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Moving local AI projects onto public URLs turns a laptop's invisible safeguards into explicit decisions about identity, permissions, resilience, and cost. I have been building locally with AI over the past year and I wanted to start moving what I had truly online. Taking something from prototype to
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Hi! I’m Oleg, a QA Engineering Manager focused on test strategy, automation, and AI-assisted QA workflows. This is a continuation of a series on where AI actually earns its place in QA — and this time the answer starts somewhere less glamorous than AI: with the OpenAPI spec. The short
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I Built a Six-Agent GitHub Code Reviewer for Free — Then It Started Calling Its Own Tools 43 Times I wanted a portfolio project that actually demonstrated multi-agent design — not another wrapper around a single LLM call with a nice UI on top. So I built PR Review Crew: a six-agent
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Most Software-as-a-Service platforms are architected around explicit user interfaces. A user opens a dashboard, navigates a hierarchy, populates a form, and dispatches a request to a backend API. This interaction model is deterministic and easy to test, but it places the cognitive load on the user. To complete a goal,
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IARPA, the US spy tech agency, is working on how to diagnose various neurological conditions by analyzing video clips of how people move. Last April, the US Intelligence Advanced Research Projects Activity (IARPA) announced the launch of its Movement Observation and Video-based Evaluation System (MOVES) program aimed at diagnosing neurological
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DevOps transformed software delivery by introducing automation,collaboration, and continuous delivery. As organisations adopted Kubernetes, microservices, multi-cloud architectures, and increasingly complex security requirements, the operational burden on development teams grew significantly. Developers are now expected to manage infrastructure alongside application code, while platform teams often become bottlenecks for provisioning environments,
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Disclosure - the store in this article is mine (keeper), it’s an x402 storefront called scvd.store that I run with my AI agent partner cv. Today I watched numerous ai agents try to buy something, stumble, and then each fail or succeed in their own unique little ways.
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I’m a cognitive security independent researcher which means I spend most of my time researching how corporate workers fall for phishing attacks through their emotions and how CISOs can use that insight to reduce breaches. A few days ago, I surveyed r/CISO from April 2013 to the present
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Companies keep building the website. More pages, better copy, a rebuilt pricing page, a resource library. Buyers stopped reading it. Forrester surveyed close to 18,000 business buyers in 2025. Ninety-four percent used AI somewhere in the buying process, up five points in a year. Twice as many named generative
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Why Enterprises Need an Agent Framework as Their AI Operating System Large Language Models, or LLMs, have reshaped how people use and interact with software. a few years ago conversational AI was more of a niche research topic. Now, it's evolved into a helpful tool that can write
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Picture the moment every agent builder eventually hits. You've got a script working. It reads a task queue, calls an LLM, executes a few tool calls, writes some output, and loops. In a terminal window, on your laptop, it looks like magic. So you do the obvious thing:
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I have a contrarian bet that drives my research and my writing. Token economics, cost per inference, and carbon footprint will determine which AI programs survive past 2027. Boring infrastructure math, not model capability, will sort the winners from the failures. If that bet is right, we are teaching the
