Europe builds deep. Korea builds fast. General argues neither is enough alone

The US Is Winning the AI Race

WhatsApp adds an incognito mode in Meta AI chats

Introducing the 6 stages at TechCrunch Disrupt 2026 — built for today’s tougher startup market

Anthropic now has more business customers than OpenAI, according to Ramp data

Where AI in Analytics Is Heading

Much of the architecture for investigation-grade tooling already exists, but in pieces.

Designing a Custom Query Language for Non-Technical Analysts

Poppy debuts a proactive AI assistant to help organize your digital life

Securitybaseline.eu

Why I'm leaving GitHub for Forgejo

Reel Friends: Building Social Discovery that Scales to Billions

On its face the new Friend Bubbles feature looks simple enough. It highlights Reels your friends have watched and reacted to. But sometimes the features that seem the most straightforward require the deepest engineering work. On this episode of the Meta Tech Podcast, Pascal Hartig chats with Subasree and Joseph, two software engineers from the Facebook [...] Read More... The post Reel Friends: Building Social Discovery that Scales to Billions appeared first on Engineering at Meta.

Software Developers Say AI Is Rotting Their Brains

“It's making me dumber for sure.”

An idiot's guide to lead optimisation for proteins

Blaise v0.7.0 (alpha) – Independent of Free Pascal compiler

The Download: making drugs in orbit and NASA’s nuclear-powered spacecraft

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A plan to make drugs in orbit is going commercial A startup called Varda Space Industries is betting that the future of pharmaceuticals lies in orbit. The company has signed a…

Sovereign Tech Fund invests over €1 million in KDE software development

Using OR-Tools CP-SAT for Scheduling Problems

Adaption aims big with AutoScientist, an AI tool that helps models train themselves

Web Server on a Nintendo Wii

Cost of enum-to-string: C++26 reflection vs. the old ways

I Moved My Digital Stack to Europe

Claude Code RCE: Exploiting Deeplink Handlers via Settings Injection

Two schools of TDD explained

This article presents two approaches of how TDD can be applied. The first is the classic one where the objects are treated as black-boxes and verification is based on the objects state. The second one is focused on interactions.

Preserving Fisher-Price Pixter

Gabriele Bartolini: CNPG Recipe 24 - Migrating from Crunchy PGO to PostgreSQL 18 with CloudNativePG

A step-by-step guide to migrating a PostgreSQL 17 cluster managed by Crunchy PGO v6 to PostgreSQL 18 under CloudNativePG. Two paths are covered: a fully declarative offline migration using CloudNativePG’s built-in pg_dump import, and an online migration using native PostgreSQL logical replication for a near-zero-downtime cutover.

A plan to make drugs in orbit is going commercial

Varda Space Industries, a startup that’s been pitching its ability to perform drug experiments in space, says it has signed up the pharmaceutical company United Therapeutics in what may be remembered as a notable step toward in-orbit manufacturing. The idea of building things in outer space for use on Earth has so far been explored…

The Boring Part of Bell Labs

Dart Live, a compiler, VM, analyzer and hot reload on the web via Wasm

"I applied to be pope"

Quack: The DuckDB Client-Server Protocol

submitted by /u/TheCrush0r [link] [comments]

European governments: 3.000 tracking sites, 1.000 phpMyAdmins, and 99% poorly

AluminiumOS, by Google: Android Reimagined for the Desktop

One Startup Is Gambling. Ten Is Mathematics

I made rust's cargo copy but for CPP

Cornelia Biacsics: Contributions for week 18, 2026

PGConf Belgium took place on 5 May 2026, organized by Wim Bertels, An Vercammen, and Grégory Gioffredi , who served also at the talk selection team. Speaker: Jan Karremans Boriss Mejias Dwarka Rao Emrah Becer Robert Treat Franck Pachot Mohsin Ejaz Afroditi Loukidou Gianni Ciolli Bruce Momijan Matt Cornillon Xavier Fisher Thijs Lemmens Josef Machytka Claire Giordano and Aaron Wislang hosted and published a new podcast episode on May 6, 2026 From MemSQL to HorizonDB, an engineer's journey ...

We tested super-resolution pre-filter for LPR OCR. It did nothing

YellowKey Bitlocker Bypass Vulnerability

DMARC Fail: 7 Causes and How to Fix Each

Deterministic Fully-Static Whole-Binary Translation Without Heuristics

What can singing mice say about human speech?

You Shipped It Fast. But Did You Ship It Right?

