BugHerd
Marker.io
Usersnap
Userback
Pastel
Bird Eats Bug
Bugasura
Bugfender
Scikit-learn
Pandas
NumPy
OpenCV
Dataiku
Exploratory
WEKA
htm.java
BugHerd is the world's leading website feedback and bug-tracking tool. Globally, thousands of leading agencies and marketing teams love it for the ease and collaboration it brings to their website projects.
BugHerd has revolutionised the way agencies collect and manage website feedback from clients and internal teams. It is perfect for teams and individuals involved in website design and development. With BugHerd you can easily pin feedback directly to specific elements of the web pages. It acts as a transparent layer on the website that is visible only to you and your team. Submitted feedback and bugs are sent to a central Kanban task board that provides all stakeholders with full visibility of the project.
Get started in 3 easy steps:
STEP 1
Go to bugherd.com and click Start 14-day Free trial.ย
STEP 2
Sign up to create your first project. You can test BugHerd out on any website. It will only be visible to you.
STEP 3
And voila! You can start collecting feedback and invite others to try it out with you. Itโs that simple.
BugHerd Scikit-learnBugHerd is particularly recommended for web development teams, digital agencies, and product managers who are responsible for maintaining and improving websites. It is also a great fit for teams who work closely with clients and require an easy way to collect and manage client feedback directly in the context of the website in question.
Based on our record, Scikit-learn should be more popular than BugHerd. It has been mentiond 40 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Solutions like https://bugherd.com/ might make the issue context capture part more accurate. - Source: Hacker News / 4 months ago
This is a great idea, but scanning through appears to be basically https://bugherd.com/ ? Source: over 3 years ago
Competitors There are a few competitors out there that do something very similar (see https://ruttl.com/, https://usepastel.com/, https://bugherd.com/, https://www.markup.io/). This seems to suggest that there seems to be a general market for such a product. Source: over 3 years ago
Currently using BugHerd for web QA (love it) and looking for something similar for email. Source: over 3 years ago
Bugherd is good for this. Used it extensively when I worked for a web agency and it saved so much time. https://bugherd.com/. - Source: Hacker News / almost 4 years ago
Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / about 2 months ago
Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
In practice, youโll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
Marker.io - Visual feedback and bug reporting tool for websites
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Usersnap - Usersnap is a customer feedback software for SaaS companies that need to constantly improve and grow their products.
NumPy - NumPy is the fundamental package for scientific computing with Python
Userback - Userback empowers product teams to collect, understand, and act on user feedback with unprecedented speed and clarity.
OpenCV - OpenCV is the world's biggest computer vision library