The math isn't complicated. It's just that nobody runs it until they get the bill. An AI agent handling a 10-turn workflow — reading files, calling tools, revising output — doesn't cost 10x a single query. Because transformer inference processes the entire context on every call, cost compounds with each additional turn. The tenth turn carries everything that preceded it: the original file reads, e
So far, we’ve covered: why MCP exists what MCP is what tools are Now let’s answer a key question: When the model decides to use a tool… who actually runs it? An MCP server is: The component that exposes tools and executes them. An MCP server is not just your backend. It is: a layer on top of your backend designed specifically for LLM interaction It has three main responsibilities: It tells the sys
Lately, I’ve been reflecting on something: The question for most developers is no longer "Are you using AI?", but rather "How and why are you using AI?". I’ve noticed AI tooling becoming increasingly embedded in my daily workflow. At this time last year, my usage of AI was limited to code autocomplete suggestions in my IDE that I would manually validate. Now I am using coding assistants to help id
Most agency onboarding fails before the kickoff call happens. Not because the team isn't good. Not because the client is difficult. Because nobody collected the right context upfront, and the kickoff call becomes the place where everyone discovers what they don't know yet. The intake form is the fix. Not a 3-question "tell us about your project" form. A real one. Here's the framework we use — 27 q
Every few years the industry rediscovers that programming languages are not religions. Then we immediately behave like they are religions. Someone posts a benchmark. Someone else says memory safety. Someone says developer experience. A distributed systems person appears from under a bridge and whispers “Erlang solved this in 1998.” A startup founder announces they are rewriting their CRUD app in R
Or: how we learned that “eventually” isn’t good enough when you’re bleeding file descriptors Or: how we learned that “eventually” isn’t good enough when you’re bleeding file descriptors Deterministic cleanup means knowing exactly when resources are freed — the difference between memory chaos and predictable system behavior in production environments. So our video transcoding service was… how d
Our goal has always been to be the go-to blockchain node platform across any chain and environment. Today, that includes the nodes you run on your own hardware. Running your own Ethereum infrastructure should be the basic right of every individual and household. Nodes should be easy. The catch? Self-hosting has always meant complexity. Manual setup, client updates, nodes falling out of sync, moni
What is Azure Storage? Azure storage is Microsoft's cloud storage solution. It allows storage of unstructured data, pdfs, file shares etc. The following steps in this article outlines how I was able to create storage for the department's testing and training. In the Azure portal, search for and select Resource groups Select + Create Give your resource group a name. For example, storagerg