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How can you use DeepSeek R1 for individual performance?

Serhii Melnyk

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I always wished to collect statistics about my efficiency on the computer. This concept is not new; there are a lot of apps developed to fix this issue. However, all of them have one substantial caution: you need to send extremely delicate and individual details about ALL your activity to "BIG BROTHER" and trust that your data will not wind up in the hands of personal data reselling companies. That's why I decided to produce one myself and make it 100% open-source for complete transparency and trustworthiness - and you can use it too!

Understanding your productivity focus over an extended period of time is vital due to the fact that it supplies valuable insights into how you designate your time, recognize patterns in your workflow, and discover areas for improvement. Long-term efficiency tracking can help you identify activities that regularly contribute to your goals and those that drain your energy and time without significant results.

For instance, tracking your performance patterns can reveal whether you're more efficient during certain times of the day or in specific environments. It can likewise assist you examine the long-term impact of adjustments, like changing your schedule, adopting brand-new tools, or videochatforum.ro taking on procrastination. This data-driven technique not just empowers you to optimize your daily routines but likewise assists you set reasonable, attainable goals based on proof instead of presumptions. In essence, understanding your efficiency focus in time is a crucial step toward creating a sustainable, effective work-life balance - something Personal-Productivity-Assistant is developed to support.

Here are main features:

- Privacy & Security: No details about your activity is sent out online, ensuring total personal privacy.
- Raw Time Log: The application shops a raw log of your activity in an open format within a designated folder, utahsyardsale.com using full transparency and user control.
- AI Analysis: An AI design analyzes your long-term activity to discover hidden patterns and provide actionable to enhance productivity.
- Classification Customization: Users can manually change AI classifications to better reflect their individual productivity goals.
- AI Customization: Today the application is utilizing deepseek-r1:14 b. In the future, users will be able to select from a variety of AI designs to match their particular needs.
- Browsers Domain Tracking: The application also tracks the time invested on specific websites within browsers (Chrome, prawattasao.awardspace.info Safari, Edge), providing a detailed view of online activity.
But before I continue explaining how to have fun with it, let me say a few words about the main killer function here: DeepSeek R1.

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

The design is open-source and can be worked on individual computers without the need for comprehensive computational resources. This democratization of AI technology enables people to try out and assess the design's capabilities firsthand

DeepSeek R1 is not great for whatever, there are reasonable issues, but it's ideal for our productivity tasks!

Using this design we can categorize applications or websites without sending any information to the cloud and hence keep your information protect.

I highly think that Personal-Productivity-Assistant might result in increased competitors and drive innovation across 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 free availability make it an exceptional option.

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

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

By operating LLMs in your area, Ollama makes sure that all information processing happens within your own environment, getting rid of the requirement to send sensitive details to external servers.

As an open-source job, Ollama gain from continuous contributions from a dynamic community, ensuring routine updates, function 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, since of deepseek-r1:14 b (14 billion params, chain of ideas).
4. Once installed, a black circle will appear in the system tray:.
5. Now do your routine work and wait some time to gather good quantity of stats. Application will store amount of second you invest in each application or site.

6. Finally generate the report.

Note: Generating the report requires a minimum of 9GB of RAM, and fishtanklive.wiki the process might take a few minutes. If memory use is an issue, it's possible to change to a smaller model for more effective resource management.

I 'd love to hear your feedback! Whether it's function demands, bug reports, or your success stories, join the community on GitHub to contribute and assist make the tool even much 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 innovative open-source application devoting to improving people focus ...

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About Me

I'm Serhii Melnyk, with over 16 years of experience in designing and carrying out high-reliability, scalable, and high-quality jobs. My technical know-how is matched by strong team-leading and communication skills, which have assisted me effectively lead teams for over 5 years.

Throughout my profession, I've concentrated on developing workflows for artificial intelligence and information science API services in cloud facilities, in addition to designing monolithic and Kubernetes (K8S) containerized microservices architectures. I've likewise worked extensively with high-load SaaS solutions, REST/GRPC API implementations, and CI/CD pipeline design.

I'm passionate about item delivery, and my background includes mentoring group members, performing thorough code and design evaluations, and managing individuals. Additionally, I've dealt with AWS Cloud services, along with GCP and Azure combinations.