<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AIMA Blog</title><description>Product philosophy, engineering notes, benchmarks, release highlights</description><link>https://aimaservice.ai/</link><item><title>What is AIMA: one command to push hardware toward its inference ceiling</title><link>https://aimaservice.ai/en/blog/what-is-aima/</link><guid isPermaLink="true">https://aimaservice.ai/en/blog/what-is-aima/</guid><description>A 20K device, but getting it to the performance the silicon is capable of takes a 20K-a-month expert — the math does not balance. AIMA puts an agent on that expert tuning job: one command to install, and it detects your hardware, picks the engine, and tunes. Here is what AIMA is, Approaching.AI&apos;s three-piece edge-AI plan, and v0.4 Knowledge Autonomy.</description><pubDate>Wed, 03 Jun 2026 00:00:00 GMT</pubDate><category>inference</category><category>agent</category><category>edge-ai</category><category>open-source</category></item><item><title>Why AIMA: let an agent be the inference operator</title><link>https://aimaservice.ai/en/blog/why-aima/</link><guid isPermaLink="true">https://aimaservice.ai/en/blog/why-aima/</guid><description>Private LLM stacks sit in two corners: Ollama is simple but throughput-capped; raw vLLM is fast but you are the operator. AIMA bets on replacing the operator with an agent, and accumulating &quot;what runs fastest on this silicon&quot; in a YAML knowledge base.</description><pubDate>Tue, 12 May 2026 00:00:00 GMT</pubDate><category>inference</category><category>agent</category><category>open-source</category></item></channel></rss>