Improving Accuracy of LLM Applications
Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.
FactTune, A Method to Fine-Tune LLMs for Factual Accuracy Without Human Feedbackdeeplearning.ai
From AI Experiments to AI Productsdeeplearning.ai
Zhipu’s GLM-Image Blends Transformer and Diffusion Architectures For Better Text in Imagesdeeplearning.ai
Independent AI Testing Authority Turns From Saturated Knowledge Benchmarks to Harder Business Testsdeeplearning.ai
Stanford Researchers Coin “Moloch’s Bargain,” Show Fine-Tuning Can Affect Social Valuesdeeplearning.ai
Breaking into AI: Advocating for Machine Learning in the Real Worlddeeplearning.ai
Tagalog Speech to TextDeepgram
Security Considerations for API-Driven Apps Deployed to CloudThenewstack
KAUST and Cerebras Named Gordon Bell Award Finalist for Solving Multi-Dimensional SeismicCerebras
Cerebras Systems Enables GPU-Impossible™ Long Sequence Lengths Improving Accuracy in NaturalCerebras
6 tips every developer should know when using Cursor and Windsurf AIMintlify
Kannada Speech to TextDeepgram
The Noise Reduction Paradox in Speech-to-Text AccuracyDeepgram
.NET zero-code instrumentationOpentelemetry
Be the Hero in Your Real-Life Cyber Heist ScriptThenewstack
Improving Pulumi’s Docker ImagesPulumi
Product updates: Running batch jobs with 1M inputs, ephemeral apps, and a new TensorRT-LLM exampleModal
React Authentication & Access ControlCss Tricks
Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models - CerebrasCerebras
DockerCon Workshops: Getting Started with Docker, AI/ML, Secure DevelopmentDocker
