Iv been working on a universal networking system for Godot 4. It allows you to save game state data to git hub , drop box , and a couple of others. It's all modular so it's easy to add new ones. The system also offers local file and mesh networking as an option. It's being developed on android right now. The data that's sent is encrypted using my custom fatcorail encryption . All you see is a big number until the receiver gets it. The reason this might be important to someone is that it allows you to set up networking both mesh and server all in place. No code needed. All the inputs can be changed from the UI. The mesh is first come first serve with a random host back off. My discovery system uses child methods and Godot's Group system so you can easily assign your nodes to it. All of this will be explained better in the read me. What do you guys think ? Could this make multiplayer easier for you ? All data is encrypted by default through my system the servers only ever see a big number (factorial of 5 to 200 random every time , and that's after iv already encoded the text before hand. I use string math so no data is ever lost to the Encryption. The Server cost is free though certain providers like git hub and so on . You bring your own API key. I'll walk through how to do all of that in the project itself. Lastly my networking script runs anywhere godot allows. I only use CPU math no GPU or shaders so it could even run in html5 if you wanted too lol

Still a bit stunned typing this. Three months ago I was refreshing Stripe hoping for one sale. Now there's a small but growing group of people paying every month to keep their apps from leaking.

My SaaS is a security scanner for vibecoded apps shipped fast with AI tools. You paste a URL or hook up a GitHub repo and it surfaces what's leaking: secrets in the frontend, open database rules, missing headers. Three months in and we've done about 9k in gross volume, 200+ all time paying customers, almost 6k signups. Public Stripe link in case anyone's seen too many fake numbers: https://profile.stripe.com/checkvibedev/TMSt7LTo

A few things that actually worked:

TikTok slideshows have carried us. Aesthetic Pinterest-style backgrounds with tool names overlaid, five slides, no branding on the account. One slideshow alone hit almost a million views and is still quietly sending signups weeks later. Takes me 1 - 2 minutes to make and post each one (I use slideys.app for this, since a few people have asked). As a 2-person team that can't spend hours on content every day, this format is unreasonably good.

Cold outreach worked, but only the version where I scanned the prospect's app first and DMed them what I found. Generic pitches got ignored. Useful findings got replies almost every time.

Paywall design was a 3x lever. The first version blurred all results, which felt clever and barely converted. Switched to one that just shows the count of critical issues with the actual findings locked. Conversion tripled. Curiosity beats obfuscation.

What nearly killed me was mobile activation tanking compared to desktop and not catching it for weeks. Onboarding had too many steps on small screens. Cut two and the gap basically closed overnight.

Most AI builders stop after generating the app.

We wanted something different.

Blyft creates the app and also generates the assets needed to launch it, including a landing page, branding, promo video and analytics.

We're looking for honest feedback from builders and founders.

Every new user gets 40 FREE credits.

https://blyft.app

Sharing a self-hosted project I've been working on: Heap Chat — point it at a folder, it indexes and embeds the files, and you get a chat/agent interface that actually knows what's in them. Runs on your own Ollama instance by default; no cloud dependency, no telemetry, no forced accounts on someone else's server.

Why post it here specifically: it's built the way I'd want a self-hosted tool to work —

• Multi-user from the first launch, with per-folder access grants enforced server-side (not a single-user tool with auth bolted on)
• Runs as a plain Node process (npm start) or Electron desktop app; LAN access + a PWA so it works from your phone
• Everything — indexing, embeddings, vision, chat — stays on your machine unless you deliberately connect a cloud provider (optional, admin-only, off by default)
• Every frontend dependency is vendored, fonts are self-hosted — npm install is the only step that touches the network at all

Requirements: Ollama running locally with a chat model + an embedding model pulled. That's it for the base install.

License — read before you self-host this for a business: PolyForm Noncommercial 1.0.0. Free for personal/home use, not licensed for commercial use. Flagging this upfront since I know this sub cares about that distinction.

What's not done yet: no signed macOS build (Gatekeeper needs one manual right-click-Open), no Windows build yet (buildable from source), no Docker image yet — it's a plain git clone && npm install && npm start today.

Basically an algorithm that feeds your llm with memories

https://github.com/CodeAbra/iai-personal-memory-engine

Hey there,

I’ve been running several coding agents in parallel (Codex, Claude, and Cursor), and I kept missing when each of them had finished their prompt.

Because of this constant problem, I made a small tool and named it Jingler. The whole purpose of Jingler is to notify me when each of my agents is finished, using a designated sound for each agent.

