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Interesting. How do you yourself use this(I am assuming of course you built it out of a need to want to have to track this data)?
How does the comp extraction work? 8-K prose has no standard format so curious whether you're running it through an LLM or using a rules-based parser, and how you handle amendments where the actual figures show up in a later filing.
The site fails to load...it just gets stuck in a fetching state. Another downside of vibe programming.
Given this is failing due to HN hug of death, might I suggest that you do a periodic batch, save the results and serve static?
Interesting, you should save part of the data to do some caching and avoid api requests for old positions.
What would also be very interesting is a graph of relationships and movements. Let's see just how incestuous the boards really are, and what's going on with serial CEOs who move from one business to the next.
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Interesting idea...tracking which execs move together or which board members overlap across companies. Maybe in next turn
> incestuous

Is it that? Or would it be similar to when you have a lot of responsibility (like leading a company) you tend to bring along people you know you can trust and can help you succeed?

Interesting part will be what do we read out of that data
100% - will be cool to see any recurring patterns, which sectors churn most, how comp varies by industry, etc. Gets more useful as the dataset grows.
Nice job! Is there a way to double click on any name to see more details on the person like previous positions or current compensation?
Is the equity piece one-time or yearly grants? I think it is adding up yearly salary with one-time equity grants. Also, how about bonuses?
Nice idea. Small thing: the categories are pretty much fixed. If you have to abbreviate a never-changing category like "Consumer Defen..." in a widget, your design doesn't work in this aspect.
did you write the SEC parsers yourself or use oss/off the shelf tech?
What's the backend? I'd recommend to migrate such project to the edge (Cloudflare, etc)
Fun project but meh on subscription. This data already exists in much better detail including full network graphs of people and many additional data points. Financial data is a hard problem because it’s not only hard to offer something new but also your only real consumer unless novel data is going to be retail.
You hit on something that AI can be really good at, which is shining light on corporate activities. Salary and movement are great, and interesting, but this could also help parse things like entry and exits into business markets where companies often quietly add or remove things from their filings. Keep going.
Getting “literally who” vibes from the list of execs that were listed with a job title but not a company name.

Mobile browser, if that makes a difference (maybe one of the people on the list helped me downsize as well at some point without me realizing it).

thats a bug. thanks for flagging. The company name is there but gets cut off on mobile. Will fix the layout.
I think one of the interesting things here is that many senior executives make similar base pay to very senior ICs. The primary compensation difference is in their equity compensation, where executives get massive PSU/RSU packages, while senior ICs get much more modest packages. A senior IC may have 30-50% of their compensation as stock, while a typical senior executives may have as much as 97% of their compensation as stock.
Says it's unable to respond at the moment.
Should be back up now. Got hit with way more traffic than expected.
You can also check any signal from publicly available sources using tools like CatchAll.

For example, "CEO and CFO appointments at US public companies in the last two weeks" found 142 records [0]

You can also set up monitors to get updates.

[0] https://platform.newscatcherapi.com/catchall/example/gtm--ex...

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Great proof of concept!

At the top it says:

> 2,100+ > CEO, CFO, Board, and other executive changes tracked in the past 30 days

Could you add a little metric there such as how many companies are being tracked, and perhaps how that compares to the previous 30 days, or 6 months ago, or 12 months ago?

Maybe a graph showing how many changes happen each month, so we can see when things are more volatile or not.

Nice work.

I remember giving this task to a summer co-op 10-12 years ago. it was alot harder to scrape the edgar site then and gather all for form 4 filings without the new api call first interface and the XBML markup in 10-K and 10-Q filings.

been mulling visualizing this for years.. nice work.