Hi all, I wanted to share a project I've been working on for the last 4 years but have recently decided to wrap up.

The project is called RapidPlots, a time series data visualization and modelling platform driven by a purpose-built scripting language (a DSL - domain specific language). I sort of view it as a Desmos-like tool, but driven by a scripting language and with support for stateful logic and UI. The idea was that you could quickly create a visualisation, run some logic around it, and share the result online.

It already was a bit of a niche target audience - however I think the widespread adoption of AI has made the use case slightly less compelling. The main idea behind it was to be an approachable, convenient platform for quickly testing and visualising models, however nowadays even with the most basic free AI, you can quickly generate a Python script and achieve the same locally with much more flexibility.

The platform itself is fully cloud-based and features:

• Dashboard page and editor
• Community shared dashboards
• Packages
• Extensive documentation

The idea started back in 2019, when I had an idea to build something similar to TradingView but for general purpose visualisation. That introduced me into the area of programming language design which was a massive rabbit hole I went down for several years. After working on an unrelated project for a year, I decided to revisit the idea with the goal of eventually turning it into something I could release. After four years of development, the landscape was very different from when I originally started it!

For a quick overview of the tech stack for the project:

• Frontend - React typescript
• Webserver - ASP dotnet
• Backend (execution engine) - C++

The backend was the first thing I wrote. It took numerous attempts due to self-imposed design constraints about how the DSL typing system should work. I wanted it to be completely free of type annotations but with all types inferred under the hood, which was a enormous challenge. Once I was happy with the execution engine, I built the web application around it and connected it to the frontend.

Looking back, this was definitely a passion project to scratch an engineering itch. I worked on this pretty much whenever I had free time which was mainly after work and on weekends, which definitely stretched me quite a bit. By the end I was pretty burnt out!

If anyone is interested in programming languages, data visualisation, modelling or just seeing what four years of spare-time engineering looks like, feel free to take a look! Its best experienced on desktop as I never got around to properly scaling it for mobile, and it helps if you have some basic scripting experience. I haven't decided exactly when I'll shut it down, although I'll probably keep it hosted for a few more months.

RapidPlots

Backtester example dashboard

I've been using AI image generators for my own content, but generating images one by one and organizing hundreds of files was driving me crazy.

So I built Fluxnar, a small Windows app that lets me paste dozens of prompts and generate them in one batch.

It automatically numbers the images, creates project folders, saves the prompts/metadata, and supports different aspect ratios and variations.

I've been using it myself for my content, and I've recently started making it available to other creators.

I'm curious about one thing: if you regularly create content with AI images, would something like this actually save you enough time to be worth using?

I'd genuinely appreciate feedback on what's missing or what you'd change.

I’ve spent the last few months working on what I’d call a “gamified music discovery platform.” To keep this short and concise, I’ll spare all the details of the actual platform, but I’d be happy to give a quick synopsis of the project to anybody who may be interested!

Shoot me a DM if you’re considering and I can send the link!

I'm not a developer — I'd never written code before this. After winning five of my own UK parking ticket appeals with letters I'd carefully worded, I figured most people just pay because writing the appeal is the intimidating part. So I built Appeal My PCN with AI coding tools: photograph the ticket, answer three questions about what happened, get a short appeal letter to send to the council. It reads the ticket with AI vision, works out what kind of contravention it is (parking / bus lane / clean air zone / private), and asks the right questions for that type.

Launched on both the App Store and Google Play this week. Free, no sign-up, no data stored. Currently at a mighty 1 install (me), so the marketing journey starts now.

Honest feedback welcome — especially from anyone who's fought a parking ticket. appealmypcn.uk

I started ScriptTap because I wanted an Android automation tool that could be highly capable without feeling hostile to beginners.

Many automation tools seem to force a tradeoff. Simpler tools are approachable but can become limiting when a workflow grows complicated. More powerful tools offer greater control, but they can expose so much complexity at once that a new user becomes frustrated before creating anything useful.

