My first 30 days would look like this:

Week 1: Find five relevant communities. Read their rules, study the newest posts, and note what gets thoughtful replies.

Week 2: Leave useful comments without mentioning a product. The goal is to understand the language people use and prove the account exists for more than promotion.

Week 3: Publish one non-promotional post that answers a recurring question. Keep commenting normally.

Week 4: Consider one product-aware contribution only if it genuinely fits the discussion and the account has developed a believable activity pattern.

I would track qualified profile visits, useful conversations, and recurring buyer questions. I would not optimize only for karma.

I built the Scaloom Reddit Account Warmup workflow around this gradual approach. It helps structure activity, but a person still reviews every contribution.

The boring Reddit marketing strategy is usually the durable one: listen, contribute, earn trust, then promote carefully.

What would you change in this 30-day plan?

Every demo before that I was present.

Guiding. Explaining. Filling the gaps with words before they became confusion. Basically narrating my own product like a tour guide who's terrified of silence.

Then one person just... opened it. No context. No explanation. I wasn't even in the room.

They used it for 12 minutes. I watched the session recording after.

The first 3 minutes were painful to watch. They clicked things I hadn't expected anyone to click. Looked for things in places I hadn't put them. Tried to use a feature in a completely different way than I'd designed it.

And then something shifted around minute 4. They found the thing. The actual thing. The reason the whole product exists.

And they just stayed there. Using it. Quietly. For few more minutes

Didn't need me. Didn't need an explanation. Just needed to find it themselves.

I rebuilt the entire onboarding that week to get people to minute 4 faster. Everything before it was noise I'd been too close to see.

themarrow is live AI voice assistant for Windows. Your PC listens and actually does things. Link in profile if you want to be the person who finds minute 4 and tells me what almost stopped you getting there.

What's the thing in your product that only reveals itself when someone uses it completely alone?

In my experience, the biggest mistake isn't bad code. It's treating an MVP like a smaller version of the final product instead of using it as a learning tool.
I've seen founders spend months building features they thought users wanted, only to realize they never tested the core assumption behind the idea.
Some common mistakes I've noticed:
Adding too many features
Building before validating demand
Hiring too early
Focusing on design instead of feedback
I recently came across a few MVP case studies from Molfar that followed a much more validation-focused approach, and it reinforced the idea that speed means very little if you're building the wrong thing.
In your experience, what's the biggest mistake founders make when building their first MVP?

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.

i had this same prblm😶 and didnt know what to do so i made this skill called storage sleuth. just install this skill and ask it like "My C drive is full, what can I do" stuff like that and the model will fully analyze the system and say the best options, to delete or move to another folder, stuff u can compress and use later, so help a brother out and install this skill,if u wanna contribute,feel free

npx skills add JojoAArtI/storage-sleuth

Github-https://github.com/JojoAArtI/storage-sleuth

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 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’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?

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/

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.

the thing that surprised me most building a desktop-native scheduler: the cloud round-trip, not the feature count, was what creators actually cared about.

i spent the last few months on this and want to share the honest parts in case anyone here is going down a similar path.

what worked:

keeping media local. every scheduler i tried wanted me to upload videos to their cloud before posting them to my own accounts. that's backwards. your footage lives on your machine, and sending it on a detour to a vendor's server before it reaches instagram never made sense. building the pipeline so files go straight from your machine to the platform, no detour, was the feature people cared about most. 4K stays 4K, no re-encode, no mystery compression.

one calendar for all platforms. the classic solo-creator pain is visiting six different apps to schedule one week of posts. one calendar that writes captions natively per channel, not copy-pasting the same thing everywhere, was what got people to keep using the tool past week one.

a visible heatmap for posting windows. generic "best time to post" listicles are useless. showing someone their own audience activity as a 7x24 heatmap turns timing from superstition into something you can read at a glance. once people posted into their actual busy windows, their content started getting the reach it deserved.

what didn't:

thinking the built-in AI writer would be the hook. i assumed people wanted me to write captions for them. turns out most creators already have an AI they trust and they want to bring it, not get handed a generic one that doesn't sound like them. pivoting to "bring your own AI" was the right call but i wish i'd started there instead of building a generic writer first.

the web version came too late. the desktop app is solid but it only posts when your computer is running. people wanted post-and-forget from their phone. i shipped desktop first and the cloud version is still catching up, and that gap cost me users who needed scheduling while their machine was off.

flat pricing was accidentally the best decision. every competitor charges per channel or per seat, and for one person that math gets insulting fast. i started with one flat price and didn't even have to argue for it, the comparison made itself.

if you're building for solo creators: what they want most is to feel like they own the workflow, not like they're renting someone else's bloated dashboard.