Most code modernization tools demo really well, showing instant results, even leading with a code conversion demo on their first sales meeting. But as these tools are met with the code at scale, suddenly they hit a wall at about the 80% mark. The latter 20% stubbornly carries all the intractable problems and edge cases. Not surprisingly this represents 80% of the modernization timeline. All large scale code modernization efforts meet this point, and it's where most modernization tools completely leave organizations at a dead end, and here is why...
Most vendors leverage some form of an LLM to translate the legacy code to a modern language. However, this means that the chain of logic occurs in the hidden layer of a probabilistic chain which is not persisted. This means that while the code can be generated it can't be globally controlled for iteration.
Large Code modernization efforts require a significant testing, tuning, and evolution of the new application. This means the code modernization is going to become part of the organization's long term operation. The prospect that the organization might not have the ability to make such changes in an automated way makes the organization feel trapped with a half baked solution that can't get across the finish line.
As testing and tuning occurs within a large code base, there often is a desire to make a surgical-yet-global change, such that the updated pattern can be propagated to all the sibling patterns in the code base. However, most every code modernization solution leaves you without any such global control.
Code has both low and high level patterns which it follows within the legacy code base. Some of these patterns are a result of developers copying prior work as a starting point. These patterns are completely eliminated in LLM-based code modernization solutions
“The extreme difficulty of scaling production of new technology is not well understood. It’s 1000% to 10,000% harder than making a few prototypes. The machine that makes the machine is vastly harder than the machine itself.”
-Elon Musk
Utilizes an AI-driven, end-to-end procedure to modernize from any stack to any stack
Feature updates, fixes, performance enhancements, etc. can all be globally propogated to the entire code base
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Enables the organization to push through the last 20% of intractable code
Eliminates technology bottlenecks, effectively organizes and utilizes data
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Holonic’s AI maintains entire state of the code which means that it can execute changes in their totality
Effectively streamlines and integrates the various competing constituencies into an effective rejuvenated system
Non-invasive process requiring no code freezes and permitting client to efficiently allocate precious resources
Eliminates security issues and protects against cyber-threats
Both the old code and the new code modeling can be brought into the intent space simultaneously where refinement, testing, and the evolution of the platform can occur globally
Adding new features are a function of modeling the behaviors you want, and decomposing that for the deterministic AI, and it will propagate that behavior both surgically and globally to the entire code base in a deterministic way. Eliminating the technical debt treadmill.
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