Benjamin Warner @benjamin_warner
Research @SophontAI. Previously answerdotai. Vaccines save lives. benjaminwarner.dev Joined September 2011-
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@KyrieBlunders Missed this last week. But yes, correctly registering new functions means compile can integrate it end-to-end without the slowdown of graph breaks and kicking back to eager mode.
Some considerations that many folks seem not to get: 1. It can be a bubble even if the tech works. (For instance, if the tech doesn't have a high-demand use case.) 2. It can be a bubble even if the tech works and has strong product-market fit. (For instance, if the tech cannot be economically viable.) 3. It can be a bubble even if the tech works, has strong product-market fit, and has a path to eventual economic viability. (For instance, if profitability takes too long to achieve or makes margin/competition assumptions that fail to materialize.) 4. It can be a bubble even if the tech works, has strong product-market fit, and is currently highly profitable. (For instance, if demand has a hard ceiling and growth stops once the ceiling is reached.) 5. It can be a bubble even if the tech works, has strong product-market fit, is currently highly profitable, and has unlimited future demand. Literally all it takes for something to be a bubble is for lots of people to over-enthusiastically bet their money on it, and subsequently get panicky. Importantly, bubbles can be attached both to things that are completely hogwash, like the Metaverse, and to world-changing developments like the Internet or railways. Bubbles don't care. They're brought into existence by the thoughts and feelings of investors, not by actual tech or products. "The bubble has burst" doesn't mean "the tech didn't work" or "people stopped using the tech." It only means that people got panicky, investor money dried up, and valuations collapsed. Internet adoption didn't stop in 2000.
Remember, for ChatGPT or Claude to work as search agents, you need to: 1) subscribe to get the smartest models 2) turn on thinking 3) make sure web search is on Then you won't make embarrassing mistakes like Kelly Sadler.
I appreciate Anthropic has provided healthcare-related evals! Let's quickly go over them. HealthBench - benchmark from OpenAI that includes 5k multi-turn conversations with patients, and rubrics for evaluation. Fable 5 achieves 62.7% vs. GPT-5.5's 56.5%. Personally, I would also appreciate HealthBench Hard scores as well in addition to the aggregate score. HealthBench Professional - another OpenAI benchmark that focuses on physician tasks. Fable 5 achieves 66.0% vs. 51.8% for GPT 5.5. HealthAdminBench - a computer-use benchmark from Stanford that evaluates the completion og various administrative tasks (prior auth, denials/appeals, etc.). Fable 5 achieves 51.9%, no GPT-5.5 score provided. Overall, this models seems to perform quite well on healthcare benchmarks. Would also appreciate additional benchmark scores like MedCalc-Bench (which was previously reported by Anthropic) and MedXpertQA (an unsaturated, hard medical MCQA benchmark). Glad to see frontier labs are more comprehensively benchmarking and reporting the medical capabilities of their models!
Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.
@KyrieBlunders Did you use the custom C++ & Cuda registration or Triton wrapping to make the custom kernel compile compatible? docs.pytorch.org/docs/stable/li…
@NirantK Claude Haiku 4.5 is the latest version of Haiku. There are no newer generations anthropic.com/claude/haiku
@johnowhitaker If the models gain agency, then the better comparison would probably be online/mobile banking.
@johnowhitaker Perhaps. Unlike banking, every coding proactivity increase so far: punch cards to assembly, compilers, IDEs, etc has led to an increased demand for programmers.
The current best comparison for agentic coding is ATMs, which increased the demand for bank tellers.
Software engineering roles are growing and concentrating in the top-paying US companies via @GergelyOrosz newsletter.pragmaticengineer.com/p/state-of-the…
Not to degrade from this work, but TurboQuant is not a competitive method nor a good benchmark. Researcher -- including me -- cannot replicate the TurboQuant paper, and even then, the performance is not great. Please. Just. Stop.
i just beat @GoogleDeepMind's turboquant introducing Shard. 10x KV cache compression on Llama-3.1-8B. zero quality loss - 10x @ 8K context, 11.2x @ 32K - NIAH recall 1.000 across 4K-32K - LongBench Δ ≈ 0 vs FP16 turboquant tops out at 4-6x at the same quality. we doubled it.
@latkins Trinity Large has quite different recommended generation settings across Hugging Face/Arcee docs: HF model card: temperature=0.45–0.6 HF generation config: temperature=0.8 Website docs: temperature=0.3 For evaluation, which settings should we be using?
Bearish signal on the practical usefulness of Mythos.
My concern for the AI era, or at least this phase of it, is that a generation is being taught that "close enough" is just fine. Take @AnthropicAI for example. Text wrapping in Claude Code has been broken for weeks. Superfluous spaces appear on the left edge. One engineer to
@eliebakouch Weird way of spelling StableAdam
It's astounding how much worse Claude Opus 4.7 still is at searching for up to date and accurate information compared to GPT-5.5 Thinking.
@Dorialexander Is it truly tokenizer-less, or is utf-8 the tokenizer?
@code_star To bad it got abandoned
@thsottiaux Codex app is missing ssh support and connecting to dev containers
Never trust financial analysis from a guy who thinks Feb 30 is a thing.
Also, check out this train wreck of a spreadsheet Ed made to estimate Anthropic's revenue for 2025. He doesn't count February 1-10, counts March 1-10 twice, counts August 21-October 21 as one month instead of two, and doesn't count October 21-November 1.
Excited that @SophontAI + @MedARC_AI has a paper accepted to ICML! Will share more details soon :)
Jeremy Howard @jeremyphoward
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