r/AIStartupAutomation

What would you say is the most difficult part about coding with AI? initial conceptualization, APIs, debugging/edge cases, hosting & implementation? pretty curious to know

Curious how others are approaching it.

Been dropshipping on Shopify for ~8 months. My biggest leak wasn’t bad products or shitty creatives — it was me making emotional calls on which products to scale or kill.

I’d look at Day 2 ROAS, panic, kill a product that would’ve been a winner. Or I’d “give it one more day” on a clear loser and burn another 80€.

So I forced myself to write down rules and stick to them. Here’s what’s actually worked:

Kill if:

•	Day 3, ROAS < 1.0, spend > 3x product cost  
•	CTR < 0.8% AND CPC > 1.50€ after 50€ spent (creative problem, not product)  
•	0 ATC after 30€ spent (offer/price issue)

Scale (20% budget bumps, not 2x):

•	3 consecutive days ROAS > 2.5  
•	CPA stable or dropping  
•	Don’t touch winning ad sets — duplicate instead

Test more:

•	ROAS between 1.2 and 2.0 → not dead, not a winner, needs new angles before judging

What changed for me: I stopped checking ads 10x/day. Check once in the morning, apply the rule, move on.

Got so tired of doing this manually I ended up building a small tool that pulls my Shopify + Meta data every night and outputs Scale / Kill / Test per product. Still in beta, looking for 2-3 more dropshippers to test free — happy to share more in comments if anyone’s interested.

Curious what rules you guys use. Anyone running stricter kill criteria?

When looking at the operational efficiency of a growing business, the hidden cash drain almost always traces back to repetitive manual work and disconnected data systems.

Many businesses invest heavily in top-tier marketing, brilliant sales teams, and advanced software, yet their day-to-day operations remain bogged down by manual data entry, manual invoicing, and copy-pasting information between apps. When a company relies on human intervention to act as the bridge between its frontend tools and backend databases, human error ceases to be an accident and instead becomes an absolute mathematical certainty.

The primary benefit of implementing end-to-end business automation is the complete elimination of this administrative friction and the costly bottlenecks that come with it. By engineering automated workflows to handle repetitive task routing, instant data synchronization, and touchless system communication, a business can completely eradicate the risk of human data typos, missed follow-ups, and delayed responses.

Furthermore, automating these repetitive tasks radically multiplies internal capacity, allowing a company to scale its overall output, processing speed, and revenue without forcing a costly parallel increase in headcount or overhead. This operational shift effectively flips the employee dynamic from low-value maintenance to high-leverage growth, allowing teams to dedicate their full focus to strategic decision-making, client satisfaction, and revenue-generating activities.

Ultimately, automation transforms an unpredictable, fragile workflow into a watertight, standardized system that operates flawlessly around the clock.

For those analyzing workflows within your own organizations, what is the single most tedious, repetitive manual process that acts as the biggest bottleneck to your daily productivity?

AI tools are incredibly useful, no doubt about that. But I have noticed something interesting lately. The more developers rely on AI for debugging, code generation, explanations and problem solving, the harder it feels to work without it for long periods.

Not in a dramatic way, but almost like a habit forming loop. You hit a problem and the first instinct becomes asking AI instead of thinking through it deeply yourself. Feels similar to how autocomplete changed writing, just at a much bigger scale.

Whats your thought on it?

👋 Hey Community,

I met up with my friend Mike yesterday and noticed he was taking notes on a piece of paper. I do that too – writing things down by hand actually helps me remember them. But it also means I end up with a stack of papers on my desk that slowly turns into chaos. Apparently Mike's whole team has the same habit. They've got Jira, Notion, and other tools set up, but the offline notes keep getting lost on people's desks.

So I made him a deal: set up a dedicated email address inside the company – something like notes@mikescompany.com – and I'd build the rest.

This is what I shipped.

📝 What it does

Snap a photo of your whiteboard, notebook page, or napkin. Email it to the dedicated inbox. Within seconds you get a Google Doc back containing the meeting title, date, attendees, summary, action items, and a full reference transcription. No app to install, no UI to learn. If you can email a photo, you can use it.

🎥 What's in the video

The walkthrough covers how the workflow is wired up node by node, and ends with a live test run using a handwritten note I scribbled down – Gmail trigger fires, the Extractor pulls the data, the Google Doc gets built, and the confirmation email lands in my inbox within seconds. Easier to see it work than to describe it.

📦 The workflow

Full JSON, sticky notes, and setup guide on GitHub: https://github.com/felix-sattler-easybits/data-extraction-workflows/blob/main/easybits-meeting-notes-to-google-doc-workflow/Whiteboard_to_Meeting_Doc.json

The link is also in the video description if you want to pull it up while watching.

This is v1, and a few people asked under the last post how it handles really bad handwriting. I've run it on a handful of examples already and the results have been solid so far, but I'd love to push the limits more. So if you've got a photo of meeting notes that you think would break it – doctor handwriting, half-erased whiteboard, napkin scribbles, multiple languages, whatever – drop it in the comments or DM it to me. I'll run it through the workflow and post the result. Genuinely curious where the breaking point is.

Also still keen on broader feedback: what else would make this genuinely useful for your team?

Best,
Felix

AI tools can generate code incredibly fast now. But I have noticed a lot of the work shifts into reviewing, correcting, re-prompting, validating edge cases, and making sure the output actually fits the system.

Sometimes it feels less like 'AI writes the code' and more like 'developers manage and refine AI-generated drafts.' The speed is real, but so is the oversight.

Even small wins matter.

Every time a creator posts something and says

"comment LINK below" — they spend the next

2 hours manually replying to every single person.

That's insane in 2025.

So I built CashPost — a tool that does this automatically:

1. You set a keyword (like "link" or "buy")
2. Someone comments that word on your post
3. They instantly get a DM with your checkout link
4. Their email is captured before checkout
5. You see the sale in your dashboard

No more manually replying. No more missed buyers.

Works on Instagram and TikTok.

I'm looking for feedback from entrepreneurs and

creators who sell digital products, courses,

or physical products on social media.

A few questions for this community:

→ Do you or someone you know sell things

through Instagram/TikTok?

→ How do you currently handle people asking

for your link in comments?

→ Would you pay $19-49/month if this saved

you 5+ hours per week and increased sales?

Waitlist is open at cashpost.live if anyone

wants to check it out. First 100 get 50% off forever.

Happy to answer any questions about the build

or the idea 👇

I personally use antigravity as an IDE and claude as the underlying LLM for coding apps. In most cases I will use the exact same setup for vibecoding ai automations - I used to love using n8n for this in the past but I feel like its kind of falling short right now.

What are you guys using?

I ve been using antigravity and claude to build software and then host it - which is pretty straight forward. I also used to use n8n a lot. But what does advanced ai coding even look like? How do you create an "Ai agent" and host it so that everyone can use it as a product and how do you build that product in the first place? whats the exact process all those "ai agent" startups use right now to ship their products? and how do you actually learn advanced ai coding/infrastructure, i cant find any tutorials online

One thing I have been noticing lately is that developers ask teammates fewer questions now because AI is always available.

• Need help debugging? Ask AI...
• Need a quick explanation? Ask AI....
• Need a starting point? Ask AI.......

It definitely improves speed and reduces interruptions, but I wonder if it also changes how knowledge gets shared across teams. Sometimes the best insights came from conversations with people who understood the system, the business context, or past decisions. Feels like AI is changing not just how we code, but how we collaborate and learn from each other.

That’s honestly the scariest part.

👋 Hey everyone,

My CEO mentioned he's got a few conferences coming up in the next weeks and he's actually looking forward to them. There's just one problem: every time he comes back from an event, he has a stack of business cards in his pocket and zero time to manually add them all to his phone.

So I went looking for a tool I could just hand him. Plenty of business card scanners exist. But every single one of them has the same baffling design choice: you have to photograph each card individually. One at a time. For 20 cards.

That's not really a scanner. That's a slightly faster version of typing them in by hand.

So I built him something better in n8n.

📸 What it does

He lays all the business cards out on a hotel desk, takes ONE photo, and sends it to a Telegram bot. The workflow extracts every contact, deduplicates against a Google Sheet (so contacts he's already saved don't get re-added), and sends back a separate vCard file for each new contact. He taps a vCard on his iPhone → "Add Contact" → done. About 15 seconds for 20 cards.

In the video above I walk through the workflow setup in n8n and do a live test run with 8 business cards in one photo – figured it's easier to see it in action than describe it.

📁 Workflow JSON

You can grab the workflow JSON here (also linked in the video description along with the easybits Extractor setup info): https://github.com/felix-sattler-easybits/n8n-workflows/tree/21d7623026008432c700cff118d1a987687a10fe/easybits-business-card-scanner-workflow

Anyone else built something similar for handling event leads? Curious whether people are pushing contacts straight to a CRM (HubSpot, Pipedrive) or keeping it in a sheet. The Sheet → vCard pattern is nice because it works for everyone, but I imagine the CRM version would be even better for sales-heavy teams.

Best,
Felix

Persistent memory keeps coming up for AI coding agents. One approach I’ve found useful: treating the knowledge layer as a compiled markdown wiki rather than just stuffing more tokens into the context window.

llm-wiki-compiler ingests docs and URLs, then the LLM builds an interlinked markdown structure. Since the output is plain markdown on disk, Claude Code reads it directly. And when you run query --save, the answer gets written back into the wiki as a page — so future queries improve.

It’s not retrieval. It’s compounding. The knowledge base gets richer instead of resetting every session.

Plain markdown, no opaque vector store, fully inspectable.

How are other agent builders solving persistent memory?

We just made TinyFish Web Search and Fetch completely FREE. Not a trial. Not a limited tier. Free.
What that means:

Search API : live, browser-rendered web search that returns structured JSON. Fresh results, not cached. Free.

Fetch API : give it any URL, get back clean markdown, JSON, or HTML. Rendered in a real browser. Also free.

No credit card required. Zero credits consumed. And before anyone asks, we already 5x'd the rate limits, so these aren't free-in-name-only endpoints with a chokehold on usage. Your agent doesn't sleep and neither do we.

If you're building agents that need to read the web, you now have a free, production-grade way to do it.

Get your API key: agent.tinyfish.ai/sign-up
Docs: docs.tinyfish.ai

Agent and Browser APIs still use credits (1 credit per agent step, 1 credit per 4 min of browser time), and you get 500 free credits on signup to try those too.

Questions? Drop them below.

Hey everyone,

I’m currently building a project called Aierex, and I wanted to share it here mainly to get feedback from developers, builders, and early users.

The idea behind Aierex is simple:

Most of the time, when we want to understand something properly, we jump between Google, Reddit, blogs, YouTube, docs, and now AI tools. Google gives links, Reddit gives discussions, and AI gives direct answers — but these experiences are usually separate.

I wanted to build something that brings these ideas closer together.

Aierex is an AI-powered knowledge community where people can explore topics, read useful content, ask questions, and join discussions around different subjects.

The goal is not to replace Reddit or ChatGPT. The goal is to create a space where:

• users can discover topic-based knowledge,
• discussions are more useful and less random,
• AI can help people understand faster,
• communities can form around real knowledge areas,
• and content can be organized better than a normal social feed.

Right now, the platform is still in beta. I’m adding seed knowledge bases in areas like cybersecurity, health & fitness, AI, startups, and other useful topics. The long-term vision is to make Aierex a place where people can learn, discuss, and explore reliable knowledge with both AI and community input.

A simple way to describe it would be:

“Where knowledge meets conversation.”

I’m still figuring out the positioning, onboarding, content structure, and what kind of users it should serve first. So I’d genuinely appreciate feedback on things like:

• Does the idea make sense?
• Would you personally use something like this?
• What would make you trust or join a platform like this?
• Should it focus more on AI answers, community discussions, or curated knowledge?
• What kind of topics would be most useful in the beginning?
• Any suggestions for improving the product or messaging?

I’m not posting this as a polished launch or paid promotion. I’m still building and learning, and I’d really value honest feedback from people who understand products, communities, and early-stage platforms.

Thanks for reading.

AI tools make it very easy to generate working code quickly. And most of the time, the output is good enough to move forward. But I’ve been wondering if that changes our standards over time. Instead of refining solutions deeply, it becomes tempting to accept code that works and revisit it later.

Sometimes that’s practical. Sometimes it slowly builds complexity that nobody fully understands. Feels like AI is changing not just speed, but also our tolerance for “good enough” engineering.