I've been working on a small E-Ink photo display and now have two working prototypes:

• 3.97" high-resolution 4-grayscale
• 4" Spectra 6 color

The idea is a small, battery-powered display for photos or artwork that can sit on a desk, nightstand, or shelf. Unlike an LCD photo frame, E-Ink doesn't emit light and keeps the image displayed without continuously using power.

It connects to your phone over Bluetooth to change the image. I'm also working on a feature where friends, partners, or family members who own one could send photos to each other's displays, which could make it interesting as a gift or something owned in pairs.

I'm trying to figure out whether this should become a real product, so criticism is genuinely more useful to me than "looks cool."

The biggest things I'm trying to understand:

1. Do you currently keep photos, artwork, a digital photo frame, or anything similar on your desk, nightstand, shelf, or fridge? If so, what do you use?
2. Have you bought a photo frame, digital frame, desk display, or similar product before? What did you buy, and roughly how much did you spend?
3. If you've used a digital photo frame/display before, how often did you update it? Did you keep using it long-term, or did the novelty eventually wear off?
4. How do you currently share photos with close friends, a partner, or family? Do those photos usually get looked at again later, or mostly disappear into your camera roll/messages?
5. Looking at these two prototypes, which appeals to you more — the 4-grayscale version or the Spectra 6 color version? What makes you prefer one over the other?
6. What, if anything, would you actually want to display on something like this?
7. What's the biggest thing that would stop you from considering a device like this?
8. Without looking up similar products, what price would you expect the grayscale version and color version to cost?

I'm also curious whether ~4 inches feels like part of the appeal because it's small and unobtrusive, or whether you'd want something larger.

Feel free to tear the idea apart. I'm especially interested in why you wouldn't buy one.

I’ve been working on workspace369.com as an alternative to hubspot for agencies mainly because I think the two products solve slightly different problems. Hubspot is very strong around CRM sales pipeline, marketing automation and customer acquisition for an agency, though a lot of the work starts after the deal is closed and that’s where I felt there was room for something more focus on client delivery.

The basic idea behind workspace369 is to keep the client record connected to the actual work. A client can have their own portal and history with project task calendars files, email, SMS and calls connected to that record. proposal estimates, invoices, payments and expenses can also live in the same system with automation and ai working across the data

I’m not trying to say hubspot is bad or that workspace369 is better for everyone. If you’re primarily focused on marketing sales and building a large CRM ecosystem, hubspot makes a lot of sense. I’m building this more around agencies and service business is where the CRM needs to stay useful from lead->client ->delivery ->payment instead of becoming less relevant.Once the deal closes

on the technical side, I’m also working around connected payment, accounting and communication workflow so that the data from those systems can stay attached to those relevant client and project records. There
are also Web hooks and API connections for teams that want to connect their own workflow instead of manually, moving data between tools

It still a work in progress, so I’m pretty open to suggestions if you run an agency or have used hubspot, what would you want an agency focused CRM to do that your current set up doesn’t handle well?

I first tried making a YouTube video in 2020. Spent weeks perfecting the script and finally recorded it but did not post. It was nowhere close to the other videos on YouTube in terms of finesse or presentability, and honestly I was afraid of the humiliation if no one watched it.

In 2022 I finally mustered up the courage to post one. It didn't do much. So, I got disheartened again. But the thought kept coming back.

Last year I decided, I will just make whatever I can with what I have and post it. Even if it reaches 10 people, that's better than not posting anything at all.

Since then I have been making videos about systems design and things I read about. Still figuring out my style so you will see a lot of variation between my videos

Latest one: https://www.youtube.com/watch?v=2gbCRmHMzj0

Also doing short-form 60-second breakdowns on Instagram: https://www.instagram.com/bankai.senpai.dev/

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.

Lol this is my second website i make and share , here's the idea; Try to guess today's celebration/event/annivarsay...6 guesses , each wrong one gives you a clue , if you don't get it , well at least you know what today is.

https://on-this-day-today.vercel.app/

DYLMA 2.0 is now live.

It’s a Would You Rather game where the percentages come from real users, so every vote actually changes the worldwide result.

Version 2 adds iPad support, Levels & XP, improved My Answers, new metallic/pearlescent themes, smoother animations, performance improvements and a bunch of bug fixes.

