We parsed 3,228 @whale_alert transactions ($548B) from the last 5 months and mapped where the money went.
The short version:
-> $128B in BTC flowing to US-regulated exchanges
-> Stablecoin mints massively outpacing burns
-> dormant wallets waking up
Full breakdown ⬇️
If you use BigQuery as your data warehouse, I want to talk to you.
I’m researching how these teams:
- query + explore data
- handle ad hoc stakeholder questions
- move from SQL —> something shareable
No pitch. Just learning.
DMs open.
🥱 50 days of feeding data, 1 million emotions. Dive into the triumphs, the “WHAT time is it?!”, and the beautifully chaotic science of keeping a tiny human fed ➡️ https://t.co/xxD3s2ypRl
1/ 🔍 Want to extract real insights from your sensor data? Learn how to use Compute Tracks to visualise, transform, and analyse raw IoT streams all in one interactive pipeline.
#IoT, #DataScience, #Analytics, #Agents
👉 Check out the full tutorial: https://t.co/rVrhArQqA8
I’m trying to test a new data science product. If you see this and have a data science need; maybe you need a model built, data cleaned or features engineered, DM me and if it’s suitable for the product we will provide a pipeline for free.
Build it once.
Run it forever.
With @ComputeTracks, you can:
⚡ Upload a CSV
🧠 Prompt the LLM
📦 Compile the pipeline
📥 Drop next week’s file into the same pipeline
It just runs.
No more redoing the same cleanup.
1/ We’re building @ComputeTracks: a new way to go from CSV ➡️ clean tables, charts, and insight pipelines in *minutes*.
Why? Because most analytics work still looks like this 👇
Let me show you a better way. 🧵
Getting much closer to something useful with @compute_tracks it can complete basic data science tasks on single files. We are embarking on a 10 improvements in 10 days period to have it perform more complex tasks. Then on to multiple files and database connections…