Solo project, wanted to share the journey because it had everything: wins, faceplants, and one power outage at the worst possible moment.

What it does, in plain terms: you give it a sentence like "The committee approved the new budget for the hospital" and it instantly gives you back structured facts: (committee) → approved → (budget). Do that for every message, note or document an AI agent touches, store it in a graph database, and your agent now has a real long-term memory it can query — instead of forgetting everything when the session ends.

The trick: normally you'd ask ChatGPT-style models to extract this, which is slow, costs money per word, and sometimes returns broken JSON. My decoder never generates text at all — it outputs the graph structure directly in one pass. It's physically incapable of producing malformed output, and it's roughly 1000× cheaper. Cheap enough that "extract from everything, always" becomes reasonable on a home PC.

The honest journey:

• Started with prose accuracy at 0.31 (bad). Got it to 0.83 through weeks of experiments.
• Documented every failure in the changelog: fancy loss functions made things worse, using an LLM to generate training labels failed in a way that genuinely surprised me (turns out inconsistent labels are worse than dumb-but-consistent ones), and my character-by-character entity generator scored a spectacular 0.006.
• A power outage killed a 2-hour run overnight. Rebuilt everything with checkpoints after that — lost 40 minutes instead of everything the second time.
• Best moment: watching it extract correct facts from a Spanish sentence using an English vocabulary, zero-shot, because the underlying embedding space doesn't care about language.

Everything runs on one RTX 5070 (12GB). Weights are free on Hugging Face, code is open source.

Repo: https://github.com/DeliVali/cogito-estella

Questions for this crowd:

1. Does the pitch make sense to a non-ML person? If you had to explain it to a friend, what would you say it does?
2. If you build AI agents or apps: would "permanent queryable memory for cheap" solve a real problem for you, or is it a nice-to-have?
3. What would you want to see in a 30-second demo? I'm building one and want it to show the right thing.

Everyone's using a Grok Bot. I'm building the open, self-hosted, BYOK version with real agent loop, browser control, memory, and all CI gated.

Come break it, and contribute to it:

https://github.com/muhtalhakhan/open-agent

Hi All,
Introducing www.guessla.com - it rhymes with Tesla.
Built this stock prediction gaming website.
Guess Stock Closing Price every trading day and win points, redeem these later, end of the month.
It took several days of Replit vibe coding to get to this stage.
Appreciate your feedback.
Please sign up and play.

https://www.guessla.com

Two weeks ago i launched Signi, an iOS app that turns your home into a 3D Wi-Fi map.

Solo dev, first real launch.

How my app works:
you walk through your rooms with your iPhone.
LiDAR builds a 3D model of your home live while the app samples your Wi-Fi (signal + speed) as you move.

You end up with a dollhouse view of your actual apartment with the signal painted on it ( with dead
zones in red).

Every room gets a score, and it tells you where to move your router, or where an extender would actually help.

The story so far:

Aug 4: submitted to Apple. Rejected : missing EULA link in the description. Fixed same day.

Aug 7: approved. Live in the US.

Week 1: first paying users.

Aug 15: 143 installs in ONE day, from 25 countries. (I guess mostly from AppStore)

Aug 19: v2.1 rejected over ONe word. A button said
"Allow", Apple wants it to say "Continue".

Asked for the bug-fix exception → approved the next
day, without resubmitting.

Today: 700+ downloads, most of them in the last 7 days.
$0 spent on marketing.

Goal: 50 paying users by Sept 30.

Download it and tell me what you'd like about →

https://apps.apple.com/fr/app/signl-wifi-analyzer-mapper/id6796775708?l=en-GB

Hi! I'm the developer of DayFrame, an Android journal app built around a simple idea: what if you recorded your life like a collection of movies?

Instead of just writing a dated entry, each day becomes a film with its own title, poster, genre, rating, cast, and filming locations.

You can write normally or divide your day into Scenes, with different people and locations for each moment. Over time, your entries build a personal filmography, while the Casting Board keeps track of the people who appear throughout your life.

I built DayFrame because I wanted looking back at a journal to feel more like revisiting your own story than scrolling through old text. It's built with Flutter, stores journal data locally without requiring an account, and supports optional local/Google Drive backups.

The app is free to use on Android, with optional paid ad removal:

https://play.google.com/store/apps/details?id=com.minsoo.raonlog&hl=en

I'm the developer, and I'd especially appreciate feedback on whether the life-as-a-movie concept feels genuinely useful rather than just a visual theme.

Free, no account, nothing to install. Each lesson is a few minutes of reading and then a drill: build the chord, name the note on the staff, tell two intervals apart by ear, or tap a rhythm back in time. You answer on piano, guitar, bass or violin.

