Machine-Learning(ML), CortexVirtual Machine(CVM)
ZKML circom adapts conv2d padding: insert pad2d op before conv2d in symbol;
Circom modified conv2d inputs & shape to adapt to the calculation of upper and lower nodes in the topology map;
Circom fixes the problem that stride=1 is hard-coded to 1, adds stride support and derivation, and requires the stride of xy to be consistent;
ZKML adds support for cvm_right_shift, cvm_clip, etc., and converts bias_add to conv operator;
ZkRollup
Add l1 and l2 signature check services (for api submission check (double verification)), tx_sender is used for api submission tx;
Implement the fee ticker for the calculation of transaction fees;
Implement the api services of accounts, block, transactions, config, fee, token, status;
Realize the circuit of block state transition, emergency exit;
Add some tool functions for zk-prover and create the main entry function of zkx_proof and exit_proof;
Define and implement some api types (api_block, api_tx, api_account..), add a simple memory cache for api query token related information;
Cortex Full Node
By combining the CTXC public chain, we can explore more possibilities of using tools in the AI field for testing;
Conduct the final stage of testing and preparation for the release of the Cortex NFT Gallery(Thalamus);
Working on Cortex NFT community & system
Continue to monitor popular market ventures and examine project logic and Web3, NFT & Defi Tokenomic models
Establish connections with several NFT artist
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!
TestNet
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