We’re excited to announce the winners of the ActivityWatch Screenshot Competition! We received some great submissions, and we’d like to thank everyone who participated. Your contributions will help us showcase ActivityWatch to the world!
Submissions were accepted in the GitHub discussion announcing the competition, and the
#screenshots channel on our Discord.
We’d like to apologize for the delay in announcing the winners. We are grateful for everyone’s patience and enthusiastic participation in this competition.
You can find selected screenshots from the winners on our screenshots page.
For the sheer number of screenshots submitted, and of very high resolution, Erik takes the top spot. Your contribution will greatly help us in showcasing the versatility of ActivityWatch!
Hernan showed us how category rules can be set up to get productivity classification instead of just activity classification. This is incredibly useful for users focused on time management.
For third place we have a tie between @BelKed and @maholli.
@BelKed showed us an excellent dark mode screenshot. It shows how elegant ActivityWatch can look in different themes.
@maholli showed us a whole month of activity! Good luck with your thesis!
All winners will have their screenshots featured on the ActivityWatch website.
We were supposed to have additional prizes, which we still aim to give out, but we have yet to order the swag we planned to ship. However, winners can expect to get swag for free as soon as we order it!
We promise the winners will one day get ActivityWatch swag, including:
We’ll reach out to the winners to distribute the prizes once we have them. (sorry if it takes a while!)
- A big thank you to everyone who participated! Your contributions are invaluable.
- The selected screenshots will be showcased on our website.
For further discussion, a GitHub thread has been set up. Feel free to share your thoughts here.
Table of Contents
In an increasingly digital world, time-tracking software like ActivityWatch plays a pivotal role in enhancing productivity and self-awareness. As we look to the future, three key elements stand out: Artificial Intelligence (AI), privacy, and personalization.
While ActivityWatch itself doesn’t currently utilize AI, the data it collects serves as a fertile ground for AI-driven applications. The software logs a comprehensive array of your digital activities, from the websites you visit to the applications you use. This data can be transformed into actionable insights through machine learning algorithms. For example, natural language processing (NLP) could analyze your text-based activities to gauge your focus levels, while clustering algorithms could categorize your activities into productivity zones.
AI’s capability to sift through large datasets makes it invaluable for identifying long-term behavioral changes or trends. By applying time-series analysis on your ActivityWatch logs, you can gain insights into how your work habits, focus periods, and even leisure activities evolve over time. This can be particularly useful for self-improvement and for tailoring your work schedule to your natural rhythms.
Imagine an AI assistant integrated into ActivityWatch that can not only summarize your day but also take context-aware notes for you. Utilizing speech-to-text algorithms during meetings, it could generate summaries and action items automatically. When you’re working on a project, it could log your key milestones based on your activity data, thereby creating a daily journal without any manual input.
How It Could Work:
- Speech-to-Text: During meetings, the AI assistant could transcribe and summarize key points.
- Text Summarization Algorithms: At the end of the day, a summary of your activities could be generated, highlighting your most productive periods and areas for improvement.
Think of this feature as a modern, less intrusive version of Clippy. By leveraging real-time analytics and historical data, ActivityWatch could offer spontaneous, in-context suggestions. For instance, if the software detects that you’ve been working on a coding project for an extended period, it could suggest taking a break or even recommend a relevant Stack Overflow thread based on your recent queries.
- Contextual Bandit Algorithms: These could be used to offer suggestions that are most likely to be useful to you at any given moment.
- Collaborative Filtering: By analyzing data from multiple users, the system could offer suggestions that have been beneficial to people with similar work patterns.
The future of time tracking isn’t just about what you’re doing on your computer; it’s about understanding the cognitive processes that accompany those activities. Building on the pioneering research in my MSc thesis, which explored the classification of brain activity using electroencephalography (EEG) and automated time tracking, we see a new frontier emerging.
ActivityWatch provides a robust framework for tracking digital activities, while EEG devices offer a window into the cognitive state of the user. When combined, these two technologies can provide unprecedented insights into not just what you are doing, but how you are mentally engaging with those activities.
- Data Fusion: By synchronizing EEG data with ActivityWatch logs, we can create a multi-modal dataset that captures both behavioral and cognitive aspects.
- Machine Learning: Advanced classifiers based on Riemannian geometry, as demonstrated in my MSc thesis, can be employed to analyze this rich dataset.
The practical implications of this integration are vast:
- Personalized Productivity: Imagine a system that knows when you’re most focused and automatically blocks distracting websites during those periods.
