标签：#zen

A competitor intelligence agent enables real-time pricing visibility, automated positioning tracking, and structured competitor briefs for brands without manual research. At the center of these workflows are a researcher agent responsible for gathering data and an analysis agent responsible for generating a summary and a downstream report. However, one of the things that makes the researcher agent's output trustworthy is its retrieval layer, since a poor retrieval-layer output can make the analysis agent's recommendation questionable. If the researcher agent searches the webpage and retrieves a challenge page, an empty response, or blocked content, every downstream conclusion becomes less trustworthy. With a 99.93% success rate on protected websites, ZenRows provides the reliable retrieval layer that makes these workflows practical in production. This tutorial shows why a CrewAI researcher agent can fail on protected competitor pages. It starts with a custom ZenRows-based tool setup, then later shows the MCP server as an alternative approach. Prerequisites This tutorial works best with Python 3.10 or newer. If you are on an older Python version, create a dedicated virtual environment with a current Python installation to avoid dependency conflicts. Python 3.10 to 3.13. CrewAI requires this range. ZenRows API key. Create an account at zenrows.com and copy your key from the dashboard. This key authenticates every scrape the researcher agent runs for data extraction. Anthropic API key. The crew uses Claude to drive both agents. Generate a key in the Anthropic Console. Install dependencies and the required packages using pip install "crewai[anthropic]" crewai-tools zenrows python-dotenv . Create a .env file and save your API keys there. Why the built-in ScrapeWebsiteTool fails on competitor pages The problem, as established earlier, starts before the analysis. A CrewAI workflow depends primarily on the information collected, because a competitor intelligence agent relie

I Built a Programming Language Called Zen Building a programming language had been something I wanted to do for a long time. What I didn't realize when I started was how much work exists beyond parsing a few tokens and generating some code. A language is not just a parser or a compiler backend. It is tooling, developer experience, documentation, installation, error handling, runtime support, and countless design decisions. After multiple attempts and many lessons learned, I'm excited to share Zen. Why a Third Attempt? Zen is not the first language project I started. My first attempts taught me a lot, but they never reached a stage where I felt comfortable sharing them publicly. The architecture was incomplete, important components were missing, and the overall developer experience wasn't where I wanted it to be. Instead of abandoning the idea, I kept iterating. Each attempt helped me better understand: Compiler architecture Language design LLVM Runtime integration Tooling and usability Error handling Project structure Zen is the result of those lessons. What Is Zen? Zen is a programming language with its own compiler pipeline and LLVM-based backend. The goal was not just to generate code, but to create a complete language ecosystem that developers can actually install and use. Zen currently includes: Lexer Parser AST generation LLVM IR generation Native executable generation through LLVM Runtime integration Standard library integration Command-line tooling Installation system Documentation website Compiler Pipeline The compilation process follows a traditional compiler architecture: Source Code ↓ Lexer ↓ Parser ↓ AST ↓ LLVM IR Generation ↓ LLVM Optimization ↓ Object Files ↓ Native Executable LLVM handles optimization and machine code generation, allowing Zen to produce native binaries. Command Line Interface Zen provides several commands for development and inspection: zen run zen build zen ir zen ast zen tokens zen clean This allows users to inspect different stage

With the release of version 1.7.9, ZenQL’s Collection API, Thor, received substantial performance improvements, largely driven by a series of memory optimization enhancements. These changes reduced unnecessary allocations, improved data handling efficiency, and significantly accelerated query execution, particularly when working with large in-memory datasets. From the early stages of development, we established a baseline benchmark to measure both correctness and performance consistently: filtering a slice of 50 million items and validating the result. The original Collection API completed this benchmark in approximately 9 seconds. Later, we introduced Thor as a replacement for the default Collection API, reducing the benchmark time to around 4 seconds. With the latest round of memory optimizations and internal improvements, Thor now completes the same benchmark in less than one second. benchmark: goos: linux goarch: amd64 pkg: github.com/malikhan-dev/zenql/collections/Thor cpu: 12th Gen Intel(R) Core(TM) i7-12700H BenchmarkQueryEngine BenchmarkQueryEngine-20 88 13636313 ns/op 22727310 B/op 0 allocs/op at Thor_Engine__test . go func BenchmarkQueryEngine ( b * testing . B ) { result := From ( & items ) . Where ( func ( search ComplexObjectToSearch ) bool { return search . Name == "Jane" && search . Flag == false }) . Collect () result2 := From ( & result ) . Any ( func ( search ComplexObjectToSearch ) bool { return ( search . Name != "Jane" ) || ( search . Flag != false ) }) . Assert () if result2 { b . Error ( "result should be false" ) } } Join the Journey We are committed to making ZenQL the fastest and most developer-friendly query engine for Go. As we continue to grow and push the boundaries of performance, we need your support! If you find ZenQL useful, please star our repository on GitHub. Your support helps us reach more developers and keep the project moving forward. Thank you!

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