Who knew I’d be this good with fabrics?

Strands #497
“Completely fabricated”
🔵🔵🔵🔵
🔵🟡🔵🔵

Strands #458
“Monster quest”
💡🔵🔵💡
🔵🔵💡🔵
💡🟡💡🔵

The sacred gates of tax season swung open today.

Naturally, I did what any rational adult would do: hid in my shell.

Slipped into terminal mode and assembled my invo CLI-based OSINT recon pipeline.

If it runs flawlessly, maybe they’ll sense I’m not emotionally ready and give me a pass. Right?

Same energy as my Amazon shopping lists.
They're not lists. They’re graveyards.

Talking down to a man who's already sold his dignity for scraps, peak pampered behavior.

Pure Trance energy—two guys acting like middle school bullies who think 'diplomacy' is just a fancy word for delivering monologues.
Speaking in a trance-like state, oblivious to the fact that they’re supposed to represent a nation, not their own egos.

The art of diplomacy... as imagined by them.

Every time I pay my health insurance bill, it feels like I'm leaving a fat tip at a Michelin-star restaurant I've never eaten at—and hope I never have to

Just brewed a decaf and poured delactosed milk—feels like a flat white that’s given up on life.

After a healthy break, I’m back!

Currently using my favorite animal to tackle:

+120 billion MXN
+32 million data frames
+9 million names
+3 million unique names

Let’s get this data unwrangled!

Not to brag but I got a USB-C into its port on the first try.
Wacha'out Tuesday!

previously = {
"roles": [
{"pos": "Investigative Journalist", "org": "MXvsCORRUPCION"},
{"pos": "Metrics Analyst", "org": "El Universal"}
],

"fellowship": {"name": "Digital Threats", "by": "GIJN"},

"workshops": [{"title": "WHOIS Investigations", "event": "COLPIN 2023"}]
}

Successful first skeet!

import pandas as pd
from bluesky import StartSkeeting

# Begin to engage
ss = StartSkeeting('Journalism, OSINT, personal')

results = ss.explore_osint_topics()

# DataFrame for these topics
topics_df = pd.DataFrame(results, columns=['Topic Name', 'Description'])

print("Successful first skeet!")