标签：#ci

Stop Fighting Scikit-Learn Pipelines: How Metadata Routing Fixes Sample Weights & Groups A couple of months ago, I stumbled upon this video by Vincent D. Warmerdam about metadata routing in scikit-learn. I'll be honest, I had no idea what "metadata routing" even meant, but Vincent's explanation completely changed how I think about building ML pipelines. The video showed me that one of the most frustrating problems in scikit-learn; passing sample weights and groups through complex pipelines finally had an elegant solution. It piqued my curiosity enough that I dove deep into the feature, tested it extensively, and honestly, I was surprised by how little coverage this gets in technical blogs and articles. So I figured, why not write about it myself and share what I learned? If you've ever struggled with imbalanced datasets, grouped cross-validation, or just wanted to pass custom information through your pipelines, this article is for you. Let's start from the very beginning. What is "Metadata" in Machine Learning? Let's start with a concrete example. You're building a credit card fraud detection model with this data: # Your training data X = transaction_features # Amount, merchant, time, location, etc. y = is_fraud # 0 = legitimate, 1 = fraud # But you also have additional information: sample_weights = [ 1.0 , 1.0 , 10.0 , 1.0 , ...] # Fraud transactions weighted 10x customer_ids = [ 101 , 102 , 101 , 103 , ...] # Which customer made each transaction Metadata is the "extra information" beyond your features (X) and labels (y): sample_weight : How important is each transaction? (Fraud = 10x more important) groups : Which customer does each transaction belong to? (For proper cross-validation) Custom metadata : Transaction timestamps, confidence scores, data quality flags, etc. Why Metadata Matters: The Credit Card Fraud Problem Imagine you're building a fraud detection system for a financial company. You have: Imbalanced data : 99% legitimate transactions, 1% fraudulent T

We constantly hear that Ansible and Python are apparently the only ways to automate networks, today I even listen in a conversation "Python is the industry standard" probably I missed the RFC document or probably the guy was referring to a sales standard, but back to us what happens when the framework, the platform or the software we are using becomes heavier than the problem to solve? There is a moment where automation becomes necessary, not because we want to look modern, not because every task deserves a framework and not simply because adding automation automatically means we are doing things better. It becomes necessary because repeating the same command collection manually across many devices is slow, risky, boring and almost impossible to diff and validate properly especially under pressure. For this reason I built the Cisco Go Collector during a real migration activity with a very practical goal: collect configuration and command outputs from Cisco devices in an easily repeatable way, without forcing every colleague involved in the process to become developers or to install an automation stack just to run a super simple flow. The idea was simple: define the devices in a CSV which is the comfort zone for everyone define the commands in the same CSV file, super simple and organized to manage one row per device run a portable Go binary against that CSV file collect the outputs in organized text files archive the result as operational evidence that can be easily diff That is it! super lightweight to run no Python virtual environment no Ansible playbook structure no inventory hierarchy no framework onboarding no additional runtime or software on corporate managed workstations just a CSV file and a compiled binary The automation and AI trap when the solution is heavier than the problem to solve I love automation and I fully support AI if used the proper way, but we have to find a balance and recognize when to choose one tool over the other and specially one progra

"Intelligence isn't about searching everywhere—it's about knowing where not to search." Artificial Intelligence is often associated with neural networks, large language models, and autonomous systems. But long before modern generative AI, computer scientists were solving a much deeper question: How do intelligent systems make decisions efficiently? Whether you're building search algorithms, recommendation systems, autonomous robots, or distributed systems, the architecture of intelligence teaches timeless lessons about solving problems under uncertainty. Let's explore five powerful ideas that shaped AI—and why they matter far beyond computer science. ✈️ 1. The Pilot's Dilemma: Why Blind Search Fails Imagine you're a pilot. Suddenly, one of your engines fails. In the next few seconds, there are hundreds of switches, buttons, and controls available. If you treated every control equally, you'd spend precious time trying random combinations. That is exactly how uninformed search works. Algorithms like: Breadth-First Search (BFS) Depth-First Search (DFS) have no knowledge of where the solution might be. They simply explore. Start ├── Option A ├── Option B ├── Option C └── ... The larger the search space becomes, the less practical this strategy is. A pilot doesn't blindly flip switches. They use additional knowledge : Engine pressure Fuel flow Hydraulic readings Warning systems Those clues dramatically reduce the number of possibilities. This is exactly what AI calls Informed Search . Instead of exploring everything, intelligent systems use knowledge to eliminate impossible paths before searching them. 🧠 2. Heuristics: The Cheat Code of Intelligence The secret behind informed search is something called a heuristic . A heuristic is simply an educated estimate. Mathematically, h(n) represents the estimated cost from the current state to the goal. One important rule always holds: h(goal) = 0 Once we've reached the goal, there's no remaining cost. Example: Finding Bucharest

Years after initial tests, researchers are now racing to see if a vaccine developed in 2011 can help fight the current Bundibugyo outbreak in Congo.

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. On TikTok, the tanned youths are explaining why they no longer wear sunscreen. In one video, a young man films […]

A French launch startup is scrapping the name of its rocket, apparently due to a trademark issue.

OpenAI’s Bonnie Xu discusses Kepler, an internal AI data analyst agent built to query 600+ petabytes of data. She explains how they overcome context window limits using MCP, automated code crawling, and RAG. Xu also shares how their team leverages scoped semantic memory for self-learning and utilizes AST-based LLM grading to build a robust, regression-free evaluation pipeline. By Bonnie Xu

CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop By Craig Risi

Researchers say these coral strongholds may help repopulate more degraded reefs.

Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the…

UK attorney general Richard Hermer told his staff his office was quitting X, according to The Guardian and The Observer.

A new report shows that government agencies failed to communicate and includes recommendations for stronger oversight in a bid to avert future disasters.

A London clinic owner has claimed he is treating people with stage 4 cancer by sealing them into a plastic bag while they're naked from the waist down and gassing them with chlorine dioxide.

"I consider this a success already, just from the fact that we're even going to try this."

In February, a Trump official refused to review the vaccine.

Taiwan's drone spending plans for defense could also boost business overseas.

Anthropic still can’t distribute Claude Mythos or Fable 5 after running afoul of the Trump administration. But no one can say exactly what the company did wrong.

On today’s Uncanny Valley, we dive into the dysfunction in Meta’s newly formed AI unit and why it’s been driving already-low employee morale even further into the ground.

It's unclear whether the system is currently intact.

The software engineers filed a complaint with Seattle’s civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs.