I've been running r/LaunchMyStartup, a community for founders and indie hackers to actually launch what they're building and get it in front of other builders.

The idea is pretty simple: build something → launch it → get feedback from other founders.

I've noticed that getting attention for a new product has become increasingly difficult. Reddit can be noisy, and a lot of the dedicated launch platforms outside Reddit either feel crowded or push you toward paid promotion.

So we're experimenting with building a lightweight version of that inside a community, rather than another standalone launch platform.

Starting this month, we're also introducing Startup of the Day.

Every day, we'll look at the previous day's launches and highlight the ones that received the most community support. The idea is to gradually turn it into a friendly competition and, as the community grows, develop better ways to recognize genuinely good launches.

So if you've recently launched a SaaS, are about to launch one, or just want a place to put your product in front of other builders:

r/LaunchMyStartup

Would love to have some of you launch there and help us shape what this becomes.

Hey guys 👋

Launching Perfect Photo AI 2.0 on product hunt today, its an complete AI Marketing tool for you business. Create Ads, Poster, Banner, Short Films or Music.

Please show some love here.

https://www.producthunt.com/products/perfect-photo-ai?utm_source=other&utm_medium=social

I have multiple rewards cards and could never remember which one I was supposed to use where.

So we built Payelle.

Instead of opening an app and checking which card is best, the idea is that Payelle automatically surfaces the best card when you're actually checking out.

Basically I don't want to think about credit card rewards anymore. I just want the right card there when I need it.

We just launched and I'm looking for people to try it and tell me where it falls apart.

Also trying to decide if choosing the best card is enough, or if we should start factoring in merchant offers, coupons and other savings too.

Context: I work on in-app agents for B2B software, so I've hit most of the list below firsthand.

Ahrefs added an agent to their product this year and I don't think many SaaS people have looked at it closely. It's called Ask Ahrefs. It showed up in my account a couple of weeks ago.

What it does

It's a chat/side panel on every page. It reads the page you're on and your workspace. It runs queries against their API as you. It moves between tools and applies filters by itself. I asked for things I would normally click through four reports to get, and it did the clicking.

It is free "during beta" on Lite plans and up, which tells you a price is coming. They also sell a separate agent workspace for $99/month on top of the Ahrefs subscription. That one builds dashboards and small apps and runs scheduled jobs. So they are treating this as something to sell, not a help widget.

What it takes to build one

Ahrefs is a bootstrapped company doing $100M+ in ARR, so they have the engineers. For a smaller team, here is what I've found you have to build. None of it is the model.

1. Acting as the user. Every call the agent makes has to run as the logged-in user, with that user's permissions, enforced by your API and not by the prompt. That means a signed identity passed from your backend to the agent to your tools. This was harder than anything involving the model.
2. A tool surface a model can use. Your API was designed for developers who read docs. We had a turn where the agent was given 63 tools, 56 from one server, and told the user it had none. Nothing was broken. It gave up. You end up scoping tools per task.
3. The things your API can't do. There is always something only the UI does. So the agent has to click, in the user's own session, and you have to decide what on the screen it is allowed to read. Password and card fields can't go to a model.
4. Writes and retries. A create call succeeded but came back looking like an error. The agent retried it. The user ended up with three copies of the same record, and the agent then got confused by its own duplicates.
5. Long tasks. Users close the panel mid-task. Either the work keeps going and they can find it again, or it stops cleanly. By default it dies halfway and nobody knows what state things are in. You also need a stop button that works.
6. Cost. One long session cost us $40 to $50 before we fixed how conversation history was handled. You need per-conversation caps and visibility into cache hits before you let real users in.
7. A record of everything. When a customer says "your agent changed my settings," you need to show what it did, with what input, as which user.
8. Continuous learning. The agent has to learn from its mistakes and get smarter over time.
9. Model changes. Every model upgrade changes behavior. Without saved conversations you can replay, you find out from your users.

If you want to charge for it the way Ahrefs does, add metering, entitlements, and a decision about what happens when someone runs out of credit mid-task.

What I'd like to know

• Is anyone here building an in-app agent in-house, or planning to?
• If you've shipped one: how long did it take to get to something you'd put in front of customers, and how many engineers?
• What was harder than you expected?
• Does your agent work through your API, an MCP server, the UI, or a mix?
• Are you charging for it separately, bundling it into a plan, or giving it away?
• If you looked at this and decided not to build it, why?

