How can you utilize DeepSeek R1 for personal efficiency?

Serhii Melnyk

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I always desired to gather statistics about my productivity on the computer system. This concept is not new; there are a lot of apps designed to resolve this concern. However, all of them have one substantial caution: you need to send extremely sensitive and personal details about ALL your activity to "BIG BROTHER" and trust that your data won't end up in the hands of personal information 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 utilize it too!

Understanding your productivity focus over a long period of time is important due to the fact that it offers important insights into how you assign your time, identify patterns in your workflow, and find locations for improvement. Long-term performance tracking can assist you determine activities that regularly contribute to your objectives and those that drain your energy and time without significant results.

For example, tracking your efficiency trends can expose whether you're more effective throughout certain times of the day or in specific environments. It can likewise help you assess the long-lasting impact of changes, like changing your schedule, embracing brand-new tools, or dealing with procrastination. This data-driven technique not only empowers you to optimize your daily regimens but likewise helps you set sensible, attainable objectives based on evidence instead of presumptions. In essence, comprehending your efficiency focus gradually is a critical 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 online, ensuring complete personal privacy.
- Raw Time Log: The application shops a raw log of your activity in an open format within a designated folder, using complete openness and user control.
- AI Analysis: An AI model evaluates your long-term activity to reveal hidden patterns and offer actionable insights to boost productivity.
- Classification Customization: Users can by hand adjust AI categories to much better reflect their personal productivity goals.
- AI Customization: Right now the application is using deepseek-r1:14 b. In the future, oke.zone users will be able to select from a range of AI designs to match their specific needs.
- 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 state a few words about the main killer function here: DeepSeek R1.

DeepSeek, a Chinese AI startup established in 2023, has recently gathered significant attention with the release of its newest AI model, R1. This model is notable for its high performance and cost-effectiveness, positioning it as a formidable competitor to developed AI designs like OpenAI's ChatGPT.

The design is open-source and can be operated on computers without the requirement for extensive computational resources. This democratization of AI innovation allows individuals to explore and examine the design's capabilities firsthand

DeepSeek R1 is not excellent for whatever, there are affordable issues, but it's perfect for our efficiency tasks!

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

I highly believe that Personal-Productivity-Assistant may 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 free availability make it an exceptional option.

The design itself will be provided to your computer through another project called Ollama. This is done for convenience and much better resources allowance.

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

By operating LLMs locally, Ollama ensures that all information processing occurs within your own environment, getting rid of the requirement to send out delicate details to external servers.

As an open-source task, Ollama gain from constant contributions from a dynamic neighborhood, ensuring regular updates, function improvements, and robust assistance.

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 ideas).
4. Once installed, a black circle will appear in the system tray:.
5. Now do your routine work and larsaluarna.se wait a long time to collect great amount of data. Application will save quantity of 2nd you spend in each application or website.

6. Finally produce the report.

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

I 'd like to hear your feedback! Whether it's function requests, ura.cc bug reports, or your success stories, join the community on GitHub to contribute and assist 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 Assistant is an advanced open-source application committing to boosting people focus ...

github.com

About Me

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

Throughout my profession, I've focused on producing workflows for artificial intelligence and data science API services in cloud infrastructure, 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 style.

I'm enthusiastic about item shipment, and my background consists of staff member, conducting comprehensive code and design evaluations, and handling people. Additionally, I've worked with AWS Cloud services, as well as GCP and Azure combinations.