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Pi-hole and Unbound DNS: DNSSEC, QNAME Minimization, Privacy

Pi-hole and Unbound DNS: DNSSEC, QNAME Minimization, Privacy

Every DNS query your devices make tells a story. When your home network sends those queries to Google (8.8.8.8), Cloudflare (1.1.1.1), or your ISP’s resolver, that provider builds a record of every domain every device visits. Your phone, your laptop, your smart TV, your thermostat: all of it. You can fix this. Run Pi-hole as a DNS sinkhole to block ads and trackers across the whole network. Then pair it with Unbound , a local recursive resolver, so your queries go straight to the DNS root servers instead of a third-party middleman.

Home Assistant AI Voice With a Local LLM: What Works in 2026

Home Assistant AI Voice With a Local LLM: What Works in 2026

Home Assistant AI voice control with a local LLM as the brain is practical in 2026. No Amazon, no Google, no cloud. The Assist pipeline already handles the plumbing: wake word, speech-to-text, a conversation agent, and text-to-speech, all on your own hardware. Setting that up is the easy part. The hard part is picking a local model that calls Home Assistant’s tools without guessing. The loop also has to be fast, or it will never feel like a real assistant. This guide covers both: the 2026 stack, the models the community actually trusts, and the latency budget that makes it work.

DIY NAS Comparison: Raspberry Pi 5 vs. Intel N100

DIY NAS Comparison: Raspberry Pi 5 vs. Intel N100

The Intel N100 is the better DIY NAS choice in 2026 if you plan to run Plex or Jellyfin, want ZFS, or need more than two drives. The Raspberry Pi 5 still wins for low-power, always-on file storage where idle power cost is what counts. The right pick depends almost entirely on what you want the box to do.

Why Build a DIY NAS in 2026? The Case Against Synology

Synology and QNAP have spent the last few years getting harder to recommend. Newer Synology units reject non-Synology drives. Those rejected drives work just like the approved ones. The DSM operating system has changed too. It used to be a handy management layer. Now it’s a closed platform that pushes cloud services you didn’t ask for. A Synology DS423+ costs about $500 with no drives. A DIY N100 build with four SATA ports runs under $200.

Why Small Language Models (SLMs) are Better for Edge Devices

Why Small Language Models (SLMs) are Better for Edge Devices

Small Language Models, sub-4B parameter models built to run on local hardware, now handle most of the edge AI work that used to need the cloud. Phi-4 , Gemma 3 , and Llama 3.2-1B run offline on Raspberry Pi boards, phones, and industrial PLCs. The economics, latency, and privacy story all point the same way: edge first.

What Counts as a Small Language Model

In 2023, “small” meant under 13B parameters. Today, three tiers matter for edge work.

Build a DIY Smart Mirror with Home Assistant Integration

Build a DIY Smart Mirror with Home Assistant Integration

A DIY smart mirror uses a two-way mirror panel, a monitor, and a Raspberry Pi running MagicMirror² . Behind the glass, the monitor shows widgets that seem to float in the reflection. Link it to Home Assistant and it turns from a novelty into a useful home panel. You see which lights are on, if the front door is locked, and your next calendar event.

Materials and Hardware Shopping List

Getting the parts right before you cut or mount saves a lot of pain. Two pieces shape the final build: the mirror and the monitor.

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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)

Gemma 4, Qwen 3.5, and Llama 4 compared on benchmarks, licensing, speed, and hardware so you can pick the right open model fast.

5 Open Source Repos That Make Claude Code Unstoppable

5 Open Source Repos That Make Claude Code Unstoppable

Five March 2026 repos extend Claude Code with autonomous ML, self-healing skills, GUI automation, multi-agent coordination, and Google Workspace access.

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DeepSeek V4 Tech Report: 3 Tricks That Cut Compute 73%

DeepSeek V4 ships 1.6T parameters and 1M context using only 27% of V3.2's inference FLOPs. Inside the hybrid attention, mHC residuals, and Muon optimizer.

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GPT 5.5 Reddit Reception: Goblins and the Cost Backlash

GPT-5.5 Reddit reception: viral goblin prompt leak, doubled pricing backlash, and 5.4 holdouts citing hallucination regressions in factual recall workflows.

What X and Reddit Users Are Saying about Claude Opus 4.7

What X and Reddit Users Are Saying about Claude Opus 4.7

How power users on X and Reddit reacted to Claude Opus 4.7: praise for agentic coding, token burn concerns, and teams' practical prompting habits.

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

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

Alibaba's sparse Mixture-of-Experts: 35B total parameters, 3B active per token. Q4 quantization runs on MacBook Pro M5, matches Claude Sonnet performance.

Alacritty vs. Kitty: Best High-Performance Linux Terminal

Alacritty vs. Kitty: Best High-Performance Linux Terminal

Alacritty vs Kitty in 2026: emoji and Unicode rendering, real benchmarks, latency, memory, maintainer reputation, and the right terminal for your workflow.

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