I really liked the main question. But I’m more interested in the structure how we journal, not if you use Logseq, OrgMode, paperback, etc.
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.
For Mixture of Expert models (like GPTs are), they can produce different results for an input sequence if that sequence is retried together with a different set of sequences in its inference batch, because of the model (“expert”) routing depends on the batch, not the single sequence: https://152334h.github.io/blog/non-determinism-in-gpt-4/
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.
To me it implicitly means: “I am selling you my distilled copy of the book at nearly the price of the book. That lets me recoup my investment, and after a few days I will take it offline, so I don’t get a law suit from O’Reilley for copyright infringement.”
Oh, wow, scummy to scammy, indeed! It’s all in the fine print… I didn’t bother reading this appendix on the first read of the blog post.
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.
That’s an interesting viewpoint, even surprising to me. Can you be more concrete about what you think is wrong with the agile software development movement Kent Beck co-founded, and how it destroys developers?
It has turned into a system that keeps developers in a constant state of crunch time. It simultaneously pushes all responsibility for delivery on to devs under the guise of “the team self organises” whilst stripping them of any real autonomy to actually feel a sense of satisfaction in completing tasks and delivering projects.
When something goes well the anonymity that “the team” creates to the wider business means that praise goes to the project managers and product owners who, realistically, probably did very little beyond the kick off to deliver.
Indeed. In agile, successes belong to the team; failures belong to the individual developer. The higher-ups will know that it was your commit that broke the build, and they know exactly how many story points you delivered -- or did not deliver -- per sprint by your JIRA metrics.
I agree on principal, but believe it is more nuanced and hybrid can work if you implement it uniformly across the company. For example, I’ve seen places where everyone can WFH Tue & Thu or Mon, Wed, Fri where hybrid have a quite smooth collaboration culture. But if it is “random”, every day ends up being WFH - that’s in effect just the same as fully remote, and you need to act accordingly. And if you work in a global company with globally distributed teams, the company already is de facto fully remote, and the picture becomes even more nuanced.
I use this. You can easily pick dates (and it correctly handles time changes in one region vs the other) and you see the time bands side by side. Finally, it has a great mobile app, too.
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?