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What is Predictive Software Quality? Software Operations in the AI Era

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Enterprise engineering teams face a widening gap between speed and reliability. Codebases are sprawling, AI now generates a significant share of code, and release cycles move faster than QA can keep up. The backlog is longer than ever, tests fail to find the most challenging edge-cases and firefighting drains time from innovation. Our systems and processes of the past can’t close this gap. Predictive software quality (PSQ), a new, AI-powered approach to operating software reliably, does. By simulating changes, […]

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Google tops OpenAI's math breakthrough — 9 to 1

Google tops OpenAI’s math breakthrough — 9 to 1

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Read Online | Sign Up | Advertise Good morning, {{ first_name | AI enthusiasts }}. Last week, OpenAI made headlines after announcing its AI cracked an 80-year-old mathematics problem. Turns out, it wasn’t the only one. Google DeepMind quietly did them eight (not one) better. The company’s AlphaProof Nexus autonomously solved nine open Erdős problems — considered some of the hardest unsolved questions in math — at a cost of a few hundred dollars per problem. In today’s […]

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When the Gravity Gates Opened at Windy Corner

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:::info Astounding Stories of Super-Science May 2001, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. A ROOM WITH A VIEW – Chapter VIII – Medieval Astounding Stories of Super-Science May 2001: A ROOM WITH A VIEW – Chapter VIII – Medieval By E. M. Forster ::: The drawing-room curtains at Windy Corner had been pulled to meet, for the carpet was new and deserved protection from […]

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New Research on Cloud Database Cost and Migration Trends

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Good enough until it isn’t: the database complacency trap A database is like a water heater. When all is well, it just does its job in the background. You don’t fantasize about replacing it or envy the one your friend just got. Really, you don’t even think about it — until something goes awry. But new research reveals a key difference: With databases, the problems don’t blindside you. Some 38% of technology leaders worry that their current database […]

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Europa no necesita cambiar de amo: necesita dejar de quemar cosas

Europa no necesita cambiar de amo: necesita dejar de quemar cosas

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Hay una forma bastante sencilla de entender buena parte de la geopolítica de los dos últimos siglos: seguir el rastro de aquello que se quema. Carbón, petróleo, gas. Quien controla los combustibles fósiles controla rutas, precios, alianzas, guerras, inflación y política exterior. Europa creyó durante años que había encontrado una solución razonable: comprar gas barato a Rusia mediante gasoductos que garantizaban suministro estable para su industria. La teoría era que la interdependencia económica haría imposible el conflicto. Hasta […]

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Token Waste: The Silent Tax on Every AI Team

Token Waste: The Silent Tax on Every AI Team

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Token Waste: The Silent Tax on Every AI Tools ChatGPT, Claude, Gemini — all three charge per token. All three are silently inflated by how most people write prompts. Here’s the research, the real cost, and a free tool that fixes it. What every major AI charges per million tokens: Sources: Anthropic docs, Google Gemini API pricing page, published pricing guides. Prices as of May 2026. The AI pricing model is deceptively simple: you pay per token. What most engineers don’t realize is that […]

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Sliding Windows Forget: Why Long-Running LLM Apps Need Memory Policy

Sliding Windows Forget: Why Long-Running LLM Apps Need Memory Policy

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Most long-running LLM failures are not pure reasoning failures. They are state-selection failures: the next model call gets incomplete, stale, or irrelevant context. In short chats, appending recent messages often works. In persistent sessions, that breaks down because durable facts vanish, stale updates return, and routine traffic consumes the prompt budget. The real question becomes: Which pieces of prior state deserve to be in the next model call? I built LLM-Context-Optimization-Engine to explore that question. It is a prototype that compares […]

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Coupled Training with Privileged Information and Unlabeled Data

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arXiv:2605.23268v1 Announce Type: new Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not be available when the model is deployed. A common strategy is to first train a model that uses all training information, then use its predictions on unlabeled examples to train a second model that only uses the inputs available at test time. However, when the extra training-only information is weak […]

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Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models

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arXiv:2605.23145v1 Announce Type: new Abstract: Individual fairness, the notion that “similar individuals should be treated similarly,” provides a strong and flexible fairness guarantee for algorithmic decision makers. However, a barrier to implementing individual fairness in practice is the difficulty of learning the similarity metric over individuals. In this work, we present an algorithm for learning a Mahalanobis similarity metric from triplet queries of the form “is individual $i$ more similar to individual $j$ or $k$?” We work in […]

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