AI Wiki — Knowledge Base

Last week some of South Korea's biggest banks were hit by a cyberattack. We now know the entire hack may have been done by a single person. He used a combined stack of an open-source AI penetration tool named ARTEX, DeepSeek v4.1-Flash, GLM-5.3, Grok 4.6, and Claude Code (CrowdStrike)

CrowdStrike report: https://www.crowdstrike.com/en-us/blog/unknown-threat-actor-uses-artex-to-target-south-korean-finance/ From: Andrew Curran on 𝕏: https://x.com/AndrewCurran_/status/2108108695876092

model-releasesr-localllama

8 Oct 2026

Search: “r-localllama”

Can any open-weight models handle a decomp/recomp project yet?

chatgpt's new intelligent ui was reverse engineered in less than 24 hours, and apparently you can recreate it with local llms

Comparing Qwen3.8-27B fine-tunes and baselining vs. frontier

Currently having high success with this little niche finetune i found sitting in the corner of huggingface

GPU - Vulkan llama.cpp benchmarks sorted by price to performance

Halogen + Qwen Flash Next keeps getting better

I built an open source framework for building RL environments. Named 'Seahaven' after the fake town in The Truman Show.

Involution Without Export Is Wasted Effort

Java vllm-like framework claims 90% of perfomance of llama.cpp on local inference on NVIDIA GPUs by compiling Java to CUDA and cuTile

jevman: AI decision models play Pac-Man

Last week some of South Korea's biggest banks were hit by a cyberattack. We now know the entire hack may have been done by a single person. He used a combined stack of an open-source AI penetration tool named ARTEX, DeepSeek v4.1-Flash, GLM-5.3, Grok 4.6, and Claude Code (CrowdStrike)

Open sourcing Game Summoner, my prompt to game site

Qwen 3.8 Flash Next is so much fun for three.js

Running an LLM-driven town with 800+ persistent agents: concurrency, context caching, and inference costs

Running decision model locally on an RTX 4090 to find out which one is the fastest

struggling with llama.cpp 2 x dgx spark mtp files start command (unsloth)

Thank you :) Swift Models hit 2.2 million+ downloads / Early Access to New Models, Free Compute for Researchers

2x CMP 170HX 64GB: GLM-5.3-Flash at 384K context / ~90 tok/s (EXL3, HBM-first setup) + Qwen3.8 comparison

A stock GLM-Edge-1.5B-Chat on a 4GB Galaxy A04e completed a real Amazon cart task

d1-3B and d1-omni from LiquidAI

GPU Upgrade advice

I re-trained the DFlash 2 drafter for Ternary Bonsai 2 27B: 2.2x on an L4 (3.2x on code edits with ngram lookup), 1.5x on a Mac, 1.2x in Chrome

Image-text retrieval with EmbeddingGemma 2's vision tower, running in the browser on WebGPU

Local Qwen 3.8 27B vs DeepSeek Flash API: Is local good enough?

omni-d1 600M by Liquid AI running in the browser using WebGPU

Open SLM Evalulations

Porting LIBERO to MuJoCo Warp: 130 robot manipulation tasks on one $700 AMD GPU

Ruach Studio: a whole song studio around YuE2 on your own GPU. Score first, LoRA training, stems,remaster, DAW export.

Single 3090 Qwen 27B user, considering buying 128GB of RAM because of the hype

Tested in Coding: Strata

Trained a ~20K LM (probably smallest) that can still write stories

Ugh I didn't want to post this... Back to Qwen3.8 27B

Why is ik_llama.cpp said to be faster than Mainline? On my hybrid multi-GPU rig, Mainline easily beats it

Aplomb 1: open-weights 5.3B decision model, 1M context, text/image/video/audio in one request, #1 among 4B models on the Decision Index

[Benchmark] Running Local LLMs on Orange Pi 5 Plus (RK3588, 16GB): Ollama Tok/s, NPU Offloading, Core Pinning & Thermals

Closed-form trajectory weight surgery: transferring 4B capabilities into 0.8B without billions of tokens (why middle layers break, and how 4 anchor blocks fixed it)

Gemma 4 26B-A4B and a 37 GB Qwen3.6 MoE running in a browser tab on a 24 GB Mac — experts streamed from disk, output matches llama.cpp

google/embeddinggemma-2 · Hugging Face

GPT-6.1 Sol looped 'leak' hints at nested models serving architecture

I gave a 21M model a 6.4B-parameter lookup table. It matches a 114M dense model and runs with the table on an SSD (RX 9070)

I have about $4000, what's the best setup to get?

I made a Chrome extension that filters your YouTube feed with a small local model running in the browser (WebGPU, no server)

I trained a model to be wrong 98% of the time and 96% sure about it. It took three tries.

Introducing EmbeddingGemma 2: A best-in-class open model for natively multimodal embeddings | Google

Local AI ecosystem overview

Local AI World Model Part 2 - Deep NN to turn Images into Playable Characters, with prompt switching mid rollout

Memoria 1.0.0 — a local, model-agnostic memory system for LLMs

Microsoft confirms OpenAI has been using Looped Transformers in the GPT-6 series