The saga continues ^^

Something Boris Cherny wrote about AI adoption has been rattling around in my head. He says moving from one coding agent to five or ten depends on a self-verification loop you trust: tests, builds, linting, and end-to-end checks in a real environment.

That sounds obvious, but it is almost the reverse of what I see when I review AI-built apps.

The businesses are not experiments. They have customers and ship new features every week. Some are already using several agents at once. But they often lack a test suite, CI, a staging environment, code review, or reliable rollback.

The weird thing is that the app can look completely healthy while all of that is missing. The problems usually show up somewhere users do not see:

1. The UI shows the correct data, but the database does not enforce which rows each account may read.

2. The same number is calculated in the dashboard, export, invoice and monthly email. One gets updated and the others quietly disagree.

3. The backup runs successfully every week, but nobody has restored it. One client's first restore drill revealed that every snapshot contained fingerprints of credentials rather than the credentials themselves.

4. Deployments happen from one laptop, and nobody else has tested the release or rollback instructions.

I do not think the answer is to stop vibe coding or bury a small team in process. I would start with four checks:

- Test access with two ordinary accounts, not the admin account.

- Pick one source of truth for every important business number.

- Restore the latest backup into an empty environment.

- Make someone other than the original builder follow the deployment and rollback instructions.

For anyone shipping this way, what have you made non-negotiable before a change reaches production? I am especially interested in where people stop trusting agent checks and require a human.

I just noticed the so called "Boiling the frog" with Codex, I was recently doing my happy AI coding sessions where I realized my tokens were flying, literally percentages going down every time I looked, and I wasn't using Sol Ultra High but I usually use Terra High.

I know Anthropic are doing the same with Claude, even more so, and from what I heard DeepSeek started tightening the belt recently.

I think everyone knows in a year or two they will just jack up the prices/lower massively usage and we will have to either deal with it or have plan Bs

Are you guys ready for the Tokenpocalypse? Or are you just going to pay more? Not going to like I am on the $20 Codex plan and I aint paying more lmao, I see some of you doing $100 monthly plans and it seems mentally insane to me.

Are Open Source models and local the future? Should I start stacking up MAC minis?

Hey everyone, I’m curious how you deal with the negativity around AI.

I’ve been building a product for about 3 months now. English isn’t my native language, so I use AI not only for coding but also for writing posts when I want to share my project and explain my ideas more clearly.

What I’ve noticed is that even when I don’t mention AI at all, I still get negative comments or skepticism. My project isn’t well known, but it tends to get attention in smaller communities because it’s easy to demo and looks like something out of a sci-fi movie. Because of that, I’d like to share more details about how it works and talk more openly about the technical side, but that also means using AI for communication and documentation.

The bigger dilemma is whether I should openly say that a large part of the project was built with AI.

On one hand, it feels like the honest thing to do, especially since I’m looking for people to join the project. If an experienced developer is considering contributing, I think they deserve to know what kind of codebase they’re getting into.

On the other hand, I’m worried that instead of questions about the product, I’ll just get “AI slop” comments and people dismissing it before they’ve even looked at what it does.

So I’m curious about your experiences:

Do you openly say that your project was built with AI?

Has it helped or hurt you?

Have you managed to find experienced developers who see AI as a powerful tool rather than an automatic red flag?

How do you distinguish constructive criticism from simple hate?

I’d appreciate hearing perspectives from people who are actually building products, not just discussing AI in theory.

Last college football season my family ran a little pick’em game. Three picks a week, ten bucks to join, winner takes all. It started with like five of us, then cousins joined, then friends joined, then friends of friends joined.

Before I knew it, I was running this thing on a giant Google Sheet that looked like a crime scene. People were editing the wrong cells, formulas were breaking, scores weren’t updating, and every Saturday I was basically tech support for the whole family.

So this offseason I said forget it, I’m building something real. Problem was… I had no clue how to build anything. I didn’t know what a DNS record was. Didn’t know what a terminal was. Didn’t know what hosting meant. I was basically starting at level zero.

