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Şevval Alper

Şevval Alper

AI Researcher
24 Articles
Stay up-to-date on B2B Tech
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

Enterprise SoftwareJun 12

MSP Automation: Acronis, ConnectWise Automate & Rewst

Managed service providers (MSPs) handle a constant operational load, including ticket management, patch management, onboarding, alert monitoring, billing reconciliation, and documentation updates. These are necessary but time-intensive tasks.

Enterprise SoftwareJun 8

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.

AIJun 5

HALC-Bench: LLM Hallucination on Long-Context Retrieval Benchmark

HALC-Bench (LLM Hallucination on Long-Context Retrieval Benchmark) measures a large language model’s resistance to fabricating evidence for a metric that does not exist in the target document by using 3 haystacks placed at the beginning, middle, and end of the model’s context window, with 204 questions. Results gpt-5.

AIJun 3

Screenshot to Code: Lovable vs v0 vs Bolt

During my 20 years as a software developer, I led many front-end teams in developing pages based on designs that were inspired by screenshots. Designs can be transferred to code using AI tools.

Agentic AIMay 26

VELC-Bench: Verification on Long Context Benchmark

The model’s ability to locate a specific metric in context, compare its value to a claim, and confirm or reject it. This tests fine-grained value matching under long-context conditions. The model must both retrieve the value and perform a precise comparison.

Agentic AIMay 26

RELC-Bench: Retrieval on Long Context Benchmark

RELC-Bench (RELC-Bench: Retrieval on Long Context Benchmark) aims to measure a model’s ability to find and extract a specific numeric value from one or more documents within its context. It tests whether the model can remember and retrieve a specific fact it just saw in the input.

AIMay 11

Best AI Code Editor: Cursor vs Windsurf vs Replit

Making an app without coding skills is highly trending right now. But can these tools successfully build and deploy an app? We benchmarked 6 AI code editors across 10 real-world web development challenges. Each task required implementations such as backend, frontend, authentication, state management.

AIMay 11

eCommerce AI Image Editing: GPT Images & Nano Banana

AI image editing tools analyze and automatically adjust product photos, allowing eCommerce businesses to enhance quality, remove backgrounds, or modify details with minimal effort. We tested the top 7 AI image editing tools on 20 images and 20 prompts across five dimensions, including prompt adaptability, realism, shadows, color rendering, and image quality.

AIMay 8

Top AI Website Generators Benchmarked

To find the most helpful prompt-to-website creator, we benchmarked the following tools: If you need to learn about no-code AI website generator tools, you can follow the links: Benchmark results We conducted this benchmark using the latest versions of the tools available as of January 2025.

AIMay 7

AI Coding Benchmark: Claude Code vs Cursor

In AI coding, the market has fragmented into two categories: Agentic CLI tools and AI code editors embedded in IDEs. Each claims to automate development. Few comparisons show how they differ under identical workloads.