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February 2025

How Core Git Developers Configure Git

Interesting discussion of git config options that the git core developers favor and that are not (yet) defaults. There are some nice suggestions. For example, I didn’t know that

1git config --global push.autoSetupRemote true

existed. If you haven’t defined an upstream for a branch yet, git will automatically set it for you. So you don’t have to run

1git push --set-upstream origin my-branch-name

anymore.

Some other options I adopted from this post:

 1# sort branches by last committed date
 2git config --global branch.sort -committerdate 
 3# sort tags by tag-number not alphabetically
 4git config --global tag.sort version:refname   
 5# better diff algorithm than the default myers
 6git config --global diff.algorithm histogram
 7# color moved code differently than added code in diffs
 8git config --global diff.colorMoved plain
 9# push branch to same-named remote
10git config --global push.default simple
11# attempt to autocorrect misspelled git commands in the cli
12git config --global help.autocorrect prompt 
13# add the diff to the commit message draft
14git config --global commit.verbose true
15# 3-way-diffing
16git config --global merge.conflictstyle zdiff3 
/ 2025-02-25 / (via) / #git
Brandon Sanderson on Building a Fiction Empire

This is an interesting podcast conversation with Brandon Sanderson (link) that I came across accidentally (I enjoyed particularly the parts from [00:37:57] onwards). He is the author of The Stormlight Archive (link) and Mistborn (link) fantasy novels. I have read The Way of Kings some years ago but didn’t yet continue the series.

A couple of interesting things he discussed:

  • He publishes and distributes his books via his company, Dragonsteel, and uses successful kickstarter campaigns (link) to finance new projects. Kickstarter is quite common for board games or video games projects, but not many people have used it for successful book projects.
  • He develops and tests his books like a Hollywood studio would test movie ideas. For example, his team developed an elaborate test reader process to understand whether they understand and/or like certain parts of a book. I find it interesting how he walks the fine line between writing about what he likes and writing for the market and commercial success.
  • He explains that ebook and audiobook deals are hard to negotiate when the market is dominated by amazon / audible.
  • He can easily deconstruct popular books into common narrative patterns, e.g. Three-act-structure (link) or The Hero’s journey (link) .
  • I found his distinction between hard magic systems (magic rules are explicitly laid out to the reader, e.g. Asimov series) and soft magic systems (rules are vague, and reader or characters will develop surprising new capabilities, e.g. Gandalf in Lord of the Rings) fascinating.
/ 2025-02-23 / (via) / #writing #fantasy
Using S3 triggers to maintain a list of files in DynamoDB

“Getting from a rough idea to a working proof of concept of something like this with less than 15 minutes of prompting is extraordinarily valuable. This is exactly the kind of project I’ve avoided in the past because of my almost irrational intolerance of the frustration involved in figuring out the individual details of each call to S3, IAM, AWS Lambda and DynamoDB.”

This describes exactly why I think that the current generation of models is already immensely valuable. There are a range of technologies and frameworks that would deliver me value if I would spend the time to adapt them to my use case. Normally this would involve a lot of googling and reading forum posts that only describe my problem to 80%. Having an LLM to guide you in the right direction, lowers the bar and time investment enough to allow much easier and quicker experimentation.

A LLM-generated proof-of-concept, followed by understanding the solution, and then a refining is what works for me. I used it to implement changes on this blog (don’t know much about Hugo), building simple apps at work (with retool), debugging package errors (homebrew, pyenv), etc.

Stuff you should have been taught in college but werent

Casey Handmer’s perspective on how to think about your job and career, especially relevant for people transitioning into tech after a PhD/Postdoc:

“It’s not enough to have mastered your job to get moved up. You also have to build trust with your management. It doesn’t matter how good you are at the mechanics of your job, if your management and colleagues don’t trust you, they’ll see you as a loose cannon and try to find ways to offboard you. I have been in this position before – and clueless about it. My job was saved because I had become critical infrastructure for too much of the system, but I was still marginalized and unable to advance, because I had broken (spectacularly!) the trust of management.”

Developer philosophy

A couple of senior developer best practices that I can relate to, especially:

“Avoid, at all costs, arriving at a scenario where the ground-up rewrite starts to look attractive.”

and

“Nobody cares about the golden path. Edge cases are our entire job. Think about ways in which things can fail. Think about ways to try to make things break.”

/ 2025-02-03 / (via) / #software-engineering
What fully automated firms will look like

I am at least sceptical that the current LLM approach will allow the necessary step change in capability, autonomy, and robustness. However, it is fun to read about hypotheses how future companies will look like:

“Everyone is sleeping on the collective advantages AIs will have, which have nothing to do with raw IQ but rather with the fact that they are digital—they can be copied, distilled, merged, scaled, and evolved in ways human simply can’t.

What would a fully automated company look like - with all the workers, all the managers as AIs? I claim that such AI firms will grow, coordinate, improve, and be selected-for at unprecedented speed.”

I find it more practical to think about how to position yourself in a world where certain aspects of your job are already automatible, i.e. the skills are available in the training dataset and they can be verified as right or wrong. This is discussed here (link) .

/ 2025-02-01 / (via) / #llm

January 2025

Life Lessons from the First Half-Century of My Career

I think most people (that get to choose) know deep down about the major things they need to do to be more productive, have a more fulfilling career and family life. Nevertheless, it’s always interesting to see other people’s focuses and learnings. I agree with most of them.

/ 2025-01-26 / (via) / #productivity
Quoting gwern

“[…] much of the point of a model like o1 is not to deploy it, but to generate training data for the next model. Every problem that an o1 solves is now a training data point for an o3 (eg. any o1 session which finally stumbles into the right answer can be refined to drop the dead ends and produce a clean transcript to train a more refined intuition).”

- gwern

/ 2025-01-18 / (via) / #llm