u/parwemic

been going down a rabbit hole of automation horror stories lately and honestly some of these are genuinely impressive in how badly they went wrong. saw a thread a while back about someone who set up an AI-connected fridge to auto-order groceries, and it ended up bulk ordering an absolutely absurd amount of bananas because it misread expiration labels. worth flagging that i can't fully verify this one so take it as a great illustrative anecdote rather than, gospel, but whether it's 100% true or not, it's exactly the kind of edge case that feels completely plausible. the ambition was there, the execution just had one tiny gap that turned into a very expensive, very yellow problem. what's interesting is this kind of thing hasn't really slowed down, it's just gotten more sophisticated. right now with AI-driven RPA being rushed into production everywhere, you're seeing a whole new generation of the same pattern. customer service bots hallucinating responses with total confidence, warehouse cobots helpfully "tidying" human workspaces mid-shift because nobody told, them the definition of tidy, supply chain optimization tools glitching out the moment real-world data variability hits them. the tools got smarter but the gap between controlled testing and actual chaos stayed exactly the same size. I reckon the most interesting fails are the ones where the idea itself was actually clever. it's not dumb setups going wrong, it's smart setups that just didn't account for one weird edge case. the gap between "works in testing" and "works when real life happens to it" is where all the chaos lives. I've seen Zapier chains that worked perfectly until an API rate limit kicked in and started flooding someone's inbox at 3am. Node-RED flows that hit an unexpected input and just. looped forever. fun stuff. what's the most creative one you've seen or built yourself? especially keen to hear about the ones where the concept was genuinely good but reality had other plans.

The cleanest way to think about this: AI didn't eliminate software work, it created a brand new category of it called "cleaning up what AI helped someone build."

The replacement narrative treats software as a fixed-size pie. It was never fixed. It was capped by who could build. Uncap that constraint and the pie doesn't just grow — it changes shape entirely.

Watch what actually happens when a non-technical founder ships their first AI-assisted build. Week one: it works, they're ecstatic, they tell everyone on LinkedIn that "coding is dead." Week three: edge cases appear, something silently fails in production, and the prompt that generated the original code is long gone. Week five: they're calling a developer to "quickly fix one small thing," which turns into a three-month rebuild.

That cycle is happening thousands of times a month right now. The volume is unprecedented.

The second-order effect nobody's pricing in yet: it's not just that more software is being built, it's that more software is being built by people who have never been taught what "production" means. The gap between "it runs on my laptop" and "it can be trusted with real customer data, real money, real SLAs" has never been wider. That gap is the entire job market for the next decade.

The underrated growth area is automation layers specifically. Non-technical teams stitch together 15 tools with a workflow builder — Latenode, n8n, whatever — and it runs fine at low volume. Then the business scales, race conditions appear, data starts silently going missing, and there's zero observability because nobody thought to build any. The person who fixes that isn't a "workflow consultant." It's a developer who understands distributed systems and can read a visual graph. That's a new hybrid skillset and the market for it is barely priced in.

So the real mental model:

- AI compresses the "can you build it" question to near-zero

- Which means "should you build it" and "can it survive contact with reality" become the entire job

- Judgment, taste, and systems thinking get more valuable, not less

- The devs who struggle will be the ones whose value prop was "I write CRUD faster than you." That was always a shaky position and AI just made it untenable.

- The devs who thrive will be the ones who can look at a half-working prototype and know which 10% needs to be rebuilt, which 90% can stay, and what the blast radius is if they guess wrong.

The winners aren't the fastest AI users. They're the people with enough scar tissue to know where the scary parts are buried.

