c/technology

It finally finished, here's the final output. Used 60% of my monthly Kimi usage on it https://t.co/TnsbOq6L8Z

Telstra skipped a patch, didn’t record changes, had no idea it was an accident waiting to happen

Week 02 of the Turing Award Series — how microprogramming turned hardware problems into software problems

Nuclideon renders terabyte-scale point clouds at full resolution in real time on standard CPUs. No GPU, no downsampling, air-gapped capable. Test it on your own data.

LeRobot 0.6.0 adds Foxglove as a native visualization backend. Pass --display_mode=foxglove to teleoperate, record, and rollout, or replay any dataset as a seekable timeline — all streaming to the Foxglove app with no extra code.

Record or upload audio, clone voices, generate speech — 100% client-side via ONNX Runtime Web. No server needed.

The kernel community (like many other free-software projects) has recently seen a large influx [...]

Suggestion for hyperscalers feeling pressure over data center water use: Buy up a few exclusive country clubs, convert the golf courses into public parks, pay for guides and binoculars to …

Injection-capable 802.11 monitor mode on WSL2 with a 99-cent AR9271. Custom kernel, embedded firmware, one-click launcher - BicycleJunkie1971/wsl2-ar9271-monitor

Factor models attempt to explain asset returns. You can approach this in two ways: define the factors you think are relevant, or use statistical learning to build the relevant factors from the data. My previous post took the first approach. This post will use principal component analysis (PCA) to let the data tell us which factors are most relevant in an FX factor model.