AI News

Building Semantic Search with Transformers.js and Sentence Embeddings

Building Semantic Search with Transformers.js and Sentence Embeddings

You’ve probably shipped this bug before, where a user types ” affordable laptop ” into your search bar and gets zero results.

Using Scikit-LLM with Open-Source LLMs

Using Scikit-LLM with Open-Source LLMs

This article will teach you how to perform a language task like text classification by integrating locally hosted large language models (LLMs) of manageable size, like Mistral, Gemma, and Llama 3: all for free thanks to Ollama — a free repository for local LLMs — and the Scikit-LLM Python library.

Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?

Scikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?

In recent years, generative AI models like LLMs (large language models) have gradually taken over classical machine learning ones for addressing certain tasks, for instance, text classification .

The Roadmap for Mastering LLMOps in 2026

The Roadmap for Mastering LLMOps in 2026

The LLMOps market is projected to grow from

Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static batching, by grouping them into fixed-size batches and processing each batch […]

Choosing the Right Agentic Design Pattern: A Decision-Tree Approach

Choosing the Right Agentic Design Pattern: A Decision-Tree Approach

Most

LLM Observability Tools for Reliable AI Applications

LLM Observability Tools for Reliable AI Applications

Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.

Implementing Prompt Compression to Reduce Agentic Loop Costs

Implementing Prompt Compression to Reduce Agentic Loop Costs

Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.

Implementing Permission-Gated Tool Calling in Python Agents

Implementing Permission-Gated Tool Calling in Python Agents

AI agents have evolved beyond passive chatbots.

The Roadmap to Mastering Tool Calling in AI Agents

The Roadmap to Mastering Tool Calling in AI Agents

Most

Implementing Statistical Guardrails for Non-Deterministic Agents

Implementing Statistical Guardrails for Non-Deterministic Agents

Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.

Agentic RAG Explained in 3 Levels of Difficulty

Agentic RAG Explained in 3 Levels of Difficulty

Traditional

Effective KV Compression with TurboQuant

Effective KV Compression with TurboQuant

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.

Building AI Agents in Python with Pydantic AI

Building AI Agents in Python with Pydantic AI

Effective Context Engineering for AI Agents: A Developer’s Guide

Effective Context Engineering for AI Agents: A Developer’s Guide

When

Text Summarization with Scikit-LLM

Text Summarization with Scikit-LLM

In a

Building AI Agents with Local Small Language Models

Building AI Agents with Local Small Language Models

The idea of building your own AI agent used to feel like something only big tech companies could pull off.

Train, Serve, and Deploy a Scikit-learn Model with FastAPI

Train, Serve, and Deploy a Scikit-learn Model with FastAPI

FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.

AI Agent Memory Explained in 3 Levels of Difficulty

AI Agent Memory Explained in 3 Levels of Difficulty

A stateless AI agent has no memory of previous calls.

Getting Started with Zero-Shot Text Classification

Getting Started with Zero-Shot Text Classification

Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.