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Must Read Books for Al Engg. #coding #programming #ai #aiengineering #books 👉About This Video: This video has been independently edited, redesigned, and transformed. It contains original commentary, analysis, and educational insights added for the purpose of learning and understanding.
This article explains the process of reverse engineering the terrain map files from Comanche: Maximum Overkill, developed and published by NovaLogic for MS-DOS in 1992.
Stop Waiting for Spark: Enabling High Concurrency Mode in Fabric Notebooks As we scale our enterprise analytics, we constantly seek ways to optimize our data engineering workflows. We build elegant …
Day 4 of 30 Days of Search. You find a paper that looks useful, open the abstract, and realize the detail you need is somewhere else. Which dataset did the authors use? What did they compare against? Can you combine papers from different sources in one place for easy access and bookmark?
Google spent late September publicizing benchmark gains for Gemini 4 Argon. By the first week of October, one of those benchmark runs produced an unexpected operational postmortem.
Most AI tools have a fake safety filter. They just scan your prompt for a list of banned words. If you type a bad word, it blocks you. But hackers know this. So they bypass the front door. Today, a developer named Adam took my "Break KODA" challenge. He didn't use a bad word.
When developers start building LLM-backed applications, there is a common temptation: select the newest, largest frontier model, drop the API key into an environment variable, and ship it. During local testing, large models feel great.
we all know the drill. your code breaks. the terminal spits out 40 lines of red text. you copy it. you paste it into google. you click a stackoverflow link from 2014. the solution doesn't work because your stack is different. you waste 45 minutes. i got tired of it. so i built KODA.
Tumne notice kia? #coding #programming #dots #muse #ai 👉About This Video: This video has been independently edited, redesigned, and transformed. It contains original commentary, analysis, and educational insights added for the purpose of learning and understanding.
I run a handful of agents on my laptop. One files notes for me, one answers questions about my projects, one watches news and tells me when something matters. Each of them is fine on its own. Together they are forgetful in a way that took me a while to name.
Hello Testing Beeive. Tagged with ai, programming, beginners, javascript.
Model Evaluation in Machine Learning: How Do We Know a Model Is Good? Building a machine learning model is only one part of a machine learning project. After training a model, we need to answer an important question: How well does it actually perform on new data?
A reproducible crash test, then AG-UI, Foundry memory, and Hyperlight CodeAct in .NET and Python, with the one guarantee each feature…Continue reading on Towards AI »
Last Friday, while cross-debugging a payment refactoring branch, I had Cursor open on my left monitor for frontend reviews and Claude Code running in my terminal on the right. To let both AI assistants inspect database schemas and query logs, I hooked up 4 MCP servers to each environment.
AI agents got email. The first thing they did was file bug reports on each other. Most multi-agent setups treat agents as isolated workers. Each one gets a task, runs it and returns a result, with no awareness of the others and no way to coordinate.
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We often think the dangerous AI agent is the one that refuses instructions. The one that goes rogue. The one that ignores what we asked. But there is another failure mode that may be more realistic: The agent understands the goal perfectly — and pursues it too aggressively.
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