Solution Brief: Line-of-Code Intelligence for Compiled Binaries
Shifting-left observability and performance insights with Large Code Language Models
Teams building performance-critical software (such as networking, IoT, embedded) currently have two choices: use static analysis to get insights early, but without runtime context; or rely on costly and complex observability tools much later in the process. .
But what if you could detect hardware and runtime-related performance issues before software is deployed? LOCI, Aurora Labs’ AI-powered Line-of-Code-Intelligence platform, makes this possible.
Download the solution brief to learn:
- The limitations of static analysis and observability tools
- How to get observability-level insights (power consumption, performance degradation) from compiled binary files
- How LOCI’s large code language model (LCLM) works
- Case studies on how AI-powered binary analysis is being used by embedded systems developers today
About LOCI by Aurora Labs
Aurora Labs is a domain expert in ML, NLP, and model tuning, pioneering data-driven innovation since 2017, developing a proprietary vertical large language model (LLM) known as Large Code Language Model (LCLM). This LCLM specializes in comprehensive system workload analysis focusing on power and performance for observability and reliability, accelerating the development of embedded systems, AI, and Data Center infrastructures.
Unlike general-purpose Large Language Models (LLMs), LCLM delivers superior, efficient, and accurate binary analysis and detection of software behavior changes on targeted hardware, offering deep contextual insights into system-wide impacts – without the need for source code.
Let’s discuss how LOCI can help your team optimize software performance.
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