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Artificial Intelligence

Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.

Explore Artificial Intelligence

LLM Market Share: Compare Usage & Adoption

LLM
Insight
Jul 2

We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: Read the methodology to see how we measured and calculated these results. The United States dominated web visits across all four months, consistently accounting for 85.5–90.5%.…

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AI Foundations
Insight
Jul 2

AGI/Singularity: 9,800 Predictions Analyzed

Artificial general intelligence (AGI) is when an AI system matches human cognitive abilities across all tasks. We analyzed 9,800 AI researchers‘, leading entrepreneurs‘, and community predictions about the AGI timeline: Will AGI/singularity happen? AGI is inevitable according to most AI experts. When will we reach AGI? Between late 2020s and early 2030s. AGI timeline shortened…

AI
Insight
Jul 2

7 Useful AI Transformation Strategies in 2026

Before choosing how to transform AI, leaders need to know where to start. We analyzed the Anthropic Economic Index (March 2026 release)31, mapping over 1 million real-world Claude interactions across 3,260 occupational tasks to the standard APQC Process Classification Framework (PCF).58 Check out high, mid, and granular-level processes by real-world AI exposure and human verification…

AI
Insight
Jul 2

AI Rollups: Funding, Investors and Industry Trends

We analyzed 30 investments involving over 130 investors from the past 3 years to understand the current trend for AI rollups. Based on our analysis, we identified investor activity and trends, including the number of investors backing AI rollups, the total funding raised for AI rollups, and the leading industries. See the methodology to learn…

Document Automation
Benchmark
Jul 2

Handwriting Recognition Benchmark: LLMs vs OCRs

OCR tools achieve over 99% accuracy on typed text in high-quality images. However, handwriting remains challenging due to variations in style, spacing, and irregularities. We introduce a cursive handwriting benchmark with 100 handwriting samples written by our team to prevent overfitting. In this benchmark, GPT-5, Gemini 3 Pro Preview, and olmOCR-2-7B-1025-FP8 are the top-performing models,…

Voice AI
Insight
Jul 2

Top 10 Voice Recognition Tool & Applications

If you’ve used virtual assistants like Alexa, Cortana, or Siri, you’re likely familiar with speech recognition and conversational AI. This technology enables users to interact with devices through verbal commands by converting spoken queries into machine-readable text. Explore the top 10 uses of voice recognition technology in voice search, customer service, healthcare, and other areas.…

AI Foundations
Insight
Jul 2

No-Code AI: Benefits, Industries & Key Differences

No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background. Recently, no-code AI has expanded…

AI Foundations
Benchmark
Jul 2

Top Image Recognition Tools Compared

We benchmarked the default API configurations of Amazon Rekognition, Google Cloud Vision, and Microsoft Azure AI Vision on 100 images across 5 object classes, and compared their pricing and feature coverage. Performance metrics for three image recognition platforms were evaluated at an Intersection over Union (IoU) threshold of 0.5, comparing mAP, F1 score, recall, and…

GenAI Applications
Insight
Jul 2

Top 13 GAN Use Cases

While GANs pioneered many early generative AI applications, particularly in image synthesis and style transfer, most consumer-facing generative AI tools today rely on diffusion-based architectures or related approaches such as flow matching and diffusion transformers (DiT). However, GANs remain important in specific domains, such as super-resolution, face restoration, the generation of synthetic tabular or healthcare…

AI Coding
Benchmark
Jul 2

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. While expecting a pixel-perfect transfer is wrong in the current state of the tools, they can give developers a foundation to work…

RAG
Benchmark
Jul 2

Multimodal Embedding Models: Apple vs Meta vs OpenAI

Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware…