I run a small fitness page on Instagram where I post workouts, tips and some beginner-friendly content.

Lately, I’ve been trying really hard to grow, so my daily routine looks like this:

• liking a lot of posts
• commenting on different accounts
• following people in my niche

The problem is  it takes a lot of time and the results are very small.

Some days I spend hours doing this, but my reach is still low and follower growth is very slow. It honestly feels like I’m stuck. I don’t want to use bots or spam people with DMs but I also don’t want to keep doing everything manually like this.

I’m looking for a more efficient and scalable way to grow something that saves time but is still organic and safe.

Has anyone found a system or workflow that actually works without burning out?

Hi everyone,

I’ve been diving deep into the tech side of the Dubai real estate market and noticed a recurring rant on Reddit: "Automation is useless if I don't have consistent leads to begin with."
Most "automation experts" try to sell CRM management, but if the funnel is empty, a fancy CRM is just an expensive digital graveyard. I’m building a ecosystem designed to fix the Acquisition + Intelligence gap first.

I wanted to get the community's take on whether these "Tiers" actually hit the pain points of a high-volume broker in the UAE:

1. Lead Hunting: Custom scrapers that find FSBO (For Sale By Owner) listings across multiple platforms before they hit the mass market.
2. AI Pre-Qual: A WhatsApp/AI layer that chats with incoming leads in Arabic/English to filter out "window shoppers" and only pings the agent when a lead is 100% ready to talk.
3. Market Intel: Automated weekly reports pulling direct data from the Dubai Land Department (DLD) so agents can send "Investor-Grade" data to their clients instantly.

My Question: If you’re an agent in Dubai, does this sound like a real "Business-in-a-Box," or is the market already too saturated with these tools? What is the one technical task you wish was off your plate forever?

Not selling anything—just building in public and want to make sure I'm solving real problems, not just writing code for the sake of it.

Hey r/SaaS,

I want to be upfront before I ask for anything.

My name is Jonathan. I have no engineering background. No CS degree. No co-founder. No funding. No team.

For the last few months I built a full B2B sales outreach platform completely alone using Claude as my only “engineering team.” I had never written a line of production code before this.

I was a vendor manager and sales guy who got tired of the tools available and decided to just build the thing myself.

The product is called ZenMode.

It runs on your desktop, uses your real browser session, and automates LinkedIn outreach the way a great sales rep would actually do it. Not the way a spammy sequence tool does it.

Here is what it does: it finds your ideal prospects on Sales Navigator, researches their profiles, writes genuinely personalized connection requests and messages using Claude, and handles the full outreach flow.

It learns from your campaign data over time so messages get better the more you use it.

No cloud-based browser farms. No API calls pretending to be you. No “we’ll run your account from our servers and definitely not get you banned” nonsense.

It is your browser, your session, your LinkedIn, running locally on your machine.

I built ZenMode because I spent years doing outreach manually and then watched every automation tool on the market either get accounts restricted or send messages so generic that reply rates were basically zero.

The AI models finally got good enough to change that and nobody had built the tool I actually wanted to use.

I just launched on Product Hunt and I am now looking for 20 beta testers who are actively doing LinkedIn outreach and want to put this thing through its paces.

I am not asking for money - I will provide you access to the tool for free.

There are lifetime deals available, but honestly what I need more than revenue right now is real users hammering on this thing and telling me what is broken, what is confusing and what is missing.

If you are a founder, SDR, or anyone doing B2B outreach on LinkedIn and you want to try something built by someone who actually does outreach for a living, drop a comment or DM me.

I will get you set up within 24 hours.

Thanks 🙏

I automated a tutoring business recently and ended up with two layers that talk to each other through a shared database.

Rule-based layer handles anything that has to be exactly right:

• Payment confirmed → create Google Calendar event
• Schedule change → send WhatsApp notification to parents

AI layer handles the messy stuff:

• Parsing scheduling requests in natural language
• Matching teacher availability (tons of edge cases)
• Drafting parent communications

Both layers read/write to the same database, so when a rule fires, the AI layer knows about it and vice versa. This solved most of the debugging headaches — you can always trace what happened and why.

I built this on Struere (struere.dev) — I'm the founder, so take that as you will. It's running in production though.