Would love to hear from anyone actually seeing their team shrink because of AI — I keep looking for that case honestly and mostly finding the opposite. If it's happening somewhere, I want to know where.

been thinking about this after seeing the nostalgia post about pre-2019 deep learning. there's something real in what people miss about that era, pure research vibes, no hype machine. but the flip side is that before cloud platforms and pre-trained models became mainstream, you, basically needed to work at Google or have a university cluster to do anything serious. now someone with a laptop and a free tier account can prototype something that would've taken a team years to set up. that's genuinely wild when you think about it. the no-code tools like Azure ML Studio and SageMaker have made it so people who, aren't ML engineers can still build useful stuff, which is cool for getting more people involved. still not sure it's as open as people claim though. the GPT-3 exclusive licensing thing a few years back was a good reminder that access to the models doesn't mean access to the actual frontier. universities are kind of getting squeezed out of large-scale training runs because compute costs are insane, and, a lot of the interesting stuff is happening behind closed doors at labs with billions in funding. so I reckon we've democratized the middle layer pretty well, prototyping, fine-tuning, deploying existing models, but the top of the stack is still pretty locked up. curious whether people here think that middle layer access is enough to actually move the field forward, or if the real breakthroughs still need the big compute that only a handful of orgs can afford.

The AI community assumes business owners want cutting-edge technology. They don't. And once you actually sit down with them, it becomes pretty obvious how much of the content in this space is written for other builders, not for the people supposedly being sold to.

Here's what they actually say when you ask them what's broken:

"I just want to stop doing the same thing over and over every day."

"I want to know when a customer is about to leave before they leave."

"I want someone to handle the follow-ups because we forget and lose deals."

"I want my team to stop spending half their day on admin."

Notice what's missing. Nobody said "I want an AI agent." Nobody said "I want a multi-agent system" or "I want a workflow orchestrated through [tool]." Nobody even used the word automation. They describe problems. They describe frustrations. They describe time they wish they had back.

If you want to sell AI services to this segment, you have to stop thinking like a builder and start thinking like a problem-solver. Walk into their world. Understand what annoys them daily. Then show them that the annoyance disappears. The best pitch I've seen for an AI service was literally "You know how your receptionist misses calls during lunch? I make sure that never happens again." That was it. No mention of AI, agents, voice models, or technology. Just the problem and the fix.

The interesting part — and the reason this cuts so deep for anyone building agents — is that the tech stack becomes almost invisible once you frame it this way. When I actually build these things, the stack is often boring: a model doing one narrow job, Latenode handling the orchestration and integrations into whatever CRM/phone system/inbox the business already uses, a couple of deterministic checks around the edges. From the owner's side, none of that matters. They see: missed calls down to zero. Follow-ups happening without anyone remembering to do them. That's the product. The agent is just the thing that makes it possible.

The mistake I see most builders making — and I've made it too — is leading with the sophistication of the system. "It's a multi-step agent with tool use and memory." Nobody cares. The buyer is measuring whether their Tuesday got easier.

Curious from others building AI services for SMBs: have you found the same thing, or are there segments where the tech-forward pitch actually lands? And for anyone who's changed how they pitch after getting this feedback from the market — what was the specific shift that started closing more deals?

The AI community assumes business owners want cutting-edge technology. They don't. And once you actually sit down with them, it becomes pretty obvious how much of the content in this space is written for other builders, not for the people supposedly being sold to.

Here's what they actually say when you ask them what's broken:

"I just want to stop doing the same thing over and over every day."

"I want to know when a customer is about to leave before they leave."

"I want someone to handle the follow-ups because we forget and lose deals."

"I want my team to stop spending half their day on admin."

Notice what's missing. Nobody said "I want an AI agent." Nobody said "I want a multi-agent system" or "I want a workflow orchestrated through [tool]." Nobody even used the word automation. They describe problems. They describe frustrations. They describe time they wish they had back.

If you want to sell AI services to this segment, you have to stop thinking like a builder and start thinking like a problem-solver. Walk into their world. Understand what annoys them daily. Then show them that the annoyance disappears. The best pitch I've seen for an AI service was literally "You know how your receptionist misses calls during lunch? I make sure that never happens again." That was it. No mention of AI, agents, voice models, or technology. Just the problem and the fix.

The interesting part — and the reason this cuts so deep for anyone building agents — is that the tech stack becomes almost invisible once you frame it this way. When I actually build these things, the stack is often boring: a model doing one narrow job, Latenode handling the orchestration and integrations into whatever CRM/phone system/inbox the business already uses, a couple of deterministic checks around the edges. From the owner's side, none of that matters. They see: missed calls down to zero. Follow-ups happening without anyone remembering to do them. That's the product. The agent is just the thing that makes it possible.

