JSON is very standard. YAML, a superset of JSON, is very readable. Got something that needs JSON, and want to write the input in YAML? Use yaml2json. Have JSON and want to view it in a more readable format? Use json2yaml.
Use with files or stdin/stdout. Pretty-print the JSON if you want.
Requires pyyaml (via pip or easy_install), aka the python-yaml Ubuntu package.
A version of these files is available at tinyurl/json2yaml and tinyurl.com/yaml2json for you to wget or curl -L
| [alias] | |
| wip = for-each-ref --sort='authordate:iso8601' --format=' %(color:green)%(authordate:relative)%09%(color:white)%(refname:short)' refs/heads | |
| def paginate(method, **kwargs): | |
| client = method.__self__ | |
| paginator = client.get_paginator(method.__name__) | |
| for page in paginator.paginate(**kwargs).result_key_iters(): | |
| for result in page: | |
| yield result | |
| # usage: | |
| import boto3 |
| { | |
| "body" : $input.json('$'), | |
| "headers": { | |
| #foreach($header in $input.params().header.keySet()) | |
| "$header": "$util.escapeJavaScript($input.params().header.get($header))" #if($foreach.hasNext),#end | |
| #end | |
| }, | |
| "method": "$context.httpMethod", | |
| "params": { |
A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."
An update by Paul Johnston (paul@roundaboutlabs.com), for a Serverless Architecture scenario. This assumes something akin to AWS Lambda + API Gateway + DynamoDB (c. 2016) Function as a Service (FaaS) solution as the basis for deployment rather than a cloud-based virtual server approach which the original paper was based upon. The FaaS solution implies each function is separately scalable and the database is inherently partitioned (assuming designed/built well).
If you agree/disagree, please fork and share with me on twitter @pauldjohnston.
this is a rough draft and may be updated with more examples
GitHub was kind enough to grant me swift access to the Copilot test phase despite me @'ing them several hundred times about ICE. I would like to examine it not in terms of productivity, but security. How risky is it to allow an AI to write some or all of your code?
Ultimately, a human being must take responsibility for every line of code that is committed. AI should not be used for "responsibility washing." However, Copilot is a tool, and workers need their tools to be reliable. A carpenter doesn't have to
| export $(aws sts assume-role --role-arn arn:aws:iam::$TARGET_ACCOUNT_ID:role/ci-access --role-session-name ci --output text --query "[['AWS_ACCESS_KEY_ID',Credentials.AccessKeyId],['AWS_SECRET_ACCESS_KEY',Credentials.SecretAccessKey],['AWS_SESSION_TOKEN',Credentials.SessionToken]][*].join(\`=\`,@)") |
| # coding=utf8 | |
| import PIL | |
| from PIL import ImageFont | |
| from PIL import Image | |
| from PIL import ImageDraw | |
| def text2png(text, fullpath, color = "#000", bgcolor = "#FFF", fontfullpath = None, fontsize = 13, leftpadding = 3, rightpadding = 3, width = 200): | |
| REPLACEMENT_CHARACTER = u'\uFFFD' | |
| NEWLINE_REPLACEMENT_STRING = ' ' + REPLACEMENT_CHARACTER + ' ' |
A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."
| # https://gist.github.com/althonos/6914b896789d3f2078d1e6237642c35c | |
| [metadata] | |
| name = {name} | |
| version = file: {name}/_version.txt | |
| author = Martin Larralde | |
| author_email = martin.larralde@embl.de | |
| url = https://github.com/althonos/{name} | |
| description = {description} | |
| long_description = file: README.md |
