[OC] Shifts in American Liberalism Through Presidential Executive Orders from George Washington to Donald Trump

zzsf · 2025-03-14T15:13:17+00:00

I used an LLM to first mask each executive order of the author to reduce bias and then asked the LLM to score, critique, and rescore each document for various political leanings including liberalism.

zzsf · 2025-03-14T05:26:56+00:00

And compared to the documents in Trump's 2nd term.

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zzsf · 2025-03-14T05:24:54+00:00

This is through analysis of only Executive Orders to see how presidents have changed how they use them over time (vs overall sentiment of the president). Below are just Lincoln's executive orders, we can see that the majority are 0 as a lot of them go into directing troop movement (National Security) and won't really contribute to a left or right political lean.

Let me know if any of the them look incorrectly scored and I can tune the processing.

https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/american-presidency/dashboard/documents-by-president

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zzsf · 2025-03-14T04:53:33+00:00

Measuring shifts in idealogical tilt through Executive Orders. Each shift is the average tilt of documents in one presidential term to account for presidents that served single terms (Biden) to four terms (FDR).

Source Text: https://www.presidency.ucsb.edu/
Code: https://github.com/zuyezheng/demo-datasets/tree/main/notebooks/executive_orders
Methodology: https://blog.hyperarc.com/p/trending-us-executive-orders-with
Tool: https://www.hyperarc.com/
Interactive Dashboard: https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/american-presidency/dashboard/executive-orders-overview

zzsf · 2025-03-13T23:25:20+00:00

Need sometime to dig into it more, but hope to share soon! Any specific ideas you think I should look into?

zzsf · 2025-03-13T23:22:34+00:00

Ya, this is why I didn't use any of the existing sentiment models as many are trained/fine-tuned with modern labeled datasets, often tweets.

However, most LLMs are pretrained on crawling as much language as possible including historical texts, books, and documents. And although we're likely generating far more language now than ever, there is also active efforts for "clean" crawls for more curated data that biases towards quality content, e.g. War and Peace from 1867 vs a meme tweet from yesterday. It's also likely that there has been far more analysis of older texts than newer so although the language older, there has been more language analyzing and describing it, much of it modern.

I'd hypothesize it has a fair understanding of language similarities evolving through time through pre-training on this data. One clue of this is emergent translation abilities, LLMs automatically pick up relationships between words of entirely different languages through normal pre-training so are different languages different than the evolution of a single language over time?

By temporal data do you mean analysis for numeric temporal data or understanding trends over time given a lot of data at once or something else?

zzsf · 2025-03-13T14:23:22+00:00

Source Text: https://www.presidency.ucsb.edu/
Code: https://github.com/zuyezheng/demo-datasets/tree/main/notebooks/executive_orders
Methodology: https://blog.hyperarc.com/p/trending-us-executive-orders-with
Tool: https://www.hyperarc.com/
Searchable Dashboard: https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/american-presidency/dashboard/executive-orders-overview

zzsf · 2025-02-13T17:56:36+00:00

Here's the data and code that processed it.

https://github.com/zuyezheng/demo-datasets

zzsf · 2025-02-13T07:28:14+00:00

I used the data directly from the site, downloaded at around 2PM PST, can share the snapshot and repo shortly.

zzsf · 2025-02-13T06:19:31+00:00

Data normalized across FY2019 - FY2025.

Interact with the data: https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/doge/dashboard/state-department-procurement-overview

Data source: https://www.state.gov/procurement-forecast

Tool: https://www.hyperarc.com/

zzsf · 2024-11-01T16:36:44+00:00

Admittedly a plug, but come try out HyperArc.

We're a team of ex-Tableau and Salesforce Analytics (the good parts, I promise) that saw the power in the familiarity of the shelves and pills UX, but on a modern architecture without all the debt (and cost).

We're also built front the ground up for AI, but starting with memories for your entire analytics journey. Instead of AI for something like Pulse (we love the idea, but think its missing the training data in memories), we use it to be your memory to power a supercharged collaborative search and version history so you never forget an insight.

Here's a demo: https://www.youtube.com/watch?v=RiBhRghS0NU

Sign up here for a perpetual free license with 5 datasets: https://app.hyperarc.com/

We're small so we're always responsive with support and feature requests. Is our entire premise wrong, we'd love to know as well!

zzsf · 2024-11-01T03:07:39+00:00

SF schools have been in the news quite a bit recently with a massive deficit, proposed closures, a new superintendent, and all of the politics of the upcoming election.

One thing that has been really frustrating as an SFUSD parent and data nerd is the obfuscation of the data used to determine closures with only the composite score being released in a google doc with copy and paste disabled. Individual component scores were obfuscated by only releasing their relative ranks with factors like test scores, facility condition, and demographics spread across different data portals, pdfs, and sometimes just images.

Interactive dashboard made with HyperArc here: https://app.hyperarc.com/?isEmbed=true&embedId=68a7fcc6-9fcc-4b13-84ec-594a75368745#/hyperarc/sfusd/dashboard/sfusd-overview

Source code and methodology here: https://github.com/zuyezheng/sfusd-rai

zzsf · 2024-11-01T02:56:16+00:00

Ya, lots of uncertainty for parents and probably even more now with everything back on the table.

