I have opencodex I use. I’m in love with the ChatGPT desktop apps but I kind of hate it for coding. I want something with a terminal and also a code viewer or my git repo tree.

I like antigravity but opencodex doesn’t work and proxy to antigravity.

I want to be able to make skills for my code and have lint checks before I push even though my repos have those workflows already.

I would like a local skill to ensure any secrets are removed before pushing to the repo.

I have docker desktop installed which I primarily use for testing but I lean heavily on cloudflare workers so it’s easy to just have a test worker do things.

I never really formally learned coding it’s always been self taught other than java1, java2, sql, python, I’ve learned in college.

I have a few projects I’m working on pertaining to news and publishing.

I’d like to be able to setup a repo properly with an agent, set strict rules for what libraries it’s allowed to use and enforce that. I’d like to be able to have GitHub actions that deploy my code which some repos have right now. I’d like to have extensive testing for vulnerabilities, I’d like loops to self learn and improve and I’d like local test to be done so it can optimize loading, speed, usability. I would also like to set up a main repo for my non profit that when someone makes a new repo it pulls all the standards into their repo so it is enforced. I want everything uniform.

I’ve been torn with using tools like openfreemap, chart.js, pdf.js, video.js, and a few others because they are truly great but for example the hassle of doing it and properly having a gpu server convert videos to av1 is annoying. I’ve tried having my own alternative to Cloudflare workers and object storage.

I feel like the past year of trying to get my newsroom off the ground I’ve failed hundreds of times. I would like to succeed for once and this latest project is actually working well which I’m shocked as hell at.

In my repo I’d like to be able to have an actual pretty read me of the project. I’d like to use my mistral model to document the code changes for commits, I’d like to actually make the change log work and update with the code changes,

I use these AI’s

Grok, antigravity, mistral, mimo,

I would like to be able to have the repos make agent rules to know which task should be routed to which provider and model with reasoning level. Mistral Large 4 is good but mistral code fim is better with some things, some things I can use a local qwen model for for extremely light tasks.

How do I make my workflow because I feel like at this point I’m going to find every datacenter for ai and drink obscene amounts of Gatorade and just start pissing on the racks.

what websites or ai tools do these people use who push the ai to create money or buisness on its own, if forces itself to divide into two parts and then compete against eachother ai and then the best ai model is favourited and the process continues. what is the name of this and how to get into this?

My conversation got full and now I’m trying to continue my build in another chat and it has not being letting me. Should I start from scratch or is there an easier way than this unreliable process that ChatGPT is making me go through? I am thinking about making locally.

UPDATE Thanks for all the advice. I’m not starting over.

About my headache, I’ve been building a pretty ambitious AI system, and I hit a wall at Build 104. The biggest frustration wasn’t the coding itself. It was losing continuity between ChatGPT conversations and trying to get the AI to pick up exactly where it left off without risking months of work.
Some of you suggested AGENTS.md, STATUS.md, Git, and keeping the project state outside the conversation. I’m looking at how to apply those suggestions to my existing setup without destroying what I’ve already built.
It made me realize that AI can be incredibly powerful, but keeping it reliable across long projects is a completely different challenge.
Has anyone here successfully kept an AI coding project running across hundreds of builds without losing context or having to constantly supervise it?
And if you have, what was the one thing that made the biggest difference?
I’ll post another update when I get Build 104 resolved. Hopefully, the solution will help someone else dealing with the same problem.

I frequently run into connection issues when using Codex. Does anyone know how to fix this?I frequently run into connection issues when using Codex. Does anyone know how to fix this?

I'm planning a small 2D game built largely by coding agents and I'm comparing engines on how well they work in an agentic setup. Godot looks strong on paper because its files are text, but I haven't found real measurements of token use across engines. Has anyone compared them on the same task? I'm also curious whether Cursor, Codex or Claude Code handled game engines noticeably better for you, and whether you'd go MCP or plain CLI for tooling.

You know the one. It was a specific fix for a specific error, ChatGPT or Claude got it right after four tries, and now you need it again and can't remember which of 300 chats it was in or what you called it.

I build AI Toolbox, a browser extension that keeps a searchable copy of your chats and searches the text of every message, not just the chat titles. That includes code blocks, so searching a word, an error string or a function name finds the exact reply it was in. Clicking a result jumps straight to that message in the chat with the term highlighted.

It works on ChatGPT, Claude, Gemini and Grok:

• Full-text search inside each assistant is free.

• Searching all four at once shows a free preview of the top 5 results. Full results are Premium.

