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One important property for a style of thinking and argumentation to have is what I call galaxy brain resistance: how difficult is it to abuse that style of thinking to argue for pretty much whatever you want - something that you already decided elsewhere for other reasons? The spirit here is similar to falsifiability in science: if your arguments can justify anything, then your arguments imply nothing.

import dspy

We are proud to present Tongyi DeepResearch, the first fully open‑source Web Agent to achieve performance on par with OpenAI’s DeepResearch across a comprehensive suite of benchmarks. Tongyi DeepResearch demonstrates state‑of‑the‑art results, scoring 32.9 on the academic reasoning task Humanity’s Last Exam (HLE), 43.4 on BrowseComp and 46.7 on BrowseComp‑ZH in extremely complex information‑seeking tasks, and achieving a score of 75 on the user‑centric xbench‑DeepSearch benchmark, systematically outperforming all existing proprietary and open‑source Deep Research agents.

1. UserPromptSubmit Hook (runs BEFORE Claude sees your message):

Python 3.14 was released at the beginning of the month. This release was particularly interesting to me because of the improvements on the "free-threaded" variant of the interpreter.

notebooks that break down the implementation.

_secret = Secret.from_name("nanochat-secrets") print("Loaded secrets from Modal") except Exception: nanochat_secret = None print("No secrets found - WandB logging disabled")

ng together semi-plausible sentences. Now we are rapidly saturating all the benchmarks we can come up with. And yet this dramatic progress has merely been the result of consistent trends in scaling up deep learning.  There have been people who have seen this for far longer. They were scoffed at, but all they did was trust the trendlines. The trendlines are intense, and they were right. The models, they just want to learn; you scale them up, and they learn more.

Dataset Download - Grab the LaTeX OCR dataset (images of equations + LaTeX code) Model Download - Download Gemma 3-4B and cache it (so we don't re-download every time) LoRA Fine-tuning - Train adapters to teach Gemma to read equations Model Export - Merge LoRA adapters into the base model (makes deployment easier) vLLM Serving - Deploy as an OpenAI-compatible API with auto-scaling

You write a simple loop that runs on your CPU-only machine, including the data or environment and the loss function. We figure out how to make the training work on a bunch of GPUs, doing the exact computation you specified, efficiently. To change the model you're working with, you only need to change a single string in your code.

The difference between being good and being great isn’t talent or formal training, but the invisible practice that happens when you're just living life.