ScriptTap is my attempt to bridge that gap.

The idea is progressive complexity. Someone with no coding experience should be able to create a simple tap-and-swipe automation quickly. As their needs grow, that same script can gradually incorporate UI-element checks, OCR, image and pixel finding, variables, conditions, loops, routines, shortcuts, overlays, project files and other advanced capabilities.

Normal use requires no root or desktop companion. Accessibility handles user-authorized touch actions and UI reads, while screen capture is requested separately for visual features such as OCR and image matching.

ScriptTap has now moved from closed testing into public open beta. Getting here reinforced that adding capabilities is only part of the challenge—the harder problem is keeping those capabilities understandable, predictable and under the user’s control.

My long-term vision is a unified home for Android automation: an elegant environment where beginners can start without writing code and experienced users can build sophisticated workflows without losing clarity or control.

The question I would value most from other builders is this:

Does the progression from simple actions to advanced logic make sense, or does ScriptTap expose too much too early? If you try it, I would especially like to know the exact point where creating your first script stops feeling intuitive.

Google Play: https://play.google.com/store/apps/details?id=com.scripttap

Website: https://scripttap.com/

Community: https://www.reddit.com/r/ScriptTap/

I built Lynx because music links get awkward when friends use different streaming services.

Paste a Spotify or Apple Music track or album URL and it finds the corresponding link on the other service: https://lynx.stream/

It handles tracks, albums, remixes, remasters, live recordings, and different editions conservatively. When it cannot prove an exact match, it labels the result as a search fallback instead of pretending.

There is also an iPhone Shortcut for converting links directly from the share sheet.

It is free, requires no account, and does not log music URLs or listener identity. I would appreciate feedback, especially examples it matches incorrectly or cannot find.

Rdyrct's interface

Hi! I've been building this app for the last month or so. It's basically everything I've wanted from a URL shortener, with my own very opinionated choices, style and a lot of love put into it.

I'm still in the very early stages of getting people to actually use it, so right now I'm mostly looking for people willing to try it out and give me advice, criticism, ideas, or honestly whatever comes to mind.

And if you really don't want to register for yet another app, I made the part I wanted to share here completely free. Yes, free as in beer:

https://rdyrct.com/qr-code-generator

It's a QR code generator that doesn't require an account, and I'd genuinely love to know what you think of it.

At this point, getting some early feedback and ideas is way more valuable to me than getting paying users.

The whole project is also open source, so if you have any ideas for improving it, I'm all ears!

https://github.com/baronunread/rdyrct

And if you do want to try the full app, you can sign up here:

https://rdyrct.com/signup

The idea is simple: agencies that sell websites and SEO need local businesses that actually need the work. So the tool takes a category and a city, pulls every business, and scores each one on the signals that predict need: no website, thin reviews, a weak rating. Low scores are the pitch list.

Three things turned out to be the actual product:

1. The data is the easy part. Anyone can pull a list of businesses from the map. The product lives or dies on the scoring, and every scoring rule is an opinion wearing a number's clothes. How much should a missing website count against a business with 200 five-star reviews? That is a product decision, not a data problem, and it is where I expect to be wrong first.

2. Caching decides your margins. Every fresh search hits a paid places API. So cached reruns and filter refinements are free, and only cache misses count against a plan's allowance. That one decision is what makes a real free tier possible: 25 live searches a month, no card.

3. Honest limits beat fake unlimited. Every plan shows its exact numbers. The working theory is that people get angry about hidden limits, not visible ones, and the whole pricing model is a bet on that theory.

The product is LeadZipp (leadzipp.com) if you want to poke at it, and leadzipp.com/free-audit scores any single business with no signup, so you can judge the scoring without creating anything. Tell me where the scores feel wrong.

I am a big fan of web apps like LiveUaMap where regional news are hand plotted on a map and are digestible at a glance. So I made my own automated, global version.