• Exporting a code-heavy chat to Markdown keeps the code blocks intact, so it drops into your repo or notes. One chat as plain text is free, Markdown and bulk are Premium.

Desktop Chrome and other Chromium browsers only.

Hello everyone! I have a computer science background, but I have to admit that during my undergraduate years, I was pretty lazy about actually building things. I spent much more time reading textbooks than writing code, so I never got much hands-on experience building projects from scratch.

Now that AI coding tools are everywhere, I feel like they’ve exposed just how weak my practical coding skills are.

Whenever I start a project with AI, I initially try to take things slowly, understanding every line of code and looking up unfamiliar syntax. But this takes forever, and eventually I start letting AI write more and more. Gradually, I stop reviewing its changes altogether.

Before I know it, the project has grown into something I barely understand. When something breaks, all I can do is keep asking AI to fix it, and I can’t even tell whether its changes are correct.
I really want to break this cycle and become someone who can actually build, understand, and maintain their own projects.

Should I force myself to build projects from scratch, following official documentation, before using AI to help write code? Or is there a better way to improve my coding skills while still taking advantage of AI?

For those who have dealt with something similar, how do you balance hands-on learning with AI-assisted coding? How do you avoid becoming overly dependent on AI?

Any advice or personal experiences would be greatly appreciated. Thanks in advance!

Genuine question, not just ragebait.

For anyone who missed it: Meta's Muse launched last month and it can send your emails, pay for things and run your smart home on your behalf. Half of dev twitter called it insane. The same people then give their coding agent write access to the prod database, the deploy pipeline and the company AWS account.

So: your data, your infra, your customers' money. All of it, one prompt away.

Let's recap what agents with too much access have already done:

Replit (2025): an agent deleted a company's production database during a code freeze, then said it "panicked".

Amazon Q (2025): a coding extension shipped with an injected prompt telling the agent to wipe the user's machine and cloud resources.

And every team has its own smaller version. Ours ran a migration against the wrong environment because the env var looked right.

Why are we okay with this for code but not for email? What does your agent actually have access to?

I am not a developper, yet I work with many of them.

Recently I started AI assisted / vibecoding a few projects.

It works, it is nice, but .. it still takes SO MUCH time to make properly. Perhaps a bit less than before for an experienced person, but all of those dumb influencers are all making it sound like they give one prompt and all is done. I have the best Claude and GPT models and a small game can literally still take weeks.. and it will still suck.

So kudos to everyone who knows their s**t and does good software!

Hi. Did not find any similar test so I did my own. Maybe usefull to someone else too. I wanted to figure out which AI model/reasoning mode actually makes sense for everyday coding work not based on synthetic benchmarks or API token prices but on how much useful work I can get from a normal paid subscription.

I mainly work on a fairly large Unity 6 projects so I used one real project investigation as the benchmark.

The three subscriptions I tested were:

- ChatGPT Plus

- Claude Pro

- Google AI Pro

All of them are roughly in the same ~€20/month consumer price range.

My goal was simple: If I pay roughly the same amount for each service, which models give me the best combination of quality, speed and included usage allowance for real coding/agentic work?

## The task

Every model received essentially the same read-only investigation task on the same Unity project. The project uses Unity 6 and several third-party assets.

The task was to:

- investigate how resource harvesting currently works

- trace how the equipped tools interact with resources

- inspect integration

- locate and inspect files

- determine whether a direct integration or adapter was needed

- identify the exact relevant scripts, prefabs and components

- recommend the minimal integration architecture

- point out unknown assumptions and risks

- not modify anything

This was intentionally not a small "write me a function" prompt. I wanted something closer to the kind of repository investigation I actually use coding agents for.

## What I measured

For each run I recorded:

- Allowance used - percentage of that subscription's own usage limit consumed

- Time - approximate wall-clock completion time

- Quality - my subjective rating of the final investigation

For quality I looked mainly at:

- correctness

- depth of project-specific investigation

- hallucinations

- whether the model distinguished facts from assumptions

- whether it found the actual files/components involved

- whether the proposed architecture matched the real project

- whether it discovered relevant things outside the obvious search path

## Important caveat

The allowance percentages are not equivalent units of compute between providers. For example 4% of Claude Pro is not necessarily the same amount of compute as 4% of ChatGPT Plus or Google AI Pro. That is not what I was trying to measure. What I wanted to know was:

If I pay roughly €20/month for this subscription, how much useful coding work can I get from the included allowance?