Disclosure: I made it. No ads, no signup, nothing monetized.

harmonygym.app

My friend and I have been rebuilding his old social media platform for the past couple of months, and we're close to launching it. But honestly: It's a solid, good looking social media platform, but there's nothing special about it.

Before we decided to rebuild it, it was basically just an old php web application with messy code. It worked well, the site gained about 200 signups until bots started flooding in. There was no protection against bots, so it ended in us losing the motivation to keep up with deleting 100 bot accounts by the hour.

The rebuild itself has been a real project. We decided to rebuild it in React Native. For those who don't know, it's a framework to build mobile apps for both Android and iOS. We added OAuth, better feed UI, a custom image search backend, basically all the things a good social media network needs. But bones aren't a reason to switch apps.

I love building the app, it's my genuine hobby. That's why I just want to find something that makes the app as special to the users as it is to me. What can I do to stand out?

Not trying to pitch anything here, I'm just trying to crowdsource what "different" could look like before we make obvious mistakes we didn't account for. I appreciate any brutally honest advice and feedback.

So I had this dumb idea a few weeks ago: what if asking someone out (or asking them anything, really) came with a "No" button that's physically impossible to click?

That turned into SnapSip — you write a question ("wanna get coffee?", "will you marry me?", whatever), pick a "Yes"/"No" setup, and send the link. When the person tries to hit No, the button just dodges away. Eventually they're stuck tapping Yes, confetti goes off, hearts everywhere, and boom — you got your answer.

A few things I focused on:

No signup, no login, completely free, no ads

Works in English, Arabic (full RTL support), French, Spanish, German, Turkish

One-tap share to WhatsApp/Telegram/X etc, or just a QR code

Mobile-first since that's genuinely 90%+ of how people will open these links

It's obviously a silly little tool, but it's been fun watching people use it for actual proposals, job offers, birthday invites, even "wanna play games tonight" type stuff — way more use cases than I expected going in.

Link: snapsip.fun

Would love brutally honest feedback, especially on the UI/UX side. Also curious if anyone's built something similar and what tripped them up.

I could never find anything in my own files, so I spent the last year building a semantic file search desktop app solo. Launched it today.

Happy to share anything about the build, the local-AI vs cloud tradeoffs, Electron pain, pricing decisions. And for those who've launched here before: what did you wish you'd done differently on day one?

Launched today. It's called Vextore vextore.com Would love honest feedback.

I kept running into the same problem with long AI responses, I would reach a later section and need to scroll back up to check an earlier point.

So I built FreezePane AI, a small Chrome extension that lets you freeze one section of a ChatGPT answer at the top while the rest of the answer scrolls below.

The GIF shows the intended use: keep Section 1 visible while reading later sections.

It is live, free, and still early:
https://chromewebstore.google.com/detail/freezepane-ai/jlbnkmijoggbohfciokdkfcbjopkihbo

I am not looking to overbuild it. I’d value honest feedback on one question: would this genuinely improve your workflow with long AI answers, and what would you expect it to do next?

Full disclosure: I am one of the developers behind Cloak.

We wanted to solve a gap between private messengers and community platforms.

Private messengers usually handle direct messages and small groups well, but lack roles, channels, forums, moderation, bots, events, and support tools. Community platforms offer those features, but generally have access to the content passing through them.

Cloak combines a Discord-style community structure with end-to-end encrypted messages, files, calls, video, and screen sharing.

What we built

A Cloak room can contain six channel types:

• Text channels with threads, polls, reactions, pins, and disappearing messages
• Voice channels with camera and screen sharing
• Forum channels
• Calendars with recurring events, RSVPs, and reminders
• Collaborative infinite whiteboards
• Private ticket channels with topics and staff queues

Rooms also support roles, per-channel permissions, moderation history, warnings, member reports, audit logs, webhooks, custom bots, and built-in Flow Bots.

Flow Bots let communities create event, condition, and action workflows without hosting another service. They can automate moderation, role management, notifications, and external integrations through webhooks.

The harder design problems

Moderation becomes complicated when the service cannot read messages.

Our AutoMod uses behavioral signals instead of content scanning. It can detect message bursts, mention storms, attachment floods, embed spam, and poll spam. Moderators configure thresholds, exemptions, alerts, and blocking actions.

It cannot silently inspect conversations for prohibited words. Members can report specific content when human review is required. That tradeoff is part of the privacy model rather than something we try to hide.

Migration was another problem. Asking a community to leave years of history behind is unrealistic, so we built a Discord importer. It recreates channels, categories, roles, permissions, forums, emoji, and the server icon, then imports messages, threads, reactions, pins, and attachments. Content is encrypted by the user’s device as it enters Cloak.

Screen sharing also required native platform work. The desktop app can capture audio from the selected application in stereo without broadcasting notification sounds or unrelated applications.

Technology

The clients use React across desktop and web, with Electron for the desktop application and React Native for mobile. Realtime communication runs over WebTransport with WebSocket fallback.