The mistake I see most builders making — and I've made it too — is leading with the sophistication of the system. "It's a multi-step agent with tool use and memory." Nobody cares. The buyer is measuring whether their Tuesday got easier.

Curious from others building AI services for SMBs: have you found the same thing, or are there segments where the tech-forward pitch actually lands? And for anyone who's changed how they pitch after getting this feedback from the market — what was the specific shift that started closing more deals?

saw a post a while back where someone built this whole Home Assistant setup with like 50+ sensors just to get a temperature alert from their fridge. we're talking ESP32 nodes flashed with ESPHome, a Matrix chatbot integration for alerts, the works. probably spent more time building it than the fridge will even last. a $20 smart plug with power monitoring would've done the job but nah, gotta go full enterprise. I'm guilty of this too tbh. spent a few weekends setting up a Node-RED flow to handle some email sorting that I could've done with a 10 line Python script. there's something about the complexity that feels productive even when it clearly isn't. and honestly it's getting worse now that agentic AI is a thing. like people are out here spinning up multi-step autonomous agents with self-healing logic just to rename files or send a weekly digest. the tooling is genuinely impressive but sometimes you gotta ask if you're solving a problem or just cosplaying as a systems architect. reckon a lot of it is just the learning value though, like you're never actually going to, need a Kubernetes cluster for your living room lights but you'll definitely learn something setting one up. curious what's the most absurd one you've come across or built yourself. was it worth it in the end or did you just quietly delete it after a month?

Karpathy's LLM wiki idea has been stuck in my head for a couple of weeks and I can't shake the feeling it reframes what "building with agents" actually means inside a company.

The usual framing: the agent is the product. You pick a model, wire up some tools, deploy it, measure adoption. The agent itself is what you're investing in.

The reframe: the agent is just the interface. The real asset is the layer of institutional knowledge that accumulates underneath it — every question someone asked, every correction an employee made, every edge case that got resolved, every "actually, we do it this way here" that got captured along the way. An agent you deploy today is roughly the same as the one your competitor deploys. A wiki that's been shaped by 500 employees asking real questions for 18 months is not something a competitor can buy, fork, or catch up on.

If that's right, a lot of choices look different. The measurement shifts from "is the agent giving good answers today" to "is it capturing what it learned today so tomorrow's answer is better." The stack shifts from "pick the best model" to "build the thing that survives model swaps." And the real work stops being prompt engineering and starts being knowledge-capture design — a much less sexy problem, which is probably why almost nobody is talking about it.

What I can't decide is whether this is actually a durable moat or just a temporary one. The optimistic read: compounding institutional context is genuinely hard to replicate and only gets more valuable over time. The cynical read: the moment a model is capable enough to infer most of that context from first principles, the accumulated wiki stops being a moat and starts being a maintenance burden.

Would love to hear from people running this inside real organisations — is the knowledge actually compounding, or is it just getting buried in logs nobody reads? And is anyone explicitly architecting for this, treating the knowledge layer as the durable asset and the agent itself as the replaceable frontend?

been going down a rabbit hole on this lately. there was a study late last year testing models on Japanese improv comedy (Oogiri) and the finding that stuck with, me was that LLMs actually agree with humans pretty well on what's NOT funny, but fall apart with high-quality humor. and the thing they're missing most seems to be empathy. like the model can identify the structure of a joke but doesn't get why it lands emotionally. the Onion headline thing is interesting too though. ChatGPT apparently matched human-written satire in blind tests with real readers. so clearly something is working at a surface level. reckon that's the crux of the debate. is "produces output humans find funny" close enough to "understands humor" or is that just really sophisticated pattern matching dressed up as wit. timing, subtext, knowing your audience, self-deprecation. those feel like things that require actual lived experience to do well, not just exposure to a ton of text. I lean toward mimicry but I'm honestly not sure where the line is. if a model consistently generates stuff people laugh at, at what point does the "understanding" label become meaningful vs just philosophical gatekeeping. curious if anyone's seen benchmarks that actually test for the empathy dimension specifically, because that seems like the harder problem.