Nazlı Şipi
She is also part of the benchmark team, focusing on web data scraping, large language models (LLMs), AI agents, and agentic frameworks.
Nazlı holds a Master’s degree in Business Analytics from the University of Denver.
Latest Articles from Nazlı
We Benchmarked the Best Web Scraping APIs
We benchmarked web data providers in categories ranging from e-commerce and social media to jobs, real estate and food delivery, sending more than 600,000 requests to 10,000+ live domains to see how they handle enterprise data collection. Our web data scraping page summarizes the results of all our web data benchmarks in one place. Below,…
Web Crawler Benchmark to Feed Websites to AI
We benchmarked four crawl APIs across three domains of varying difficulty at three max depth levels (5, 10, 20) with a 1,000-page limit, measuring crawl coverage, execution time, link discovery, markdown link quality, and title extraction accuracy. If you aim to: You can read our benchmark methodology. Firecrawl consistently crawled around 100 pages on theregister.com…
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 40 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. See our benchmark methodology to learn our testing procedures. Every model scores lower on visual logic…
Best AI Web Scraping Tools Benchmarked
We benchmarked top AI web scraping tools with 25,000 requests, 5,000 per tool, across 500 URLs on each of 10 of the most visited domains to see their performance, and also compared how each one leverages AI. See the benchmark methodology section for more details on the testing process. For each domain we asked for…
15 AI Agent Observability Tools: AgentOps & Langfuse
We tested 15 observability platforms for LLM applications and AI agents. Each platform was implemented hands-on through setting up workflows, configuring integrations, and running test scenarios. We benchmarked 4 observability tools to measure whether they introduce overhead in production pipelines. We also demonstrated a LangChain observability tutorial using Langfuse. We integrated each observability platform into…
Top LLMOps Tools & Compare them to MLOPs
LLMOps platforms handle the operational side of running large language models: deployment, monitoring, evaluation, and cost management. We examined top LLMOps tools, their core features, pricing models, and how they differ from each other to help identify the best fit for various use cases. A breakdown of each metric is provided below: LLMOps platforms support…
AI Hallucination Detection Tools: W&B Weave & Comet
We benchmarked three hallucination detection tools: W&B Weave HallucinationFree Scorer (part of CoreWeave Forge, formerly Weights & Biases), Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency. We tested 100 responses (50 correct, 50 hallucinated) from factual Q&A scenarios against their source…
LLM Latency Benchmark by Use Cases
You can find details on how we measured latency here. We report reasoning and non-reasoning models separately. Reasoning models spend several seconds thinking before the first visible answer, so comparing them directly with non-reasoning models on latency would be misleading. Some models also change behavior by task: GPT-5.2 answers Q&A without reasoning but reasons on…
Mobile AI Agents Tested Across 65 Real-World Tasks
We spent 3 days benchmarking four mobile AI agents (DroidRun, Mobile-Agent, AutoDroid, and AppAgent) across 65 real-world tasks using an Android emulator with applications such as calendar management, contact creation, photo capture, audio recording, and file operations. See benchmark results including real-world performance comparison, costs and execution times: Highest success rate (43%) with high cost…
LLM Observability Tools: Weights & Biases, Langsmith
LLM applications have expanded from single-turn chats into multi-step agents that use tools, query databases, and coordinate with other models, making their behavior harder to interpret. LLM observability provides continuous visibility into these complex workflows, helping organizations monitor quality, detect failures, troubleshoot issues, and manage performance and costs. W&B Weave is Weights & Biases‘ LLM…
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