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jcmarchi · 5 months

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The Rise of AI Software Engineers: SWE-Agent, Devin AI and the Future of Coding

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The Rise of AI Software Engineers: SWE-Agent, Devin AI and the Future of Coding

The field of artificial intelligence (AI) continues to push the boundaries of what was once thought impossible. From self-driving cars to language models that can engage in human-like conversations, AI is rapidly transforming various industries, and software development is no exception. The emergence of AI-powered software engineers, such as SWE-Agent developed by Princeton University’s NLP group, Devin AI, represents a groundbreaking shift in how software is designed, developed, and maintained.

SWE-Agent, a cutting-edge AI system, promises to revolutionize the software engineering process by autonomously identifying and resolving GitHub issues with unprecedented speed and accuracy. This remarkable tool leverages state-of-the-art language models like GPT-4, streamlining the development cycle and enhancing developer productivity.

The Advent of AI Software Engineers

Traditionally, software development has been a labor-intensive process, requiring teams of skilled programmers to write, review, and test code meticulously. However, the advent of AI-powered software engineers like SWE-Agent has the potential to disrupt this age-old paradigm. By harnessing the power of large language models and machine learning algorithms, these AI systems can not only generate code but also identify and fix bugs, streamlining the entire development lifecycle.

One of the key advantages of SWE-Agent is its ability to autonomously resolve GitHub issues with remarkable efficiency. On average, it can analyze and fix problems within 93 seconds, boasting an impressive 12.29% success rate on the comprehensive SWE-bench test set. This level of speed and accuracy is unprecedented in the software engineering realm, promising to significantly accelerate development timelines and reduce the overall cost of software projects.

At the core of SWE-Agent’s success lies the innovative Agent-Computer Interface (ACI), a design paradigm that optimizes interactions between AI programmers and code repositories. By simplifying commands and feedback formats, ACI facilitates seamless communication, empowering SWE-Agent to perform tasks ranging from syntax checks to test execution with remarkable efficiency. This user-friendly interface not only enhances performance but also accelerates adoption among developers, making AI-assisted software development more accessible and approachable.

SWE agent LLM

LLM Agents: Orchestrating Task Automation

LLM agents are sophisticated software entities designed to automate the execution of complex tasks. These agents are equipped with access to a comprehensive toolkit or set of resources, enabling them to intelligently determine the best tool or method to employ based on the specific input they receive.

The operation of an LLM agent can be visualized as a dynamic sequence of steps, meticulously orchestrated to fulfill the given task. Significantly, these agents possess the capability to use the output from one tool as input for another, creating a cascading effect of interlinked operations.

BabyAGI: Task Management Powerhouse One of the most notable LLM agents is BabyAGI, an advanced task management system powered by OpenAI’s cutting-edge artificial intelligence capabilities. In tandem with vector databases like Chroma or Weaviate, BabyAGI excels in managing, prioritizing, and executing tasks with remarkable efficiency. Leveraging OpenAI’s state-of-the-art natural language processing, BabyAGI can formulate new tasks aligned with specific objectives and boasts integrated database access, enabling it to store, recall, and utilize pertinent information.

At its core, BabyAGI represents a streamlined version of the Task-Driven Autonomous Agent, incorporating notable features from platforms like GPT-4, Pinecone vector search, and the LangChain framework to independently craft and execute tasks. Its operational flow comprises four key steps: extracting the foremost task from the pending task list, relaying the task to a dedicated execution agent for processing, refining and storing the derived result, and formulating new tasks while dynamically adjusting the priority of the task list based on the overarching objective and outcomes of previously executed tasks.

AgentGPT: Autonomous AI Agent Creation and Deployment AgentGPT is a robust platform tailored for the creation and deployment of autonomous AI agents. Once a particular objective is defined for these agents, they embark on a relentless loop of task generation and execution, striving tirelessly to meet the stipulated goal. At the heart of its operation lies a chain of interconnected language models (or agents) that collaboratively brainstorm the optimal tasks to meet an objective, execute them, critically assess their performance, and iteratively devise subsequent tasks. This recursive approach ensures that AgentGPT remains adaptive, learning and refining its strategies with each loop to inch closer to the objective.

https://arxiv.org/pdf/2308.00352.pdf

Code Assistants: Enhancing Developer Productivity

Code assistants are advanced tools designed to assist developers in the code-writing process, often implemented as Integrated Development Environment (IDE) plugins, extensions, or add-ons. These assistants are capable of suggesting code completions, identifying and rectifying bugs, providing optimization recommendations, and simplifying recurring coding tasks. By incorporating generative AI models, they analyze coding patterns and furnish insights that streamline the development workflow, accelerating code generation and elevating the quality of output.

