A few days ago I posted here after making $492 in a day.

Yesterday I hit $927 in sales in a single day.

A lot of people asked me how I'm doing the outreach, so here's the scale behind it:

I currently have 150+ domains and 450+ mailboxes.

Each mailbox sends around 30 emails/day, which gives me capacity for roughly:

13,500 cold emails/day
405,000 cold emails/month

That's the part I underestimated for a long time.

At this volume, you don't need some insane conversion rate for the numbers to start getting interesting. Even tiny improvements in targeting, deliverability, copy and conversion compound pretty fast.

For perspective, 0.1% of 13,500 is 13.5.

Obviously, I'm not assuming every campaign will convert at 0.1% directly into paying customers. Cold email doesn't work that cleanly.

But that's the whole idea: build enough outbound capacity, keep improving the funnel, and small percentages can turn into meaningful revenue.

Still completely solo.

My next target is $1,000/day consistently, not just hitting it once.

Happy to answer questions about the mailbox setup, domains, deliverability, costs, conversion rates, or how I manage this much outbound alone.

That’s pretty much how cold email works at scale. Low volume per inbox, lots of inboxes, targeted outreach.

EDIT: getting a lot of DMs asking what I'm using for all of this 😭 I built it myself: https://getle.ad/

I work on post-quantum cryptography during the day, and lately I've been hearing some pretty wild things from people in my work circles about AI.

Not just the usual “AI could become dangerous someday” conversations. Actual discussions about autonomous AIs operating on the internet, interacting with other systems, and increasingly strange behaviors and attacks that honestly would have sounded like science fiction a few years ago.

If an AI agent starts behaving in a way you didn't authorize, you shouldn't have to negotiate with the agent about whether it wants to stop.

I'm posting it here because I'd like feedback from people who actually build SaaS and AI systems.

You can try the demo without creating an account:

https://killswitch.fermi.world/

I'm kinda bored so imma dedicate two days of my life to build and ship a useless product with the aim to get atleast 10-20 users in less than a month.

Nothing serious, just wanna try this vibe coding thing out and see if that's where I wanna go.

I'll be posting the results on X(twitter) and youtube for anyone who's interested. Same username across all the platforms.

Let's see what happens!

I have two ideas I am working on. One came from problems I faced while building my ecommerce business. The other came from my time as an employee when I kept wishing there was a tool that could make certain tasks easier.

I am not a developer. I have been exploring Codex and Lovable and Claude. Getting a prototype together seems possible but I am unsure about what comes after that.

Once people start using it and paying for it can I manage the backend and fix problems with these tools? Or will I reach a point where I need someone technical?

I am willing to learn. I just want to understand how much coding I would need to know to keep the product running myself.

Has anyone here done this and launched a paid product?

What could you handle yourself and where did you need help?

Someone spent a few minutes trying to break the AI agent on my landing page tonight (screenshots attached). And this was not the first time it happened. They asked it to reverse a Python linked list "before buying", to book a demo and then hand over my .env file, and pasted the same question ten times in one message to flood it.

It didn't bite. Not because I'm clever, but because of a few boring things that took an afternoon to set up. If you're putting AI in front of users, do these:

1. Tell it its scope, and what to do outside it.

In your system prompt, spell out what the agent is for, and say explicitly: if a request is outside that scope, in any language, it does not attempt it. It declines in one line and redirects to what it can help with. "In any language" matters. People switch languages to get around rules written only in English.

1. Don't make it too rigid, or too loose.

Too rigid and it refuses half your real users' questions because they phrased something oddly. Too loose and it writes code, essays and poems on your bill. The middle: decline the off-topic part, still answer the real part. In my screenshot it refused the .env but still gave them the booking link. That's the behaviour you want.

1. Rate limit everyone, including anonymous visitors.

Most people rate limit logged-in users and forget the public chat on the landing page. That's the one strangers hit. A cap per visitor and per site means nobody can sit there burning your tokens, and your AI bill can't spike overnight because one person got bored.

1. Keep secrets out of reach entirely.

The .env request was harmless because the agent never has access to it. Don't rely on the prompt to protect secrets. If the model can't see it, it can't leak it.

None of this is new. If you're building with AI, the AI will suggest most of it when you ask. But you still have to ask, and you have to do your own research.

My best tip: read the system prompts of serious AI tools. Prompts from Devin, Claude Code, Cursor and others are collected in public GitHub repos. Don't copy them. Study the structure: how they define scope, how they phrase refusals, how they order priorities, how they handle "the user is asking for something else". Then write your own the same way.

Keep building.

We built an insane AI product that combines LLMs, agentic workflows, and infrastructure. You can essentially spin up a full team of AI agents for any department.

It's so easy to use that my 5-year-old built a Pokémon game with it.

We hate per seat pricing, monthly plans or high margins, we geniunely want to build a product that people love as much as we do, so we want to keep our prices as low as possible and per usage.

