AI Coding

Token optimization is the critical skill separating cost-effective LLM applications from budget-draining experiments.

The Model Context Protocol (MCP) is revolutionizing how AI assistants interact with external data sources and tools. In this guide, we’ll explore how to build MCP servers in Python, with examples focused on web search and scraping capabilities.

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This guide provides a comprehensive overview of available Go SDKs for Ollama and compares their feature sets.

Here is a comparison between Qwen3:30b and GPT-OSS:20b focusing on instruction following and performance parameters, specs and speed:

In this post, we’ll explore two ways to connect your Python application to Ollama: 1. Via HTTP REST API; 2. Via the official Ollama Python library.

Ollama’s GPT-OSS models have recurring issues handling structured output, especially when used with frameworks like LangChain, OpenAI SDK, vllm, and others.

Here’s a side-by-side support comparison of structured output (getting reliable JSON back) across popular LLM providers, plus minimal Python examples

Large Language Models (LLMs) are powerful, but in production we rarely want free-form paragraphs. Instead, we want predictable data: attributes, facts, or structured objects you can feed into an app. That’s LLM Structured Output.

Here is an up-to-date GitHub Copilot cheat sheet, covering essential shortcuts, commands, usage tips, and context features for Visual Studio Code and Copilot Chat

Here we have a description of The Model Context Protocol (MCP), short notes on how to implement an MCP server in Go, including message structure, protocol specifications.

Since standard Ollama doesn’t have a direct rerank API, you’ll need to implement reranking using Qwen3 Reranker in GO by generating embeddings for query-document pairs and scoring them.

This little Reranking Go code example is calling Ollama to generate embeddings for the query and for eache candidate document, then sorting descending by cosine similarity.

In the Ollama models library there are models that able convert HTML content to Markdown, which is useful for content conversion tasks.

Vibe coding is an AI-driven programming approach where developers describe desired functionality in natural language, allowing AI tools to generate code automatically.