NOV., 2022: My new book, Learn Functional Programming The Fast Way, is currently an Amazon Java and functional programming #1 new release. The book is now available in three formats: PDF Format $10 on Gumroad.com Paperback $30 on Amazon Kindle $10 on Amazon
Agentic Commerce and Payments
Artificial Intelligence
Terraform
AI Agents
Homelab & Self-Hosting
Search Engines
Network Security
Generative AI
Computational Complexity
Artificial Intelligence
AI Agents
Artificial Intelligence
xAI
Database Internals
Computer Vision
Large Language Models
Data checksums are one of those Postgres features that, when they are doing their job, are easily forgotten. They sit quietly in the header of every data page as a small integer fingerprint, forever waiting to thwart the threat of cosmic rays or errant hardware failures. Most clusters run from cradle to grave and never trip a single one.For years, that decision was etched in stone at the time of database initialization. It wasn't until version 12 that Postgres introduced the pg_checksums util...
VPN and Tunneling
AI Coding Tools
Network Security
Startups and Venture
Linux Kernel
Artificial Intelligence
Compilers
WebGPU
Startups and Venture
Open Source
For PostgreSQL administrators, DBAs, SREs, and platform teams, understanding how backup data moves through a system is just as important as knowing when a backup completed successfully. Questions about repository layout, WAL handling, metadata, integrity, and recovery usually surface when troubleshooting, validating a backup strategy, or preparing for recovery. The Storage and Recovery Guide is written to answer those questions Rather than focusing on commands or configuration, it follo...
AGI and AI Safety
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. The UK’s generational tobacco ban might not work. I’m supporting it anyway. —Jessica Hamzelou As the parent of two little girls, I often think about how their childhood is different from…
Agentic Commerce and Payments
As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking. That was not the prevailing sentiment when I was young.…
This week, we discuss the Supreme Court, the private jet, and AI on the TV.
Unlearned parameters exhibit a correlation with retrained parameters via contraction mapping that facilitates the approximation of unlearned parameters with minimal gradient reliance.
Yesterday, I had the pleasure of presenting at the Postgres User Group Estonia, and that was a delightful experience! Many thanks to Ervin Weber, who literally spent three years trying to make it happen. I was happy to give back to one of my favorite places in the world – the city of Tallinn. I was a little bit hesitant when Ervin indicated his preference to listen to my pg_acm talk. I thought that this talk was often viewed as “too specialized”, “niche,” and not interesting enou...
This article describes how Allegro built “Commander,” a keyboard-first ⌘+K command palette designed to instantly search and manage over 150,000 entities within Allegro’s developer portal, built on Spotify’s Backstage framework. It highlights the tool’s underlying stack-based mini-router architecture, which combines advanced TypeScript and Zod for zero-boilerplate type safety alongside client-side caching for instant performance.
Ryan welcomes Benny Chen, co-founder of Fireworks AI, to the show to explore what actually makes an AI application good or not, how to balance qualitative signals with quantitative metrics when evaluating AI, and how open-source eval protocols and community efforts are setting the standard for AI evaluation.��...
Anthropic
Computer Networks
RAG
arXiv:2607.01244v1 Announce Type: new Abstract: The growing number and complexity of technical regulations represent an important challenge for all professionals in regulated industries. This paper describes a case study, from design to deployment, of building a Retrieval-Augmented Generation system for the consultation of complex technical regulations in the railway domain. Although developed for the railway sector, this testimony of an industrial experience is of particular value for techn...
RAG
arXiv:2607.01243v1 Announce Type: new Abstract: The rapid growth of scientific publications makes it increasingly difficult to track and synthesize research progress. While Large Language Models (LLMs) can support automated survey generation, existing methods retrieve unstructured data and require models to infer conceptual, methodological, and taxonomic relations from raw text at generation time. We introduce STRUCTSURVEY, a hierarchical multi-agent framework that shifts structural reasonin...
Large Language Models
arXiv:2607.01242v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference m...
RAG
arXiv:2607.01241v1 Announce Type: new Abstract: Existing prompt compression methods treat text as flat token sequences, failing to capture the distributed nature of important information, which is often spread across multiple locations and connected through both local syntactic dependencies and global semantic relations. Such relational structure is naturally represented as a graph, where tokens or sentences become nodes and their dependencies become edges. To this end, we propose RAGP, whic...
LLM Evaluation
arXiv:2607.01240v1 Announce Type: new Abstract: Count-based F1 is widely used as a proxy for LLM error-detection quality, but this paper shows that it can rise dramatically without a corresponding improvement in span localization, a gap termed F1 Inflation. The paper introduces ErrorBench, a controlled stress-test protocol for prompt-induced count distortion. ErrorBench evaluates six contemporary LLMs under five prompt conditions over 4,290 responses from 143 CoNLL-2014 passages. Under CoNLL...
Prompt Engineering
arXiv:2607.01239v1 Announce Type: new Abstract: Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural mechanism: BPE tokenization fragments safety-critical words into sub-word pieces, and the three public alignment datasets we surveyed contain no intentionally fragmented inputs. The mechanism is a chain, tested end-to-end on five model families (Qwen-3-4B, Qwen-2.5-7B, Gemma-3-4B, Llama-3.1-8B, M...
Generative AI
arXiv:2607.01238v1 Announce Type: new Abstract: Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapping between text and acoustics, they rely on grapheme-to-phoneme (G2P) systems that fail to capture speaker-specific acoustic variation. Prior work demonstrates that grapheme-based models outperform phoneme-based systems at scale, but not in low-resource settings. In this paper, we propose SPARC...