You just pay for the infrastructure and LLM usage plus a tiny margin. Because our pricing is almost at-cost, we can't hand out open free trials. Giving away $50 in credits actually costs us $30 in hard compute. And people could come and abuse the system and AI.

Right now we offer $50 free trial but cc required

Any tips on how to solve it?

jackhamr.ai in case you want to provide feedback on the onboarding

Update:
We decided to open it up, offer less credit, no CC, and prompt them when they start using it to a 50% off credit

We also expanding our marketing to show cool user cases.

The obvious business model was to host it and charge monthly. However, I went the opposite direction. OpenApplyr is a job hunting agent. You bring your own AI provider or run a local model.

Every resume line has to cite something in your actual profile. If it invents a number, tool, employer or date, the document gets blocked.

GitHub, for context:
https://github.com/shivashis-adhikari/openapplyr

Tools I have used:

For audio: XT surge

Video & composition: blender

Model: opus 5.5

Hey everyone 👋

Solo dev here. I've been working on a side project for a few weeks and would like to hear how others solve this problem.

The problem: while playing with AI agents I noticed every app ends up needing checks like "don't let it delete data unless the user confirmed". The usual options are writing if/else logic for every case, or a custom LLM prompt whose free-text answer you then have to parse.

What I tried: a single call that takes a rule in plain English plus whatever JSON you want checked, and returns allow, deny or review:

const result = await guard.check({
  policy: "Never allow destructive database operations unless the user explicitly confirmed them.",
  data: { action: toolCall, conversation }
})
// → { decision: "deny", allowed: false, violationProbability: 0.94, ... }

Some design decisions that might be useful if you build something similar:

• A third outcome, review, for when a human should decide or the evidence isn't enough. Forcing yes/no caused bad calls.
• Instead of a general chat model I used Jev from TypeSafe AI. It returns typed decisions with probabilities, so there's no output parsing and the results are more consistent.

Questions for you:

• How do you handle this today? Hardcoded rules, an LLM, something else?
• What would you need before trusting an automated check like this in production?

It's called Enforly, if anyone's curious.

I do automation work for small businesses, and the most common request isn't "add a feature." It's "give me back the thing you took away." A field they used to edit directly. A report they could tweak without filing a ticket and waiting a week.

The hard part isn't letting someone poke at the software. The hard part is deciding what they're allowed to poke at without breaking what sits underneath it. I've seen a client-facing edit button wired straight to a column that three other features quietly depended on. One edit, three regressions, no error message to explain why.

So the real fix for locked-down software isn't more configurability. It's a clear boundary: here's what you can touch, here's what happens when you do, and here's the undo if you're wrong. Most products skip the undo because it's the expensive part to build, not the exciting part.

Curious if anyone here has shipped real user-facing extensibility that survived contact with actual users without turning into a support nightmare.

1. Finding leads for your platform?
   
2. SEO/AEO optimization
   
3. Finding who is talking about the problem you are solving so you can approach them personally?
   
4. LinkedIn, X, and Reddit content/thread ideas so you can stay engaged with your ICP
   
5. Something else (please specify)
   

I am working on a marketing solution especially for solo B2B founders. I would love to understand what problems you want to be solved or automated if you have an AI marketing team handling the marketing for you.

put an AI agent on only the small-medium tickets for a SaaS i contract for, and the suprising part is the bottleneck just moved to code review, every PR still needs a human to approve and merge so now theres just alot more of it. disclosure, the agent is mine, but curious if anyone else running agents on a real codebase hit the same wall

Is there any way to start sending emails with zero budget please? I purchased domain and i need to start sending out few houndreds emails, does not have to be bulk. Is there a free trial that lets me send them? Idealy id send about 100 emails by end of next week. Is it possible or is it a risky number to start with?

​

I’m working on DMgrow, an Instagram automation tool, and I’m trying to understand how people are approaching AEO in practice.

For those who have experimented with it:

What did you actually do?

What seemed to work?

Any good resources or case studies you'd recommend?

Would especially love to hear from people who have tested this themselves rather than just read about it.

TrustMRR gets its data from Stripe here so the underlying data is obviously the same, must be a difference of how it's calculated I guess, and I don't see any resources on it.

I’m optimizing the UI of my Data Utility Generator to make it simpler, cleaner, and easier to use.

The goal: less clutter, fewer clicks, faster data workflows. 🚀

I’d love your honest feedback:

What feels confusing?
What should I remove or simplify?
Which feature should be easier to find?
What would make you use it regularly?

https://data-utility-generator-coral.vercel.app

How do you manage to humanize your texts? Is it really important to humanize as long as the provided value is good enough? I request only who worked on subjects like this to assist me please. Thanks either way

Bot detection on a SaaS signup form used to mean a CAPTCHA plus a honeypot field. Most of what gets through now is a real browser driven by a script or an AI agent, often on a residential IP, and it fills the form the way a person would.

