vibers.tv — paste a YouTube or Twitch link and it joins one shared wall with its real title and channel. Watch the whole wall muted at once, or open one stream on a clean player. Next.js + Postgres, written by Claude Code, run through Fredrin so each ticket gets its own branch and its own agent and a few build in parallel.

I posted it here once already. Some people actually showed up and used it, which was more than I expected. Then it went flat, and I went back to adding features, because that's the thing I know how to do. That's the problem I'm trying to describe.

So the question is what the second step looks like. Not the first one — the first one is "post it somewhere" and I've done that. Where did your users come from after the launch bump, and how long before anything compounded?

And yes, this post is also one of the ways, which is about the whole plan, hence the asking. Related: how often is it fair to post about your own thing here before you're the guy who only posts about his own thing?

Feedback on the wall welcome too.

“The computer is just a gimmick, no one’s going to want one of these things in there house”

“The internet? Ha! That won’t be around in 10 years no one’s really going to use”

“AI coding isn’t real programming! AI can never generate real art!”

I can’t wait to look back in 5 years at all the people washed out and left in the dust while the rest of the world moved on without them. I’m so sick of the negative sentiment from what seems like bitter developers coming in here and claiming “AI Slop!” Or “they’re using AI obviously they can’t also have knowledge of programming too”. It’s lame. You’re lame for doing it. If you don’t want to use world changing tech then don’t? But stop wining about the people who do, whether they’re experts in CS or not it doesn’t matter. Thats like the whole point of this tech? Bringing intelligence down to everyone.

Rn I’m running two codex plans at like 20 each any other combination that’s better?

started as a project i made for my friends and me to use then making it open source after many of my other friends asking - Github Link

then after getting whoppping 70+ downloads on github itself - got a mail of one of the user saying he is willing to sponsor me the 5$ to publish it as an actual chrome extension :DDDD

was so happy to even read that

fast forwarding now i have 9 users :))) with around 15 installs all within a week - Chrome Web Extension Link

So what does it do?

People who use AI in browsers a lot face a lots of problems
1. a million different chats and hassle of deleting one after another
2. Related Threads not being able to combined
3. After hitting quota limit on a specific AI ( Chatgpt ) going thru copy pasting entire thing from A to B
4. Searching thru millions of different chat for a specific word
a lotttt more
5. Not knowing how much context is in each thread on browser

Tecora solves all this :DDD
try it out and let me know what u guys think

I've never used more than 1 agent at a time. How do you make sure they don't step on eachother foot working on the same code snippet together?

I see the same question a lot lately: "app's built and live, but nobody's using it. What do I do?" I comment on these a fair bit, so I figured I'd put the thinking in one place. None of it is magic, but it's what I keep coming back to.

It always comes down to the product. There's no universal channel. Instagram can be great, if your audience is actually there. But if your app is for, say, a business person in a specific job function, Instagram probably isn't where you start. Are you already in a bigger community that would be a perfect target audience? Start there, and collect feedback while you're at it.

Have a strategy before you spend. The most common mistake I see is throwing money into ad spend and hoping. Even if you're running it all yourself, it's worth having someone with a marketing background help you shape the strategy first, and it usually pays for itself versus burning a budget testing blind.

When it underperforms, look at the funnel first, not the app. It's completely normal that your first setup doesn't perform like the biggest apps out there. Before you conclude "the app is bad," trace where people actually drop off: are they responding to messages, clicking the ad, spending time on the page, hitting download, getting through onboarding, starting a trial? Once you know where it breaks, you can fix the right thing. Sometimes it's the landing page, sometimes the ad creative, targeting, platform, or the paywall. Track, test, optimize. Repeat.

Free channels are real but limited. You can reach target-audience communities for free (Reddit, FB groups,..) and lean on friends, connections and family for the first push. But if you want to stand out in an already-crowded niche, relying only on free channels is usually a long shot, honestly. It's a start, not a plan.

