![]() ![]() Watch this space for our new package cli that allows a consistent bi-directional stream to enhance your developer workflow at speed with Codebox registries such as a private npm registry.That's exactly what the array of refs is used for. We are only touching the surface of what this potentially could do to help assist engineers to get on and focus on building features rather than repetitive tasks day to day. Security With all of this data potential advisories such as our integration with the Node Security Platform could recognize them and flag them immediately as you code. Teacher A way of helping people learn to code, drawing of knowledge across the whole of the open source eco system and your local project (in the piepline) this could assist from less experienced to senior engineers generate code based on the frameworks and coding styles your project and the wider open source community. ![]() It can also help lessen the load of repetition day to day and help alleviate RSI. Potential Use CasesĪccessibility An assistant such as this could help greatly with accessibility and helping people code with certain disabilities. These models we hope to host and have many types that can help understand and assist developers when writing code within their editor as naturally as possible. We believe this is the key in perhaps unlocking the ability to a more natural phrase for an assistant. This model is then sent the prop type name and we then get a probability of what the prop type “should” be. Once we have a huge set of sample data we then feed this into AWS Machine Learning as a multi-class classification model.ĭemo of our prediction, we send “age” and get a prediction it will be of type number: We scrape the data from the public GitHub source code to generate a model that stores prop type names and the type in which they have been set. For our example you notice we did not specify the types for the props “name, age and gender”. We for see each framework library such as ‘ ‘ ‘ etc an implementation of phrases it is aware off based on the tokenized phrases.Īfter the code is generated in parallel we also have a phase of machine learning that you can attach to each of the different parts of the code AST generated. For this we use Babel to generate the AST then output the code based on the tokenized phrase generated. So you end up with a tokenized string such as:Īfter we have this we can then move on to the next phase to then generate the syntax AST. Lexing Phase The lexing phase looks at our example phrase and starts tokenising it so that it understands the context in which it needs to then generate the relevant code. The API allows it to improve accuracy when speaking such terms that are not in the normal dictionary for a specific language. We still need to work on stemming, so apologies for the dupes o) ![]() In this case we pass in the following terms for our demo: const TERMS = export default TERMS In addition the Google Cloud Speech API allows us to feed it the relevant framework terms. Speech Recognition Quite simply we can use the Google Cloud Speech API to stream audio to recognise phrases into text form. In order to start digging into this and hooking it up so that we can generate code we need some how to start understanding the phrase in which your voice is saying. “create hello world component with props name, age and gender as a span element”Īs an example of our current API this is currently how it looks, (prone to many changes): import lexer from ' import = transform(phrase) PhasesĪs per the demo let’s work with the phrase: The above has machine learning enabled and additional error checking for React DOM elements. ![]()
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