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How can you use DeepSeek R1 for personal efficiency?

Serhii Melnyk

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I constantly wished to collect stats about my performance on the computer system. This concept is not new; there are a lot of apps created to resolve this problem. However, all of them have one considerable caution: you need to send highly sensitive and personal details about ALL your activity to "BIG BROTHER" and trust that your data will not end up in the hands of personal data reselling companies. That's why I decided to develop one myself and make it 100% open-source for complete openness and trustworthiness - and you can use it too!

Understanding your productivity focus over an extended period of time is necessary because it provides important insights into how you assign your time, recognize patterns in your workflow, and find locations for improvement. Long-term productivity tracking can help you pinpoint activities that regularly contribute to your goals and those that drain your energy and time without significant results.

For instance, tracking your productivity patterns can expose whether you're more efficient during certain times of the day or in particular environments. It can also assist you assess the long-term impact of changes, like altering your schedule, embracing brand-new tools, or taking on procrastination. This data-driven method not only empowers you to enhance your daily regimens but likewise assists you set practical, attainable goals based on evidence rather than assumptions. In essence, understanding your efficiency focus with time is a vital action towards producing a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is designed to support.

Here are main functions:

- Privacy & Security: No details about your activity is sent online, making sure total privacy.
- Raw Time Log: The application shops a raw log of your activity in an open format within a designated folder, providing full transparency and user control.
- AI Analysis: An AI design evaluates your long-term activity to discover covert patterns and supply actionable insights to enhance efficiency.
- Classification Customization: Users can manually change AI categories to better reflect their individual performance objectives.
- AI Customization: Today the application is using deepseek-r1:14 b. In the future, users will have the ability to pick from a variety of AI models to match their particular requirements.
- Browsers Domain Tracking: The application likewise tracks the time spent on individual websites within web browsers (Chrome, funsilo.date Safari, Edge), providing a detailed view of online activity.
But before I continue explaining how to have fun with it, let me say a couple of words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI startup founded in 2023, has actually just recently garnered considerable attention with the release of its most current AI design, R1. This model is significant for its high efficiency and cost-effectiveness, positioning it as a formidable rival to established AI designs like OpenAI's ChatGPT.

The model is open-source and can be operated on desktop computers without the need for comprehensive computational resources. This democratization of AI innovation enables people to experiment with and evaluate the model's abilities firsthand

DeepSeek R1 is not excellent for everything, there are affordable issues, but it's best for our efficiency jobs!

Using this model we can classify applications or websites without sending any data to the cloud and therefore keep your data protect.

I strongly think that Personal-Productivity-Assistant may cause increased competition and drive development throughout the sector of similar productivity-tracking services (the combined user base of all time-tracking applications reaches tens of millions). Its open-source nature and totally free availability make it an excellent option.

The model itself will be provided to your computer by means of another job called Ollama. This is done for benefit and much better resources allowance.

Ollama is an open-source platform that allows you to run big language designs (LLMs) in your area on your computer system, enhancing data privacy and control. It's compatible with macOS, Windows, and Linux operating systems.

By running LLMs in your area, Ollama ensures that all information processing occurs within your own environment, getting rid of the requirement to send sensitive details to external servers.

As an open-source project, Ollama gain from continuous contributions from a vibrant neighborhood, making sure regular updates, function improvements, 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, because of deepseek-r1:14 b (14 billion params, chain of ideas).
4. Once set up, a black circle will appear in the system tray:.
5. Now do your routine work and wait some time to gather great of data. Application will keep quantity of second you invest in each application or site.

6. Finally create the report.

Note: Generating the report requires a minimum of 9GB of RAM, and the procedure might take a couple of minutes. If memory usage is an issue, 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 demands, bug reports, or your success stories, join the neighborhood on GitHub to contribute and help make the tool even better. Together, we can form the future of efficiency tools. Check it out here!

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

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

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in developing and implementing high-reliability, scalable, and premium projects. My technical competence is complemented by strong team-leading and interaction abilities, imoodle.win which have assisted me effectively lead groups for over 5 years.

Throughout my profession, I have actually concentrated on developing workflows for artificial intelligence and data science API services in cloud infrastructure, in addition to developing monolithic and Kubernetes (K8S) containerized microservices architectures. I've also worked thoroughly with high-load SaaS solutions, REST/GRPC API executions, and CI/CD pipeline style.

I'm enthusiastic about product shipment, and my background consists of mentoring staff member, carrying out extensive code and style evaluations, and handling people. Additionally, I've dealt with AWS Cloud services, in addition to GCP and Azure combinations.