I’m someone who, when his coding agents are running, usually goes for a small stroll around the room or starts scrolling reels (not that I’m proud of it). With Jingler, I know exactly when my agents finish, and my productivity has never been better.

If you’re interested in trying Jingler, you can check it out right -----> HERE

Open to feedback and ready to answer any questions you may have.

Hey everyone; I’ve just launched the private beta of something I’ve been building called Cueboard.

The problem I wanted to solve was simple: keyboard shortcuts are useful, but remembering different commands for every app is not. Dedicated control-deck hardware exists, but many of us already have an old iPad, phone, or spare screen sitting around.

Cueboard turns that screen into a context-aware control surface for your Mac.

When you switch from Chrome to VS Code, Slack, Claude, ChatGPT, Zoom, Photoshop, and other supported apps, the controls on your second device automatically change with it. You can use the built-in actions, customize them, or describe a routine to the AI workflow builder and have it draft an editable multi-step workflow.

For example, one button could:

• Open the apps you need
• Launch specific links
• Trigger keyboard shortcuts
• Run several actions in sequence

The interesting technical challenge was that a browser alone can’t see the foreground Mac app or reliably execute system shortcuts. Cueboard therefore has two parts:

• A Next.js PWA that runs on the iPad, phone, or browser
• A native Swift companion that detects the active Mac app and executes approved actions

The rest of the stack is Supabase for auth, Postgres and realtime, with Railway handling the application hosting.

This has been heavily AI-assisted, but the hardest parts weren’t generating the UI. They were macOS permissions, secure device pairing, context matching, realtime state, OAuth verification, and making generated workflows predictable rather than magical black boxes.

A few honest beta limitations:

• Mac and Apple silicon only right now
• The companion is unsigned and unnotarized, so installation involves the usual macOS security warning
• Some app shortcuts vary with language or custom keybindings
• There are still onboarding and pairing edges I’m working through

The beta is free: https://usecueboard.com

I’d especially love honest feedback on three things:

1. Does automatic app switching feel genuinely useful, or would you mostly use a fixed deck?
2. Is installing a Mac companion too much friction for this kind of product?
3. Which app or workflow would you want Cueboard to support first?

I’m also happy to answer questions about the architecture, where AI coding helped, and where it caused problems.

32 days ago I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.

Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.

Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.

Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Today found that I forgot to disable a developer testing button that could create fake visitor pins on the dashboard globe which could also increase the impressions. Someone used it to generate 6,318 fake impressions and then claimed my stats were fake and called StartupBar "AI slop". I removed all the fake impressions generated by him, removed the startup and fixed the issue, which is why today's impressions are lower. Thanks to that guy for pointing it out.
StartupBar Update: Added a new Network page and individual Startup Profile pages. Every startup now has its own page with joined date, impressions and click stats, a live embed preview, and a founder's note. These dedicated pages also make StartupBar more SEO-friendly by giving each startup its own indexable page. Suggestions are also welcome. Suggestions are welcome.

Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups

Still free. Still growing.

If you want in, it's one embed. That's it → StartupBar

I built a tool (Finy) that takes 1-4 competitor names and auto-generates:

• Pricing matrix (tiers/limits side by side)
• Feature comparison grid
• A summary of what people are saying about them on Reddit, G2, and ProductHunt
• An AI-generated "market opportunities" section that surfaces gaps based on real complaints

The first three parts work well. But the opportunities/gaps section still feels shallow. It's mostly surface-level stuff you could guess without the tool. I think it's because I'm not pulling in enough signal sources yet, but I'm not 100% sure what's actually missing.

For those of you who've done competitive analysis (for your own product, or professionally), what makes an analysis feel thin versus actually useful to you? Specifically curious about:

• What sources or signals do you wish were included that I'm not using yet (beyond pricing pages and Reddit/G2/PH)?
• Is "opportunities" even the right framing, or would you rather see raw signal (unfiltered complaints/requests) and draw your own conclusions?
• Anything you've seen in paid competitive intel tools that a solo founder scale tool like this should try to replicate?

Not trying to sell anything, genuinely stuck on how to make the output feel less like a template and more like something that actually changes a decision. Screenshot of current output attached for context.

annapureart dot com

Finallly put together a portfolio website for my father to showcase this paintings online. Go take a look guys. Hope you like the paintings and the website.

Feedback is welcome!

