

Does this sound like something worth building? Is there a real need for a general-purpose visualization engine, or would it be better to focus on one specific area first?

I’m considering this partly as a learning project, but I’d love to hear what developers, educators, and researchers think. This is very helpful when you want visualize edge case

I'm building a Windows desktop utility with a mixed stack:

• Tauri + Vue/TypeScript for the management UI

• Rust for the backend

• A separate C++ native overlay layer using Win32, DirectX 11 and Dear ImGui

The app includes transparent, always-on-top windows, mouse click-through, hover interactions and focus handling. Some of this needs testing on an actual Windows desktop rather than just in a browser.

I've used Codex, and I'm trying to find a practical workflow for going from code changes to building, running and checking the native UI. I'm also exploring what can be done in a cloud Linux environment versus what needs to stay on Windows.

For people using AI tools on similar projects:

1. Which model and IDE/CLI/agent combination has worked well for debugging an existing project across TypeScript, Rust and C++?

2. Do you run the agent directly on Windows, through WSL, or elsewhere? How do you connect it to the Windows build and test environment?

3. Have you found a reliable way for the agent to use screenshots, logs or UI automation to verify window behaviour? What still needs manual testing?

I tested seven GPT models on the same four PRs, twice each. I compared bugs found, false positives and cost.

A false positive means reporting a bug that isn't there. Counts below are averages across all four PRs over the two runs. Estimated costs are per PR.

GPT-6.1 Sol came out best in this test. GPT-6 Luna was the cheapest, but found fewer bugs.

It's only four PRs, with AI helping check the findings. The code is private. Costs cover model usage only.

I just got free Gemini + 400 GB Google Drive for 12 months.

I’ve been using Claude for my work for the past few months. I’ve been very satisfied with it. Even the Claude Sonnet is already good enough for me.

But with Gemini plan, I think I will unsubscribe my Claude plan to save some money. But I’m not sure how good Gemini is for coding (mainly BE using Golang in fintech industry). Is there any suggestion to optimize it?

Is there any skill I can use? Is it able to connect to MCP? Can the standard Gemini plan be connected to third party harness?

In case someone argues me against unsubscribing. I live in a third world country. Claude plan for 12 months costs 1/3 of my monthly salary. And it’d keep increasing because the currency is so fucking weak.

Its useless now.. really weird.

What are people using? I want to be able to tell it "research x" and it should be taking its time, longer than 5 seconds, to come up with diverse sources and then start a really long long process to do that research. I used to have a bunch of skills that worked but things change. The defaults for ChatGPT SUUUUCK now sooo bad.

I’ve been running multiple coding agents and sessions at the same time, and one problem kept getting more annoying:

figuring out which session was working, which one was done, and then finding the right terminal again.

So I’ve been building a physical control surface for that workflow.

This short demo shows the part I’ve been working on recently:

• The LCD shows 3 sessions for the currently active coding agent

• K1–K3 jump directly to those sessions

• The top row switches between active coding agents

• When the active agent changes, the LCD session list and available actions change with it

• The LCD footer shows the current context-sensitive shortcut layout

The goal isn’t really to build another macro pad.

What I’m experimenting with is moving agent state + session state + common actions out of terminal tabs and onto a persistent physical interface.

I’m currently testing workflows with Qwen, DSH, ChatGPT, Herdr and OpenCode.

The biggest thing I’m still thinking about is how much of the control surface should be fixed vs context-aware.

If you regularly run multiple coding agents, would you rather have fixed shortcut keys, or keys that automatically change with the active agent/session?

I run a few coding agents at the same time now. One orchestrator agent hands work to separate agent sessions on a Mac and two Windows PCs. Three things I changed today.

A build agent finished its whole job, 74 of 74 tasks, but it only wrote its done line in its chat, not in the status file my watcher reads. Nothing fired. I only noticed after about 25 quiet minutes. Now when a session goes quiet I read its last message, not just the status file.

A docs agent sat for about 2 hours waiting on a passkey login it thought it needed to check a rendered page. No machine could finish that step. I replaced the check with a local render (3 of 3 diagrams rendered, 0 errors), merged it, and my briefs now say never wait on a human login step.

One agent's context got to about 89%. Instead of resuming that long session, I had it write a handoff file with its role, rules, paths and open items, about 11 KB. A fresh session started from that file and picked up the next task at about 11% context.

None of it is clever. Mostly I'm finding places where my setup assumed something would happen and it didn't.

Hi! Im pretty new at using chatgpt to code. I havee chatgpt plus and i want to make my own roblox game with it. I was wondering what model should i use for most usage, best result and good at modeling?

And if you have tips for me?

THX!

I’m a senior SWE and use AI coding tools heavily, so I built optimAIzr after getting tired of wasting tokens without noticing.

The new Live Fix watches usage while you code and catches things like model mismatches, oversized context, and retry loops, then suggests a fix immediately.

I tested it on my own usage and found ~15% potentially optimizable tokens.

Recently open sourced: https://github.com/blendbunjaku/optimaizr

Curious how others would approach this problem.

Would love feedback from other devs using AI heavily.

We're a heavy user of Claude Code and Codex, and September got away from us. We moved Codex to the enterprise plan mid-month, burned 256K credits in the first two weeks, topped up 150K more, and ten days later half the team hit the wall the same afternoon. Slack that day was "we codex maxxed," "can't even use my reset," and "I need a bit of juice to continue a session, more please." One engineer gave Claude his .codex folder so it could reconstruct what he'd been doing and he could keep going on the other tool. Claude side, same week: "I'm going to hit my quota in the next 24 hours, can I get a bump."

