Category: Blog

  • Topics Everyone Is Talking About No307

    Rusts New v0 Symbol Mangling Explained • Microsoft Copilot AI Lands on LG TVsand Cant Be Removed • Should AI That Replaces Humans Also Pay Taxes? • The Gorman Paradox: Why Dont We See AI-Built Apps Yet? • The Cat Gap: When Felines Vanished from North America

  • Topics Everyone Is Talking About No306

    AI and the Ironies of Automation Part 2 • 2002: Last.fm and Audioscrobbler Pioneered the Social Web • Kimi K2 1T Model Runs on Dual 512GB M3 Ultras • TOON: Token-Oriented Object Notation for AI Data • Jubilant: Python Subprocess Meets Go Codegen

  • Expert: designing ethical governance frameworks for AI

    Artificial intelligence has moved from research labs into core business infrastructure, but governance has not kept pace. Designing ethical governance frameworks for AI requires blending technical understanding with organizational accountability, ensuring systems remain transparent, fair, and controllable. This post dives deep into the engineering, policy, and design principles behind AI governance in 2025 and beyond.

  • Empirical: Parquet vs ORC compression benchmarks

    Parquet and ORC are the heavyweights of columnar storage in modern data engineering, each designed for high-performance analytics on massive datasets. In this post, we empirically benchmark both formats under post-2024 workloads, comparing compression ratios, read/write throughput, CPU utilization, and query latency across common engines like Spark, Trino, and DuckDB. The results shed light on…

  • Tools: aiohttp and anyio for async workflows

    Asynchronous programming in Python has evolved from an experimental niche to a production-grade requirement. Libraries like aiohttp and anyio have matured into indispensable tools for handling high-concurrency workloads. This article explores how these frameworks integrate into modern async workflows, comparing their use cases, performance trade-offs, and integration with today’s most popular Python ecosystems.

  • Intro to dimensionality reduction

    Dimensionality reduction helps simplify complex datasets by reducing features while retaining essential information. This post introduces the fundamentals of PCA and other popular techniques like UMAP and t-SNE, explaining their mathematical foundations, real-world applications, and the latest tools driving high-performance data analysis in 2025.

  • Topics Everyone Is Talking About No305

    Ask HN: What Are You Working On? December 2025 • Elevated Errors Across Many Models • GraphQL: The Enterprise Honeymoon Is Over • Claude CLI Deleted My Home Directory and Wiped My Mac • A Distraction-Free Writing Environment…

  • Topics Everyone Is Talking About No304

    Building JustHTML with Coding Agents • Accelerating Double-to-String Conversion • A Non-Scientific Guide to Post-Quantum Cryptography Security • Myna v2.0.0 Beta Adds APL Support and Style Variants • Baseline: Operation-Based Evolution and Versioning of Data

  • Topics Everyone Is Talking About No303

    What Exactly Is a Build System? • Stop Writing If Statements for Your CLI Flags • Editors Should Offer an Opt-In for Less Assistance • Using nvi as a Minimal and Fast Text Editor • VPN Location Claims Often Dont Match Reality…

  • Topics Everyone Is Talking About No302

    Want to sway an election? Heres how much fake online accounts cost • Solar power goes 247 as battery costs plummet • I fed 24 years of my blog posts to a Markov model