u/nevesincscH

my dad passed in late 2024 but his Gmail still auto-forwards to my mom's inbox from when he was sick, and last week she forwarded me one of those emails, which was a personalized recruiting outreach offering him a CTO role at a stealth startup.

The opener referenced an article he wrote for an industry trade journal in 1997, which was a specific enough detail that the recruiter clearly used a data provider that scraped his linkedin a while back and just kept the record on file.

As someone who works in GTM at a small B2B SaaS using FullEnrich and Apollo for our outbound, I was able to figure out which of the major vendors still had his record listed.

And none of them had him as currently active anymore, but I found his profile in a smaller vendor that the recruiter was using and they had him flagged as active CTO at a company he left in 2009.

i wrote to them asking them to scrub him from their database, and they did it within 48 hours but couldn't tell me how many other dead profiles were still in there, and the recruiter who sent the email will never know why her prospect never replied.

Aren't we just guessing which of our prospects are even alive at this point?

We replatformed our commerce backend and I lead paid social on a small in-house team, and meta & google performance has been wobbly since the cutover.

the issue is our meta pixel is firing purchase events inconsistently, the AOV showing up in ads manager is way lower than what's hitting the store on the backend, and a chunk of conversions just aren't getting attributed.

So CPAs are noticeably higher than they were on the old setup, our optimization signal is noisy enough that the algorithm keeps thrashing audiences, and the leadership team is asking why we haven't gotten back to baseline yet.

We're stuck between rebuilding the pixel from scratch on the current setup or jumping to a server-side rebuild. if you were in my place, which way would you go?

was reading about manchester united's new online shop last week after they dropped the case study, and ended up looking at who pockets the money on the other side of the checkout.

seemingly, the money trail kept ending in the same country since every layer is german…

the shop itself is built on SCAYLE (which is part of the zalando group), zalando also sells the exact same kits direct on their website, and adidas (also german) just signed through 2035 to make the kits at £90M/yr.

the club is american-owned, the badge is english, and the entire consumer side underneath is german.

I don't have any friends to share this win with so Reddit it is :))

I worked at Deloitte and then Bain for about 6 years total. The work was genuinely interesting but I was pulling 80 hour weeks, and the thing that kept nagging me was watching small ecom brands with worse products than the clients I advised absolutely eat market share because they just understood the short form content game and the big guys didn't. I kept thinking I could do that so I went for it.

My hypothesis was simple. There's real underserved demand in the niche i wanted to attack (clothing), slightly premium basics, and the actual edge is never the product as it’s heavily saturated, it's creating a tight community, creative volume, and knowing how to talk to the customer. I noticed most brands in my space pour everything into one hero video and then wonder why the ads stop working in 2 weeks. I went the other way and treated creative like a content channel, not an ad budget.

8 months in I did just over $34k in revenue last month, margin sitting around 27% after ad spend and COGS. It's not life changing by any means but it’s a step in the right direction.

Back to the point of creative volume which was the big unlock for my brand. Early on I was producing these perfect UGC ads and burning out, going dark for 2 weeks at a time. Eventually I built a workflow combining Argil and Descript to spin out the UGC and FAQ style variations way faster, which took me from 4 or 5 new creatives a month to 20-something + i’ve been using CapCut for the quick edits. That volume is what dropped my CAC. On the boring technical side, I started on Shopify and I'm still there. When orders picked up in Europe i went with Scayle. Also Klaviyo for email has paid for itself ten times over in the meantime. So that’s when it comes to my stack so far, still adding/removing as i scale more.

Stay with me on this IMPORTANT point. People want to feel something before they buy, and a series of honest slightly imperfect branding beats any polished over produced videos or photos, focus on building and nurturing your community and differentiating yourself from the HUGE competition.

On a personal note, i really teared up looking at the numbers because 18 year old me who moved to France as an immigrant with nothing would not believe this is real life.

Anyway, sending some good karma into the world, iif anyone has questions I'm happy to get into it. cheers for more wins!!

Right, going to be completely honest, i've gone round in circles on this for months so just looking for what's actually working for people now.

Our core product is a desktop thing that's been around forever. it's a whole mixed bag in there, bit of C#, bit of C++, some .NET, and a couple of the newer screens are basically HTML sat inside a frame. so nothing clean about it. and every tool and tutorial out there assumes you're on a web app, which we're mostly not. We did have a proper go at automating it years back. the suite we used was object and selector based, and the trouble was every time the devs changed the UI even slightly, half of it broke, and not for real bugs, just because the thing it was looking for had moved. by the time we'd fixed the tests we'd already manually tested the build anyway. and you needed someone who was essentially a coder to keep it alive, which on the QA side we didn't really have. ultimately the rest of the team stopped trusting it because it was so fragile, so we just let it dwindle and went back to manual regression. which is now the bottleneck because the product's huge and the test team's relatively small.

So we're having another go, and things have moved on a fair bit since we last looked. right now i've got a few on the shortlist to have a play with, the commercial ones like Ranorex and TestComplete, the free stuff like WinAppDriver and FlaUI, Squish since some of it's Qt, and a couple of the newer ai/vision ones like Askui and testRigor. haven't committed to any of them.

What i actually want from people running real desktop apps is which of these survive the UI moving around, and which don't need a team of SDETs to babysit. less bothered about the marketing, more what's holding up day to day.

The US just put export controls on AI models and that's not a one-off. it's the first taste of the next decade and half my feed is already demanding radical measures, some European manhattan project to make it right.

it won't come from there. change never does. What i mean here is that the EU can fund things, it can write the checks and the regulation, it just can't found anything. that part has always been on us, technical founders, engineers, and builders.

What's sad is that most of us talk ourselves out of it. I've watched brilliant friends from ETH and TUM decide they need "more experience first," take the safe job at Nvidia or Google, and never build anything interesting again. the ones who matter are the ones who didn't flinch. the Mistral founders could've taken openai-sized paychecks abroad and built a frontier lab here instead. the people behind Argil looked at a category American tools assumed they owned and started shipping anyway.

It's never the most qualified person who does it. it's the one who sees what others don't and refuses to quit. Mistral became the lab the whole continent points to every time this export fight comes up. ASML quietly became the company the entire planet's chips depend on. Someone decided European companies shouldn't have to route their payroll and contracts through a US platform, and built Workmotion. someone in Hamburg runs commerce for brands as big as Levi's on Scayle. Someone looked at the US health apps everyone here imports by default and built Lucis instead, and many more examples out there.

My point is that our education is an opportunity most of the world never gets. We get to chase the ambitious thing instead of just any job. nobody expects you to spend it on something this big which is exactly why it's worth doing.

if we want Europe to stop renting its future from someone else, you don't wait for it to be handed back. you build the alternative, or you choose it. it starts with you.

What do you think is the reason stopping our builders from giving it a go? the money, the cofounder, or the paycheck they would walk away from? curious to hear your thoughts!

For a Solopreneur Clay's new pricing model is a bit stiff. Are there any good cheaper alternatives? Or alternate techniques to do lead enrichments in the b2b space?

Thank you!

I read a post here from a 19yo who's been trying to earn passive income since he was 16. Crypto, stocks, print on demand, affiliate blogs, he tried all of it and got zero results. He then asked if making money online was even possible anymore.

Passive income is REAL but it requires capital to generate returns. Dividend stocks require a portfolio, real estate requires a down payment, crypto requires buying power. This is completely unrealistic for a young person to have. Except if you inherit a family fortune lol

So what's the answer you might say? The step 80% people skip is building active income first. Specifically, building a skill that compounds, that has real demand, and that generates enough cash flow to actually save and reinvest. That's the bridge between zero and passive. Not another passive income idea. 

Now you might ask, which skill i should learn? i got obsessed over this too and spent the last two months analyzing more than 700 interviews from successful freelancers, service business owners who built active income before passive ones.

I used Claude to filter for ones with multiple sources reporting real income and low upfront cost. I concluded that the skills worth building in 2026 are all AI-native, meaning using AI to help solve a business problem. Here is the list of what’s worth building in 2026:

1. AI content agency for local businesses: gym owners, real estate agents, med spas, and restaurants need 8 to 12 videos per month for Instagram and TikTok but have no time or budget to film. You sell a monthly retainer between €600 and €1,500 and fulfill everything using AI. Workflow: you record the client once for two minutes, train an AI clone using Argil, script with Claude, generate videos, schedule with Buffer. Ten videos takes 2 to 3 hours once the system is set up. People niching into real estate agents and lawyers are replacing their salary with 3 to 4 clients. Upfront cost under €100.
2. AI prospecting service for B2B companies: sales teams in construction, manufacturing, and many old school sectors are still cold calling from outdated spreadsheets. Here you can build a prospecting system using something like Leadbay, enrich contact data with FullEnrich, and run sequences through Instantly. Deliver a weekly qualified prospect list on retainer. People claim they're charging €800 to €2,000 per client per month. France and Germany especially because American tools completely ignore these markets. The barrier to entry is almost zero.
3. AI Automation consultant: every small business has repetitive manual work like client onboarding, invoice follow-ups, CRM updates. They know they need to automate it but they have no idea how. What you do is you map their process, build the workflow in for example Relay, connect their existing tools like HubSpot, Notion, or Gmail, add AI steps for intelligence, add human approval checkpoints for judgment. Charge €500 to €2,000 setup plus monthly retainer. People doing 3 to 5 client setups per month report €4,000 to €8,000 monthly within 90 days. Zero coding required.
4. GTM automation for startups: early stage B2B startups have products and services but no sales motion. You build and run their outbound process, prospect research, enrichment, sequencing, reply handling, reporting, with something like Yalc, FullEnrich, and Instantly. People running this for 2 to 3 clients simultaneously are making €6,000 to €10,000 per month as fractional GTM freelancers.
5. AI agent setup and management service: founders know they should be running AI agents across operations. They don't know how to configure them or trust them enough to let them run. You do the setup using something like Pancake which configures agent roles across growth, content, and ops, connect their existing tools like Slack, Notion, and GitHub, train the team on oversight and approvals. People charging €1,500 to €3,000 for implementation plus €500 to €800 per month ongoing. Recurring revenue compounds fast because once clients see agents running reliably they want more of them.
6. AI QA and testing: software companies spend enormous amounts on QA. Most don't know the tools that now make it possible for one person to do what used to require a team. You offer agentic testing and automated documentation as a freelance service using something like AskUI which handles testing across desktop, mobile, embedded, and browser, and generates audit-ready reports automatically. Pair that with Claude Code for writing test definitions in plain language and Jenkins for plugging into their existing CI/CD pipeline. People with engineering backgrounds charging €150 to €300 per hour. The automotive and embedded software market is desperate for this.

Oh BuT noNE of theSE arE paSsiVe. Yes, if you’re reading up until now, that's the whole point. These are skills that generate real cash flow that pay well enough to start building the thing you actually want, which is assets that work while you sleep.

Pick one of these and go all in for a year, then you can enjoy the passive fruits of your labor.

Hope this helps a 19yo out there.

The current conversation about AI in business is still mostly about adding tools to existing structures. Hire the same team, add Copilot, move faster. Thing is, that's not what the interesting founders are doing right now, or at least moving towards.

What I'm seeing in the startups actually ahead of this is a fundamentally different model. Not a smaller version of a traditional company. A different answer to the question of what a startup even needs to be.

The organizing principle is simple. We humans own judgment, relationships, and strategy. AI owns execution. Everything that used to require a person because it was time-consuming, not because it required genuine human judgment, gets handled by the stack.

Here's what that looks like in some of the function i analyzed:

Content and brand: Traditional model: content team, video producer, social media manager, copywriter. AI-native model: one person who owns the creative strategy and brand voice, and a production layer that executes it. The companies doing this well are using tools like Argil for video, Claude for scripting, and Opus Clip for repurposing. The human still decides what to say and why. The production is fully automated. One person is doing what used to require four.

Prospecting and sales intelligence: Traditional model: SDR team, data providers, manual research, spreadsheets. AI-native model: a domain-specific intelligence layer that understands your market, scores your existing accounts, and surfaces new prospects continuously without a team of people maintaining it. The companies I keep seeing do this well in traditional industries (construction, manufacturing, distribution, hospitality) are running tools like Leadbay for prospect discovery, FullEnrich for contact enrichment, and Instantly for outreach sequencing. The sales rep shows up to the right company at the right time because the system told them to, not because they spent three hours researching on Google.

