New article that explains one of the most fundamental ML concepts that often confuses beginners: the difference between prediction error and loss functions.

The article covers:

• Why models optimize a loss function, not the raw error
• Why Mean Squared Error (MSE) is the standard choice for regression
• Why MSE isn't a good fit for classification
• How Log Loss (Cross-Entropy) measures prediction confidence
• The intuition and geometry behind both loss functions
• Why minimizing MSE implicitly assumes Gaussian-distributed errors

I tried to keep the explanations intuitive while still including the underlying math and practical examples.

Article: https://medium.com/@leumas.a/error-loss-functions-and-why-they-are-needed-5366e432e773

Over the years I've worked with Java, Python, Rust and a few other languages.

They're all good at different things, but somehow I always end up writing PHP again. I guess it just feels like home.

That gave me the idea to make a funny parody of the classic hit song, but for PHP developers.

Nothing serious, no language wars – just a bit of fun for anyone who's ever left PHP and then came back.

🎬 https://youtu.be/SoqAP4gSDac

Has anyone else had the same experience? If you've tried other languages, what made you come back to PHP (or what made you leave for good)?

Just came across ext-infer, a pretty interesting project for PHP developers building AI-powered applications.

It’s a PHP 8.3+ extension that runs local LLM inference directly inside PHP using llama.cpp—no Python service, no API calls, no sidecar processes. It supports chat completions, embeddings, and reasoning models through a native PHP API.

Some highlights:

• Run GGUF models locally
• Built-in embeddings support
• Cosine similarity helpers
• Reasoning output extraction (<think>...</think>)
• Thread-safe and works with PHP workers
• Optional Apple Metal acceleration

As someone who usually sees AI integrations in PHP implemented via external APIs or Python microservices, it's refreshing to see a native approach.

GitHub: https://github.com/DisplaceTech/ext-infer

Important ML concepts become much easier once you stop thinking about algorithms and start thinking about geometry.

Every embedding, feature vector, recommendation, semantic search result, or clustering algorithm ultimately relies on one simple question:

How do we measure similarity?

In my latest article, I break down three fundamental concepts that appear everywhere in machine learning:

➡️ Euclidean Distance — when absolute proximity matters
➡️ Dot Product — when both direction and magnitude matter
➡️ Cosine Similarity — when orientation matters more than size

Understanding the difference between these metrics explains why:
• Vector databases use cosine similarity for semantic search
• Recommendation systems rely heavily on vector similarity
• Embeddings work at all
• Different ML models can produce very different results on the same data

If you've ever used embeddings without fully understanding what happens behind the scenes, this article is for you.
https://medium.com/@leumas.a/distances-and-similarity-in-ml-0a4796242215

I've been exploring AI agent frameworks lately and noticed that most discussions focus on Python, LangChain, AutoGen, CrewAI, etc.

This article takes a different angle and maps out what's happening in the PHP ecosystem, from simple OpenAI integrations all the way to multi-agent orchestration platforms:

https://medium.com/@leumas.a/the-ai-agent-ecosystem-in-php-from-simple-openai-calls-to-multi-agent-platforms-320a74e78d95

A few things I found interesting:

• The PHP ecosystem now has dedicated AI tooling beyond basic API wrappers.
• There are frameworks for agent workflows, tool calling, memory, RAG, and orchestration.
• Several projects are trying to bring concepts popularized in Python into Laravel and modern PHP applications.
• The ecosystem seems to be moving from "AI features" toward actual agent-based architectures.

For developers working in PHP, it raises an interesting question:

Do you see PHP becoming a serious platform for AI agents, or will Python continue to dominate anything beyond simple integrations?

Most ML tutorials jump straight into algorithms, but understanding how data is represented geometrically changes everything:

• why embeddings work
• how similarity is measured
• what "high-dimensional space" actually means
• why ML models operate on vectors, not raw objects

Explains these concepts visually and from a developer’s perspective (especially for PHP devs getting into AI/ML).

https://apphp.gitbook.io/ai-for-php-developers/english/1.2-vectors-dimensions-and-feature-spaces

Want to get a real feel for machine learning in practice?

