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

Serhii Melnyk

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I always desired to collect data about my productivity on the computer system. This concept is not brand-new; there are plenty of apps developed to resolve this issue. However, all of them have one significant caution: you must send highly sensitive and individual details about ALL your activity to "BIG BROTHER" and trust that your data will not end up in the hands of personal data reselling companies. That's why I decided to produce one myself and make it 100% open-source for total transparency and dependability - and you can utilize it too!

Understanding your performance focus over an extended period of time is essential because it provides valuable insights into how you allocate your time, recognize patterns in your workflow, clashofcryptos.trade and find areas for improvement. Long-term productivity tracking can help you identify activities that regularly add to your goals and those that drain your time and energy without significant outcomes.

For instance, tracking your performance trends can reveal whether you're more effective throughout certain times of the day or in particular environments. It can likewise assist you assess the long-term impact of modifications, like changing your schedule, adopting brand-new tools, or taking on procrastination. This data-driven technique not just empowers you to optimize your daily routines however also assists you set realistic, attainable objectives based on evidence instead of assumptions. In essence, understanding your performance focus gradually is an important step toward developing a sustainable, effective work-life balance - something Personal-Productivity-Assistant is developed to support.

Here are main features:

- Privacy & Security: No details about your activity is sent over the internet, making sure complete privacy.
- Raw Time Log: The application stores a raw log of your activity in an open format within a designated folder, offering complete transparency and user control.
- AI Analysis: An AI design examines your long-term activity to discover hidden patterns and offer actionable insights to boost efficiency.
- Classification Customization: Users can by hand adjust AI classifications to much better reflect their personal efficiency objectives.
- AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will have the ability to choose from a range of AI designs to fit their specific requirements.
- Browsers Domain Tracking: The application likewise tracks the time invested in individual sites within browsers (Chrome, Safari, Edge), offering a detailed view of online activity.
But before I continue how to play with it, let me say a couple of words about the main killer function here: DeepSeek R1.

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

The model is open-source and can be operated on individual computer systems without the need for substantial computational resources. This democratization of AI technology allows individuals to explore and examine the design's capabilities firsthand

DeepSeek R1 is not good for whatever, there are reasonable concerns, however it's best for our efficiency jobs!

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

I strongly think that Personal-Productivity-Assistant may result in increased competitors and drive development across 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 model itself will be provided to your computer system by means of another job called Ollama. This is done for benefit and much better resources allotment.

Ollama is an open-source platform that enables you to run large language models (LLMs) locally on your computer, boosting information privacy and control. It works with macOS, Windows, and Linux operating systems.

By running LLMs locally, Ollama makes sure that all information processing occurs within your own environment, getting rid of the requirement to send sensitive details to external servers.

As an open-source project, Ollama gain from constant contributions from a lively community, making sure regular updates, function 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, since 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 regular work and utahsyardsale.com wait some time to gather great amount of data. Application will keep quantity of 2nd you spend in each application or site.

6. Finally generate the report.

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

I 'd love to hear your feedback! Whether it's feature requests, bug reports, or your success stories, users.atw.hu sign up with the community on GitHub to contribute and assist make the tool even better. Together, we can form the future of performance tools. Check it out here!

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

Personal Productivity Assistant is an advanced open-source application committing to enhancing people focus ...

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About Me

I'm Serhii Melnyk, with over 16 years of experience in developing and implementing high-reliability, scalable, and premium tasks. My technical competence is matched by strong team-leading and interaction abilities, which have actually assisted me effectively lead groups for over 5 years.

Throughout my career, I've concentrated on producing workflows for artificial intelligence and information science API services in cloud infrastructure, along with creating monolithic and Kubernetes (K8S) containerized microservices architectures. I have actually also worked extensively with high-load SaaS options, REST/GRPC API executions, bphomesteading.com and CI/CD pipeline style.

I'm passionate about product delivery, and my background includes mentoring employee, performing extensive code and design reviews, and handling people. Additionally, I have actually worked with AWS Cloud services, in addition to GCP and Azure integrations.