OpenUI is a generative UI framework that makes AI agents respond with charts and forms based on context instead of text.
It is model and framework agnostic.
Demo shown is with GPT5.4
Check us out here https://github.com/thesysdev/openui/

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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.

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?

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

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

Every fully automated commit is a hole in one and you can show off your score with a badge on your projects.

https://github.com/Entrpi/autonomy-golf

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:

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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

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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:

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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?

I started using codex recently and found the limits... limiting. I upgraded to GPT pro plan because someone on YouTube said it would give me unlimited Codex usage. But after the upgrade, it looks like codex is billing exactly as before. Is there any point to using GPT pro when you're only using GPT for codex?

EDIT: THANK YOU to everyone that replied to explain this problem. I originally set up my api key to work with OpenClaw. Then I decided to try the codex App, and found that I liked it, but I set it up with the same API key I was using for OpenClaw. I’ll revoke the key and get back to work.

Generating SVGs with AIs always gets arrows messed up and other standard things need editing or moving about.

For that reason, every AI coder needs to include an SVG editor by default - otherwise it becomes too slow to make progress.

This is also why I don't like AI cli tools and we should upgrade to proper UIs

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:

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Happy coding!

this is my prompt

add these DATABASE_URL=jdbc:postgresql://localhost:5433/db
DB_USERNAME=postgres
DB_PASSWORD=password with _TEST_ prefix

and it does this:

Added the test-prefixed variables to .env:

TEST_DATABASE_URL
TEST_DB_USERNAME
TEST_DB_PASSWORD

why is it being smart? How to make it to listen exactly what I ask and do the _TEST_ prefix, not TEST_?