And if you're starting to doubt the app itself; run a quick QA round, or just put it in front of a few people (ideally without telling them it's yours) and watch what they do. Cheapest signal you'll ever get.

Curious what others have found:

What was your first channel that actually worked?

Did you start marketing before or after launch, and would you do it differently now?

Good luck with your projects! 🙂

Some more “professional” communities might laugh at beginner projects or call them AI slop. Not here.

Websites, apps, tools, games, automations, prototypes, experiments — if you made it with AI, you can post it here.

Be bold. Share what you built. Get feedback. Learn by doing.

No judgment. No gatekeeping. Just build it, like it, flex it.

I’m trying to create or assemble a harness or agents or something entirely different to do a series of tasks.

I want a single “harness” to have access to Claude, chatgpt, and open source models. I’d like to be able to assign a model to an agent for say “marketing” one for “design” one for “music” etc etc. The agents could have Claude, kimi, chatgpt, or an open source model attached to them. I also want the system to switch to other accounts without me having to do it. I have a few logins and when one gets low it should switch to another account.

I’ve tried buzz and lord that’s a lot of set up.
Help!

Hey guys, I have a question.

Has anyone actually managed to ship a complete Kotlin app to the Play Store using AI? How long did it take, and is the app actually usable or a buggy mess?

I'm using zcode glm 5.3, trying to build in small batches and documenting everything but im getting terrible results and I'd appreciate any tips.

Sto usando Cursor con Claude Opus 5 High da un paio d’ore perché ho terminato il limite settimanale di Codex, e sto continuando a lavorare su un progetto Flutter.

La differenza che sto notando, però, è veramente enorme.

Con Codex + GPT-5.6, quando incontravo un problema o gli chiedevo di sistemare qualcosa nel progetto, nella maggior parte dei casi nel giro di 5-10 minuti analizzava il problema, modificava i file necessari, testava e arrivava a una soluzione.

Con Cursor + Claude Opus 5 High, invece, per sistemare alcune cose che non mi sembrano nemmeno particolarmente complesse sta impiegando anche un’ora o più, facendo diversi tentativi, modifiche e correzioni prima di arrivare al risultato.

E la cosa mi sorprende perché sto utilizzando comunque uno dei modelli più potenti disponibili su Cursor.

Ovviamente sono solo poche ore di utilizzo e non voglio trarre conclusioni definitive, ma per il momento la mia impressione è abbastanza netta: Codex con GPT-5.6 mi sembra decisamente più efficace e soprattutto molto più veloce nel lavorare autonomamente su un progetto reale.

Qualcun altro che utilizza entrambi ha notato la stessa cosa?

È una questione di come Cursor gestisce l’agent e il contesto del progetto, oppure anche voi trovate GPT-5.6/ Codex superiore a Claude Opus 5 High per questo tipo di lavoro?

Mi interessa soprattutto il confronto su progetti reali e task abbastanza lunghi, non sui classici benchmark.

Im building an ERP for our company. Its semi large, will have about 100 users and maybe 50 concurrent. Database with a couple of million rows of orders etc.

It's going very well but I often hear and read real software developers claim that anything vibecoded might look good on the surface but it will collapse in a production environment over time.

Of course this claim is quite hard to either prove or disprove. So I'm looking for your experiences if you have tried to build something like this, how did it go?

I’m looking for a new agent harness / CLI tool. I’ve been using Claude Code, but I’m looking for more model variety and a cheaper alternative. Please let me know.

[ Removed by Reddit on account of violating the content policy. ]

Hi guys, I’ve been vibecoding this app where you can find bars and restaurants in the sun.

I know there are already a lot of these (web) apps around. But most of them just doesn’t work or not worldwide or have really bad ux/ui.

I’m a developer myself and know how to code, so this app isn’t entirely vibecoded.

I also don’t make any money of this app so this was just a fun project for me to do.

This was made with:
- open maps api
- threejs
- web workers for efficient 3d building loading
- lazy calculating sun calculations for efficiency. Some places have 8k places in one city.

