How can you use DeepSeek R1 for individual performance?

Serhii Melnyk

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

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

For instance, tracking your productivity patterns can reveal whether you're more efficient during certain times of the day or in particular environments. It can likewise assist you examine the long-term effect of modifications, like changing your schedule, embracing brand-new tools, or dealing with procrastination. This data-driven approach not only empowers you to enhance your daily regimens but likewise helps you set reasonable, attainable goals based on proof instead of assumptions. In essence, comprehending your performance focus in time is an important step towards producing a sustainable, effective work-life balance - something Personal-Productivity-Assistant is designed to support.

Here are main functions:

- Privacy & Security: No details about your activity is sent out over the internet, guaranteeing total privacy.

- Raw Time Log: wikitravel.org The application stores a raw log of your activity in an open format within a designated folder, using complete transparency and user control.

- AI Analysis: An AI model evaluates your long-term activity to uncover concealed patterns and provide actionable insights to improve efficiency.

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

- AI Customization: Right now the application is using deepseek-r1:14 b. In the future, users will have the ability to pick from a range of AI designs to match their specific needs.

- Browsers Domain Tracking: The application likewise tracks the time invested in specific sites within web browsers (Chrome, Safari, Edge), offering 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 start-up founded in 2023, has actually just recently gathered significant attention with the release of its latest 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 wikibase.imfd.cl can be operated on individual computers without the requirement for comprehensive computational resources. This democratization of AI technology allows people to explore and examine the model's abilities firsthand

DeepSeek R1 is not good for everything, there are reasonable concerns, however it's ideal for our performance jobs!

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

I strongly think that Personal-Productivity-Assistant might cause increased competitors and drive development across 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 totally free availability make it an excellent alternative.

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

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

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

As an open-source task, Ollama gain from continuous contributions from a lively neighborhood, ensuring regular 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, since of deepseek-r1:14 b (14 billion params, chain of thoughts).

4. Once set up, prawattasao.awardspace.info a black circle will appear in the system tray:.


5. Now do your regular work and wait a long time to gather excellent amount of data. Application will store quantity of 2nd you invest in each application or site.

6. Finally create the report.

Note: Generating the report needs a minimum of 9GB of RAM, and the procedure might take a few minutes. If memory use is an issue, it's possible to switch to a smaller sized model for more effective resource management.

I 'd love to hear your feedback! Whether it's function demands, 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 form the future of efficiency 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 individuals focus ...

github.com

About Me

I'm Serhii Melnyk, with over 16 years of experience in developing and executing high-reliability, scalable, and high-quality jobs. My technical proficiency is matched by strong team-leading and interaction skills, which have actually assisted me successfully lead teams for over 5 years.

Throughout my career, I've concentrated on creating workflows for artificial intelligence and data science API services in cloud infrastructure, in addition to designing monolithic and Kubernetes (K8S) containerized microservices architectures. I've likewise worked thoroughly with high-load SaaS services, REST/GRPC API applications, and CI/CD pipeline style.

I'm passionate about product shipment, wiki.woge.or.at and my background includes mentoring team members, conducting comprehensive code and style reviews, and managing individuals. Additionally, I have actually dealt with AWS Cloud services, along with GCP and Azure integrations.