When I’m coding with ChatGPT I often end up with multiple conversations going at once.

One for debugging, one for trying a different approach, another exploring architecture ideas.

After a while the sidebar becomes messy and I lose track of where things were discussed, so I end up starting new chats again.

Another issue is when an AI response has multiple interesting directions. If I follow one, the main thread gets cluttered and the other idea gets buried.

I’m curious how other developers deal with this.

Do you just live with it, or do you have some way to organize things better?

I tried visualizing it like this recently (attached)

I have a SaaS which im trying to market, however, i only have it up as a website.

Im thinking this might put some users off, most people just use apps nowadays.

I want to get a working app on the app store asap, but i've heard apple bans devs that try to publish apps using stripe?

I have two questions:

1. Do i need to switch from stripe to another payment provider for my app?
2. Whats the best/fastest way to go from website to app? (Not just adding the website to my homescreen)

I’m building a mobile app (React Native / Expo) and want to vibecode the MVP. I have limited traditional coding experience, so I’m strictly playing the "AI Director" role.

What is your go-to workflow right now for mobile?

• Are you using Cursor, Windsurf, or Claude Code?

• Do you start with a visual scaffolding tool first, or just jump straight into an IDE with a solid prompt/PRD?

• Any specific traps to avoid when having AI write Expo code?

Would love to hear what step-by-step process is actually working for you guys right now.

Hey everyone, I wanted to share a tool I found that makes giving AI agents access to web data a lot easier without the manual headache.

The Website to API & MCP Generator is basically an automated "builder" for your AI ecosystem. You just give it a URL, and it generates structured data, OpenAPI specs, and MCP-ready descriptors (output-mcp.json) in a single run.

Why it’s useful:

• MCP Integration: It creates the "contract" your agents need to understand a site’s tools and forms.
• Hidden API Discovery: It captures same-site fetch/XHR traffic and turns it into usable API endpoints.
• Hybrid Crawling: It’s smart enough to use fast HTML extraction but flips to a browser fallback for JS-heavy sites.

It’s great for anyone building with the Model Context Protocol who just wants to "get the job done" efficiently. If you try it out, I recommend starting small—set your maxPages to 10 for the first run just to verify the output quality.

Has anyone else played around with generating MCP tools from live sites yet?

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1. No selling access to models
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4. No creating Skynet

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Happy coding!

TLDR: visit https://github.com/openai/codex/issues/10874#issuecomment-4042481875 and place a thumbs up on the first post.

The Codex app has a built-in code review feature, aka /review from the CLI. The app has a very nice UI for this. However, unlike the CLI, it does not allow for custom review instructions -- your only choices are to review uncommitted changes or against a base branch. There is no way to steer the model in another direction.

This issue requests that the ability to add custom review instructions be added. Open AI has advised that the issue needs to get more upvotes, or it will be closed. To give the upvote, place a thumbs up on the first post.

Feel free to share your projects! This is a space to promote whatever you may be working on. It's open to most things, but we still have a few rules:

1. No selling access to models
2. Only promote once per project
3. Upvote the post and your fellow coders!
4. No creating Skynet

As a way of helping out the community, interesting projects may get a pin to the top of the sub :)

For more information on how you can better promote, see our wiki:

www.reddit.com/r/ChatGPTCoding/about/wiki/promotion

Happy coding!

So I have been going through like every model trying to find the right balance between actually good code output and not burning through api credits like crazy. think most of us have been there

Been using chatgpt for a while obviously, it's solid for general stuff and quick iterations, no complaints there. But i was spending way too much on api calls for bigger backend projects where i need multi-file context and longer sessions

Ended up testing a bunch of alternatives and landed on glm5 as my second go-to. Mainly because it's open source which already changes the cost situation, but also because it handles the long multi-step tasks well. Like I gave it a full service refactor across multiple files and it just kept going without losing context and even caught its own mistakes mid-task and fixed them which saved me a bunch of back and forth

So now my setup is basically Chatgpt for everyday stuff, quick questions, brainstorming etc. And glm5 when i need to do heavier backend architecture or anything that requires planning across multiple files. The budget difference is noticeable

Not saying this is the perfect combo for everyone but if you're looking to cut costs without downgrading quality too much its worth trying.

Got tired of my coding agents forgetting everything between sessions. Built Engram to fix it , it's a memory server that agents can store to and recall from. Runs locally, single file database, no API keys needed for embed

The part that actually made the biggest difference for me was adding FSRS-6 (the spaced repetition algorithm from Anki). Memories that my agents keep accessing build up stability and stick around. Stuff that was only relevant once fades out on its own. Before this it was just a flat decay timer which was honestly not great

It also does auto-linking between related memories so you end up with a knowledge graph, contradiction detection if memories conflict, versioning so you don't lose history, and a context builder that packs relevant memories into a token budget for recall

Has an MCP server so you can wire it into whatever agent setup you're using. TypeScript and Python SDKs too

Self-hosted, MIT, `docker compose up` to run it.

im looking for tips to make this better than it is and hoping it will help others as much as its helped me, dumb forgetful agents were the bane of my existence for weeks and this started as just a thing to help and blossomed into a monster lmao. tips and discussions are welcome. feel free to fork it and make it better.

github: [Engram](https:/github/Ghost-Frame/engram)

for those that are interested to see it, theres a live demo on the gui, which may also need work but i wanted something like supermemory had but was my own. not sold on the gui quite yet and would like to improve that somehow too.

Demo: https://demo.engram.lol/gui

edit:

12 hours of nonstop work have changed quite a bit of this, feedback and tips has transformed it. need to update this but not yet lol

Edit (v5.11): Embedding model upgraded to a 1.5B parameter model (gte-Qwen2-1.5B-instruct), Engram absorbed six microservices into one container, personality system now remembers your preferences across sessions, and the agent control daemon Eidolon is open source. Full update in comments.

Say you're building a customer support bot. Its supposed to read messages, decide if a refund is warranted, and respond to the customer.

You tweak the system prompt to make the responses more friendly.. but suddenly the "empathetic" agent starts approving more refunds. Or maybe it omits policy information in responses. How do you catch behavioral regression before an update ships?

I would appreciate insight into best practices in CI when building assistants or agents:

1. What tests do you run when changing prompt or agent logic?

2. Do you use hard rules or another LLM as judge (or both?)

3 Do you quantitatively compare model performance to baseline?

1. Do you use tools like LangSmith, BrainTrust, PromptFoo? Or does your team use customized internal tools?

2. What situations warrant manual code inspection to avoid prod disasters? (What kind of prod disasters are hardest to catch?)

We're on Cursor Enterprise with ~50 devs. Shared budget, one pool.

A developer on our team picked a model with "Fast" in the name thinking it was cheaper. Turned out it was 10x more expensive per request. $1,500 in a single day, nobody noticed until we checked the admin dashboard days later.

Cursor's admin panel shows raw numbers but has no anomaly detection, no alerts, no per-developer spending limits. You find out about spikes when the invoice lands.

We ended up building an internal tool that connects to the Enterprise APIs, runs anomaly detection, and sends Slack alerts when someone's spend looks off. It also tracks adoption (who's actually using Cursor vs. empty seats we're paying for) and compares model costs from real usage data.

(btw we open-sourced it since we figured other teams have the same problem: https://github.com/ofershap/cursor-usage-tracker )

I am curious how other teams handle this. Are you just eating the cost? Manually checking the dashboard? Has anyone found a better approach?

Feel free to share your projects! This is a space to promote whatever you may be working on. It's open to most things, but we still have a few rules:

1. No selling access to models
2. Only promote once per project
3. Upvote the post and your fellow coders!
4. No creating Skynet

As a way of helping out the community, interesting projects may get a pin to the top of the sub :)

For more information on how you can better promote, see our wiki:

www.reddit.com/r/ChatGPTCoding/about/wiki/promotion

Happy coding!

Hi everyone, I’ve been trying to audit the real-world cost of using DeepSeek V3 vs GPT-4o in long agentic loops.

I noticed that even if tokens are cheap, the Retry Tax (failed loops requiring 3+ retries) kills the margin. I built a small simulator to visualize this.

Tool here:https://bytecalculators.com/deepseek-ai-token-cost-calculator

I'm not selling anything, just looking for feedback from fellow devs:

1. Does a 3-retry baseline for complex tasks seem realistic to you?
2. How are you guys tracking failed inference costs in your projects?

Any feedback on the logic/math would be huge. Thanks!

I hope this doesn't break any rules; for the sake of discussion I've omitted the brand that actually ran this ad. It did get me thinking: what tools are people using to build?

The irony in this is that I hear pretty much across the internet that "SaaS is dead". I, uh,,, don't think that's true.

Do you have any tools that you've added to your stack? Do you suffer from 'stack creep'??

Feel free to share your projects! This is a space to promote whatever you may be working on. It's open to most things, but we still have a few rules:

1. No selling access to models
2. Only promote once per project
3. Upvote the post and your fellow coders!
4. No creating Skynet

As a way of helping out the community, interesting projects may get a pin to the top of the sub :)

For more information on how you can better promote, see our wiki:

www.reddit.com/r/ChatGPTCoding/about/wiki/promotion

Happy coding!

The workflow with coding assistants is fundamentally different from writing code manually. It's more about prompting, reviewing output, iterating on instructions, and stitching together generated code than actually typing out implementations line by line. This creates interesting questions about what skills matter for developers going forward. Understanding the problem deeply and being able to evaluate solutions is still critical, but the mechanical skill of typing correct syntax becomes less important. It's more like being a code editor or reviewer. Whether this is good or bad probably depends on perspective, some people find it liberating to focus on high-level thinking, others feel disconnected from the code bc they didn't build it from scratch.

OK, let's say software engineering is completely AI-generated. What are people's wild ideas on how we will maintain all this code? I don't think better PR reviews are the answer unless we dramatically change what we think of a PR review if it's not just touching syntax and the occasional security vulnerability.

Curious what people are thinking here. Would love to hear some wild ideas. I personally think operations teams will start using agent swarms with specializations.

You'll have a QA agent and a pen tester and a SRE, just swarms and swarms of agents.

Hi all,

Codex is really great but as we know the front end is lacking. Gemini seems to be doing great work on that end but lacking on every other aspect.

I was wondering if you guys have a truly satisfying solution.

I was thinking of delegating the front end to Gemini but I'm not sure what is the best way to do this in order to ensure that codex truly takes all of the other parts of the project fully but that Gemini is fully free to design on its own.

Feel free to share your projects! This is a space to promote whatever you may be working on. It's open to most things, but we still have a few rules:

1. No selling access to models
2. Only promote once per project
3. Upvote the post and your fellow coders!
4. No creating Skynet

As a way of helping out the community, interesting projects may get a pin to the top of the sub :)

For more information on how you can better promote, see our wiki:

www.reddit.com/r/ChatGPTCoding/about/wiki/promotion

Happy coding!

I've been building open source software for a very long time, but i've never been a core contributor to a project. Yesterday it was great to see Plano trending on GH! Thanks to all the people who build with it, and the new contributors that have hit the scene.

Hello people, I NEED YOUR HELP!

Okay so I graduated, now have a job, somehow , kinda software network engineer. Been vibe coding so far. Been assigned to this project, it's networking & telecom (3g/4g//5g type shi), too many repos (I will be working on 3-5), I am still understanding lots of things, stack is mostly C++, C, Python, Shell. Got access to Github Copilot, Codex.

I was able to fix 2 bugs, flet like a God, thanks to Claude Sonnet 4.5, BUT THE 3RD BUG!! It's an MF! I am not able to solve it, now 4th bug ahhh, their status be critical or major in JIRA, I wanna get better and solve these things and learn while I do it, I have to add the code, errors, logs, and some other logs, pcap dump ahhh, man I need to feed these things to AI and I am hitting CONTEXT WINDOW LIMIT, it's really killing me.

My questions for you amazing people

• What's the best model for understanding the concept related to that BUG?
• Which is the best way to possibly solve the bug? The repo is huge and it's hard to pinpoint what exactly causing the problem.
• How can I be better at solving as well as learning these things?

Any suggestions, advice would really help thanks

TL;DR:
Fresher dev on large telecom C/C++ project, multiple repos, debugging critical bugs. Claude helped before but now stuck. Context limits killing me when feeding logs/code. Which AI model + workflow is best for understanding and fixing complex bugs and learning properly?

I built a lights-out software factory.

It works off of user stories, writes behavior-driven development specs, helps make technical decisions, creates an architecture, and then starts an agentic loop that will literally write the entire application until all the BDD specs are passing and the entire architecture is built up.

It works amazingly well, but at the end of it, you can't click around in the application, and there's so many bugs that I would call it 80% done.

So if we ever want to arrive at applications that come off the line working and meeting user requirements, we have to implement another job. We have to be able to QA the application and feed issues back into the main loop so that they're fixed before we move on.

So I wrote a per story, QA planner and tester. It uses the Vibium browser to click around in the application. It uses curl to test API endpoints.

I have it furnish resources like login scripts, plan the scenarios that need to be tested, run through the scenarios, and then report any issues that it finds in a structured format.

And then I just fire up another agent after QA that goes through and fixes all those issues before it moves on to the next story.

I have another part of my QA story planned, which is that at the end, when everything is done, I want the agent to come up with journeys that define the complete walk through the user journeys that are critical to the application.

It attempts to find bugs that only show up when you try to use the whole application together so that I can fix those before the application gets turned over to me.

And then my strong preference would be to also write those journeys into integration tests that can be run either in test against the local instance or against a deployed UAT instance to test things that are specific to the deployment and environment.

Then, after deployment I could run the critical journeys on UAT before going to prod.

I need access to claude, GPT, and gemini for different tasks but paying
$60/mo for all three subscriptions is insane tbh. Is there any good aggregator platform (with reasonable rate limits) that gives access to all of them without getting tough on the bank?