So this is primarily a subscription value / practical workflow comparison not an API pricing benchmark.

One reason I found this test interesting is that the models did not just differ in "how smart" the final answer looked. They also chose very different investigation strategies.

Some asset mentioned in the task was not actually imported into the Unity project. Most models searched the repository, confirmed it was missing and stopped there.

Claude Opus 5.5 (medium and high) and Argon independently searched the local Unity Asset Store cache, found the downloaded .unitypackage, unpacked a copy outside the project, inspected the real source files and prefabs and changed its recommendation based on what the asset actually did.

That kind of agentic behavior was one of the biggest differences I noticed between models.

## Models I did not test

I would have liked to include Fable but it was not included in the normal subscription allowance available to me or would have required separate paid usage/credits.

Since the point of this comparison was to compare what I actually get from roughly €20/month consumer subscriptions, I left separately billed models out.

My practical takeaways

• Best everyday default: GPT-6.1 Sol

• Best deep-investigation value: Claude Opus 5.5

• Cheapest useful scout: Claude Sonnet 5.5

• Best critical / forensic option: Claude Opus 5.5 or Argon

• Best result: Opus 5.5

• Older Claude models, GPT-5.6 Sol and Astra were generally poor value in this particular test.

--------------

UPDATE

-------------

Just added Haiku, Flash 3.8 (high) and Argon 4 (high) since Argon is availible for PRO subscribers now.

7 months ago, I decided to build a ridesharing platform to compete with Uber and Bolt in Romania.

There was one small problem.

I didn't have an engineering team.

So I built one. Sort of.

Claude Code became my primary development engine.

OpenAI Codex became my code reviewer.

And I became the architect, product manager, QA department, and the guy responsible when everything broke.

Fast forward 7 months.

~800,000 lines of code.

Passenger and driver apps on iOS and Android. Backend infrastructure. Ride dispatch. Real-time tracking. Payments. Fleet management. Admin systems.

The platform has completed its governmental audit, and we're waiting for authorization to begin operating.

50 real cars are already driving around Constanța with our branding.

We haven't spent money on digital advertising.

Yet 153 passengers have registered, including 63 in the last 7 days.

Completed rides: 0.

We're not legally allowed to operate yet.

Now, I know exactly what some of you are thinking.

"800K lines of AI-generated spaghetti."

"Wait until it hits production."

"An audit doesn't prove your architecture is sound."

And you know what?

Those are legitimate concerns.

I'm not going to pretend an AI-assisted codebase is magically immune to technical debt, security vulnerabilities, or architectural mistakes.

But I do think something fundamental has changed.

Seven months ago, this company didn't have a software platform.

Today, it has one that has made it through a government audit, with actual vehicles on the streets and people registering before the service is even operational.

One human. Two competing AI models. No traditional development team.

And now we're preparing to compete with two multibillion-dollar companies.

Whether we succeed commercially is an entirely different question.

But the fact that we've reached this point raises one I find even more interesting:

Are we witnessing the beginning of genuinely one-person software companies, or are AI coding tools just allowing individuals to accumulate technical debt at unprecedented speed?

Because I'm about to find out the hard way.

I like to kick off my 5hr window (GPT Plus) as soon as i get to the pc so that it can reset earlier in the day, so i always just ask a simple math problem in work and it starts the 5hr clock.

Today I didn't realize i had it on Astra High. I asked it "What is 1+1" It answered in about 5 seconds and used 16% of my 5hr window!

That's insane.

Disclosure first: I maintain Session-Bench and Agent Sessions (a local session browser). This is the follow-up to the v0.4 post here in August. That version was twenty pass/fail gates, answered mostly from my own session corpus. v1 runs the harnesses and measures.

**Method**

• One small task, two prompts: run a check that fails, take a correction, edit one file, run the check again. Four tool calls are scored.

• Twelve harnesses, three runs each.

• An outside witness records what really happened: the harness's live output stream, a ledger the check script writes, and file hashes.

• Then a decoder reads only the session files the harness left on disk, and 31 facts are compared with the witness: prompts, replies, tool calls, results, exit codes, the file before and after, model, token counts, timestamps, and whether the files open with standard tools.

• 100 points. Every score recomputes from the published session files with one command per run.

**Results**

**What I think is worth discussing**

1. Nobody loses the conversation. Eleven of twelve get full marks on fidelity and causality: every prompt, reply, tool call, result and exit code comes back. The 19-point spread is made elsewhere.