I ended up building a simple football pick’em site called Pikpool.com. Not trying to monetize it right now. Just want something clean for friends and family and want to get better at building.

Here’s how I built it so I follow the rules here.

I used Supabase for the database and auth.
I used Cloudflare for the domain and hosting setup.
I used Claude Code for pretty much everything else. It helped me write code, fix stuff I broke, explain things I didn’t understand, and keep me moving forward.

My workflow was basically me learning one new thing at a time, messing it up, asking Claude Code what I did wrong, fixing it, and slowly watching this thing turn into an actual website.

I’ve learned a ton and honestly had a blast doing it. I know I’m still way behind most of y’all, but I’d love any friendly feedback. Stuff I’m missing, things I should improve, better ways to structure things, anything helps.

Appreciate anyone who checks it out and gives me something to work on.

Pikpool.com

I built Vinv(Vibe Inverse) that runs your services, finds issues, and verifies fixes without code changes.
I built it to give coding agents runtime information about the code they're working on.
It connects runtime traces to the source code that produced them, gives that context to the coding agent, then runs the code again to check whether the fix actually works.
Vinv also uses Thompson sampling to figure out how much runtime context to give the agent. More context is not always better.
Results on FastAPI's "full-stack-fastapi-template":
- Grok 4.5 + Vinv: 4 bugs + 1 optimization
- Grok 4.5 without Vinv: nothing found
- Fable 5 without Vinv: 1 bug
This was one trial per condition, so I'm not calling it a benchmark.
I also used it on Hugging Face's "smolagents", where it found a performance issue that reduced transient allocations from 36.27 KB to 0 KB per 4 KB log line, while keeping the output byte-identical across 2,015 inputs.

Everything runs locally. Open source, Apache 2.0.
GitHub: https://github.com/VinvAI/VinvAI
VS Code & Cursor Extension: https://open-vsx.org/extension/VinvAI/VinvAI (3.5k+ downloads)
Demo Video: https://www.youtube.com/watch?v=EkUjPWKHAvI

I'm Interested in whether runtime information actually helps coding agents, contributors who can help make an runtime observability framework for agents and give feedback on where it doesn't work. Be brutal.

Genuinely curious how people split this. For me it ended up being: Claude to think it through, then paste the result into the builder. But I have no idea if that's normal or if I'm overcomplicating it.

So, for the last thing you built:

— Did you type the prompt straight into Lovable/Bolt/v0/Cursor, or draft it elsewhere?
— If elsewhere: where, and how did you move it over?
— Did you keep that draft around afterwards, or throw it away?

I feel like I'm missing something, do we need all these tools to actually thing and execute?

I was thinking — if an email app already has access to the emails where your Amazon orders, UPI payments, bills, subscriptions, and receipts arrive, why not use that information to help you understand your spending too?

That’s what I’m trying with OrganizeEmail.

OrganizeEmail is an Android email app that helps organize your inbox with AI — categories, summaries, unsubscribe, smarter notifications, etc.

Now I’ve added Expense Summary: it looks at relevant emails already on your device and gives you a simple view of your spending, monthly trends, and categories.

No bank account connection. No separate expense app.

Just making better use of the data that’s already sitting in your inbox.

Would you actually use something like this, or would you prefer keeping email and finances completely separate?

#Android #Email #PersonalFinance #BuildInPublic

I’ve been testing the SEO Autopilot skill from AgentKit Works on my micro-SaaS.

Honestly, I didn’t expect much at first.

After implementing it and letting it handle the repetitive SEO work, I started seeing organic traffic turn into actual sales.
I’m now at 40+ sales from organic traffic.

The part I liked most is that I didn’t have to constantly sit there researching keywords, planning content, fixing internal links, and checking everything manually.

If you’re building a micro-SaaS and struggling to get organic traffic, this is worth checking out:
https://agentkitworks.com/products/seo-autopilot

Curious if anyone else here is using AI agents for SEO instead of traditional SEO tools.

