AI Cost Compression: Using Quantization to Scale AI Efficiently
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Deploying AI models at scale shouldn't require a blank check for computing. Quantization offers a path to cost-compression, allowing teams to run larger, more capable models with significantly less compute resources. In this article, we explain how to bridge the gap between research-grade precision and production-grade efficiency to scale your AI initiatives without scaling your budget.
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Institutional Latency and the Quest for Information Dominance
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Artificial intelligence is expanding software delivery capacity across national security environments, but many organizations still struggle to translate that speed into mission advantage. This article explores how institutional latency in governance, acquisition, and decision-making is becoming the critical limiting factor in the pursuit of information dominance.
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NoCap Development: How AI's Capacity Is Shifting Delivery Constraints
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As AI continues to reshape what is possible in software delivery, are the platforms and methodologies we have built our strategies around still the right ones? In this article, we explore how AI's capacity is shifting the constraint from building to thinking — and what that means for low-code platforms, agile methodologies, and security practices.
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A Guide to Advanced Prompting with LangChain
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Large Language Models (LLMs) are powerful tools, but harnessing their full potential often comes down to one crucial skill: effective prompting. Enter LangChain. This post explores several powerful prompting techniques you can leverage with LangChain to build more intelligent and reliable LLM applications that unlock the potential of Generative AI.
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