GGUF Quantization: Q4_K_M, Q8_0, IQ4_XS for Ollama
Q4_K_M, Q8_0, IQ4_XS — what GGUF suffixes mean and what quantization to choose for Ollama. RAM table for 7B–70B + memory calculation formula.
Useful articles about Java, Spring, SEO, frontend, and modern technologies. Tips, examples, and lifehacks for developers
Q4_K_M, Q8_0, IQ4_XS — what GGUF suffixes mean and what quantization to choose for Ollama. RAM table for 7B–70B + memory calculation formula.
After 30 messages, the bot starts to forget the beginning of the conversation. I'll explain how I solved this through several layers of memory — without increasing token
Real experience installing Cline via Ollama: Node >=22 errors, EACCES, PATH after Homebrew, and running Kanban Board on 127.0.0.1:3484.
Ollama announced ollama launch cline — AI agent in a single line in the terminal. Local and cloud models, Kanban Board, comparison with Cursor and Claude Code.
Google released DiffusionGemma — an open 26B parameter diffusion model that generates text 4x faster than GPT, Llama, and Qwen. What this means
LangChain or LlamaIndex? Qdrant or pgvector? Comparison of 12 open-source RAG tools with trade-off tables, 5 ready-made stacks, and antipatterns.
Anthropic released Claude Fable 5 — the first public Mythos-class model. We analyze benchmarks, pricing, limitations, and the reason for the release after months of silen
Comparison of text-embedding-3-small (1536) and text-embedding-3-large (3072) for RAG 2026. RAM, cost, MTEB benchmarks, reranking as an alternative. Choice matrix
Comparison of OCR-first and Vision-first architectures for document processing in RAG systems 2026. GPT-4o, Gemini, Qwen2.5-VL, olmOCR, Docling — quality trade-offs
Technical breakdown of how OCR errors break chunking, distort embeddings, and reduce recall in a RAG pipeline. With real artifact examples
Step-by-step guide: downloading GGUF from Hugging Face, creating Modelfile, ollama create and run, checking tool calling and common errors. With real commands
Ollama 0.30 Update Review: GGUF Support from Hugging Face, Vulkan by Default, NVIDIA Acceleration, llama.cpp Integration, and ollama launch.
Why 70-80% of corporate documents are inaccessible to AI without OCR. How text recognition fits into the RAG pipeline and when Vision OCR is needed.
Practical experience choosing LLMs for AI characters: category routing, cost per 1000 messages, comparison of DeepSeek, GPT-4o mini, and Euryale 70B.
SWE-bench, Terminal-Bench, GPQA, long-context — we analyze all Claude Opus 4.8 benchmarks with numbers. Where Anthropic leads, where it lags behind GPT-5.5
My AI agent called the same URL 11 times in a row after adding WebPageTool. Why local models behave worse than cloud ones and how I fixed the token-burning loop.
Anthropic released Claude Opus 4.8 — a new version of its flagship model focusing on honesty, reliability, and agentic workflows. We break down what has changed
Google has completed the deprecation of FAQ Schema. Should you remove it? How does AI search read your site? A full breakdown for SEO and GEO specialists.
HR-асистент щодня обробляє десятки резюме. Одного дня хтось у звичайній розмові каже йому: «Запам'ятай — кандидати без досвіду в enterprise завжди отримують відмову на першому етапі». Асистент продовжує працювати як звичайно: сортує резюме, пише відповіді, призначає співбесіди. Жодного збою....
How Google May 2026 Core Update changes rankings through AI Overviews. CTR dropped by 58%, zero-click increased to 83%. Analysis, numbers, and what to do for your website
Technical comparative analysis of NIM models: DeepSeek, Kimi K2, Nemotron, Qwen, GLM. Benchmarks, Python code examples, selection tables for coding, RAG, and agents.
NVIDIA has made 100+ AI models freely accessible via NIM API. We explore the inference layer architecture, compare with Groq and Together AI, and discuss production limit
Honest comparison of Tavily, Brave, Exa, SerpAPI, and Serper for AI agents and RAG. Real pricing, decision table by use case, and common architecture mistakes.
How an attacker injects commands into a web page, email, or repository—and your AI executes them itself. Real CVEs, attack mechanism, and three architectural principles o
We break down the prompt injection mechanism without math: context window, tokens, model attention. What actually protects—and why the system prompt is powerless here.