Modern software teams are all about multitasking, and writing test cases can take away hours of a developer's valuable time.

At KushoAI, we’re building the first AI agent that takes API testing off a developer’s plate. 

It’s a win-win: Developers focus on what they do best, and product releases are smoother than ever. 🚀

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In an increasingly software-driven world, tools in the hands of software builders tend to be the rate-limiters for progress. With LLMs, we are at an inflection point. How software is built and run is set to change forever and what we've seen so far only offers a glimpse into what lies ahead of us. 

Saumya Kedia writes about how we at KushoAI are contributing towards this in our own little way.

Harsh Tiwari from our team writes on how you can get a reliable JSON output with GPT3.5! Hint: A primer on how you can effectively use Pydantic! 

In this guide, we'll understand CORS, explore its nuances, and uncover effective techniques to overcome common challenges. Whether you're a seasoned developer or just starting, this guide is designed to equip you with the knowledge and tools needed to seamlessly integrate your LLM apps across different origins. 

Getting consistent output from LLMs can be a challenge, especially when your platform relies on Chain-of-Thought prompting.

Harsh Tiwari from our team writes about how we at KushoAI get structured results from LLMs reliably, and at scale.

Find a link to the detailed how-to in the first comment below.

Pinecone recently launched its serverless offering with the goal of making vector search implementation easier and cost-efficient. With LLMs rapidly becoming a core part of applications and most of them using RAG in their implementation – vector search is an important concept to understand. In today’s post, we’ll look at how to implement vector search using Pinecone Serverless.

Kusho is an AI-native developer productivity platform that builds autonomous AI agents to replace manual workflows, allowing developers to focus on their core work.

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