After 4,000 interviews with developers, you’re basically a senior engineer in trauma vocabulary and opinion parsing
I'm Not a Developer. But I've Spent 10 Years Writing for Them.
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@[Ayush_SIngh] Great question, Ayush. Honestly? I'm not sure it has changed as much as people think.
When I started writing for developers, engineers, and architects in 2015, I learned fast that this audience has a finely tuned BS detector. They've been pitched vaporware for decades. They don't trust claims — they trust results. And they're not going to believe something works until they can test it themselves for their specific use case.
That skepticism hasn't gone away. If anything, it's more justified. The volume of noise has increased, but the tolerance for it hasn't.
What has changed is the format. Shorter. More direct. Less preamble. But the underlying demand — just give me the facts, show me what it actually does — that's been consistent from day one.
The writers who struggle with this audience are usually the ones trying to sell something. The ones who do well treat the reader like the smartest person in the room. Because usually, they are.
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@[yogirahul] While, nobody knows, patterns from the last 10 years provide some good signals.
The clearest trend I see continuing is accessibility. Technology that once required a team of specialists to implement is increasingly within reach of business owners and non-technical professionals. AI is accelerating that shift faster than anything I've seen.
The bigger change might be cultural. There's far more emphasis now on starting with the business problem rather than the technology. For years, organizations adopted solutions and then figured out what to do with them. That approach burned a lot of people. The ones succeeding today are flipping that equation — defining the problem first, then finding the right tool.
If that discipline holds over the next decade, I think we'll see technology that's not just more powerful, but more purposeful. And honestly, more useful to more people.
Thanks for the question.
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I specialize in LLM evaluation, prompt engineering, and RLHF (Reinforcement Learning from Human Feedback) methodologies. My focus is helping developers integrate LLMs into production systems: model fine-tuning strategies, prompt optimization, agentic workflows, AI-powered DevOps, and building reliable AI applications that actually work.
Having trained the core Google Bard model and interviewed 4,000+ technology executives across AI/ML infrastructure, I write about real-world LLM implementation challenges—not theoretical possibilities. I attend major tech conferences to understand what developers actually face when deploying AI in production environments. Show less
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