Bryan Cantrill's 'The Peril of Laziness Lost' argues that LLMs have zero cost to write code and no motivation to abstract. Humans must serve as the 'deletion engine' or systems will bloat endlessly.
I tested local Vision LLMs (Gemma 3, Qwen2.5-VL, Llama 3.2 Vision, Gemma 4) to see if they could look at character illustrations and pixel art and generate RPG-style stats in JSON format.
A paper claims that a single binary operator eml(x, y) = exp(x) - ln(y) combined with the constant 1 can express all elementary functions — arithmetic, trig, logarithms, even pi. I read the paper and tested it in 5 languages.
Benchmarking NII's LLM-jp-4-32B-A3B-thinking on EVO-X2 (Ryzen AI Max+ 395) with ROCm. 62.9 t/s vs Qwen3.5-35B-A3B's 44.7 t/s. Covers thinking control issues, KV cache trade-offs, knowledge cutoff, Japanese quality comparisons, code generation tests, and training data composition.
Tested See-through (SIGGRAPH 2026), which decomposes a single anime character image into a 23-layer PSD with front/back hair separation and hidden-area inpainting. What the Live2D prep actually looks like and what still needs manual work.
After updating to AMD Software 26.3.1 on a GMKtec EVO-X2 (Ryzen AI Max+ 395), Vulkan backend fails to allocate device memory properly and falls back to CPU. Investigation and workaround by changing BIOS VRAM allocation from 48GB/16GB to 32GB/32GB.
An update switched Qwen Image Edit inference from FP16 to BF16, and M1-M3 Macs emulate BF16 at half speed. Benchmarks of the 80s to 10min regression, why --force-fp16 gives black images, and the --fp16-vae --fp16-unet config that lands at 2:30.
Changes from v1 to v2 of Kana Chat, an AI agent built around official CLI wrappers. Covers dual-model router, Heartbeat memory, planner mode, image input, speech transcription, PWA push notifications, and the lessons learned from a month of daily use.
The three-stage pipeline of BERT perplexity scan → LLM judgment → escalation packaged as a cross-platform Python tool. The installer automatically downloads llama-server and GGUF models.
Bundling NDLOCR-Lite's DEIMv2 + PARSeq with ONNX Runtime Mobile in an iOS app to run camera capture → perspective correction → layout detection → text recognition → confidence-based correction entirely on device.
