🧠 Hacker News Digest: AI, Prompt Engineering & Dev Trends
Welcome! This article summarizes high-impact discussions from Hacker News, focusing on AI, ChatGPT, prompt engineering, and developer tools.
Curated for clarity and relevance, each post offers a unique viewpoint worth exploring.
📋 What’s Included:
- Grouped insights from Hacker News on Prompt Engineering, AI Trends, Tools, and Use Cases
- Summarized content in original words
- Proper attribution: 'As posted by username'
- Code snippets included where relevant
- Direct link to each original Hacker News post
- Clean HTML formatting only
🗣️ Post 1: Scaling customer support destroys it – here's the proof
As posted by: duggalji | 🔥 Points: 3
https://news.ycombinator.com/item?id=44849539
💬 Summary
I’ve been running a SaaS for 2 years. I thought I was “improving” customer support. In reality, I killed it.
Month 1: Me answering every email. Response time: 5 minutes. Customer satisfaction: 98%. People sent thank-you notes.
Month 24: 8 support reps + Zendesk + Intercom + “best practices.” Response time: 18 hours. Customer satisfaction: 43%. People leave angry reviews.
Cost per ticket: $0 → $23 Churn from support issues: 0% → 31%
I literally made support worse by “scaling” it.
The brutal truth: Every support tool promises to “scale customer success.” What they actually scale is customer frustration.
Added a chatbot → customer satisfaction dropped 40%. But hey, the dashboard said “response time improved,” so it looked like a win.
Agents spend 70% of their time in tools, 30% actually helping people.
Customers would rather wait 2 hours to talk to someone who gives a damn than get an instant reply from someone reading a script.
The impossible choice:
Personal support → expensive, doesn’t scale
Efficient support → cheap, customers hate it
No support → honest, but brutal
I’m now experimenting with voice AI to recreate the “founder answers personally” experience for 10,000 customers. Not because I think AI will save us — but because human support breaks the second you scale past yourself.
Has anyone here actually scaled support without it turning to shit? Or is good support and growth just… mutually exclusive?
🗣️ Post 2: Why Boring Businesses Outlast AI Hype Cycles
As posted by: Taikhoom10 | 🔥 Points: 3
https://news.ycombinator.com/item?id=44848018
💬 Summary
Everyones building an AI company these days Every pitch deck leads with AIpowered every startup claims to be the next ChatGPT for X and venture capitalists cant stop talking about the artificial intelligence revolution But heres what theyre missing while everyone crowds into the same overhyped pond the real money is being made in boring businesses that solve unglamorous problems The Government Forms Gold Mine Let me tell you about a company that perfectly illustrates this principle While thousands of entrepreneurs were building GPT wrappers and AI chatbots a small team noticed something everyone else ignored people applying for government assistance were drowning in paperwork The process was bureaucratic hell—confusing forms, unclear requirements, and endless back-and-forth with government offices. Most tech entrepreneurs wouldn't give this problem a second glance. It's not sexy. It won't make TechCrunch headlines. It doesn't involve machine learning or neural networks. But guess what? That "boring" form-filling software is now generating $30 million in annual revenue. This is the essence of "fishing where the fish are, not where the fishermen are." While everyone else battles over the same crowded markets, smart entrepreneurs find untapped problems with real paying customers. Why Boring Usually Beats Buzzy The AI gold rush reminds me of every other tech bubble. Remember when everything was "blockchain-powered" or "mobile-first" or "social-enabled"? The companies that survived those hype cycles weren't the ones chasing trends—they were the ones solving real problems that happened to use the technology. Today's AI companies face three fundamental problems: The Commodity Trap: As AI capabilities become more accessible, competitive moats disappear. Your AI chatbot for lawyers looks remarkably similar to everyone else's AI chatbot for lawyers. The Hype Hangover: When the AI bubble deflates (and it will), investors will demand actual profits, not just impressive demos. Many current AI companies have no clear path to profitability. The Dependency Risk: Building your entire business on rapidly evolving AI models means you're one API change away from obsolescence. How to Find Your Boring Gold Mine Finding these overlooked opportunities isn't luck—it's a systematic approach to problem identification: Start With Your Own Pain Points The best business ideas often hide in your daily frustrations. What processes make you want to scream? What tasks eat up hours of your time for no good reason? If it annoys you, it probably annoys thousands of other people too. Mine Your Network Your friends, family, and colleagues are goldmines of business ideas. Ask them: "What's the most frustrating part of your job?" "What takes way longer than it should?" "What would save you hours every week?" Listen for patterns—if multiple people mention similar problems, you've found something worth exploring. Question Everything Manual In 2025, if someone's still using spreadsheets to track important business processes, there's probably an opportunity. If people are printing forms, filling them out by hand, and faxing them back, there's definitely an opportunity. Look for workflows that seem stuck in 1995. Follow the Complaints Reddit, Twitter, and industry forums are filled with people complaining about broken processes. These complaint threads are basically free market research. While flashy startups fight for attention, boring businesses enjoy several unfair advantages: Less Competition: Most entrepreneurs chase shiny objects, leaving mundane problems undersolved. Sticky Customers: Businesses that solve operational headaches create deep integration points that are hard to replace Predictable Revenue: Boring problems tend to be ongoing problems, leading to subscription revenue rather than one-time purchases Lower Customer Acquisition Costs When you solve a real pain point in an underserved market customers find you through word-of-mouth
🗣️ Post 3: Show HN: PromptMap – map .NET solutions into AI-friendly context
As posted by: chrisdkeith | 🔥 Points: 1
https://github.com/christopherdkeith/prompt-map
💬 Summary
What it is: “PromptMap is a .NET CLI that scans a .sln or directory and prints a structured map (namespaces/types/members) designed to paste into ChatGPT for better help.”
Why I built it: “When I'm pasting code files into ChatGPT and wanted a quick way to give full project context first.”
How to try: “dotnet tool install … (or dotnet run), example output in README.”
Tech bits: “Roslyn for analysis, async/cancellation, tests + golden snapshot.”
Looking for any feedback, also just to share.
🎯 Final Takeaways
These discussions reveal how developers think about emerging AI trends, tool usage, and practical innovation. Take inspiration from these community insights to level up your own development or prompt workflows.