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How can you make use of DeepSeek R1 for personal performance?

Serhii Melnyk

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I constantly wished to gather statistics about my performance on the computer. This idea is not brand-new; there are lots of apps developed to fix this concern. However, all of them have one substantial caution: you should send highly delicate and personal details about ALL your activity to "BIG BROTHER" and trust that your information won't end up in the hands of individual information reselling firms. That's why I chose to create one myself and make it 100% open-source for total transparency and credibility - and you can utilize it too!

Understanding your performance focus over an extended period of time is essential since it supplies valuable insights into how you assign your time, identify patterns in your workflow, and discover locations for improvement. Long-term performance tracking can help you identify activities that regularly add to your goals and those that drain your time and energy without meaningful outcomes.

For instance, tracking your performance patterns can expose whether you're more effective throughout certain times of the day or in specific environments. It can likewise assist you examine the long-term impact of modifications, like altering your schedule, adopting new tools, or dealing with procrastination. This data-driven method not just empowers you to enhance your daily routines however likewise assists you set practical, attainable goals based upon proof rather than presumptions. In essence, comprehending your performance focus in time is a critical step toward creating a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is designed to support.

Here are main features:

- Privacy & Security: No details about your activity is sent online, making sure total privacy.
- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, providing complete transparency and user control.
- AI Analysis: An AI design evaluates your long-lasting activity to uncover hidden patterns and provide actionable insights to enhance efficiency.
- Classification Customization: Users can manually adjust AI categories to better reflect their personal productivity objectives.
- AI Customization: Today the is using deepseek-r1:14 b. In the future, users will be able to pick from a variety of AI models to suit their particular needs.
- Browsers Domain Tracking: The application likewise tracks the time invested in individual sites within internet browsers (Chrome, Safari, Edge), providing a detailed view of online activity.
But before I continue explaining how to have fun with it, let me state a couple of words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI start-up founded in 2023, has actually recently gathered considerable attention with the release of its newest AI model, R1. This design is significant for its high performance and cost-effectiveness, placing it as a powerful rival to developed AI models like OpenAI's ChatGPT.

The model is open-source and can be run on personal computers without the need for extensive computational resources. This democratization of AI innovation allows individuals to experiment with and assess the model's capabilities firsthand

DeepSeek R1 is bad for whatever, there are reasonable issues, but it's ideal for our efficiency tasks!

Using this model we can categorize applications or websites without sending out any information to the cloud and it-viking.ch thus keep your data secure.

I highly think that Personal-Productivity-Assistant may result in increased competition and drive innovation throughout the sector of comparable productivity-tracking services (the combined user base of all time-tracking applications reaches 10s of millions). Its open-source nature and complimentary availability make it an outstanding alternative.

The model itself will be provided to your computer system via another job called Ollama. This is done for benefit and better resources allotment.

Ollama is an open-source platform that allows you to run big language models (LLMs) in your area on your computer, improving information personal privacy and control. It works with macOS, Windows, and Linux operating systems.

By operating LLMs in your area, Ollama ensures that all data processing takes place within your own environment, eliminating the need to send sensitive details to external servers.

As an open-source project, Ollama gain from constant contributions from a vibrant community, making sure regular updates, feature enhancements, and robust assistance.

Now how to set up and run?

1. Install Ollama: Windows|MacOS
2. Install Personal-Productivity-Assistant: Windows|MacOS
3. First start can take some, due to the fact that of deepseek-r1:14 b (14 billion params, chain of thoughts).
4. Once installed, a black circle will appear in the system tray:.
5. Now do your regular work and wait a long time to collect good quantity of data. Application will save amount of 2nd you invest in each application or site.

6. Finally produce the report.

Note: Generating the report needs a minimum of 9GB of RAM, and the procedure may take a few minutes. If memory use is a concern, it's possible to switch to a smaller sized design for more effective resource management.

I 'd love to hear your feedback! Whether it's feature requests, bug reports, or your success stories, sign up with the neighborhood on GitHub to contribute and help make the tool even better. Together, we can form the future of productivity tools. Check it out here!

GitHub - smelnyk/Personal-Productivity-Assistant: Personal Productivity Assistant is a.

Personal Productivity Assistant is an advanced open-source application dedicating to enhancing individuals focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in designing and carrying out high-reliability, scalable, and premium tasks. My technical knowledge is complemented by strong team-leading and interaction abilities, which have helped me effectively lead groups for over 5 years.

Throughout my profession, I have actually concentrated on creating workflows for artificial intelligence and information science API services in cloud infrastructure, as well as developing monolithic and Kubernetes (K8S) containerized microservices architectures. I've likewise worked thoroughly with high-load SaaS options, REST/GRPC API applications, and CI/CD pipeline style.

I'm passionate about item delivery, and my background consists of mentoring staff member, performing comprehensive code and design reviews, and managing individuals. Additionally, I have actually worked with AWS Cloud services, as well as GCP and Azure integrations.