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What I Learned Building a Real-Time Thin-Film Thickness Monitoring Pipeline
Dev.to · raymond zhao 📐 ML Fundamentals ⚡ AI Lesson 1w ago
What I Learned Building a Real-Time Thin-Film Thickness Monitoring Pipeline
If you've ever had to instrument a coating line, you know the hard part isn't the optics. It's...
The Best Anomaly Detector I Know Optimizes Nothing
Dev.to · Nishant Banginwar 📐 ML Fundamentals ⚡ AI Lesson 1w ago
The Best Anomaly Detector I Know Optimizes Nothing
Classic Machine Learning Through the Eyes of an SRE — Part 9: Isolation Forest The algorithm in one...
Lists in Python for Beginners
Dev.to · Vijayan Chakravarthi 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Lists in Python for Beginners
A List can be considered as a dynamic array. It is denoted by [ ]. The values inside a list are...
Deploying ClearML as an Azure ML Alternative
Dev.to · Sanskriti Harmukh 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Deploying ClearML as an Azure ML Alternative
Azure Machine Learning ties experiment tracking, pipelines, and model serving to Azure-specific APIs...
Measure Every Token: A SQLite-Backed Call Tracker for Free Model Endpoints
Dev.to · Dakota Huang 📐 ML Fundamentals ⚡ AI Lesson 1w ago
Measure Every Token: A SQLite-Backed Call Tracker for Free Model Endpoints
Free model endpoints are not free of mystery. You get a response, but no dashboard. No latency...
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets
Dev.to · Fenix 📐 ML Fundamentals ⚡ AI Lesson 1w ago
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets
mcp-tool-sanitizer v0.1.0: Making the MCP approval-view match the bytes the model gets A...
A Higher Pass Rate Can Mean a Worse Model. The Math Is Simpson's Paradox.
Dev.to · Maya Andersson 📐 ML Fundamentals ⚡ AI Lesson 1w ago
A Higher Pass Rate Can Mean a Worse Model. The Math Is Simpson's Paradox.
We shipped a model update last quarter that moved our aggregate pass rate from 81.2% to 83.6%....
A judge that agrees with your humans 92 percent of the time can be at 60 percent where the gate actually decides
Dev.to · Maya Andersson 📐 ML Fundamentals ⚡ AI Lesson 1w ago
A judge that agrees with your humans 92 percent of the time can be at 60 percent where the gate actually decides
TL;DR: Judge-human agreement is almost always reported as one number over a whole validation set....
Six Posts on Ensembles and Tuning, and the Uncomfortable Thing They All Turned Out to Be About
Dev.to · Sachin Kr. Rajput 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Six Posts on Ensembles and Tuning, and the Uncomfortable Thing They All Turned Out to Be About
The One-Line Summary: I set out to write six posts comparing ensemble and tuning methods, and every...
Decision Trees vs Random Forests: When Should You Use Which?
Dev.to · AheadMint Official 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Decision Trees vs Random Forests: When Should You Use Which?
Choosing between a Decision Tree and a Random Forest is one of the first architectural decisions in...
Free Models as Test Doubles: A Dev/Prod Split That Saves Real Money
Dev.to · Casey Li 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Free Models as Test Doubles: A Dev/Prod Split That Saves Real Money
The most expensive place to call a paid model API is your own laptop during debugging. Every retry,...
I Built a Free CLI + MCP Server for GEO Audits - Here is What 1,200 Sites Taught Me
Dev.to · L D (一π狐言) 📐 ML Fundamentals ⚡ AI Lesson 2w ago
I Built a Free CLI + MCP Server for GEO Audits - Here is What 1,200 Sites Taught Me
I spent two months building a free GEO (Generative Engine Optimization) audit tool with a CLI and an...
Adaptive compute techniques yield significant inference speedups across models
Dev.to · Papers Mache 📐 ML Fundamentals 📄 Paper ⚡ AI Lesson 2w ago
Adaptive compute techniques yield significant inference speedups across models
FlashMorph slashes the cost of designing hybrid attention models, needing only 20 M tokens and...
The cheapest model on my plan loses every benchmark. It still beats models charging 14x more.
Dev.to · Michael Amachree 📐 ML Fundamentals ⚡ AI Lesson 2w ago
The cheapest model on my plan loses every benchmark. It still beats models charging 14x more.
I ran the $0.14 model against the $0.44 model expecting a close fight. It lost 4-0. Then I looked at what it does to everything priced in between.
Blending and Voting: Four Noses, One Bottle, and the Blender Who Graded His Own Homework
Dev.to · Sachin Kr. Rajput 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Blending and Voting: Four Noses, One Bottle, and the Blender Who Graded His Own Homework
The One-Line Summary: Voting has no learned parameters, so it cannot overfit and it cannot lie to...
GC-OPD: Reconciling Teacher Likelihood with Verified Task Success
Dev.to · Prabhakar Chaudhary 📐 ML Fundamentals ⚡ AI Lesson 2w ago
GC-OPD: Reconciling Teacher Likelihood with Verified Task Success
The mismatch inside standard on-policy distillation On-policy distillation trains a...
JX N-Body Engine 0.1.0: Arbitrary-Precision Python and Numerical Validation
Dev.to · Lino Avila 📐 ML Fundamentals ⚡ AI Lesson 2w ago
JX N-Body Engine 0.1.0: Arbitrary-Precision Python and Numerical Validation
A Newtonian N-body engine built around a sixth-order Yoshida integrator, an independent Decimal...
Beyond the Vector: Why Graph Neural Networks are the Strategic Choice for Enterprise Generative AI on GCP
Dev.to · Kutluk Atalay 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Beyond the Vector: Why Graph Neural Networks are the Strategic Choice for Enterprise Generative AI on GCP
In the current epoch of Artificial Intelligence, the industry remains singularly preoccupied with the...
Don't Trust the First Token: A Streaming Latency Autopsy on Free Model Servers
Dev.to · Jordan Huang 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Don't Trust the First Token: A Streaming Latency Autopsy on Free Model Servers
Streaming changes everything. Or so I thought. Then I measured it. The first token is a...
Go 1.27's SIMD ties with NumPy until the data fits in cache
Dev.to · Efrain Garay 📐 ML Fundamentals ⚡ AI Lesson 2w ago
Go 1.27's SIMD ties with NumPy until the data fits in cache
I measured Go 1.27's experimental simd package against NumPy. They tie out of cache and lose inside it, and the reason is not the language.