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How can you utilize DeepSeek R1 for individual efficiency?

Serhii Melnyk

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I always wished to gather stats about my on the computer system. This idea is not new; there are a lot of apps developed to solve this concern. However, all of them have one substantial caveat: you must send out highly delicate and personal details about ALL your activity to "BIG BROTHER" and trust that your information will not wind up in the hands of individual information reselling companies. That's why I decided to produce one myself and make it 100% open-source for total openness and reliability - and you can utilize it too!

Understanding your productivity focus over an extended period of time is vital since it provides valuable insights into how you assign your time, determine patterns in your workflow, and discover areas for improvement. Long-term efficiency tracking can assist you identify activities that consistently add to your objectives and those that drain your energy and time without meaningful outcomes.

For example, tracking your productivity trends can reveal whether you're more reliable during certain times of the day or in specific environments. It can likewise assist you evaluate the long-lasting effect of adjustments, like changing your schedule, embracing brand-new tools, pediascape.science or dealing with procrastination. This data-driven technique not just empowers you to enhance your daily regimens but also helps you set realistic, attainable objectives based upon proof instead of assumptions. In essence, comprehending your productivity focus in time is a vital step toward creating a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is developed to support.

Here are main features:

- Privacy & Security: No details about your activity is sent online, guaranteeing total privacy.
- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, offering complete transparency and user control.
- AI Analysis: An AI design examines your long-term activity to reveal covert patterns and chessdatabase.science offer actionable insights to improve productivity.
- Classification Customization: Users can by hand adjust AI classifications to much better show their individual efficiency goals.
- AI Customization: Today the application is using deepseek-r1:14 b. In the future, users will have the ability to choose from a variety of AI designs to fit their specific requirements.
- Browsers Domain Tracking: The application also tracks the time spent on specific sites within internet browsers (Chrome, Safari, Edge), using a detailed view of online activity.
But before I continue explaining how to play with it, let me say a couple of words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI start-up founded in 2023, has just recently amassed substantial attention with the release of its newest AI model, R1. This design is notable for its high performance and cost-effectiveness, positioning it as a formidable competitor to established AI models like OpenAI's ChatGPT.

The model is open-source and can be operated on computers without the requirement for comprehensive computational resources. This democratization of AI technology allows individuals to explore and oke.zone assess the model's capabilities firsthand

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

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

I strongly think that Personal-Productivity-Assistant may lead to increased competitors and links.gtanet.com.br drive development across 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 free availability make it an excellent option.

The model itself will be provided to your computer through another project called Ollama. This is done for benefit and better resources allowance.

Ollama is an open-source platform that enables you to run large language models (LLMs) in your area on your computer system, boosting information personal privacy and control. It's compatible with macOS, Windows, and Linux running systems.

By running LLMs in your area, Ollama guarantees that all information processing occurs within your own environment, eliminating the requirement to send out delicate details to external servers.

As an open-source task, Ollama gain from constant contributions from a lively neighborhood, guaranteeing routine 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, because 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 routine work and wait some time to gather great amount of stats. Application will store quantity of 2nd you invest in each application or site.

6. Finally create the report.

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

I 'd like to hear your feedback! Whether it's function demands, complexityzoo.net bug reports, or your success stories, join the neighborhood on GitHub to contribute and assist make the tool even better. Together, we can shape the future of performance tools. Check it out here!

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

Personal Productivity Assistant is an innovative open-source application devoting to improving people focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in developing and carrying out high-reliability, scalable, and top quality jobs. My technical competence is complemented by strong team-leading and interaction skills, which have actually helped me effectively lead teams for over 5 years.

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

I'm passionate about product delivery, tandme.co.uk and my background includes mentoring employee, conducting thorough code and design reviews, and handling individuals. Additionally, I have actually dealt with AWS Cloud services, along with GCP and Azure integrations.