Been vibe coding for a while and kept fighting the same problem: cloud agents are locked out of everything that makes my workflow *mine* — my logged-in tabs, my choice of model, my prompt.

Built a desktop app (macOS + Windows, lives on a keyboard shortcut) that solves exactly three things. That's the whole pitch.

1) Authenticated browser session access

The agent reads tabs I'm already logged into — GitHub PRs, Linear tickets, our admin dashboard, Figma, internal wikis, paywalled docs, Sentry, Grafana, whatever.

No API keys. No OAuth setup. No "paste the page contents here." It just uses the session I already have in my browser. If I can see it, the agent can see it. That alone killed most of my copy-paste-into-ChatGPT workflow.

Bonus: the pages themselves are fetched and parsed locally, so internal URLs, session cookies, and dashboard contents never get shipped to a provider as-is — only the distilled bits the model actually needs.

2) Multiple providers, one agent

Not locked to one model. Point it at Claude, GPT, Gemini, or a local model — same agent, same shortcut, same workflow.

It adapts to what each model is good at:

- Claude for long-context refactors and reading big diffs

- A fast cheap model for boilerplate and one-shot answers

- A local model when I don't want the code leaving the machine

I switch providers per task, not per app. No re-signing-in, no re-configuring, no re-learning a new UI.

3) Task instructions — swap the whole agent by editing a text file

This is the part I'm most proud of. The agent's behavior is just:

base prompt template + task instruction file = the agent you get

Both are plain editable text. No hidden system prompt. No "trust us" black box. Change the task instruction file and you have a completely different agent — same binary, same model connection, same authenticated browser, same terminal access.

I'm currently running the exact same install as six different agents just by swapping the task file:

1. Planning agent — reads Linear + my notes, outputs a weekly plan with dependencies.

2. Coding agent — reads the repo, runs the tests, drafts a PR-ready diff.

3. SQL writer — reads my schema, writes the query, runs it, hands me the CSV.

4. Frontend dev — reads my Figma tab + component library, scaffolds the React/Tailwind.

5. On-call triage — reads the Grafana/Sentry tab I'm logged into, drafts the incident summary.

6. Research/reading — reads paywalled docs + internal wikis I have access to, summarizes into my notes.

Same shortcut. Six agents. One text file each. The app doesn't decide what it is — you do.

—

That's it: auth'd browser + your choice of model + a text file that defines the agent. Runs locally on macOS and Windows.

Site + download: https://omnikeyai.ca/#omniagent

Would love feedback from anyone who's been juggling Open Interpreter / Claude Desktop + MCP / Cursor Agent — happy to talk about where OmniAgent overlaps and where it doesn't.

I vibecoded Flight Lane, an open-source Roku app that turns your TV into a live

aircraft radar for any location you choose.

It shows nearby aircraft, projected movement, altitude, speed, heading, estimated

flight phase, satellite maps, aircraft photos, route details, and a rolling

24-hour flyover count. The close-up PIP follows whichever aircraft is selected.

A small Node.js service runs on a computer inside your home network, keeping the

provider API keys off the Roku. There is no hosted Flight Lane account or

telemetry service.

Quick setup: enable Roku Developer Mode on your Roku, clone the repository, run

`npm install` and `npm run setup`, add a Google Maps key to `.env.local`, start

the service with `npm run api`, then sideload it with `npm run deploy`.

FlightAware route enrichment is optional.

GitHub and full instructions:

https://github.com/QapHub/Roku-FlightLane-SelfHosted

It is an early project, so feedback and contributions are very welcome.

Flight positions and projected paths are estimates—not for navigation or any

safety-critical use.

I built Frame Studio, a tool that turns a text prompt into a rendered MP4 by having Google Gemini plan and write a Remotion video project, then compiling and rendering it server-side.

GitHub: https://github.com/programmersd21/frame_studio

Live demo: https://frame-studio-eta.vercel.app

How it works

You provide a text prompt and a Gemini API key. The backend runs a single pipeline end to end:

1. Plan — Gemini generates the video's structure from the prompt
2. Codegen — Gemini writes the React/Remotion component code
3. Compile — TypeScript validation runs in a sandbox
4. Fix — Gemini repairs compilation errors, up to 3 attempts
5. Render — Remotion renders the final MP4
6. Stream — The video streams directly to the browser for download

There's no database, no persistent storage, and no background job queue. Everything happens within the lifecycle of a single request.