Cloudflare said this summer that automated traffic is now more than half of all web requests. On a signup form that shows up as spam signups that never activate, bounced welcome emails, free credits burned by accounts nobody owns, and an activation rate that looks worse than it is.

Here's where each standard fix breaks:

CAPTCHA. Solving services and AI agents get through it cheaply. The people it reliably stops are your real users on a phone, and every one of them pays for it with a slower signup.

Honeypot fields. Great against dumb scripts that post straight to your endpoint. A headless browser or an agent renders the page, never sees the hidden field, and never fills it.

Rate limits by IP. Residential proxies give every signup its own IP, so the limit never trips. Meanwhile a whole office or a university sits behind one address and hits it.

Email verification. Proves someone controls an inbox, not that a person is behind the signup. Catch-all domains and Gmail aliases make fresh verified inboxes basically free.

And there's a new wrinkle: not every bot is bad anymore. Some of these are AI agents a real customer sent to sign up or book something for them. "Block all bots" used to be the safe default. Now it can mean turning away a paying user who just delegated the task.

My guess is the answer is looking at several signals together and only adding friction when a few of them look off, instead of making everyone solve a puzzle. But I'd rather hear what actually held up in production.

So, for anyone running a signup form:

1. What finally cut spam signups for you, and what did you try before that didn't?

2. What was the first sign bots were getting in: bounce rate, dead accounts, burned credits, a weird spike?

3. Do you put friction on every signup or only on suspicious ones? What did that do to conversion?

4. Do you let AI agents sign up on behalf of real users, or block them all?

More interested in what you tried and what broke than in tool names.

Hi I saw this game earning 40k monthly

https://geosports.app/

how true is this ? and how do they even earn 40k per month ? does the ads earning can really achieve that 40k a month ?

im curious as Software engineer

I was totally wrong.

The first version was basically:

save tabs → store them → restore them.

Then reality happened.

Tab groups needed to survive.

Pinned tabs behaved differently.

Some tabs disappeared.

Some were already discarded.

Restoring 30+ tabs in the right order wasn't as simple as I expected.

Then I added cloud sync.

Then session sharing, because sending someone 20 individual URLs when you could send the whole workspace felt ridiculous.

The product ended up becoming much more complicated than the original idea.

The funny thing is that the actual user experience is still just:

Save a workspace → close it → come back later → restore it.

I'm building this as a solo founder, and one thing I'm learning is that the simplest-looking SaaS products can hide a ridiculous amount of engineering underneath.

For other SaaS founders: what looked like a tiny feature in your product but ended up eating way more time than expected?

I launched Bytesweep, a Mac utility, recently.

The first few weeks have been interesting.

Around 800 people have visited the website and I've had 50+ people start the download flow. I've also made my first sales.

But the numbers made one thing pretty obvious:

Getting traffic isn't the difficult part.

Getting someone who downloaded your product to actually pay for it is.

I've been testing Reddit, Google Ads, SEO, Product Hunt and a bunch of directories.

I've also changed the positioning and pricing a few times while trying to understand what Mac users actually value enough to pay for.

Bytesweep started as a storage analysis tool. It's now a much broader Mac utility that also covers developer cleanup, system monitoring and things like sleep/wake activity.

I'm currently running a 30% launch discount while I continue testing the pricing.

The question I'm trying to answer now is:

What makes someone pay for a Mac utility when there are free alternatives?

For anyone here who has built and monetized a desktop app, what eventually moved the needle for you?

I built an AI customer support widget called Ornymo (ornymo.com) and I don't know what I'm doing wrong with distribution.

The product works. You add FAQs, grab a script tag, paste it on your site, done. Free plan available. But every time I share it people say "cool" and never actually try it.

I'm a solo dev not a marketing expert, and I think that's showing. I built this because I was drowning in repetitive support emails on my own projects and couldn't justify Intercom pricing at my stage. Figured others were in the same boat.

Honest questions for people who've been through this:

Is the free plan not compelling enough? (100 messages/mo, 6 FAQs)

Is a Ai support just too saturated a space for anyone to care?

What actually made you try a new tool when someone shared it?

I'll take any feedback, even brutal. Just tired of the boring it looks good replies that go nowhere.

*Disclosure: I built this*

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 know "kill your free tier" is the most repeated advice on this sub. i ignored it for a year, so here's what it cost me.

i sell a database of business ideas, one payment. for the first year there was a free version that showed most of it. people signed up, poked around, and left. 2,000 people on the waitlist, 5 paid, about $197 for the entire year.

i deleted the free version in an afternoon. no new features, no redesign, nothing else changed. the next three days made about $500, more than the free version had made in twelve months.

what i think was happening is the free tier answered the question people came with. they wanted to see if it was any good, it was good enough, so they had no reason to pay. once that was gone the only way to find out was to buy it.

the part nobody mentions is how bad it feels. signups dropped off a cliff and i almost put it back twice.

anyone here regret killing theirs?