🐍 Using UUIDv7 in Python, Django, and PostgreSQL
A hands-on resource for teams upgrading to PostgreSQL 18 or modernizing Django backends, emphasizing real-world UUIDv7 use cases and security implications.
A practical guide to implementing UUIDv7 in Python 3.14, Django 5.2, and PostgreSQL 18. It explains why UUIDv7 improves upon UUIDv4 with better index locality and predictable ordering, provides code samples for both backend and database integration, and discusses when to generate UUIDs at the app or DB layer. The article also introduces UUIDv47 for timestamp-masked identifiers in public APIs.
🔗 Read more 🔗
⚙️ Writing Type-Safe Generics in C
An insightful exploration of C metaprogramming that brings modern, type-safe patterns to low-level systems programming.
A deep tutorial on implementing generic programming in C using macros and header instantiation. It shows how to achieve type safety without duplication, avoid type erasure, and keep code clean. Includes practical examples for creating generic data structures like vectors and handling compilation challenges.
🔗 Read more 🔗
🧠 Smoothsort Demystified: Understanding Dijkstra’s Adaptive Sort
A masterful explanation of one of Dijkstra’s most elegant and underappreciated sorting algorithms, revealing both its theory and engineering elegance.
An in-depth look at Dijkstra’s Smoothsort algorithm—an adaptive sorting method that approaches linear time on partially sorted data while using only O(1) extra space via Leonardo heaps. The article explains its mathematical logic, implementation details, and performance compared to heapsort and mergesort.
🔗 Read more 🔗
🔍 Show HN: Epstein Files Organized and Searchable
🔗 Read more 🔗
🤖 Structured Outputs Arrive on the Claude Developer Platform
A major reliability boost for AI developers, bridging the flexibility of LLMs with predictable, schema-safe behavior for production systems.
Anthropic introduces structured outputs for the Claude Developer Platform, enabling models to produce responses that strictly follow predefined JSON schemas. This feature improves reliability for AI agents and reduces parsing complexity, supporting enterprise-grade stability for LLM-based applications.
🔗 Read more 🔗