Clairvoyance - idmontie's Portfolio

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    children: "I made react-cornea to do image diffing of react components for CI/CD environments."
  });
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I made react-cornea to do image diffing of react components for CI/CD environments.


I made react-cornea to do image diffing of react components for CI/CD environments.


A testing utility for generating visual diffs of your React components.


react-cornea

/*@jsxRuntime automatic @jsxImportSource react*/
const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];
function _createMdxContent(props) {
  const _components = Object.assign({
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  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "The Clarity Hub Infer API provides a fast and intuitive way to create, manage, and deploy NLP models based on labelling utterances."
    }), "\n", _jsx(_components.p, {
      children: "This API used the Universal Sentence Encoder (USE) to create embeddings from utterances, then used cosine similarity to find the most similar utterances to a given utterance."
    })]
  });
}
function MDXContent(props = {}) {
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  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
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return {
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The Clarity Hub Infer API provides a fast and intuitive way to create, manage, and deploy NLP models based on labelling utterances.
This API used the Universal Sentence Encoder (USE) to create embeddings from utterances, then used cosine similarity to find the most similar utterances to a given utterance.


The Clarity Hub Infer API provides a fast and intuitive way to create, manage, and deploy NLP models based on labelling utterances.

This API used the Universal Sentence Encoder (USE) to create embeddings from utterances, then used cosine similarity to find the most similar utterances to a given utterance.


NLP infer API to create and label utterances.


Clarity Hub Infer API

/*@jsxRuntime automatic @jsxImportSource react*/
const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];
function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p"
  }, props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "Clairvoyance finds the best answers that have worked in the past and suggests them to teams using Intercom when answering similar questions."
    }), "\n", _jsx(_components.p, {
      children: "Clairvoyance was built on AWS using S3, SNS, SQS, RDS, PostgreSQL, MongoDB, Elasticsearch, and React. We created a webapp, extensions, and a microservice backend to bring real time suggestions to customer success agents using Intercom."
    })]
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}
function MDXContent(props = {}) {
  const {wrapper: MDXLayout} = props.components || ({});
  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
    children: _jsx(_createMdxContent, props)
  })) : _createMdxContent(props);
}
return {
  default: MDXContent
};


Clairvoyance finds the best answers that have worked in the past and suggests them to teams using Intercom when answering similar questions.
Clairvoyance was built on AWS using S3, SNS, SQS, RDS, PostgreSQL, MongoDB, Elasticsearch, and React. We created a webapp, extensions, and a microservice backend to bring real time suggestions to customer success agents using Intercom.


Clairvoyance finds the best answers that have worked in the past and suggests them to teams using Intercom when answering similar questions.

Clairvoyance was built on AWS using S3, SNS, SQS, RDS, PostgreSQL, MongoDB, Elasticsearch, and React. We created a webapp, extensions, and a microservice backend to bring real time suggestions to customer success agents using Intercom.


Clairvoyance (renamed to Clarity Hub) finds the best answers that have worked in the past and suggests them to teams using Intercom when answering similar questions.
