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

Serhii Melnyk

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I constantly wanted to gather statistics about my performance on the computer. This idea is not new; there are a lot of apps developed to solve this concern. However, all of them have one significant caveat: you must send out extremely delicate and personal details about ALL your activity to "BIG BROTHER" and trust that your data won't wind up in the hands of individual data reselling firms. That's why I decided to produce one myself and make it 100% open-source for total openness and trustworthiness - and you can use it too!

Understanding your productivity focus over an extended period of time is important since it supplies valuable insights into how you designate your time, determine patterns in your workflow, and discover locations for enhancement. Long-term performance tracking can assist you determine activities that consistently add to your objectives and classifieds.ocala-news.com those that drain your time and energy without significant results.

For instance, tracking your productivity patterns can expose whether you're more effective throughout certain times of the day or in specific environments. It can likewise assist you assess the long-lasting effect of changes, like altering your schedule, embracing new tools, or dealing with procrastination. This data-driven approach not just empowers you to enhance your daily regimens however likewise helps you set practical, attainable objectives based on proof instead of assumptions. In essence, comprehending your efficiency focus over time is a crucial step toward producing a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is created to support.

Here are main features:

- Privacy & Security: No details about your activity is sent over the internet, ensuring total personal privacy.

- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, providing full transparency and user control.

- AI Analysis: pediascape.science An AI model evaluates your long-lasting activity to uncover covert patterns and supply actionable insights to improve performance.

- Classification Customization: Users can manually adjust AI categories to much better reflect their personal performance goals.

- AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will be able to choose from a variety of AI models to suit their specific requirements.

- Browsers Domain Tracking: The application likewise tracks the time invested in individual websites within web 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 few 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 latest AI model, R1. This design is notable for its high performance and cost-effectiveness, positioning it as a powerful rival to established AI designs like OpenAI's ChatGPT.

The design is open-source and can be worked on individual computers without the need for substantial computational resources. This democratization of AI technology allows individuals to try out and examine the design's abilities firsthand

DeepSeek R1 is bad for everything, there are affordable concerns, bbarlock.com but it's best for our efficiency tasks!

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

I highly believe that Personal-Productivity-Assistant might cause increased competition and drive development across the sector of similar productivity-tracking services (the integrated user base of all time-tracking applications reaches 10s of millions). Its open-source nature and complimentary availability make it an exceptional alternative.

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

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

By running LLMs locally, Ollama guarantees that all data processing occurs within your own environment, eliminating the requirement to send sensitive details to external servers.

As an open-source project, Ollama gain from constant contributions from a dynamic neighborhood, making sure routine updates, function improvements, and robust support.

Now how to install and run?

1. Install Ollama: utahsyardsale.com Windows|MacOS

2. Install Personal-Productivity-Assistant: Windows|MacOS

3. First start can take some, drapia.org due to the fact that of deepseek-r1:14 b (14 billion params, chain of thoughts).

4. Once set up, a black circle will appear in the system tray:.


5. Now do your routine work and wait a long time to gather great amount of data. Application will keep quantity of 2nd you spend in each application or website.

6. Finally generate the report.

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

I 'd love to hear your feedback! Whether it's feature requests, bug reports, or your success stories, join the community on GitHub to contribute and help make the tool even much better. Together, we can shape the future of efficiency tools. Check it out here!

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

Personal Productivity Assistant is an innovative open-source application committing 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 high-quality jobs. My technical knowledge is complemented by strong team-leading and communication skills, which have actually helped me successfully lead groups for over 5 years.

Throughout my profession, I've concentrated on producing 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 options, REST/GRPC API executions, and CI/CD pipeline style.

I'm passionate about product delivery, and my background includes mentoring employee, performing extensive code and style evaluations, and managing individuals. Additionally, I have actually dealt with AWS Cloud services, along with GCP and Azure integrations.

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