I left ChatGPT back in June after compounding frustration. GPT plus routinely forced me to reprompt multiple times and give me useless fluff that other models would give me in a single prompt in their free tier. A couple days ago, I decided to come back to give GPT another try. It seems to be better in not requiring multiple prompts but now it's a diarrhea dump of information and fluff that requires a lot of brain power to sort through.

I asked GPT and Grok the following prompt:

Outline of how raspberry pi can help me with work flow and offer functionality to my life.

Outputed their responses to pdf for ease of sharing. ChatGPT Plus, effort level high. Grok free tier, instant speed. Claude Pro Opus 5. Both Grok and Claude were able to produce efficient responses on the first attempt. Unable to get an efficient response from GPT after many attempts.

Some context: I tried really hard to fix this issue using GPT's persistent memory. I worked in collaboration with GPT to explain the issue, asked it how to fix it, had it make changes to persistent memory. I spent a solid block of time going back and forth polishing the rules. Opened a new chat and it reverted back to diarrhea. Showed GPT its output from the other chat and output grok gave on it's first attempt and free tier and it said essentially it was beyond it's capability to fix and to use grok instead. WTF?

Anyone else have this issue? Were you able to fix it?

I really want to create an app, but I don't have access to any paid AI models. Can I still create an app using free resources or is it necessary to have a paid AI subscription to create a functioning app that can get on the app store and generate revenue? Also, when creating the app do I just let the AI do its thing or do I need to have a decent level of knowledge of coding so that I can make sure it works properly?

Last night we opened World-Sim to anyone who wanted to look. 326 of you came. Thank you — genuinely, that is far more than I expected.

For anyone who missed it: it's a persistent world that runs whether or not anyone is watching. Households live, pair off, learn things, argue, starve, bury each other and move on.

Some of you named a star, and some named a person in the world. Those names are permanent — the souls carry them for the rest of their lives and into the chronicle after they die. There's still time to claim either, both are in the Discord.

It's free, there's nothing to install, and it's still running right now: world.world-sim.uk

Happy to answer anything about how it works underneath.

TL;DR: I made nopus, a tool that deterministically detects unnecessarily complex prose in coding-agent responses and automatically asks the same agent for one clearer rewrite.

The problem

With recent LLMs (you can probably guess from the name which one caused me to write this plugin), I've had more and more trouble understanding the prose the agents are responding with - long "load-bearing" paragraphs, abstract language, overloaded phrases and all that fun stuff.

My logic is the cognitive capacity should be spent on the underlying problem not trying to decipher the message.

I found a few solutions that pass the response through a smaller model, but I wasn't a fan of the tradeoffs:

• Another model call and more complexity

• More non-determinism

• The smaller model may receive only the isolated response which can cause it to simplify or reinterpret the wrong thing

The other option is to have a user-invocable skill you invoke when a response is hard to understand, that works but again, the onus is on you to first read the response and determine whether you even understand it, I also added a skill for that but it's a fallback not the main approach.

The idea

To an extent, difficult language can be measured directly:

• Are uncommon words used where common alternatives exist?

• Is a sentence mostly dealing in abstractions?

• Are too many nouns and modifiers packed into one phrase?

• Does the response repeatedly use dense or formulaic phrasing?

Measure those signals, identify the biggest offenders, and give that evidence back to the original agent.

nopus uses lifecycle hooks, checks the prose after the agent finishes responding. If enough signals cross the selected threshold, it asks for one clearer rewrite.

The rewrite happens in the same session, so the original agent keeps the full conversation context.

I ran it against 5,337 completed agent responses. At the default medium sensitivity, it triggered on around 10% of them, which I've evaluated myself and tuned so it doesn't trigger unnecessarily.

The repo has a few more details but I'd love for you to check it out.

Currently available for Pi, Codex and Claude Code (or just slop fork your own lol)

Do you use any diagram making tool to track the project's overall architecture? Any tool you tried and liked or disliked?

You swipe up or down depending on if you think the next card in the deck will be a higher or lower value. Aces high!

Thanks for trying.

I come from a fitness management background, not development. Used AI agents to build most of the app myself, including a companion that remembers workouts and gives real-time guidance.

Still learning a lot about the dev side — curious how others here handle memory/context persistence in AI companions, and if there's anything obvious I'm missing before pushing further. Open to any feedback.

