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Microsoft reports AI is more expensive than paying human employees
The latest news from Microsoft has sent shockwaves through the industry.
The AI Practitioner Desk
The editorial desk behind AI Practitioner — applied AI for product and engineering teams. We write about LLMs in production, agent design, evaluation, cost, and shipping AI that works, with sources for every claim.
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Memory has grown to nearly two-thirds of AI chip component costs
It's becoming increasingly evident that memory has become a significant component of AI chip costs.
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Is AI Profitable Yet?
It's essential to examine the current state of its profitability. The question of whether AI is profitable yet has been a topic of discussion among industry experts,…
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MAI-Code-1-Flash
Recent developments have brought attention to MAI-Code-1-Flash. The introduction of this technology has been making waves, with discussions and announcements popping…
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AI has a multiplying effect on existing technical skills
It's becoming increasingly evident that AI has a multiplying effect on existing technical skills.
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AI Agent Guidelines for CS336 at Stanford
Recent developments at Stanford University's CS336 course are at the forefront of this change.
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Why Your AI Demo Worked but Your Production Pilot Failed
The gap between a compelling AI demo and a successful production pilot is one of the most demoralizing experiences in applied AI. The demo worked on every example you showed. The pilot ran for six weeks and users reported that the feature was unreliable, slow, and sometimes wrong in embarrassing
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The AI Product Manager's Checklist for Shipping Responsibly
AI products fail in ways that traditional software products do not. A bug in a CRUD application either works or throws an error. An AI feature can produce plausible-looking wrong answers with high confidence, behave differently on Tuesday than it did on Monday without any code change, and fail in
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Building an AI Agents Observability Stack from Scratch
Running a single LLM call is simple to debug: you have an input, a prompt, and an output. Running an agent that orchestrates multiple LLM calls, tool invocations, and conditional branches is a different operational category. When an agent produces a wrong answer or gets stuck in a loop, you
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Fine-Tuning vs RAG: When to Use Each (and When Neither Works)
The fine-tuning versus RAG debate is mostly a false binary. In practice, the right architecture depends on three variables specific to your use case: the nature of the knowledge your application requires, your latency tolerance, and how frequently that knowledge changes. Getting this wrong is expensive. Fine-tuning a large model
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The Evaluation Framework That Actually Predicts LLM Production Performance
Perplexity is a terrible proxy for production usefulness. A model with excellent perplexity on a held-out text corpus can still produce confidently wrong answers, miss edge cases in your specific domain, or fail at the structured output requirements your application depends on. Teams that select models by benchmark performance and
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Building a Content Pipeline with n8n and a Local LLM
The default AI content pipeline assumption is that you need cloud API access to do anything useful. That assumption is wrong, and increasingly expensive to maintain as you scale content volume. A local LLM running on commodity hardware, combined with n8n for orchestration, handles the majority of structured content automation