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

Serhii Melnyk

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I constantly wanted to collect stats about my productivity on the computer. This concept is not new; there are plenty of apps designed to fix this problem. However, all of them have one considerable caution: you should send out highly delicate and individual details about ALL your activity to "BIG BROTHER" and trust that your information will not wind up in the hands of personal data reselling firms. That's why I chose to develop one myself and make it 100% open-source for total openness and reliability - and you can utilize it too!

Understanding your productivity focus over a long period of time is important since it supplies valuable insights into how you allocate your time, recognize patterns in your workflow, and find locations for enhancement. Long-term efficiency tracking can help you identify activities that regularly contribute to your goals and those that drain your time and energy without meaningful outcomes.

For instance, tracking your performance trends can expose whether you're more reliable during certain times of the day or in specific environments. It can likewise help you assess the long-term effect of adjustments, like altering your schedule, embracing brand-new tools, or dealing with procrastination. This data-driven approach not just empowers you to enhance your daily routines however likewise assists you set realistic, attainable objectives based on evidence instead of presumptions. In essence, understanding your efficiency focus over time is a crucial action toward creating a sustainable, effective work-life balance - something Personal-Productivity-Assistant is created to support.

Here are main features:

- Privacy & Security: No details about your activity is sent out over the web, guaranteeing 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 model analyzes your long-lasting activity to uncover concealed patterns and supply actionable insights to boost efficiency.
- Classification Customization: Users can manually change AI classifications to better reflect their individual performance goals.
- AI Customization: Today the application is using deepseek-r1:14 b. In the future, users will be able to select from a range of AI models to match their .
- Browsers Domain Tracking: The application also tracks the time invested on private websites within browsers (Chrome, Safari, Edge), offering a detailed view of online activity.
But before I continue explaining how to have fun with it, wiki.eqoarevival.com let me state a couple of words about the main killer feature here: DeepSeek R1.

DeepSeek, a Chinese AI start-up founded in 2023, has recently gathered substantial attention with the release of its newest AI model, R1. This design is significant for its high efficiency and cost-effectiveness, positioning it as a powerful competitor to established AI designs like OpenAI's ChatGPT.

The design is open-source and can be operated on personal computer systems without the requirement for comprehensive computational resources. This democratization of AI innovation enables people to experiment with and assess the design's abilities firsthand

DeepSeek R1 is not great for everything, there are reasonable issues, but it's perfect for our efficiency jobs!

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

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

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

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

By operating LLMs in your area, Ollama ensures that all information processing occurs within your own environment, removing the need to send out delicate details to external servers.

As an open-source task, Ollama gain from continuous contributions from a lively neighborhood, guaranteeing regular 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 ideas).
4. Once installed, a black circle will appear in the system tray:.
5. Now do your routine work and wait some time to collect excellent amount of stats. Application will save amount of 2nd you spend in each application or site.

6. Finally produce the report.

Note: Generating the report needs a minimum of 9GB of RAM, and the process may take a few minutes. If memory usage is a concern, it's possible to switch to a smaller model for more efficient resource management.

I 'd enjoy to hear your feedback! Whether it's function demands, bug reports, or your success stories, sign up with the community on GitHub to contribute and akropolistravel.com assist make the tool even better. Together, we can shape the future of productivity tools. Check it out here!

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

Personal Productivity Assistant is an advanced 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 executing high-reliability, scalable, and premium tasks. My technical know-how is matched by strong team-leading and communication abilities, which have assisted me effectively lead teams for over 5 years.

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

I'm enthusiastic about item delivery, and my background consists of mentoring employee, performing extensive code and style evaluations, and managing people. Additionally, I've dealt with AWS Cloud services, as well as GCP and Azure combinations.