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

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

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Home Assistant Dashboards: 6 Conditional Card Types and HACS Extensions

Home Assistant Dashboards: 6 Conditional Card Types and HACS Extensions

Yes, Home Assistant ships a built-in conditional card. It shows or hides any dashboard card based on live state: entity value, time of day, who is home, screen size, and more. Add template sensors and a few HACS cards, and you can build dashboards where morning shows weather and coffee buttons, evening shows media and light scenes, and an empty house shows cameras and alarm controls. Cards pop in and out without leaving blank gaps, all through the stock Lovelace frontend. No custom code needed.

OpenAI Codex CLI: The Rust-Powered Terminal Agent Taking on Claude Code

OpenAI Codex CLI: The Rust-Powered Terminal Agent Taking on Claude Code

OpenAI Codex CLI is an open-source (Apache 2.0), Rust-built terminal coding agent. It has over 72,000 GitHub stars. It pairs GPT-5.4’s 272K default context window, which you can push to 1M tokens, with OS-level sandboxing. That sandbox runs on Apple Seatbelt on macOS and Landlock plus seccomp on Linux. Here is the key point: Codex CLI is the only major AI coding agent that enforces security at the kernel level, not through application-layer hooks. With codex exec for CI pipelines, MCP client and server support, and a GitHub Action for PR review, it is the most infrastructure-ready rival to Claude Code in 2026.

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

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

Qwen3.6-35B-A3B is Alibaba Cloud’s Apache 2.0 sparse Mixture-of-Experts model released April 14, 2026. It carries 35 billion total parameters but activates only about 3 billion per token, and on agentic coding suites it beats Gemma 4-31B and matches Claude Sonnet 4.5 on most vision tasks. A 20.9GB Q4 quantization runs on a MacBook Pro M5, which is the reason this release has taken over half the AI timeline for the past week.

Structured Output from LLMs: JSON Schemas and the Instructor Library

Structured Output from LLMs: JSON Schemas and the Instructor Library

The Instructor library (v1.7+) patches LLM client libraries to return validated Pydantic models instead of raw text. It does this with JSON schema enforcement in the system prompt, auto retries on validation failure, and native structured output modes where the provider supports them. It works with OpenAI, Anthropic, Ollama , and any OpenAI-compatible API. You define your output as a Python class and get back typed, validated data. No regex parsing, no json.loads() wrapped in try/except, no manual type casting.

What Are the Best Ergonomic Split Keyboards for Programmers (2026)?

What Are the Best Ergonomic Split Keyboards for Programmers (2026)?

The three best ergonomic split keyboards for programmers in 2026 are the MoErgo Glove80 ($399, best overall comfort with contoured key wells and aggressive tenting), the ZSA Voyager ($365, best portable option with a low-profile design and magnetic tenting legs), and the Kinesis Advantage360 Pro ($499, best for deep key well fans with wireless ZMK firmware). All three offer full Linux support, open-source firmware tweaks, and columnar stagger layouts that cut finger strain on long coding days.

WireGuard Site-to-Site VPN: 400-500 Mbps on Raspberry Pi

WireGuard Site-to-Site VPN: 400-500 Mbps on Raspberry Pi

To connect two remote LANs over WireGuard , you configure a WireGuard peer on one gateway device at each site, set AllowedIPs to include the remote site’s subnet, enable IP forwarding on both gateways, and add routing so LAN clients send cross-site traffic through the tunnel. Once configured, every device on either LAN can reach devices on the other LAN transparently - no VPN client installation on individual machines. A single UDP port open on at least one side is all you need.

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What X and Reddit Users Are Saying about Claude Opus 4.7

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Running Gemma 4 26B MoE on 8GB VRAM: Three Strategies That Work

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Run Google Gemma 4 26B MoE with sparse activation on budget 8GB GPUs using aggressive quantization, GPU-CPU layer offloading, and tensor parallelism techniques.

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Local Image Models in 2026: Qwen vs FLUX vs SDXL on VRAM

Compare the best local image generation models on text-in-image accuracy, prompt adherence, VRAM, speed, and license to find your quality-per-VRAM sweet spot.

AI Coding Benchmarks in 2026: Why the Leaderboard You Pick Decides the Winner

AI Coding Benchmarks in 2026: Why the Leaderboard You Pick Decides the Winner

AI coding benchmarks produce wildly different rankings. Which models win depends on which benchmark you choose and which agent framework wraps them.

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