GitHub Copilot: AI-Powered Programming Companion GitHub Copilot, developed through a collaboration between GitHub and OpenAI, harnesses the capabilities of the Codex generative model, aiding developers in writing code more efficiently. Described as an AI-powered programming companion, it presents auto-complete suggestions during code development. GitHub Copilot keenly discerns the context of the active file and its related documents, proposing suggestions directly within the text editor. It boasts proficiency across all languages represented in public repositories.

Copilot X, an enhanced version of Copilot, builds upon this foundation, offering an enriched experience with chat and terminal interfaces, enhanced support for pull requests, and leveraging OpenAI’s GPT-4 model. Both Copilot and Copilot X are compatible with Visual Studio, Visual Studio Code, Neovim, and the entire JetBrains software suite.

AWS CodeWhisperer: Real-Time Coding Recommendations Amazon CodeWhisperer is a machine learning-driven code generator that offers real-time coding recommendations. As developers script, it proactively presents suggestions influenced by the ongoing code. These propositions range from concise comments to elaborately structured functions. Currently, CodeWhisperer is attuned to a multitude of programming languages, including Java, Python, JavaScript, TypeScript, and many more. The tool seamlessly integrates with platforms such as Amazon SageMaker Studio, JupyterLab, Visual Studio Code, JetBrains, AWS Cloud9, and AWS Lambda.

Bard to Code: Conversational AI for Code Generation Bard, often categorized as conversational AI or a chatbot, demonstrates an adeptness in producing human-like textual responses to a diverse spectrum of prompts, owing to its extensive training on a myriad of textual data. Moreover, it possesses the dexterity to produce code across various programming languages, including but not limited to Python, Java, C++, and JavaScript.

SWE-Agent vs. Competitors: Democratizing Access to Advanced Programming Capabilities

In a landscape dominated by proprietary solutions like Devin AI and Devika, SWE-Agent shines as an open-source alternative, democratizing access to cutting-edge AI programming capabilities. Both SWE-Agent and Devin boast impressive performance on the SWE-bench benchmark, with SWE-Agent achieving a competitive 12.29% issue resolution rate. However, SWE-Agent’s open-source nature sets it apart, aligning with the collaborative ethos of the software development community.

By making its codebase available to developers worldwide, SWE-Agent invites contributions and fosters an ecosystem of innovation and knowledge-sharing. Developers can freely integrate SWE-Agent into their workflows, harnessing its power to streamline software development processes while simultaneously contributing to its evolution. This collaborative approach empowers developers of all backgrounds and skill levels to optimize their workflows, enhance code quality, and navigate the complexities of modern software development with confidence.

Beyond its technical prowess, SWE-Agent holds the potential to catalyze a paradigm shift in software engineering education and community collaboration. As an open-source tool, SWE-Agent can be integrated into educational curricula, providing students with hands-on experience in AI-assisted software development. This exposure can help shape the next generation of software engineers, equipping them with the skills and mindset necessary to thrive in an increasingly automated and AI-driven industry.

Moreover, SWE-Agent’s collaborative nature encourages developers to share their experiences, best practices, and insights, fostering a vibrant community of knowledge exchange. Through open-source contributions, bug reports, and feature requests, developers can actively participate in shaping the future of AI-powered software engineering. This collaborative approach not only accelerates the pace of innovation but also ensures that SWE-Agent remains relevant and adaptable to the ever-evolving needs of the software development ecosystem.

The Future of Software Development

While the emergence of AI-powered software engineers like SWE-Agent presents exciting opportunities, it also raises important questions and challenges that must be addressed. One critical consideration is the potential impact on the software development workforce. As AI systems become more capable of automating various aspects of the development process, there may be concerns about job displacement and the need for reskilling and upskilling initiatives.

However, it’s important to recognize that AI is not a replacement for human developers but rather a powerful tool to augment and enhance their capabilities. By offloading repetitive and time-consuming tasks to AI systems like SWE-Agent, human developers can focus on higher-level tasks that require critical thinking, creativity, and problem-solving skills. This shift in focus could lead to more fulfilling and rewarding roles for software engineers, allowing them to tackle more complex challenges and drive innovation.

Another challenge lies in the ongoing development and refinement of AI systems like SWE-Agent. As software complexity continues to increase and new programming paradigms emerge, these AI systems must be continuously trained and updated to stay relevant and effective. This requires a concerted effort from the research community, as well as close collaboration between academia and industry, to ensure that AI-powered software engineers remain at the forefront of technological advancements.

