SoberStack is a free sobriety streak tracker built around one idea: progress is not erased by a slip. Track your current streak, log urges in the moment, and see a history graph that preserves your past. Private, simple, no accounts required.
Tap a thing and the counter resets. A quiet, local-first day counter for the things you keep meaning to do. No account needed, no spreadsheet.
sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Which is more popular?
Based on our record, Scikit-learn
seems to be a lot more popular than SoberStack App.
While we know about 41 links to Scikit-learn,
we've tracked only 2 mentions of SoberStack App.
social mentions
41 vs 2
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
207 vs 25
Base details
Website, pricing, platforms and company facts side by side.
Ease of Use Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
Extensive Documentation and Community Support The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
Integration with Other Libraries Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
Variety of Algorithms It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
Performance Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.
Possible disadvantages
Limited Deep Learning Support Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
Not Ideal for Large-Scale Data While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
Lack of Online Learning Algorithms The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
Less Flexibility in Customization It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
Dependency Overhead Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.
Sobriety Tracking SoberStack provides a clear and straightforward way to track sober days, helping users maintain accountability and visualize their progress over time.
Community Support The app offers community features that connect users with others on similar recovery journeys, providing encouragement and shared experiences to help maintain sobriety.
Simple and Clean Interface SoberStack features an intuitive, minimalist design that makes it easy for users to navigate and use without feeling overwhelmed, which is important during recovery.
Motivational Features The app includes milestones, badges, or motivational prompts that help keep users engaged and inspired to continue their sobriety journey.
Privacy-Focused SoberStack appears to prioritize user privacy, which is crucial for people dealing with sensitive personal health and recovery information.
Analysis
An editorial look at what each product does well and who it suits.
Scikit-learnSoberStack App
Overall verdict
Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.
Why this product is good
Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.
Recommended for
Beginners learning machine learning concepts and application.
Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.
Overall verdict
SoberStack appears to be a helpful digital tool for individuals pursuing sobriety, offering tracking and support features that can complement a recovery journey, though it should not replace professional treatment for those with serious addiction issues.
Why this product is good
Sobriety tracking helps users visualize progress and milestones, which can boost motivation and accountability
Digital tools are accessible anytime, providing support during moments of temptation or difficulty
May include community or support features that reduce feelings of isolation common in recovery
Can help users identify triggers and patterns through logging and reflection
Convenient and private way to monitor personal goals without requiring in-person meetings
Recommended for
Individuals in early recovery looking for daily accountability and motivation
People who want to track sobriety milestones and celebrate progress
Those seeking a private, self-directed complement to formal treatment or group support
Users who benefit from digital reminders and habit-tracking tools
Anyone wanting to reduce alcohol or substance use and build healthier habits
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised...
Recommendations tracked on public social media and blogs since March 2021.
Scikit-learn41 mentionsSoberStack App2 mentions
Where to Learn Applied ML for Incident Response: Start at Scoping
Reachability says who could be compromised. Behavior says who probably is. Sysmon Event ID 1 records every process with its parent. Reduce each to a parent>child token, keep only tokens that are new to each host since the intrusion...
- Source: dev.to
/
30 days ago
Detecting Ingress Tool Transfer (T1105) with Python
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,...
- Source: dev.to
/
5 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....
- Source: dev.to
/
6 months ago
SoberStack: a sobriety/recovery tracker built around the idea of focusing on sober days rather then streaks so it's not all or nothing. It uses a Github style contribution graph. https://soberstack.app/.
- Source: Hacker News
/
5 months ago
I've been building SoberStack ([https://soberstack.app](https://soberstack.app)). It's the first side project I've taken on in a few years. It's a free sobriety app for any bad habits I built for myself. Most sobriety apps reset your...
- Source: Hacker News
/
7 months ago
Alternatives to Scikit-learn and SoberStack App
When comparing Scikit-learn and SoberStack App, you can also consider the following products.
Pandas
NumPy
OpenCV
Dataiku
Exploratory
WEKA
htm.java
I Am Sober
Sobriety Counter – Bad Habits
Sobriety Tracker Clock
HabitBull
Nomo
Sober Tracker
Streaks
