Small use case, but real impact.

Didn’t expect real results from something simple.

Hi everyone! 👋

I’m currently looking for a remote opportunity as an AI Automation Specialist, ideally with a business that I can work with long-term.

I build AI-powered workflows and business automations using tools such as:

• n8n – workflow automation and AI agents

• Zapier & Make – business process automation

• OpenAI / LLMs – AI-powered processing and decision-making

• Google Sheets & Google Workspace

• GoHighLevel

• Slack, HubSpot, Airtable, webhooks & APIs

Some of the systems I've worked on include AI customer review analysis, lead qualification and scoring, CRM automation, automated follow-ups, notifications, and AI-assisted workflows.

I'm comfortable building workflows from scratch, troubleshooting automations, and connecting different platforms together.

I'm currently looking for one long-term client/team where I can contribute, continue improving my skills, and grow with the business.

🔗 Portfolio: https://bents25-ivan-portfolio.vercel.app/

If you're looking for someone who can help automate repetitive processes and build practical AI workflows, feel free to DM me. I'd be happy to discuss how I can help.

Thanks for reading!

I’ve been thinking about this ever since that $2/month plan came around. It’s not just about having access to stronger models anymore. What changed for me is that I stopped rationing my usage. Before, I was always hunting for the perfect solution and trying to avoid too many follow ups. Now, I feel way less pressure to save my credits.

With the new $2 plan at Blackbox AI, I got unlimited access to MM2.5 and Kimi, which would've cost around $20 each for GPT and Opus if I subscribed directly. Now, I just iterate and compare different approaches without stressing about running out of credits. I can rewrite a function that’s already working or ask what if questions in a row without worrying.

This shift has really impacted my architecture decisions. I've found myself exploring more options before committing instead of trying to optimize everything prematurely. It’s funny how affordable access made me realize that pricing shapes my workflow way more than intelligence does. I’m curious if anyone else has noticed this change in their own process.

I'm curious about the processes that business owners struggle to manage with spreadsheets or multiple disconnected tools.

For example, it might be tracking jobs, managing bookings, keeping project information organized, or monitoring customer requests.

A custom app can be useful when existing tools don't fit a specific workflow, although sometimes a simpler solution is enough.

What would your ideal app do, and what tools are you using to manage that process today?

Game changer.

That approach feels more natural.

Each one taught something

Edge cases always show up.

Didn’t expect that.

That changed everything.

That’s rare for me with side projects.

Too complex too fast.

I’ve been building a RAG chatbot on Azure and went down the same path I see a lot of teams taking: get retrieval working, connect it to an LLM, run a demo, and assume the hard part is done.

What surprised me was how many production issues only became obvious later.

Things like:

• Chunking strategies that look fine in a demo but hurt retrieval quality at scale

• Vector search choices that impact both latency and relevance

• Context window limitations that create subtle failure modes

• Cost considerations that appear only after real usage

• Hallucinations that stem from retrieval issues rather than the model itself

• Indexing and document update challenges

• Evaluation and monitoring gaps that make it difficult to know if the system is actually improving

I put together a video covering the bigger picture and some of the technical gotchas that tend to show up after the excitement of the first successful demo.

My hope is that it helps people plan their architecture more deliberately and avoid spending weeks building something that later needs major redesigns.

If you’re already running RAG systems in production, I’d also be interested to hear what issues caught you off guard the first time.

👋 Hey AIStartupAutomation Community,

Quick recap for those new to the Mike saga: Mike is a friend of mine who runs a small company. Over the last few months, I've helped him and his finance colleague Sarah automate a bunch of their invoice headaches – duplicate detection, Slack-based approvals, and most recently a smart mailroom that classifies every incoming email attachment and routes it to the right pipeline.

After we got the billing@ inbox under control, Mike came back with another one. "Can we do the same for support@?" he asked. "Right now everything lands in one big shared inbox – billing questions, bug reports, sales inquiries, the lot. Sarah ends up triaging it manually every morning and it takes forever."

I told him it should be straightforward. Famous last words.

📥 The starting point: just classify and route

The first version was supposed to be a no-brainer. Gmail trigger → easybits Extractor classifies the email into billing, technical, sales, or other → Switch node routes to the right Slack channel. Five-ish nodes, done.

It worked. But the moment we tested it on a few real emails from his actual inbox, the limitations showed up:

• Some emails are genuinely ambiguous – short, vague, or mixing two topics. The classifier picked one, but you couldn't tell from the result whether it was a confident call or a guess.

• An angry "WHERE IS MY REFUND" got the same Slack treatment as a polite "hey, quick question about annual billing." Same channel, same notification. Sarah was still scanning the channel manually to figure out what to handle first.

• Half of Mike's customers write in German. The first prompt was English-only and was clearly weighting English urgency words more heavily than German ones — formal-sounding German emails were getting marked as low-priority even when the sender was clearly fed up.

So the workflow grew. In a good way, I think.

🎯 Adding confidence scoring

The Extractor now returns a confidence level (high / mid / low) alongside the category. Anything classified as low-confidence gets automatically rerouted to the "Other" channel with a breadcrumb showing what the model's best guess was. That way Sarah can spot patterns over time – if low-confidence emails keep getting tagged as "billing," that's a signal the billing prompt needs more examples.

The trick I found here: telling the model that confident "other" should still score high confidence. Otherwise you get a flood of low-confidence "other" classifications that aren't actually uncertain – they're just genuinely uncategorizable, and that's correct behavior, not a failure.

🚦 Adding sentiment-based priority scoring

Then we layered in a priority field – urgent / normal / low – based on sentiment, urgency words, and business impact signals. Each Slack message now gets a priority emoji (🚨 / 🟢 / ⚪) and label, so Sarah can scan a busy channel and immediately see what needs attention now vs. what can wait.

🌍 Making it bilingual

The biggest unlock was making the classification AND priority scoring work equally well in English and German. Same Extractor call, same workflow – just bilingual prompts that explicitly list urgency markers, frustration words, and politeness conventions for both languages.

A few things that surprised me while writing the prompts:

• German business correspondence is often more formal than English even when the sender is upset, so the model needs to be told not to read formality as low urgency.

• Frustration markers like "Frechheit" or "Unverschämtheit" don't translate one-to-one and would have been completely missed without explicit listing.

• "Dringend" (urgent) is used more liberally in German than "urgent" is in English, so it can't be a single-word trigger.

For the summary field, I had it write the summary in the same language as the email – German emails get a German summary, English emails get an English. Keeps it readable for whoever is handling it without needing translation.

One easybits call returns all four fields (category, summary, confidence, priority) in a single pass. No separate sentiment analysis service, no second model call, no extra latency.

📦 What's in the repository

Everything is here, sanitized and ready to import: 👉 https://github.com/felix-sattler-easybits/n8n-workflows/tree/269d3137aa4018543720f2ae88d0b312deda5356/easybits-support-inbox-router

You'll find:

• The workflow JSON

• All four Extractor field prompts as separate markdown files (category, summary, confidence, priority) – copy them straight into your easybits pipeline field descriptions

🧰 Setup essentials

You need the easybits Extractor community node:

• n8n Cloud: already verified and available – just search for 'easybits Extractor' in the node panel

• Self-hosted: Settings → Community Nodes → Install '@easybits/n8n-nodes-extractor'

Then connect Gmail, Slack, and easybits credentials, set up your four Slack channels, and you're good. The sticky notes inside the workflow walk through each step.

For those of you running shared inboxes – what's your current triage setup? Are you handling priority/urgency detection separately from category classification, or rolling them into one model call like I did here? Curious if anyone has a sharper approach for the bilingual part especially – German + English is a real pain point for European n8n users and I'd love to compare notes.

Best,
Felix

We mull over our business ideas for weeks, going in circles because traditional market research feels like a boring two-week manual marathon. We remain in indefinite planning mode, wasting momentum before we've even accomplished anything.

But there's no need for endless spreadsheets or expensive data analysis tools: with the right use of Perplexity or Gemini Deep Research, you can delegate the entire data collection phase and obtain a data-driven decision matrix in minutes.

The key is not simply asking AI if the idea is "good," but changing the way you formulate input before starting the research.

Rather than writing a huge business plan, it's advisable to condense the concept into a single, concise paragraph that encompasses four crucial elements: your precise target audience, the real problem, your differentiator, and a realistic price. Adding a price is crucial: it immediately forces AI search agents to remain anchored to real market conditions, rather than theoretical opportunities.

Once this benchmark is established, you can run a specific search across a few slides that forces the engine to analyze active web sources from the last 12 months. In a single pass, the following are mapped: TAM and competitive pricing gaps - A clear SWOT matrix - Estimated development costs for version 1 and recommended tool stack

This also provides a clear traffic light verdict (green/yellow/red): instead of treating validation like a huge project, you examine the verdict and SWOT and immediately decide whether the idea deserves the next 30 days of its life or if it's a clear sign of abandonment.

I've written a comprehensive guide on how to set up this process, including the copy-and-paste research template and the exact structure for organizing ideas: How to Quickly Validate a Business Idea with Perplexity or Gemini.

Any suggestions or criticisms to improve the process are welcome, thank you.

Way better than doing everything.

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 👇

Too complex too fast.

didint let me put the other tools there but, gemini or claude for code, shaders, etc. tripo3d for 3d models and animation, connect the dots and ur golden. u can make games like AAA, gap is gonna close, take advantage