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Show HN: Record, branch, and compare command runs in stdlib-only Python

Hi, this is a package I developed for keeping track of short toy-model development cycles and explaining what changed between runs.The core idea: commands run through the package, and their declared parameters (or injected parameters with --substitute) and outcomes go into a dependency-free ledger stored in the project. Because that ledger lives in the project directory, it has worked well for passing context between different agents. I’ve also found some use for it in CI work. Comments URL: https://news.ycombinator.com/item?id=50417782 Points: 3 # 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

Faster Retrieval for Better, More Efficient RAG

Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query. As the number of keys grows to millions and vector dimensions reach thousands, the computational cost of running an exhaustive…

Will the real Anthropic please stand up

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. After weeks of frenzied speculation, Anthropic has filed its IPO prospectus. The 335-page document (100 pages or so are focused on risks) lays out the company’s unique view of the technology it…

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chDB Durable Layer for agent memory

The chDB Durable Layer keeps agent memory fast to query locally while making its analytical state recoverable across laptops, CI jobs, and short-lived sandboxes.