Felipe Kitamura

“A friend left a party at 9:30 on a Saturday. Not tired. Not sick. He wanted to get back to his agents. Nobody questions it anymore. Half the room is thinking the same thing. The other half are probably checking the progress of their agents. At a party” writing.nikunjk.com/p/token-anxiety

A Note of Thanks to our wonderful reviewers for their time and expertise! doi.org/10.1148/ryai.2… #reviewers #ML #MachineLearning

Supporting communication in Radiology AI with ROADMAP, a new ontology doi.org/10.1148/ryai.2… @cekahn @abhisuri97 @_ragonzales #Radiology #ontology #MachineLearning

Supporting communication in Radiology AI with ROADMAP, a new ontology doi.org/10.1148/ryai.2… @cekahn @abhisuri97 @_ragonzales #Radiology #ontology #ML

The Checklist for AI in Medical Imaging addresses #AI applications that include classification, segmentation, and reconstruction of medical images rsna.org/claim #MachineLearning #Radiomics #ArtificialIntelligence

New data resource with 4-view 2D mammograms and corresponding BI-RADS assessment, breast density, machine ID, and proof of benign or malignant outcome doi.org/10.1148/ryai.2… @FelipeKitamura @haritrivedimd @emoryradiology #breast #BreastRad #cancer

A new narrative review describes the evolution of AI applications for radiology from LLMs to autonomous agents doi.org/10.1148/ryai.2… @k_bressem @ShahriarFaghani @Khosravi_Bardia #agent #agents #AgenticAI

Karpathy's llama2.c showed you could train a real transformer in pure C with no frameworks. A solo researcher (and Claude Code) just took that same model, Stories 110M, Llama2 architecture, trained on real text and ran it on Apple's M4 Neural Engine (ANE) for less than a watt. He reverse-engineered the undocumented private APIs, bypassed CoreML, and found Apple's abstraction layer was hiding 2-4x of the chip's real throughput. The ANE delivers 6.6 TFLOPS per watt, roughly 80x more efficient than an Nvidia A100. The real implication here is inference: there are hundreds of millions of Apple devices with one of the most efficient AI accelerators ever shipped in consumer hardware, and Apple's own software stack is the thing standing between developers and its actual performance. h/t @maderix

@Starlink " "Trying to predict the future is a discouraging and hazardous occupation... The only thing we can be sure of about the future is that it will be absolutely FANTASTIC " -Arthur C. Clarke - Starlink 🛰

Establishing normative, age-based, pediatric kidney length and volume percentiles from a large US dataset with a natural language processing model doi.org/10.1148/ryai.2… @ChildrensPhila #USRad #AI #MachineLearning

New data resource with 4-view 2D mammograms and corresponding BI-RADS assessment, breast density, machine ID, and proof of benign or malignant outcome doi.org/10.1148/ryai.2… @FelipeKitamura @haritrivedimd @emoryradiology #mammogram #breastcancer #MachineLearning

Even in the age of digital articles, conciseness matters radiologyai.substack.com/p/why-word-cou… #sciencewriting #technicalwriting #MachineLearning

New mammography dataset with BI-RADS, breast density, machine ID, and outcome data freely available to the research community doi.org/10.1148/ryai.2… @FelipeKitamura @haritrivedimd @emoryradiology #mammogram #cancer #OncoRad

Bought a new Mac mini to properly tinker with claws over the weekend. The apple store person told me they are selling like hotcakes and everyone is confused :) I'm definitely a bit sus'd to run OpenClaw specifically - giving my private data/keys to 400K lines of vibe coded monster that is being actively attacked at scale is not very appealing at all. Already seeing reports of exposed instances, RCE vulnerabilities, supply chain poisoning, malicious or compromised skills in the registry, it feels like a complete wild west and a security nightmare. But I do love the concept and I think that just like LLM agents were a new layer on top of LLMs, Claws are now a new layer on top of LLM agents, taking the orchestration, scheduling, context, tool calls and a kind of persistence to a next level. Looking around, and given that the high level idea is clear, there are a lot of smaller Claws starting to pop out. For example, on a quick skim NanoClaw looks really interesting in that the core engine is ~4000 lines of code (fits into both my head and that of AI agents, so it feels manageable, auditable, flexible, etc.) and runs everything in containers by default. I also love their approach to configurability - it's not done via config files it's done via skills! For example, /add-telegram instructs your AI agent how to modify the actual code to integrate Telegram. I haven't come across this yet and it slightly blew my mind earlier today as a new, AI-enabled approach to preventing config mess and if-then-else monsters. Basically - the implied new meta is to write the most maximally forkable repo and then have skills that fork it into any desired more exotic configuration. Very cool. Anyway there are many others - e.g. nanobot, zeroclaw, ironclaw, picoclaw (lol @ prefixes). There are also cloud-hosted alternatives but tbh I don't love these because it feels much harder to tinker with. In particular, local setup allows easy connection to home automation gadgets on the local network. And I don't know, there is something aesthetically pleasing about there being a physical device 'possessed' by a little ghost of a personal digital house elf. Not 100% sure what my setup ends up looking like just yet but Claws are an awesome, exciting new layer of the AI stack.

Open-Source Dataset for the RSNA Screening Mammography Cancer Detection Challenge doi.org/10.1148/ryai.2… @FelipeKitamura @haritrivedimd @emoryradiology #OncoRad #breastcancer #MachineLearning

How was the show Silicon Valley so ahead of its time?

GPT-5.3-Codex is now available in Codex. You can just build things. openai.com/index/introduc…

Teaching MRI to Read the Report: Text-Image Alignment for Brain MRI at Scale doi.org/10.1148/ryai.2… @_Satyam_ #NeuroRad #MRI #ML

New Engineering blog: We tasked Opus 4.6 using agent teams to build a C compiler. Then we (mostly) walked away. Two weeks later, it worked on the Linux kernel. Here's what it taught us about the future of autonomous software development. Read more: anthropic.com/engineering/bu…

Interpretable Machine Learning Model Using Digitized US Features for Classifying Complex Thyroid Nodules doi.org/10.1148/ryai.2… @SIO_Central #SIO2026 #IRad #IROnc #OncoRad #radiology