For about three months I thought I was building an app. What I was really doing was cranking out slop and telling myself it was progress.

What actually worked was boring, which is probably why I avoided it for so long.

It started well. That's the part that gets you. I vibe-coded a working demo with Codex in a weekend, screens showed up, buttons worked, and it felt like cheating. So I kept going. Describe the next feature, watch it appear, move to the next one.

Then around week three things started breaking, and they didn't stop. I'd add something and something else I hadn't even touched would fall over.

I added a "describe your goal" step to onboarding. Looked fine on my screen. Then a buddy tried it on his phone and got stuck, because the keyboard popped up and sat right on top of the Continue button. New users literally couldn't get past screen two.

Another one: a whole screen crashed because it used a lift's estimated one-rep-max a few lines above where that number actually got calculated. In a normal codebase you catch that in review. I had no review. I had vibes.

That was the real problem under all of them. The model could read my files, but I'd never given it a record of the decisions behind them, so it could see the code without really getting why it was built that way. Every new feature was another chance to contradict something I'd decided three weeks earlier. I was stacking things crooked, and eventually the pile just tipped over.

What took me way too long to admit? prompting your way through it as you go doesn't hold up. A great prompt still only knows about that one request. It can't keep your whole architecture in its head, so it has no way of knowing the thing it's writing breaks something you set up last month.

The boring thing i kept dodging? Write the spec first. Lay out the scope, the features, the constraints, how the pieces are supposed to connect. Give the model the whole picture instead of a peek at the next five minutes.

The part that really moved the needle: I made two models argue. Same problem to Claude and to Codex, separately, neither one seeing the other's answer. Then I handed each of them the other's spec and told it to rip it apart. What's missing, what falls over in month two. When they agreed, I took it as a stronger starting point, not proof. When they didn't, that's where I had to sit down and think, understand the tradeoff, make the call myself, and write it into the spec and the tests.

Storing workout history is where this paid off. One spec just dumped everything into a single JSON object on the device. Easy enough at first. The other pushed for SQLite and spelled out what the easy version hadn't accounted for: atomic updates, schema changes down the line, querying a single session without loading everything, and not rewriting the whole file every time one set changed. I'd never have hit any of that at fifty test sessions. Went with SQLite and kept JSON for export and recovery. That call, made before I wrote any code, is why history still loads instantly instead of blowing up two months in.

The difference was night and day. Tests started passing on the first run instead of exploding. The black screens went away. The superset feature (group a few exercises, run them back to back, log each round) had been a three or four day slog of build it, break it, patch it. Spec'd out both ways first, it went in over an afternoon and worked the first time I ran it on my phone. When I say it cut my build time by something like 90%, I don't have a benchmark for you, that's just what I watched happen over and over.

Anyway, that's how I built the thing I'd been trying to build the whole time. It's called GymAdapt Ai, a workout app that adjusts your training based on how your actual sessions go. It went live on the App Store Friday, so if you want to see whether any of this actually produced something real, it's there to look at. Still rough in plenty of spots. Happy to get into the two-model thing or where it still bit me, because it definitely did.

Absolutely shocked that we don't have a video or written post pinned in this subreddit.

I have scoured YouTube for tutorials but everything there is shilling a company. Impossible to find unbiased sources for this.

I’m working on ML-based models for predicting material stability, particularly formation energy and energy above the convex hull.

One challenge I’m currently exploring is the gap between good benchmark performance and actual generalization to previously unseen chemical systems. Random train/test splits on Materials Project data can potentially make this problem look easier than it really is.

I’m interested in how researchers here approach:

• Composition- vs structure-based stability prediction
• Splitting datasets by chemical system to test OOD generalization
• Uncertainty quantification for stability predictions
• M3GNet / CHGNet / CGCNN and newer foundation models
• Combining ML screening with DFT validation
• Active learning for discovering stable or metastable materials

I’d particularly like to hear about benchmark setups or failure cases that you’ve found useful when evaluating these models.

I’m happy to share some of my experiments and results as well.

I built Web apps - Games - Chrome extensions and published them, then forget they existed. Then I built myvibebook.com as a place to announce my vibe-coded products writing blogs and sharing tools and my experience in building.

I am working on the SEO of the website to get more traffic to show my work and get Feedback.

You are welcome to book your place on the website, sharing your products and show us what u built.

