Les news de la curation sur des nouveautés et l’humour
It’s recommended to use Lambda instead of the anonymous class, but there are some pitfalls, such as the potential NoClassDefFoundError.
In this post, I will explore this error and how to avoid it. I have two classes, RequiredObject and OptionalObject. The latter one is optional at runtime, and optional dependency is common especially for this framework.
Cangibrina is a Python-based multi platform admin dashboard finder tool which aims to obtain the location of website dashboards by using brute-force, wordlists, Google, Nmap and robots.txt.
It is multi-threaded, supports modifying your user agent, using a TOR proxy, custom dorks, Nmap integration and can use both DuckDuckGo and Google.
Cangibrina Admin Dashboard Finder Requirements
Cangibrina Usage to Find Admin Dashboards
usage: cangibrina.py [-h] -u U [-w W] [-t T] [-v] [–ext EXT] [–user-agent]
[–tor] [–search] [–dork DORK] [–nmap [NMAP]]
Fast and powerful admin finder
optional arguments:
-h, –help show this help message and exit
-u U target site
-w W set wordlist (default: wl_medium)
-t T set threads number (default: 5)
-v enable verbose
–ext EXT filter path by target extension
–user-agent modify user-agent
–sub-domain search for sub domains instead of directories
–tor set TOR proxy
–search use google and duckduckgo to search
–dork DORK set custom dork
–nmap [NMAP] use nmap to scan ports and services
There are other specific tools in this area like WPScan for WordPress and DruPwn for Drupal – and in those cases the dashboard URLs are already known.
Read the rest of Cangibrina – Admin Dashboard Finder Tool now! Only available at Darknet.
In this post we will talk about creating Python Lists of Tuples and how they can be used.
Lists in Python are simply an array. Here is a basic list of my favorite WoW Classes:
The Amazon SageMaker machine learning service is a full platform that greatly simplifies the process of training and deploying your models at scale. However, there are still major gaps to enabling data scientists to do research and development without having to go through the heavy lifting of provisioning the infrastructure and developing their own continuous delivery practices to obtain quick feedback. In this talk, you will learn how to leverage AWS CodePipeline, CloudFormation, CodeBuild, and SageMaker to create continuous delivery pipelines that allow the data scientist to use a repeatable process to build, train, test and deploy their models.
Below, I’ve included a screencast of the talk I gave at the AWS NYC Summit in July 2018 along with a transcript (generated by Amazon Transcribe — another Machine Learning service — along with lots of human editing). The last six minutes of the talk include two demos on using SageMaker, CodePipeline, and CloudFormation as part of the open source solution we created.
Whenever something serious happens, we usually try and determine cause and effect. What was it that caused this thing to unfold the way it did? Whilst the theory is nice, we often employ some rather dubious explanations to try and explain the series of events. Superstitions perhaps, or correlation rather than causation.
There have been attempts in the past to generate mathematical models for general causality, but they haven’t been particularly effective, especially for more complex problems. A new study from the University of Johannesburg, South Africa and National Institute of Technology Rourkela, India, has attempted to use AI to do a better job.
Several interesting announcements from last week’s Google Next conference.
Knative, a new OSS project built by Google, Red Hat, IBM, and others to build, deploy, and manage modern serverless workloads on Kubernetes. Built upon Istio, with 1.0 coming soon and managed Istio on GCP. It includes a build primitive to manage source-to-Kubernetes flows, that can be used independently. Maybe it is the new standard to define sources and builds in Kubernetes. Read more from Mark Chmarny.
From ride shares to smart power grids and from healthcare to our online lives, AI is being propelled out of labs and into our daily lives. Microsoft is betting that conservation-focused AI can save our planet, while Facebook sees it as a silver bullet for rooting out harmful content. Tesla CEO Elon Musk and the late physicist Stephen Hawking both warned society of the potential for weaponized AI.
At CA, we wanted to gain insight into how the AI Ecosystem has developed over the past year. We partnered with Quid, a San Francisco-based startup, whose platform can read millions of news articles, blog posts, company profiles, and patents — and offer immediate insight by organizing that content visually. From its global dataset of 1.8 million companies, Quid classified companies that mentioned a specific focus in "Artificial Intelligence" or "Deep Learning."
Depuis la fin juillet 2018, le CERT-FR constate une nouvelle campagne de courriels distribuant le rançongiciel Locky touchant actuellement la France. Les messages sont accompagnés d’un lien hypertexte encourageant à télécharger la facture d’une commande. Le taux de blocage par les …
Source de l’article sur CERT-FR
When people look for metrics collection for their environment they often look towards the same few solutions like Collectd, Telegraf, etc. This is for good reason: those options provide flexible & extensible metrics collection…and so can Sensu.
Sensu works quite well for metrics. I’d like to show you how to set it up.
The imaginary fiction in the scientific movies is now real stuff to talk on. Artificial Intelligence and Machine Learning are taking technology to the next level of advancement. Many giant companies are endeavoring to leverage this technology to understand the customer’s demands and engage for better success. Even the social marketing giant Twitter has joined the league.
Further, in a recent announcement, the company declares that they are going to use insightful Machine Learning technology to recommend tweets to its users.