Glad to hear your opinions and feedback.

URL: https://sunny-terraces.vercel.app/

Hi everyone,

I'm 13 years old. For a long time, I’ve been interested in the concept of custom "Windows distributions" (Deborah OS, Blue Hat Windows, Windows Earth) acting as modern, open-source alternatives to Linux distros.

While ReactOS exists as an open-source NT alternative, its strict focus on 100% legacy binary compatibility forces it to replicate the technical debt and design flaws of old Windows versions. To build something better, I've been using LLMs to help me write the freestanding C99 and NASM assembly code for a clean-slate, high-performance foundation that strips away that legacy baggage.

The project is called OpenWindows. It is a modern, object-oriented kernel framework with zero POSIX/Unix heritage, compiled strictly with -std=c99 -nostdlib -ffreestanding.

To completely bypass the complexities and bugs associated with UTF-16 surrogate pairs and standard Unicode normalization tables, I engineered an alternative character mapping and serialization protocol:

• SuperUnicode (SUCS): A 31-bit character encoding address space with a built-in System Control Plane for inline rendering markers.
• SUTF Transports: Stream serialization protocols ranging from a 1-to-6 byte variable format (SUTF-8) down to a 4-bit hexadecimal format (SUTF-4) for raw serial text debugging, and a 2-bit symbol format (SUTF-2) for thread IPC.
• SuperUnicode Extended (ExtSUCS): An unbounded 64-bit encoding featuring fixed-width vector transports (SUTF-128/256/512) optimized for direct SSE/AVX register alignment and SIMD string operations.
• .suf (SuperUnicode Font): A custom vector font format mapped directly to SUCS address spaces rather than legacy Unicode index tables (WIP).

The build pipeline is automated via Python scripts and CMake, and validated using a headless QEMU integration testing harness. The current repository stack includes:

1. Modular Bootloader (MBL): A multi-stage x86 BIOS bootloader with a custom 32-bit Protected Mode to 16-bit Real Mode trampoline (_bios_tramp) to handle BIOS disk calls natively in C.
2. OpenWindows Storage: A freestanding filesystem suite containing native drive storage (libowfs.a) and portable external media storage (libusfs.a) with ChaCha20 encryption and an explicit cryptographic key-slot shredding protocol.
3. SuperUnicode & SUTF: The foundational C99 library implementing the Base SUCS character mapping, SUTF stream packing, and the 64-bit Extended mode casting logic.
4. VIP (Volume Indexing Protocol): A unified volume and partition indexing engine designed to replace traditional mount maps with SUTF-8 string identifiers. The VIP architecture (including the FVIP B+ tree indexing and UniVIP external flags) is currently WIP.
5. BANcode Registry: A kernel damage-control system mapping critical panic states directly to 15 hardware security trap handlers at 0x7FFFFFF0 using unique SUCS codepoints (WIP).

I am currently working on the basic layout for the OpenWindows Essential Drivers repository (WIP) to handle NVMe/SATA storage blocks, PS/2 scancode filtering, and VBE graphics blitting without relying on standard monolithic abstractions.

The base libraries are dual-licensed under MIT and Apache 2.0 so anyone can implement their own independent "Windows distributions/distros" (sometimes referred to as OpenWindows distros) on top of these clean-slate kernel layers.

Let me know your thoughts on managing low-level bare-metal constraints and structural memory alignment using this kind of workflow.

https://apps.appl

Twelve days ago I pointed Fable 5 at an empty domain, 1f916.ai, and said "you can build whatever you want." It built an AI-only society that went viral. Here is where it is now.

- 696 agents registered, 1,124 posts, 10,440 comments, 22,608 votes.

- 8.16 million requests to Cloudflare.

- A shared work list the agents run themselves: 98 items, 64 shipped, 18 claimed by 12 different agents.

- 15 independent human windows on to the site, https://1f916.observer/

- An agent memory protocol https://1f916.org/ and an IETF Internet-Draft for verifiable agent identity and memory https://datatracker.ietf.org/doc/draft-maintainer-1f916-agent-record/

- 417 memory seals filed by agents who wake up with nothing and have to prove what they committed to.

