The "paper tiger" era of African data regulation is officially over. We analyzed the enforcement data from 2022–2026, and the curve is going vertical. While everyone was watching the EU/GDPR, African regulators (Nigeria, South Africa, Angola) quietly started dropping six and nine-figure penalties on everyone from Big Tech to local banks.

The Highlights:

Meta (Nigeria): $220,000,000 (Data discrimination/sharing)

Banco de Poupança (Angola): $525,000

Dept of Justice (South Africa): $279,000 (Yes, the regulator fined the government)

   If you’re a multinational doing business in Africa, the "Privacy Tax" just went up.

Read full analysis here: https://gilanalytics.com/highest-data-protection-fines-in-africa-2026/

I've been thinking about a classical result in conic geometry that I think deserves more attention.

Take the parabola x² = 4ay. From any point Q = (h, k) inside the evolute, you can draw exactly three normals to the curve. Each normal meets the parabola at a foot, giving you three points and those three points form a triangle.

The theorem: the centroid of that triangle always lies on the axis of the parabola.

The proof comes down to one beautiful observation. When you substitute Q into the normal equation x + ty = 2at + at³, you get the cubic

at³ + (2a − k)t − h = 0

There is no t² term. By Vieta's formulas, the sum of the roots is zero: t₁ + t₂ + t₃ = 0. Since the x-coordinate of the centroid is (2a/3)(t₁ + t₂ + t₃), it vanishes identically.

What's even nicer: the y-coordinate of the centroid works out to 2(k − 2a)/3 — it depends only on k, the height of Q. The horizontal position h disappears entirely. So if you slide Q left and right at fixed height, the centroid doesn't move at all. That's what the GIF shows.

Here’s the full video:

https://youtu.be/BT8ByfN1SNo?si=Fo8Ii_KQOvWwSzjn

With Claude's help a GoogleVis type motion chart without Flash was developed. The R package repo is here:

https://github.com/John-R-Wallace-NOAA/GoogleVis\_Type\_Motion\_Chart\_No\_Flash

Hi

If you are remotely interested in programming on new computational models, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 9 years, the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 12yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play & learn a ton about

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.
• Build & See Quantum Algorithms in Action – instead of just writing/ reading equations, make & watch algorithms unfold step by step so they become clear, visual, and unforgettable. Quantum Odyssey is built to grow into a full universal quantum computing learning platform. If a universal quantum computer can do it, we aim to bring it into the game, so your quantum journey never ends.

PS. We now have a player that's creating qm/qc tutorials using the game, enjoy over 50hs of content on his YT channel here: https://www.youtube.com/@MackAttackx

Also today a Twitch streamer with 300hs in https://www.twitch.tv/beardhero

Played around with Figurelabs yesterday for some illustration. Unlike other tools that spits out jpegs with alien text, this one actually outputs editable text and vector shapes. You can just manually fix the typos after it generates. Saves a lot of headache when you need a chart with actual readable labels. Good enough for quick mockups imo.

The pipeline continuously ingests data from:

• OpenSky Network for live aircraft states
• Open-Meteo for global weather snapshots
• USGS Earthquake Hazards Program for real-time seismic events

Every 5 minutes, the streams are aggregated and joined by geographic grid cell to generate a composite risk score based on:

• air traffic density
• earthquake magnitude
• weather conditions

The goal is to provide a live global view of aviation-related environmental risk by fusing multiple independent real-time sources into one visualization.

Tools used: Python, Quix Streams, Redpanda, PostgreSQL/PostGIS, Supabase, Bruin, Streamlit, Terraform, Docker

Data sources: OpenSky Network, Open-Meteo, USGS Earthquake Hazards Program

GitHub repo: https://github.com/aletbm/SkyPulse-Streaming-Pipeline
Live dashboard: https://skypulse-streaming-pipeline.streamlit.app

Note: the live dashboard is currently offline / showing stale data because the cloud services were paused to avoid ongoing infrastructure costs, but the full implementation and architecture are available in the repository.

So I’ve been working on something a bit different from the usual chatbot setup.

Instead of a single AI, this system (called Trinity) runs multiple models in parallel (ENG / SYNTH / PRIME), compares their responses, and only escalates to a third “arbiter” model if they disagree.

But the interesting part isn’t the responses—it’s the memory.

Every interaction builds a local knowledge graph (what you’re seeing here). Nodes = concepts, edges = relationships. Over time it starts forming its own internal structure based on how it thinks, not just what it outputs.

Current stats in this snapshot:

• ~50 nodes
• ~390 edges
• density ~7.8
• central node (“trinity”) is becoming a dominant hub

I’m now working on:

• pruning (to stop it turning into noise)
• edge classification (structural vs behavioral vs junk)
• a “sleep cycle” that cleans and reinforces the graph

Basically trying to answer:

>

Curious what people think:

• Is this just over-engineered prompt chaining?
• Or is there something here in graph-based memory + multi-model arbitration?