Introduction

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

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

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

Here’s a snapshot of 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 in the field for over three years and have been actively engaged in technical writing and project development throughout my academic and professional journey.

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 Workstream Materials.

Before coming across Get Pieces, I used to keep track of small workstream materials like code snippets in a mix of different places. I often saved code snippets and error stacks 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 unorganised. It was hard to find specific pieces of information when I needed them, and I often spent a lot of time searching through different files and notes. There was no central place to keep everything together, which made it inefficient and time-consuming.

Why I Chose Pieces and Its Productivity Benefits.

What initially interested me about Pieces was the need for a centralised location to organise and manage the various types of information I work with. As a machine learning engineer and technical writer, I often multitask and handle a lot of information at once, such as code snippets, links, screenshots, and error stacks. Previously, I would save these in different places or bookmark links as I went along, which was disorganised and inefficient.

When I came across Pieces, I saw an opportunity to streamline my workflow by having a single tool for all my storage needs. With Pieces, I can keep everything—whether it’s code, screenshots, or links—in one central place. Additionally, the built-in chatbot feature allows me to quickly learn more 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 useful to review Pieces because it solves common problems with organising information. Many people, including my peers, struggle with keeping track of code, links, and notes. By sharing my review, I hope to show how Pieces can make their work easier and more organised. It’s a tool that can help save time and reduce hassle, which could be really helpful 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 myself and impacts our productivity and ongoing projects.

Challenges and Areas for Improvement

Just like any other development tool out there, Get Pieces has areas that need improvement and may not fit everyone's needs perfectly. As a user, I have experienced some of these issues firsthand and would like to highlight these areas.

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

2. Resource Intensive: Pieces OS enables the running of Large Language Models (LLMs) locally on your machine, ensuring that all operations, processing, and data handling are performed on your own device without relying on external servers. While this enhances privacy and security, it comes with a significant downside: resource intensity.

Running LLMs locally can be extremely demanding on your computer's resources, requiring substantial CPU, GPU, and memory capacity. This high demand can lead to system freezes, where your machine becomes unresponsive, and other tasks slow down considerably. This issue is especially pronounced on lower-end or older hardware, making it less feasible for those without high-performance systems.

3. Potential security and privacy concerns: The context-aware feature of Pieces provides substantial productivity enhancements, but it's important to be mindful of privacy and security concerns to safeguard sensitive information. Although Pieces is not trained on users' data, as a writer, I might have concerns when documenting sensitive APIs. Ensuring that this data is securely managed is essential to prevent unauthorised access and potential breaches*.*

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Strengths and Highlights

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

1. Live context feature: As someone who regularly engages in writing, including coding, documentation, and blogging, I often encounter errors. These errors, which are typically due to incorrect inputs, can be disruptive to my workflow. I have always wanted a tool that could help me identify and correct these mistakes in real-time. Get Pieces has proven invaluable in this regard.

One notable instance was while 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 enhanced my productivity and the quality of my work.

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2. Multi-Modal Capability: One of the standout features of Get Pieces is its multi-modal 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 certain features, such as image processing, require a subscription.

For instance, with Get Pieces, I can easily upload a screenshot of code and obtain detailed information about it without needing to pay for this feature. This capability has been a major boost to my productivity, allowing me to work more efficiently and effectively without worrying about additional tool costs. Overall, Get Pieces provides a cost-effective solution for handling diverse input data, making it an invaluable asset for my workflow.

3. Universal Model Access: Another feature that stands out on Pieces is the Universal Model Access. This is a game-changer for users seeking flexibility and variety 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 with and leverage various models without additional costs. The ease of switching between different models and integrating them into various workflows makes Pieces a powerful tool for enhancing productivity and creativity. Whether for coding, content generation, or data analysis, this feature streamlines access to cutting-edge AI capabilities, providing unparalleled convenience and versatility.

Moreover, these models are typically available as cloud-based options, but that's not all. Users also have the flexibility to utilise similar powerful models directly on their local devices. This dual availability ensures that users can access high-performance AI tools both online and offline, enhancing versatility and convenience.

4. Ask Copilot: As a developer with experience using various copilots, I find that Pieces Copilot truly stands out. The Pieces Ask Copilot feature is particularly impressive, offering insightful information about files, snippets, or terminal outputs. For instance, when encountering 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 favourite AI chatbot, like ChatGPT. However, with Pieces Copilot, you can resolve bugs faster without leaving your IDE, making your development process more efficient and seamless.

Final Thoughts

Pieces for Developers is undoubtedly a must-have tool for anyone looking to streamline their workflow. With everything you need in one place, it simplifies and enhances the development process. As a machine learning engineer, I find myself using this tool consistently and can’t imagine working without it. Whether you're a student, intern, or experienced developer, I strongly urge you to adopt this innovative tool.