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How can you utilize DeepSeek R1 for personal productivity?

Serhii Melnyk

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I constantly wanted to collect stats about my efficiency on the computer system. This concept is not brand-new; there are lots of apps designed to solve this concern. However, users.atw.hu all of them have one significant caution: you must send highly delicate and personal details about ALL your activity to "BIG BROTHER" and trust that your data won't wind up in the hands of personal data reselling firms. That's why I chose to one myself and make it 100% open-source for total openness and dependability - and you can utilize it too!

Understanding your efficiency focus over a long period of time is important since it offers important insights into how you assign your time, determine patterns in your workflow, and morphomics.science find areas for enhancement. Long-term performance tracking can assist you determine activities that regularly add to your objectives and those that drain your time and energy without meaningful outcomes.

For example, tracking your performance patterns can reveal whether you're more effective during certain times of the day or in particular environments. It can likewise help you examine the long-lasting effect of adjustments, like altering your schedule, adopting new tools, or tackling procrastination. This data-driven technique not just empowers you to optimize your daily regimens however likewise assists you set reasonable, attainable goals based on proof instead of presumptions. In essence, comprehending your performance focus in time is a vital step towards producing a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is created to support.

Here are main functions:

- Privacy & Security: No details about your activity is sent out over the internet, making sure total personal privacy.
- Raw Time Log: 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 reveal hidden patterns and offer actionable insights to boost efficiency.
- Classification Customization: Users can manually change AI categories to better show their personal efficiency goals.
- 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 particular requirements.
- Browsers Domain Tracking: The application likewise tracks the time invested in private sites 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 startup established in 2023, has actually recently garnered significant attention with the release of its newest AI model, R1. This design is significant for its high efficiency and cost-effectiveness, placing it as a powerful rival to established AI designs like OpenAI's ChatGPT.

The model is open-source and can be run on individual computers without the need for comprehensive computational resources. This democratization of AI technology permits people to experiment with and evaluate the model's abilities firsthand

DeepSeek R1 is bad for everything, there are sensible issues, but it's ideal for our performance jobs!

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

I strongly think that Personal-Productivity-Assistant might result in increased competitors and drive development across the sector of similar productivity-tracking services (the combined user base of all time-tracking applications reaches tens of millions). Its open-source nature and free availability make it an exceptional option.

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

Ollama is an open-source platform that enables you to run large language designs (LLMs) in your area on your computer, improving data privacy and control. It's suitable with macOS, Windows, and Linux operating systems.

By operating LLMs locally, Ollama guarantees that all data processing occurs 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 community, 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, since of deepseek-r1:14 b (14 billion params, yogaasanas.science chain of ideas).
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 collect excellent quantity of data. Application will save quantity of 2nd you spend in each application or website.

6. Finally produce the report.

Note: Generating the report requires a minimum of 9GB of RAM, and the process might 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 like to hear your feedback! Whether it's function demands, bug reports, or valetinowiki.racing your success stories, sign up with the community on GitHub to contribute and help make the tool even much better. Together, we can shape the future of productivity tools. Check it out here!

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

Personal Productivity Assistant is a revolutionary open-source application dedicating to improving people focus ...

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About Me

I'm Serhii Melnyk, with over 16 years of experience in creating and implementing high-reliability, scalable, oke.zone and high-quality jobs. My technical competence is matched by strong team-leading and communication skills, which have helped me effectively lead teams for over 5 years.

Throughout my profession, I have actually concentrated on producing workflows for artificial intelligence and data science API services in cloud facilities, in addition to designing 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 style.

I'm enthusiastic about item shipment, and my background consists of mentoring staff member, performing thorough code and design evaluations, and handling people. Additionally, I have actually dealt with AWS Cloud services, along with GCP and Azure combinations.