MontagFTB

I’d like my Pip-Boy in matte black please.

I have split up the data model into a handful of layers in order to separate concerns. The evidentiary layer are the raw bits - the media, sources, clippings, photos, etc. From there I derive "personas", which are the (incomplete and possibly inaccurate) representations of people from those media bits. Above that is the curation layer, where the curator takes those personas and compiles persons from them.

So if there are a bunch of articles that talk about someone's birthday but a couple give conflicting data, that is all present and recorded in the evidentiary layer, and it is the responsibility of the curator to decide which is the actual birthday for that person.

edit: the "below/above" terminology is a dependency relationship. The facts undergird the conclusions drawn from them. To flip that relationship would be to "retcon" facts to fit the conclusions a curator has already drawn, which would be a mistake.

I’ve written my own genealogy app as well, it has reinvigorated my efforts. I have a multi-level data model (evidentiary below, curated above) that has helped me piece together people across a multitude of disparate sources. Claude has been my sidekick, and it’s amazing- especially for the more toilsome tasks like extracting people’s details from newspaper articles.

Their infrastructure is buckling under increased demand from AI generated code.

Imagine the software development lifecycle is a river blocked by several dams- necessary processes that also slow the water’s volume and speed. Writing the code was a very big dam upstream, and successive dams didn’t need to handle a lot of water. With that dam busted wide open, the torrent of code is inundating downstream processes. GitHub happens to host many of them.

Earlier this year the GitHub COO shared a post on Twitter with explosive numbers they were facing. I cannot seem to find it at the moment. (Edit: shared in other replies to this thread.)

I think I’ve seen that movie.

> it just reads as more evidence that LLMs are better at replacing leadership than anybody else.

I believe this counts as having “more confidence in AI’s advice than in that of colleagues”

In a 30M+ LOC repo we blew out Qwen3.6’s context thinking about a code review. This was via direct call to model. Do harnesses facilitate better context management, or is there something else to accommodate its smaller context window?

I have heard of some high school classes reverting to typewriters. So your suggestion holds some weight.

TLDR proof of care from the article: a low bandwidth process (e.g., from handwriting to tattooing it on your body) that you voluntarily put your words through to convey their level of personal importance.

Some of his examples were tongue in cheek. But even handwriting feels a little too laborious when what we lost that needs replacement is manual typing.

This article was tremendous. My dad and I rebuilt an old GPW as my car in high school. His brother bought it as his car for high school a generation earlier. This article and the video are a love letter to that old car- a quirky, heavy, underpowered, beautiful legend.

Do you have links to what you found? That solution sounds plausible in this case.

iPhone Dynamic Crease (tm)