| { | |
| "meta": { | |
| "manifest_version": 3, | |
| "locale_version": "1.3", | |
| "locale_name": "简体中文", | |
| "locale_type": "zh_CN", | |
| "locale_last_updated": "2020-08-07 18:00:00 UTC", | |
| "locale_author_name": "moelody", | |
| "locale_author_email": "yfsmallmoon@gmail.com", | |
| "locale_source_url": "https://gist.github.com/moelody/3159316ce726fc629fae15278bbce429" |
| [HttpPost] | |
| public HttpResponseMessage CreateCustomer(string name, string billingInfo) | |
| { | |
| Result<BillingInfo> billingInfoResult = BillingInfo.Create(billingInfo); | |
| Result<CustomerName> customerNameResult = CustomerName.Create(name); | |
| return Result.Combine(billingInfoResult, customerNameResult) | |
| .OnSuccess(() => _paymentGateway.ChargeCommission(billingInfoResult.Value)) | |
| .OnSuccess(() => new Customer(customerNameResult.Value)) | |
| .OnSuccess( |
| Github.com ui .currently does not natively supoport search for multiple topic tags as of now. However their api allows you to query multiple tags. Below is a simple example to query github.com with ecs and go topic tags. | |
| curl -H "Accept: application/vnd.github.mercy-preview+json" \ | |
| https://api.github.com/search/repositories?q=topic:ecs+topic:go | |
| Response from the github can be rather verbose so lets filter only relavant info such repo url and description. | |
| curl -H "Accept: application/vnd.github.mercy-preview+json" \ | |
| https://api.github.com/search/repositories\?q\=topic:ecs+topic:go | jq '.items[] | {url:.url, description:.description}' |
| """ | |
| Тренировка модели болталки Axioma на FRED T5 для проекта https://github.com/Koziev/chatbot | |
| Эксперимент с файнтюном: токены истории диалога не включаем в backprop, присваивая соответствующим целям (labels) значение -100 | |
| Прочие хинты по тренировке: https://kelijah.livejournal.com/315826.html | |
| """ | |
| import os | |
| import json | |
| import sys | |
| import io |
Optional - Set format on save and any global prettier options
npm i -D eslint prettier eslint-plugin-prettier eslint-config-prettier eslint-plugin-node eslint-config-node
| <?php | |
| /* | |
| Backup script for trakt.tv (API v2). | |
| Live demo: https://darekkay.com/blog/trakt-tv-backup/ | |
| */ | |
| // create a Trakt app to get a client API key: http://docs.trakt.apiary.io/#introduction/create-an-app | |
| $apikey = "CLIENT_API_KEY"; |
| def de(fobj, bounds, mut=0.8, crossp=0.7, popsize=200, its=500): | |
| dimensions = len(bounds) | |
| # random (0-1) positions for all particles | |
| pop = np.random.rand(popsize, dimensions) | |
| min_b, max_b = np.asarray(bounds).T | |
| diff = np.fabs(min_b - max_b) | |
| # scale particle positions according to bounds | |
| pop_denorm = min_b + pop * diff | |
| # initial objective function value | |
| fitness = np.asarray([fobj(ind) for ind in pop_denorm]) |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
| import network | |
| import socket | |
| import time | |
| import struct | |
| from machine import Pin | |
| NTP_DELTA = 2208988800 | |
| host = "pool.ntp.org" |