How can you Utilize DeepSeek R1 For Personal Productivity?

How can you utilize DeepSeek R1 for individual productivity?

Serhii Melnyk

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I always wanted to gather data about my efficiency on the computer system. This concept is not brand-new; there are a lot of apps designed to fix this issue. However, all of them have one considerable caution: you should send out extremely delicate and personal 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 create one myself and make it 100% open-source for complete transparency and credibility – and you can use it too!

Understanding your productivity focus over a long duration of time is vital due to the fact that it provides important insights into how you allocate your time, determine patterns in your workflow, and find areas for improvement. Long-term performance tracking can help you identify activities that consistently contribute to your objectives and those that drain your time and energy without significant results.

For example, tracking your productivity patterns can reveal whether you’re more efficient throughout certain times of the day or in specific environments. It can likewise help you examine the long-term effect of adjustments, like altering your schedule, adopting new tools, or dealing with procrastination. This data-driven technique not just empowers you to enhance your daily routines however also assists you set reasonable, attainable objectives based upon evidence rather than assumptions. In essence, understanding your productivity focus in time is a critical step towards producing a sustainable, efficient 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 web, making sure total privacy.

– Raw Time Log: The application shops a raw log of your activity in an open format within a designated folder, using full openness and user control.

– AI Analysis: An AI model analyzes your long-lasting activity to uncover surprise patterns and offer actionable insights to boost performance.

– Classification Customization: Users can manually adjust AI categories to better show their personal performance objectives.

– AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will be able to choose from a variety of AI models to suit their particular requirements.

– Browsers Domain Tracking: The application also tracks the time invested in private sites within internet browsers (Chrome, Safari, opentx.cz Edge), offering a detailed view of online activity.

But before I continue explaining how to have fun with it, let me say a couple of words about the main killer feature here: DeepSeek R1.

DeepSeek, a Chinese AI startup founded in 2023, has actually recently amassed considerable attention with the release of its most current AI design, R1. This model is significant for its high performance and cost-effectiveness, positioning it as a powerful rival to established AI designs like OpenAI’s ChatGPT.

The model is open-source and can be operated on desktop computers without the need for extensive computational resources. This democratization of AI innovation permits people to experiment with and evaluate the design’s capabilities firsthand

DeepSeek R1 is bad for whatever, there are reasonable concerns, but it’s best for our efficiency tasks!

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

I highly think that Personal-Productivity-Assistant may result in increased competition and drive innovation across the sector of comparable productivity-tracking services (the integrated user base of all time-tracking applications reaches 10s of millions). Its open-source nature and complimentary availability make it an outstanding alternative.

The model itself will be delivered to your computer via another task called Ollama. This is provided for convenience and much better resources allocation.

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

By running LLMs locally, Ollama ensures 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 task, Ollama gain from constant contributions from a vibrant community, ensuring 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 ideas).

4. Once installed, a black circle will appear in the system tray:.

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

6. Finally generate the report.

Note: Generating the report requires a minimum of 9GB of RAM, and the process might take a few minutes. If memory usage is a concern, it’s possible to change to a smaller sized model for more effective resource management.

I ‘d enjoy to hear your feedback! Whether it’s function requests, bug reports, or your success stories, sign up with the neighborhood on GitHub to contribute and help make the tool even better. Together, we can shape the future of performance tools. Check it out here!

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

Personal Productivity Assistant is an advanced open-source application dedicating to enhancing individuals focus …

github.com

About Me

I’m Serhii Melnyk, with over 16 years of experience in creating and implementing high-reliability, scalable, and premium tasks. My technical proficiency is complemented by strong team-leading and communication skills, which have actually helped me successfully lead groups for over 5 years.

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

I’m enthusiastic about product delivery, and my background includes mentoring staff member, performing thorough code and design evaluations, and managing individuals. Additionally, I have actually dealt with AWS Cloud services, along with GCP and Azure combinations.