u/Shot-Hospital7649

So I went ahead and bought it.

The program includes 28 weeks of weekend live sessions and no-code AI automation consumer use cases and does not provide a certificate at the end. $60.

Now I'm in, I'm starting to wonder if I just paid for something I could've learned entirely on my own. Everything they're covering feels like it exists somewhere on YouTube or in free documentation already.

7 months of weekends is a serious time commitment. And I'm not sure a live session format is actually faster than just picking a tool and building something yourself.

Has anyone here been in a similar situation, paid for a long, structured course, and then realized self-learning would've gotten you there quicker?

If you think these kinds of courses aren't worth it, drop the resources you'd actually recommend in the comments. Free docs, YouTube channels, communities, anything that helps someone build real AI automation projects without needing a 28-week handhold.

I'm trying to figure out if I should stick with it or just cut my losses and go build stuff on my own.

So I went ahead and bought it.

The program includes 28 weeks of weekend live sessions and no-code AI automation consumer use cases and does not provide a certificate at the end. $60.

Now I'm in, I'm starting to wonder if I just paid for something I could've learned entirely on my own. Everything they're covering feels like it exists somewhere on YouTube or in free documentation already.

7 months of weekends is a serious time commitment. And I'm not sure a live session format is actually faster than just picking a tool and building something yourself.

Has anyone here been in a similar situation, paid for a long, structured course, and then realized self-learning would've gotten you there quicker?

If you think these kinds of courses aren't worth it, drop the resources you'd actually recommend in the comments. Free docs, YouTube channels, communities, anything that helps someone build real AI automation projects without needing a 28-week handhold.

I'm trying to figure out if I should stick with it or just cut my losses and go build stuff on my own.

So I went ahead and bought it.

The program includes 28 weeks of weekend live sessions and no-code AI automation consumer use cases and does not provide a certificate at the end. $60.

Now I'm in, I'm starting to wonder if I just paid for something I could've learned entirely on my own. Everything they're covering feels like it exists somewhere on YouTube or in free documentation already.

7 months of weekends is a serious time commitment. And I'm not sure a live session format is actually faster than just picking a tool and building something yourself.

Has anyone here been in a similar situation, paid for a long, structured course, and then realized self-learning would've gotten you there quicker?

If you think these kinds of courses aren't worth it, drop the resources you'd actually recommend in the comments. Free docs, YouTube channels, communities, anything that helps someone build real AI automation projects without needing a 28-week handhold.

I'm trying to figure out if I should stick with it or just cut my losses and go build stuff on my own.

I read a breakdown of what loop engineering actually means and wanted to share the parts that stuck with me.

The core idea:
An agent is just a while loop. The model reads context → calls a tool → the result goes back into context → it repeats. That part is basically solved. Every serious framework lands on the same ~6 lines. Nobody's competing on the loop itself.

What people are actually competing on is everything around it:

The stopping problem:
When an agent stops calling tools, that's the end of its turn, not the end of the task. Confusing those two is apparently the most common way loops go wrong. "Done" should mean the tests pass, not the agent feeling good about its work.

Context rot:
The longer a loop runs, the more junk piles up: dead ends, stale reasoning, and old tool outputs. performance drops as that pile grows, which produces worse decisions, which adds more noise. They call it the "doom loop". The fix is treating context like a budget, not a bucket.

Tool design:
A tight set of focused, non-overlapping tools beats 100 vague ones. Also, anything that writes state needs to be safe to call twice, because loops retry.

The critic problem:
A loop with no checker is just an agent nodding at its own work. Separate the maker from the checker.

The framing that clicked for me:
Prompting is steering the agent move by move. Loop engineering is building the system that steers it, then stepping back. Your job shifts from giving instructions to designing the goal, the brakes, and the verifier.

From what I can tell, most people are still mostly in prompt engineering. I'm curious where others are on this. Are you actually building loops yet or still one-shotting everything?

The article link is in the comments.

I've been wanting to build an AI automation system for lead generation for a while now.

But I am stuck on the very first step. Like, what do you even build first?

From what I can tell, there are so many tools: Clay, n8n, Make, Instantly, Apollo, etc. And I get confused on which one to start with.

Is it data enrichment first? or outreach sequencing? or lead scoring? not sure what the actual order is supposed to be.

