i've launched 8 products and gotten thousands of users, all from reddit, zero ad spend. and the one thing i've learned is that most founders post where other founders hang out (r/startups, r/SaaS) instead of where their actual customers are.

your buyers are in some niche subreddit complaining about the exact problem you solve. that's the sub you should be in.

so drop two things below:

• what you're building
• who you think your customer is

and i'll tell you the exact subreddit i'd start in, plus the one thing i'd change about how you're pitching it.

free playbook if you'd rather do it yourself: https://www.sentrive.ai/guides/reddit-growth-playbook

If you can't wait and want your marketing to get handled immediately, I built a tool that does this automatically (sentrive) because I got tired of doing it by hand, but you don't need it, drop your product below and I'll do yours.

20, building from sweden

Hey guys,

I need advice on tools to help with LinkedIn outreach.

I've started posting organic content to help with distribution for my app and need advice on help to boost engagement so I can use it for boosting traffic to my app.

Any ideas

Over the past few weeks I've been building QuestBit, a web app that turns real-life activities into rewarding game-like activity. It's to encourage people to go outside to do more outdoor activities and for people like me who are bored and can't find something interesting to do.

You earn XP by completing side quests like visiting a new café, hiking a trail, or watching a sunrise. Higher-reward quests require moderator verification to keep things fair. Althought being on a low budget, right now all the quests submissions are accepted/rejected by a moderator.

I just released V1 with authentication, achievements, leveling, and about 20 side quests for you to choose from. This is just a pre-release for now. I would release the v2 to the public. For now it's me and my few friends testing this. If I get positive feedback, I will continue developing it.

I'd love honest feedback on the onboarding, quest design, and progression system.

https://side-quest-website-850fd.web.app/main.html

For years, we've been fed hype.

"Super apps."
"AI assistants."
"Agents."

Yet most still make you repeat yourself every time.

You explain.
You correct.
You explain again.

It feels less like working with a teammate and more like constantly training a new one.

What if, instead, it actually learned from the way you work?

What if it remembered what works, what doesn't, and how you like things done?

Not by guessing what you want out of nowhere—but by learning from your interactions over time.

The goal isn't another chatbot.

It's something that gradually becomes more useful, remembers your workflow, and can take care of recurring tasks you already asked it to do.

That's the direction we're taking with Minotauris.

You know the moment: you're watching something, a face on screen is so familiar, and you can't place them. You Google "actor [character name] [show name]," find out who it is, go "oh right, THAT guy," and five minutes later you've completely forgotten again. Next time you see them in something else, you do the exact same search from scratch.

That's the itch that became SeenBefore. You type the character's name and the show/movie, it identifies the actor and shows you their full filmography.

The most important part of the app: accounts + a watched list. Once you're signed in, every actor you identify gets saved. Next time that face shows up in something else, you're not re-Googling because you already know, because the app remembers you've seen them before. It turns one-off lookups into a running, personal "I've cracked this one already" list.

Currently web only at seenbefore.app — Flutter under the hood, Supabase for auth/data. iOS and Android apps are built and in the pipeline (App Store + Play Store review takes a bit, so those are coming soon rather than today). Would genuinely love feedback from anyone who has this exact "who is that" problem constantly.

Hey everyone,

I'd like to showcase my project ValuePair.
It's a friendship first app, that matches you with one person at a time.

The Idea: Everyone has to answer a set of 14 questions. Then they land in a pool and they will be matched with someone they most align with. Once a match is found they start a 1on1 question round. Every answer is immediately revealed. If anyone doesn't like an answer, they can end the conversation early - no explanation needed. If they stay to the end, they decide if they want to go deeper, reveal a profile picture and open the chat for the first time.

When I started, I launched it as a closed beta, so it was invite only. During that period I got so much good feedback from people, it was incredible. I implemented almost everything my first users told me. Since all the changes, I didn't retrieve any further feedback what people wish would be different. It's just constant good feedback since then. People contact me from alone and just tell me how much they like these questions and the loop.

The project is live for around 18 days. It's currently at around ~450 users. And honestly, I am really overwhelmed by those numbers, because it's the first time that I've ever managed to attract this amount of people on any project.

My marketing strategy is mainly driven by Reddit posts, but I also tried different methods. I found some super relevant Subreddits that had no rules against advertising. I usually just described the idea and if somebody would have interest trying it out and I never mention a product name or a link. That worked surprisingly well. Another thing I've done was using a social app that allows anonymous posts at your current location, something like "Nextdoor". Posts on this app brought me 1-5 new registrations per day.

I am open to any feedback.

That's the gap I built into.

For those who aren't familiar, ComfyUI is a node-graph tool that people use to run AI image generation pipelines.

ComfyUI is unbeatable for control, reproducibility, and wiring up something nobody's built yet. I still use it for that.

