How can you use DeepSeek R1 for individual productivity?

Serhii Melnyk

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I always desired to collect data about my productivity on the computer. This concept is not new; there are lots of apps created to solve this issue. However, all of them have one substantial caution: you should send out extremely delicate and personal details about ALL your activity to "BIG BROTHER" and trust that your information won't wind 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 credibility - and you can use it too!

Understanding your efficiency focus over a long duration of time is necessary since it supplies valuable insights into how you assign your time, determine patterns in your workflow, and discover locations for improvement. Long-term performance tracking can help you pinpoint activities that consistently add to your objectives and those that drain your time and energy without meaningful results.

For example, tracking your performance patterns can reveal whether you're more reliable during certain times of the day or in specific environments. It can likewise help you examine the long-lasting impact of changes, like altering your schedule, embracing new tools, or taking on procrastination. This data-driven method not just empowers you to optimize your daily regimens but also assists you set practical, attainable goals based on evidence rather than presumptions. In essence, understanding your performance focus with time is a crucial step towards creating 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 online, wikitravel.org making sure total privacy.
- Raw Time Log: The a raw log of your activity in an open format within a designated folder, providing complete openness and user control.
- AI Analysis: An AI model examines your long-lasting activity to discover surprise patterns and offer actionable insights to enhance productivity.
- Classification Customization: Users can manually change AI classifications to better show their individual performance objectives.
- AI Customization: Right now the application is using deepseek-r1:14 b. In the future, users will be able to select from a variety of AI models to match their specific requirements.
- Browsers Domain Tracking: The application also tracks the time invested on individual sites within web browsers (Chrome, Safari, Edge), providing a detailed view of online activity.
But before I continue explaining how to have fun with it, ghetto-art-asso.com let me state a couple of words about the main killer function here: DeepSeek R1.

DeepSeek, addsub.wiki a Chinese AI start-up established in 2023, has just recently garnered substantial attention with the release of its latest AI model, R1. This design is notable for its high efficiency and cost-effectiveness, positioning it as a powerful rival to developed 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 permits people to explore and evaluate the design's capabilities firsthand

DeepSeek R1 is bad for everything, there are sensible issues, but it's perfect for our performance jobs!

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

I strongly believe that Personal-Productivity-Assistant might lead to increased competitors and drive innovation across the sector of comparable 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 provided to your computer system by means of another job called Ollama. This is provided for benefit and better resources allowance.

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

By running LLMs in your area, Ollama guarantees that all data processing takes place within your own environment, getting rid of the need to send sensitive details to external servers.

As an open-source job, Ollama gain from constant contributions from a vibrant community, ensuring regular updates, feature improvements, and robust support.

Now how to install 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 second you invest in each application or website.

6. Finally generate the report.

Note: Generating the report requires a minimum of 9GB of RAM, and the procedure might take a few minutes. If memory usage is a concern, it's possible to switch to a smaller design for yogicentral.science more effective resource management.

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

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

Personal Productivity Assistant is an advanced open-source application committing to boosting individuals focus ...

github.com

About Me

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

Throughout my career, I've focused on developing workflows for artificial intelligence and information science API services in cloud infrastructure, along with designing monolithic and Kubernetes (K8S) containerized microservices architectures. I've likewise worked thoroughly with high-load SaaS options, REST/GRPC API implementations, and CI/CD pipeline style.

I'm passionate about product shipment, and my background includes mentoring staff member, carrying out comprehensive code and style evaluations, and handling people. Additionally, I've dealt with AWS Cloud services, as well as GCP and Azure combinations.