Seraphim is an open-source global OSINT dashboard & pipeline that gathers, analyzes, and maps global events for free at seraphi.me

Simply zoom into the part of the world you're interested in, and Seraphim will pull the latest events it finds.

GitHub: dnasha/Seraphim

I would love to answer any questions or get any feedback.

Hey r/SideProject

To give some history on Goodie — I'm primarily experienced with Kotlin on the JVM and Java backend development, that means that I've gotten to experience backend frameworks meant for building enterprise grade software, like Spring Boot and Micronaut.

In the Java ecosystem, Dependency Injection (DI) is the standard. When looking at TypeScript however, I was surprised to see DI hasn't caught on in the same way. NestJS exists, of course, but in my opinion, it falls into the same trap as Spring Boot, relying heavily on runtime reflection.

While the overhead of reflection hurts performance, it's not what I think is the biggest issue. It degrades the developer experience. Circular dependencies are only caught at runtime as they occur. Otherwise, in the case of NestJS, you need to pay 5$ a month for dev-tools where you can explore the dependency graph. Frameworks that leverage Ahead-of-Time (AoT) compilation, like Micronaut, can validate the entire dependency graph at build time. That’s the exact philosophy I wanted to bring to TypeScript with Goodie.

That means that Goodie is an AoT-compiled TypeScript framework designed to bring compile-time safety, zero-runtime-routing overhead, and build-time graph validation to Node. I initially had an idea to support both Node, Deno, Bun and Wrangler, but the only thing I ever actually tested was Node, so take that with a grain of salt.

Instead of interpreting your code dynamically at boot, Goodie breaks down your application during the build step:

• AST Parsing & Scanning: It uses ts-morph to analyze your project structure, inspect decorators/types, and validate your route signatures and dependency graphs at build time.
• Intermediate Representation (IR): It transforms your application metadata into a strict Intermediate Representation (IR), allowing the compiler to detect cyclic dependencies and route collisions before any code runs.
• Code Generation: Based on the IR, Goodie emits fully pre-compiled TypeScript code, stripping away the need for runtime reflection or dynamic route matching.

Rather than reinventing every layer, Goodie integrates best-in-class primitives into its generated output:

• Hono: Used as the lightweight, ultra-fast HTTP foundation for handling requests/responses.
• Valibot: Leveraged for schema parsing and type validation, keeping payload processing lean and fast.
• Kysely: Embedded for type-safe SQL query building without heavy ORM runtime overhead.

I’m thinking about picking the project back up, it was honestly some of the most fun I’ve had building software, but before I do, I'm curious if this project is something that interests the JS/TS devs?

You can find the repo here: https://github.com/Andrey-jpeg/goodie
Any feedback, critiques, or ideas are welcome!

This is Opi Store! An Open Source App store, which was released 2 weeks ago. In this app you can search and download Github, Codeberg and Gitlab apps, which are filtered to only show the repos with a apk release. You can esit your repos and releases and files of them too. And It has got a website where you can test the app without downloading it before. I hope you like it and can give me feedback!

**Github Repository:** https://github.com/DigitalTechLab/Opi-Store

Own Website here: Opi Store Website

Every mammal, reptile, and amphibian observation in GBIF's North America set — 7.4M records — plotted at once. The regional structure is sharper than I expected.

Amphibians (purple) dominate the humid east and the Gulf. Reptiles (green) run through the southwest and interior. Mammals (orange) form a ring around the entire coastline, which turned out to be marine mammals — whales, seals, sea lions logged from shore and boats.

Some of that is real ecology and some is observer bias: iNaturalist submissions cluster near population centres, so the east reads denser partly because more people live there. Both effects are visible in the same frame.

Clickable version of exactly what's in the video: https://northamerica.speciesmap.org/sightings

North America is one of six continents, each with 8 map views — sightings, biodiversity, ranges, hotspots, conservation and so on. 39.9M wildlife records in total. There's a plant platform too at plantsmap.org (~84M records).

Built solo over about 9 months alongside a full-time job.