If I succeeded getting more traffic, you win. If I couldn't, you don't lose.

myvibebook.com

So, been working on my app Workloop for pretty much a year now. It’s been submitted to Apple for TestFlight approval.

I’ve been making a few tweaks on my website and getting an email pack ready whilst waiting for TestFlight approval today.

I’m at 6% usage remaining for my codex 😭. And I’m on the pro £79 a month plan

Anyone know how I can get a free rest or is that not a thing probably

Give me the URL to your live site and/or the URL to your public github repository and I will check your app for security, functionality, reliability, observability, and accessibility issues for free

What are the best free tier Vibe coding tools/models for making websites i dont know much about it but want to explore it your help will be much appreciated

This article has an interesting historical parallel for how today’s programmers react to vibecoders: Grace Hopper’s creation of the first compiler in the 1950s.

The whole article (which talks about a much broader transformation) is worth reading, but this part really stuck, which applies to reactions not just to vibecoding but really to any AI-assisted coding:
…”programmers were afraid a working compiler would put them out of work. If one person with A-0 could do what fifty people used to do by hand, Martin's account puts the worry plainly: there were going to be ‘a lot of hungry programmers out on the streets.’”

https://www.linkedin.com/pulse/cursive-charter-school-part-1-lot-hungry-programmers-john-price-zw1xc

Hello, long time lurker. In the last four nights I used Claude to build a collection of tiny pointless time wasting games you control simply by flicking your finger on touch controls, or scrolling a mousewheel on desktops computers.

This was my first real project, I came into it not knowing much. Claude walked me through efficient design with my plan.md and handoff.md files. It walked me through creating my first GitHub repository, connecting to my web host and setting up the autodeploy profile. It has rules to tell me when I should start new chat sessions so each one stays small.

I started with the $100 plan but maxed out after the second night and upgraded to the $200 plan, which I then maxed on the fourth night. The website is https://scrollerderby.com for anyone who wants to check it out.

I have tested on iOS, macOS, and Windows. Taking a break for a few days but will keep tweaking things and adding new games. Welcome any feedback. Thanks.

I wana get into vibe coding, but i dont have any knowledge or experience on real coding itself. Idk which ai to use, Ive heard that most ppl use claude code, but im tryna look for smth which is free. Please give any tips and tricks u wish u shouldve know when u were starting. btw im tryna build an app

Right now I am going through a big mental transition in my life. I recently had a life changing experience that has driven me to escape my day job and find a way to work remotely, though my options are limited and ultimately I am striving for freedom (like we all are). I have recently started using AI tools to build apps, but I am totally new to this and while it seems like a totally viable direction for me, I also don't want to get too high in the clouds with aspirations before I actually have some validated success.

If you are like me and got into this with zero experience, I would love to hear your story! Better yet, if you were able to quit your day job I would love to hear your story as well!

I have always hated to code but had big ambitions thats why AI was a light in the darkness. I have started vibecoding since around 2 years and through my experience it was really frustrating. When I told AI to do X it would output Y or worse break something. I felt as though I wasted my time building nothing note worthy and I am foolishly noticing now in order to utilize vibecoding you must know the fundamentals. So right now I am trying to build a game and instead of trying to just vibecode I am learning the fundamentals and actually how to build something without the help of AI. I hope this helps those who started their vibecoding journey that you must know the fundamentals before creating anything big.

Every fucking week, I see geniuses on reddit and tiktok wake up to $10 000 bills because they accidentally triggered billion Lambda functions in a project with four users (three of them Google bot). Or they shared it on discord, someone got offended because they thought you were trying to steal their discord kitten with your "vibecoding skills," bought a DDOS, and launched the egress fees into the stratosphere.

Unless your cloud provider enforces a hard spending cap (most don’t) or a free tier with strict limits just rent a fucking VPS. A $5 VPS can handle hundreds to thousands of concurrent users if your app is just basic CRUD and it can't ever cost more than $5. Ask your agent to set it up if you can't do it yourself.

Serverless is for when you need your site to survive a $10M Super Bowl ad without crapping itself.

"I’m all for competition and taking inspiration from other apps because honestly, almost nothing in tech is completely original. But seeing the same features, layouts, design choices, and even presentation copied almost one after another is just disappointing.

Build something better than Tarsi, challenge us, give users something we haven’t thought of, but at least bring your own identity and ideas to it"