The Work AI Cannot Replace: My Mother and Higher-Value Care. AI abundance forecasts assume labor scales. Care work does not. A daughter’s hours are the hours the forecast does not see. The wall is closer than they say. caregiving aging-and-care ai-economics ai-careers Artificial Intelligence (AI) abundance forecasts share a sentence. The sentence promises automation will …
addresses the fundamental challenge of slow, sequential token generation in language model inference. DFlash speculative decoding support for CPUs was recently enabled in vLLM v0.25.0. In our testing with Qwen3.5-9B on an r8i AWS instance, powered by Intel® Xeon® 6 processors with Performance-cores, DFlash increased average token generation throughput to 3.92x that of the autoregressive …
Speculative Decoding on CPUs: Nearly 4x Faster Token Generation with DFlash Read More »
Venture Capital and AI: Sized for Fifteen Winners a Year. Old venture capital was a basketball team sized for fifteen winners a year. The redesign that scaled: share economics, centralize control, and grow sideways. ai-markets ai-strategy ai-economics For thirty years, every venture firm in the country was sized for the same number. About fifteen technology …
Sized for Fifteen Winners a Year · Hanh D. Brown Read More »
Muse Glimmer is gaining attention in the local AI community and is being compared with Qwen’s 27B-class models. In many cases, it is performing better, especially for local coding and agentic workflows. Meta looks strong in the open-model space, and with a few more iterations, models like this could start competing closely with proprietary systems. …
Run Muse Glimmer for Local Vibe Coding with llama.cpp, DFlash, and Pi Read More »
Open Weights AI: Distribution Decides Whose Values Win. Open Artificial Intelligence models win distribution because they ship cheaper and modifiable. Distribution decides the global default. Values ride along with the winner. ai-policy ai-markets ai-safety Powerful countries are not picking the smartest Artificial Intelligence (AI). They are picking the cheapest one to deploy and the easiest …
Distribution Decides Whose Values Win · Hanh D. Brown Read More »
This post was co-authored with Max Silfverberg (Data Scientist, AI Solutions Lead), Antti Hallavo (Lead AI Software Engineer), and Pontus Huotari (Lead Data Scientist). We work at Alma Media, a Finnish digital services, marketplaces and media company. One of our focus areas is developing AI/ML solutions for real estate listing services, where understanding image content plays an important role. services handle hundreds of thousands of listings a year. Most of those come with …
Why We Fine-Tuned SigLip (And Why That’s Not Always the Right Call) Read More »
How to Read AI News: Three Layers, One Sentence Skipped. AI news conflates three different capabilities. The most important sentence is usually the one the announcement does not say. Here is the method for reading it. reading-ai-news ai-and-work ai-strategy Notice the word Artificial Intelligence (AI) in any news story you read this morning. The word …
Three Layers, One Sentence Skipped · Hanh D. Brown Read More »
Agentic AI has officially moved from the research lab into enterprise production. In 2026, the AI narrative has shifted dramatically from conversational chatbots — systems that wait for human prompts to generate text — to autonomous AI agents. These systems can plan, execute, and adapt multi-step tasks across external tools, databases, and APIs without …
5 Real-World Use Cases for AI Agents Transforming Industries Read More »
Will AI Take My Job? Three Questions That Tell You How Long. Three questions tell you how long your job lives past AI. Name the capabilities. Rank them by AI improvement rate. The slowest one decides. ai-careers ai-and-work reading-ai-news Artificial Intelligence (AI) does not reshape every job at the same speed. It improves some capabilities …
Will AI Take My Job? Three Questions That Tell You How Long · Hanh D. Brown Read More »
? Let me provide a real, personal example. We fine-tuned a 7B parameter model which completely blows foundation models out of the water, but just for this very narrow subtask: Filling out synoptic reporting templates for breast cancer. This is a hellishly difficult task with complex input formats, branching logic, and fields that must appear in …