Why AI-accelerated teams keep breaking production — and what the ones that don't are doing differently

How Braze’s CTO is rethinking engineering for the agentic area

Jon Hyman, co-founder and CTO of Braze, shares how he's led the company's engineering organization over nearly 15 years of growth — and how they transformed into an AI-first team in just a few months.

LEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection

arXiv:2605.10980v1 Announce Type: new Abstract: Diffusion Language Models (dLLMs) have garnered significant attention for their potential in highly parallel processing. The parallel capabilities of existing dLLMs stem from the assumption of conditional independence at high confidence levels, which ensures negligible discrepancy between the marginal and joint distributions. However, the stringent confidence thresholds required to preserve accuracy severely constrain the scalability of paralle...

PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks

arXiv:2605.10977v1 Announce Type: new Abstract: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddi...

Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation

arXiv:2605.10975v1 Announce Type: new Abstract: Graphs with heterophily, where adjacent nodes carry different labels, are prevalent in real-world applications, from social networks to molecular interactions. However, existing spectral Graph Neural Network (GNN) approaches tailored for heterophilous graph classification suffer from hub-dominated (node with large degree) aggregation and oversmoothing, as their suboptimal polynomial filters introduce approximation errors and blend distant signa...

Vertex-Softmax: Tight Transformer Verification via Exact Softmax Optimization

arXiv:2605.10974v1 Announce Type: new Abstract: Certified verification of transformer attention requires bounding the softmax function over interval constraints on the pre-softmax scores. Existing verifiers relax softmax ndependently of the downstream objective, leaving avoidable slack. We prove that the exact optimum of this score-box problem is attained at a vertex of the constraint box, and establish a threshold structure theorem showing that, after sorting the objective coefficients, the...

Rotation-Preserving Supervised Fine-Tuning

arXiv:2605.10973v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) improves in-domain performance but can degrade out-of-domain (OOD) generalization. Prior work suggests that this degradation is related to changes in dominant singular subspaces of pretrained weight matrices. However, directly identifying loss-sensitive directions with Hessian or Fisher information is computationally expensive at LLM scale. In this work, we propose preserving projected rotations in pretrained singul...

Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models

arXiv:2605.10971v1 Announce Type: new Abstract: Discrete diffusion language models (DLMs) generate text by iteratively denoising all positions in parallel, offering an alternative to autoregressive models. Controlled generation methods for DLMs, imported from autoregressive models, apply uniform intervention at every denoising steps. We show this uniform schedule degrades quality, and the damage compounds when multiple attributes are steered jointly. To diagnose the failure, we train sparse ...

MMTB: Evaluating Terminal Agents on Multimedia-File Tasks

arXiv:2605.10966v1 Announce Type: new Abstract: Terminals provide a powerful interface for AI agents by exposing diverse tools for automating complex workflows, yet existing terminal-agent benchmarks largely focus on tasks grounded in text, code, and structured files. However, many real-world workflows require practitioners to work directly with audio and video files. Working with such multimedia files calls for terminal agents not only to understand multimedia content, but also to convert a...

Mechanism Design for Quality-Preserving LLM Advertising

arXiv:2605.10964v1 Announce Type: new Abstract: Embedding advertisements into large language model (LLM) outputs introduces a fundamental tension: revenue optimization can distort content and degrade user experience. Existing approaches largely ignore this trade-off, often forcing irrelevant ads into responses. We propose a quality-preserving auction framework that explicitly integrates content fidelity into the mechanism design. Built on retrieval-augmented generation (RAG), our approach tr...

QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization

arXiv:2605.10959v1 Announce Type: new Abstract: There is currently no unified metric for evaluating the efficiency of quantized neural networks. We propose QuIDE, built around the Intelligence Index I = (C x P)/log_2(T+1), which collapses the compression-accuracy-latency trade-off into a single score. Experiments across six settings -- SimpleCNN (MNIST, CIFAR), ResNet-18 (ImageNet-1K), and Llama-3-8B -- show a task-dependent Pareto Knee. 4-bit quantization is optimal for MNIST and large LLMs...

Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation

arXiv:2605.10947v1 Announce Type: new Abstract: EEG microstate analysis segments continuous brain electrical activity into brief, quasi-stable topographic configurations that reflect discrete functional brain states. Conventional approaches such as Modified K-Means operate directly in electrode space with hard assignment, offering no learned latent representation, no generative decoder, and no mechanism to decode latent configurations into verifiable scalp topographies, limiting both model t...