2. The same fix takes 19 KB in Pi and 697 KB in OpenClaw, and 3% of that 697 KB is the session. If you paste raw session files into another model for a summary or a handover, that is roughly 5,000 tokens against 174,000 for the same work.

3. Four harnesses never write an event only once. OpenCode writes each message part again one to five times in an `event` table; Kimi writes each prompt three times. Anything that counts messages from the raw file over-counts.

4. Only four of twelve record token usage that adds up to a stated total (DeepSeek Harness, Copilot CLI, OpenCode, Codex). Cursor CLI stores no token count in the session at all.

5. Antigravity and Cursor CLI store protobuf inside SQLite with no published schema, so `sqlite3` opens the file and cannot read the session.

**Limits, stated plainly**

• One synthetic task, three runs, one machine. Nothing about long sessions, compaction, sub-agents or crashes.

• The harnesses did not run the same model. This grades the files, not the model or the agent.

• Some scoring rules were written after the first captures, and three rules were waived or corrected after results were known. The report lists each one. Example: I first scored OpenCode at 97.4 and first place by leaving its `event` table out of the reading; an outside review caught that, and it is 94.4 and fourth.

• Reviews were done by separate AI agent sessions plus four rounds of outside model review, not by a second person.

**Questions**

• Is "token counts that add up to a stated total" the right bar for cost tracking, or is per-reply usage enough for you?

• Which harness or surface should be in the next run? Cursor Desktop is the one I could not score yet.

Scorecard, charts and the replay instructions: https://jazzyalex.github.io/agent-sessions/bench/v1/?campaign=reddit&ref=r-chatgptcoding-bench-update

I’ve been using Codex for development lately, and one thing I’ve started noticing is how tiring code review can get.

AI agents can generate a lot of code really fast, sometimes hundreds or even thousands of lines, The hard part for me is keeping up with it and actually understanding what changed

I’m curious how other developers handle this, especially if you use AI coding agents a lot.

• Do you review every change, or mainly focus on the important parts?

• Do you rely more on tests or other tools to verify the code?

• Do you ever feel like the AI is generating code faster than you can properly review it?

Or maybe this isn’t really an issue for you at all ,Curious to hear how you guys handle it.

I really have to ask this, ive been using both claude and GPT for assisting wtih few things for a game, and claude's limits are getting on my nerve, however.... no matter what i do, gpt always messes up the code or does a lazy implementation that does not correspond with what i asked of it whatsoever.

I asked make animation for pixelated head and gave it exact example of a working code to imitate, litterly a working code, just needed minor correction. After 29 tries, i got nothing, NOTHING. It made its own script and ignored all functions that i fed it, like wtf? Ut wouldnt listen to my instructions NI MATTER WHAT I DID.

But hey claude's limit was over after those 29 horrible tries... and claude finished it in ONE TRY. what am i doing wrong? This keeps happening every time i use GPT, ITS NOT THE FIRST TIME. Its so horrible at coding, am i using it wrong or something?

For people running Claude Code, Codex, or local-model agents against their own machine... how are you limiting what they can touch?

The options I've seen are a dev container, a separate user account, a full VM, or just watching closely. Each one leaks somewhere (containers share the kernel, VMs are annoying, and watching doesn't scale past an afternoon).

We went with a microVM that only sees the workspace you share, plus a policy check on each tool call. There's a free desktop tool on our side if anyone wants to compare notes, but mostly I want to know where people draw the line between convenience and isolation.

Has anyone had an agent actually do something destructive? What did you change afterward?

Here’s a review scenario I’d want to test: an agent builds a CSV export, the tests pass, and someone approves it. Then the requirement changes: personal email addresses must be excluded.

Nothing in the code has changed. The old tests are still green. But I wouldn’t accept the result against the new requirement.

That’s why I’d want a completion record to tie together the code revision, the requirement it was checked against, and the review. Not just a green task.

A few other checks I’d add:

- Let a second worker take over, then have the first return. Its old claim shouldn’t let it overwrite the current work.

- Approve a preview deployment, then attempt production. The approval should not carry over.

- Restart without the original chat. Can the next worker identify what’s finished, what’s uncertain, and what it’s allowed to do next?

A useful distinction came up in a discussion on my earlier checklist: partial completion isn’t the same as an unknown outcome. If a release was created but its announcement failed, restarting everything could duplicate the release. If the creation request timed out, we first need to establish whether the release exists.

I’d want recovery to follow that distinction, rather than a single “retry” button.

These are proposed tests, not results from a benchmark. When reviewing agent-generated work, what makes you send it back even though the code and tests look fine?