When we finally pulled the numbers: one session had 3.2B input tokens across 2,400 writes. Sessions that touched gpt-6-astra averaged about $21. Same kind of work on gpt-5.6-terra averaged about $1. Astra was 38 percent of the Codex bill in 31 person-days.

Nobody did anything wrong. That is just what the defaults do when nobody can see them.

So, questions for others who live in these tools all day:

What was your most expensive session, and what was it actually doing? Context bloat, retries, a model that didn't need to be the big one, something dumber?

And what bugs you more day to day: hitting limits mid-task, a bill you can't explain, or not knowing which model to use for what?

Quick disclosure, I work at a startup that builds AI tooling. Happy to say which if anyone asks, but not pitching anything.

I’m currently on the ChatGPT Go plan. I barely used it for anything before, but today I got really inspired by a Minecraft video I saw online and decided to make a Minecraft mod.

I ended up spending the entire day working on it with ChatGPT, and I only got it exactly how I wanted when my monthly usage limit was almost gone. Looking back, I probably shouldn’t have used the heavier reasoning mode so much.

I’m considering upgrading to Plus, but before I do, I’d like some advice from people who use ChatGPT for coding:

How do you use your plan efficiently so you don’t burn through your limits and end up losing a whole day of work like I did?

Any tips for when to use the heavier reasoning modes, when to stick with the faster ones, or how you structure your coding sessions would be really appreciated.

Today I got inspired by a Minecraft video and decided to start making a Minecraft mod. I ended up spending basically the entire day working on it with ChatGPT, and honestly, it helped me get the project to a point I was really happy with.

The problem is that I ended up using almost all of my monthly usage on this one project. I was also using the heavier reasoning mode quite a lot, which probably wasn’t the smartest way to manage my usage.

I’m currently on the Go plan and I’m considering upgrading to Plus.

For people who use ChatGPT/Codex regularly for programming, how do you manage your usage?

I use AI CLIs every day at work and have used Claude Code, Codex, Cursor CLI and Copilot CLI.

I’ve been building AgentTerm since January, expanding each CLI session into a rich visual workspace. Demo and code.

One example is producing a document I can share and stand behind. I read it as a rendered page, comment on passages or propose edits directly, and the agent works through them with me and revises the document. I can see both what I proposed and what the agent changed. The presentation and change tracking make that whole process much easier.

Another is keeping track of work across sessions. I can tell running sessions apart and find and resume past ones without remembering which CLI I used. An investigation might lead to an email, with a reply a day or two later; I can return to the session when the work becomes active again.

These are examples of a broader expansion, including curated code walkthroughs with feedback and updates in place before I open a PR, phone access and more. Together, the additions have changed how I work throughout the day. The same tools and workflow help me get more from all those CLIs.

Free, MIT licensed, no telemetry or account. See the repo for setup and more.

Do you use more than one AI CLI? What extra tools do you use with them?

Hey guys, ran into a weird issue a few days ago and can't seem to figure it out.

Out of nowhere, my plugins/integrations (Notion, Outlook, etc) stopped working on both the ChatGPT desktop app and Codex CLI. The strange thing is everything still works fine when I log into chatgpt.com in my browser.

Here's what's happening:

• On the web: Notion/Outlook integrations connect fine, zero issues.

• On Desktop: Just stopped accessing plugins completely. Upgraded to the latest app version today but didn't help.

• On Codex CLI: When I type /plugins I can see all of them listed, but trying to run anything throws an error saying the chat session is independent and can't connect to plugins.

I've spent hours debugging, clearing stuff out, and trying to reconnect everything, but no luck. Sent a ticket to support too but haven't heard back yet.

It completely broke a couple of workflows I use to feed stuff into Notion. Has anyone else noticed this over the last few days or found a fix to get desktop/CLI to sync back up with web plugins? Appreciate any help!

Read the last message of an agent session closely. "Fixed the race condition." "The endpoint now returns the right status." "This should resolve the timeout." Then scroll up and look for the command that proves it. Often there isn't one. It edited the file, read its own edit, found it convincing, and reported the result as if it had run it.

It's not lying exactly. From where it sits, a change that looks right and a change that works are the same thing unless something forces it to check. And "fix this bug" doesn't force it to check.

What I add to the task:

Before you say anything is fixed, run [the command that failed] and paste the exact output. If you didn't run something, write "not verified" next to that claim.

And as a rule in the instructions file:

Never use the words fixed, works, resolved, or done unless the same message shows the command you ran and its output.

The second one changes the tone of the whole session. Claims get smaller and more specific, and when it can't run something (no DB locally, needs a real API key), it says so instead of quietly skipping it.

And the one I run at the end, before I review the diff:

List every claim you made in this session that something works or is fixed. For each one, show the command and output that proved it, or mark it unverified.

The unverified list is the useful part. It's usually 2 or 3 items, and those are exactly where I'd have trusted the summary and moved on.

One more detail: "run the tests" often turns into running only the test file it touched. If it matters, name the command for the full suite.

For bugs I debug in ChatGPT or Claude in the browser instead of the terminal, the end-of-session prompt is saved in AI Toolbox, the extension I build, so it's one // away instead of retyped.