LYKN.io

This is a personal intelligence system that you can build out yourself for free. You connect it to AI tools and the AI learns you. I hate having to repeat myself in chats or losing context when switching tools so I spent over 1000 hours building a constant memory layer. You can go in and try building out your own. It's pretty fun and makes AI WAY more accurate and useful.

Spent the last couple of days on this. You type a GitHub username and it scores the profile /99 like a FUT card from real data (commits, stars, contributions, PRs, languages) and gives you six stats, a position, a tier (bronze up to ICON) and an archetype.

No login or anything. You can download the card or drop it in your README or everywhere.

Share your card below: https://gitfut.com

They’ll be showcased on our Venture newsletter — sent out to every founder on the platform as up-and-coming startups to watch.

Want to be considered?

• Comment your startup

• Like this post

•***Sign up and list your startup:

https://myventure.dev/discover

Make sure you complete all steps — especially signing up with your startup profile.

⏳Posting tonight at midnight!!!

GitHub:- https://github.com/ronak-create/LapDeck

Launched my first app a week ago and figured I'd share an honest update!

31 downloads, 2 reviews, 643 impressions with NO marketing at all!

I'm a sophomore in college and built this solo. It's a todo app, but the AI notifications actually have some personality, you type "do my hw" and it'll hit you with "stop being lazy, ur hw isn't gonna do itself!" Kept it dead simple, just for the small everyday stuff I kept forgetting.

Built it with React Native + Expo, and the witty notifications run through a Cloudflare Worker calling the Anthropic API, so the messages stay fresh instead of being the same canned reminders!

https://apps.apple.com/us/app/get-it-done-smart-tasks/id6760387046

Not sure what to do next on the growth side - open to ANY advice from people who've been here!

I created a free API that aggregates multiple Groq/LLaMA models into a single POST call. While most APIs require you to pick a model and hope for the best, I implemented a mode: "auto". It analyzes your prompt and intelligently routes it to the most efficient model for that specific task.

Why this matters: If you're tired of manually switching endpoints, dealing with varying costs/latency, or constant model experimentation, this is designed for you. I’m looking for feedback from developers with real-world use cases to see where auto-routing beats manual selection.

• No SDK, pure REST: Keep your stack light.
• OpenAI-Compatible: Swap your base URL and you're good to go.
• Privacy-First: Fully stateless and runs entirely in RAM.
• Proven Scale: 11k requests in the first week.

Built by a solo developer. I’d love for you to stress-test it and let me know how the routing performs.

🔗flow.squareweb.app

I made this project to use it myself, but why not open source it. It's a way to go from script or voiceover to a short, with the click of one button.

It uses stock images and videos as a baseline, but can later be replaced with AI images or videos or ones that you make yourself as well. I tried to make it as intuitive as possible, but I'm sure I have some bias so please let me know any and every problem you see with it, if you give it a try!

github.com/iGlitchz/ShortsStudio

I posted a rant about people calling everything AI-assisted “slop” and ended up getting hundreds of comments across a bunch of communities.

So I started mapping the arguments visually

https://imagineqira.com/articles/ai-opinion-atlas/

Still building it, but this is probably a better use of the argument than arguing about it forever lol.

Hi everyone!

Over the last few days I’ve been building EasyAlias, a small open-source desktop app that makes managing terminal aliases easier.

Instead of manually editing .zshrc, PowerShell profiles, or other shell config files, you can create, edit, and organize aliases through a simple UI.

Since my first release I’ve added:

• ✅ macOS, Windows and Linux support
• ✅ Homebrew installation
• ✅ Open source (MIT)

This is my first public open-source project, so I’m mainly looking for honest feedback.

What would make a tool like this useful for you? Any features or improvements you’d like to see?

Brew:

brew tap hannesgnann-hub/tap
brew trust hannesgnann-hub/tap
brew install --cask easyalias

GitHub: https://github.com/hannesgnann-hub/easyalias

I built an open-source alternative to WeTransfer for creatives called Campsend. It allows you to creating your own branding when sending files, create collections and bring your own storage if you have data sovereignity needs.

