Machine-Learning(ML), CortexVirtual Machine(CVM)
ZKML trains RNN language sequence prediction model;
ZKML subgraph interface storage is optimized, and parent and child traces share node pointers (hash);
ZKML investigates accuracy parameters and tests the impact of parameters on the quantitative model;
ZKML Make config class for quantization procedure(with statement);
The test ran boojum's sha256 circuit. It succeeded (very quickly) when the preimage < 2kb, but would get stuck at 4kb and 8kb;
L1Client is divided into querying client and signing client to interact with L1 and fix errors in other modules caused by reconstruction;
add the generic support for RocksDB storage;
Some chore refactor: migrate workspace dependencies, use basic_type::web3 as a replacement for direct ethers-rs;
The circuit benchmark results are very poor (release mode), you need to consider switching to boojum (fri) or halo2;
core/state: move UpdateContractCode before the trie hash is computedore/state: move UpdateContractCode before the trie hash is computed;
p2p: move ping handling into pingLoop goroutine (#27887);
all: remove trailing whitespace (#27741);
build: upgrade -dlgo version to Go 1.20.7 (#27835);
cmd/bootnode: fix timer leak (#27754);
R&D the current progress of ZKML;
R&D the technical progress of the current MPC wallet;
R&D the cross-chain bridge ecosystem;
Collaboration with several NFT Artists;
Continue to monitor popular market ventures and examine project logic and Web3, NFT & Defi Tokenomic models
About Cortex 😇
Cortex’s main mission is to provide state-of-the-art machine-learning models on the blockchain in which users can infer using smart contracts on the Cortex blockchain. One of Cortex’s goals also includes implementing a machine-learning platform that allows users to post tasks on the platform, and submit AI DApps (Artificial Intelligence Decentralized Applications).
Cortex is the only public blockchain that allows the execution of nontrivial AI algorithms on the blockchain. MainNet has launched. Go build!
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