Woah! @Nubank just got authorization to become a bank in Mexico. 15% of Mexican adults were already Nu customers before it was legally a bank. The numbers from the press release: - 15 million customers, adding 12,000 per day (!!!) - $5.9bn in deposits - 54% of customers got their first-ever credit card from Nu - Breakeven in Q1 2026, with the efficiency ratio improving 78 percentage points - A projected $4.2bn investment in Mexico through 2030 Nubank is a monster. Just a monster. And the detail that got me: Nu built all of this on a SOFIPO license, a wrapper originally designed for community savings co-ops. The company calls the conversion "unprecedented" because no SOFIPO has ever transformed into a bank before. Nu is the first. Which makes Mexico arguably the most competitive digital banking market anywhere right now. Revolut launched its first bank outside Europe there in January. Plata (built by the ex-Tinkoff team, now a $3.1bn valuation) got its de novo license in February. Ualá and Klar bought theirs. Santander launched Openbank. Hey Banco spun out of Banregio. Every region's neobank war produced a winner, and those winners have never fought each other on the same turf. Brazil's champion, Europe's most aggressive fintech, the Tinkoff diaspora, Argentina's Ualá. Mexico is the first market where all the playbooks collide. Nubank enters that fight with 15 million customers, a deposit book, and breakeven economics. What I wonder about is the next product. A bank license unlocks nómina, and payroll is how Mexican banks lock customers in. Whoever wins payroll wins the market. And Nubank's take on that could be fascinating.

What if Your Neural Network Was Forced to Obey Physics? Physics-Informed Neural Networks (PINNs) are neural networks trained to satisfy a differential equation by building the PDE residual directly into the loss. They emerged from a very practical problem...classical PDE pipelines can be brilliant, but they often demand heavy discretization work (meshes, stencils, stability tuning), and the method you build is usually tied to one geometry and one solver setup. A PINN flips the workflow by representing the solution itself as a smooth function uᵩ(x,t) and enforcing the physics everywhere you choose to sample the domain. People often meet PINNs in the least helpful way...via a flashy solution plot, and almost no explanation of what was enforced to get it. In this series we keep the enforcement visible. We pick a differential equation, represent the unknown solution as a flexible function, measure how well that function satisfies the equation across the domain, and train it to reduce that mismatch everywhere we sample. A normal neural net learns from labels...you give it inputs and target outputs. A PINN learns from a differential equation...you give it inputs (x,t) and it gets punished whenever its output fails the PDE. By punish we mean that the loss increases when the mismatch is large we reward it if the loss decreases as the mismatch gets smaller. The network isn’t replacing physics, it’s becoming a flexible function that is forced to satisfy the same calculus you’d impose on any candidate solution. The math breakdown: We start with a PDE we want to solve on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown function u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we would have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or a known initial condition. The training objective is just a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are the collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is that the derivatives inside rᵩ are computed by automatic differentiation ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … So we can differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. This is the whole idea behind PINNs. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold...a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t): each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large (color encodes the sign). As training runs, those threads go slack across the domain not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs# #PhysicsInformedNeuralNetworks# #ScientificMachineLearning# #PDE# #DifferentialEquations# #Optimization# #MachineLearning# #AppliedMath# #ComputationalPhysics#

Nu checked the entry paths across five of Dr4v3n's contacts. No repeated node sequences, no repeated timing intervals. The variation was too consistent for randomness and too perfect for improvisation. The pattern was deliberate. @FinalbosuX

NU Silver Arrows Radio Show, Episode 3: Montreal Unpacked Go behind the scenes of the Canadian Grand Prix as the Mercedes-AMG PETRONAS F1 Team breaks down a weekend of highs, lows, and everything in between. Watch Episode 3 now on the team’s YouTube channel

Nu Holdings - Q1 2026 Earnings $NU 11.7 [-9.5% AH] ⚪ Revenue: $5.0B (Est: $5.0B) ✅ Adj. EPS: $0.21 (Est: $0.19) Additional Metrics: Net Income: $871M [+41% YoY] ROE: 29% Net Interest Income: $3.25B [+12% QoQ] Total Customers: 135M+

AT Nu Udra is really fun!! Once you know what to do its very great hunt Btw for lance, the charge counter timing vs the nuke attack is when he dashes backwards with his fire, release the counter immidietly when he slides away and you can nullify the entire thing ☺️

$CELH Alani Nu's share of TikTok Energy Drinks Discussion rose significantly, nearly up 5pp to over 30% market share, after a string of new flavors including Cotton Candy, Voodoo Vanilla, and Witch's Brew gained consumer attention.