TECH DIVE: IN-DEPTH TECHNICAL ARTICLES
Kubernetes for GenAI: Why it makes so much sense
Learn about the benefits of utilizing Kubernetes as a platform for GenAI, such as GPU enablement, storage integrations, and workload automation, and how it can enhance inference.
How Meta trains large language models at scale
Meta details their efforts to scale large language model training, highlighting challenges and innovations in hardware, software, networking, and infrastructure to support massive AI workloads requiring hundreds of thousands of GPUs working in concert.
LangChain vs. LlamaIndex
What are the key differences between LlamaIndex and LangChain - find out which AI framework best suits your project needs in this comparison.
Human insight + LLM grunt work = creative publishing solution
A seamless method to integrate Google Docs and Markdown for efficient software documentation, leveraging the power of LLMs to simplify your workflow.
Postgres is all you need, even for vectors
Using PostgreSQL with the pgvector plugin alongside relational data challenges the need for specialized vector databases in many AI and machine learning applications.
Revolutionizing distributed software with WebAssembly component model
Discover how the WebAssembly Component Model transforms server-side software with unprecedented performance, security, and portability.
Leveraging AI for efficient incident response
Meta leverages AI to revolutionize system reliability investigations, achieving a remarkable 42% accuracy in root cause identification using cutting-edge heuristic and large language model-based techniques.
Why your brain is 3 milion more times efficient than GPT-4
Take a deep dive into the world of vector databases with Olaf Górski as he compares the leading options, revealing why our brains are still millions of times more efficient than GPT-4, and explores the intricacies of embeddings, HNSW, and ANNS from a production project perspective.
