Took 53 cleaned images prepared for WAI-IL and trained a WAI-Anima character LoRA with AnimaLoraToolkit + RunPod. Training itself ran for $1.22, but at inference the side ponytail direction wouldn't shift with Danbooru tags or natural language. Verification record showing the issue is a directional bias inherited from Anima base (preview3-base onward).
I dropped the nervous sample identified as the culprit last time, plus 5 others, and retrained the LoRA under otherwise identical conditions. The sweat drops on ep08 angry are gone, and as a bonus, ep06 produced the closed-mouth restrained anger that the previous training never managed to reproduce.
Using Veilora's VeilShift™ as a lens, this piece breaks down what DPI looks at, and what VLESS + XHTTP + REALITY, uTLS, and xPaddingBytes can and cannot hide.
OpenAI shipped GPT-5.5 and GPT-5.5 Pro on the API. A practical rundown of the 1M+ context, the new reasoning.effort default, image input behavior, prompt caching, and pricing.
Training an Illustrious-XL LoRA on RunPod for around $1 by doing env setup on a $0.08/hr CPU Pod and renting the 4090 only for actual training. Network Volumes attach to both pods at the same time, so there's no idle GPU billing. Four sd-scripts gotchas hit on the way included.
Hands-on running inclusionAI Ling-flash-2.0 (100B / 6.1B active, MXFP4 quant, 54.7GB) on SwiftLM via mlx-swift-lm on an M1 Max 64GB. Covers bailing_moe + MXFP4 support check in mlx-swift, the startup surprise, and what --stream-experts actually saves.
WAI-Illustrious SDXL v17 tested on M1 Max 64GB ComfyUI against v16 with the same seed. Hires fix now auto-corrects hands and feet, the four rating tags (general/sensitive/nsfw/explicit) still drive NSFW output, and v16-trained LoRAs mostly carry over — with one case where they don't.
In its April 23 update, Vercel disclosed customer accounts compromised prior to and independently of the Context.ai incident. Covering the Lumma Stealer infection path, the ShinyHunters $2M BreachForums listing, and what non-sensitive environment variables actually mean.
Reading Anthropic's postmortem and the DEV operational report in the same week reveals that Claude Code's quality degradation and Max weekly quota exhaustion are not separate incidents—they're two sides of the same quality×cost design decisions. A timeline and operational priority walkthrough.
A hands-on build and run of the Swift-based LLM inference server SwiftLM on an M1 Max 64GB. Covers Qwen3.6-35B-A3B and Qwen3.5-122B-A10B, with the same BST, BBS, and persona tests used in the existing Ollama and MLX-lm write-ups.