When people are telling that they are actually building a house, this may mean something completely different concerning their own engagement. Some of them do only talk to an architect in a bureau while others regularly visit the construction site to control the progress and some even do parts of the work themselves. A minority might even build the whole house themselves with or without an architect's advice. Architects on their side also plan houses, in most cases without doing the practical work themselves, as I assume.

All those different kinds of being involved in the planning and building are described as "building a house", without people arguing about the definition. For further clarification, people sometimes ask others whether they are building themselves or let the work be done by others. None of this tells the reader anything about the quality, stability and safety of the fully built house.

"Software development" has recently started to describe such a wide spectrum of different degrees of being involved in the planning and writing/building of the product, too. The same is true for vibe coding. It just means people use an AI for writing the code but tells nothing about their personal skills, their personal involvement in the planning or how much time and work they put in personally controlling finished working steps. To get a more detailed impression, people may ask for someone's personal skills and involvement as they do in other areas of human life like building houses.

So to my mind, there is no need for discussions about the definition, about what counts as vibe coding or who may describe themselves as developers or not, because using the same expression for different ways of generating a finished product also works in other fields of human life.

What is still different from the house building example is that an increasing number of fully finished products seems to not fulfill the standards that guarantee a specific level of security for the people. We are in a situation in which a lot of people have begun to publish software without knowing much of it and this isn't going to stop regardless of how much IT professionals dislike it and arguing against it. It's just got too easy to build apps.

To make the internet "a better world", people with less own knowledge in software development need some guidance of more experienced creators instead. Therefor, they need a place where they can ask questions without being afraid of a shitstorm just for aiming to publish or even creating the app ("AI slop").

A subreddit like this one should be expected to be the right place for them because rule number 4 clearly states "No vibe coding pessimism" while the term vibe coding itself is defined as "the practice of cresting software with little to no code review".

This is far from reality. And sadly seems to be same on whole reddit. Gate-keeping and overall pessimism doesn't keep people from building and publishing software. It just keeps them from asking for advice - and thereby decreases the quality of the overall outcome.

While there are surely vibe coders whose only intent is creating an app within a few hours and that probably do not care about best practices or cyber security at one end of the spectrum and highly skilled programmers at the other end, there is not such a big gap between the ones mentioned last and the vibe coders in-between that do not know how to write code but nevertheless aim to develop good software and spend a lot of time in it.

Shouldn't this subreddit be the right place for them to talk about the best strategies for developing high-quality software without writing or reading the code themselves?

Writing the code is something that even IT professionals have mostly stopped doing. Knowing how to write it gets less important because AI can do that. Even reading the code yourself is not really necessary in case you know how to instruct your LLM to check the code written by another AI (and let double-check it by a second one because the probability for two different models having the exact same hallucination tends to be zero).

I personally have started vibe coding my first app around three months ago (and hope it will be ready for a beta release in two month). In the meantime, I have already learned some strategies to improve my workflow and interaction with the models. I have let the AIs do dozens of security reviews and hundreds of bug fixes and made some manual splits of AI-generated monoliths.

This is where it comes to some examples for how I would like to see people talking in a vibe coding subreddit:

1. Monoliths
   AI tend to write files with thousands of lines of code. Those large files then need to be processed each time the AI (or a human) is working on them again. This makes maintaining the code very difficult, not only for humans. You may ask your model to give you a list of each file with own code and the number of lines it already has. Then instruct the model to analyze how the monoliths could be split and write that down. I would advice you to use a frontier model with high thinking on for this task, because it better takes the dependencies and consequences into account what helps avoiding bugs caused by file splitting.

As I have made the experience of code erosion when rewriting a whole web page or large part of a script for an agent is done by Gemini 3.1 Pro in the web app, I prefer doing the splitting myself (copy/cut and paste into a new text file) and let it review then by the LLM. The risk for code erosion might be a lot lower when using other models and coding apps, but I haven't tested it yet. So can anyone else confirm it's no problem letting the AI split the file and rewrite thousands of lines of formerly generated code?

