fnl
Joined 16 years ago
Joined 16 years ago
For example, I try to journal by treating my work as “experiments” and tie those in turn to goals and challenges. And my experiments consist of a plan, prediction, the actual data, and the evaluation (of the data against the prediction). Finally, I constantly track the current state relevant towards the goal. If that sounds familiar to the Toyota Kata, that is because it is. And it should sound familiar to anyone trying to apply the scientific method.
I’d be curious to learn HOW others journal?
> Set it to refs/notes/commits to enable rewriting for the default commit notes.
Why wouldn’t that at least be the default? Why is rewriting off by default in the first place?
And in general, binary floating point arithmetic cannot guarantee associativity - i.e. `(a + b) + c` might not be the same as `a + (b + c)`. That in turn can lead to the model picking another token in rare cases (and it’s auto-regressive consequences, that the entire remainder of the generated sequence might differ): https://www.ingonyama.com/blog/solving-reproducibility-chall...
Edit: Of course, my answer assumes you are asking about the case when the model lets you set its token generation temperature (stochasticity) to exactly zero. With default parameter settings, all LLMs I know of randomly pick among the best tokens.
Disclaimer: I do really enjoy this book, as it reminds my of uncle Bob’s Clean Code, but a short version, and with the focus on what to do after writing code, when you need to change it or want to understand it better.
Yes, it is structured very much like a collection of blog posts. Which is great for me, as I typically work on learning one “tidying” a day or less. So I am not yet done with the book, end-to-end.