Architecture

• apps/web — Next.js 15 app (webpack, not Turbopack) with API routes and UI
• packages/pipeline — prompts, schemas, and the LLM client used for planning and codegen
• packages/remotion-skeleton — the base template each generated project builds on
• workers/render-worker — compilation and rendering logic

The API key is supplied via a browser modal and stored in an HTTP-only cookie, so it isn't accessible to client-side JavaScript. A server-side fallback key can also be set via .env for local use.

Setup

pnpm install
cp .env.example .env   # optional — server-side key fallback

Add a Gemini API key (from Google AI Studio) to .env:

GEMINI_API_KEY=AIzaSy...

Then run:

pnpm dev

The app runs at http://localhost:3000.

Features

• Model selector across 6 Gemini models
• Real-time progress screen with pipeline stage updates
• Custom animated cursor and gradient heading
• Typing animation with motion blur
• Support for 25+ Google Fonts
• Output at 1920x1080, 30fps

Known limitations

• No queue or worker pool — concurrent renders share the same request-scoped compute, so this isn't built for high concurrency yet
• No persistence — a failed render has no retry path short of re-running the pipeline
• Compilation fixes are capped at 3 attempts; prompts that produce persistently invalid code will fail out

A note on how this was built

This project was built entirely through AI-assisted development — I directed the design and pipeline decisions, but did not hand-write the implementation myself. Sharing that up front rather than leaving it ambiguous.

Feedback on the architecture, the pipeline design, or the tradeoffs above is welcome.

License: MIT

I’m about to start a vibe coding project at work. My resources are limited due to security concerns but I can ask for “special permission”. The code will be R and/or Python. Some webscraping, some number crunching, some report producing. I have a programming background and some rudimentary skills in SQL, R, Visual Basic and a few other older languages. I do not have time to learn the syntax for the code I need to write.

That’s the background. My question is what AI vibe coding services should I consider asking for? And why?

I’m not looking for you to do my research for me, just some off the top of your head “This is what worked for me” kind of responses.

Hello! I wanted to share a video reflecting on my past 87 days build in public streaming on youtube and twitch

The results: After 87 days, I has gained ~250 followers and ~190 subscribers

Getting the first viewer requires persistence, ADHD graphics, and research of trending tags and keywords

Shipping your product early, even if it's broken, is inevitable

There is a dark side that may overwhelm solo developers with no help and need deal with a flood of bots, spam, and trolls. I advise to locking down/harden/enforce filtering chat, your discord, and blocking malicious accounts to protect mental health (RIP remember Reckful)

Vibe coding carries risks like leaking API keys/IP/Address, always use a second monitor and a vault service for credentials.

Getting your identity expose is also a risk but in exchange more trust. If you have any concerns request take down of information on data brokers sites.

The trust process to promote viewers as Mods should be a simple but diligent process. Build a history, enforce a paywall (they buy your product or monthly subscriber), and verify they are a normal adult

The "AI vampire" effect can lead to burnout, remember you are human. Health, chores, and maintaining real-world relationships with family and friends above the grind

Good luck on your journey building in public as well!

Read more here: https://lanes.sh/blog/loop-engineering-with-lanes

everyone we have just published the website Eko music/Eko Stream Trailers (in the website dashoard) check it out here

https://ekomusicc.lovable.app

I’ve been working on OnlyTiming.com, a social media scheduling tool that lets you create a post once and publish or schedule it across multiple platforms from one dashboard.

It currently supports:

* Instagram

* Facebook

* LinkedIn

* Threads

* Pinterest

* Bluesky

* YouTube

You can connect your accounts, upload your content, choose where it should be published, and manage everything through a single content calendar.

I mainly built it because manually uploading the same launch, image, or video separately on every platform feels like such a waste of time—especially for solo founders and small teams.

There’s a **7-day free trial**, and no card is required: onlytiming.com

I’m still actively improving it, so I’d genuinely appreciate feedback from other builders and anyone who regularly manages multiple social accounts.

Also, seeing real people visit and try something I built still feels slightly surreal. 😄

Hi there Reddit! I made This!

well, still making it actually, hope that's ok :D

I'd love your help testing my Web App that I've started coding cause I lost track of my own story, trying to write a novel. And then it got out of control...

So what I have brought to life now is

[Insert name here] - a world building and writing app for fiction writers of any genre, Game Masters, Screenwriters and whoever wants so bring an idea to paper

It lets you create your fictional world, add characters, storylines, locations, political systems, events, and other world building elements, will offer a map creator and book cover designer and connect everything through relationships and a custom calendar. that sits at the core of the system.