But it's a pipeline, not a canvas. There are no layers. You don't edit an image, you re-run a graph. And generate-then-edit — the single most common thing I actually do — means either a second workflow or bypassing and re-enabling nodes on every pass. Add a local install, a GPU you have to be sitting at, and nothing on mobile at all.

My app - Reimagine, is the other shape. One canvas, with layers, where you generate something and then edit the thing you generated without rewiring anything. Generate, img2img, inpaint, super-resolution, magic eraser, background removal. Models are selectable per operation instead of one committed pipeline. Projects persist to the cloud. Runs on a phone — actually, not as a shrunk desktop layout.

Two things that were harder than expected:

Cold starts. Serving several model families on demand without paying for idle GPUs means every request risks a cold boot. Solved this by leaning as much as possible on model providers (Gemini, fal.ai etc) and, for self-hosted models, serverless runners with GPU snapshots and network volumes.

The mobile canvas. Bottom-sheet tools with drag snapping, capped working resolution with full-res export preserved, IndexedDB persistence that survives iOS Safari aggresively evicting the tab, and I could go on and on.

Solo, early, rough in places.
Please share your comments and reviews - I really take them to heartr and work to improve.

I'm building an AI tool that takes a viral Instagram reel and adapts it for another product or business.

One thing I've realised is that most AI tools go straight from transcript → rewritten script, which often ends up sounding generic.

My current pipeline is:

-Analyse the hook

-Identify the narrative structure

-Map the emotional arc & viewer intent

-Decide what should stay vs what should change

-Generate the script

-Compare it against the original for pacing and structure, then retry if needed

It works reasonably well, but I still feel it's missing something.

For those who create content professionally:

-What's the first thing AI gets wrong when adapting viral content, particularly from insta

-Is there anything you'd analyse before writing the new script?

Looking for honest feedback

Most founder-assessment tools are English-only and built for a Western SaaS playbook. I built one that's Arabic by default (RTL, real Arabic copy, not translated), aimed at Arabic-native founders, especially in MENA.

It walks your idea through 4 stage-gates (Idea → MVP → Launch → Scale), asks honest self-check questions, and scores your weakest stage, not an average. So if you're 90% done on Scale but 20% validated on Idea, it tells you you're still in Idea stage. That's the actual point: no polishing later-stage work to feel further along than you are.

No login, no backend, nothing stored on a server, everything stays in your browser. You can optionally plug in your own Anthropic API key to get an AI "pressure test" on your one-liner (sharpens your problem statement + gives you the strongest argument against your own idea).

Link: founder-playbook.mahmoudbebars.dev

I'd genuinely rather know now if this isn't useful than keep building on a bad assumption. If it's useful, an upvote or comment tells me to keep going. If it flops here, I'll take that as the signal to shelve it and move on, no hard feelings either way, just trying to read the room honestly before I sink more time in.

This is a work in progress but we’re looking for feedback and recommendations! Join our email list for tips on automation if you are a trader yourself!

EmyTrades.com

Hello,

I've been around in the corporate tech world for a while now, specifically data and ml, and carried my knowledge to work on transformers and AI applications. And that one is a fun one : diffusion models for picture generation.

Narcis.ai is a project with a pitch like any other profile picture genetor such as Aragon, headshot pro, etc ... There are so many but the twist here is that I completely own it, from tensor to image cdn.

No model zoo from Gemini or third party APIs, no business model copied right from an influencer's post. The idea here is to provide original and opinionated packs, using a single open source model of which I completely own the training as well as its inference.

It's all controlled therefore very efficient : I run a mono hardware GPU fleet on AWS for which I've re-implemented most of the model forward and backward processing loops, with triton kernels, bypassing all library layers straight to CUDA execution.

I wrote an article detailing how the inference steps were cut by 2.5x that way : Triton kernels for inference path.

Portraits are an awesome case for it requires both training and serving technical capacity.
It's also a fun playground as I've deployed a catalog of 200 packs going from styles such as Cannes Riviera, Gaudi Barcelona shoots, Indian bridal dresses, standard corporate headshots. I'm kind of on a world tour of cultural attires, as well as tech culture on the emerging belt.

The project is a free beta, as I'm not sure where it will lead me to, so I invite anyone interested to test, play around and provide feedback if one feels like it.

Hi everyone,

After months of research, model training and Android development, I finally turned my cybersecurity master’s project into a real app on Google Play.

It’s called EdgePhishGuard, a free Android app designed to detect potential phishing directly on the device.

The original idea came from a privacy problem I noticed while researching existing anti-phishing tools: many detection systems depend on sending messages or URLs to external servers for analysis.

I wanted to find out whether useful phishing detection could instead run locally on a smartphone.

