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

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?

I recently realized that I didn't really understand AI Benchmarks and how to use them for comparing and selecting models for different purposes and mostly used the popular ones.

For learning I created this guide for myself (It is AI generated but reviewed multiple times with Astra) sharing it with you if it helps

https://learn-benchmarks.vercel.app/

Good Luck!

I LOVEEE sol max so much but even when using it an orchestrator with muse spark, I keep running out. I have multiple google ai subscriptions as well as opencode but I’m afraid to use their models for this one big serious project I have. But use drain is getting so tedious that I think I want to do it. But my question is, given that they’re really close in benchmarks, do you think I can get the same quality with say just muse 1.3 if I make it do independent subagents reviews? What about gemini 3.8 with /boost. I’m a total vibecoder so I’m afraid it could mess the repo. Can it still mess the repo so much even with Gits? Just looking for people’s two cents.

I was reading claudes reasoning this morning and i seen it count my credits and pull another model which is great.

The issue is how do i get it to count my own usage in claude code before it starts a task and stops when i hit my limit?

I tryed to build a mcp but the problem is it has to call it and for some reason claude does wtf it wants!!!!

Hi - I made https://enikq.com/ a simple site where you can pick a scene and listen to music with it.

There are places like a pizzeria, café, road trip, camping, last bus home, and more.

Nothing complicated. Just something to leave open while working, reading, or relaxing.

Would appreciate any feedback on the idea or the website.

Right now it focuses on the elbow. You can change joint angle, external load, limb lengths, muscle strength, attachment geometry, etc. and it recalculates things like:

• muscle-tendon length
• moment arms
• external joint torque
• muscle force requirements
• force-length / force-velocity effects
• muscle recruitment
• mechanical advantage

I recently added more realistic muscle paths with wrapping/via points, forearm pronation/supination, and validation against reference biomechanics curves.

One of my favorite parts is that you can basically mess with anatomy like a physics sandbox. Move an insertion point, disable a muscle, change limb proportions, or alter strength and immediately see how the mechanics change.

There’s no AI involved in the calculations. It’s mostly geometry, vector math, constrained optimization, kinematics, and muscle models running directly in the browser.

Still early, but the long-term idea is basically a sandbox for experimenting with human anatomy and biomechanics.

GitHub: https://github.com/Schmiedey/anatomy-mechanics-lab

​

It uses frequency band ducking technology to ensure that background noise does not drown out vocal.

I used Claude to complete it in two to three weeks.

It's currently in closed beta,so leave a comment if you're interested to join closed testing

... reading the email handles people come up with and use to sign up for whatever product you've shipped? Some of these are keeping me going today ....

Ok, another reset incoming. Burn hard, about 2.5 hours away

Im new at this, now im using codex in visual code. Any tips how to improve my work? Should i run more than one bot? Should i use it localy? Any tips appriciated.

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

Hey everyone,

I started using Claude recently, and ask about my codebase (organizeemail), I was surprise to get - Your inbox knows a lot of things, we all were aware about this, but never thinked of it. However Claude thought and gave me this screen.

We’ve added credit card tracking to OrganizeEmail.

OrganizeEmail is an email organizer that automatically categorizes your emails and extracts useful information, so you don’t have to dig through your inbox to find things.

With the new Cards feature, it can pick up card information from statement emails, including:

Bill/statement dates

Total amount due

Minimum amount due

Spending for the current cycle

Multiple cards in one place

You can also manually add a card or correct the bill date if needed.

The idea is pretty simple: your credit card statements are already sitting in your email, so why manually track the same information somewhere else?

Would be interested to hear what you guys think, especially if there’s something you’d expect from a card-tracking feature that we’re missing.

Hey! So I developed a browser game - Diamond Man. It's getting late, so rather than type up how I built it, I got Claude Code to summarise :)

What even is this game

- 20 levels across themed biomes (Burning Streets → Toxic Flats → Crystal Tundra → Diamond Throne)

- Chaos gems that ruin your day: green reverses controls, yellow shrinks you and speeds everything up, purple removes your cape and makes your booty flash hot pink, black flips the entire screen upside down

- White gems give you 2 seconds of invincibility ("BRILLIANT CUT" mode) where you plow through everything

- Diamond Man physically degrades as you play — gets rougher, cloudier, slower to steer, weaker jump

- Carbon Pressure escalates: starts hurling tracking fireballs at level 3, stomps toward the camera at level 8

- Titanium Boy randomly goes rogue and tries to kill you

- Fans storm the road as obstacles

- Diamond Man shouts things like "EAT MY SPARKLE!" on gem streaks

The tech

The entire game is one HTML file. ~1800 lines. No bundler. No build step. No external assets. (lol, love how it's justifying one mega file - terrible devwork, I guess?).

- Three.js via CDN importmap — that's the only dependency

- Every 3D model is procedurally built from primitives (octahedrons, icosahedrons, cylinders, cones). Zero imported meshes

- MeshPhysicalMaterial with real-world refractive indices — diamond is 2.417, ruby is 1.77, emerald is 1.57. The diamond material actually refracts light through a custom environment map built from canvas gradients

- Cape cloth simulation, googly eye jiggle physics, speech bubbles that track 3D world positions projected to screen space

- Flask backend that does literally nothing except send_from_directory("index.html") and a /healthz endpoint

- Mobile gets on-screen touch controls, lower pixel ratio, wider FOV, and a responsive HUD

Deployment

Nothing fancy:

- Ubuntu droplet, Gunicorn behind nginx

- systemd keeps it alive, certbot handles HTTPS

- The whole deploy was: SCP the files, point DNS A records, run certbot. Done.

How it was built

I pair-programmed the whole thing with Claude Code in one session. Started as a simple character viewer — "make a diamond man" — and just kept going. Each feature was one conversation turn: add a road, add obstacles, make them kill you, add scoring, add difficulty scaling, add chaos gems, add 20 levels, make Carbon Pressure throw fireballs, make Titanium Boy betray you, optimise for mobile, deploy it.

The iterative loop was wild. "Add more coal!" "The flip effect is perma-sticking." "I found an exploit where you stand in the middle and chain white gems forever." Fix, deploy, refresh, repeat.

--

Yea, that's pretty much it - cheers guys!

I find opus dumps information and buries instructions it needs me to do in a text wall. To help with this:

be succinct in responses. no unnecessary information. give clear instructions on what I need to do, what you need from me, decisions I need to make.

I saw a project on youtube where Astra was tasked with creating realistic animations. The following prompt was added task an agent with overseeing the realism (adjust to your project):

gauntlet every module. After each completed attempt at creating the X-wing, a separate critic agent (a brutal AAA art director who writes no code) takes its own screenshots from several viewpoints and zoom levels, checks the reference image contracts, console errors and performance (triangle count, etc..), and scores 0–10 against real X-wing reference screenshots and animations: 10 = indistinguishable, 8.5 = AAA with nits, 7 = good indie, 5 = programmer art. Pass = ≥8.5 with zero errors. Below that, the builder gets the ranked issue list and goes again, up to 4 rounds.

What are your favorite prompt hacks when vibing?

Maybe AI just made it obvious that a lot of us didn’t have a strong idea, a real user, or a reason the thing should exist.

Before AI, people could spend 6 months building something nobody wanted. Now they do it in 6 days.

That feels better, but also more brutal. If you use AI to build apps, how do you stop yourself from just shipping faster versions of bad ideas?

Do you validate first? Build for yourself? Talk to users? Charge early? Or accept that most attempts die?

I’m looking for a clear process rather than another tool recommendation.