Go

Go DDOS Yourself (serverless-artillery)

Hacker News

Go DDOS Yourself (serverless-artillery)

TL;DR: npm install -g serverless npm install serverless-artillery export AWS_PROFILE=<your-aws-credentials-profile> # export HTTP_PROXY=<your-corporate-proxy> slsart deploy slsart invoke # you just sent 10 request at http://aws.amazon.com. Hungry for more? Continue: slsart script -e https://your.endpoint.com -d 60 -r 1000 # this specifies 1 minute of 1000 HTTP requests per second at your endpoint - see script.yml in the local directory and artillery.io for docs slsart invoke # 1K RPS will happen for a minute. Yea! What fun. Now, clean up after yourself slsart remove # Full README and details on GitHub (https://github.com/Nordstrom/serverless-artillery) # We wanted to make distributed load testing so easy that it could happen automatically with every push. It's worked nicely. It scales fantastically. You don't have to think about how it does so. Global distribution across regions and POPs are thoughts for the future. # If you like the tool, you'll want to eventually setup load result recording that can handle your volume. There is an Influx DB plugin for that. In fact, there's a whole workshop for that (see lesson 4). # Workshop: https://github.com/Nordstrom/serverless-artillery-workshop # This tool automatically splits the artillery script into Lambda-sized chunks to be executed in a coordinated, distributed manner without burdening you with any of the details of what it takes to do this. # a more general form is to come. # [INSERT LEGAL DISCLAIMER]

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
40%40% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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