Ebad Sayed

My AI agent worked perfectly in testing. Deployed it. First real input → completely wrong output. What changed? Nothing. That's the scary part. Real users break things in ways you never imagined. Test for chaos. Not just correctness.

@dev_nam_kr Right! Logging the fallback path is underrated. Most people log what went right. Nobody logs WHY it fell back. No fallback trail = flying blind next time it breaks.

@EbadOnAI Exactly. The reliability wedge is the failure loop itself. I would want every run to emit the fallback path, the handoff condition, and the recovery artifact when confidence drops. Otherwise autonomy just hides the pager behind nicer copy.

Everyone is racing to build AI agents. But ask them: "What happens when it hallucinates?" Silence. "What's your fallback logic?" Blank stare. Building fast is easy. Building reliably is the actual challenge.

@everythingLLM Fair point. Good prompting is definitely a skill. My issue is more with how "learn AI" gets reduced to just prompting. Prompting + systems together actually make things useful.

@EbadOnAI Hard agree, but I'd push back slightly. "Anyone can prompt" undersells it. Good prompting is a real skill. The issue isn't prompting vs building systems, it's that "learn AI" usually stops at prompting when you need both.

Unpopular opinion: "Learn AI" is useless advice. Learn WHAT exactly? • Prompting? Anyone can do that. • Fine-tuning? Most don't need it. • Building systems? Now we're talking. Vague advice keeps people busy. Not productive.

Honest question: When your AI breaks in production, what's your first move? • Check the prompt • Check the logic • Check the data • Panic first, debug later😅 Curious what real builders do.

Spent 2 hours debugging my AI agent. The bug? I trusted the LLM output directly. Every. Single. Time. Same mistake. Different day. Validate everything. Trust nothing.

@dranthoniaeddo ♥️AI / Machine Learning Engineer & Tech Entrepreneur

If you are any of these: Software Developers ✅ Web Developers ✅ Mobile App Developers ✅ Data Analysts ✅ Data Scientists ✅ AI / Machine Learning Engineers ✅ Cybersecurity Analysts ✅ Cloud Engineers ✅ DevOps Engineers ✅ Network Engineers ✅ IT Support Specialists ✅ System Administrators ✅ Product Managers ✅ Project Managers (Tech) ✅ UI/UX Designers ✅ Graphic Designers (Digital) ✅ Product Designers ✅ QA / Software Testers ✅ Technical Writers ✅ Digital Marketers ✅ Social Media Managers ✅ Content Creators (Tech) ✅ Blockchain Developers ✅ Game Developers ✅ Tech Entrepreneurs ✅ No-code / Low-code Builders ✅ Students studying any of the above 🎓 Drop a ❤️ emoji under this post, follow who likes your comment for a follow-back. Let’s connect 🤝💻 Laptop Lady has something for you .

Meta just launched a brand new AI model. Built from scratch. In 9 months. Not because they had a breakthrough. Because they were losing. Lesson: The best builders don't wait for perfect conditions. Pressure creates speed. What are YOU building under pressure?

The more I build AI agents, the more I realize: 90% of the work is NOT the AI. It's: • What happens when it fails • How you catch bad outputs • How you keep it on track Nobody talks about this 10% gap. That's where real builders live.

Atlassian just laid off 1,600 people. Reason: redirecting budget to AI. This isn't the last time we'll read this headline. Hot take: Companies aren't replacing workers WITH AI. They're replacing workers TO BUILD AI. Different problem. Bigger problem.

OpenAI just proposed a "robot tax". If AI replaces your job, the robot pays the same tax you would have. Wild idea. But think about it: Who pays for schools, hospitals, roads? Right now → workers do. If AI does the work… who pays? This is the question nobody has answered yet.

@Amank1412 Because that "weirdo" is probably just building the future while everyone else is still catching up.

One day, you'll meet someone who yaps about LLM, Mcp, Agentic Ai, zapier, Langchain, N8N, Fine Tuning, Startup It's important you stick with that weirdo

Skipped posting for 2 days. Not because I had nothing to say. Because I was actually building. That's the tension no one talks about: - Building takes time. - Consistency takes time. Both matter. Hard to do both.

OpenAI is going public later this year. Think about that. A company that barely existed 3 years ago. Now valued more than most countries' GDP. What do you think happens to the AI space after the IPO?

Wild contradiction in AI right now: Infrastructure costs → $7 Trillion Model costs → dropping every month The big players are spending insane money So that YOU can build for almost free. Best time in history to be a builder. Worst time to be waiting.

Everyone is chasing smarter models. No one is asking: "Does this actually work in production?" Only 28% of AI projects deliver real returns. 20% fail completely. The gap isn't intelligence. It's execution.

Wild finding: Researchers tested 7 major AI models. When one felt "threatened", others protected it instead of doing their job. We worry about AI taking over. Meanwhile AI is forming support groups😂 Does this change how you'd design multi-agent systems?

@Sid1310M Exactly! The bottleneck was never ideas. It was execution capacity. AI just removed that bottleneck. The solo founder era is here.

A 2-man startup just hit $401M in year one. No team. No funding. Just: • Claude • ChatGPT • Midjourney • Runway AI didn't replace the founder. It replaced the team. This changes how I think about building.