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

Serhii Melnyk

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I always wanted to collect stats about my efficiency on the computer system. This idea is not new; there are a lot of apps created to resolve this problem. However, all of them have one considerable caution: you need to send out extremely sensitive and personal details about ALL your activity to "BIG BROTHER" and trust that your data won't wind 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 transparency and trustworthiness - and you can use it too!

Understanding your productivity focus over an extended period of time is essential because it provides important insights into how you designate your time, determine patterns in your workflow, and find areas for enhancement. Long-term performance tracking can help you pinpoint activities that consistently contribute to your objectives and those that drain your energy and time without significant outcomes.

For instance, tracking your productivity patterns can expose whether you're more reliable during certain times of the day or in particular environments. It can likewise assist you assess the long-term effect of adjustments, like changing your schedule, adopting brand-new tools, or taking on procrastination. This data-driven approach not just empowers you to enhance your daily routines but also assists you set sensible, attainable goals based upon evidence instead of presumptions. In essence, comprehending your productivity focus in time is a crucial step toward 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 online, making sure complete personal privacy.
- Raw Time Log: lespoetesbizarres.free.fr The application stores a raw log of your activity in an open format within a designated folder, providing full transparency and user control.
- AI Analysis: An AI model analyzes your long-term activity to uncover hidden patterns and supply actionable insights to boost productivity.
- Classification Customization: Users can by hand change AI categories to better show their personal performance goals.
- AI Customization: Today the application is utilizing deepseek-r1:14 b. In the future, users will be able to select from a variety of AI models to fit their specific requirements.
- Browsers Domain Tracking: The application also tracks the time invested in private sites within web browsers (Chrome, asteroidsathome.net Safari, Edge), using a detailed view of online activity.
But before I continue explaining how to play with it, let me state a few words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI startup established in 2023, has actually just recently garnered substantial 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 rival to developed AI models like OpenAI's ChatGPT.

The model is open-source and can be operated on computers without the need for substantial computational resources. This democratization of AI innovation allows individuals to experiment with and examine the design's capabilities firsthand

DeepSeek R1 is bad for whatever, there are reasonable issues, but it's ideal for our productivity jobs!

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

I highly believe that Personal-Productivity-Assistant might result in increased competitors and drive development across the sector of comparable productivity-tracking services (the combined user base of all time-tracking applications reaches 10s of millions). Its open-source nature and complimentary availability make it an exceptional option.

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

Ollama is an open-source platform that allows you to run big language models (LLMs) in your area on your computer, boosting data privacy and control. It works with macOS, Windows, and Linux operating systems.

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

As an open-source job, Ollama gain from continuous contributions from a dynamic neighborhood, making sure regular updates, function enhancements, 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 routine work and wait a long time to gather good amount of statistics. Application will store amount of 2nd you spend in each application or website.

6. Finally create the report.

Note: Generating the report requires a minimum of 9GB of RAM, and the procedure may take a few minutes. If memory usage is an issue, it's possible to switch to a smaller 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 engel-und-waisen.de help make the tool even 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 is an innovative open-source application committing to boosting individuals focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in developing and carrying out high-reliability, scalable, and premium jobs. My technical know-how is complemented by strong team-leading and interaction abilities, which have assisted me effectively 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 designing monolithic and Kubernetes (K8S) containerized microservices architectures. I have actually also worked extensively with high-load SaaS solutions, REST/GRPC API applications, and CI/CD pipeline style.

I'm enthusiastic about product delivery, and my background includes mentoring team members, conducting comprehensive code and design reviews, and managing individuals. Additionally, I've dealt with AWS Cloud services, in addition to GCP and Azure combinations.