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Hands-on experience with AI, self-hosting, Linux, and the developer tools I actually use

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Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

Gemma 4 vs Qwen 3.5 vs Llama 4: Which Open Model Should You Actually Use? (2026)

A head-to-head comparison of Gemma 4, Qwen 3.5, and Llama 4 across benchmarks, licensing, inference speed, multimodal capabilities, and hardware requirements. Covers the full model families from edge to datacenter scale.

5 Open Source Repos That Make Claude Code Unstoppable

5 Open Source Repos That Make Claude Code Unstoppable

Five GitHub repositories released in March 2026 push Claude Code into new territory. From autonomous ML experiments running overnight to multi-agent communication and full Google Workspace access, these open source tools solve real workflow gaps that Claude Code cannot handle alone.

Claude Opus 4.7: What X and Reddit Users Are Saying

Claude Opus 4.7: What X and Reddit Users Are Saying

A 48-hour snapshot of how power users on X and Reddit reacted to Anthropic's Claude Opus 4.7 release on April 16, 2026. Covers the dominant praise for agentic coding and the new Claude Design tool, the three loudest complaints, token-burn economics, and the practical prompting habits teams are already adopting.

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Qwen3.6-35B-A3B: Alibaba's Open-Weight Coding MoE

Alibaba's Qwen3.6-35B-A3B is a sparse Mixture-of-Experts model with 35B total and 3B active parameters, released April 2026 under Apache 2.0. It scores 73.4 on SWE-bench Verified, matches Claude Sonnet 4.5 on vision, and runs locally as a 20.9GB Q4 quantization on an M5 MacBook. A close look at the architecture, benchmarks, features, and honest trade-offs.

Alacritty vs. Kitty: Best High-Performance Linux Terminal

Alacritty vs. Kitty: Best High-Performance Linux Terminal

A practical comparison of Alacritty and Kitty for high-performance Linux terminal workflows in 2026, including latency, startup time, memory use, and heavy-output responsiveness. The analysis covers design philosophy differences between minimalist and feature-rich terminal environments, plus Wayland behavior and real-world configuration trade-offs. It also situates Ghostty and WezTerm in the current landscape and explains when each terminal model fits best for daily development.

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

MiniMax M2.7: Model That Almost Matches Claude Opus 4.6

A practical review of MiniMax M2.7: the 230B-parameter Mixture-of-Experts reasoning model that scores 50 on the Artificial Analysis Intelligence Index, runs on a 128GB Mac Studio, and costs roughly a tenth of Claude Opus 4.6. Covers benchmarks, self-hosting hardware, the license catch, and when to pick the API over local inference.

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How to Use Service Workers for Caching on Static Sites

How to Use Service Workers for Caching on Static Sites

Service workers give you a programmable network proxy right inside the browser. They sit between your page and the server, intercept every fetch request, and let you decide whether to serve a response from cache or from the network. For static sites - where every page is a pre-built file and every asset has a predictable URL - this is a natural fit. A well-configured service worker makes your static site load in single-digit milliseconds on repeat visits, work fully offline, and pass every Lighthouse PWA audit. The entire implementation fits in a single JavaScript file under 100 lines.

 Javascript, Static-Sites, Hugo, Optimization
10 Claude Code Plugins to 10X Your AI Development Projects

10 Claude Code Plugins to 10X Your AI Development Projects

If you want better output from Claude Code , adding more AI is rarely the answer. Pairing it with the right CLI tools and skills is. By combining purpose-built integrations like Supabase CLI, Playwright, and GitHub CLI with structured orchestration frameworks like GSD, you can build a development stack where Claude Code handles code generation, entire deployment pipelines, research workflows, and browser automation - without constant hand-holding.

Most developers discover Claude Code and immediately try to connect every available MCP server they can find. Within a week, they notice that the agent is slower, less decisive, and often picks the wrong tool for the job. The fix is almost always a smaller, more deliberate toolset.

 Claude, Ai-Coding, CLI, Developer-Tools
Claude Code Agent Teams: Orchestrating Multiple AI Sessions on One Project

Claude Code Agent Teams: Orchestrating Multiple AI Sessions on One Project

Claude Code Agent Teams is an experimental feature - available since v2.1.32 (February 2026) - that lets you run 2-16 Claude Code sessions coordinated by a single team lead. Each teammate operates in its own context window with full tool access, while communicating through a shared task list and direct peer-to-peer messaging. You enable it with one config change, describe the team you want in natural language, and Claude handles spawning, assignment, and coordination. The feature works best for parallelizable work like multi-file refactors, cross-layer feature builds, and research-and-review workflows, but it costs 3-7x more tokens than a single session and has no session resume capability.

 Claude, Ai-Coding, Ai-Agents, CLI
CLAUDE.md Productivity Stack: Skills, Git Worktrees, and Hooks for Parallel Development

CLAUDE.md Productivity Stack: Skills, Git Worktrees, and Hooks for Parallel Development

The single most important file in any Claude Code project is CLAUDE.md - a persistent instruction set that loads every session and shapes how the agent reads, writes, and verifies code. But CLAUDE.md alone is not what separates productive setups from fragile ones. The real productivity stack in 2026 combines CLAUDE.md conventions with on-demand skills, deterministic hooks, and git worktree isolation for running 10-15 parallel sessions against a single repository. Each session is scoped to one task, operating in its own branch, turning a solo developer into a small engineering team .

 Claude, Ai-Coding, Developer-Tools, Productivity
How to Automate Database Migrations with Alembic and SQLAlchemy

How to Automate Database Migrations with Alembic and SQLAlchemy

Alembic is the standard migration tool for SQLAlchemy projects. You run alembic init, point it at your SQLAlchemy models, and use alembic revision --autogenerate to produce migration scripts. Alembic then applies those scripts in order with alembic upgrade head, giving you repeatable, reviewable schema changes that work identically everywhere your application runs . As of March 2026, the latest stable release is Alembic 1.18.4, which supports SQLAlchemy 2.0 (currently at 2.0.48) and its modern declarative and typed APIs natively.

 Python, Databases, Developer-Tools, Automation
How to Build a Local Code Interpreter Agent with Ollama and Docker

How to Build a Local Code Interpreter Agent with Ollama and Docker

You can build a fully local, sandboxed code interpreter agent by pairing Ollama (running a reasoning model like Llama 4 Scout or DeepSeek R1) with a Docker container that executes the generated Python code. The agent sends a user prompt to the local LLM, which produces Python code; that code gets injected into a locked-down Docker container with no network access and strict resource limits; the stdout/stderr output is captured and fed back to the LLM for reflection and iteration. The entire loop runs on your machine with zero cloud API calls, giving you a private, free, ChatGPT Code Interpreter-style experience.

 Ollama, Docker, Python, Local-Ai
How to Control Room LEDs with Your Voice Using WLED and Home Assistant

How to Control Room LEDs with Your Voice Using WLED and Home Assistant

Flash WLED 0.15 onto an ESP32 over USB in under five minutes using the web installer at install.wled.me , wire up a WS2812B or SK6812 addressable LED strip with a properly sized 5V power supply, then add the device to Home Assistant via auto-discovery and configure voice control through the built-in Assist pipeline. You get hands-free color changes, effects, and brightness control with zero cloud dependency. Total cost is under $30 for a basic setup, and the whole thing takes about an hour.

 Home-Assistant, Iot, Esphome, Voice-Assistant
How to Replace Cron with Systemd Timers for Modern Linux Task Scheduling

How to Replace Cron with Systemd Timers for Modern Linux Task Scheduling

Systemd timers should replace cron for nearly all scheduled tasks on modern Linux systems. They provide structured logging through the journal, built-in dependency management, randomized delays to avoid resource stampedes, and persistent timers that catch up on missed runs after a reboot. The only reason to stick with cron is legacy compatibility on minimal systems that do not run systemd. If your Linux distribution shipped in the last decade, you already have everything you need to make the switch.

 Linux, Automation, CLI
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