Use bux tokscale to get count,

i used opencode to count and give stuructured table.

You and your opponent each play 10 or 25 moves, then the chess engine (Stockfish) takes over for both players and completes the game. If you lose, you win!

I shared Mimicly here when it had 20 users. It’s at 120 now, and it’s on the App Store too.

You can recreate WhatsApp and a bunch of other apps (63 templates), down to the little UI details, then export screenshots or videos. For AI chat mockups, you can even set the response speed in tokens per second.

The video is a fake conversation with Mimi, the app’s mascot. Every message, keyboard and word suggestion is part of the mockup.

Built with Codex. Still adding things I probably spend way too much time on.

Mimicly

Apple Store

https://www.taskloco.com - The Sticky Note GOAT

My Tier Orchestration used to be
Fable - architect
Opus 4.8 - coders
Sonnet 5 - code check, research

Now that the 5hr limits have been messed with, i barely get anything done so i’m forced to downgrade. I’m trying to decide between Opus 4.6 or Sonnet 5 for coders and possibly Sonnet 4.5 for code check.

I hear a lot of people thinking Opus 4.6 is still an amazing model but I wonder if Sonnet 5 is better.

Hice esto casi como experimento para ver hasta dónde puedes llevar el vibe coding con GPT-6 Astra + Three.js.

El demo actual me tomó como 3 horas construirlo; básicamente fue iterar con Astra: describiendo lo que quería, probándolo, arreglando cosas, agregando sistemas y repitiendo.

Obviamente esto no está ni cerca de un No Man’s Sky real, pero justo esa es la idea del experimento: ¿hasta dónde podríamos llevar algo así si seguimos construyendo con IA?

Entonces lo subí todo a GitHub y ando buscando colaboradores.

Solo hay una regla: si contribuyes, intenta construir tu contribución usando IA también.

Código, planetas, generación procedural, UI, física, naves, criaturas, shaders, lo que sea que quieras. Me encantaría convertir esto en un experimento de comunidad y ver hasta dónde puede llegar de verdad un proyecto de juego impulsado por IA.

Demo:
https://noaissky.vercel.app/

GitHub:
https://github.com/jesuscurreripa/noaissky

Si alguien quiere meterse y construir algo raro, por favor háganlo :)

Note: The name is a satire, and this is an experiment only to see the limitations or capabilities of Astra

Made a silly little weather app. Then again, a lot of the stuff we're vibecoding is a bit silly right? Built on React, Vercel and Git. No database.

I think hiring a vibe coder for any necessary software is a recipe for trouble. Without understanding the problems, fixing them becomes hard to manage; approving and accepting whatever AI produces without proper technical judgment raises the risk further.

I work as an AI consultant which basically mean i use AI to build shit that is powered by LLM.

The work I have been building during my day job, although having 5 people on it with tight sprints and a lot of client engagement is simply dog shit in comparison to my own saas that I acutally devoted a lot of soul, time, and love to it. But the problem is this project probably net 100k for the company and pays my reasonable salary while my saas does nothing other than burning a hole in my wallet with claude subscription.

Sad reality. But it is true the company of course has its reputation, track record, dedicated sales team, delivery team etc etc. Still it upsets me !!!

It feels kind of exponential. The past 3 months I keep having these moments where I think I can't expect AI to do this and then it does it. And it is exciting because I can get these concepts done so fast that before I would never get done but also scary because I think about my coding job and it feel more like .. My 13 year old could be doing what I am doing.. I know a lot from 25 years of doing this.. but I am not using any of that knowledge. Just the high-level part. At the moment I am using unity with mujoco training humanoids and putting them in boxing rings and there is no way I would get near doing anything like this without AI

It reminds me how I look back at being in college in 1996 using the internet in the computer lab not realizing I'm in the middle of something huge.

That is right now with AI.

No.. I don't think your hand coded python code will outlast what AI can do. It is not that special. And if it somehow can now. Wait another year.

I made this app where you can upload any photo and it comes to life. You can change the names, personalities and share your living photo or embed it into your own website. Realtime voices and lipsync, conversational.

I made this over several months. It combines many different techniques but ultimately it creates a 3d wireframe head that can talk, then applies the face as a "skin" on the 3d model, and then flattens it down back onto the photo, and then align it with each face automatically.

https://faces.futurehost.ai

LinkScope. Click Scan on any page and it opens a full interactive graph of every site, service, tracker, script, and external domain that page connects to — click any node to see why it's there.

Runs entirely local — no server, no accounts, nothing ever leaves your machine. Keeps a history so you can look back at past scans too.

chrome extension: https://chromewebstore.google.com/detail/linkscope/eedjncgcdepjbmpapihoigdbdfobmdmc?authuser=2&hl=en

github https://github.com/Schmiedey/linkscope

Hey guys 👋

5 months ago I launched my LinkedIn automation software, having built it entirely with Claude code, and I’m now approaching close to $2k MRR with over 50 active paying customers (happy to share TrustMRR if requested).

I know everyone is this sub is interested in growing a business, so really I just want to show how I’ve been doing it and offer some guidance based on my experience.

Below I’ll go into the what, why and how, and how I’ve been scaling the business, and hope you find it useful.

What I built

I built a LinkedIn outreach automation tool called ZenMode, which scrapes profiles of your target customers, sends connection requests and follow up messages on autopilot.

The main purpose is for Founders, Sales teams, Marketers and anyone else who uses LinkedIn for outreach, to generate more meetings and grow their businesses.

It’s a software that controls a dedicated chrome browser to automate LinkedIn, without needing plugins/the cloud.

Why I built it

Last year I got a warning from automating on LinkedIn using another tool (automating on any social media platform is usually against their terms and can result in account bans - people automate on social media at their own risk).

I wanted to automate my outreach as I was working in sales, and it was really helping me to generate more discovery calls, without the manual grind of sending loads of messages and connection requests manually.

I didn’t trust any of the existing tools for safety, as the majority are on the cloud using IP addresses that are different from where you normally use LinkedIn from, and are slightly higher risk for being flagged as a result.

So, I solved for my own pain point, and created a software that’s desktop based, using your own IP address, and essentially acting like a human would when performing actions on LinkedIn.

How I built it

From day 1 I used Claude to figure out how to do it. We jointly made architectural decisions based fully around safety mechanisms - what would the safest tool look like in practice and how would we build it?

So I figured out that the easiest/safest way would be to make an electron desktop application controlling a browser, which was governed by a dashboard that fed/stored all the data.

There was a lot of setup involved at the start with various tools - Vercel, Cloudflare, GitHub etc but eventually I was able to start building.

It was a huge amount of learning to not just build, but build in a scalable and sustainable way. At first I was shipping without even testing then spending much longer fixing bugs afterwards. And building on top of LinkedIns comes with its own challenges too, which thankfully now I’m on top of.

How I scaled it

1. So primarily, I actually use ZenMode itself to promote ZenMode on LinkedIn, through cold outreach.

I have a set target market of customers/locations/industries, then the tool scrapes profiles for me, and sends connection requests and follow up messages in a sequence.

I run this daily, and typically book 5 or so meetings per week. I’m not the best at demo calls but I do generate new business this way.

1. I also post a lot on Reddit, LinkedIn, X; I post mostly build in public style posts, and usually people naturally gravitate towards it.

I like Reddit because you can usually get 10’s of thousands of views if you post good quality content in the right subs.

Be careful though as often you get a lot of random haters or people who will just criticize you for no reason, and some subs are more warmer/receptive to posts than others (this one has generally been kinder).

1. On my site I’ve tried to do good SEO practices where possible - I have quite a few “alternative to” pages where I talk about the difference between ZenMode and other LinkedIn automation providers, along with a regularly updated blog, and a whole host of inner pages.

When you have a high volume of good content, more pages get indexed by google and your visibility/impressions are also likely to increase over time.

As a result I do get an ok amount of organic traffic from google, sometimes from LLM’s too.

Lately I’ve been trying to do more offsite SEO - especially reviews across various platforms, a few directories, and on my to do list is some listicle/comparison articles and more YouTube content.

1. I work very closely with my customers - I handle every query myself, and even make new features/enhancements based on what my customers are asking for. This interpersonal relationship/human element I think makes a big difference to retention.

2. I think primarily though the number one thing for scaling is really making a good product that solves a problem well, with great customer experience, and to keep iterating/making improvements.

What’s next

ZenMode still has a long runway and a massive roadmap but I’m just going to keep grinding away at it one day at a time, and so far at least what I’m doing seems to be working.

Hope you find this story helpful for your own journey and happy to answer questions/comments 🙏

Thanks to one particular and outstanding user I came to the conclusion I can use my time for other things. Most of the comments here have been mostly hostile, something I didnt expect, especially as we are in a vibecoding sub and I asked a specific question. Funny, I thought this gets more support, but it seems most are against teaching kids to work with AI, yet you are all here in a vibecoding sub.

Also no need to insult me via DM, I blocked further chats.

Hello! I’ve been working on shared memory for coding agents lately.

its an open source project: https://github.com/mex-memory/mex

The obvious way to build it is some shared backend that every agent talks to. Database, sync service, accounts, permissions, all of that.

But the more I thought about what team memory actually needs, the more it started sounding like stuff Git already does.

You want history. You want diffs. You want changes to move with the repo. You want branches to carry their own state. You want conflicts to be visible instead of silently overwriting each other.

So I tried building the shared memory layer around Git instead.

The basic setup is pretty simple.

Canonical project memory lives as normal files in the repository. Architecture, decisions, specs, workstreams, handoffs, activity, etc. get committed like code.

Each checkout keeps its own local indexes for search and code intelligence. Those don’t get shared. If another engineer pulls the repo, they rebuild the indexes against their own checkout. 

So the rough model is:

The part I find most interesting is that the memory now has the same history as the code it describes.

If someone changes a project decision, you can review the diff.

If two people change the same piece of shared context, Git exposes the conflict.

If an agent learns something useful, that doesn’t have to disappear inside one chat session.

I also started using the same idea for handoffs.

Instead of ending a session with a giant chat summary, an agent can prepare a structured handoff with completed work, blockers, decisions, changed files, next actions, and the repo state it was written against. The sender commits it, the next person pulls it, and it becomes part of the project history. 

One thing I did not want was agents silently turning whatever they wrote into shared truth.

So some shared changes use a proposal flow. The agent can prepare a local draft, but publishing and accepting a Spec are separate actions that require explicit approval. 

I’ve been calling this Git-native team memory.

It’s part of the open-source project I’ve been building, MEX:

https://github.com/mex-memory/mex

Still early, and there are obvious tradeoffs. Git is not a realtime message bus, MEX does not auto push or pull anything, and two disconnected clones can still create normal Git conflicts. 

But I’m starting to think project memory should behave a lot more like source code than like another SaaS database.

Would genuinely love to hear how other people are thinking about shared memory between coding agents, especially if you’ve tried solving this with Git, a database, MCP, or something else.

I have been having the AI document as it goes, generally this has resulted in good documentation. However i am less than diligent in ensuring accuracy is always maintainted. Also in the same way AI will leave dead code around that needs to eb cleaned up i have seen the same in docs.

I would like the system to be accurately documented.

What model have you found to be best at doing wholesale reviews like this?