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# find a file by filename
# fz .json fx
# fz .json "jq -C '.' | less -R"
fz() {
  f $1 | fzf --bind "enter:execute(${2:-bat} {})" --preview "bat --color=always {}"
}

# find a file by content
# az ingest open
az() {

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[ { date_of_birth: { order: 'asc' } } ]

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Convenience utility functions for NiPyApi, not really intended for external use
"""

from __future__ import absolute_import, unicode_literals
import logging
import json

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import json
import sys
import traceback
import csv
from java.nio.charset import StandardCharsets
from org.apache.commons.io import IOUtils
from org.apache.nifi.processor.io import StreamCallback
from org.python.core.util import StringUtil

class PyStreamCallback(StreamCallback):

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Could you introduce your app in a few sentences? What does it do, who is it for? Which platforms? Personal app or for company?

Lexi is an open source iOS app for displaying and dictating a random Greek Word of the Day, with history and favourite features.

It was developed as a learning project (primarily) and for my parents who would find a random word of the day interesting in their efforts to learn the

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_speak = (language, text) => {
  const { pitch, rate } = this.state;

  const start = () => {
    this.setState({ inProgress: true });
  };

  const complete = () => {
    this.state.inProgress && this.setState({ inProgress: false });
  };

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curl 'http://demo.searchkit.co/api/crimes/_search' -H 'Pragma: no-cache' -H 'Origin: http://localhost:3000' -H 'Accept-Encoding: gzip, deflate' -H 'Accept-Language: en-US,en;q=0.9' -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.94 Safari/537.36' -H 'Content-Type: application/json;charset=UTF-8' -H 'Accept: application/json, text/plain, */*' -H 'Cache-Control: no-cache' -H 'Referer: http://localhost:3000/?q=theft' -H 'Connection: keep-alive' --data-binary \@request.json --compressed

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# Insert your preferred key mappings here.
map h goBack
map l goForward

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name: "VGG_ILSVRC_16_layers"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {

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192:~/Sandbox/dd% docker run -p 8080:8080 --name dd -v /Users/gavin/Sandbox/dd:/opt/models beniz/deepdetect_cpu    
DeepDetect [ commit 978401f3d1f23a327d0ebfef24cb0a0d7c543c6e ]

INFO - 09:30:47 - Running DeepDetect HTTP server on 0.0.0.0:8080

INFO - 09:30:53 - Initializing net from parameters:

INFO - 09:30:53 - Creating layer / name=vgg_face / type=MemoryData
INFO - 09:30:53 - Creating Layer vgg_face
INFO - 09:30:53 - vgg_face -> data