Foundations
ML Fundamentals
Neural networks, backpropagation, gradient descent — the maths behind AI
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Dev.to · Dakota Huang
📐 ML Fundamentals
⚡ AI Lesson
3d ago
Differential Testing Is the Proof Your Extraction Is Missing
Most extractions break at merge, not at edit time. The build passes. The tests pass. The function...

Dev.to · Taylor Lin
📐 ML Fundamentals
⚡ AI Lesson
4d ago
The Score Is a Symptom: A Diagnostic Tree for Free-Model Evals
A free model scores 30% on your eval. The obvious conclusion: the model is weak. Swap it, and the...

Dev.to · Emery Li
📐 ML Fundamentals
⚡ AI Lesson
4d ago
Your Eval Set Is Rotting: A Theme-Fingerprint Drift Check
Your evaluation set has an expiration date. Last month it helped you pick a model. This month it is...

Dev.to · raymond zhao
📐 ML Fundamentals
⚡ AI Lesson
4d 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...

Dev.to · Nishant Banginwar
📐 ML Fundamentals
⚡ AI Lesson
4d 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...

Dev.to · Vijayan Chakravarthi
📐 ML Fundamentals
⚡ AI Lesson
4d 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...

Dev.to · Sanskriti Harmukh
📐 ML Fundamentals
⚡ AI Lesson
5d ago
Deploying ClearML as an Azure ML Alternative
Azure Machine Learning ties experiment tracking, pipelines, and model serving to Azure-specific APIs...

Dev.to · Dakota Huang
📐 ML Fundamentals
⚡ AI Lesson
6d 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...

Dev.to · Fenix
📐 ML Fundamentals
⚡ AI Lesson
6d 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...

Dev.to · Maya Andersson
📐 ML Fundamentals
⚡ AI Lesson
6d 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%....

Dev.to · Maya Andersson
📐 ML Fundamentals
⚡ AI Lesson
6d 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....

Dev.to · Sachin Kr. Rajput
📐 ML Fundamentals
⚡ AI Lesson
6d 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...

Dev.to · AheadMint Official
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Casey Li
📐 ML Fundamentals
⚡ AI Lesson
1w 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,...

Dev.to · L D (一π狐言)
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Papers Mache
📐 ML Fundamentals
📄 Paper
⚡ AI Lesson
1w 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...

Dev.to · Michael Amachree
📐 ML Fundamentals
⚡ AI Lesson
1w 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.

Dev.to · Sachin Kr. Rajput
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Prabhakar Chaudhary
📐 ML Fundamentals
⚡ AI Lesson
1w ago
GC-OPD: Reconciling Teacher Likelihood with Verified Task Success
The mismatch inside standard on-policy distillation On-policy distillation trains a...

Dev.to · Lino Avila
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Kutluk Atalay
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Jordan Huang
📐 ML Fundamentals
⚡ AI Lesson
1w 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...

Dev.to · Efrain Garay
📐 ML Fundamentals
⚡ AI Lesson
1w 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.

Dev.to · Efrain Garay
📐 ML Fundamentals
⚡ AI Lesson
1w 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.

Dev.to · Roman Koropets
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Computing WHO growth percentiles on-device
Every baby tracker shows growth percentiles. "Your daughter is in the 72nd percentile for weight." It...

Dev.to · sagar jain
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Model Routing in Production: Cheap First, Escalate on Doubt
Route most requests to the cheapest model that passes your evals, and send a request to the expensive model only when a cheap, checkable signal says the...

Dev.to · ömer faruk aydın
📐 ML Fundamentals
⚡ AI Lesson
1w ago
How I built a crypto signal generator that beat fixed-weight strategies by 37% Sharpe
Combining 13 technical indicators in an XGBoost model with Bayesian-optimized hyperparameters - a complete Python pipeline.

Dev.to · Alok Kumar
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Part 1 — What Actually Happens When Code Runs
When we write: const result = add(10, 20); Enter fullscreen mode Exit fullscreen...

Dev.to · Nerav Doshi
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Compared Quantization Levels: Q4 vs Q8 vs FP16 on llama3.2:1b
Context: A model's weights — the numbers it uses to reason — are normally stored at high precision,...

Dev.to · Christian Pichichero
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Point-in-Time Fundamentals for Numerai Signals: Killing Lookahead in Your Feature Join
If you build features for Numerai Signals from fundamentals, the single most common way to silently...

Dev.to · Josef Doornink
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Choosing the Right GPU for Your Model — A Sizing Method, Not a Guess
Choosing the Right GPU for Your Model — A Sizing Method, Not a Guess OK,...

