Use Astro DB for local-first development and host the production database on Bunny.net's managed libSQL instead of Turso. Schema, seeding, remote push, and deploy, step by step. Read more ›
The main focus of machine learning (ML) is making decisions or predictions based on data. There are a number of other fields with significant overlap in technique, but difference in focus: in economics and psychology, the goal is to discover underlying causal processes and in statistics it is to find a model that fits a data set well. In those fields, the end product is a model. In machine learning, we often fit models, but as a means to the end of making good predictions or decisions. Read more ›
Recently, I’ve been fascinated by the trend of building data storage systems on object storage. Systems like TurboPuffer are all the rage in the search space right now. New storage primitives like SlateDB make creating bespoke storage systems feasible for a single developer or small team. Read more ›
Windows 11's Low Latency Profile CPU boost is now available for older PCs too. I tested it on a Lenovo ThinkCentre M700 with a 6th Gen Intel Core i3-6100, no Turbo Boost, and single-channel RAM. Here's what the CPU scheduler actually does on decade-old hardware, and whether it makes a difference. The post appeared first on Read more ›
In this post, we show you how to use EXPLAIN plans to diagnose and improve query performance in Amazon Aurora DSQL. We introduce a three-layer filter model as a practical framework for understanding where your predicates are evaluated, and walk through the architecture differences that make Aurora DSQL plans unique, the anatomy of an EXPLAIN output, access method selection, and a step-by-step query improvement workflow. Read more ›
This paper proposes a hybrid reinforcement learning–assisted distributionally robust optimization (RL–DRO) framework for robust and economically efficient energy management in interconnected multi-microgrid systems under renewable, demand, and price uncertainty. The framework integrates deep reinforcement learning to generate adaptive scheduling policies with a Wasserstein-metric distributionally robust optimization formulation that enhances robustness against probability distribution shifts ... Read more ›
Track ML experiments with MLflow in under 10 minutes — log params, metrics, and models in 3 lines of Python. Real benchmarks on sklearn and PyTorch. Read more ›
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine fault-tolerant state-machine replication (BFT-SMR) is the replicated service that totally orders client transactions before execution. While prior research has primarily focused on designing novel consensus algorithms with improved performance, recent studies have shown that further gains can be achi... Read more ›
A benchmark focusing on the performance of Postgres client libraries for Node.js, brianc/node-postgres VS porsager/postgres - nigrosimone/postgres-benchmarks Read more ›
A native macOS app for fine-tuning LLMs on Apple Silicon — fully on-device, fully open source. Read more ›
Hey LET! Hassan here, founder of Hostodo. We’re bringing a new set of KVM VPS specials in Detroit, Las Vegas, and Tampa. Matched pricing, a little extra disk, and Pure NVMe storage. We’re a no-frills infrastructure provider focused on simple, stable, and affordable virtual machines. Our platform is custom-built in-house, from billing to deployment, with instant provisioning and a modern control panel. All VMs are provisioned on nodes powered by AMD EPYC 7742 CPUs. 🟢 These prices a... Read more ›
So, you adopted event-driven architecture because your system was a rat’s nest of coupling, and events were the answer to decouple it. But now debugging is a nightmare. You have events coming in out of order. You have retries causing duplicates and multiple different side effects. Local development is a pain. It’s frustrating, right? But… Read More »Stop Blaming Event-Driven Architecture The post appeared first on <a href=" Read more ›
A machine learning-powered simulation is giving researchers a new window into the processes that create some of the universe’s heaviest elements. Where do the gold in jewelry, the uranium in nuclear fuel, and many of the universe’s heaviest elements come from? Scientists believe they are forged in some of the most violent events in the [...] Read more ›
Everyone repeats the analogy that PagedAttention is virtual memory for the KV cache. But I think the more interesting story is what comes along with it: copy-on-write, swapping, thrashing, a missing MMU, a timing side-channel that leaks other users' prompts, and new ways to optimise serving LLMs. Read more ›
Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures... Read more ›
A practical AI Infra review of Agentic AI reliability, covering a five-dimension framework, fault tolerance, recovery, observability, and hybrid architecture design. Read more ›
I remember reading about Claude playing Pokemon Red when Sonnet 3.7 was released and thinking to myself "this is the wrong way to 'solve' Pokemon". An LLM ta... Read more ›
Built on for PostgreSQL SQL and PL/pgSQL. Point your editor at it for .sql files and get diagnostics, navigation, completion, and formatting backed by real PostgreSQL grammar rather than regex heuristics. Features Diagnostics — Parse errors from tree-sitter reported as LSP diagnostics Semantic Tokens — Syntax highlighting via semantic token classification Document Symbols — Outline of DDL statements (tables, functions, views, etc.) Workspace Symbols — Search across all open files Go to Defini... Read more ›
Every time a new Java release drops, the question surfaces again in Slack channels and pull-request threads alike: “Are we still on G1? Should we switch to ZGC?” With Java 26 landing in March 2026, that conversation got a meaningful update. Two JEPs directly touched garbage collection — one gave G1 a solid throughput boost and the other … Read more ›