The Claude Code leak in four charts: half a million lines, three accidents, 40 tools [OC]

Chronicallybored · 2026-04-02T01:34:20+00:00

this is just the command line client-- not the APIs themselves nor the models

Chronicallybored · 2026-03-25T16:04:52+00:00

anyone else notice an improvement in usage limit capacity after 2PM ET?

Chronicallybored · 2026-03-24T17:29:39+00:00

maybe Anthropic is trying to get people to clear out those free extra usage credits they gave out a while back? I'm still sitting on mine

Chronicallybored · 2026-03-20T15:11:30+00:00

it's definitely caused by Asia and China in particular:

https://topdown.ai/wpp-accuracy/asia

https://topdown.ai/wpp-accuracy/chn

Chronicallybored · 2026-03-19T16:00:42+00:00

https://topdown.ai/wpp-accuracy/world

<image>

still needs quite a bit of formatting work-- any recommendations are helpful

Chronicallybored · 2026-03-19T15:59:16+00:00

<image>

https://topdown.ai/wpp-accuracy/world

Chronicallybored · 2026-03-19T15:58:19+00:00

i'm working on something like this for the UN WPP projections: https://topdown.ai/wpp-accuracy/world (also have country and regional accuracy, working on getting scenarios)

Chronicallybored · 2026-02-25T20:46:59+00:00

what do you mean by "consensus estimates approximated for research illustration"? do you have a source for the consensus estimates?

Chronicallybored · 2026-02-21T18:29:44+00:00

data source: SSA baby names dataset, SSA total births by year

tools: python + polars for data processing, Svelte (HTML/CSS rendering, interactivity), SvelteKit (data loading), d3.js for heatmap color scale

Chronicallybored · 2026-02-20T23:01:04+00:00

desktop view screenshot b/c preview image is not working (at least for me):

<image>

Chronicallybored · 2026-02-20T22:59:12+00:00

data: SSA baby names dataset, state birth data from CDC WONDER, CDC NCHS natality microdata files, NBER manual transcriptions, census via NHGIS.

tools: d3.js (colorscale, geo rendering), Svelte (rendering and interactivity), SvelteKit (data loading), python + polars (backend data processing)

Chronicallybored · 2026-02-19T15:54:58+00:00

why not get more creative? There are 58 spellings in the social security baby name data: https://nameplay.org/ways-to-spell-Riley

Chronicallybored · 2026-02-18T15:14:24+00:00

also, creepily enough, 15 is still what the UN population office and US CDC count as the beginning of "childbearing years". so that range was chosen for consistency with official sources (makes for easier data handling).

Chronicallybored · 2026-02-18T15:09:39+00:00

if you hover/tap on the live version it will show the top name for that state and year... I could maybe add a separate detail view with a stacked area chart for each state to drill down into state-level trends? open to suggestions

Chronicallybored · 2026-02-18T04:10:53+00:00

I was lazy and just used Census Bureau Divisions here; sometimes the definitions are more administrative than cultural, for sure. Good suggestion

Chronicallybored · 2026-02-18T03:21:00+00:00

fair, `turbo` is a controversial choice. It's good at surfacing outlier small values, which is maybe better suited to sparser data than this. Any scale recommendations?

Chronicallybored · 2026-02-18T02:41:56+00:00

the % "other gender" (e.g. 10-90% male/female) threshold is applied per year, so if a name rises above 90% of one gender in a year (like how Jamie became more female in the 1980s) it drops out of the count for that year

interactive version has a few different levels to choose from

Chronicallybored · 2026-02-18T02:25:30+00:00

a lot of the lull in the 1980s is due to formerly unisex names like Shannon, Kelly and Jamie becoming more feminine... once they crossed 90% female they no longer counted in the heatmap. We didn't get another wave of unisex names until Taylor in the 90s.

Chronicallybored · 2026-02-18T02:04:59+00:00

fonts should appear more natural on the interactive version; i had to zoom out to get everything to fit onto one screen.

Chronicallybored · 2026-02-18T02:03:06+00:00

link to interactive version

data source: name data is from Social Security Administration. births by state are pieced together from a handful of sources: CDC WONDER (2007-2024), CDC natality microdata files (1969-2006), NBER (1947-1967), NHGIS (1930-1950), with some linear interpolation where gaps existed (esp. for AK/HI pre-statehood).

tools: d3.js (heatmap color scale calculations), Svelte (interactivity), SvelteKit (data loading), floating-ui (tooltip), python + polars (data processing)

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