conorbranagan

trace_graph_query.py provides a simple CLI for getting the trace graph data. It will use the org

Usage

usage: trace_graph.py [-h] [--api_key API_KEY]
                      [--application_key APPLICATION_KEY]
                      [--name {service,host}]

optional arguments:

enricofoltran

package main

import (
	"context"
	"flag"
	"fmt"
	"log"
	"net/http"
	"os"
	"os/signal"

myshov

console.log(1);
(_ => console.log(2))();
eval('console.log(3);');
console.log.call(null, 4);
console.log.apply(null, [5]);
new Function('console.log(6)')();
Reflect.apply(console.log, null, [7])
Reflect.construct(function(){console.log(8)}, []);
Function.prototype.apply.call(console.log, null, [9]);
Function.prototype.call.call(console.log, null, 10);

codefromthecrypt

I've had many people ask me questions about OpenTracing, often in relation to OpenZipkin. I've seen assertions about how it is vendor neutral and is the lock-in cure. This post is not a sanctioned, polished or otherwise muted view, rather what I personally think about what it is and is not, and what it helps and does not help with. Scroll to the very end if this is too long. Feel free to add a comment if I made any factual mistakes or you just want to add a comment.

So, what is OpenTracing?

OpenTracing is documentation and library interfaces for distributed tracing instrumentation. To be "OpenTracing" requires bundling its interfaces in your work, so that others can use it to time distributed operations with the same library.

So, who is it for?

OpenTracing interfaces are targeted to authors of instrumentation libraries, and those who want to collaborate with traces created by them. Ex something started a trace somewhere and I add a notable event to that trace. Structure logging was recently added to O

alexeygrigorev

import requests
import base64
from tqdm import tqdm


master_json_url = 'https://178skyfiregce-a.akamaihd.net/exp=1474107106~acl=%2F142089577%2F%2A~hmac=0d9becc441fc5385462d53bf59cf019c0184690862f49b414e9a2f1c5bafbe0d/142089577/video/426274424,426274425,426274423,426274422/master.json?base64_init=1'
base_url = master_json_url[:master_json_url.rfind('/', 0, -26) + 1]

resp = requests.get(master_json_url)
content = resp.json()

kevinlebrun

PoC Keep Session during domain migration

The idea is simple: pass the session cookie from the old domain to the new one.

Usage

$ go run server.go

Go to http://session and http://nosession.

kevinlebrun

bins = [
    '01010000',
    '01101100',
    '01110101',
    '01100111',
    '00100000',
    '01101001',
    '01101110',
    '01110100',
    '01101111',

kevinlebrun

package main

import (
	"fmt"
	"os"
	"path"
	"path/filepath"
	"strings"
)

dannguyen

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

yang-wei

Should be work with 0.18

Destructuring(or pattern matching) is a way used to extract data from a data structure(tuple, list, record) that mirros the construction. Compare to other languages, Elm support much less destructuring but let's see what it got !

Tuple

myTuple = ("A", "B", "C")
myNestedTuple = ("A", "B", "C", ("X", "Y", "Z"))