1. 10X faster serialization than protobuf c++ 2. Shared/Circular reference tracking Support 3. Polymorphism and tagged union support 4. Schema evolution support 5. idiomatic domain object API

Flatbuffer Example:

namespace demo;

table Node {

  parent: Node (fory_weak_ref: true);

  children: [Node] (fory_ref: true);

  cached: Node (fory_ref: true, fory_thread_safe_pointer: false);

}

Protobuf Example:

syntax = "proto3";

message TreeNode {

  TreeNode parent = 1 [(fory).weak_ref = true];

  repeated TreeNode children = 2 [(fory).ref = true];

}

If you already have .proto or .fbs schemas and you want shared/circular reference support, with the Fory compiler you can keep those schemas, add a small set of Fory options, then foryc generates idiomatic native models across Fory-supported languages

With fory first-class object graph serialziation support, you can serialize object graphs without manual *_id link reconstruction or rewriting your entire schema into Fory Schema.

1. GitHub: https://github.com/apache/fory

2. Compiler docs: https://fory.apache.org/docs/compiler

Tiny Schema demo

message Customer { string id = 1; string name = 2; }

message Order {

  string order_id = 1;

  ref Customer customer = 2;

  list<string> items = 3;

}

1. Docs https://fory.apache.org/docs/compiler

2. GitHub https://github.com/apache/fory

3. Benchmarks https://github.com/apache/fory/tree/main/docs/benchmarks

Fory C++ serialization blog: https://fory.apache.org/blog/fory_cpp_blazing_fast_serializa...

This project is for running local CLI/agent loops on macOS/Linux/Windows while executing commands inside browser-hosted terminals for cloud servers. The key goal is output/exit-code parity so local automation can trust remote runs.

GitHub Repo: https://github.com/inclusionAI/asystem-awex

Awex is a weight synchronization framework between training and inference engines designed for ultimate performance, solving the core challenge of synchronizing training weight parameters to inference models in the RL workflow. It can exchange TB-scale large-scale parameter within seconds, significantly reducing RL model training latency. Main features include: Blazing synchronization performance: Full synchronization of trillion-parameter models across thousand-GPU clusters within 6 seconds, industry-leading performance; Unified model adaptation layer: Automatically handles differences in parallelism strategies between training and inference engines and tensor format/layout differences, compatible with multiple model architectures; Zero-redundancy Resharding transmission and in-place updates: Only transfers necessary shards, updates inference-side memory in place, avoiding reallocation and copy overhead; Multi-mode transmission support: Supports multiple transmission modes including NCCL, RDMA, and shared memory, fully leveraging NVLink/NVSwitch/RDMA bandwidth and reducing long-tail latency; Heterogeneous deployment compatibility: Adapts to co-located/separated modes, supports both synchronous and asynchronous RL algorithm training scenarios, with RDMA transmission mode supporting dynamic scaling of inference instances; Flexible pluggable architecture: Supports customized weight sharing and layout behavior for different models, while supporting integration of new training and inference engines. GitHub Repo: https://github.com/inclusionAI/asystem-awex