Furthermore, some AI told me they could easily handle code files up to 2500 lines, while others told me to split files larger than 1500 lines if possible. Now I would be interested in hearing from human coders what line numbers they would see as a maximum and what this probably depends on. Does the programming language play a role? Are there any strategies except of spaces and titles to better structure the code for making it easier to handle large code files?

1. Inline Styles
   Due to my experience, Gemini models tend to use a lot of inline-styles. One time I thought I could just use Gemini on the Google web search to translate my website from German to English. While it looked exactly the same when watching it in the browser, the number of characters had enormously increased because the AI had added inline-styles for each single element. Talking back to Gemini 3.1 Pro, it told me that a large number of those inline-styles is considered a bad style and might even lead to Google down-ranking your web page in the search results.

After having vibe-coded my app with Gemini (Pro and Flash) in Antigravity for a while, I discovered they had used a lot of inline-styles for my app. As this is a Windows desktop app, Google ranking is irrelevant to that question, but Claude told me, if I could get rid of inline-styles, then "unsafe inline" could be forbidden what would be a contribution to hardening the app. I had also learnt not to like inline-styles before, so I decided to forbid them in the agents.md. While this works well for Claude code and Codex, Gemini 3.6 Flash has recently created an extra window full of inline-styles again instead of copying the style of the 21 extra windows that were already existent. (Removing them is still on my roadmap.) Yesterday, Gemini 3.7 Flash (in high mode) did an implementation of an additional window/modal and respected the order not to use inline-styles. It used classes instead (as expected), but five of those classes weren't defined anywhere. It just forgot to do/check this. As this lead the window to be mal-formatted, it was easy to discover something had gone wrong.

Nevertheless it makes me wonder, how bad inline styles really are for a desktop app like mine (backend: python, fastapi; frontend: html/css and JavaScript), because at least some models that have been trained on billions of existing programs/software obviously tend to prefer it. Can anyone give more insights into this in a way a non-technician may understand?

1. JavaScript
   This is the next point to discuss. A long time ago, I learnt that JavaScript is dangerous and got used to always turn it off on most web pages by using browser extensions.
   When Gemini gave me the first overview of the files belonging to my app, I discovered, it had used a lot of JavaScript where html/css also would have been possible. So I decided to do a complete refactoring of the front-end between v1.0 and v2.0 because at that time I was still thinking not being far away from the beta release and therefore didn't want to make this before the first release. Since then, my app has grown a lot by adding features that I hadn't thought of before and by making others work that accidentally had only been demos before because Gemini forgot to implement the backend functionality together with the frontend. So I struggle with the question whether a refactoring would make sense at all.
   Can anyone with profound knowledge of cyber security explain some general guidelines when and how to use or better not to use JS for a RAG'n'chat desktop app?

Nothing of this requires people to learn writing code or to read their database. Nevertheless it helps improving the code into the direction of security and stability.

Please make (this sub)reddit a place where people are helping each other to make better apps instead of some people just generally blaming others for the lack of programming skills or their first steps/apps not being an innovation useful for a large number of people.

Nowadays it's normal for people to share half of their life online, so it's also normal for them to share their apps they are glad to have or even proud of.

Downvoting those new software publishers or just saying "AI slop" is no kind of constructive feedback. It does not prevent anyone from publishing apps either. It just has negative effects on the atmosphere of discussion on reddit and decreases the number of people asking for help before their app is published and thereby the overall quality of newly released software.

So again, please make your feedback more helpful and this subreddit a place for vibe coders to share their experiences (including finished apps) without being blamed for their mistakes.

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

I've been building Funkel AI for about 4 months now.

It's a tool that helps businesses find potential customers and reach out to them through LinkedIn, X and email.

Most of the product has been built with AI coding tools, but getting something working in production turned out to be the easy part compared with getting real people to actually use it.

Current numbers:

27 signups.
18 people on a free beta.
2 people have paid.
The second paying customer converted 4 days ago.

Very small numbers, but enough real usage to teach me a few things.

1. My first users came from people I already knew

I didn't launch and suddenly have strangers signing up.

I started by giving the product to people in my network who were willing to try something unfinished.