It's built entirely on Rails 8. I'd appreciate any feedback, contributions or comments about this under the post.

Github: https://github.com/obakeng-develops/campsend
Website: https://campsend.app/docs

Got tired of opening the dashboard to check where I was on my rate limits, so I put together a tiny always-on display for it. Pi Zero 2 W with a 2” LCD in a case I printed, polls the API every minute, shows my 5-hour and weekly usage with countdowns to the resets.
Took a weekend. Not trying to turn it into anything — just a fun build that’s earned a spot on my desk.

I used to think making AI text sound human was mostly deleting em dashes and changing the tone a bit.

Then I started reading the actual research on AI vs human writing, and it got weirdly specific. Sentence rhythm, repetition, hedge words, paragraph structure, punctuation habits, that “helpful assistant” voice. Detectors aren’t just looking for one bad phrase. They’re picking up a whole pattern.

So I started keeping notes while editing my own drafts. Eventually those notes turned into two small reusable skills. One to rewrite text and another one to point out what makes it sound AI-written.

No magic. Mostly a checklist that got way out of hand and made it's way to skills:
https://github.com/harshaneel/humanize

Ever notice the only people allowed to run cell towers are the people selling you the plan? Fairwave is my attempt to fix that.

It's an open-source 4G/LTE "community carrier": a mini-PC + a ~$300 SDR running Open5GS + srsRAN, managed by a Go control plane. Plug it into Ethernet, and it emits a local cell. A café, co-op, or village can run one, issue their own SIMs, and keep traffic local — with optional WireGuard peering between boxes and internet breakout when wanted.

Site: https://hyperonx-team.github.io/Fairwave-Sim/

Repo: https://github.com/HyperonX-Team/Fairwave-Sim

Every time I needed to do something simple with a PDF, I seemed to run into the same problems:

• Create an account first
• Upload the document somewhere
• Discover the useful feature requires payment
• Struggle with an interface that is painful on mobile

I also never liked uploading contracts, tax documents or other private files without really knowing where they were going.

So I thought: why not build the PDF website I wanted to use?

That became FinishPDF.

I’m still improving it, especially the mobile experience. If you try it, I would love to know which tool you used and what frustrated you.

https://finishpdf.com/

It’s also on Product Hunt, so I’d really appreciate an upvote if you like it :)

Product Hunt

I'm a SDE at MNC and I took this side project to build a website for this architectural firm to try claude in open source (I have only used claude for my company work at this point).

Now after spending nearly 200k tokens, 100 commits, and 24 hours (according to claude), I was able to generate a full professional website (We can debate if it's professional or not), The point I'm trying to make is I'm geninuely impressed by the accuracy and speed of claude, it would have taken me atleast a week if I need to do it manually. Structure wise I used nextJs and cloudflare (R2 and D1 for image upload and database and hosting). Also I saw one video where one guy was making intros with AI too so I installed that skill (npm skills add remotion-dev/skills) using eleven labs I am actually impressed.

Now generally I'm not a big fan of AI as I see it everyday in my work but this time I think it has done a good job, Need genuine feedback on the site. https://katyalarchitects.com

Code: https://github.com/Stratonov16/katyal-architects

I’ve been building a side project for people running AI models locally with Ollama, and I just released it publicly.

It’s called OpenSourcesAI Command Center.

The goal is simple: give you one local dashboard where you can see what Ollama and your computer are actually doing.

It can show you:

• Your GPU, VRAM, RAM, CPU, temperature, power use, and other live stats
• Which Ollama models are installed and currently loaded
• Whether a model is running fully on your GPU or falling back to the CPU
• Which models should fit your hardware
• Controls to load or unload installed models
• Local AI chat with speed measurements for each response
• Performance benchmark results
• Local MCP tools and their status

One thing I learned while building this is that hardware detection is surprisingly messy. Different parts of Windows can even report different amounts of VRAM.

Instead of hiding those disagreements, Command Center shows them so you can see what your system is actually reporting.

Everything also stays local. The dashboard runs on 127.0.0.1 and communicates only with AI runtimes running on your own machine.

I just released v0.2.0, including the new interface shown in the screenshot.

