February 2026

ChatGPT grew from zero to 100 million users in two months. Today it has over 800 million. That makes it one of the fastest-growing applications in history. And their database? PostgreSQL. The same 30-year-old relational database running your side project. Most engineering teams would have panicked at 10 million users and started a multi-year migration to Cassandra, DynamoDB, or some other NoSQL system. OpenAI did not. They scaled PostgreSQL itself. And the techniques they used are not exotic […]

Ver mais

TL;DR: Is our reality a simulation? Maybe. But if you look at the oldest documentation we have—specifically the Book of Job—it looks less like a “sim” and more like a chaotic production environment where the Lead Architect is running stress tests on the user base. Here is why your suffering might just be a feature, not a bug. I’ve been thinking a lot about Simulation Theory lately. Not because I watched The Matrix again, monitoring a chaotic situation, […]

Ver mais

Recent advances in scientific machine learning (SciML) have enabled neural operators (NOs) to serve as powerful surrogates for modeling the dynamic evolution of physical systems governed by partial differential equations (PDEs). While existing approaches focus primarily on learning simulations from the target PDE, they often overlook more fundamental physical principles underlying these equations. Inspired by how numerical solvers are compatible with simulations of different settings of PDEs, we propose a multiphysics training framework that jointly learns from both […]

Ver mais

In the rapidly evolving landscape of 2026, “AI-first” is no longer a buzzword — it is an architectural requirement. For fintech institutions, the ability to automate credit decisions while maintaining strict compliance is the holy grail In this deep dive, we will build a production-grade Bank Loan Approval Workflow. We won’t just move entities from “Draft” to “Approved.” We will inject a cognitive layer into the state machine using symfony/workflow and symfony/ai-bundle. Our system will automatically score loan applications and dynamically route them: high-scoring applications get […]

Ver mais

Hiring talented people with the right attitude and right skills is not enough, they also need the right environment to thrive—are leaders in the organization only focused on business deliverables or do they also invest in their team’s learning and growth? Do they encourage people to speak up and share their disagreements or do they shut down voices that differ with their own perspective? Are people in the team clear on their priorities and aligned with a long-term […]

Ver mais

We demonstrate that modern machine-learning methods can autonomously reconstruct several flagship analytic structures in scattering amplitudes directly from numerical on-shell data. In particular, we show that the Kawai–Lewellen–Tye (KLT) relations can be rediscovered using symbolic regression applied to colour-ordered Yang–Mills amplitudes with Mandelstam invariants as input features. Using standard feature-selection techniques, specifically column-pivoted QR factorisation, we simultaneously recover the Kleiss–Kuijf and Bern–Carrasco–Johansson (BCJ) relations, identifying a minimal basis of partial amplitudes without any group-theoretic input. We obtain the […]

Ver mais

Federated learning (FL) enables distributed model training across edge devices while preserving data locality. This decentralized approach has emerged as a promising solution for collaborative learning on sensitive user data, effectively addressing the longstanding privacy concerns inherent in centralized systems. However, the decentralized nature of FL exposes new security vulnerabilities, especially backdoor attacks that threaten model integrity. To investigate this critical concern, this paper presents the Layer Smoothing Attack (LSA), a novel backdoor attack that exploits layer-specific vulnerabilities […]

Ver mais

Learning from electronic health records (EHRs) time series is challenging due to irregular sam- pling, heterogeneous missingness, and the resulting sparsity of observations. Prior self-supervised meth- ods either impute before learning, represent missingness through a dedicated input signal, or optimize solely for imputation, reducing their capacity to efficiently learn representations that support clinical downstream tasks. We propose the Augmented-Intrinsic Dual-Masked Autoencoder (AID-MAE), which learns directly from incomplete time series by applying an intrinsic missing mask to represent naturally […]

Ver mais