Moreover, as AI systems become more integrated into the software development process, concerns around security, privacy, and ethical considerations must be addressed. Robust measures must be put in place to ensure the integrity and trustworthiness of the generated code, as well as to mitigate potential biases or unintended consequences. Ongoing research and dialogue within the software engineering community will be crucial in navigating these challenges and establishing best practices for the responsible development and deployment of AI-powered software engineers.

Conclusion

The rise of AI-powered software engineers like SWE-Agent represents a pivotal moment in the evolution of software development. By leveraging the power of large language models and machine learning algorithms, these AI systems have the potential to revolutionize the way software is designed, developed, and maintained. With their remarkable speed, accuracy, and ability to streamline the development lifecycle, AI software engineers promise to enhance developer productivity and accelerate the pace of innovation.

However, the true impact of AI software engineers extends beyond mere technical capabilities. As open-source solutions like SWE-Agent gain traction, they have the power to democratize access to advanced programming capabilities, fostering a collaborative ecosystem of knowledge-sharing and empowering developers of all backgrounds and skill levels.

As we embrace the era of AI-assisted software development, it is crucial to recognize the challenges and opportunities that lie ahead. While job displacement concerns and the need for reskilling exist, AI systems like SWE-Agent also present an opportunity to redefine the role of software engineers, allowing them to focus on higher-level tasks that require critical thinking and creativity.

Ultimately, the successful integration of AI-powered software engineers into the software development ecosystem will require a collective effort from researchers, developers, and industry leaders.

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tastydregs · 1 year

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Meet BabyAGI — The Autonomous AI Agent to Streamline Your Tasks

Explore 10 Task Examples for Utilizing BabyAGI

BabyAGI is an autonomous Artificial Intelligence agent developed by Yohei Nakajima. This autonomous AI agent is designed to generate and execute tasks based on given objectives. It harnesses cutting-edge technologies from OpenAI, Pinecone, LangChain, and Chroma to automate tasks and achieve specific goals. In this blog post, we will dive into the unique features of BabyAGI and explore how it can streamline task automation.

BabyAGI and Project Managers: A Comparison

BabyAGI can be seen as a digital project manager. Just like a project manager, BabyAGI works towards a specific objective by creating a list of tasks, prioritizing, and executing them. It also adapts to changes and makes necessary adjustments to ensure the goal is met.

Like project managers, BabyAGI possesses the ability to learn from previous experiences and make informed decisions.

A Closer Look at BabyAGI

BabyAGI is an AI-driven personal assistant for your computer. By interpreting a given objective, it creates a list of required tasks and then executes them. After each task, BabyAGI evaluates the results and adjusts its approach accordingly.

Unique to BabyAGI is its ability to learn from feedback through trial-and-error, making human-like cognitive decisions. It can also write and run code to achieve specific goals and excels in areas such as cryptocurrency trading, robotics, and autonomous driving.

Using BabyAGI can free up your time to focus on higher-value tasks like decision-making and creative projects.

BabyAGI’s Modus Operandi

BabyAGI follows a systematic approach to automating tasks and making complex decisions:

Setting clear Objectives: To begin, the user sets a high-level objective that BabyAGI will accomplish. For example, the objective may be to “Add 1000 Twitter followers in 30 days?”

Task Generation (Agent): Next, BabyAGI will use language models such as GPT-4 to break down the objective into a list of potential tasks. These tasks can be research-oriented, such as “Identify and follow Twitter accounts relevant to your niche to increase your visibility and gain new followers”, “Utilize Twitter analytics to track your progress and adjust your strategy accordingly” etc. The list of tasks is then stored in long-term memory (vector database) for future reference.

Task Prioritization (Agent): Once there is a list of tasks, BabyAGI will use its reasoning capability to evaluate and prioritize the tasks based on their importance and dependency to achieve the final outcome. It will decide which task to execute first.

Task Execution (Agent): BabyAGI will then execute and complete a task. The results of the execution and the information gathered will also be saved in long-term memory for future use.

Evaluation & Creating New Tasks: After executing a task, BabyAGI will evaluate the remaining tasks and the results of the previous execution using its reasoning capability. Based on the evaluation, it will create new tasks to be done to achieve the final objective.

Repeat: These steps are repeated until BabyAGI achieves the original objective or until the user intervenes. BabyAGI will continually evaluate the progress of the objective and adjust the task list and prioritization accordingly to efficiently reach the desired outcome.

