Şevval Alper
Şevval is an AI researcher at AIMultiple. She has previous research experience in pseudorandom number generation using chaotic systems.
Research interests
Şevval focuses on AI coding tools, AI agents, and quantum technologies.
She is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand various emerging technologies and their applications.
Professional experience
She contributed to organizing and guiding participants in three “CERN International Masterclasses - hands-on particle physics” events in Türkiye, working alongside faculty to facilitate learning.
Education
Şevval holds a Bachelor's degree in Physics from Middle East Technical University.
Latest Articles from Şevval
OCR Benchmark: Text Extraction / Capture Accuracy
OCR accuracy is critical for many document processing tasks, and SOTA multi-modal LLMs are now offering an alternative to OCR.
Text-to-Video Generator Benchmark
A text-to-video generator is an AI system that turns written prompts into short videos by generating visuals, motion, and sometimes audio directly from natural language.
Code Execution with MCP: A New Approach to AI Agent Efficiency
Anthropic introduced a method in which AI agents interact with Model Context Protocol (MCP) servers by writing executable code rather than making direct calls to tools. The agent treats tools as files on a computer, finds what it needs, and uses them directly with code, so intermediate data doesn’t have to pass through the model’s memory.
Top 10 Google Colab Alternatives
Google Colaboratory is a popular platform for data scientists and machine learning scientists, but its limitations and pricing may not meet your needs. Several alternatives offer unique features and capabilities that cater to different data science needs and scenarios.
LLM Parameters: GPT-5 High, Medium, Low and Minimal
New LLMs, such as OpenAI’s GPT-5 family, come in different versions (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) and with various parameter settings, including high, medium, low, and minimal. Below, we explore the differences between these model versions by gathering their benchmark performance and the costs to run the benchmarks. Price vs.
AI Agents: Operator vs Browser Use vs Project Mariner
AI agents are increasingly marketed as end-to-end digital workers, but real-world performance can vary widely depending on the task, tools, and execution environment. To understand what these systems can genuinely deliver today, we conducted hands-on benchmarking across practical business scenarios.
Speech-to-Text Benchmark: Deepgram vs. Whisper
We benchmarked the leading speech-to-text (STT) providers, focusing specifically on healthcare applications. Our benchmark used real-world examples to assess transcription accuracy in medical contexts, where precision is crucial. Speech-to-text benchmark results Based on both word error rate (WER) and character error rate (CER) results, GPT-4o-transcribe demonstrates the highest transcription accuracy among all evaluated speech-to-text systems.
Vibe Coding: Great for MVP But Not Ready for Production
Vibe coding is a new term that has entered our lives with AI coding tools like Cursor. It means coding by only prompting. We made several benchmarks to test the vibe coding tools, and with our experience, we decided to prepare this detailed guide.
Top 4 AI Search Engines Compared
Searching with LLMs has become a major alternative to Google search. We benchmarked the following AI search engines to see which one provides the most correct results: Benchmark results Deepseek is the leader of this benchmark, by correctly providing 57% of the data in our ground truth dataset.
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