บทเรียน AI

Learn how to build a productivity agent with Qwen 3.7 Max via OpenRouter, that plans, researches, writes, and self-reviews its own output in a Streamlit app.

Google's Managed Agents let you deploy autonomous agents with a single API call. Learn how to build one that analyzes data, runs Python, and generates charts.

Use Google's Antigravity CLI to orchestrate dynamic subagents that clean, analyze, and visualize a dataset in parallel, producing an interactive HTML dashboard.

Learn how to write a spec, turn it into a plan, and let Claude Code build using spec-driven development. Compare Superpowers, Spec Kit, and BMAD-METHOD to find the right tool for your workflow.

Connect Xiaomi MiMo-V2.5-Pro to OpenCode with Olostep MCP and the MiMo Token Plan, then put it to the test by building a CLI tool and a Reflex web app.

Run Qwen3.6 27B on an RTX 3090 and learn how Multi-Token Prediction (MTP) with llama.cpp can boost local LLM inference by almost 2x without upgrading your GPU.

Learn how OpenAI's gpt-realtime-2, gpt-realtime-translate, and gpt-realtime-whisper differ, then test each one with working Python WebSocket code.

Build a Python exercise trainer that logs workouts, remembers user history with Supermemory, and suggests personalized sessions across separate AI agent runs.

Learn what Perplexity Computer is, how its parallel sub-agents work, what credits really cost in a hands-on test, and where Pro and Max fit real research workflows.

Build an AI agent with Kimi K2.6, Olostep, and the OpenAI Agents SDK that reads your CV, finds live roles, filters noise, and tells you exactly where to apply.

Customize Claude Code in the terminal with permissions, CLAUDE.md, plan mode, /loop, hooks, /voice, and cost tracking. Seven upgrades that pay off fast.

Learn how to run the full DeepSeek V4 Flash model on a single GPU using a modified llama.cpp build and a compatible GGUF file in this hands-on tutorial.