Reduce LLM Costs: Token Optimization Strategies
Cut LLM costs by 80% with smart token optimization
Token optimization is the critical skill separating cost-effective LLM applications from budget-draining experiments.
RAG
Converting HTML to Markdown with Python: A Comprehensive Guide
Python for converting HTML to clean, LLM-ready Markdown
Converting HTML to Markdown is a fundamental task in modern development workflows, particularly when preparing web content for Large Language Models (LLMs), documentation systems, or static site generators like Hugo.
Go clients for Ollama: SDK comparison and Qwen3/GPT-OSS examples
Integrate Ollama with Go: SDK guide, examples, and production best practices.
This guide provides a comprehensive overview of available Go SDKs for Ollama and compares their feature sets.
Comparison: Qwen3:30b vs GPT-OSS:20b
Comparing Speed, parameters and performance of these two models
Here is a comparison between Qwen3:30b and GPT-OSS:20b focusing on instruction following and performance parameters, specs and speed:
Integrating Ollama with Python: REST API and Python Client Examples
+ Specific Examples Using Thinking LLMs
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.
Structured output comparison across popular LLM providers - OpenAI, Gemini, Anthropic, Mistral and AWS Bedrock
Slightly different APIs require special approach.
Here’s a side-by-side support comparison of structured output (getting reliable JSON back) across popular LLM providers, plus minimal Python examples
Constraining LLMs with Structured Output: Ollama, Qwen3 & Python or Go
A couple of ways to get structured output from Ollama
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.
Reranking documents with Ollama and Qwen3 Reranker model - in Go
Implementing RAG? Here are some Go code bits - 2...
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.
Reranking texts with Ollama and Qwen3 Embedding LLM - in Go
Implementing RAG? Here are some codesnippets in Golang..
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.
Qwen3 Embedding & Reranker Models on Ollama: State-of-the-Art Performance
New awesome LLMs available in Ollama
The Qwen3 Embedding and Reranker models are the latest releases in the Qwen family, specifically designed for advanced text embedding, retrieval, and reranking tasks.
Beautiful Soup Alternatives for Go
Continuing the topic of extracting data from html
- For a direct Beautiful Soup analogue in Go, use soup.
- For CSS selector support, consider goquery.
- For XPath queries, use htmlquery.
- For another Beautiful Soup-inspired option, look at Node.
If you’re looking for a Beautiful Soup equivalent in Go, several libraries offer similar HTML parsing and scraping functionality:
Convert HTML content to Markdown using LLM and Ollama
LLM to extract text from HTML...
In the Ollama models library there are models that able convert HTML content to Markdown, which is useful for content conversion tasks.
Cloud LLM Providers
Short list of LLM providers
Using LLMs is not very expensive, might be no need to buy new awesome GPU. Here is a list if LLM providers in the cloud with LLMs they host.
How Ollama Handles Parallel Requests
Configuring ollama for parallel requests executions.
When the Ollama server receives two requests at the same time, its behavior depends on its configuration and available system resources.
Testing Deepseek-R1 on Ollama
Comparing two deepseek-r1 models to two base ones
DeepSeek’s first-generation of reasoning models with comparable performance to OpenAI-o1, including six dense models distilled from DeepSeek-R1 based on Llama and Qwen.