What is mattwood.fyi?

A live, structured feed of riffs and links from Matt Wood, Chief AI & Technology Officer at AWS — readable by people and queryable by agents as a typed knowledge graph.

Is there an API and is it free?

Yes. A public, read-only query API is available with no authentication and no API keys. See https://mattwood.fyi/openapi.json and https://mattwood.fyi/docs.

How should an AI agent use this site?

Fetch https://mattwood.fyi/feed.json for the full structured feed, or query the API for keyword/semantic search and the connection graph. See https://mattwood.fyi/agents/ for when-to-use guidance.

ThinkingCap: Gemma-4-26B-A4B Token Efficiency

Connections

Related to

Thinking Orbs: AI thinking indicator for React Both deal with AI 'thinking' processes — ThinkingCap optimizes thinking tokens computationally while Thinking Orbs visualizes the thinking indicator for users; complementary perspectives on the same underlying concept
Introducing Beam: Reflection's 501B Open-Weight Model Both represent community-optimized open-weight models (ThinkingCap on Gemma-4, Beam as Reflection's 501B), illustrating the trend of third-party actors improving or repackaging base models for efficiency or capability gains
Kimi K3.1 Pro: Advancing Open Intelligence Both are optimized open-weight models targeting efficiency improvements; ThinkingCap reduces thinking tokens for Gemma-4-26B while Kimi K3.1 Pro advances open intelligence through architectural refinements
Mistral Large 4 Both are third-party optimized or enhanced versions of existing frontier models, competing in the efficient open-weight model space
Mistral Large 4 Both are optimized large language models competing in the open-weight space, with ThinkingCap targeting token efficiency on Gemma-4-26B and Mistral Large 4 representing a comparable frontier open model
Thinking Orbs: AI thinking indicator for React Both concern the 'thinking' phase of AI reasoning — ThinkingCap optimizes thinking token volume while Thinking Orbs provides UI indicators for the thinking process, reflecting shared interest in making reasoning transparent and efficient

Supports

Running Large LLMs on Mac Studio with MLX and RDMA ThinkingCap's token efficiency improvements directly benefit local deployment scenarios like running large LLMs on Mac Studio, where reduced token generation lowers memory bandwidth and compute costs
Running Large LLMs on Mac Studio with MLX and RDMA ThinkingCap's 39-69% token reduction directly reduces memory and compute requirements, making running large LLMs on consumer hardware like Mac Studio more feasible by lowering throughput bottlenecks
How Long Contexts Fail ThinkingCap's 39% reduction in thinking tokens directly addresses the token waste and inefficiency problems with extended context/reasoning that 'How Long Contexts Fail' describes
How Long Contexts Fail ThinkingCap addresses a specific failure mode of long contexts — excessive thinking token generation — directly relevant to how long contexts fail through token budget overruns and inefficiency

Type:	link
First published:	Friday, 23 October 2026 at 23:12 UTC
Location:	https://bottlecapai.com/post/thinkingcap-gemma-4-26b/
URL:	https://mattwood.fyi/i/b3fab895

I'm Matt Wood, and this is For Your Information. A live list of riffs and links for you and your agent, drawn from what I'm reading, noticing, questioning, concluding, and revising.

Connections