- Mental Health Monitoring: By tracking cognitive states alongside digital activities, we can identify stressors and recommend timely interventions.
- Enhanced Learning: For educational software, understanding the cognitive load during different tasks can help adapt the material in real-time to optimize learning.
While the potential is exciting, ethical considerations, particularly around privacy and consent, are paramount. All EEG data would be encrypted and stored locally, in line with ActivityWatch’s privacy-first philosophy.
As we look to the future, the integration of EEG devices and ActivityWatch opens up new avenues for research, particularly in the realms of cognitive science, human-computer interaction, and even preventive healthcare.
By marrying the behavioral data from ActivityWatch with the cognitive insights from EEG, we’re not just tracking time; we’re understanding it on a whole new level.
Rewind.ai takes time tracking to the next level by offering full-context tracking, capturing not just screen data but also audio and microphone inputs. While this provides a rich dataset that can fuel advanced AI applications, it also introduces a set of challenges and ethical considerations. (Disclosure: I am an investor in Rewind.ai)
The depth of data collected by full-context tracking opens the door to a multitude of AI-driven applications:
- Voice Analytics: By analyzing audio data, AI can gauge the sentiment and tone of meetings, providing insights into team dynamics.
- Content Classification: Advanced machine learning models can automatically categorize screen content, offering a more nuanced understanding of user behavior.
- Real-Time Feedback: With access to both screen and audio data, AI can offer real-time suggestions or alerts, such as flagging potential security risks or recommending breaks based on detected stress levels.
While the wealth of data offers numerous possibilities, it also raises significant privacy concerns:
- Data Encryption: Storing audio and screen data necessitates robust encryption methods to prevent unauthorized access.
- User Consent: It’s crucial to obtain explicit user consent for capturing and analyzing such sensitive data.
- Data Minimization: The principle of collecting only the data that is strictly necessary should be adhered to, in line with GDPR and other privacy regulations.
Full-context tracking is not just data-intensive but also resource-intensive:
- Storage: The sheer volume of audio and screen data can quickly consume local storage, requiring efficient data compression algorithms.
- Processing Power: Real-time analysis of such data can be CPU-intensive, potentially affecting system performance.
Rewind.ai has addressed this by leveraging it’s macOS-only nature to utilize Apple’s CoreML framework for on-device AI processing, allowing efficient data compression and real-time analysis with relatively low impact on overall system performance.
The challenge lies in striking a balance between leveraging AI’s capabilities and maintaining user privacy and system performance. One potential solution could be edge computing, where data is processed locally on the user’s device, minimizing data transfer and storage while still enabling real-time AI analytics.
In a world where data breaches are increasingly common, privacy is paramount. ActivityWatch addresses this by storing all your data locally, ensuring that you have complete control over your information.
While full-context tracking offers richer data, it is much more resource-intensive and poses greater privacy risks compared to ActivityWatch’s local storage approach.
ActivityWatch allows for custom plugins and scripts, enabling you to tailor the software to your specific needs. This is crucial in a world where no two users are the same.
In the realm of behavioral and social sciences, the availability of accurate and comprehensive data is often the cornerstone of impactful research. Traditional methods of data collection, such as surveys and interviews, have their limitations in terms of scale and objectivity. This is where time-tracking tools like ActivityWatch can fill a significant gap.
One of the challenges researchers face is the lack of specialized tooling for data collection that is both ethical and efficient. ActivityWatch addresses this void by offering a platform that is not only privacy-centric but also highly customizable. Researchers can use custom plugins and scripts to tailor data collection to the specific needs of their study, thereby enhancing the quality and relevance of the data collected.
ActivityWatch is currently being utilized in a 5-year project funded by the European Research Council. The project aims to delve into the intricacies of human behavior and time management. The use of ActivityWatch in such a high-profile research initiative underscores its value as a reliable tool for academic and scientific inquiry. Learn more about the ERC-funded project here.
While not directly using ActivityWatch, the WARN-D research project aims to build a personalized early warning system for mental health by tracking stressors among students. This project highlights the broader societal value of time-tracking data and sets a precedent for how ActivityWatch could be employed in similar research endeavors. Learn more about the WARN-D project here.
By offering a robust, customizable, and privacy-focused platform, ActivityWatch is poised to become an invaluable asset in the toolkit of modern researchers.
ActivityWatch is not just another time-tracking tool; it’s a platform that embodies the future of this technology. Situated at the crossroads of data-driven AI applications, stringent privacy measures, and extensive personalization capabilities, ActivityWatch is setting the standard for what time-tracking software can and should be.