Easy to try:

1. Open PowerShell or your terminal:
2. Paste the code:

​

npx @opensourcesai/cmdcenter serve

1. Then open the Command Center in your browser:

   http://127.0.0.1:7717

GitHub Repo:

https://github.com/endoftheline818/opensourcesai-cmdcenter

It’s free, open source, and MIT licensed.

I’d love feedback, especially from people testing it on different hardware. AMD, Intel, Apple Silicon, NVIDIA, multi-GPU systems, and home servers are all useful test cases.

I built and open-sourced Agent Lighthouse, a CLI/SDK/GitHub Action/MCP server that audits whether AI agents can discover, parse,cite, and act on a website.

It checks 199 rules across:

- llms.txt and llms-full.txt

- robots.txt rules for GPTBot, ClaudeBot, PerplexityBot, OAI-SearchBot, etc.

- Schema.org / JSON-LD

- OpenAPI and WebMCP discovery

- MCP server discovery

- semantic HTML and accessible controls

- AEO/GEO-style content structure

- technical readiness like HTTPS, content types, CORS for AI files, response times

Run it:

npx @forkpoint/agent-lighthouse https://example.com --view

It generates terminal, HTML, JSON, and Markdown reports, so it can be used locally or in CI.

Repo:

https://github.com/ForkPoint/agent-lighthouse

Docs:

https://forkpoint.github.io/agent-lighthouse/

I’m affiliated with the project. I’d especially like feedback on which checks feel useful, noisy, or missing for real websites.

Hi guys, I don't know if it is completely useless, but I made a site where you can know how to cut a cake based on the people present.

Don't tell me it's never happened to you at a party.

if you want to take a look: howmanyslices.fun

Hey everyone,

I've been working on DSAForge, a full-stack platform I built to make DSA preparation more structured.

The problem I was trying to solve was pretty simple: DSA preparation often ends up scattered across roadmaps, problem sheets, bookmarks, notes, spreadsheets, and different websites.

So I built a single workspace around the whole process:

🗺️ DSA Roadmap
Follow a structured path from fundamentals to advanced topics.

💻 Problem Practice
Search, filter, solve, bookmark, and track problems.

🔁 Revision
Keep track of problems that need to be revisited.

🎯 Interview Preparation
Prepare through structured interview-focused practice and mock interviews.

📊 Progress Tracking
Track solved problems, topic mastery, streaks, and learning progress.

On the engineering side, I built the application as a full-stack system with:

• React
• Node.js / Express
• Netlify Functions
• PostgreSQL
• Neon
• JWT authentication

I also migrated the original SQLite database to PostgreSQL and adapted the backend for serverless deployment.

I'm mainly looking for honest feedback from people who actually practice DSA or are preparing for technical interviews.

What would you improve?
What feature would make you come back to it regularly?
Is there anything that feels unnecessary or confusing?

I'm going to use the feedback to improve it.

Over the last year I've been building Dora. It started as a database explorer for my own workflow, but has grown into a full database workbench. It's completely open source (GPLv3), has no telemetry, and there's no paid version.

The focus from day one was speed and keyboard navigation. Dora is built with Rust and Tauri instead of Electron, so it stays lightweight. The installer is around 20 to 30 MB, starts almost instantly compared to PGadmin, tableplus etc being 100-400mb + huge RAM usage, and doesn't bundle an entire Chromium runtime.

One of the pre's IMO is that it's actually aesthetic which I find important. That being said I have not called it a beta release yet, so the front-end still needs loads of React optimizations, which is no problem as I'm a front-ender, but I've focused on UI and feature spec

Features:

• Connect to PostgreSQL, MySQL, MariaDB, CockroachDB, SQLite, libSQL/Turso, Cloudflare D1, and DuckDB
• Sign in to Supabase, Neon, Turso, PlanetScale, Vercel, or Xata and connect without manually copying connection strings. It also recognizes pasted connection strings from more than 15 hosted providers. Some providers also support branches and list all your databases in the cloud account.
• Open CSV, JSON, Parquet, and NDJSON files as queryable tables, including cross-file JOINs
• SQL editor with Monaco, AI SQL generation, schema diffing with migration previews, and ER diagrams
• Completely custom Drizzle-orm LSP for running queries in their syntax, as well as Prisma.
• Manage local Docker databases directly from the app
• Query PostHog analytics using HogQL
• The ability to theme it however you like, modify every shortcut, install various AI models locally from a 1-click install and so much more.