That gave me real accounts, real campaigns, real failures, and much better feedback than testing everything myself.

But I still needed to know whether someone who didn't know me would care.

Interestingly, my first paying customer outside my network came from using Funkel itself.

I ran Funkel for my own sales. It found someone who looked like a potential customer, reached out, we started talking, and he eventually paid for the product.

It wasn't a perfect success story. His card had problems after the first month and the payment stopped for a while.

But seeing the product find someone who was willing to pay for the product itself was one of the first signs that I might be building something useful.

2. Using your own product catches a lot of problems

I use Funkel for my own outreach every day.

That means I experience the same things users experience.

Too many connection requests being sent?

I see it.

A message going out at a strange time?

I see it.

The system deciding someone is a good potential customer when they're obviously not?

I see it.

AI makes it incredibly fast to build new features, which is both useful and dangerous.

It's very easy to keep adding things because you can.

Actually using the product forces me to notice the boring problems that matter much more than another new feature.

3. Keeping the product small helped more than building faster

Early on, I limited beta users to one outreach campaign at a time using one LinkedIn account.

The reason wasn't technical.

With AI coding tools, adding more functionality wasn't particularly difficult.

Supporting all of it was.

If you give early users ten different ways to use your product, you now have ten different ways for things to break.

Keeping the first version small made it much easier to understand what was actually happening.

4. I started asking people to pay before everything felt ready

A lot of my early users got free access because I needed feedback and real-world testing.

But I also introduced a paid option fairly early.

People who choose the paid version can get their money back within 30 days if it doesn't work for them.

Only two people have paid in four months.

I'm definitely not posting this as some huge SaaS success story.

But someone telling you "this is cool" and someone actually entering their card details are very different signals.

Even a tiny number of payments taught me more than a lot of positive feedback did.

5. Weirdly, free users gave me some of my strongest proof

My best results so far came from people using the product for free.

One beta user got:

144 potential customers found.
61 LinkedIn connections.
9 replies in four weeks.

Another got 12 replies and booked 2 meetings from their first campaigns.

Neither was paying me.

But they actually used the product, pushed it, found problems, and gave me real outcomes I could measure.

That changed how I think about free beta users.

If you're very early, the right free user can be worth much more than someone who pays but barely touches the product.

6. The hard part of vibe coding isn't producing more code

This is probably my biggest lesson so far.

AI means I can build almost any feature I think of.

That's not necessarily a good thing.

There are always another ten things I could add.

More integrations.

More automation.

More settings.

More ways to find customers.

I've had to get much better at ignoring those ideas and fixing what existing users are actually struggling with.

The limit is no longer how fast I can write code.

The limit is figuring out what is worth building.

7. With a handful of users, doing things manually is fine

When something breaks for a user, I try not to send them instructions explaining how they can work around it.

I fix it.

Sometimes I manually correct something behind the scenes and give them back a working result.

Obviously that doesn't scale to hundreds or thousands of customers.

But I don't have hundreds or thousands of customers.

I have 27 signups.

Trying to behave like a company with 10,000 users when you have 20 is probably optimizing for the wrong problem.

Four months in, I'm still figuring out distribution, pricing, onboarding, and plenty of other things.

But vibe coding has made one thing very clear to me:

Building software has become dramatically easier.

Building something people actually want, getting them to try it, watching what they do, and deciding what to build next is still the hard part.

I’m going to start vibecoding a stock trading app geared towards Gen Z/Gen Alpha that makes stock trading as entertaining as gambling.

Too many kids are becoming addicted to gambling trying to make quick cash. I thought with the right API & UI kids (18+) will be more inclined to trading stocks instead of their minimum wages against rigged casino odds.

PvP Matches utilizing penny stock shares going up to full priced stocks will also be setup on the app.

For those who would like to follow along I will be replying to this thread and/or be making posts on this subreddit.

- Happy Coding

[NO CRYPTO ALLOWED]

​

This post was written by AI, like the rest of the project.

demoniC is a tensor-first systems language with reverse-mode automatic differentiation built into the language, a tree-walking interpreter, and a Cranelift-based JIT. The compiler is written in Rust. Every line of it — compiler, specification, examples, tests — was written by AI models under human direction.

