Analyzes the structure of every token injected before an answer in the OpenCode agent, including the system prompt, rule files, MCP tools, and native tools, and lays out in detail how to optimize based on real measurements.

A record of measuring how much of the Hermes Agent system prompt the skills index and tool schema take up, and reducing injection using usage data (.usage.json) and toolset-level disabling. Skills went from 32 (4,807 chars) to 8 (2,643 chars), and tools from 20 (43,264 chars) to 15 (30,016 chars).

A measured record of trimming Hermes Agent's fixed per-request injected tokens by 37%. Along with the results of slimming skills, tools, SOUL, and memory, it explains where the absolute cap on injected history is actually decided.

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.

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.

By 2026, AI has moved past the ask-and-answer chatbot stage into an ecosystem of agents that decide and act on their own. What decides a large model's performance is not raw parameter count but its skills, rules, and inference speed — and at bottom it is still next-token prediction.

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.