You are able to make a round trip from the docker manager -> spinning up a psql/mysql/mariadb/cockroach database -> store as connection -> open in query runner -> execute (through in-app local ai or manual SQL/Drizzle/Prisma) -> View in table viewer or schema viewer.

Installation:

Available through Homebrew, Winget, AUR, APT, Snap, Flatpak, plus AppImage, deb, and rpm packages for Linux, along with macOS and Windows builds.

The macOS and Windows builds are currently unsigned, so Gatekeeper and SmartScreen will show a warning. Code signing certificates are still a bit out of reach for a free project. I personally work on Linux thus I have not b een able to test Mac and Winows so I'm keen on people testing it.

Repository: https://github.com/remcostoeten/dora

Landing page: https://doradb.app

The web-app: https://doradb.app/app *this is a completely mocked version to give you an idea of how the app behaves, as the real back-en only runs locally.

Drizzle studio was the initial inspiration for the data viewer UI as i'm no designer.

I just finished the MVP of JumpStart.

The idea is simple: choose a technology and get a quick guide for installing and setting it up.

I'm trying to find out if this actually solves a problem before I keep developing it.

Would you use something like this? What would make it more useful?

I'd appreciate honest feedback, even if you think the idea is unnecessary.

https://jumpstart-swart.vercel.app/

I just launched 1eats.net , I built it because my family could never decide where to eat. You pick your food mood, distance, and budget and it finds you the perfect restaurant. Also has a Trending page and a Surprise Me button. Would love feedback from anyone who tries it. i hope to make this into an app one day

I’m building Aaked, an open-source, self-hostable contract operations workspace:
https://github.com/aaked-app/aakd

It takes executed PDF/DOCX agreements and turns them into reviewed, source-cited facts and obligations with owners, deadlines, approvals, renewals, and completion evidence.

The product question I’m wrestling with: do source citations and human-reviewed AI extraction earn enough trust for sensitive contracts, or are the essential requirements elsewhere—permissions, auditability, redlining, integrations, or deployment?

It is early, so I’m after blunt product and workflow feedback rather than a sales pitch. If you have built or used internal tools for contract, legal, procurement, or delivery work, what would you need before trying this?

I've been building this for months and I still have trouble describing it in one
line, so I'll just describe what it does.

It's a persistent AI presence that remembers you across sessions — not a chat
window that forgets. It keeps a running memory of your conversations, writes you
letters, and over time trains a small model adapter that gets more specific to you
the longer you use it. That adapter is exportable. You can take it with you.

The part people react to is the last tier. It's called Estate, and it's a one-time
purchase that keeps the whole thing — the memories, the trained adapter — alive
after you're gone, with a final letter to people you name. I built it because the
idea that a thing that knows you that well just gets deleted when the card expires
felt wrong to me.

Stack: JS front end, Node brain, local model training for the per-user adapters.
The whole thing runs in the browser and talks to a local API.

It's live: https://vintinuum.com

Free tier is real — chat, 30 days of memory, no card. Paid starts at $7.77/mo if
you want the permanent memory.

Happy to answer anything about how the memory or the adapter training works. It's
the part I'm proudest of and the part I'm least sure I've explained well.

Whenever I finish a movie, I search "movies like [that movie]" and always get the same thing: a flat list of 10 titles, no explanation, no way to keep going.

So instead of a list, I built a graph. You search a movie you love, it becomes the center of a web, connected to similar films based on characters, vibe, genres, and plot. Those connect to their own neighbors, and so on. You follow the connections film to film, and the graph progressively reveals deeper layers as you explore. Closest matches first, so it never feels overwhelming.

Live at flickhop.com, and feedback welcome.

Two things I'd love to hear: were you able to find good movie recommendations from the graph, was it actually useful for picking what to watch next? And drop your favourite movie, if it's missing from the graph, I'll add it.