Agents2026-07-10
AI agents for sales 2026: automating lead generation and correspondence
AI agents for sales in 2026: how to automate lead generation, enrichment, and outreach, build an SDR agent in Python, and connect to CRM. A guide for
Blog
Practical guides on AI agents, LLMs, RAG, local inference and the open-source tools we track daily.
Voice and Media2026-07-09
Sora 2 and video generation 2026: what new models can do and where the limit is
Sora 2 and video generation 2026: analysis of text-to-video models, comparison with Runway, real limits, API code, and where AI video currently breaks
RAG and search2026-07-08
Private offline RAG 2026: your document search without internet
How to build a local RAG in 2026 without internet: privacy, vector database, local LLM and self-hosting. Stack, code, embedding comparison, and typica
Models2026-07-08
DeepSeek V4 in 2026: Chinese Model Outperforms GPT on Price and Code
DeepSeek V4 2026: Open‑source LLM that dominates coding benchmarks at a fraction of GPT’s cost. Review, pricing, benchmark, and real‑world example via
AI and Development2026-07-07
AI Text Detectors 2026: Do They Work and How Are They Bypassed?
AI Detector in 2026: Does GPTZero Work, What Is Perplexity and Watermarking, Why AI Text Detection Fails, and How It's Bypassed. A Practitioner's Revi
Code Tools2026-07-07
Claude Code vs Cursor in 2026: What’s Actually Faster for Development?
Claude Code vs Cursor in 2026: an honest comparison of two AI IDEs. Speed, price, agent autonomy, real‑world use cases — which coding assistant should
Agents2026-07-06
MCP vs function calling 2026: how they differ and what to choose
MCP vs function calling in 2026: exploring tool use, the Anthropic standard, differences for agents. Comparison, code, table — what to choose for your
Agents2026-07-06
20 Best MCP Servers 2026: Connecting LLM to Anything
Top 20 MCP Servers of 2026: Anthropic's Model Context Protocol, ready-to-use tools for connecting LLMs to databases, APIs, files, and code. Comparison
Models2026-07-05
Running a local LLM on RTX 4090 in 2026: Which models fit and how
Local LLM on RTX 4090 in 2026: Which models fit in 24 GB, how to choose quantization, launching via Ollama and llama.cpp. Practice, numbers, ready-to-
RAG and search2026-07-04
RAG or fine-tuning in 2026: when to choose what and how much it costs
RAG or fine-tuning in 2026 — we break it down by numbers: when to choose LLM fine-tuning vs vector database. Cost, hybrid schemes, selection checklist
Agents2026-07-03
AI agents for crypto trading in 2026: do they actually work?
AI agents for crypto trading in 2026: we examine whether automated trading really works, how a bot-agent differs from old bots, and where money gets l
Models2026-07-02
Qwen 3: Why Developers Are Massively Switching to Alibaba's Model
Qwen 3 from Alibaba — an open source model that smashes benchmarks in code and multilinguality. Analysis of architecture, MoE, local deployment, and m
Models2026-07-01
LLM Code Ranking 2026: Who Writes the Best Code Right Now
LLM Code Ranking 2026: compared Claude, GPT, and open models on HumanEval, real tasks, and price. Who actually writes the best code right now.
RAG and Search2026-06-30
Vector Databases 2026: Qdrant, Milvus, pgvector, Chroma Benchmark on Real Workload
Comparison of vector databases 2026: Qdrant, Milvus, pgvector, Chroma under real load. Benchmark latency, RPS, memory, filtering. What to choose for R
Agents2026-06-29
How to build an AI agent from scratch in 2026: full guide with code
Full guide on how to build an AI agent from scratch in Python and LangGraph in 2026: tool calling, memory, reasoning loop, and working code. No fluff.
Models2026-06-28
Best embedding models 2026: what to choose for search and RAG
Embeddings 2026: comparing BGE, E5, Qwen3 and Voyage for search and RAG. Choosing a model for Russian language, dimensionality, latency, and vectoriza