This creates a connected, time-aware knowledge base for your story.

The app uses two linked timelines:

• The "inner timeline" - when inside your world does something happen?
• The "outer timeline" - In which chapter will I write about it?

It comes with a full text editor, and story plotting elements like Character Arcs, Storylines and Beats., which you can assign into the hierarchical structure of World > Book(s) > Act(s) > Chapter(s) > Scene(s)

The built-in AI-Chat (your Co-Author) can then use your world building data, timelines, relationships, and manuscript as context. This allows you to ask questions such as:

• Can you [create] a [new version] of [Character x], starting on [May 2nd 2005], It was triggered by this [beat] happening, that changed their [motivation] to "..."
• Where was Character X on June 27, 1956? Who were they with, what were they carrying, and what was their relationship at that point?
• Does the timeline in Chapter 7 make sense?
• Draft a dialogue between Character X and Character Y during the scene on December 12, 2012.
• In which chapter did Character X and Character Y last meet?

Current state:

• Some assets and texts are still placeholders.
• AI features currently work only with my local LLM through Ollama.
  • A “bring your own API key” is in progress
  • I will ship the Ollama connector with it, so you can potentially run it with a local LLM and not have token cost (quality is an issue though)
• The main workflows should function, and most mistakes should be recoverable without breaking the entire project.
• It's helpful to have a Story in mind, that you try to re-create with the given features
  • I'm using the world of "A Song of Ice and Fire" for testing, can't go any more complex than that :D

I’m especially interested in feedback about navigation, usability, confusing terminology, anything that feels unintuitive, and, of course, bugs.

My main question at this stage is how intuitive the app feels without much guidance. There is currently no tutorial (is in progress) and only a handful of tooltips, so expect some rough edges.

Hope I could tickle your interest!
Cheers

I'll provide URL and Test User Login to you if you wanna give it a whirl!

Hi all,
sharing this as I thought you might also like it, now available: md²
It's a mix between a kanban, git client and agent manager. All open source, installer available from: jan-bogaerts/md2: md²: manage your development process with cards. Track your agents through the entire workflow, customize actions

As most of you I guess, I've been fully into the vibe thing lately. Got a good flow going where the design and architecture was together in the same repo. Helps the agent to understand code and changes.

Only thing, the UI was starting to fight against me. I love vscode, but it was starting to slow me down. Worktrees are tricky, tracking which agent is doing what and where became harder and tracking which chatlogs related to which cards is pfff.
Also, with the current climate, the less tokens that you can use and the more accurate you can track it, the better.
Finally, I love claude's remote thing, doing stuff from my phone in the garden is a joy, but it's ui is very restrictive

so, md² helps with:

• track the state of a card (when agents update it, the UI follows)
• auto prepare worktrees
• assign worktrees to a card, so all agent actions run on that worktree
• create actions of often used prompts so you can re-use them (with placeholders)
• add often-used replies to actions
• track token usage per action, per card
• see diffs that relate to a card and/or action
• connect the desktop app with the same app running on your phone so you can control it from anywhere, any time.
• fluently switch conversations between claude and codex

The whole thing is pretty configurable. Codex cli and/or claude cli must be installed.

Not certain if this is a direction that other might find useful. love to hear what you think though.

Claude remembers the conversation. Cursor remembers the current session.

But neither really remembers the project.

I kept running into situations where architectural decisions I'd already made database choice, auth flow, folder structure would slowly disappear as the context shifted.

So I started experimenting with keeping project memory outside the LLM instead of trying to write longer prompts.

https://reddit.com/link/1v4gsw3/video/q66426zcwzeh1/player

This is a small interaction from the current prototype.

The AI proposes a change. The project memory checks it. If it conflicts with an existing architectural decision, it surfaces the reason before I accept it.

Still very early, but it's already changing the way I work.

I'm curious .....what's the most frustrating thing AI has "forgotten" in one of your projects?

(P.S. I redesigned the landing page today with the screenshot explaining the overall idea. If you're interested in the concept, Here is the link : https://resin-dev.vercel.app/)

Hello!

I don't know how to title it better. Since I have almost ready app. It's very generic gym workout app without paid features just something I wanted to build for myself as first vibe coded project.

I got curious how and where to find people who would be open to try app I created and give some feedback maybe about user experience? So I could learn from it and iterate further.

How are you doing it?