1d ago · 4 min read · Most discussions of AI infrastructure stop at the model. The model is the part that is easy to talk about — it has a name, a version number, and a benchmark score. The harder and less-discussed part i
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3d ago · 12 min read · We've all seen the demo: an AI agent books a flight, updates a CRM, and sends a Slack message, all from a single chat prompt. It looks like magic. But when you try to connect that agent to your 20-yea
Join discussionMay 22 · 77 min read · The landscape is already saturated with "AI wrappers" and hype-driven demos, I been building something that reflects true Systems Engineering principles. For the last few months—working before and aft
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May 12 · 13 min read · [Experimental Series - Status: In Development] The Emperor Is All But Dead. The Light Remains. An experimental governance framework for software teams of humans and AI agents — and a request to be arg
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May 8 · 8 min read · A few speakers even had to update their slides overnight because something had been released the previous evening. That sounds funny until you realize this is now the operating model for engineering t
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Apr 29 · 59 min read · How do you write a test for a response that is confidently wrong? This is the most urgent open question in software quality right now — and most teams have no answer. Target Audience: AI Engineers ·
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Apr 29 · 34 min read · Introduction: The Graveyard Nobody Talks About You have spent months building a machine learning model. The data scientists are celebrating. The accuracy numbers look phenomenal. The Jupyter notebook
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Apr 22 · 5 min read · Local AI development moves fast. Teams pull models, test variants, tweak prompts, and move on. A few weeks later, people are asking the same questions: Which model are we actually using in production
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Apr 19 · 11 min read · Github repo: https://github.com/SubhanshuMG/agents-as-state-machines The thesis in one paragraph Stop calling them agents. They are state machines that invoke LLMs at certain transitions. The multi-a
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