Discover effective prompting techniques to prevent large language models from reinforcing your biases. Learn strategies for critical thinking, structured interactions, and extracting unbiased, robust information.
Explore the cutting-edge tools and models for creating a local, low-latency, and open-source speech-to-speech assistant, including ASR, LLM, and TTS pipelines, hardware considerations, and real-time integration tips.
Explore the critical debate around AGI definitions, the limitations of LLMs, and the next-generation approaches like world models. Discover insights into the future of general artificial intelligence and potential challenges.
Explore how AI coding impacts COBOL development, from strict formatting and compliance hurdles to its unexpected utility as a productivity tool. Discover strategies for leveraging LLMs effectively in legacy systems.
Explore how developers are creatively automating coding work with AI, from codifying principles to using LLMs as pair programmers. Learn key strategies for boosting productivity while maintaining code quality and managing AI interactions.
Discover how businesses are moving past productivity gains to generate significant revenue and cut costs by strategically integrating LLMs into their operations. Learn from real-world examples of successful implementation and key patterns for monetization.
Burned out from years in tech? Discover diverse career paths, practical sabbatical advice, and introspective strategies to redefine happiness and find your next fulfilling chapter.
Explore cutting-edge methods for providing continuous context to AI models, focusing on agentic search, intelligent memory management, and preventing context drift for more efficient and coherent interactions.
Users report fluctuating performance from large language models like Claude Opus, often seeing degradation during peak hours. Explore theories ranging from dynamic model routing to compute allocation, and discover user-shared strategies to mitigate these issues.
Discover practical strategies for preventing LLM hallucinations in production systems, focusing on robust external validation and treating LLM output as untrusted input. Learn how to build reliable AI applications by separating model proposals from deterministic execution.