👟Jim “🈶️” Liu👟 @ founder mode

@itsj.im

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#BlackLivesMatter // Beta Tester🙋🏻‍♂️; @CascadiaJS startup fair @Developer_DAO Dev #3915; @DWebSeattle; He/him 🫔DM me about your startup

Adam Jude’s X post on Andrés Muñoz’s kick change
Power rangers
Perplexity Perplexity is a measure often used alongside cross entropy loss to evaluate the performance of models in tasks like language modeling. It can provide a more interpretable way to understand the uncertainty of a model in predicting the next token in a sequence. Perplexity measures how well the probability distribution predicted by the model matches the actual distribution of the words in the dataset. Similar to the loss, a lower perplexity indicates that the model predictions are closer to the actual distribution.
Guest passes for CLEAR plus for 3 months
Screenshot of Jason’s Xeet from X
Appendix A: Introduction of PyTorch
The three main stages of coding a large language model (LLM) are implementing the LLM architecture and data preparation process (stage 1), pretraining an LLM to create a foundation model (stage 2), and fine-tuning the foundation model to become a personal assistant or text classifier (stage 3). Each of these stages is explored and implemented in this book.
Picture of Building A Large Language Model from Scratch book
Jim is screaming internally
Tofino was beautiful
Bluesky in Seattle
White Peony
No roughing gif from Arrested Development
Lemon glaze cupcake
Football x Sunset
Japanese Maple
Jim got mints!
“It’s physical social media in the real world”
Jim’s list for baseball news
Workout session with Future