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Indie Game Development with AI

Article URL: https://www.sandromaglione.com/articles/indie-game-development-with-ai Comments URL: https://news.ycombinator.com/item?id=50432523 Points: 22 # Comments: 16

Show HN: Neural Network Formal Verification

Article URL: https://github.com/giovanni-norbedo/awesome-nn-formal-verification Comments URL: https://news.ycombinator.com/item?id=50412710 Points: 1 # Comments: 0

Show HN: Nino – Fastest C# serializer, safe on untrusted input, also TS/Go/Rust

Nino is a source-generated binary serializer for C#, and as far as we know the fastest one. In the same BenchmarkDotNet run on GitHub runners against MemoryPack 1.21.4, it serializes classes and polymorphic types 3.5–7.2x faster and deserializes them 1.7–2.1x faster (arrays of unmanaged structs are on par). Deserializing into existing objects allocates nothing. Reproduce: https://nino.xgamedev.net/en/docs/v4/benchmarksIt is built to read bytes you do not trust: lengths are checked before allocating, depth is bounded, hash flooding is limited, and the read paths are fuzzed in CI.The bytes are a spec, not a memory dump, so CoreCLR, Mono, IL2CPP and 32-bit hosts write the same bytes, and runtimes for TypeScript, Go and Rust pass the same conformance corpus.Trade-offs: positional by default (VersionTolerant or Keyed for stored data), a 4-byte type id on polymorphic values, no System.Type, needs C# 9 and Roslyn 4.3+.Docs: https://nino.xgamedev.net Comments URL: https://news.ycombinator.com/item?id=50413601 Points: 1 # Comments: 0

Amazon QuickSight’s layered maps now support GeoJSON point and line features, enabling the visualization of locations and routes together.

Amazon QuickSight’s layered maps now support the GeoJSON formats for Point and LineString geometries, enabling users to overlay store locations, delivery routes, or flight trajectories on the same map. This capability requires no third-party services, but it does demand that GeoJSON files adhere to the proper structure, with matching keys and static updates.

Timeline and related coverage

Introducing Qwen2-Math

GITHUB HUGGING FACE MODELSCOPE DISCORD 🚨 This model mainly supports English. We will release bilingual (English and Chinese) math models soon. Introduction Over the past year, we have dedicated significant effort to researching and enhancing the reasoning capabilities of large language models, with a particular focus on their ability to solve arithmetic and mathematical problems. Today, we are delighted to introduce a series of math-specific large language models of our Qwen2 series, Qwen2-Math and Qwen2-Math-Instruct-1.