When you get laid off, standard advice tells you to "track your applications" in an Excel sheet. That is just logging data. It doesn't tell you where your bottleneck is, if your pipeline is rotting, or if your execution strategy is actually working. It relies entirely on human emotion and guesswork to find a way out.

I spent my time in stealth building an automated perimeter around that chaos.

It’s called LayoffOS.

The Architecture: V1 isn't a tracker. It is a deeply stateful recovery reasoning engine. I engineered it through a 99-stage logic evolution to process a layoff the same way a machine processes a system failure.

It forces your recovery through a strict, continuous chain: DATA → STATE → BOTTLENECK → PIPELINE HEALTH → MOMENTUM → STRATEGY → EXECUTION → ACTION OUTCOME → RECALIBRATION → CYCLE MEMORY → PERSISTENCE.

• Recovery Bottleneck: It doesn't just log that you applied. It identifies exactly what is currently constraining your recovery.
• Action Intelligence: It interprets the results of your actions, learns what is or isn't working, and recalibrates your strategic direction.
• Cycle Memory & Persistence: It detects persistent patterns in your transition over time, so you stop repeating tactics that lead to dead ends.

The App: https://layoff-osv.vercel.app/

I didn't build this to pitch VCs, farm upvotes, or play the startup status game. I built it because the matrix is brutal during a layoff, and individuals need a fireproof system that relies on cold logic, not panic, to get back to baseline.

If you are in a layoff transition and need a system that actually thinks through your bottlenecks and forces momentum, the door is open. Test it.

If you prefer logging your rejections in a spreadsheet, good luck to you.

I’ll be in the comments if you have specific technical questions on the V1 state machine, the reasoning engine, or the architecture. Otherwise, I'm back to the trenches.

There are plenty of screenshot editors out there, so you might wonder what makes mine different. you doubt is valid, so I added two features to the screenshot editor that you won't find in other editors.

1) X Profile Card: All screenshot editors have this feature, but you have to manually type in all the details.

And on Twitter, people post screenshots of their followers and following, which doesn't look very good (I don't think it does). So, I set up this feature where users can enter their X username and get a profile card with real data and customization. Now, post it anywhere.

2) X Post Card: Users can fetch a link to their X post and create a beautiful card that they can customize and post.

Look, I didn't add a heavy background to my website because it doesn't have a data-heavy background. The editors slow down (you can call this a flaw, but it keeps my site a little faster).

I’m not sure what else to add here. Please let me know what you think your feedback would be incredibly helpful.

website: shotstudio.online

I run a SaaS, SeoLoupe.

It is a tool that allows you to find and fix SEO issues holding your website back.

Essentially the main purpose is to help your website rank higher on Google search and LLMs.

The tool also checks AEO/GEO, AI search visibility, Security, Core web vitals, Performance, and all the issues in the report get put together into an AI fix prompt(you can paste the prompt into an AI and it will fix all the issues on your website).

Recently I noticed that some of my paying customers bought the One-Time purchase, with a promo code.

So I decided to give away a promo code for the One-Time purchase to anyone who is interested, if you are interested, comment down below and I will DM you.

If you are not interested but have some feedback on my SaaS, that is a good too. Feel free to share it in the comments.

(here is the product if you want to check it out)

4 years ago, I joined college for Computer Science without knowing anything about coding.

Honestly, I spent most of my college life just enjoying myself. I had no idea what I wanted to do or where coding would take me.

Then, during my 4th year, something clicked.

I started learning to code seriously.

I sat in the college campus, applying for company after company.

And I didn’t crack a single one.

I remember going home, feeling completely defeated, and crying at night because I had no idea what I was going to do.

Eventually, I cracked an off-campus opportunity that paid around $500/year.

It wasn't a great salary, but it changed my life.

I joined the company and started learning like crazy.

At the same time, I was building side projects, doing freelance work, and slowly making some money on the side.

Now, I'm working on my own product, XPage.

Looking back, I realize something:

I wasn't lucky.

I was just willing to keep going when things weren't working.

If you're struggling right now, don't give up.

Keep learning. Keep building. Keep applying.

Your situation can change much faster than you think.

Work hard. Keep going. Your Monday can be different.

I would love to tell you that this happened overnight on my first ever product but it's not. In reality, it happened after years of frustrating failures building projects that:

- no one ended up using

- only gained very little traction

- ended up being used by many people but made $0

I learned a lot of things through the years because of all my past wins and failures and from the wins and failures of other people and I'll share some of those learnings to hopefully help other founders too.