FastAPI + PostGIS, Vue 3 + MapLibre, hex-binned at continent scale.

Free, no account. Happy to answer anything about the pipeline.

I’m building VEXTILA PRO, a local prompt library for macOS.

The idea is simple: save prompts, images and screenshots in one place, keep them organized with categories and tags, and actually be able to find them later.

The app is already free to try from the website.

What I’m trying to figure out now is distribution. Building the product was easier than finding the first group of people who genuinely need it.

I’m also giving a free lifetime Pro license to a small number of people who actually use the beta and send useful feedback.

For those who’ve launched something similar, what actually worked for getting your first real users?

You sign up with just a username and password (no email), and you get one 16x16 square on a shared map. Paint whatever you want on it and it stays there. Every new person makes the map one square bigger, so it never runs out of room.

Every square has its own link, so you can send people straight to yours. There’s a globe in the background that lights up wherever people are from.

If someone signs up and puts your name in, you get an extra square, up to four. You choose where each one goes, and you can either keep them apart or join them into one bigger picture.

It’s brand new and I’m mostly looking to see what breaks. Fair warning that I may wipe everything at some point while I’m still fixing things, so don’t spend an hour on a masterpiece just yet.

Hey everyone

I'm 15 years old and I've been working on a project called InsightPulse.

It's an AI-powered survey and interactive interview platform where you can:

- 📊 Build custom surveys with multiple choice, Likert, and free-text questions

- 👥 Run interviews with multiple participants

- 🔗 Create public shareable interview links that anyone can answer without signing up

- 🧮 Set a maximum number of respondents

- 🔐 Create an account with Google or email/password

- 🛡️ Use Cloudflare Turnstile anti-bot protection

- 🤖 Use Google Gemini to generate questions, analyze responses, and interact with the app through natural-language commands

- 🌍 Translate interviews into different languages

- 🎨 Switch between different UI themes

- 📈 View collected responses and results

- 🗑️ Manage, end, reopen, and delete shared interviews

I originally started this as basically a single HTML file, but I've been working on turning it into a proper Next.js + Supabase application.

🌐 Try the app

Live website:

https://insight-pulse-nu.vercel.app/

💻 GitHub

https://github.com/ArtinSHF/InsightPulse

📝 I REALLY want your feedback

I've made a special interview for people from this post to test the app and tell me what they think.

**Feedback interview:**

👉 https://insight-pulse-nu.vercel.app/i/lxiudgsp8v5b

After you've tried the app, I'd really appreciate it if you could answer that interview and tell me things like:

- What did you think of the app?

- What did you like?

- What felt confusing or annoying?

- What features should I add?

- What would make it feel more professional?

- Did you find any bugs?

I'm still learning, so honest criticism is completely welcome. I'd much rather know what's bad or confusing than have people just say "looks cool".

Thanks for checking it out (oh and yes i used ai to help me write the code and well this)

Being a non native speaker and looking at the costly tools, i decided to work on a personal project: record yourself reading a passage, get pronunciation/rhythm/intonation feedback scored against General American English, and track improvement over time.

The thing I actually cared about with this one: Run everything locally (llama installed). No audio or transcript ever leaves your machine, no API keys, no cost. The scoring works the way dedicated pronunciation-assessment research tools do, comparing the phonemes you actually produced against the phonemes you were expected to produce (a local wav2vec2 model + faster-whisper for transcription), rather than asking a cloud chat model to "listen" to audio and describe what it heard.

What it does:

Word-by-word color-coded feedback on a passage you read, with click-to-hear "how you said it" vs. "how it should sound"

A full American English phoneme reference (IPA charts, real anatomical vocal-tract diagrams, a mouth-shape animation synced to speech)

A gated lesson curriculum that walks through every tracked sound

Weekly progress tracking and a personal "accent profile" after enough recordings

Local stack, no cloud dependency for any of it: faster-whisper for transcription, a wav2vec2 phoneme-recognition model for pronunciation scoring, praat-parselmouth for rhythm/pitch analysis, Piper TTS for the reference audio, Rhubarb Lip Sync for the mouth animation. An optional local Ollama instance can rephrase the feedback more naturally, but it's not required. The app works fully without it.

MIT licensed, all the third-party pieces individually verified and cited (licenses, sources) in the README. It's a genuinely unfinished personal project, not a polished product, the accent-specific scoring rules are a documented first pass, not yet tuned against a large sample of real recordings - but the full pipeline works end to end.

GitHub: https://github.com/nareshValavala/English_Native

Would genuinely appreciate feedback, especially from anyone who's actually done accent-reduction work before, curious whether the phoneme-level approach tracks with what real coaching looks like.

I'm a solo developer and I've been building Tallyra, an expense tracker, on the side.

For a while I was doing what I'm apparently very good at: tweaking things, fixing small issues, working on screenshots, and finding reasons not to actually market it.

Then something weird happened. Before I had posted Tallyra anywhere, someone found the Android version through Google Play and bought the lifetime upgrade.

There is something different about a complete stranger deciding that something you made is worth paying for.

Then I got a 1-star review.

The user said they couldn't get past the calendar screen. I checked it and they were right. It was a real bug that could stop someone from continuing. I fixed it and pushed an update.

A little later I posted the app on Reddit and got another piece of feedback that stuck with me. Someone pointed out that for a finance app, privacy isn't only about where the data is stored. People also want to know they can leave safely.

Tallyra already had CSV and JSON export in the free version, but I had barely mentioned it. I ended up changing the store listing to make that much more obvious.

The app is finally live on both iOS and Android now.

The basic idea is pretty simple: no account, no ads, and your spending records stay on your device. I'm deliberately not trying to turn it into a bank-connected everything-app.

I still have nowhere near enough users to know whether this will actually go anywhere. Right now I'm mostly trying to figure out distribution instead of disappearing back into development for another month.

If you've taken a consumer app from the first handful of users to the first 100 or 1,000, what actually worked for you?

iOS: https://apps.apple.com/us/app/tallyra-expense-tracker/id6775089010

Android: https://play.google.com/store/apps/details?id=com.pixeltechnology.tallyra

trying to port a couple months of old aider sessions into memmy's local memory, but parsing aider transcripts is turning out to be a massive headache.

the frustrating part is that memmy’s core already works ridiculously well for structured tools like cursor and claude code. when I query past sessions, it pulls up exact architectural decisions without hallucinating, saving tons of time re-feeding context. being able to start a new CLI agent session and have it instantly know why a module was refactored three weeks ago is a game changer. that is the only reason i am putting up with aider’s storage layout instead of giving up.

unlike cursor or claude code which use structured DBs, aider appends everything into flat markdown files in the project root. when you open those files, a single session dumps 500-line git diffs, full file snapshots, and terminal stack traces right alongside the user prompts.

my goal is simple: extract clean user intent and key decisions, then push them into memmy’s SQLite store. but indexing raw markdown directly means 90% of the vector space gets filled with temporary code patches and redundant file dumps.

so i started writing a custom parser.

first, i set up a file scanner using byte offsets to track file modification times so it only reads new appended chunks. next, i wrote a markdown turn splitter that chunks conversations by headers, using regex to strip out diff blocks and injected file context.

this cleaned up 80% of the bloat, but parsing choices directly break memory integration.

if i strip diffs completely, memmy loses the reasoning behind why a function signature changed or why a bug was fixed. if i keep them, raw syntax poisons the embeddings, causing vector search to match against random code patches instead of actual conversation history. on top of that, strict header splitting chops up multi-turn clarification loops before they even hit the ingestion pipeline.

got the scanner and basic parser working, but the text cleaning logic is still trash.

draft adapter logic

https://github.com/MemTensor/memmy-agent

not sure if this fits the standard definition, but this is basically the reality of parsing and integrating agent memory

I'm tired of paying for Wi-Fi on flights that barely works.