Picture from Yohei Nakajima’s web site

In summary, BabyAGI works by using artificial intelligence to interpret objectives, create a set of tasks, execute them, evaluate the results, and make decisions based on the outcome. It’s a powerful tool that can automate routine tasks and make complex decisions, allowing you to focus on more high-value tasks. The re-prioritization step is a key feature that ensures that BabyAGI is always working towards the objective in the most efficient way possible.

The Pros & Cons of Using BabyAGI

Using BabyAGI to automate tasks has several advantages. Since it can make complex decisions, it can be used for decision-making tasks that require controlling parameters and making complex decisions. This makes it an effective tool for any project that involves identifying tasks based on logical reasoning (To do list type tasks). BabyAGI can also constantly learn feedback from prompts and task results on a trial-and-error basis, which enables it to make human-like cognitive decisions.

While BabyAGI is a powerful tool, it has some limitations. Since it is trained on real-world scenarios and simulated environments, its performance is only as good as the extent of its training data. This makes its usage limited to certain fields. Additionally, BabyAGI does not have access to apps and services available on the internet, which limits its functionality. Please note that this was the case with the original BabyAGI, the latest iteration of BabyAGI implemented on top of Langchain removes this limitation by swapping LLM for execution to Langchain agent.

An Example of BabyAGI in Action

In the past, it has to be a elaborate setup to try and run BabyAGI. Thankfully now we have a web based app we can use. To use BabyAGI, access the web app at https://github.com/miurla/babyagi-ui or the hosted version at babyagi-ui.vercel.app. Input the objective “Add 1000 Twitter followers in 30 days?” and run BabyAGI. BabyAGI UI was created by Yoshiki Miura.

You will need your OpenAPI key, which can be entered in the settings. And remember to limit the number of iterations to save API cost.

Here is an example run of BabyAGI using the web interface at https://babyagi-ui.vercel.app/. Remember that you’ll need to use your OpenAPI key for this process.

Enter the Open API key to run BabyAGI

Here is the start of a new objective to “Add 1000 twitter followers in 30 days?”

Here is the details of the run…

🎯 Objective

Add 1000 twitter followers in 30 days?

📝 Task List

How to add twitter followers?

👉 Next Task

How to add twitter followers?

✅ Task Result

There are several ways to add Twitter followers, including:

Engage with your audience — respond to comments, retweet relevant tweets, and participate in Twitter chats.

Use relevant hashtags to increase the visibility of your tweets.

Share valuable content that is informative or entertaining to your followers.

Run Twitter ads to target specific audiences and increase your reach.

Collaborate with other Twitter users in your niche by retweeting each other’s content and engaging with each other’s followers.

By implementing these strategies consistently over the course of 30 days, you can successfully add 1000 Twitter followers to your account.

Now, BabyAgi will use the new results and the past tasks to generate a list of prioritized tasks as shown below and execute the 1st task in that list. You will see Tags like Generating Tasks, Prioritizing tasks and Executing tasks.

📝Task List

Identify and follow Twitter accounts relevant to your niche to increase your visibility and gain new followers

Utilize Twitter analytics to track your progress and adjust your strategy accordingly

Create and share visually appealing graphics and images to increase engagement and attract new followers

Collaborate with influencers or industry leaders in your niche to gain exposure to their followers and increase your own following

Host a Twitter giveaway or contest to incentivize new followers and increase engagement

👉Next Task

Identify and follow Twitter accounts relevant to your niche to increase your visibility and gain new followers

✅ Task Result

Understood. To achieve the objective of adding 1000 Twitter followers in 30 days, my task is to identify and follow Twitter accounts that are relevant to the user’s niche. This will help increase their visibility and gain new followers. Based on the previously completed task of how to add Twitter followers, I will use various strategies such as following accounts of industry leaders, engaging with relevant tweets, and using appropriate hashtags to attract new followers. I will also monitor the user’s account regularly to ensure that the number of followers is increasing steadily.

Now, the process continues. BabyAgi will use the new results and the past tasks to generate a list of prioritized tasks as shown below and execute the 1st task in that list. Note that the list of overall tasks have been completed updated between what was initially gnerated and what it has the list of tasks.

📝Task List

Conduct a thorough analysis of the user’s niche to identify relevant Twitter accounts that could potentially increase their visibility and attract new followers

Utilize Twitter’s advanced search feature to find tweets and accounts related to the user’s niche and engage with them by liking, retweeting, and commenting on their posts

Use appropriate hashtags in tweets to attract new followers who are interested in the user’s niche

Collaborate with influencers or industry leaders in the user’s niche to gain exposure to their followers and increase their own following

Create and share visually appealing graphics and images to increase engagement and attract new followers

Host a Twitter giveaway or contest to incentivize new followers and increase engagement

Utilize Twitter analytics to track progress and adjust strategy accordingly

Regularly monitor the user’s account to ensure that the number of followers is increasing steadily and adjust the strategy accordingly

Process continues here. 1st task in the above list will get executed and the BabyAgi will use the new results and the past tasks to generate a list of prioritized tasks and execute the 1st task in that list. If you notice the initial task list and each the subsequent task list are very different. It works out well sometimes and in other times it leads to a path where it keeps repeating the same tasks in a loop.