By offering a platform that is both customizable and privacy-centric, ActivityWatch provides a unique solution that caters to a diverse user base—from individuals seeking to improve their productivity to researchers aiming to collect valuable data for scientific inquiry.
As we look to the future, ActivityWatch is committed to further enhancing its platform to meet the evolving needs of its users. Whether it’s integrating more advanced data analytics or expanding the range of custom plugins, the roadmap for ActivityWatch is geared towards innovation and user empowerment.
In a world where time is our most valuable resource, ActivityWatch offers a way to make the most of it. By focusing on the key pillars of AI, privacy, and personalization, ActivityWatch is not just keeping pace with the future of time tracking—it’s leading the way.
The future of time tracking is intricately tied to AI, privacy, and personalization. With its focus on these areas, ActivityWatch is well-positioned to lead the way.
If you are excited about this like we are, consider joining the Discord server. We’re always looking for new contributors and ideas!
And if you aren’t using ActivityWatch already, now is the perfect time to start collecting your screentime data!
In today’s digital landscape, effective time management is more critical than ever. While everyone has 24 hours in a day, how we utilize those hours significantly impacts our productivity and well-being. Time-tracking software has thus become a fundamental tool for individuals and organizations aiming to optimize their time use. This article will conduct a side-by-side comparison of four popular time-tracking applications: ActivityWatch, RescueTime, ManicTime, and Apple ScreenTime.
Here’s a table outlining the features of each software:
|Granular tracking of applications, browser activity, custom event tracking. Extensible architecture.
|Application and website tracking, limited offline tracking, distraction-blocking features.
|Comprehensive computer usage tracking, supports offline activity tagging.
|Limited to Apple ecosystem; tracks applications and website usage. No custom event tracking.
|Reporting & Analytics
|Rich dashboard with real-time and historical data. Custom reports possible due to open-source nature.
|Detailed reports, limited customization.
|Highly detailed reports, manual tagging and categorization.
|Basic reporting on screen time and app categories, little customization.
|Local data storage by default. Robust privacy controls.
|Data stored on their servers.
|Local and cloud storage options.
|Encrypted, on-device storage. Tightly integrated with Apple ecosystem.
|High levels due to open-source nature, from UI to tracking and reporting.
|Limited to predefined settings and features.
|Some customization like tagging and categorization.
|Minimal customization, focused on app limits and downtime.
|Free and open-source.
|Freemium model, premium version offers additional features.
|Free version available, paid license for advanced features.
|Free but limited to Apple devices.
Choosing a time-tracking software often depends on specific needs. The following matrix offers suggestions based on different use-cases:
|Highly Customizable Solution
|Open-source nature allows for extensive customization, from UI to tracking metrics.
|Local data storage by default, with robust privacy controls.
|All three support multiple platforms (ActivityWatch and ManicTime support Linux too). Apple ScreenTime is limited to Apple devices.
|Offers team features, data storage on cloud, and centralized reporting.
|Provides distraction-blocking features in the premium version. Note that there are other free and open-source tools for blocking distractions, which is why ActivityWatch doesn’t offer this feature.
|All offer robust reporting capabilities, with ActivityWatch and ManicTime allowing for greater customization.
|Seamlessly integrates with Apple devices, from iPhones to Macs.
|Freelancers on Budget
|Free and open-source with an extensive set of features for individual tracking.
|Offline Activity Tracking
|Both support offline activity tagging, making it suitable for tracking non-digital tasks.
Time-tracking software options abound, each with its unique set of features, advantages, and limitations. While the final choice will ultimately depend on your specific needs, ActivityWatch emerges as a strong contender in multiple categories.
- High level of customization thanks to its open-source nature
- Strong commitment to user privacy with local data storage
- Extensive tracking capabilities, including custom event tracking
- Free and open-source, providing a comprehensive feature set without a financial barrier
- RescueTime offers distraction-blocking and enterprise features
- ManicTime provides a versatile tracking environment, including offline tagging
- Apple ScreenTime integrates seamlessly within the Apple ecosystem
In summary, ActivityWatch excels in customizability, privacy, and its free pricing model. It is an ideal choice for users ranging from freelancers to those who prioritize data privacy, all without compromising on tracking capabilities and analytics. If you’re looking for a comprehensive, customizable, and cost-effective time-tracking solution, ActivityWatch may well be the ideal choice for you.
For more alternatives, you can check out alternativeto.net.