How Can You Utilize DeepSeek R1 For Personal Productivity

How can you use DeepSeek R1 for personal performance?


Serhii Melnyk


Follow


--


Listen


Share


I constantly desired to collect stats about my efficiency on the computer system. This idea is not new; there are lots of apps developed to solve this issue. However, all of them have one significant caveat: you should send out extremely delicate and individual details about ALL your activity to "BIG BROTHER" and trust that your information won't wind up in the hands of individual information reselling companies. That's why I chose to create one myself and make it 100% open-source for total openness and credibility - and you can utilize it too!


Understanding your efficiency focus over an extended period of time is essential due to the fact that it offers important insights into how you allocate your time, identify patterns in your workflow, and find locations for enhancement. Long-term efficiency tracking can assist you determine activities that consistently contribute to your objectives and forum.batman.gainedge.org those that drain your energy and time without significant results.


For example, tracking your efficiency patterns can reveal whether you're more efficient during certain times of the day or in particular environments. It can also help you evaluate the long-term impact of modifications, like altering your schedule, adopting new tools, or dealing with procrastination. This data-driven approach not only empowers you to enhance your daily regimens but likewise helps you set realistic, attainable goals based upon evidence instead of presumptions. In essence, comprehending your efficiency focus with time is an important step towards creating a sustainable, efficient work-life balance - something Personal-Productivity-Assistant is designed to support.


Here are main functions:


- Privacy & Security: No details about your activity is sent over the internet, guaranteeing total privacy.

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

- AI Analysis: An AI design evaluates your long-term activity to uncover hidden patterns and supply actionable insights to boost efficiency.

- Classification Customization: Users can manually adjust AI categories to better reflect their individual performance 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 suit their particular needs.

- Browsers Domain Tracking: The application also tracks the time invested in specific websites within internet browsers (Chrome, Safari, Edge), using 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 feature here: DeepSeek R1.


DeepSeek, a Chinese AI start-up established in 2023, has recently amassed significant attention with the release of its latest AI design, R1. This model is noteworthy for its high performance 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 worked on computers without the need for substantial computational resources. This democratization of AI technology enables people to explore and examine the design's abilities firsthand


DeepSeek R1 is bad for whatever, there are sensible concerns, however it's best for our productivity jobs!


Using this model we can categorize applications or mariskamast.net sites without sending out any information to the cloud and therefore keep your information protect.


I highly believe that Personal-Productivity-Assistant may cause increased competitors and drive innovation throughout 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 complimentary availability make it an outstanding option.


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


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


By operating LLMs in your area, Ollama ensures that all data processing happens within your own environment, removing the need to send out delicate details to external servers.


As an open-source job, Ollama gain from constant contributions from a dynamic community, making sure routine updates, feature enhancements, and .


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, because 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 a long time to collect great quantity of stats. Application will save quantity of 2nd you spend in each application or site.


6. Finally produce the report.


Note: Generating the report requires a minimum of 9GB of RAM, and the procedure may take a couple of minutes. If memory usage is a concern, it's possible to change to a smaller design for more effective resource management.


I 'd love to hear your feedback! Whether it's feature requests, bug reports, or your success stories, join the neighborhood on GitHub to contribute and assist make the tool even much 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 devoting to boosting people focus ...


github.com


About Me


I'm Serhii Melnyk, with over 16 years of experience in developing and executing high-reliability, scalable, and high-quality projects. My technical know-how is complemented by strong team-leading and interaction skills, which have actually helped me successfully lead groups for over 5 years.


Throughout my career, I've concentrated on creating workflows for asteroidsathome.net artificial intelligence and information science API services in cloud infrastructure, in addition to creating monolithic and Kubernetes (K8S) containerized microservices architectures. I have actually likewise worked thoroughly with high-load SaaS solutions, REST/GRPC API applications, and CI/CD pipeline design.


I'm enthusiastic about item delivery, and my background consists of mentoring employee, performing thorough code and style reviews, and handling individuals. Additionally, I've worked with AWS Cloud services, as well as GCP and Azure combinations.