GitLab

How can you use DeepSeek R1 for personal performance?

Serhii Melnyk

Follow

--

Listen

Share

I always wished to collect data about my efficiency on the computer system. This idea is not brand-new; there are plenty of apps developed to resolve this concern. However, all of them have one considerable caution: you should send out highly sensitive and personal details about ALL your activity to "BIG BROTHER" and trust that your information won't end up in the hands of individual data reselling firms. That's why I decided to create one myself and make it 100% open-source for total transparency and dependability - and you can utilize it too!

Understanding your performance focus over a long period of time is important because it supplies important insights into how you designate your time, recognize patterns in your workflow, and discover locations for enhancement. Long-term productivity tracking can help you identify activities that consistently contribute to your goals and those that drain your energy and time without significant results.

For example, tracking your performance patterns can expose whether you're more effective during certain times of the day or in specific environments. It can likewise help you evaluate the long-lasting effect of changes, like altering your schedule, adopting new tools, or wiki.dulovic.tech dealing with procrastination. This data-driven method not only empowers you to enhance your daily routines however likewise assists you set realistic, attainable goals based upon evidence rather than assumptions. In essence, comprehending your productivity focus over time is a critical action towards 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 over the internet, making sure complete privacy.
- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, providing complete openness and user control.
- AI Analysis: An AI design analyzes your long-term activity to discover surprise patterns and provide actionable insights to enhance productivity.
- Classification Customization: Users can manually adjust AI classifications to much better reflect their personal productivity goals.
- AI Customization: Right now the application is using deepseek-r1:14 b. In the future, archmageriseswiki.com users will have the ability to pick from a variety of AI designs to match their specific needs.
- Browsers Domain Tracking: The application likewise tracks the time spent on individual websites within internet 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 couple of words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI start-up established in 2023, has actually just recently gathered considerable attention with the release of its latest AI model, R1. This model is significant for its high performance and cost-effectiveness, placing it as a powerful competitor to established AI models like OpenAI's ChatGPT.

The model is open-source and can be operated on desktop computers without the need for substantial computational resources. This democratization of AI technology allows individuals to try out and evaluate the design's capabilities firsthand

DeepSeek R1 is bad for whatever, there are affordable issues, however it's best for wiki.asexuality.org our performance tasks!

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

I strongly think that Personal-Productivity-Assistant might cause increased competition and drive development throughout 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 totally free availability make it an outstanding option.

The design itself will be delivered to your computer system via another job called Ollama. This is provided for benefit and tandme.co.uk much better resources allotment.

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

By locally, Ollama guarantees that all data processing takes place within your own environment, removing the requirement to send sensitive 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 support.

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, 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 regular work and wait some time to gather excellent quantity of data. Application will keep amount of second you spend in each application or site.

6. Finally produce 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 change to a smaller sized model for more effective resource management.

I 'd like to hear your feedback! Whether it's feature requests, 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 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 individuals focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in creating and executing high-reliability, scalable, and top quality projects. My technical know-how is complemented by strong team-leading and communication skills, which have actually helped me effectively lead teams for over 5 years.

Throughout my profession, I have actually concentrated on creating workflows for artificial intelligence and information science API services in cloud infrastructure, as well as creating monolithic and Kubernetes (K8S) containerized microservices architectures. I've also worked extensively with high-load SaaS services, forum.altaycoins.com REST/GRPC API applications, and CI/CD pipeline style.

I'm passionate about item shipment, and my background consists of mentoring staff member, conducting extensive code and design evaluations, and managing individuals. Additionally, I've dealt with AWS Cloud services, as well as GCP and Azure combinations.