AI Coding Tools in 2026: The Complete Guide to What Works, What Doesn't, and What's Coming
Everything we've covered on AI coding tools — comparisons, pricing, privacy, agents, and the security risks nobody expected. Updated April 2026.
Posts
Gemini CLI Tutorial: Setup, Configuration, and a Real Python Project
Set up Google's free Gemini CLI in 5 minutes, configure GEMINI.md, add MCP servers, and build a Python project — all on the 1,000 requests/day free tier.
AI Agent Guardrails That Work: 4 Production Wipes, 4 Fixes
AI agent guardrails from 4 real production wipes — PocketOS, Replit, Amazon. Scoped tokens, destructive-action gates, isolated backups, plan-first mode.
Big Tech Is Trading 100K Jobs for $725B in AI Infrastructure
$725B in AI capex, 100K+ layoffs, and 275K unfilled AI roles. The numbers behind big tech's 2026 human-to-GPU trade — and what it means for your career.
MarkItDown vs Docling vs Marker: PDF to Markdown for LLMs
Three open-source PDF-to-Markdown tools for RAG and LLM pipelines, tested on real documents. Speed, table fidelity, install pain, and which one to pick.
GPT-5.4 vs Claude Opus 4.7 vs Gemini 3.1 Pro for Coding (May 2026)
Three weeks rotating between GPT-5.4, Claude Opus 4.7, and Gemini 3.1 Pro on real coding work — benchmarks, token costs, and the per-task winner for each.
FastMCP in Python: Build a Real MCP Server (2026 Guide)
Build a production-ready MCP server in Python with FastMCP 3.2 — tools, resources, prompts, GitHub OAuth proxy, MCP Inspector, and Claude Desktop hookup.
Python t-strings (PEP 750): A Practical Tutorial With Real Examples
Python 3.14 t-strings (PEP 750) let you intercept values before string interpolation. Hands-on tutorial with SQL safety, HTML escaping, and structured logging.
AI Brain Fry: What BCG Found in 1,488 Workers
AI brain fry hit 14% of AI-using workers in the BCG-Harvard study. Devs near the top, behind marketing and HR. Productivity drops once you cross 4 tools.
AsyncTLS: 4.7x Faster Long-Context LLM Inference With Two-Level Sparse Attention
AsyncTLS sparse attention fuses block filtering, token selection, and async KV cache offloading for 1.3-4.7x throughput gains at 48k-96k token contexts.