Insights on AI, machine learning, data privacy, and building efficient AI infrastructure from the Corewood team.
Understanding how to effectively work with Large Language Models by treating them as context synthesizers rather than instruction followers. Learn why traditional command-based approaches fail and how to shape AI outputs through information flow.
Most software projects fail not because of technical complexity, but because teams build the wrong thing. Learn how engineers can take ownership of requirements and build solutions that actually solve problems.
Exploring how the foundational principles of Unix continue to guide modern AI development, from modularity to transparency and human-centric design.
Discover how we eliminated JSON parsing overhead and achieved 10-100x faster tokenizer startup times with a custom binary format for DeBERTa tokenizers in production ML inference.
For forty years, engineering teams optimized for speed. Then came privacy laws. Explore how the fundamental disconnect between performance-first systems and compliance requirements is reshaping software development.
Healthcare software innovation is stifled by legacy privacy approaches. Learn why compliance-first thinking creates inefficiency and how dynamic privacy protection can unlock better user experiences, lower costs, and faster development.
When trying to do the right thing drives the wrong results. Explore how zero-copy architecture transforms healthcare data compliance from an expensive constraint into a manageable foundation.
Building AI systems that deliver real business value requires navigating a complex landscape of technical considerations that directly impact your bottom line.
Many companies are demoing AI capabilities they fundamentally cannot afford to operate. Learn about the sobering reality of AI economics and sustainable alternatives.
Your model is only as effective as the infrastructure it runs on. Learn how the right architecture can dramatically improve AI performance and cost-efficiency.
On-premises AI deployment offers better control, data privacy, and long-term cost benefits compared to cloud-based alternatives. Learn how to implement it effectively.
Despite billions in investment, current machine learning approaches are failing to deliver on their promises. Learn why the 'bigger is better' approach has hit its ceiling and how targeted intelligence can help.
Moving beyond impressive AI demos to real business value requires infrastructure, talent, and implementation expertise. Learn how to achieve actual ROI from your AI investments.