For anyone doing similar setups: how do you decide what stays rule-based vs what you hand off to AI? I keep going back and forth on where to draw that line.

She'd been copying order details from emails into a spreadsheet.

30 to 40 times a day. Every single day. For two years.

I fixed it in three hours.

She cried.

Not because it was technically impressive. Because she suddenly realized how much of her life she'd handed to a task that never needed a human.

I've automated 20+ businesses this year. And the thing that surprises every single client is never the big flashy AI stuff.

It's always something embarrassingly simple.

The follow up that didn't get sent because things got busy. The report someone pulls manually every Friday afternoon. The same email rewritten 15 times a week with tiny changes. The data copied from one tool to another because nobody set up a connection.

None of this sounds impressive. But it's the stuff people actually pay for. And keep paying for. The pattern I keep seeing is this - the less exciting the task sounds, the more grateful the client is when it's gone. Because it wasn't just the time. It was the mental load of remembering to do it. Every. Single. Day.

If you're a business owner - what's the task you've just accepted as part of the job that probably shouldn't exist?

Drop it below. Genuinely curious.

And if it sounds automatable, I'll tell you if it is.

Hey guys,

I’m planning to learn AI Automation and sell it to businesses as a service (AAA). I have two quick questions:

1. Is there still good money in this, or is it just hype?

2. How long does it realistically take to learn the tools (Make/Zapier/APIs) well enough to start charging clients?

Would love to hear from anyone actually doing this. Thanks!

I want to share a story about a sourcing workflow I finally got working after a lot of painful trial and error. Hopefully this saves someone else the headaches I went through.

The problem

I run a small outdoor apparel brand (just me and one partner). Earlier this year we needed to find new manufacturers because our existing supplier in Guangdong kept missing delivery windows and quality was slipping. We needed to source technical fleece jackets from a factory that could handle DWR coatings, had relevant certifications (OEKO TEX, BSCI), and ideally wasn't in China because of the tariff situation.

Sounds simple enough, right?

What I tried first (and why it failed)

Alibaba: This was the obvious starting point. I spent about a week messaging suppliers. The experience was exactly what you'd expect: tons of trading companies pretending to be factories, gold supplier badges that mean nothing except that someone paid for them, and a flood of copy paste responses that didn't address my actual specs. I got maybe 40 responses out of 120+ messages sent, and maybe 5 of those were from actual manufacturers. The paid ranking system makes it almost impossible to find the best fit versus whoever spent the most on ads.

ImportYeti: I tried using this to look up US customs records and see where competitors like Alo Yoga and similar brands were sourcing from. The raw data was genuinely useful as a starting point. I could see shipment records, factory names, volumes. But it's basically a big database dump. No way to filter by capability, no compliance info, no way to actually contact suppliers through it. I ended up with a massive spreadsheet of factory names that I then had to manually research one by one. Cross referencing government registrations, checking certifications, finding contact info, translating emails into Vietnamese and Chinese. After two weeks I had vetted maybe 15 suppliers and sent personalized outreach to 8 of them. Two responded.

DIY n8n automation: Being on r/automation, naturally I tried to build my own pipeline. I set up an n8n workflow that would scrape supplier directories, enrich the data with a GPT node, auto generate outreach emails, and send them via SMTP. It sort of worked for the email generation part, but the data quality was garbage. I had no reliable way to verify which suppliers were real factories versus middlemen, no certification data, and the personalization was surface level at best. Suppliers could tell it was automated and I got almost zero meaningful responses.

At this point I'd burned about three weeks and had exactly two viable supplier conversations to show for it.

How I found a solution that actually worked

A friend who runs a DTC brand mentioned he'd been using a platform called SourceReady. I was skeptical because I'd already been burned by Alibaba alternatives that turned out to be the same thing with different paint. But he showed me his workflow and I was genuinely impressed by the data depth.

SourceReady is basically an AI sourcing engine built on top of cross verified supplier data from customs records, government registrations, trade show directories, and certification databases. The key difference from something like ImportYeti or ImportGenius is that instead of just giving you raw import records, it integrates all that data into an actual workflow with AI matching, automated outreach, and quote comparison.

Implementation (step by step)

Step 1: AI supplier search. I typed in something like "technical fleece jacket manufacturer, DWR coating capability, BSCI certified, low tariff country, MOQ under 500 units." Within about 10 seconds it returned around 90 results, each with an AI explanation of why that supplier matched and a percentage score. I could see verified export history, which brands they ship to, certifications, estimated capacity. The fact that I could see a factory ships to known premium brands (the platform shows this from customs data) was a huge quality signal that would have taken me days to piece together manually.

Step 2: Compliance screening. This was the part that really surprised me. The platform flagged two suppliers that had potential UFLPA (Uyghur Forced Labor Prevention Act) risks based on upstream material sourcing. I would never have caught this on my own, and getting a shipment detained at customs would have been devastating for a small brand. Alibaba and Global Sources don't offer anything like this; they rely entirely on supplier self disclosure.

Step 3: Automated outreach. I wrote one inquiry template with my specs, target pricing, and timeline. The AI personalized it for each supplier (referencing their specific capabilities and certifications), translated it into the appropriate language, and sent it out. It also handled follow ups automatically. This replaced the entire n8n workflow I'd been trying to build, except it actually worked because the underlying data was verified.

Step 4: Quote comparison. As responses came in, the platform extracted key data points from each quote and put them in a side by side comparison. No more manually copying numbers into spreadsheets.

Results

Within 48 hours I had 23 supplier responses (compared to 2 after three weeks of manual work). The AI had pre scored and compared all the quotes. I narrowed it down to 4 finalists in a single afternoon.

I ended up placing an order with a factory in Vietnam that I never would have found on Alibaba. They had verified BSCI and OEKO TEX certifications, a documented export history to several mid tier outdoor brands, and their pricing came in about 18% cheaper than what I was paying my previous Chinese supplier (before even accounting for the tariff differential).

Total time from search to purchase order: about 5 days. Previous process took 6 to 8 weeks.

What I learned

1. Raw data isn't automation. Tools like ImportYeti give you useful raw material, but turning customs records into actionable supplier decisions still requires enormous manual effort. The real value is in platforms that layer intelligence and workflow on top of verified data.
2. Supplier verification matters more than supplier volume. Alibaba has millions of listings but the signal to noise ratio is terrible. Having 1.2 million cross verified suppliers (SourceReady's claim) with actual customs data and certification records is infinitely more useful than 10 million unverified listings.
3. Compliance automation is underrated. With UFLPA enforcement ramping up, the ability to automatically screen for sanctions and forced labor risks isn't a nice to have anymore. It's essential. I've heard of brands losing six figures on detained shipments.
4. The outreach automation is the real time saver. Finding suppliers is one thing. Actually reaching out, following up, translating, negotiating, and comparing quotes across dozens of conversations simultaneously is where most of the time gets burned. Having an AI agent handle that 24/7 was the single biggest efficiency gain.

I'm still on the free tier of SourceReady (200 credits plus 30 daily refresh) and it's been sufficient for my scale. They have a $25/month plan if you need more volume. No transaction fees or commissions, which is refreshing compared to marketplace models.

Happy to answer questions about the specific workflow or how I set things up. Sourcing automation was the last piece of my business that was still painfully manual, and I'm honestly a little annoyed I didn't find this sooner.

Hi everyone,

I’m an AI and Automation Developer looking for a new laptop. I currently use a Lenovo ThinkPad provided by my company for work, and I’ve had a great experience with its reliability and keyboard. I'm looking for something similar for my personal projects.

My Requirements:

Stack: Python (FastAPI, Flask, Django), Docker containers, self-hosted n8n, and light GenAI work.

Budget: ₹50,000 INR (Strict).

OS: I have my own Microsoft license, so DOS/No-OS is preferred to save money. I plan to dual-boot Ubuntu.

Desired Specs:

CPU: I'm targeting an i5 13th Gen H-series (like the i5-13420H). I need the higher TDP for virtualization/Docker. Is this achievable at 40k, or should I look at 12th Gen H-series?

in

RAM: Must be 16GB. Ideally a model with an expandable slot to reach 24GB+ later.

Storage: 512GB NVMe SSD.

Current Shortlist:

Lenovo V15 G4 (i5-13420H) - Seems like the best professional fit.

Lenovo IdeaPad Slim 3 - Concerned about whether the RAM is soldered or expandable.