How can you make use of DeepSeek R1 for individual efficiency?

Serhii Melnyk

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I constantly desired to collect statistics about my efficiency on the computer system. This concept is not brand-new; there are lots of apps created to solve this problem. However, all of them have one significant caution: you need to send out extremely delicate and individual details about ALL your activity to "BIG BROTHER" and trust that your data won't end up in the hands of individual information reselling firms. That's why I decided to produce one myself and make it 100% open-source for complete openness and trustworthiness - and you can use it too!

Understanding your efficiency focus over a long period of time is essential due to the fact that it offers valuable insights into how you allocate your time, determine patterns in your workflow, and find locations for improvement. Long-term performance tracking can assist you pinpoint activities that regularly contribute to your goals and those that drain your time and energy without significant outcomes.

For example, tracking your efficiency trends can reveal whether you're more efficient during certain times of the day or in particular environments. It can also help you examine the long-lasting impact of changes, like altering your schedule, embracing brand-new tools, or dealing with procrastination. This data-driven method not only empowers you to enhance your daily regimens but likewise assists you set sensible, attainable objectives based on evidence rather than presumptions. In essence, understanding your productivity focus in time is a vital action towards creating a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is designed to support.

Here are main features:

- Privacy & Security: No details about your activity is sent over the web, wiki.rrtn.org making sure complete personal privacy.

- Raw Time Log: The application shops 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 evaluates your long-term activity to reveal surprise patterns and supply actionable insights to improve productivity.

- Classification Customization: Users can by hand adjust AI categories to much better show their individual productivity objectives.

- AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will be able to select from a range of AI designs to suit their particular requirements.

- Browsers Domain Tracking: The application likewise tracks the time invested in individual websites 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 state a couple of words about the main killer feature here: DeepSeek R1.

DeepSeek, a Chinese AI start-up established in 2023, has actually just recently amassed significant attention with the release of its newest AI design, R1. This design is notable 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 worked on individual computer systems without the requirement for substantial computational resources. This democratization of AI technology permits individuals to explore and assess the design's capabilities firsthand

DeepSeek R1 is not good for everything, there are sensible issues, but it's perfect for our efficiency tasks!

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

I strongly think that Personal-Productivity-Assistant may cause increased competition and drive development across the sector of comparable productivity-tracking services (the integrated user base of all time-tracking applications reaches tens of millions). Its open-source nature and totally free availability make it an outstanding alternative.

The model itself will be provided to your computer system by means of another project called Ollama. This is done for convenience and better resources allowance.

Ollama is an open-source platform that allows you to run big language designs (LLMs) in your area on your computer system, enhancing information privacy and control. It's compatible with macOS, Windows, and Linux operating systems.

By running LLMs in your area, Ollama makes sure that all data processing occurs within your own environment, eliminating the need to send out delicate details to external servers.

As an open-source job, Ollama gain from constant contributions from a dynamic community, ensuring routine updates, feature improvements, and robust assistance.

Now how to install 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 ideas).

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


5. Now do your routine work and wait a long time to gather excellent quantity of statistics. Application will save quantity 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 few minutes. If memory use is a concern, it's possible to switch to a smaller sized design for more efficient resource management.

I 'd like to hear your feedback! Whether it's function requests, bug reports, or your success stories, sign up with the neighborhood 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 dedicating to boosting people focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in creating and carrying out high-reliability, scalable, and premium tasks. My technical competence is matched by strong team-leading and communication skills, which have actually helped me successfully lead groups for over 5 years.

Throughout my career, I have actually concentrated on creating workflows for artificial intelligence and data science API services in cloud facilities, as well as designing monolithic and Kubernetes (K8S) containerized microservices architectures. I've also worked thoroughly with high-load SaaS services, REST/GRPC API implementations, and CI/CD pipeline design.

I'm passionate about product shipment, and my background includes mentoring team members, performing comprehensive code and design reviews, and managing individuals. Additionally, I've worked with AWS Cloud services, in addition to GCP and Azure integrations.