Dev.to · Tom Jones
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Who your model works with matters more than which model you picked
The short version, for anyone who does not benchmark models for a living Every few weeks a...

Dev.to · Shashi Bhushan Savarn
📐 ML Fundamentals
⚡ AI Lesson
1w ago
Predicting CPU Spikes
Predictive System Health Checks: What I Learned Testing ARIMA, SARIMA, and Prophet on Infrastructure...

Dev.to · Timevolt
📐 ML Fundamentals
⚡ AI Lesson
2w ago
From Zero to Hero: Preparing for FAANG Interviews in 3 Months – A Journey Inspired by *The Lord of the Rings*
The Quest Begins (The "Why") Honestly, I used to stare at a blank editor and feel like...

Dev.to · Serguey Asael Shinder
📐 ML Fundamentals
⚡ AI Lesson
2w ago
Make the Model Show Its Work
Don't just ask for the answer. Ask how it got there. A model will hand you a conclusion with total...

Dev.to · Samuel Umoh
📐 ML Fundamentals
⚡ AI Lesson
2w ago
Building Fault-Tolerant, Event-Driven Kafka Pipelines in Go: Reliable Reprocessing & Dead Letter Queues
A practical guide to building reliable event-driven systems in Go using Apache Kafka. Learn how to...

Dev.to · Quinn Li
📐 ML Fundamentals
⚡ AI Lesson
2w ago
A Free Server Is Enough to Test a New Model Before You Trust It
You do not need a large budget to find out whether a freshly announced model fits your system. A...

Dev.to · Ashkan Ahmadi
📐 ML Fundamentals
⚡ AI Lesson
2w ago
How We Hardened a Multilingual TypeScript Text Filter Against Real Bypasses and False Positives
Text filtering looks deceptively simple when the first version works on isolated examples. Give a...

Dev.to · Quinn Sun
📐 ML Fundamentals
⚡ AI Lesson
2w ago
Replay Your Last Ten Bugfixes Before You Trust a New Coding Model
Consider a small team that sees two model releases in the same week. One is DeepSeek-V4-Pro-0813,...

Dev.to · Dinesh Kumar Ramasamy
📐 ML Fundamentals
⚡ AI Lesson
2w ago
From API to GPU, Week 5: Tensors, the Data Structure Behind Every Model
Phase 2 of 8: Enough ML to understand inference. Week 5 of 32. Phase 1 was about running models....

Dev.to · Finley Zhou
📐 ML Fundamentals
⚡ AI Lesson
2w ago
A Free Model Endpoint Replied Twice, Then Went Silent. The Fix Was a C++ Replay Envelope, Not Retries
Late on a Tuesday, a C++ tooling team noticed their warning classifier was duplicating...

Dev.to · Alexander Kopylkov
📐 ML Fundamentals
⚡ AI Lesson
2w ago
Why Making AI Answer Faster Is Worth $1.5 Billion
Keeping an AI model fast enough to use is turning out to be the expensive part of building...
Simon Willison's Blog
📐 ML Fundamentals
⚡ AI Lesson
3w ago
GitHub Models is now retired
GitHub Models is now retired I missed this news until today, when the GitHub Actions run for my simonw/research repository failed with this error message: GitHu

Dev.to · sagar jain
📐 ML Fundamentals
⚡ AI Lesson
3w ago
Build, Buy, or Call an API: How We Actually Decide
Clients ask me why we don't just build our own model. It's a fair question, and most of the time the honest answer is that building our own would be the slowest

Dev.to · Aviral Srivastava
📐 ML Fundamentals
⚡ AI Lesson
3w ago
Serving Models with TensorFlow Serving
Unleash Your AI: Serving Models Like a Pro with TensorFlow Serving So, you've poured your...

Dev.to · oleg kholin
📐 ML Fundamentals
⚡ AI Lesson
3w ago
A Stroke Instead of a Picture: The Evolution of Recognition Paradigms as Exemplified by Speech and Handwritten Input
The problem of speech recognition in contemporary artificial intelligence systems can be described as...

Dev.to · dayu2333-jinyul
📐 ML Fundamentals
⚡ AI Lesson
3w ago
I Could Not Mentally Calculate Bitwise XOR in a Coding Interview — So I Built a Visual Calculator
After bombing a bitwise operations question, I built a free visual calculator that shows AND, OR, XOR, NOT, NAND, NOR, XNOR with binary alignment.

Dev.to · Li Zhuojun
📐 ML Fundamentals
⚡ AI Lesson
3w ago
I measured his app with his own code. He measured my claim with his own corpus.
This is part five of a series about pointing an append-only audit log at things that count tokens....
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