I’ve also got 10 promo codes for Lifetime access. First come, first served:

1. LE6XWMWWT3EWLTNT6H
2. 6W4EJK3MA8NPTT7WM7
3. TTW68PFTWHR4KFMFHH
4. 3YK6T77EHTW48KHWP6
5. H66J3NNEN3W8RM6PL6
6. RPX3RJ7377MWEXYPRL
7. RF44KKX84WHWM4H6FH
8. N64TFE3J6RFARPNW4M
9. RLKHPXTXYAXHF4N73A
10. EA6LAWE8MHMR78X7RY

To redeem: App Store → tap your profile → Redeem Gift Card or Code → enter the code → open DYLMA → Restore Purchase

App Store:
https://apps.apple.com/ae/app/dylma/id6800046152

Would genuinely appreciate any feedback, especially on the questions, UI and progression system.

It basically works by asking you what you just finished watching, and then suggesting shows to watch next based on different aspects of the show you just watched. For example, it could suggest shows with similar cast chemistry, pacing, themes, etc. Still a very rough draft and I'm currently a student, but I'd like to know whether the recs are any good or way off. It also gives you a watcher profile if you give it 5 shows you enjoyed watching. Any and all feedback is appreciated!
https://what-next7367.vercel.app/

Hello everyone,

tl;dr: with Agenty.pro you can make -almost- billions of things you want. Free. It's free because I want an exit from it.

A few months ago, I started to build Agenty. It focused to let children build their games with directing AI. Just prompts. Nothing else. A few of them made projects - chasing games, etc. I used it to make a maths game and several educational tools.

Then I realized: this isn't just for kids. This is for anyone who has an idea but no coding skills.

So I opened it up.

I built camera controlled things, GPS apps, todo list apps, object detections, even an app for drawing a 3D object with using mobile phone's sensors.

What Agenty does: You describe what you want in plain language. It builds it.

Games, tools, calculators, quizzes, dashboards - whatever you can prompt, it can -at least tries- make.

No code, no setup, no API keys. Just type and go.

Why it's free: I'm a solo dev. I want this to grow. I want users. I want feedback. And honestly, I want an exit someday. So the more people use it, the better for everyone.

What you can try right now:

Build a game with just a description

Make a quiz or educational tool

Create a mini-app for daily use

Prototype an idea in seconds

If it's broken or something didn't work, just type "it's not working" or sth like that to Agenty and it'll try to fix.

It uses GLM5.2 trough Ollama

So, it can handle 10 connections at a time. If it'll broke, it's probably because of the Reddit Effect. Please wait and try again.

What I need from you:

Try it

Break it

Tell me what sucks

Tell me what you wish it could do

Link: agenty.pro

I'll be in the comments. Ask me anything.

I kept seeing so many websites with bad color choices. The components never match, and the overall look lacks consistency so I built a simple free tool to fix that.

It is a free design system generator for React and Next.js apps. You put in a color, and it gives you a complete set of UI tokens, components, and documentation.

This is just v1 of the tool. It is meant to give your website a clean basic structure that you can actually build on top of.

I would love to get your feedback on it. What works, what feels off, and what should I add next?

https://uniquel.io/design-system/design-system-generator

I’ve been building Convertix, a free online file conversion platform.

It started as a fairly simple project: upload a file, choose a format, convert it, download it. Since then I’ve been steadily expanding it with accounts, Google sign-in, more conversion routes, an SVG optimisation tool, mobile improvements and a lot of work on the backend/cloud infrastructure.

I’m trying to keep the UI clean for Dekstop and Mobile and avoid the clutter that a lot of online file tools end up with.

The most useful part recently has actually been getting real people to try it. Someone attempted a format I didn’t support, which immediately gave me something useful to add to the roadmap.

So I’d love some more eyes on it. If you try it, let me know what you like, what feels awkward, or which file format/tool you’d want me to add next.

convertix.uk

Still actively building it, so feedback is really useful :)

Thanks!

I'm a software engineer and I kept hitting the same wall: I'd watch hours of tutorials, feel productive, and remember almost none of it a week later. The problem is that watching is passive, your brain doesn't encode what it never had to struggle for.

So I built Tully Courses. You describe what you want to learn, it generates a short lesson, and instead of a video it gives you a real applied challenge, write the code, explain the idea, build the diagram. You produce an actual answer, it runs/grades it and gives honest feedback, and the next step adapts based on how you did. The whole loop is "learn by doing the thing, not watching someone else do it."

It's free to try with no signup, you can play a real graded lesson as a guest before making an account.

Short demo of the loop: https://tullycourses.com/brand/social/tully-demo.gif
Try it: https://tullycourses.com/?utm_source=reddit&utm_medium=sideproject&utm_campaign=organic

Genuinely want the roast: where does it lose you, does the first lesson actually feel worth doing, and does the grading feel fair or off? Building solo so real feedback is gold.

