Şevval Alper

Sep 1

Audience Simulation: Can LLMs Predict Human Behavior?

In marketing, evaluating how accurately LLMs predict human behavior is crucial for assessing their effectiveness in anticipating audience needs and recognizing the risks of misalignment, ineffective communication, or unintended influence. Audience simulation with LLMs enables the modeling of virtual audiences, helping organizations anticipate reactions to content or products without relying on costly surveys or focus…

Agentic AIAug 28

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. We spent more than 40 hours testing the top 5 AI agents to see if…

Aug 27

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…

Aug 24

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. We benchmarked each agent across 10 full-stack web development tasks, performing ~600 atomic validation checks per agent and more than 9,600…

AI Agent Platforms Benchmark: Claude Managed Agents vs Google Vertex Agent Engine

We benchmarked 4 AI agent platforms across 3 dimensions: task completion (10 coding tasks × 3 runs), harness-specific capabilities (steering, reconnection, long-conversation recall, large-file handling), and cost. Claude Managed Agents and Vertex AI Agent Engine both achieve 100% pass rates on the task suite, with Vertex winning on cost ($1.45 vs $2.50). For harness-specific features…

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. claude-fable-5 scores 97.0% on the 100 direct-recall items,…

MCP Benchmark: Top MCP Servers for Web Access

We benchmarked 8 MCP servers across web search and extraction, as well as browser automation tasks, by running 4 different tasks 5 times on all suitable MCPs. We also performed a load test involving 250 concurrent AI agents. *Web search & extraction tasks are run with Bright Data’s default MCP server, browser automation tasks are…

Insight

LLM Parameters: GPT-5 High, Medium, Low and Minimal

Some 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. We used the GPT-5 family in our…

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. claude-fable-5 answered all 204 traps correctly at every haystack…