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How can you use DeepSeek R1 for personal efficiency?

Serhii Melnyk

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I always wished to gather statistics about my efficiency on the computer. This concept is not brand-new; there are a lot of apps created to resolve this concern. However, all of them have one considerable caveat: you need to send out extremely delicate and individual details about ALL your to "BIG BROTHER" and trust that your data won't end up in the hands of personal information reselling firms. That's why I decided to develop one myself and make it 100% open-source for complete transparency and dependability - and you can use it too!

Understanding your efficiency focus over a long duration of time is necessary since it offers valuable insights into how you designate your time, recognize patterns in your workflow, and discover locations for enhancement. Long-term efficiency tracking can help you determine activities that regularly add to your goals and those that drain your energy and time without meaningful results.

For example, tracking your efficiency patterns can reveal whether you're more efficient during certain times of the day or in particular environments. It can likewise help you examine the long-lasting impact of adjustments, like altering your schedule, adopting brand-new tools, or dealing with procrastination. This data-driven technique not only empowers you to enhance your daily regimens however likewise helps you set reasonable, attainable goals based upon evidence instead of assumptions. In essence, comprehending your performance focus with time is a critical action toward creating a sustainable, effective 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, drapia.org making sure total privacy.
- Raw Time Log: The application shops a raw log of your activity in an open format within a designated folder, offering full transparency and user control.
- AI Analysis: An AI design examines your long-lasting activity to discover hidden patterns and supply actionable insights to improve productivity.
- Classification Customization: Users can by hand adjust AI categories to better reflect their individual performance goals.
- AI Customization: Today the application is utilizing deepseek-r1:14 b. In the future, users will be able to select from a range of AI models to match their specific needs.
- Browsers Domain Tracking: setiathome.berkeley.edu The application likewise tracks the time invested on individual websites within internet browsers (Chrome, Safari, Edge), providing 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 startup established in 2023, has actually just recently amassed substantial attention with the release of its most current AI design, R1. This design is significant for its high performance and cost-effectiveness, placing it as a powerful competitor to established AI designs like OpenAI's ChatGPT.

The design is open-source and can be worked on individual computers without the need for extensive computational resources. This democratization of AI innovation permits individuals to experiment with and examine the design's abilities firsthand

DeepSeek R1 is not excellent for whatever, there are sensible issues, however it's perfect for our productivity jobs!

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

I highly believe that Personal-Productivity-Assistant may lead to increased competition and drive innovation throughout the sector of similar 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 excellent alternative.

The model itself will be provided to your computer through another job called Ollama. This is provided for convenience and better resources allocation.

Ollama is an open-source platform that enables you to run large language designs (LLMs) in your area on your computer system, enhancing 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 information processing occurs within your own environment, eliminating the requirement to send out delicate details to external servers.

As an open-source job, Ollama gain from constant contributions from a vibrant community, making sure routine 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 ideas).
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 2nd you spend in each application or website.

6. Finally create the report.

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

I 'd enjoy to hear your feedback! Whether it's function requests, bug reports, or your success stories, elearnportal.science sign up with the community on GitHub to contribute and assist make the tool even much better. Together, we can shape the future of performance 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 enhancing individuals focus ...

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About Me

I'm Serhii Melnyk, with over 16 years of experience in designing and executing high-reliability, scalable, and high-quality projects. My technical know-how is matched by strong team-leading and interaction abilities, which have helped me successfully lead groups for over 5 years.

Throughout my career, I have actually concentrated on producing workflows for artificial intelligence and data science API services in cloud infrastructure, in addition to creating monolithic and Kubernetes (K8S) containerized microservices architectures. I have actually also worked thoroughly with high-load SaaS options, REST/GRPC API executions, and CI/CD pipeline design.

I'm enthusiastic about item shipment, and my background includes mentoring group members, conducting extensive code and style reviews, and handling people. Additionally, I have actually dealt with AWS Cloud services, as well as GCP and Azure integrations.