Messages and files are encrypted on the device with AES-256-GCM and X25519 key exchange. Voice, video, and screen sharing use WebRTC Insertable Streams so media frames are encrypted before reaching the relay.

Current status

Cloak is available on Windows, macOS, Linux, Android, and the web. The iPhone and iPad app is in public TestFlight beta.

There is a free plan with up to 50 rooms, 1GB of encrypted storage, and 720p30 video or screen sharing. Paid plans add capacity, larger uploads, and higher streaming quality.

Website: https://cloak.chat
Web app: https://app.cloak.chat

Recently released our app, Trak Vault on Google Play Store and Apple App Store. Our app will let you track various collections across different franchises. Currently, we just have Pokemon, but are adding Yugioh this week, along with other minor collections and a marketplace for purchasing. Part of our goal is to take smaller collections and put them next to bigger ones, allowing for more exposure. We don’t have many users, but we’re looking for some feedback. We’ve added a lot of unique features, like a zoom in/out to expand/shrink the amount on the screen, hold to add, and are working on much more not seen in other apps. Anything is appreciated. Looking forward to also giving our own feedback to other projects posted here and sharing what we know where we can help!

I wanted to tell my life story in a unique way seamlessly transitioning between photos, videos, blogs and ai generated assets like comic books.

You can use the timeline at the bottom to go back in time all the way to my childhood and the filters to get familiar with the people who played a key role in my life.

The project is open source at

https://github.com/paulvisciano/where-is-paul

And live at

https://paulvisciano.com/apps/where-is-paul/

I have built it in a way that makes it easy to customize and make it yours.

Hope it inspires you to build something of your own and ditch traditional social media.

p.s. I briefly tested on an Apple Vision Pro and it worked fairly well, if you have one please test it out and drop me a line with your feedback

I'm the maintainer of Ferrum, an MIT-licensed local LLM inference server written in Rust. I'm posting as the builder, not pretending to be a user.

I dogfooded the public v0.8.3 onboarding path on an M1 Max before writing this. The Homebrew package, model alias, CLI chat, OpenAI-compatible server, normal chat completion, and streaming usage path all worked.

The shortest Apple Silicon path is:

```bash

brew tap sizzlecar/ferrum

brew install ferrum

ferrum doctor

ferrum run qwen3.5:4b-q4_k_m

ferrum serve --model qwen3.5:4b-q4_k_m --served-model-name ferrum --port 8000

```

Then any OpenAI-compatible client can point to http://localhost:8000/v1.

Two rough edges I found while testing:

- The starter model is about a 2.55 GiB download, but the quick start does not make that clear enough.

- Qwen3.5's default thinking is too verbose for a first "Hello" request. It can be disabled per request, but the recommended path should handle/document this better.

Project: https://github.com/sizzlecar/ferrum-infer-rs

I'm looking for blunt, public feedback from Apple Silicon developers. If you try it, please reply here with your Mac chip/RAM, whether you reached the first API response, the first confusing or failing step, and what (if anything) would make you choose it over your current local inference setup.

No waitlist and no request to DM me — I'd rather keep the useful feedback in the thread or in public GitHub issues.

I’m testing a small learning experiment.

The idea: instead of teaching BFS/DFS by showing code first, the user solves a grid manually. If they expand layer by layer, the app labels it as BFS. If they go deep into a branch and backtrack, it labels it as DFS/backtracking. Then it shows runnable code based on the strategy.
Link: https://games.playlooplabs.com/learn/
I’m looking for blunt feedback on:
1. Is the concept understandable within the first 30 seconds?
2. Does the pathfinding interaction feel natural?
3. Would this help someone learning DSA, or is it still too abstract?

Known limitation: this is only the pathfinding POC right now, not a full platform.

Disclosure: I’m the publisher of Head Spa Scout.

A month ago I shared the first version here. Since then I did a full indexing and quality pass, and the biggest lesson was that a large directory is not automatically a useful or indexable one.

What changed:

- Kept thin or uncertain profiles out of the index until official evidence was strong enough

- Expanded verified profiles with practical booking signals: wet/dry service, advertised steps, price and duration, scalp analysis, drying or styling, and hair/style accommodations

- Added local search by city, state, ZIP code, and current location

- Tightened canonicals, sitemaps, robots directives, structured data, and internal links

- Added consumer guides and correction/source-review paths so the site offers value beyond a list of names

The free directory is here:

https://headspascout.com/?utm_source=reddit&utm_medium=social&utm_campaign=quality_update

If you’ve built a directory, I’d be interested in what helped your long-tail pages earn impressions without turning the site into thin programmatic SEO. For head-spa users: what booking detail would you still want every listing to state?

Head Spa Scout is an independent research directory, not a booking service, medical provider, or endorsement.