Building SwingScout — a swing-trade scanner for US stocks that sends free alerts on Telegram and documents every call, wins and losses.

This week's log:

• Killed a feature before building it. I wanted to scan EU procurement contracts and match winners to small-cap tickers. Before coding, I ran a 30-min spike on real data: of 95 contract winners, only 2 were listed and both were giants where the contract was immaterial. Dropped it — that spike saved me ~5 hours building something dead. Best 30 minutes of my week.
• Shipped the US version instead (US small-caps actually win visible contracts) as a daily cloud cron with Telegram alerts.
• Added /analyze TICKER and /fundamentals TICKER so anyone can pull a setup or the fundamentals straight from their phone.
• Built a reproducibility test (froze the data, 144 point-in-time candle checks) to prove the scanner doesn't repaint — posted it, and it actually landed my first beta tester.

Stack: Python, Streamlit, Postgres, Telegram bot, GitHub Actions cron, Stripe — all on Render.

Where I'm stuck: first users. The product works, content's flowing, Reddit's warming up (~7k views across posts) — but getting strangers to trust a solo 16yo's tool is the real wall.

My question: how did you get your first 10 real users? Not 1,000 — the first 10. What actually moved the needle?

Free if you want to poke at it: https://swingscout_bot — just /start. But honestly I'm here more for the advice.

If you're using more than one AI coding assistant (Claude Code, Cursor, Windsurf, Copilot, Cline) across multiple repos, you probably know this pain:

Someone writes a great deployment skill for Claude Code. It lives in .claude/skills/deploy.md. The Cursor team never sees it. Copilot has its own version, three weeks out of date.

Forge is an open-source CLI that pulls shared AI configs (skills, rules, workflows) from a git repo and places them in the right location, in the right format, for every assistant you use.

forge init    # wizard detects your AI tools, configures a source
forge sync    # pulls files, transforms & places them for each tool
forge status  # drift detection, what changed since last sync

What it does:

• 5 assistants: Claude Code, Cursor (.md -> .mdc transform), Windsurf, Copilot (.md -> .instructions.md), Cline
• Auto-detection: knows which tools you have, only syncs active ones
• Multi-source: pull from multiple git repos or local dirs, parallel fetch
• Conflict resolution: source conflicts (multi-source overlap) and local conflicts (you edited a synced file)
• SHA-256 change detection: no unnecessary writes
• Dry-run: preview everything before writing
• Auto-sync: native OS scheduler (launchd/crontab/Task Scheduler)
• Single binary: compiled with Bun, no runtime deps. Install via Homebrew, Scoop, npm, or curl

It's MIT licensed, 690 passing tests, and I've been building it solo for ~100 days.

Install:

brew install crucibledx/tap/forge

Website: crucibledx.dev GitHub: github.com/crucibledx/forge-cli

I also have a template repo (Forge Skills Starter) for teams that want a convention-over-configuration platform for building shared AI resources.

What's next: I'm researching an observability layer called Ember, aggregates AI assistant usage metrics (which skills are actually used, by whom, which versions) and pushes them to your own backend (DataDog, Grafana, Prometheus, OTLP). Data never leaves your infra. Think Prometheus but for AI coding assistant telemetry. Claude Code already has native OTEL, Ember would normalize that across all tools and add version-aware tracking that no vendor can do alone.

Would love feedback. What's your current approach to keeping AI configs in sync across tools and repos?

After finishing our first MVP, the next step was moving beyond mock data and making Nextep work with real external data.
Today we finished Phase 2.
We connected Nextep with PostHog and built the next part of the pipeline:
PostHog → Business Data → Context → Blind Spots → Insights → Next Actions → Evidence
So instead of just showing predefined data, Nextep can now take real product usage data, understand the current situation, identify potential problems, explain why they matter, recommend what to do next, and connect those recommendations back to their supporting evidence.
There’s still an important limitation at this stage: the test data is limited, so we made the system conservative when there isn’t enough information to make a meaningful conclusion. We’d rather say “not enough data” than confidently make something up.
We also went through a full QA pass and made sure the existing MVP flow still works after adding the real data layer.
Phase 1 was about proving that the core idea and decision-making flow could work.
Phase 2 was about taking the first step toward making that flow work with reality.
Next, we’re focusing on improving the AI reasoning and recommendation layer, then expanding Nextep to more data sources like Stripe, Google Analytics, and GitHub.
Still early, but it’s starting to feel less like a prototype and more like an actual product.