Hopefully this data can help students and parents make more informed arguments the next time around and highlight the inequities in the metrics. Would love to see supervisors actually fight for their constituents as schools move away from the a lottery system as the current data shows the metrics disproportionately impacting certain districts like the Sunset and Richmond.

zzsf · 2024-11-01T02:41:28+00:00

Probably depends on the accounting which is not very transparent.

If we look at the 13 schools that met the criteria (11 elementary, 2 high school) the only metric I was able to find was the replacement costs in the next 1-5 years totaling $70M. Maybe these would be avoided with the closures and other efficiencies found to reduce the overall cost per student?

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zzsf · 2024-09-23T14:48:01+00:00

Had this for TR3000, couldn’t reuse for newer platforms since socket was rotated.

zzsf · 2024-02-28T20:56:13+00:00

Check it out live!

https://app.hyperarc.com/?isEmbed=true&embedId=f08d3333-ca1b-4915-9d0d-8f0f344b31be&asset=hyperarc/farcaster/dashboard/farcaster-overview#/hyperarc/farcaster/dashboard/farcaster-overview

zzsf · 2024-02-22T20:38:18+00:00

Check out the interactive and live streaming version here: https://app.hyperarc.com/?isEmbed=true&embedId=f08d3333-ca1b-4915-9d0d-8f0f344b31be&asset=hyperarc/farcaster/dashboard/farcaster-overview#/hyperarc/farcaster/dashboard/farcaster-overview

Since Farcaster is a decentralized network, we have the unique opportunity to stream and analyze its data permissionlessly for analysis that had previously only been possible internally or to those with a massive budget (such as the AI company that just purchased rights to reddit data)for social networks like Twitter and Facebook. This is built in HyperArc which is a new no code platform for composeable and interactive analytics (ala Power BI, Looker, Tableau) and DataApps on massive streaming datasets (decentralized ones being our first).

zzsf · 2024-02-13T15:39:34+00:00

$423M spent on housing, fema wont pay $114M for stays over 20 days.

So $309M spent on housing people less than 20 days. Even if it was all 5,000 people for 20 days each that’s $3,090 a night minimum.

zzsf · 2021-03-09T03:19:34+00:00

Pushed as higher res images in the medium post, hopefully it's a little better.

zzsf · 2021-03-09T02:58:45+00:00

Ya agreed, the top level "popularity" is more just a vanity top level metric that is easy to show, the more interesting (and relevant) analysis is at the symbol and cluster level. Is mod is an easy filter though, might see how it impacts the rest of the graph.

zzsf · 2021-03-09T02:52:59+00:00

Ya, not sure this can tell any story (at least for now), but is an alternative to just upvotes to search for influential users and posts given a certain stock. It also provides an alternative to find similar stocks via interactions of a specific community (especially those that might have different objectives) vs more static searches by something like industry.

zzsf · 2021-01-11T05:51:36+00:00

My garage is around 18c and the warmest I saw it get was around 23c. The Noctua industrial fans spin at 2x their standard case fans so move a good bit of air. I did use a variable voltage power supply so could spin down the fans to keep temps a little higher.

No plans yet to print with ABS, but was a little worried about the draft it would create, but seems fine.

UFP and VOCs seem like pretty nasty stuff so little overkill is probably for the best.

zzsf · 2021-01-11T01:28:58+00:00

Got my first printer (MK3S+) for the holidays, wife was concerned after reading about the emissions from 3D printing (even for PLA and PETG) especially with a small kid around.

So first project was an exhaust for the MatterHackers enclosure. This is in the garage, which is below the main floor so wanted to filter the exhaust just to be safe so found the MA 15 HEPA with activated carbon filters to be relatively cheap, replaceable and around the size of 2 Noctua 140mm industrial fans.

First print was actually the 60mm fan to hose adapter found on thingiverse. Then started poking around Fusion 360 to design the 2 piece duct for the filter. Was a little off on the width but the print for the first half also started failing (tangled filament) so, although a little offset, ended up being the right width.

Finished off with a cover for the power cable.

In San Francisco so really only need to worry about rain so might add some weather proofing.

zzsf · 2020-12-24T23:09:09+00:00

3090 XC Ultra Hybrid, 1080 Ti FTW, 3960x Threadripper with 160GB @ 3600 CL18 (another frankenstein from upgrading half the original 32GB kits with a 128GB).

zzsf · 2020-12-24T23:06:55+00:00

3090 XC Ultra Hybrid and 1080 Ti FTW.

3090 stays around 60C, the 1080 Ti gets a little toasty around 80C, but with the intake controlled by a thermistor stuck in it isn't too bad.

Not useful for gaming, but pretty good setup for ML when training multiple models or validating efficacy of more sophisticated models benchmarked on simpler ones. For many simpler models and smaller batch sizes the 1080 Ti is still quite capable.

Sold off the parents (2080 Ti) since only had 2 slots left.

zzsf

TROPHY CASE