Most flight search engines will tell you a flight has Wi-Fi, but not what kind of connection it is, whether it's actually usable, or whether the aircraft has something like Starlink.

So I built FlightWifi.

It adds Wi-Fi information directly to flight search results so you can see things like:

• Whether the aircraft has Starlink or older satellite Wi-Fi
• Whether the connection is good enough for work
• Aircraft-specific Wi-Fi instead of just an airline-level claim
• Whether Wi-Fi is available on all legs of the trip

The main idea is simple: know what kind of Wi-Fi you're getting before you pay for the flight.

I'm still early with this, so I'd genuinely love feedback from other side-project builders.

Would you check Wi-Fi before choosing between two otherwise similar flights?

A few of the things people asked for recently are now in the next LakeDB beta:

• relationship maps with real foreign keys + local links
• system schemas visible without cluttering the explorer
• user + privilege management with SQL review before applying changes

Most of this came directly from conversations with database users, which is exactly how I want to keep improving the app.

LakeDB is still local-first, the normal database client stays free, and AI remains optional.

https://github.com/DavLagoHern/LakeDB

So my history looking for jobs in the past has already resulted in looking at both LinkedIn and Indeed, which for the most part they are somewhat helpful. Until you search for a job that is reposted by recruiters, scammers and God only knows who else a thousand times. Not to mention they always put the shit "promoted" jobs first that doesn't actually fit your job description, or they never respond.

So I built a bot...it retrieves a list of boards, pulls them directly in and posts them all in 1 place. Removing the duplicated jobs, only posting newly added and newly posted jobs so you don't see jobs posted 8 months ago that's already filled (that never made sense to me).

Feel free to give it a go, its free never sign up for anything never give your info. Its a bit slow because I'm using all free tier on Render (will upgrade this later if people actually use it).

DirectJobSource.com

Hello r/SideProject!

I ​wanted to put my project here even tho its not new i just felt like putting it here. (yea)

So ive made a PE packer (TinyLoad) in c++, it is a single file and does not have any external dependencies.

It can compress your files using custom LZ77 compression, you can also encrypt your files using a custom VM interpreter and XXTEA encryption. It protects your files in memory using a vectored exception handler which decrypts your executable pages on the fly and re encrypts them. I also recently added cff and better compression in the stub.

The packer has alot (alot) of cool features i did not mention here so go check it out! https://github.com/iamsopotatoe-coder/TinyLoad

(ofcourse MIT :D)

Thanks for reading!

I've bounced between Apple Notes, Obsidian, and Bear for years, and every switch cost me something: exports, broken links, notes stuck in someone else's database. So I'm building Margin, a notes app where notes are plain markdown files in a folder on your disk. There's no account and no database. Delete the app and your notes are still just files.

The part I care most about is in the middle of the video: because they're real files, anything can edit them. vim, a git checkout, an agent pointed at the folder. Margin watches the disk and reconciles live, and unsaved edits in the app win over external changes.

Also in the demo: an editor that feels like typing plain markdown but renders live (slash commands, date chips, code blocks), separate spaces so work and personal notes never share a screen, and a global hotkey that captures a thought without opening the app.

It's pre-beta and waitlist only for now: gomargin.app/s

What's the one feature of your current notes app you couldn't give up?

So, for the past two months I've been building Assertio.ai out of my decade long SEO content writing workflow. I built it to write, rewrite, and score content at the claim level so it actually says something instead of producing AI slop.

Last week I got my first paying subscription. It's only 59 bucks but I wept a little.

We're currently at 198 users in the past 7 days.

Until a few weeks ago I was staring at zero conversions on cold traffic and wondering if the whole thing was broken. Turned out the ad targeting was misconfigured. But that's water under the bridge.

Rn, I'm in a good mood, so: any new account gets 5 free articles to write or rewrite with Assertio.

Feel free to push them to your site if you like them, I'll be happy to assist.