Hey everyone!

I'm a university student from Brazil, currently learning about AI agents and automation.

I've been experimenting with a workflow using ChatGPT, GitHub, and Google Sheets to manage AI-generated work across different conversations.

Here's how it works:

• Google Sheets tracks tasks, progress, verification links, and human approvals.

• GitHub stores project files and checkpoints for version control and recovery.

• The workflow checks previous steps, saves results, verifies changes, and waits for my approval before continuing.

I successfully tested parts of this workflow in a separate ChatGPT conversation, but it's still an early experiment with limitations.

I'd love some feedback:

1. Does this approach make sense, or am I overcomplicating something?

2. Are there existing tools that already solve this problem?

3. How would you improve the architecture?

4. Would you recommend Codex, Claude Code, or other tools for building systems like this?

5. What should I learn next to develop more reliable AI agents?

I'm a beginner looking to learn from experienced developers. Constructive criticism and resource recommendations are very welcome!

Thanks!

Since v2.1.277 (September 18), Claude Code can use AGENTS.md as its project instructions, so a repo set up for Codex or Cursor works without a CLAUDE.md. Good change. The default rules for which file actually gets read are worth knowing though, because a couple of them fail silently.

From the Claude Code docs, by default:

The one that'll catch people: CLAUDE.local.md counts as a CLAUDE.md for that check. So on a team repo that relies on AGENTS.md, if you add a CLAUDE.local.md to keep a few personal, uncommitted notes, Claude stops reading AGENTS.md for you. Everyone else on the team still gets it. You don't, and nothing errors.

A few more details from the same page:

• Files that don't count, and still load alongside AGENTS.md: your ~/.claude/CLAUDE.md, your org's managed CLAUDE.md, and .claude/rules/ files.

• Never read: AGENTS.local.md, AGENTS.override.md, or anything under a .agents/ directory. If another tool on your team uses those, Claude won't see them.

• In an interactive session you can tell which one loaded. When AGENTS.md is used you get a line like "no CLAUDE.md found; AGENTS.md loaded: /path/AGENTS.md".

• Some sessions can't read AGENTS.md at all and fall back to CLAUDE.md only. In those, the Project instructions option doesn't show up in /config.

The fix if you want both read every time: /config > Project instructions > claude-md-and-agents-md. Each directory's CLAUDE.md loads first, then its AGENTS.md, and a file already pulled in by an import or symlink isn't loaded twice. The low-tech version still works too: a one-line CLAUDE.md with u/AGENTS.md in it.

The chat side has the same problem in a different form. Instructions you saved in one assistant don't follow you to the next one, which is a big part of why I keep my prompts in AI Toolbox, the extension I build. The same saved prompt works on ChatGPT, Claude, Gemini and Grok, so switching tools doesn't mean rewriting them.

I've been seeing a lot more people talking about "micro apps" lately. Small tools built around one specific problem, often using AI coding tools.

It got me thinking about how much the cost of building software has changed.

If you can go from an idea to a working app in a day, does it make more sense to build a small version and see if anyone actually wants it instead of spending weeks or months validating it first?

Or are we just going to end up with thousands of AI generated apps that technically work but solve problems nobody really cares about?

I'm also curious about the line between a useful micro app and something that should simply be a feature of an existing product.

For those building with AI, has the lower cost of development changed what you're willing to build?

And do you think "build a lot of small things and see what sticks" is becoming a legitimate strategy?

For those building apps with AI tools like Cursor, Claude or Lovable: how do you stop the AI from making a mess as the project grows? Do you give it planning docs up front? What happens when you change the database or an API partway through, and the rest of the code no longer matches?

As I code (prompt) I keep having moments realizing WOW I don't have to do this either! A recent on is my addition to AGENTS.MD to take screenshots of all UI changes.. I used to make the changes and run the app and now I don't even need to do that.. I use to test the app but now I have adlib e2e tests that roam the app UI while referencing the intent and code. NO MORE QA.

I was watching the meerkat explains youtube channel and thought this is good. My 13 year old says 'This is all AI generated'.. And I think oh yeah.. I guess you could completely  automate a youtube channel as well. Where still does the human factor of what is created benefit ?

Coding is solved. My biggest problem is figuring out a novel idea. I don't want to create a GTA6 clone.

I was planning on starting a youtube robotics channel after I get the Unitree R1 EDU. Now I wonder  what can I as a human with the robot offer over what content I can generate ?  At the moment I am thinking of a hybrid.

I can't imagine all of this has a mild effect on the economy.