Zero-native – Build native desktop apps with web UI

The vi family

Referer Reality

AI shouldn't have a meter. Unlimited tokens. Forever

You Don't Look Like a Gamer: On Toxicity, Gatekeeping, & Women Who Share Gaming

Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases

arXiv:2605.09472v1 Announce Type: new Abstract: Positional encoding in transformers is commonly implemented through positional embeddings, attention masks, or bias terms, but formal connections between these mechanisms remain limited. We study attention with positional bias through the lens of locality-sensitive hashing (LSH), focusing on Attention with Linear Biases (ALiBi). We show that the ALiBi bias matrix is the expectation of contiguous block-diagonal binary masks induced by a ``positi...

FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media

arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent in digital communication, have potential as compact indicators of investor sentiment, which can be critical for predicting market trends. Our study examines whether emojis alone can serve as reliable proxies for financial sentiment and how they compare with traditional text-based analysis. We co...

Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition

arXiv:2605.09468v1 Announce Type: new Abstract: Multimodal Intent Recognition (MIR) aims to understand complex user intentions by leveraging text, video, and audio signals. However, existing approaches face two key challenges: (1) overlooking intricate cross-modal interactions for distinguishing consistent and inconsistent cues, and (2) ineffectively modeling multimodal conflicts, leading to semantic cancellation. To address these, we propose a novel Cognitive Dual-Pathway Reasoning (CDPR) f...

The Impossibility of Simultaneous Time and I/O Optimality for The Planar Maxima and Convex Hull Problems

arXiv:2605.09464v1 Announce Type: new Abstract: We prove that no deterministic output-sensitive algorithm for the planar convex hull and maxima problems can obtain both optimal time and I/O complexity, where the optimality is defined with respect to both the input and output sizes. This explains why the best previous algorithms achieved an optimal I/O bound at the cost of sub-optimal running time (Goodrich et al. [FOCS, 1993]). To the best of our knowledge, the impossibility of simultaneous ...

Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven

arXiv:2605.09463v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks. However, their deployment in long-context scenarios faces high computational overhead and information redundancy. While soft prompt compression has emerged as a promising way to mitigate these costs by compressing sequences into compact embeddings, existing paradigms remain fundamentally constrained by position bias: they primarily rely on learnable tok...

VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection

arXiv:2605.09461v2 Announce Type: new Abstract: Automated vulnerability detection is a fundamental task in software security, yet existing learning-based methods still struggle to capture the structural dependencies, domain-specific vulnerability knowledge, and complex program semantics required for accurate detection. Recent Large Language Models (LLMs) have shown strong code understanding ability, but directly prompting them with raw source code often leads to missed vulnerabilities or fal...

When Few Steps Are Enough: Training-Free Acceleration of Identity-Preserved Generation

arXiv:2605.09460v1 Announce Type: new Abstract: Identity-preserved image generation is typically built on many-step diffusion backbones, making personalized generation expensive at deployment time. We show that this cost is often unnecessary for identity-conditioned FLUX generation. A frozen InfuseNet identity adapter trained with dev transfers directly to the distilled schnell backbone without retraining. This two-line replacement -- changing the backbone path and disabling classifier-free ...

RAwR: Role-Aware Rewiring via Approximate Equitable Partition

arXiv:2605.09457v1 Announce Type: new Abstract: While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of GNNs often drops when prediction tasks depend on long-range interactions. These limitations are attributed to phenomena such as oversquashing, where structural bottlenecks restrict signal propagation across the network topology. To address this challenge, we introduc...

Traceway: MIT-licensed observability stack you can self-host in ~90s

Christophe Pettus: All Your GUCs in a Row: backend_flush_after

PostgreSQL's complicated relationship with the Linux page cache spawns four GUCs to manage writeback—and backend_flush_after is the conservative one.

Fc, a lossless compressor for floating-point streams

Medicare’s new payment model is built for AI, and most of the tech world has no idea

Ming Ying: ParadeDB is Officially on Render

Deploy ParadeDB on Render with one click. Full-text search, vector search, and hybrid search over Postgres — now available on your favorite cloud platform.

My graduation cap runs Rust

ParadeDB is Officially on Render

Deploy ParadeDB on Render with one click. Full-text search, vector search, and hybrid search over Postgres — now available on your favorite cloud platform.

AI load breaks GitHub – why not other vendors?

Not a Security Issue

Lanzaboote – NixOS Secure Boot

EFF to 4th Circuit: Electronic Device Searches at the Border Require a Warrant

Bullshit Machines

The Cloudflare 20% Cut and the Rise of "Layoff Washing"

Show HN: I submitted 316 AI-generated PRs to open source

Meta employees protest against mouse tracking tech at US offices