Issue 114 — 2026-09-14
Fake copyright strikes holding Instagram creators hostage
Scammers are filing bogus copyright complaints against Instagram creators. The strikes knock posts offline and threaten accounts. Then the scammers reach out, demanding payment to withdraw them.
The scheme works because creators don't always know how to fight back. A single strike feels small — but they stack up, and the platform can shut you down if you get too many.
This is the same playbook as the YouTube copyright trolls, but now it's on Instagram, where the audience is wider and the creators are smaller. Your tía's page, your cousin's food account — they're all in the crosshairs.
The fix is straightforward: use Instagram's built-in dispute process, keep receipts, and don't pay the first demand that shows up. The fake complainants are counting on you being too busy to fight.
Why this matters for us: la gente with pages and feeds are getting nickel-and-dimed by strangers who know the process better than they do — and paying up is the easy way out.
Google's TabFM handles any table without retraining
Google released TabFM, a foundation model for tabular data. Unlike older approaches that retrain for every dataset, it works zero-shot on any table. It handles everything from customer records to sensor readings.
The model treats each table as a sequence — rows and columns become tokens the model can reason over. You feed it a table, it spits out predictions. No fine-tuning required.
This is the kind of thing the cousin running a warehouse management side business would use. Tables of inventory, orders, supplier lead times — now one model covers them all.
Why this matters for us: the gente who run small operations with spreadsheets and databases can finally get ML without hiring data scientists — the model learns once and just works.
Denormalize early and often — it's the opposite of what your database professor taught you.
— cybertec-postgresql.com
#too-many-tables-you-re-probably-hurting-yourself-19cc66Hudi indexes on data that never stops changing
Hudi is a table format for Apache Iceberg. It sits on top of your data lake and lets you write records without rewriting the whole file. The trick is it keeps an index—like a table of contents—that gets updated as records arrive.
Most table formats are built for batch work: you dump files, then read them. Hudi is built for the opposite. Data keeps flowing in—clicks, sensor readings, transactions—and the index keeps up. Without it, every query has to scan the whole dataset just to find the rows that changed.
The moving-target problem is real. When a table is being written to, a query might see stale rows or miss new ones.