GTM and outbound: Traditional model: BDR team, sequencing tools, manual personalization, a RevOps person to keep everything running. AI-native model: a GTM operating system that runs research, enrichment, sequencing, and reporting as configured agents while one operator manages the strategy layer. The stack people are building around tools like Yalc, Unipile for LinkedIn automation, and Crustdata for data enrichment replaces most of what a three to five person outbound team used to do. The human writes the playbook. The system runs it.

Operations and orchestration: Traditional model: ops manager, project coordinator, admin headcount for recurring work. AI-native model: an agent workforce configured around the actual shape of the business. Growth agents, content agents, ops agents running continuously inside existing tools like Slack, Notion, GitHub with human approval on anything sensitive. Tools like Pancake, Relay for workflow automation, and Make for simpler trigger-based flows are building around this model specifically. The org chart is a set of Markdown files. The employees are agents that don't sleep.

Engineering and QA: Traditional model: QA team, manual test cycles, documentation overhead that scales with headcount. AI-native model: agentic testing across every surface, audit-ready documentation generated automatically, one engineer managing the validation layer instead of a team maintaining brittle test scripts. In automotive and embedded software specifically, tools like AskUI, Claude Code for test definition in natural language, and Jenkins for CI/CD integration are closing the loop between AI-generated code and certifiable hardware validation in a way that used to require a dedicated team.

The pattern across all these is the same. The function still exists, but the headcount doesn't.

The companies doing this aren't lean because they're cheap, but because the execution layer is genuinely automated and the humans they do have are doing work that actually requires humans. The output per person is an order of magnitude higher than a traditionally structured team.

The interesting question now is how long it takes for this to become the default expectation rather than just an exception.

Your thoughts on this?

I’ve been building agentic stuff for a few months now and I keep running into the same problem when shipping. The demos always look clean, but the reality is messier.

I think my assumption going in is what’s screwing me up, how i think about it is i should connect the APIs, chain the models, and then ship the automation. That worked kinda fine until I started working on complex workflows that exist outside of API land.

The invoicing tool my client uses is a desktop app from a decade ago. The supplier portal I needed to automate blocks anything that isn't a human browser session. One internal admin panel has an API technically but it hasn't been updated since ages and half the endpoints return nothing useful. the HR system lives inside a Citrix environment. None of this is unusual btw, it's just what actual business software looks like.

However whats interesting was realizing the model itself wasn't the main issue, the reasoning/planning were fine. The problem was interaction and getting an agent to actually operate software the way a human does. so I started experimenting more seriously with what people call computer use and tried a bunch of different approaches. spent time with browser-use and OpenClaw for the web layer, i then tested Claude and OpenAI's computer use for exploratory stuff. also been testing Pancake as an autonomous ops layer to orchestrate some of the longer-running workflows and AskuI for the actual GUI interaction piece. This was mainly aimed at the part where the agent needs to physically operate an interface rather than call an endpoint.

The experiment is messier than I expected and maintenance is real. I'm still figuring out where the actual lines are between what browser agents handle well versus what needs proper OS-level computer use.

What are people actually using when workflows leave API-land? curious whether others are going hybrid or trying to find one thing that handles everything.

I grew an AI influencer Tiktok account posting quotes and personal growth content aimed at girls to big numbers but i have no idea how to monetize it.

I've always been camera shy so this is the only way i found that i can build some form of audience that i can later sell something too, but i don't know which route to take with what i have right now. I feel like ebooks are outdated and nobody reads anymore. I thought about offering private consultations/coaching but i really want to stay behind the scenes.

One idea that is sounds realistic and where i can provide immediate value is to teach what i did with this account for people who want to grow their Tiktok accounts, and build distribution for their services products and learning valuable skills such as script writing, editing and video generation (Argil, elevenlabs, premierepro...), and social media management. The question now is how do i make this passive?

I want to build something long term here and completely passive, how should i approach this?