Level Up Coding
“Level Up Coding” delves into the intricacies of Python programming, data augmentation for machine learning, and the challenges of enterprise RAG implementations. It explores the importance of understanding the speed of Python, the significance of short-term memory in AI applications, and the utilization of tools like Langchain for conversation history retention. Additionally, it discusses the pitfalls of treating data as static in the realm of generative AI and emphasizes the need for systems to not just read documents but truly understand and interact with dynamic big data.
How to Stop an AI From Living in the Past
A major European bank deployed a GenAI assistant to help compliance officers interpret Basel III and the EU’s Capital Requirements Regulation (CRR3) — two sets of rules that govern how banks manage fi...
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Linus Torvalds Said the Quiet Part Out Loud About AI and Code Quality.
Two days ago in Mumbai, Linus Torvalds said something that has been sitting with me since I read it. He said he is no longer a programmer. Not in the sense that he has lost the ability. In the sense t...
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Loop engineering isn’t new. Don’t reinvent the wheel.
always has been; always will be A lone senior developer with no spec, no tests, and no code review ships bugs. Not sometimes. Reliably. This is why we invented all the ceremonies, the design documents...
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How OAuth and JWTs Actually Work: The Access Token System Silicon Valley Keeps Breaking
On September 25, 2018, Facebook’s engineering team noticed something odd: an unusual spike in a feature called “View As,” which lets you preview your own profile the way someone else would see it. By ...
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The Perceptron: The Building Block of Neural Networks
1. Introduction My main interest is Large Language Models (LLMs). Like many developers, I’ve spent a lot of time using them, building applications around them, and reading about how they work. Eventua...
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Two AI Teams, Human-Gated: Running a Dev Team and a Content Team as Claude Code Subagents
A year of building AI into real business operations in public — assistants, an MCP server, multi-agent, a self-built ontology engine, an order-to-cash series. This isn’t a new build; it’s how I work: ...
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The Go Error You See Is Never the Real Error
It was 3:07 AM when the pager went off. Not a gradual alert build-up, not a "low priority — acknowledge within 4 hours" kind of thing. Full-red, P0, "payment service is down and revenue has stopped fl...
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SUMX vs CALCULATE in DAX: Which One Is Really Faster?
A Deep Dive with Real Benchmarks Generated with Gemini For years, Power BI practitioners have repeated a single phrase like a mantra: “CALCULATE is faster than SUMX.” “Avoid SUMX because it’s a slow ...
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Odin: An Underrated C Alternative That Few Programmers Know About
C23 introduced some interesting features for C programmers, but we know that C won’t evolve into a modern, system-programming-ready, general-purpose language, mostly due to compatibility reasons. On t...
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Loop Engineering with Claude Code: Rebuilding Snake
Eight prompts, ten minutes of agent time, one failed prediction, and two bugs introduced by the human-in-the-loop. Continue reading on Level Up Coding
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The Senior Engineer Who Quit Being a “Verification Layer” Was Sitting on the Only Skill the Agents…
I spent the second week of June reviewing code I did not write. Not most of the week. All of it. Monday through Friday, my whole job was to sit in front of pull requests that three coding agents had g...
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Chinese AI Labs Innovate Where US Labs Scale. Incentive to Innovate vs Adapt
Free version MLA: 57x KV cache reduction. GQA: 4–8x. MLA published May 2024. It is now July 2026. No major US lab has adopted it in production. That is not a rounding error. That is fourteen months of...
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