I have said stop after 10 iterations. So it stopped with the following result.

✅ Task Result

Based on the user’s target audience and content strategy, I recommend the following schedule for consistent posting on Twitter:

Monday: Share an educational article or blog post related to the user’s industry or niche. Tuesday: Post an inspirational quote or image that resonates with the target audience. Wednesday: Share an informative infographic or video that offers valuable insights or tips. Thursday: Ask a thought-provoking question or conduct a poll to encourage interaction and engagement. Friday: Share a behind-the-scenes glimpse into the user’s business or personal life to humanize the brand. Saturday: Retweet or share content from other thought leaders or influencers in the user’s industry. Sunday: Post a motivational message or call to action that inspires followers to take action.

By posting consistently and providing a mix of valuable content, the user can attract new followers and increase engagement, ultimately leading to the goal of adding 1000 followers in 30 days.

You can see the full run of BabyAGI here for this objective.

I tried this for another objective “Give a detailed weather report for the capital of New York ?” just to illustrate if it can get to latest weather.

You can see the results from this run here. As you can see the list of tasks its generating is good and logical but the results are incorrect as its not looking at the latest weather.

This is something that will get addressed using BabyAGI with Langchain tools. See here the example from Langchain. The execution chain in the original BabyAGI was pure LLM and this has been swapped to Langchain agent so it can access the internet.

Links to try out BabyAGI

BabyAGI : https://github.com/yoheinakajima/babyagi

BabyAGI with Langchain : https://python.langchain.com/en/latest/use_cases/agents/baby_agi_with_agent.html

BabyAGI with Langchain Colab notebook : https://colab.research.google.com/drive/1IzYc6kLzuRMXfUI4aCNxWCYfzZHtdCf-?usp=sharing

BabyAGI UI : to run locally access the web app at https://github.com/miurla/babyagi-ui or the hosted version at babyagi-ui.vercel.app.

Ten examples of where BabyAGI can be used

BabyAGI is a versatile AI tool that can perform a range of tasks, including managing to-do lists, conducting research, and creative writing. For instance, it can assist in generating a list of tasks and prioritizing them to achieve a specific goal. It can also help with product research by analyzing user feedback and market trends. Additionally, BabyAGI can generate ideas for creative writing, such as generating plot-lines, characters, and themes for a novel or screenplay. Its ability to adapt to changes and re-prioritize tasks based on previous outcomes makes it a valuable asset for various industries.

Objectives that involves creating logical tasks are prime candidates for using BabyGPI. Some examples are given below. Remember that as of the current release, BabyGPI cannot leverage internet so all the use cases are restricted to logical reasoning tasks by the LLM. But if you use the one with Langchain integration and provide the serp api key, BabyAGI can connect to the internet and do research as well. I tried many of these but did not let it run to the end (to save on API cost). I have linked those use cases below.

Add 1000 twitter followers in 30 days?

Get 200 Facebook likes on a post in a week.

Increase Pinterest followers by 300 in a month.

Give a detailed weather report for the capital of New York ?

Plan a birthday party at a vegan place in NYC

Come up with 5 AI use cases to use in manufacturing?

What are the top 3 podcast ideas based on latest news?

Find the top 3 braces for handpain?

Create a competitive report for wearable heart rate monitor watch?

What are the top use cases for using Autonomous agents?

As you can see its trending in the right direction to complete these tasks. As technology matures, this will continue to go in the right direction and become very valuable.

Wrapping Up

In conclusion, BabyAGI is an innovative AI tool that automates complex decision-making tasks in various use cases, such as creative writing, research and to-do tasks. It efficiently breaks down high-level objectives into tasks, prioritizes and executes them based on logical reasoning, and continuously evaluates and adjusts its approach.

Leveraging advanced language models like GPT-4, Pinecone, LangChain, and Chroma, BabyAGI can learn from previous tasks and adapt to changing circumstances. Its ability to make complex decisions and control parameters makes it an invaluable asset for automating task management. As AI technology continues to evolve, we can expect even more impressive advancements in the realm of autonomous agents like BabyAGI.

Meet BabyAGI — The Autonomous AI Agent to Streamline Your Tasks was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

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