AI Foundations
Explore foundational concepts, tools, and evaluation methods that support the effective development and deployment of AI in business settings. This section helps organizations understand how to build reliable AI systems, measure their performance, address ethical and operational risks, and select appropriate infrastructure. It also provides practical benchmarks and comparisons to guide technology choices and improve AI outcomes across use cases.
Artificial Superintelligence: Opinions, Benefits & Challenges
The prospect of artificial superintelligence (ASI), a form of intelligence that would exceed human capabilities across all domains, presents both opportunities and significant challenges. Unlike current narrow AI systems, ASI could independently enhance its capabilities, potentially outpacing human oversight and control. This development raises concerns regarding governance, safety, and the distribution of power in society.
Top 9 AI Infrastructure Companies & Applications in 2025
Many organizations invest heavily in AI, yet most projects fail to scale. Only 10-20% of AI proofs of concept progress to full deployment. A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models.
Top AI Note Takers Tested: Motion, Fellow, Otter, and TL;DV
We tested each AI note taker to evaluate their accuracy and features during real world meetings. Follow the links to view our detailed reviews: AI note taker benchmark results Methodology for evaluating AI note taking tools: We tested AI note takers during a strategy session focused on improving sales in the rural market. 1.
Claims Processing with Autonomous Agents [2025]
We’ll use Stack AI workflow builder for claims automation and create an AI agent to enable users to upload accounting documents like claim forms and automatically convert them into structured JSON using OCR and GPT-based processing. The extracted data can then be sent to a Google Sheet or used in custom apps and databases.
Hands-On Top 10 AI-Generated Text Detector Comparison
We conducted a benchmark of the most commonly used 10 AI-generated text detector.
How to Measure AI Performance: Key Metrics & Best Practices
Measuring AI performance is crucial to ensuring that AI systems deliver accurate, reliable, and fair outcomes that align with business objectives. It helps organizations validate the effectiveness of their AI investments, detect issues like bias or model drift early, and continuously optimize for better decision-making, operational efficiency, and user satisfaction.
Model Context Protocol (MCP) and Its Importance in 2025
Model Context Protocol (MCP) is an open protocol that standardizes how applications, databases and tools provide context to LLMs. More simply, it enables applications to connect to AI models, helping to achieve standardized results. Why is it important? MCP servers are becoming more popular because of their integration capabilities with AI systems.
AI Reasoning Benchmark: MathR-Eval in 2025
We designed a new benchmark, Mathematical Reasoning Eval: MathR-Eval, to test the LLMs’ reasoning abilities, with 100 logical mathematics questions. Benchmark results Results show that OpenAI’s o1 and o3-mini are the best performing LLMs in our benchmark.
AI Deep Research: Claude vs ChatGPT vs Grok in 2025
AI deep research is a feature on some LLMs that offers users a wider range of searches than AI search engines. We tested the following tools with two tasks and evaluated them across 5 dimensions: Results We evaluated them in terms of accuracy and the number of sources.
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.