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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.

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.

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