Exploring Get Pieces for Developers: A Personal Assessment

Introduction

Since the emergence of generative AI, completing tasks has never been easier. Whether it’s coding, writing, researching, or even studying, generative AI allows people to accomplish in minutes what used to take hours or even days.

I am a machine learning engineer and technical writer. My day-to-day consists of developing and documenting machine learning projects. I write mostly in Python, often using Jupyter notebooks for experimentation and development. For documentation, I frequently use Google Colab.

My workflow has remained consistent since early 2021, a time when tools like ChatGPT were still new and not widely adopted. Initially hesitant to try AI tools, I quickly discovered how they could dramatically streamline my tasks, from coding to writing.

Here’s a look at my typical work environment:

• Operating system: I develop on both Mac and Linux.
• Tools: I regularly use Chrome, Visual Studio Code, Slack and ChatGPT.
• Projects: My projects often involve developing machine learning models, creating technical content, and contributing to open source documentation.
• Experience: I have been working in the field for over three years and have been actively engaged in technical writing and project development throughout my academic and professional career.

Integrating AI into my workflow has made my tasks more manageable and efficient, allowing me to focus on more complex and creative aspects of my work.

Previous methods of tracking workflow materials.

Before I discovered Get Pieces, I used to keep small workflow items, like code snippets, in different places. I often saved code snippets and piles of errors in text files or sticky notes on my computer. For things like links and screenshots, I used browser bookmarks and a folder on my desktop.

This process was quite disorganized. It was difficult to find specific information when I needed it and I often spent a lot of time searching through different files and notes. There was no central place to put everything together, making the process inefficient and time-consuming.

Why I chose Pieces and its productivity benefits.

What first drew me to Pieces was the need for a centralized place to organize and manage the different types of information I work with. As a machine learning engineer and technical writer, I’m often multitasking and managing a lot of information at once, like code snippets, links, screenshots, and piles of errors. Previously, I would save them in different places or bookmark links as I went, which was disorganized and inefficient.

When I discovered Pieces, I saw an opportunity to streamline my workflow by having one tool for all my storage needs. With Pieces, I can keep everything from code to screenshots to links in one central location. Plus, the built-in chatbot feature allows me to learn more quickly about the information I’ve saved, making the process even more productive and seamless.

Why I reviewed Pieces and how it can help others.

I thought it would be helpful to review Pieces because it solves common problems with organizing information. Many people, including my peers, struggle to keep track of code, links, and notes. By sharing my review, I hope to show how Pieces can make their work easier and more organized. It’s a tool that can help save time and reduce hassle, which could be very useful for anyone managing a lot of different information.

In this blog, we’ll explore the strengths, weaknesses, and challenges of Get Pieces, and examine how it affects machine learning engineers like me and impacts our productivity and ongoing projects.

Challenges and areas for improvement

Like any other development tool, Get Pieces has areas that need improvement and may not perfectly meet everyone’s needs. As a user, I have encountered some of these issues first-hand and would like to highlight these areas.

1. Feature overload: Get Pieces comes with a plethora of features that can be very useful. However, having too many features can sometimes overwhelm users, making the tool less user-friendly. This is especially true for new users, who may need extra time to learn how to use all the features effectively.

2. Intensive resources: Pieces OS allows large language models (LLM) to be executed locally on your machine, ensuring that all operations, processing, and data management are performed on your own device without relying on external servers. While this improves privacy and security, it comes with a major drawback: resource intensity.

Running LLM locally can be extremely demanding on your computer’s resources, requiring significant CPU, GPU, and memory capacity. This high demand can lead to system freezes, where your machine stops responding and other tasks slow down significantly. This issue is especially pronounced on low-end or older hardware, making it less feasible for those without high-performance systems.

3. Potential security and privacy issues: Pieces’ contextual functionality offers substantial productivity improvements, but it’s important to keep privacy and security concerns in mind to protect sensitive information. While Pieces isn’t trained on user data, as an author I may have concerns when documenting sensitive APIs. Ensuring that this data is handled securely is critical to preventing unauthorized access and potential breaches*.*

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Highlights and strengths

After using Get Pieces for a considerable period of time, I have seen significant positive effects on my workflow. Here are some key areas:

1. Live Context Feature: As someone who writes regularly, including coding, documenting, and blogging, I often encounter errors. These errors, which are usually caused by incorrect input, can disrupt my workflow. I always wanted a tool that could help me identify and fix these errors in real-time. Get Pieces has proven to be a great help in this regard.

A notable example occurred when I was working on a blog using an online editor. The Get Pieces browser extension seamlessly integrated into my workflow, allowing me to spot and correct errors in my Markdown text before publishing. This feature significantly improved my productivity and the quality of my work.

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2. Multimodal capacity: One of the most notable features of Get Pieces is its multimodal capability, which allows users to work with different types of input data without incurring additional costs. This is a significant advantage over other chat copilots like ChatGPT, where some features, such as image processing, require a subscription.

For example, with Get Pieces, I can easily download a screenshot of the code and get detailed information about it without having to pay for this feature. This ability has significantly improved my productivity, allowing me to work more efficiently without worrying about additional tool costs. Overall, Get Pieces offers a cost-effective solution for managing various input data, making it an invaluable asset to my workflow.

3. Universal access to the model: Another standout feature on Pieces is Universal Model Access. This is a new addition for users looking for flexibility and diversity in AI tools. This feature consolidates access to a diverse range of language models, including premium options like GPT-4, PaLM 2, and Anthropic, all within a single platform. It eliminates the need for multiple subscriptions or separate interfaces, allowing users to experiment and leverage different models at no additional cost. The ease of switching between different models and integrating them into various workflows makes Pieces a powerful tool for improving productivity and creativity. Whether for coding, content generation, or data analysis, this feature simplifies access to cutting-edge AI capabilities, providing unmatched convenience and versatility.

Additionally, these models are usually available as cloud-based options, but that’s not all. Users also have the option to use similar powerful models directly on their local devices. This dual availability ensures users have access to high-performance AI tools both online and offline, improving versatility and convenience.

4. Ask the co-pilot: As a developer with experience using various copilots, I find Pieces Copilot to be a real standout. The Pieces Ask Copilot feature is particularly impressive, offering relevant information about files, snippets, or terminal output. For example, when you encounter an error, you can simply highlight the error message and, with one click, ask the copilot for details about the error. Fascinating, right? Normally, you would copy the error message and paste it into your favorite AI chatbot, like ChatGPT. However, with Pieces Copilot, you can fix bugs faster without leaving your IDE, making your development process more efficient and smoother.

Final Thoughts

Pieces for Developers is definitely a must-have tool for anyone looking to streamline their workflow. With everything you need in one place, it simplifies and improves the development process. As a machine learning engineer, I use this tool constantly and can’t imagine working without it. Whether you’re a student, intern, or experienced developer, I highly recommend adopting this innovative tool.