Product Reviews

After 6 months of wrestling with deepset-ai/haystack: It’s good for prototypes, painful for anything real.

When I decided to give Haystack a shot, my goal was to integrate a simple search solution into a mid-sized application. We were building a knowledge base feature for a web application servicing around 10,000 users, hitting peak loads of

LangChain in 2026: An Honest Review After a Year of Use

After spending an entire year wrangling with LangChain, I can confidently say that while it has some great features, it also comes with more than its fair share of pain points.

Context

Over the last year, I’ve integrated LangChain into multiple projects ranging from

n8n vs. Make: An In-Depth Look for Side Projects

n8n boasts 180,218 stars on GitHub—a significant number that reflects a passionate community. Make, however, tends to lurk behind with less visibility, despite being a well-known tool in the automation space. Stars don’t give you functionality, though, and deciding between n8n and Make for your side

The Battle of vLLM vs TensorRT-LLM: A Developer’s Perspective
vllm-project/vllm boasts an impressive 73,811 stars on GitHub. In contrast, TensorRT-LLM isn’t quite as popular but has its own following. Depending on your production requirements, the choice between these two can significantly impact your project. To help you make a decision, let’s get into the specifics.

How to Add Vector Search with LangChain (Step by Step)

I’m going to show you how to add vector search functionality using LangChain, a library that’s gaining traction in the AI community. With a staggering 130,274 stars on GitHub, 21,484 forks, and 474 open issues, it’s clearly making waves. This isn’t just a buzzword; vector