Berk Kalelioğlu
Research interests
Berk focuses on machine learning, agentic AI tools, and large and small language models (LLMs and SLMs).He is part of the AIMultiple benchmark team, conducting assessments and providing insights to help readers understand emerging technologies and their real-world applications.
Professional experience
He began his career as a Tech Project Lead at ODTU IVME-R, where he led a project to build physical quantum and pseudorandom number generators.After his tenure at IVME-R, he co-founded a game development company and released a game on Steam.
He later shifted his career toward AI and joined AIMultiple as a Researcher.
Education
Berk holds a Bachelor’s degree in Mathematics from Ankara University.Latest Articles from Berk
AI VC Benchmark: 16 AI Agents on Client Identification
Client identification is part of commercial due diligence. We tested 16 models on client-list research for three research and review sites, one of them AIMultiple, scoring their submissions against answer keys we compiled and the pages they cited. We did not set reasoning effort for any model. Each ran at its agent program’s default, and…
OpenClaw Alternatives: Hermes vs ZeroClaw vs Grok Bot
Autonomous AI agents, such as OpenClaw and Hermes agent, automate multi-step tasks that would normally require constant human input. While OpenClaw has become the most widely adopted always-on autonomous agent, many users are seeking alternatives due to its challenging deployment process and complex configuration requirements. We provide 5 leading OpenClaw alternatives, highlighting their key capabilities…
Computer Use Agents: Benchmark & Architecture
Computer-use agents operate real desktops and web apps. Their designs, limits, and trade-offs are often unclear. We break down how leading systems work, how they learn, and how their architectures differ. We also reference a focused UI-grounding benchmark on 100 desktop screenshots, across 4 task types and 5 runs per sample. It isolates the quality…
Time Series Classification Benchmark: Foundation Models vs Classical Methods
We benchmarked 13 time series classification methods, from pretrained time series foundation models to a 22-feature baseline from 2019, on 33 UCR/UEA datasets under one frozen protocol. That is 14,638 recorded method-dataset-resample cells, 11,874 of them scored. The chart compares 12 methods on the 15 univariate datasets every one of them completed, each dataset run…
VPS Benchmark: Hetzner vs Digital Ocean
We benchmarked 6 Virtual Private Server (VPS) providers by running ~1,200 automated tests per server across CPU, memory, disk I/O, and network speed using sysbench, fio, and speedtest-cli. We also documented the full signup-to-SSH experience for each provider. We used 4 vCPU (Shared) / 8 GB Plans of each provider, without adding any extras or…
AIM Agentic Marketing Benchmark
We are introducing the AIM Agentic Marketing Benchmark, which measures agent performance on three marketing workflows: competitive gap analysis, ABM target list preparation, and a personalized sales deck. We also ran a separate website reputation audit in which agents examined AIMultiple’s English-language content and reported verifiable issues with factual accuracy, citations, consistency, freshness, functionality, grammar,…
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