lagartoJuancho

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "VpcDiscovery",
      "Effect": "Allow",
      "Action": [
        "ec2:DescribeAccountAttributes",
        "ec2:DescribeAddresses",
        "ec2:DescribeAvailabilityZones",

forkyau

venkat-dand-fhr

Three corrections. Change nothing else.

1. Layer 4 is architecturally wrong. DynamoDB does not invoke anything.
   Remove the arrow from DynamoDB to API Gateway (WebSocket).
   Draw TWO arrows, both leaving "Any backend Lambda":
     - down to DynamoDB tos-connections, labelled "DynamoDB query"
     - across to API Gateway (WebSocket), labelled "HTTPS post to connection"
   Keep API Gateway (WebSocket) -> Browser, "WebSocket push".

2. The red box in layer 3 is mislabelled. The systems under test are SaaS

Pryodon

Computer Information:
Manufacturer: Gigabyte Technology Co., Ltd.
Model: 970A-DS3P FX
Form Factor: Desktop
No Touch Input Detected

Processor Information:
CPU Vendor: AuthenticAMD
CPU Brand: AMD FX-8370 Eight-Core Processor

Katie-at-ScriptRunner

# REST Endpoint Usage Logging (ScriptRunner for Jira & Confluence)

Custom REST Endpoints don't show *who* is calling them anywhere in the ScriptRunner UI by default.

This snippet adds one small piece of logging to an endpoint so that every time it's called, you get a log line showing the **endpoint name**, the **user who called it**, and the **time** it happened.

The same approach works in both **ScriptRunner for Jira** and **ScriptRunner for Confluence** — the setup steps are identical, but the code used to identify "who called this" is slightly different between the two products, so two versions are provided below.

> ⚠️ **Confluence version note:** this uses the standard Atlassian SAL `ComponentLocator` pattern (the standard way non-Jira products fetch the current user), rather than an example pulled directly from Adaptavist's own docs. Test it in a sandbox instance first.

csh1668

{"targetLanguage":"Korean","translatorName":"Gemini","translatorModel":"gemini-2.5-flash-lite","date":"2026-09-09 21:19:43","translationCount":33233}

yakyram

namespace CSLight
{
    internal class Program
    {
        static void Main(string[] args)
        {
            Random random = new Random();

            int minValueRandom = 10;
            int maxValueRandom = 26;

solon

{"client_id":"https://gist.githubusercontent.com/solon/87dcbf6322d817bef9d616d79b300a3f/raw/clade-opaque-probe.json","client_name":"Clade Development OAuth Verification Probe","redirect_uris":["https://app.clade.dev/oauth-probe-callback"],"token_endpoint_auth_method":"none"}

ZEEZOSEIL

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.metrics import classification_report, f1_score
from tensorflow.keras import layers, models

# -------------------------------------------------------------------
# 1. Dataset Loading & Preprocessing
# -------------------------------------------------------------------
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

Charlson-Chen

#dailyR_09/09/2026

library(tidyverse)
x_range<-seq(-4,3,.01)
y<-dnorm(x_range,mean=0,sd=1)
data<-data.frame(x=x_range,y=y)

g<-ggplot(data,aes(x=x,y=y))+
  geom_line()+
  geom_area(data=subset(data,x>=-1&x<=2),aes(x=x,y=y),fill="steelblue",alpha=.4)+