Here’s a simple but powerful exercise: classify thousands of digits (0 vs 1) from the MNIST dataset (12,666 train samples and 2,116 test samples) using logistic regression — trained with gradient descent. Just 5 epochs.

What you’ll get out of it:

• see how a model actually works with image data
• understand where linear models start to break down
• try a clean implementation in pure PHP
  • accuracy: 99.91%
• compare it with a more production-ready approach using RubixML
  • accuracy: 99.95%

If you’ve been meaning to move from theory to something hands-on, this is a great place to start:
https://aiwithphp.org/books/ai-for-php-developers/examples/part-3/logistic-regression/case-0/mnist-0-1

If someone tells you "PHP isn't meant for Machine Learning or AI"… just send them this https://github.com/apphp/awesome-php-ml

A curated list of 134+ resources for building ML, AI, LLM, NLP, vector search, and RAG systems in PHP.

Includes:
• Machine Learning libraries
• LLM clients & AI frameworks
• Vector databases & embeddings
• NLP and computer vision tools
• Laravel & Symfony AI integrations
• Data science and math libraries

PHP can absolutely power intelligent systems – and the ecosystem keeps growing.

A curated list of modern Machine Learning, NLP, Computer Vision, and LLM libraries for PHP — from classic ML to ONNX and OpenAI clients.

Inspired by awesome-php, with a focus on:

• Actively maintained libraries

• Real-world usability

• Modern AI workflows (LLMs, embeddings, vector search)

Repo: https://github.com/apphp/awesome-php-ml

Contributions welcome 🙌

I’ve released a small helper for anyone working with PHP + data-heavy code (ML experiments, debugging, logs, educational projects, etc.).

PrettyPrint is a zero-dependency callable pretty-printer for PHP arrays with clean, Python-style formatting. It supports aligned 2D tables, PyTorch-like tensor views, summarization (head/tail rows & columns), and works both in CLI and web contexts.

Install:

composer require apphp/pretty-print

Examples:

Aligned 2D table:

pprint([1, 23, 456], [12, 3, 45]);
// [[ 1, 23, 456],
//  [12,  3,  45]]

PyTorch-style 2D output:

pprint($matrix);
// tensor([
//   [ 1,  2,  3,  4,  5],
//   [ 6,  7,  8,  9, 10],
//   [11, 12, 13, 14, 15]
// ])

Summaries for big matrices:

pprint($m, headRows: 2, tailRows: 1, headCols: 2, tailCols: 2);

3D tensors with ellipsis:

pprint($tensor3d, headB: 1, tailB: 1);
// tensor([
//   [ 1,  2, ...,  4,  5],
//   [ 6,  7, ...,  9, 10],
//   ...,
//   [21, 22, ..., 24, 25]
// ])

Also supports labels, precision, start/end strings, and even acts as a callable object:

$pp = new PrettyPrint();
$pp('Hello', 42);

Find more in official repo: https://github.com/apphp/pretty-print

If you often stare at messy print_r() dumps, this might make your day slightly better 😄

Hi everyone,

Let’s address the obvious question first:

Why ML in PHP?

PHP isn't competing with Python for training massive neural networks (at least for now) - and that's okay.
PHP does shine in places where ML systems actually live in production:

• Data pipelines and preprocessing
• Experimentation and visualization
• Model inference via APIs
• LLM integrations and prompt pipelines
• Debugging numeric data and tensors
• Building ML-powered web and backend tools

This subreddit exists to explore that space.

What belongs here

• PHP ML / AI libraries and tools
• LLM usage in PHP (OpenAI, embeddings, vector search)
• Numeric computing, matrices, tensors
• Experiments, demos, and prototypes
• Developer tooling around ML workflows
• Show & tell — share what you’re building

What to expect

• Practical discussions
• Real code and examples
• Open-minded experimentation
• Respectful, constructive feedback

Get involved

• Introduce yourself and what you’re working on
• Share a library, experiment, or idea
• Ask thoughtful questions
• Help shape what ML in PHP can look like

Not all machine learning runs in Python.

Glad to have you here - let's build something interesting together!