Top 10 Technology Trends
A 2026 editorial refresh of the old 2019 trend list, focusing on which technology themes proved durable and which ones matter most now.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A 2026 editorial refresh of the old 2019 trend list, focusing on which technology themes proved durable and which ones matter most now.
A refreshed guide to classical and modern text similarity approaches, from edit distance and token overlap to embeddings and hybrid retrieval.
A practical guide to data science in media and entertainment, covering use cases such as personalization, churn reduction, monetization, forecasting, and audience intelligence.
A practical comparison of speech processing APIs for speech-to-text, text-to-speech, streaming transcription, customization, and modern voice AI workloads.
An executive-friendly guide to the main branches of data science and how managers should think about the field, supported by a mindmap.
A practical comparison of top cloud computer vision APIs and vision services, focused on fit, tradeoffs, and when to use a managed API instead of custom models.
A 2026 comparison of text processing APIs across Google Cloud, AWS, Azure, and IBM, covering sentiment analysis, entity extraction, translation, customization, and platform fit.
A refreshed take on how to analyze startup geography, using the original state-level study as a historical example of location-based exploratory analysis.
A practical guide to data science in retail, covering analytics and AI use cases such as forecasting, pricing, personalization, merchandising, fraud prevention, and omnichannel operations.
A practical comparison of Python NLP libraries, focused on when to use NLTK, spaCy, scikit-learn, Gensim, Polyglot, and Transformers.
A practical overview of data science in insurance, including ten high-value use cases across underwriting, fraud detection, claims automation, retention, and operations.
A refreshed comparison of Python, R, and Scala for data science, including how the languages differ and which library ecosystems still matter most.