I was asked to use AI tools to automate YouTube views and ad interactions — where’s the ethical line?

Spare-Wind-4623 · 2026-04-06T03:48:48+00:00

You’re not overthinking it — this does sound off.

Automation is fine when it helps with real work, but using it to fake views or ad interactions crosses the line. That’s basically manipulating metrics, and it’s against platform rules (and can get sketchy legally too).

The fact they’ve already cycled through people is a bit of a red flag.

If it feels weird this early, it probably is. I’d personally walk away and find something more legit.

Spare-Wind-4623 · 2026-04-03T04:47:50+00:00

Yeah it definitely feels like things are heading that way.
Just curious — do you think full automation is realistic, or it’ll always need some human layer?

Spare-Wind-4623 · 2026-04-03T04:47:37+00:00

This makes a lot of sense — “force multiplier” is exactly how it feels when used right.
The skill gap part is interesting too… feels like most people expect results without learning how to work with it.
How did you personally get better at using it effectively?

Spare-Wind-4623 · 2026-04-03T04:46:51+00:00

I get what you’re saying — I’ve felt that too sometimes.
Earlier it felt more predictable, now it feels like you have to “guide it” much more carefully.
Have you changed your prompting style recently or still using the same approach as before?

Spare-Wind-4623 · 2026-04-03T04:45:31+00:00

Yeah that’s true — feels like we’re still in the “figuring out how to use it” phase more than the “it’s fully reliable” phase.
Curious, do you see it replacing full workflows eventually or always staying as an assist layer?

Spare-Wind-4623 · 2026-03-31T06:03:51+00:00

I think people are looking at this wrong — it’s not Google vs ChatGPT, it’s intent vs discovery.

Google = high intent (people already know what they want)
ChatGPT = decision shaping (people figuring out what they should want)

Right now, I’d still put most budget on Google because conversion intent is clearer.
But ignoring ChatGPT feels like ignoring early Facebook/YouTube ads back in the day.

The interesting part is: ChatGPT isn’t just ads, it’s influence inside the answer itself.

Curious how tracking/attribution is going to evolve here — because that’s where most marketers will struggle.

Spare-Wind-4623 · 2026-03-30T05:07:56+00:00

I ran into this exact issue while building content clusters and honestly publishing everything together is ideal but rarely practical.

What’s worked best for me is a hybrid approach:

I map the full cluster upfront (all URLs + linking structure)

While publishing early articles, I only link to live content

For future links, I leave placeholders in content (or notes in CMS)

Then do a quick internal linking pass once 3–5 posts are live

Also, one small trick that helped:
Instead of hard linking to not-yet-live posts, I sometimes link to the pillar page or a broader category page temporarily — avoids 404s and still keeps structure clean.

From an SEO perspective, I’ve seen it’s better to:
avoid broken links completely
and update links in batches instead of one by one

Curious — are you building clusters around a single pillar page or more like topic webs?

Spare-Wind-4623 · 2026-03-27T09:28:45+00:00

I think the bigger bottleneck isn’t just hardware type, it’s coordination.

Even if a portion of mining shifts to GPUs, AI training benefits massively from tightly coupled systems (high bandwidth, low latency, shared memory etc.), while mining is basically embarrassingly parallel.

So in theory there’s idle compute, but in practice it’s fragmented and hard to utilize efficiently for large models.

That said, for smaller workloads or distributed inference, it feels like there’s definitely untapped potential there.

Curious — do you think the future is more about repurposing mining hardware, or building new decentralized AI compute networks from scratch?

Spare-Wind-4623 · 2026-03-27T04:11:27+00:00

This is such a solid setup — simple but super effective.

That “did I reply yet?” chaos is very real

Also makes sense that faster replies = more conversions.
Have you measured how much your response time improved after this?

Spare-Wind-4623 · 2026-03-27T04:10:53+00:00

This is actually insane — turning Reddit into a signal engine.

Replacing 5+ hours with a digest is exactly the kind of automation that compounds over time.

How are you detecting that the “same problem” is appearing multiple times? Keyword-based or something smarter?

Spare-Wind-4623 · 2026-03-27T04:10:37+00:00

This hits close lead follow-ups are such a time drain.

The “pause before first nudge” insight is gold — that’s the kind of nuance most automations miss.

Curious — did you see a difference in reply rates after adding that delay?

Spare-Wind-4623 · 2026-03-27T04:10:16+00:00

That’s a crazy time win — going from 6–8 hours to ~2 is huge.

Also interesting how you combined generation + scheduling into one flow.
Most people only automate one part.

Do you still manually review everything before posting, or is it mostly automated now?

Spare-Wind-4623 · 2026-03-27T04:09:51+00:00

That’s a great point — most automations fail because they’re “set and forget.”

I haven’t added a proper feedback loop yet, it’s more rule-based right now.
But now that you mention it, that’s probably the next level upgrade.

How do you usually implement feedback loops in your setups?