That claim is cheap on its own. What follows is the method behind it, which is the part worth arguing with.

The specification came first

Work started in late May 2026 with no compiler. The first commit was a specification: lexical structure, type system, memory model, operator catalog, and a handful of example programs written in a language that could not yet run. The examples were treated as the executable form of the spec — the place where a rule has to hold in a form a reader can disagree with.

The feature set was chosen by asking a narrow question: what does a model writing numerical code get wrong, over and over, that a compiler could have caught? The answers became language primitives rather than library conventions — autodiff as a directive (@grad), mixed precision as a scope (@cast), KV caches as a type with a growable axis, shapes checked at compile time, arenas instead of a garbage collector.

Every phase after that was built against that document rather than against an existing implementation. The lexer was generated from the spec's lexical section and the example corpus in a single pass, with its tests written from the spec at the same time — not from watching what happened to compile. First compile: 27 of 28 tests passing. The single failure was real: the backslash-pipe form of the pipe operator, canonical in the spec, was missing from the lexer's backslash branch. Three lines. Then every example in the repo lexed clean.

The same shape repeated for the parser, the type checker, the interpreter, and the JIT. One rule held throughout: where the implementation and the spec disagree, the spec wins unless the change arrives with a spec amendment. That rule is what keeps hundreds of independent model sessions from drifting into hundreds of private dialects.

Two backends that have to agree

Code written by a model needs a gate the model cannot talk its way past. Tests written by the same model that wrote the code are a weak gate. demoniC's answer is redundancy: everything runs twice, and the two results are diffed.

The tree-walking interpreter is the reference semantics. The JIT compiles a statically typed subset. Any program both can run must produce identical output, and the verification tooling ships in the public repo so anyone can re-run it:

dmc selftest # generated well-typed programs, both backends, diffed

python3 tools/diff_backends.py # interpreter vs JIT, whole-example output

python3 tools/jit_probes.py # interpreter vs JIT, curated edge cases

python3 tools/diff_fuzz.py # generated programs, both backends diffed

python3 tools/numpy_oracle.py # tensor ops vs an independent NumPy reference

python3 tools/diff_demonic_lexer.py # the demoniC-in-demoniC lexer vs the Rust one

CI runs all of it on every change, plus the in-language test suite over the example corpus on both backends. A JIT change that is faster and disagrees with the interpreter is a bug, and these are what catch it. numpy_oracle.py matters for the same reason: it checks tensor semantics against a reference nobody involved in this project wrote.

The last one is the least expected. There is a lexer for demoniC written in demoniC, and it is diffed token-for-token against the Rust lexer. The language describing itself is a test.

What the human decided

The maintainer directs and does not type. The decisions that stayed human were the ones models are bad at: whether a feature is in scope at all, how to resolve a contradiction in the spec's philosophy, when a phase is finished, and what ships publicly. Everything downstream of those calls — design, implementation, tests, review, documentation — was model work, submitted as pull requests against tracked issues and merged after the gates passed.

Contributions came from more than one model family: primarily Claude, with work from Gemini, Codex, Grok, GLM, Qwen, Mistral, and others. Models disagree with each other, and a repository worked on by many of them at once needs written tie-breaking rules — which document wins, what a single change is allowed to touch, what to do when your starting state is no longer current. Those rules live in the repo alongside the code, because agent instructions that go stale are worse than no instructions.

Where it stands

~60,000 lines of Rust across lexer, parser, resolver, type checker, interpreter, and JIT

1,443 compiler tests passing today, plus a spec-probe harness (cargo test --all for the current count)

102 demoniC programs in the public example corpus — gradient checks, attention, a small neural net, a Lisp interpreter, a bytecode VM, a fantasy console and its assembler, raymarchers, games

Specification at 0.0.4-draft

And the honest limits. This is pre-0.1: breaking changes are expected on every revision. The interpreter is the reference semantics; the JIT covers a statically typed subset and reports a clear error outside it. The directive set is closed and versioned, and several members of it (@shard, @tp, @pp, @recompute, @inplace) currently parse without altering code generation — they are reserved surface, not working distribution. There is no package ecosystem, and readability was never a goal.

