Botmonster Tech

Radon is a colorless, odorless radioactive gas that seeps from soil and rock. It is the second-leading cause of lung cancer after smoking. The only way to know your exposure is to measure it over months, not minutes. Indoor levels swing by ten times between calm winter nights and breezy summer afternoons. The cleanest build I have found in 2026 pairs the FTLab RD200M sensor with an ESP32 running ESPHome . The RD200M is a pulsed ion chamber with a simple UART output. The node sends live and rolling-average readings to Home Assistant . It also fires alerts when your annual average creeps toward the EPA action level of 4 pCi/L (148 Bq/m3) . Total parts cost is about 150 USD, roughly half the price of an Airthings View Plus, and you own every byte of the data.

To batch migrate GitHub to Gitea, migtea gets you past one small limit: Gitea’s built-in migrator moves exactly one repository per submission. migtea is an open-source terminal tool I built that moves your whole account in one pass, wikis, LFS, and issues included. Here is how it works, screen by screen.

Everything here was tested against Gitea 1.26.4, gh 2.96.0, tea 0.12.0, and uv 0.11.2.

Why won’t Gitea migrate all your GitHub repos at once?

Gitea has a built-in migration page, but it moves only one repository at a time. You paste one GitHub URL and a token, click Migrate, and wait. The repo arrives complete, with its wiki, LFS objects, and issues. Then you start over with the next one. For a hundred repos, that is a hundred rounds of the same form. migtea does them all in one go.

Reddit’s first-week verdict on GPT-5.6-Sol is split. Hands-on testers praise it as a cheaper, token-efficient coding workhorse, yet say Claude Fable 5 still builds better-looking interfaces. The loudest reaction is price: one reader pegged a benchmark run at $8.39 for Sol against $21.63 for Fable, and that gap is pulling paying Claude users toward Codex.

This reading comes from nine threads on old.reddit.com captured during launch week, spanning r/OpenAI, r/ClaudeAI, r/ClaudeCode, r/codex, r/singularity, r/ChatGPT, and r/vibecoding. Every thread ran between 550 and 1,234 upvotes. Treat it as early launch-week reception, still forming.

The WD Black SN8100 (Gen5, TLC, up to 14,900 MB/s) is the best overall NVMe SSD for homelabs in 2026, while the WD Black SN7100 (Gen4, TLC) offers the best value for most builds. Avoid QLC drives for write-heavy NAS workloads - TLC endurance is worth the small price premium when your data matters.

Picking an SSD for a homelab is different from picking one for a gaming PC. Your homelab drives run 24/7, handle sustained write workloads from VMs and containers, and need to last years without surprise failures. The wrong choice - a cheap QLC drive in a write-heavy Proxmox setup, for instance - can burn through its endurance rating in under a year. This guide covers which drives are worth buying right now, what to skip, and how to keep them healthy once they’re installed.

The best eGPU enclosures for Linux in 2026 are the Razer Core X V2 ($349, Thunderbolt 5, 80 Gbps) for maximum bandwidth and the Sonnet Breakaway Box 750 eX ($349, Thunderbolt 4) for proven Linux reliability. Thunderbolt 5 enclosures have finally closed the bandwidth gap that made external GPUs feel like a compromise, and Linux kernel 6.12+ delivers stable hot-plug support that actually works.

External GPUs spent years as a niche curiosity - the bandwidth penalty was too steep, driver support too fragile, and the cost math rarely made sense. That calculus has shifted. If you run GPU workloads on Linux - local LLM inference, Stable Diffusion, CUDA development, PyTorch training - an eGPU setup now gets you 85-95% of internal PCIe performance depending on the workload. This guide ranks the enclosures that work best on Linux, walks through the setup process, and sets realistic expectations with actual benchmark numbers.

DuckDB crunches gigabytes of CSV and Parquet with no database server, no import step, and no waiting around. You aim a SELECT straight at a file on disk and it answers. On 41 million rows of raw NYC taxi data, I clocked a full group-by aggregation in 20ms and a two-table join in another 20ms, read straight off Parquet with nothing loaded, copied, or indexed first. That is a multi-gigabyte analytical query returning before you lift your finger off Enter key! Every number in this post comes from a benchmark you can run yourself; the scripts and raw results live in a GitHub repo .