GitLab

How can you make use of DeepSeek R1 for individual efficiency?

Serhii Melnyk

Follow

--

Listen

Share

I always desired to collect stats about my productivity on the computer system. This idea is not new; there are plenty of apps developed to fix this problem. However, buysellammo.com all of them have one substantial caution: you must send extremely delicate and individual details about ALL your activity to "BIG BROTHER" and trust that your information won't end up in the hands of personal data reselling firms. That's why I decided to develop one myself and make it 100% open-source for total openness and dependability - and you can use it too!

Understanding your performance focus over an extended period of time is important because it offers valuable insights into how you designate your time, recognize patterns in your workflow, and discover locations for improvement. Long-term efficiency tracking can help you determine activities that regularly add to your objectives and those that drain your time and energy without meaningful outcomes.

For instance, forum.batman.gainedge.org tracking your productivity patterns can reveal whether you're more reliable throughout certain times of the day or in particular environments. It can also help you examine the long-lasting impact of modifications, like changing your schedule, adopting brand-new tools, or dealing with procrastination. This data-driven approach not just empowers you to enhance your daily routines but also helps you set realistic, attainable objectives based on proof rather than presumptions. In essence, understanding your productivity focus over time is an important action towards developing a sustainable, effective work-life balance - something Personal-Productivity-Assistant is created to support.

Here are main features:

- 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, offering complete transparency and user control.
- AI Analysis: An AI model analyzes your long-lasting activity to uncover hidden patterns and supply actionable insights to boost efficiency.
- Classification Customization: Users can manually change AI categories to much better reflect their individual performance goals.
- AI Customization: Right now the application is utilizing deepseek-r1:14 b. In the future, users will have the ability to select from a variety of AI designs to match their particular needs.
- Browsers Domain Tracking: The application also tracks the time invested in private websites within web browsers (Chrome, Safari, Edge), using a detailed view of online activity.
But before I continue explaining how to have fun 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 just recently garnered significant attention with the release of its most current AI design, R1. This model is noteworthy for its high performance and cost-effectiveness, placing it as a powerful competitor to established AI models like OpenAI's ChatGPT.

The model is open-source and can be worked on desktop computers without the need for comprehensive computational resources. This democratization of AI innovation permits individuals to try out and assess the model's abilities firsthand

DeepSeek R1 is not great for whatever, there are affordable concerns, but it's ideal for our productivity jobs!

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

I highly think that Personal-Productivity-Assistant may result in increased competition and drive development throughout 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 complimentary availability make it an outstanding alternative.

The model itself will be provided to your computer system by means of another task called Ollama. This is done for convenience and much better resources allotment.

Ollama is an open-source platform that allows you to run large language designs (LLMs) in your area on your computer, improving data 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, getting rid of the need to send delicate details to external servers.

As an open-source task, Ollama gain from continuous contributions from a dynamic neighborhood, ensuring routine updates, feature enhancements, 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, a black circle will appear in the system tray:.
5. Now do your regular work and wait some time to collect good quantity of stats. Application will save quantity of 2nd you invest in each application or site.

6. Finally produce 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 a concern, thatswhathappened.wiki it's possible to switch to a smaller sized model for more effective resource management.

I 'd like to hear your feedback! Whether it's requests, animeportal.cl bug reports, or your success stories, sign up with the neighborhood on GitHub to contribute and assist make the tool even much better. Together, we can shape 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 improving individuals focus ...

github.com

About Me

I'm Serhii Melnyk, photorum.eclat-mauve.fr with over 16 years of experience in designing and carrying out high-reliability, scalable, and premium projects. My technical expertise is matched by strong team-leading and communication abilities, which have actually assisted me successfully lead teams for over 5 years.

Throughout my profession, I've focused on creating workflows for asteroidsathome.net artificial intelligence and thatswhathappened.wiki data science API services in cloud facilities, in addition to creating monolithic and Kubernetes (K8S) containerized microservices architectures. I have actually also worked extensively with high-load SaaS solutions, REST/GRPC API executions, and CI/CD pipeline style.

I'm enthusiastic about product delivery, and my background includes mentoring employee, performing extensive code and style reviews, and managing people. Additionally, I've worked with AWS Cloud services, along with GCP and Azure integrations.