If anyone here has actually built or is building something like this, I would love to know what you built first and what came after. Even a rough order of steps would help a lot.

also would genuinely like to connect with people who've built or are building things like this.

What was your starting point?

I've been wanting to build an AI automation system for lead generation for a while now.

But I am stuck on the very first step. Like, what do you even build first?

From what I can tell, there are so many tools: Clay, n8n, Make, Instantly, Apollo, etc. And I get confused on which one to start with.

Is it data enrichment first? or outreach sequencing? or lead scoring? not sure what the actual order is supposed to be.

If anyone here has actually built or is building something like this, I would love to know what you built first and what came after. Even a rough order of steps would help a lot.

also would genuinely like to connect with people who've built or are building things like this.

What was your starting point?

I've been wanting to build an AI automation system for lead generation for a while now.

But I am stuck on the very first step. Like, what do you even build first?

From what I can tell, there are so many tools: Clay, n8n, Make, Instantly, Apollo, etc. And I get confused on which one to start with.

Is it data enrichment first? or outreach sequencing? or lead scoring? not sure what the actual order is supposed to be.

If anyone here has actually built or is building something like this, I would love to know what you built first and what came after. Even a rough order of steps would help a lot.

also would genuinely like to connect with people who've built or are building things like this.

What was your starting point?

I just read an article on agent memory that cleared up something I'd been thinking about.

My assumption was always more memory = smarter agents.

Save call transcripts, CRM notes, docs, and Slack messages and give the agent access to everything.

But the article argued that more memory often makes agents worse because they retrieve the wrong information at the wrong time. The human ends up manually providing context, correcting mistakes, and repeating the same instructions over and over.

The interesting part was that the problem wasn't memory. It was retrieval.

Two ideas stood out:

• Every correction from a human should become a future rule; otherwise, the same mistakes keep happening.
• When multiple systems disagree (CRM vs. call transcript, for example), the agent needs a defined source of truth instead of guessing.

The article also suggested answering a few questions before automating anything:

• What should the agent never see?
• Which corrections keep repeating?
• How does a correction become a permanent rule?

Made me realise that most agent workflows probably fail at retrieval rather than memory.

Have any of you run into this with your own agents?

Link is in the comments.

Claude Design dropped an interesting update, and most of it is aimed at one problem: AI-generated UI looking cool but having nothing to do with your actual codebase or brand.

From what I can tell, here's what changed:

Imports: You can now pull your design system straight from a repository, a design file, or your existing codebase. So instead of Claude guessing what your buttons should look like, it actually knows.

Self-correction: this is the interesting one for agents, people. Claude builds using your real components and checks its own output against your design rules before it even shows you anything. basically a verification loop before the output reaches you, not after.

Canvas controls: drag, resize, and align directly. less prompting back and forth for small layout tweaks.

Bi-directional sync: you can hand a design off to Claude Code or start in the terminal and sync changes back to the canvas. Design and code stop being two separate worlds.

Also added PDF/PPT exports and some new tool integrations. It's in beta now for all paid plans, web and desktop.

What I find interesting here is the self-correction loop specifically. It feels like the same pattern showing up across agent design lately: don't just generate something and hope it's right; verify it against ground truth before calling it done. I just didn't expect to see that pattern land in a design tool first.

I'm curious if anyone's actually used the new self-correction feature yet. Does it actually catch real brand violations, or is it more surface level right now?

Link is in the comments.

Recently, I read this article trying to understand what people mean when they say "AI agent" because everyone throws that word around, and I never really got it. And honestly, it helped.

In short it explained : just chatting with AI is one thing, but when the AI can actually go do stuff on its own, remember things from before, and keep working, that's when it becomes an "agent."

But now I'm trying to figure out how that actually helps with lead generation marketing automation, which is what I am working on now. The post talks about setting up a bot that can write emails, do research, and that kind of thing, but I'm not sure how that turns into an actual system that finds leads, figures out who's worth chasing, and follows up with them without me doing it manually every day.

Like, is this something where you build one "thing" that does all of it, or do you need separate little bots? finding leads, deciding if they're worth pursuing, writing the outreach, and all that.

If anyone's actually built something like this for lead gen, even something basic, I'd genuinely love to hear how you set it up.

Article link is in the comments.