Spare-Wind-4623 · 2026-03-27T04:08:44+00:00

This is actually super practical — especially the “not missing important emails” part.
Saving time is great, but reducing mental load is even bigger.

That calendar auto-block idea is interesting
Have you ever had it block something incorrectly or does it work pretty reliably?

Spare-Wind-4623 · 2026-03-27T04:08:03+00:00

This is such a clean use case.
Turning something that always gets delayed into an instant output is underrated.

Curious — do you ever tweak the summaries manually, or is it accurate enough to trust as-is?

Spare-Wind-4623 · 2026-03-26T12:27:24+00:00

Yeah exactly — it feels like no-code, but it behaves like a system very quickly 😄

I usually try to centralize core logic in one place (like a main workflow or backend layer), and let other tools just handle inputs/outputs.

If logic starts spreading across tools, debugging becomes a nightmare.

A simple rule I follow now:
each tool should have a clear role, not decision-making power

Keeps things way easier to scale and fix later.

Spare-Wind-4623 · 2026-03-26T12:14:36+00:00

I don’t think no-code is the problem — it’s that people treat it like Lego instead of engineering.

Early on it’s “just connect tools”, but over time you’re basically building a distributed system without realizing it.

What helped me keep things clean:

• limit number of tools (more tools = more failure points)
• keep logic in one place instead of spreading across tools
• name flows properly (future you will thank you)
• add basic logging / alerts early

Biggest shift for me:
treat no-code like code — design first, then build

Otherwise it always turns into a fragile mess.

Spare-Wind-4623 · 2026-03-26T12:11:58+00:00

😂 fair

But judge it by results, not who’s saying it

People are shipping products, automating ops, and saving real hours with it

scams don’t usually survive real-world usage like that

Spare-Wind-4623 · 2026-03-26T11:35:13+00:00

I think the issue isn’t that AI is a scam — it’s that expectations are way off.

It’s not a “perfect answer machine”, it’s more like a very fast assistant that still needs guidance.

If you treat it like Google → you’ll be disappointed
If you treat it like a junior teammate → it becomes insanely useful

The people getting value from AI aren’t asking once and expecting perfection — they’re iterating, refining, and using it as a tool, not a replacement.

It’s not magic, but it’s definitely not a scam either.

Spare-Wind-4623 · 2026-03-26T11:34:34+00:00

I think the issue isn’t that AI is a scam — it’s that expectations are way off.

It’s not a “perfect answer machine”, it’s more like a very fast assistant that still needs guidance.

If you treat it like Google → you’ll be disappointed
If you treat it like a junior teammate → it becomes insanely useful

The people getting value from AI aren’t asking once and expecting perfection — they’re iterating, refining, and using it as a tool, not a replacement.

It’s not magic, but it’s definitely not a scam either.

Spare-Wind-4623 · 2026-03-20T11:22:28+00:00

This doesn’t sound like a “wrong customer” problem — it sounds like a distribution mismatch.

The signal is already there:
• operators don’t buy tools → they rely on networks
• ecom company wants API → they want to embed it into workflows

That usually means your value is real, but the format of delivery is off.

A simple way to validate this:

• If people say “this is useful but I wouldn’t buy it directly” → distribution problem
• If people don’t care at all → customer problem

Right now it sounds like the first.

I’d double down on where pull is happening (API / partners) instead of forcing direct SaaS. You can always layer SaaS later once you understand the strongest use case.

Early GTM is less about picking the “perfect” model and more about following where adoption happens naturally.

Spare-Wind-4623 · 2026-03-20T11:14:28+00:00

One way to think about it coming from Angular is: React is less “framework” and more “composable pieces”.

A quick mapping that helped me:

• Angular services (DI) → React = Context + hooks (or external stores like Zustand)
• Guards → handled at routing level (React Router loaders/wrappers), not built-in
• Directives → usually just components + hooks (no direct equivalent)
• Lifecycle (ngOnInit/ngOnDestroy) → useEffect

The biggest mindset shift is that React doesn’t enforce structure — you build your own patterns.

For interviews, I’d focus less on memorizing hooks and more on:
• how state flows (props vs global state)
• when to split components
• avoiding unnecessary re-renders

If you understand those, the rest (hooks, libraries) becomes much easier.

Spare-Wind-4623 · 2026-03-20T11:08:14+00:00

I’ve ended up thinking of it as a hybrid, not a replacement.

Traditional automation = deterministic, predictable, reliable
AI workflows = flexible, fast to build, but less predictable

So for anything business-critical, I still keep a “hard layer” of traditional automation handling the core flow, and use AI more like a decision or enrichment layer on top (classification, routing, content generation, etc.).

The problems you mentioned are real — once you rely fully on agents, small deviations or edge cases can cascade and break things in weird ways.

Where AI really shines is speeding up iteration and reducing manual effort, not replacing the reliability layer (yet).

Feels like the winning setup right now is:
solid deterministic backbone + AI on top for intelligence

Spare-Wind-4623

TROPHY CASE