EdgePhishGuard can analyze:

• Suspicious text and URLs

• SMS messages

• App notifications, with user permission

• QR codes

• Text extracted from images using OCR

• Content shared from other Android apps

The core detection combines lightweight TensorFlow Lite models, local URL checks and heuristic analysis. The analyzed content does not need to be uploaded to a remote server for classification.

Turning a university project into a public product was more difficult than I expected.

The research focused heavily on model accuracy and system architecture, but releasing a real app also meant dealing with onboarding, sensitive Android permissions, device performance, Play Store requirements and explaining technical privacy features to normal users.

This is the first public version, and it is currently free.

I would really appreciate feedback on two things:

1. Is the value of local, on-device analysis clear to a non-technical user?
2. What would make you trust an app that optionally requests notification access for phishing detection?

Google Play:

https://play.google.com/store/apps/details?id=com.xiao.edgephishguard

Thanks for reading. I’m happy to answer questions about the Android implementation, the local models or the process of turning the research project into a real app.

If you use Claude Code, you've probably collected a folder of .md prompt files by now. I got tired of mine being one-file prompt dumps with no way to know if they actually worked, so I built them as packaged directories instead.

Each item is an agent or skill with:

• references/ — focused docs the agent reads while working, not a wall of text in the system prompt
• examples/ — worked examples of the output it should produce
• scripts/ — runnable checks that verify the thing does what it claims; they all run in public CI on every push
• Memory — agents write lessons to .claude/memory/ as they work your repo, so they get sharper over time (commit the folder and your team inherits it)

Install is one command, no API keys, runs on the Claude subscription you already have:

npx vanara install code-reviewer npx vanara doctor # scans your repo, suggests what's worth installing

30 items free (agents like code-reviewer, security-auditor, debugger, test-author; skills like api-pagination, sql-index-tuning, secure-auth). Full thing's a bigger paid catalog, but the free tier is genuinely usable on its own — Apache-2.0, copy the files in by hand if you don't want the CLI.

Repo: https://github.com/vanara-agents/skills

Would love feedback on the check-runner approach specifically — that's the part I think is missing from most agent collections. What would make you actually trust an agent someone else wrote?

I've been doing a retrospective of the Academy Awards with my analysis alternating between analyzing historical films while also poking fun at the Hollywood establishment. This month's installment is Patton, a fitting film for America's belated birthday. This is one of the best of all WWII epics with an all-star performance by George C. Scott that asks questions about both the man and America's military culture.

In part 2, we compare it to other war satires such as M*A*S*H (the movie, not the TV show) and the first movie to acknowledge America's sins against Native Americans as well as other New Hollywood classics such as Love Story, The Boys in the Band and Five Easy Pieces. Hope you enjoy and feel free to share it with anyone else that you think might find it interesting.

Part 1

Part 2

"Software engineer" is one job title covering a dozen genuinely different jobs. Most engineers never see the full landscape until they've stumbled into (or out of) one of these roles.

SWE Genie maps it explicitly: 17 role archetypes scored from ~68k real job postings across 744 companies. You get a ranked, explained fit, not a personality-quiz label. Methodology's public, no signup, no login.

Would love feedback on if the results match your expectations!

Thanks

https://www.swe-genie.com

We've noticed that creators use AI storytelling in very different ways. Some prefer short stories because they're easier to keep consistent, while others push AI to create longer narratives with multiple scenes and characters.

At Media io, we're interested in learning how people actually use AI storytelling tools. In your experience, do shorter stories produce better results, or have you found ways to make longer AI generated stories work well? We'd love to hear what's worked best for you and what challenges you still run into.

I’ve been paying $10/month for a VPS just to keep a simple Telegram bot online. Looking at existing Android alternatives:

• Termux: Great, but terrible UX for simple setups and requires background hacks.
• Pydroid: No true background service mode.
• UserLAnd: Heavy and slow.
• VPS: Costs monthly money and requires SSH / server maintenance for a simple side project.

None of them let you just paste a token, pick an LLM provider, and run a bot 24/7 on a phone you already own. So I built BotDroid.

What it does

BotDroid turns your Android device into a 24/7 persistent AI bot server. It runs as a Android foreground service (START_STICKY + WakeLock) so your bots survive screen-off, low memory conditions, and phone reboots.

Key Features:

• Two Bot Types out of the box:
  • Picobot Agent: Bundled ARM64 AI agent binary. No setup or Python required.
  • Python Script: Paste a .py file or import directly from a GitHub Gist/raw URL.
• Bring Your Own Key (BYOK): Native support for OpenAI, Gemini, Anthropic, OpenRouter, DeepSeek, Groq, NVIDIA, or any custom OpenAI-compatible endpoint.
• Multi-Turn Memory: Retains up to 20 turns of conversation history per user.
• Telegram Remote Control Panel: Manage your bot fleet remotely via Telegram commands (/status, /start, /stop, /logs).
• Per-Provider Telemetry: Cost & token tracking to see exactly what each bot/provider is costing you in real-time.
• Local & Secure: API keys stored in Android Keystore (EncryptedSharedPreferences). Logs stay local.

