Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
Enterprise Document Intelligence [Vol.1 #10B] - The LLM as last line of defence, then two real escalations walked end to end: a flat table to Azure, a figure to a vision model
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Top 5 MCP Servers for High-Performance Agentic Development
Here are five that are genuinely worth wiring into a high-performance agent development setup, chosen for what they do to an agent's actual capability rather than their star count.
Backpropagation Explained for Beginners (Part 1): Building the Intuition
Let's discover how neural networks learn, step by step
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Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
Enterprise Document Intelligence [Vol.1 #6quinquies] - Prompt engineering, then context engineering, then loop engineering. On the question side, the loop is small by design: read the doc, ask what is missing, re-parse.
The post Loop Engineering for RAG Question Parsing: The Small Loop That Runs Bef...
Your AI Agent Passed Every Eval. Finance Still Killed It.
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced.
The one metric that predicts whether an agent survives production, and how to measure it without a rebuild.
The post Your AI Agent Passed Every ...
Stop Using If-Else Chains: Use the Registry Pattern in Python Instead • 5 Real-World SQL Projects to Build Your Data Portfolio • 10 YouTube Channels Keeping You Ahead in AI • Structured Language Model Generation with Outlines
A Git worktree is a separate directory checked out from the same repository. You can have as many as you need, each on its own branch, all coexisting simultaneously on your filesystem.
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
Everyone is talking about loop engineering, but most discussions assume an LLM sits at the center of the loop. I wanted to isolate the architecture itself. So I built a deterministic, zero-dependency Python benchmark that replaces the model with simple rules, allowing me to measure one question dire...
Agentic AI Security: Defending Against Prompt Injection and Tool Misuse
In this article, you will learn what prompt injection and tool misuse are in the context of agentic AI systems, and which defense strategies experts...
Prepare These 5 Assets Before Your AI Agents Take On More Work
How to define recurring work, give AI the right context, explain what high-quality work looks like, and decide where human judgment is still needed.
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The most interesting thing about Pi isn't any single feature; it's that the project treats "what we didn't build" as documentation worth writing, which is rare enough on its own to take seriously.
Building Trustworthy Production RAG Systems Through Continuous Evaluation
A practical guide to building an evaluation workflow that catches retrieval failures, hallucinations, and performance drift before they reach users
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In this article, you will learn how scikit-ollama bridges the scikit-learn interface with locally running Ollama models to perform zero-shot text classification; no cloud API...
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
Enterprise Document Intelligence [Vol.1 #7quinquies] - Hallucination is usually garbage-in. Fix retrieval, and the model has nothing left to make up
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How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
I measured the actual GPU electricity for eight local models on one RTX 3090 — and the cheapest wasn't the smallest, nor the priciest the biggest.
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Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs
How to stop parsing JSON by hand and start trusting your model's output
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