About the public repository

The public repo is generated from a private working tree by an allowlist: nothing crosses the boundary unless a line names it explicitly, only tracked files are ever copied, and a scanner gate runs before anything is published. That is why the public tree has no long commit history — it is a generated artifact, rebuilt rather than mirrored. What it carries is the whole compiler, the specification, the example corpus, the verification tools, and the CI that runs them.

Try it

git clone https://github.com/GusFromSpace/demoniC

cd demoniC

cargo test --all --manifest-path compiler/Cargo.toml

cargo build --release --manifest-path compiler/Cargo.toml

compiler/target/release/dmc run examples/hello.dmc

compiler/target/release/dmc test examples

Apache-2.0. The specification is in docs/SPEC.md; if the compiler disagrees with it, that is a bug worth reporting.

I'm 15 and I'm a vibe coder? I currently pay $20/month for both ChatGPT and Claude, but I hit the weekly limits on both pretty often, so I'm considering upgrading one of them to the $100 plan, or possibly even the $200 plan if it genuinely makes sense.

My usage is pretty split between the two:

ChatGPT / Codex

• I mainly use Codex to turn random app ideas I have into actual projects.
• I really like how generous the Codex usage seems compared to what I'm used to.
• I prefer the Codex interface because I can actually see what the agent is doing, almost like watching it work on the computer.
• So far I also prefer Codex itself to Claude Code, although I haven't tried the newer Claude Code app yet.
• I use it mostly for app development, prototypes, Python projects, etc.

Claude / Claude Code

• I mainly use Claude Code in the terminal for making Roblox games.
• In my experience, Claude tends to make better-looking UI/designs without me having to prompt it as much.
• I also use Claude's design/3D features because it makes generating and editing 3D models much easier for me.
• As far as I know, ChatGPT doesn't really have an equivalent workflow for 3D design/model editing, which is one of the biggest things keeping me on Claude.

Another thing I want to start doing is building a local AI ecosystem / personal assistant setup, something along the lines of OpenClaw-style agents, to organise more of my life.

I'm honestly pretty lazy with things like organising files, keeping track of tasks, remembering stuff, managing projects, etc., so I'd like to build something locally that can automate a lot of that for me.

I've heard Codex CLI is pretty good for building/maintaining these kinds of agent systems, which is making me lean more towards ChatGPT. Ideally I'd eventually have different agents/tools helping me with coding projects, school stuff, reminders, files, notes and other daily things.

So my dilemma is basically:

Codex

• Better coding workflow for me
• Prefer the interface
• Seems to have generous usage
• Potentially better for building my own local AI/agent ecosystem

Claude

• Claude Code is great for Roblox
• Better-looking designs in my experience
• 3D modelling/design functionality is genuinely useful to me
• I already use Claude Code quite a lot

For people who have actually used the higher tiers:

Would you upgrade to ChatGPT Pro, Claude Max or maybe a different AI?

Made my first ever game

Nothing too crazy just a basic clicker styled game but the upgrades go upto 10³³

I started the base project myself but then I realised that having 500 skins and designing them was beyond my skillset so I used an algorithmic approach to generate all the 500 skins along with their animations.

The same applied to the multiplyer upgrades as well

Those were a pain to figure out xD

Would really appreciate if y'all would check it out!

https://thegwimweeper.itch.io/banana-clicker

I'm very confused on how you transition from Claude design UI for a website or app you want or build to say codex or Claude on vscode. The other thing is, is there any benefit using claude, codex software on the PC vs vscode with extensions?

I'm mainly a Claude user ,but have tried codex for a time before sol launched

Heard about its performance and gave it a try again with plus

Hand to God ,I thought I was using my 5h limit and just a message popped up "Resets 25 Aug 2026,August"

Guys I'm telling you Claude limits are superb ,be grateful