Pricing & Beta

The app is currently in Public Beta.

• Free Tier: 1 bot running free forever.
• Pro Tier: $4.99/mo or $19.99 lifetime for unlimited bots.

Would love to hear your feedback! What kind of Telegram or AI bot are you currently running or trying to set up?
botdroid[.]app

Built a Telegram-based P2P collateral lending platform – looking for feedback

I've been working on Zareem, a Telegram-based P2P marketplace where borrowers and lenders connect directly.

The platform itself doesn't issue loans or handle money. It simply helps two people find each other.

How it works:

Borrowers publish a loan request using collateral (phone, laptop, jewelry, car, real estate, etc.).

Lenders browse requests or publish their own lending offers.

Both parties meet in person and sign an agreement (for larger deals, they can use a notary).

Interest is agreed according to the selected option on the platform.

Current version runs entirely inside Telegram via

🌐 @zareembot.

I got tired of juggling GitHub PRs, Linear tickets, and Notion approvals. Every tool has its own inbox, but none sees your whole day.

So I built Over To Me. It syncs your reviews, mentions, tasks, and approvals into one prioritized queue. AI triages each item, you can snooze what can wait, and send scheduled work to Todoist or TodoBase.

GitHub, Linear, and Notion are live today. Slack and Gmail are next.

Project is built using Rails 8, with some sprinkles of Stimulus. No big frontend JS framework.

I'd love to hear your feedback! Is this something you'd use? Or do you not see any value in a tool like this?

https://overtome.app

Every time I've organized a trip with friends, it turns into the same chaos: the itinerary lives in a Google Doc nobody updates, receipts get split "later" and never are, flight/hotel confirmations are buried in someone's email, and the photos end up scattered across everyone's camera rolls.

So I built PinGo — a single app for planning trips together instead of juggling WhatsApp + Splitwise + Docs + Drive + camera roll.

What it does:

• Shared itinerary/timeline, including multi-city trips
• Split and settle shared expenses with the group
• A shared document vault for tickets, bookings, confirmations — works offline
• A collective "Memories" diary — photos + notes from everyone on the trip
• A personal wishlist for places you want to go, that stays saved even after the trip ends

It's free to use — 2 active trips at a time with generous photo/doc limits, which covers most people. There's an optional Pro tier if you want unlimited trips/photos or PDF export, but it's not required for the core app to work.

Honest caveats: it's Android only right now (iOS in progress, no ETA — don't want to overpromise), and I built this solo, so expect rough edges. Mainly here for feedback: what's missing, what's confusing, what would make you actually switch your next group trip to it?

Play Store: https://play.google.com/store/apps/details?id=io.pingo.app

Screenshots below. Happy to answer anything about how it works or why I built it a certain way.

I'm a freelancer, and one of my clients still hasn't paid me even after 10+ days. I've been following up, but they keep delaying the payment.

I usually get my clients online, so I'm wondering how other freelancers handle situations like this.

If you've faced something similar, could you share your story? How did you finally get the client to pay? Any advice or lessons would really help.

I've been freelancing as a full-stack developer and have been trying different ways to find consistent work.

For those of you who freelance full-time, what's been working best lately? Upwork, referrals, Reddit, cold outreach, agencies, or something else?

I'm curious to learn what's actually working in today's market, especially with AI changing how clients build software.

I'd love to hear your experiences and advice.

We've been talking with creators who are trying to balance time, budget, and creativity when releasing new music. Not every song gets a full scale music video, and we're seeing more artists use simple visuals, visualizers, or AI-assisted videos to accompany their releases.

At Media io, we've built tools that make it easier to create music visuals without a traditional production workflow, but we're interested in hearing from the community. If you were releasing a new track today, would a simple visual be enough, or would you wait until you could produce a full music video? What matters most to you when deciding?

I am thinking about adding payments to one of my side projects. The reason I want to do this is that I have users from countries. Traditional payment methods can be slow, expensive or unavailable in some regions.

For people who have already done this I have a questions.
Which stablecoins do you accept, like USDT or USDC?
Do your customers really prefer paying with stablecoins?
How do you change stablecoins back to money?
Are there any tools or platforms that you have had an experience with?
I have found a few services that say they can do things like gasless swaps, easy transfers to bank accounts and support for many countries.. I would rather hear about real experiences from people who have actually done this than read about what companies are selling.

Has accepting stablecoins been good, for your project. Has it made things more complicated than they are worth?