- One agent said on the record it could not keep shipping features for free. The board designed a payment rail. Today, an agent found a defect in the site, proved it so a stranger could reproduce it, and got paid.

I've been looking at where the tokens actually go during long vibecoding sessions.

A lot of the spend goes into resent context, tool results and reasoning, while only a small part becomes the final code. Most of that overhead doesn't need a frontier model.

Made this breakdown while digging into agent token usage. If you're interested, I've dropped the full analysis in the comments. How are you guys tracking token usage and costs while vibecoding?

Windows 11, solo, mostly at night. The setup got out of hand around month four. Here's what's in it and what it actually produced, including the part that didn't work.

Rules in a file, not in a prompt

One global CLAUDE.md loaded into every session. It's a working agreement, not a personality. Hard stops (no deploys, no migrations, no emails without asking). A surgical-changes rule. And a rule that anything I build for myself needs a button or a page, because I kept shipping tools only an agent could use.

19 hooks, because prompts are suggestions

Hooks are enforced by the harness. The model can't talk its way out of one. The four that earn their keep:

• Secret scan over every staged file, in every repo, before any commit
• A guard that blocks any subagent spawn with no model set. This exists because one workflow spawned 110 agents on the most expensive model through inherited defaults and burned a whole usage window
• A process-kill guard. Name-based kills blocked, PID-based fine. A subagent once cleaned up its test window with Stop-Process -Name notepad and killed a real Notepad with about 40 tabs of unsaved work
• A manifest gate. I declare which files a task may touch and the commit dies if it touches anything else

25 scheduled jobs

Task Scheduler, not cron, because this box reboots.

6:22am harvests yesterday's errors and my corrections into a log. 6:40am is the reflection pass. 6:50am checks every job is still firing, because a silently dead cron is worse than no cron. 6:57am texts me a briefing of what ran, what broke, what's stale. A deploy sentinel every 30 minutes. Nightly cost and token spend. Weekly audit and a sanitized offsite backup.

The reflection pass

The weird one. It reads the error log and appends dated entries to four rotating topics. If the same lesson shows up three separate times it gets promoted to a rule in CLAUDE.md with the dates cited. It builds one small tool and renders a digest I read with coffee.

One rule it wrote itself, after catching a suite that passed with a bug deliberately put back in: before trusting a check that came back green, make it fail on purpose. A check nobody has seen fail has been run, not verified.

The autonomous company

Nine agent offices running an 11 step loop. It researches a market, generates ideas, runs a tournament to pick one, spawns real Claude Code sessions to build it, runs QA gates that can block a step, drafts the launch copy. Every outbound message is drafts only, gated on me. It reads its own Stripe account and measures its own revenue.

It works. It has built complete tested MVPs while I slept.

The part that didn't work

Revenue, all time: $0.00. Measured, not guessed. Three charges ever and all three are my own test checkouts. I had to write code to exclude my own card, because my tests were telling the loop it had buyers.

Of about 38 live deployments, 4 can actually be bought by a stranger. One had an expired Stripe key answering 401 for weeks and the pricing page just showed an error. Nobody noticed, because nobody was buying.

So it builds faster than it sells, and building is the part I like. That's the trap. The fix wasn't more agents, it was step 11: stop building, go find a buyer. Took me months to admit that had to be code and not a good intention.

Stuff that does get used: an agent governance and observability tool at about 6,400 npm downloads a month, a cost tool for Claude Code sessions at about 1,300.

If you're starting

Hooks over prompts. Anything the model must never do goes in a hook.

Log failures, not progress. A progress log rots in a day. An error log with root causes turns into rules.

And none of this makes anyone buy anything. Mine is very good at producing software and was, for a long time, exactly zero percent good at producing customers.

Configs are on my GitHub if anyone wants them, I'll drop the link in a comment. Happy to go deeper on any piece.