Kore - Binary File Format
50x faster than JSON, 50-70% smaller. 6 language bindings.
What it does
Binary format for modern data systems. Parse 100MB in 50ms (vs JSON 3000ms). 50-70% smaller files. Type-safe: Python, Java, JS, Go, C#, Ruby. Production benchmarks: 20x faster logs, 64% size reduction. Zero deps. VS Code extension included.
Does a similar job
all alternatives →- AFApache Fory Rust – 10-20x faster serialization than JSON/ProtobufOct 2025 · fory.apache.org · ▲67
Serialization framework with some interesting numbers: 10-20x faster on nested objects than json/protobuf. Technical approach: compile-time codegen (no reflection), compact binary protocol with meta-packing, little-endian layout optimized for modern CPUs. Unique features that other fast serializers don't have: - Cross-language without IDL files (Rust ↔ Python/Java/Go) - Trait object serialization (Box) - Automatic circular reference handling - Schema evolution without coordination Happy to discuss design trade-offs. Benchmarks:…
- JAJSON as Fast as Binary2015 · hperadin.github.io · ▲12
- JFJSON For You – Visualize JSON in graph or table views2024 · github.com · ▲211
After two years of improvement, I think it's time to share it with you all. Here’s a quick overview: - Common features include validation, formatting, minification, and more. - Visualize JSON in a graph or table view. - Structured comparison with fallback to text comparison. - Navigate though JSON using JSON pointer. - Supports jq. Would love to hear the community's questions, thoughts and comments!


- UJuDSV.js – A faster CSV parser2023 · github.com · ▲67
Hey folks! I know CSV parsers (especially in JS) aren't terribly exciting and someone writes a "better" one every week. I'm in the middle of my parental leave, and this was a project that came out of me looking for the fastest/smallest CSV parser. It all started so innocently, and then turned into a benchmark-validation-athon; the library itself took ~2 weeks to write, but the performance comparisons took another ~4 weeks (on and off). The benchmarks were a huge effort, but I think they are the most thorough to date, both in breadth and in depth, so hopefully you find them useful:…
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- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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