As the growth lead for Steve AI, my role was to ensure that our product reached and resonated with the right audience. We conducted extensive market research, identified target segments, and crafted compelling messaging to highlight the unique value proposition of Steve. Since we were targeting a very niche audience, it was important to keep in mind the hurdles engineers face in getting clarity on their work. All in all, it was an amazing experience, and very excited for V2

Hey guys, Working on Steve was a Steep learning experience. Here is how Steve helps engineering teams that use Linear: Steve is the perfect tool for engineering teams that want to improve their efficiency, productivity, and quality. Sign up for the waitlist today to be the first to try Steve! - Steve is powered by artificial intelligence, so it can learn and adapt to your team's specific needs. - Steve is easy to use and can be integrated with your existing Linear workflows. - Steve is a cost-effective way to improve your engineering team's performance. **Code Overview** The code is designed to automate data collection and processing by using the langchain library. It uses a Linear API to fetch data, transforms it into CSV format for analysis, and interacts with the langchain agent for further operations. **Key Components** 1. API Interaction and Data Fetching: - The Linear API is utilized to retrieve data with the help of an API key. - The API response, which is in JSON format, is converted into CSV for better processing. - The data fetched includes ten specific fields. 2. CSV Data Conversion: - The JSON data fetched from the Linear API is converted into CSV format. - The CSV conversion involves the creation of a CSV file and writing the fetched data into it. - Currently, the data is not being saved into any database, but the CSV file is used for further operations. 3. Langchain Agent Interactions: - The code leverages the 'csv_agent' from the langchain library. - This agent specializes in handling data in the CSV format and is used for the analysis of the collected data. - Other agents, such as the JSON agent, can also be used, or custom agents can be created according to requirements. 4. Data Management: - To manage the responses efficiently, the langchain's 'prompt' feature is employed. - This feature helps in controlling the flow of operations. **Code Execution The following code components facilitate the operations: 1. Import Necessary Libraries:Import langchain and other necessary libraries, also setting up the API keys for langchain and SERPAPI. 2. Initialize Langchain: Initialize langchain with the given parameters. 3. Query Execution: Run the agent to execute a specific query. 4. Function Definitions: Define functions that handle the conversion of JSON data to CSV, and the main function that fetches the data, converts it, saves it, and interacts with the langchain agent. 5. Running the Main Function: The main function is called to execute all the operations sequentially.

Congratulations on the launch, this looks epic! 👏👏

Congratulation on the launch of Steve!

Looks Great. Will test it out

As the function writer for Steve AI, I had the privilege of shaping its intelligence and capabilities. The code is designed to automate data collection and processing by using the langchain library. It uses a Linear API to fetch data, transforms it into CSV format for analysis, and interacts with the langchain agent for further operations. I poured hours into crafting the underlying logic that powers Steve, ensuring it can provide valuable insights and assistance to users. Seeing Steve in action and witnessing its impact on users is incredibly rewarding. I’m proud to have contributed to its success and to be part of a team that is revolutionizing the way we interact with AI.

Steve seems to be a real game-changer! Can't wait to try it out!

Our journey in building Steve AI has been an exhilarating one. As the lead engineer, I had to make sure we create robust and scalable architecture we created to handle complex natural language processing tasks. Also, we plan on launching further versions which will include natural language processing and other key features. The database, workflows, and integrations were done keeping in mind our product roadmap. It was a challenge, but seeing Steve in action and delivering accurate responses makes all the hard work worth it.

Looks epic bakar!

Linear can get really messy and I waste my time looking up something specific, if Steve helps me with this, its going to be my very next buy

Don't spam community Slack groups asking for upvotes. Specifically Abubakar. Not cool. ✌

Congratulations on the launch 🎊

Hey there! 👋 I must say, Steve sounds like an incredible tool for engineering teams using Linear. The potential to enhance efficiency, productivity, and quality is truly valuable. I'm excited to sign up for the waitlist and be among the first to try out Steve! The fact that Steve is powered by artificial intelligence is impressive. With the ability to learn and adapt to a team's specific needs, it can provide tailored solutions and make a significant impact on workflow optimization. The seamless integration of Steve with existing Linear workflows is another standout feature. It's always a plus when a tool is easy to use and seamlessly fits into established processes without disruption. Moreover, the cost-effectiveness of Steve makes it even more appealing. Improving engineering team performance while being mindful of the budget is a win-win situation. Moving on to the code overview, it's great to see the level of detail and thought put into the design. The use of the langchain library for automating data collection and processing is commendable. Leveraging the Linear API and transforming the fetched data into CSV format shows a practical approach to analysis and further operations. The key components outlined in the code, such as API interaction, data conversion, langchain agent interactions, and data management, demonstrate a well-structured and organized approach to handling and analyzing the collected data. The code execution steps provided make it easier for users to understand how to implement and run the code successfully. The inclusion of necessary library imports, initializing langchain, executing queries, defining relevant functions, and running the main function showcase a clear pathway for execution. Overall, I'm impressed by Steve's capabilities and the attention to detail in the code explanation. It's evident that a lot of thought and effort has gone into creating a tool that can truly benefit engineering teams. I can't wait to give it a try and see how it can enhance my team's performance. Kudos to the Steve team for this fantastic product! 👏

I'm a huge fan of linear, so I'm excited to start using this.

Steve looks promising! Congratulations on your launch! 🚀🚀🚀