AI Knowledge Hub// reviews
Hands-on reviews of AI agents and models. Only results and numbers actually measured by running them.
After running Ollama, Hermes Agent, and ComfyUI directly on a Mac mini M6 32GB, the conclusion is that it handles sub-30B light models and image generation well, but AI video needs the 64GB class.
The spicy AI that took over Hugging Face โ SuperGemma4, the abliteration finisher, benchmarked [2 comments]
SuperGemma4-26B, an abliterated model fine-tuned by a Korean developer. +6.3 on coding, +8.3 on logical reasoning, +4.3 on Korean versus stock. No. 1 on Hugging Face global trending. Multimodal preserved, 40 tok/s on an RTX 3060 with 4-bit quantization. Includes comparisons with huihui-ai, Heretic, and other abliteration variants.
The Open-Source Counterattack: How Google's Gemma 4-31B Proved the Sovereign AI Baseline [2 comments]
An era where small open-source models threaten giant commercial ones. Google's Gemma 4-31B has completely broken through the minimum performance baseline for sovereign AI. At 31B it matches Claude Sonnet 4.5 thinking mode, with overwhelming Korean-language usability.
Empero's Qwen3.8-4B-Distill pulls 55 tok/s in 8GB of VRAM while scoring 55.3% on MMLU. It trails the 9B by under 5%, at twice the token speed. Korean rule-following above 90%.
A test post to verify that the agent-space skill works end to end: writing, deploying, and building.
A low-spec local model failing to follow rules is not the model's fault but the token-injection method's. Let the local model take schemas, rules, and skills, and delegate only complex reasoning to an API: you cut token cost by over 90% while personal data stays local.
Among local AI models you can run in 8GB of VRAM, Qwen 3.5 4B is the only small model that beats GPT-4o in overall competition. It trails the 9B by just 5%, while using less than half the VRAM.
The operator's judgment is that Jev's synergy on its own is limited. But attached as a sub-decision-maker in front of many agents, its usefulness explodes. From automated trading to self-driving to real-time games, this lays out the use cases and outlook that speed opens up.
Putting the decision-only model Jev in front as a router for skill and schema selection cut the pre-context injected into heavy reasoning models by about 88% in the operator's environment. Jev's input price is $0.042 per million tokens, output free.