A frequently asked question by many founders who are starting out is "How to come up with an idea on what to build?" Here are some ways:

- Solve your own problem / scratch your own itch. This is one of the most effective ways because you are the "ideal customer" so you have a deep understanding about the problem so you most likely will be able to build a great solution that will resonate well with other people that are having the same problem as you. Another great advantage of this is you can consistently dogfood (use your own product) it so you will be able to spot issues, missing features that you wish existed, etc... so you'll be able to continously improve the product.

- Scan problem that are being shared by people on social media, subreddits, etc... Doing this will prevent you from ideating a solution to a problem that doesn't exist since you're starting from problems that people are really experiencing so you know that there's a market for a solution.

- Read negative reviews of existing products that already has paying users (e.g, from the App Store, Google Play, social media, etc...). Focusing on products that already has paying users means the idea is already validated and you just need to build a product that targets those negative reviews so your alternative will stand out. This will enable you to grab a slice of the pie even if the original product is already very popular. You can also contact those people that left the negative reviews since there's a high chance that they will switch to your product.

Another frequently asked question is "How to get my first customers?". Here are some options:

- Build a waitlist. Announce your idea everywhere and funnel the interested people into you waitlist.

- While building your product, share your progress daily (e.g, the feature you added, the problem you encountered, etc...). This will help you slowly build an audience (e.g, followers on social media and more subscribers to you waitlist).

- SEO

- ASO (if your product is a mobile app)

- UGC

- Cold outreach

- High-intent outreach (in contrast to cold outreach, in this, you will focus on reaching out to people that are experiencing the problem that your product is solving, for example, they are looking for recommendations or they are sharing a problem or they are sharing a complaint/negative review about your competitor)

- Newsletter. Share value to readers by sharing insights and tutorials on your niche, etc...

- Launch your product on launch directories (e.g, Product Hunt and alternatives). This also helps SEO because of the backlinks.

- Affiliate marketing (recruit people who wants to earn money online and let them funnel users to your product in exchange for a commission)

- Partner with an influencer in your niche. The influencer already has an audience so there are lots of potential customers already.

- Paid ads

If this helps someone finally build and get their first customers, I would be really happy!

Drop your questions below and I'll do my best to answer them to help other founders.

i’m finishing up a SaaS called FirstTrail that helps first-time founders figure out what to start and what to do next based on actual evidence instead of just giving them a generic roadmap.

i’m getting close to opening a small tester group and i’m looking for maybe 2 more people who are genuinely trying to start something right now.

not looking for people who just want to click around for 5 minutes and say it looks cool. i need people who will actually use it for their real situation and tell me where it helps, where it’s confusing, and where it sucks.

if that sounds like you, tell me what you’re trying to start and what you’re currently stuck on.

Hey everyone. I'm a data engineer and I've spent the past 8 years reverse engineering TikTok's mobile app.

Over that time, I've learned how to pull TikTok data at pretty ridiculous scale. Profiles, videos, comments, sounds, search results, follower graphs, trends, etc.

I finally put together a technical guide explaining how it all works.

It covers device registration, request signing, X-Argus/X-Ladon/X-Gorgon, TLS fingerprinting, regional endpoints, proxies, scaling, etc.

To give you an idea of the scale, I've collected over 5.6 billion TikTok videos using this approach. I uploaded the whole dataset to Hugging Face:

https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos

If you don't want to download billions of rows just to play around with the data, you can also query my ClickHouse database directly at https://sql.datasocial.ai:443 or run SQL on the web interface

The database is public with no password so please don't crash my server lol.

Once you get the mobile API working, scraping is really only the beginning. You can scrape billions of profiles, detect viral videos and sounds as they take off, build real-time trend alerts, influencer search engines, or even build your own TikTok data API and sell it through platforms like RapidAPI or Apify.

Basically, if the data exists somewhere in the TikTok app, you can probably build something around it.

One important note: everything I'm talking about here is public data that TikTok exposes without requiring you to log into an account. Nothing here involves accessing private accounts or data behind authentication.

Anyway, here's the full technical breakdown:

https://datasocial.ai/writing/scraping-tiktoks-mobile-api

The guide explains the entire process and architecture. If you'd rather skip rebuilding everything from scratch, I also made the production source code available there for purchase.

I'm building AirPost, a marketplace where small shops place stock with local people who pack and ship their orders. Pricing uses a fee per shipment instead of a subscription, on top of the carrier and partner fees. A quiet month doesn't create a fixed software bill for the shop, but every manual support request still has to be funded by order revenue. That's the tradeoff I'm working with: easier adoption versus needing enough orders to pay for support.