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How can you use DeepSeek R1 for individual performance?

Serhii Melnyk

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I constantly wished to gather stats about my performance on the computer system. This idea is not brand-new; there are lots of apps developed to fix this problem. However, all of them have one substantial caveat: you must send out extremely sensitive and individual details about ALL your activity to "BIG BROTHER" and trust that your data won't wind up in the hands of individual information reselling companies. That's why I decided to develop one myself and make it 100% open-source for complete openness and credibility - and you can use it too!

Understanding your efficiency focus over a long duration of time is important since it provides important insights into how you assign your time, determine patterns in your workflow, and discover locations for enhancement. Long-term productivity tracking can assist you identify activities that consistently add to your objectives and those that drain your energy and time without meaningful outcomes.

For example, tracking your productivity trends can expose whether you're more effective throughout certain times of the day or in particular environments. It can also assist you examine the long-lasting impact of modifications, like changing your schedule, adopting new tools, or dealing with procrastination. This data-driven technique not only empowers you to enhance your daily regimens but likewise assists you set practical, attainable objectives based upon proof instead of presumptions. In essence, comprehending your productivity focus gradually is a vital action towards producing 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 over the internet, guaranteeing complete personal privacy.
- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, offering complete openness and user control.
- AI Analysis: An AI model examines your long-term activity to discover covert patterns and provide actionable insights to boost performance.
- Classification Customization: Users can by hand adjust AI categories to better show their individual performance goals.
- AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will be able to pick from a variety of AI designs to suit their particular requirements.
- Browsers Domain Tracking: The application also tracks the time invested on specific websites within web browsers (Chrome, Safari, Edge), providing a detailed view of online activity.
But before I continue explaining how to play with it, let me say a few words about the main killer feature here: DeepSeek R1.

DeepSeek, a Chinese AI in 2023, has actually just recently gathered substantial attention with the release of its newest AI design, R1. This model is noteworthy for its high performance and cost-effectiveness, placing it as a powerful rival to developed AI models like OpenAI's ChatGPT.

The design is open-source and can be run on individual computer systems without the requirement for extensive computational resources. This democratization of AI technology enables people to try out and evaluate the model's abilities firsthand

DeepSeek R1 is not excellent for whatever, there are sensible issues, but it's best for our performance tasks!

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

I strongly believe that Personal-Productivity-Assistant might result in increased competition and drive development throughout 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 free availability make it an outstanding option.

The design itself will be delivered to your computer through another project called Ollama. This is done for benefit and better resources allocation.

Ollama is an open-source platform that allows you to run large language models (LLMs) locally on your computer, enhancing data personal privacy and control. It's compatible with macOS, Windows, and Linux running systems.

By operating LLMs in your area, Ollama makes sure that all information processing occurs within your own environment, getting rid of the need to send delicate details to external servers.

As an open-source project, Ollama gain from constant contributions from a dynamic neighborhood, making sure routine updates, feature improvements, 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 routine work and wait a long time to gather good quantity of stats. Application will keep quantity of second you invest in each application or website.

6. Finally create the report.

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

I 'd like to hear your feedback! Whether it's feature demands, bug reports, or your success stories, join 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 committing to boosting people focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in designing and carrying out high-reliability, scalable, and premium tasks. My technical proficiency is complemented by strong team-leading and sciencewiki.science interaction abilities, which have assisted me effectively lead teams for over 5 years.

Throughout my career, I've concentrated on creating workflows for artificial intelligence and information science API services in cloud facilities, engel-und-waisen.de along with creating monolithic and Kubernetes (K8S) containerized microservices architectures. I've also worked thoroughly with high-load SaaS options, REST/GRPC API applications, and CI/CD pipeline design.

I'm passionate about product shipment, and my background consists of mentoring team members, carrying out thorough code and design reviews, and managing individuals. Additionally, I have actually worked with AWS Cloud services, along with GCP and Azure integrations.