Howard Pearlmutter

What does the real system need to do?

A useful technical conversation can connect questions that are often separated:

• What must remain reliable while new capabilities are introduced?
• Where do native execution, middleware, data and application behavior meet?
• Which failure modes will a demonstration or a passing test leave unexplored?
• How should performance, concurrency, lifecycle and platform constraints shape the architecture?
• What evidence would make an implementation decision more convincing?
• How will the people using and operating the result experience it?

Bring an existing system, a proposed product, an unfamiliar integration or several competing paths. The work can begin with understanding the relationships and making the important questions visible.

Working code across native, enterprise and media boundaries

During his Telos Alliance engagement, Howard produced working code, prototypes and demonstrations spanning Apple Silicon multicore CPU/GPU execution, Macintosh MPSGraph, Java Foreign Function & Memory integration, Netty, Vert.x, Quarkus and Jakarta EE CDI.

Work around Dynamic Media Facility and Media eXchange Layer concepts joined high-performance computing, digital media, enterprise software, graphics and dynamic simulation. A facility simulation exposed statistics from the overall facility down to media grains and flows while the standard was still emerging.

In multiple demonstrations, his mastery of AI tooling for software development was very evident, not just for explaining concepts, but for applying AI-assisted engineering to low-level implementation, multiple programming languages, native/platform integration, scalable middleware, and domain-specific media workflows.

What might be hiding between the layers?

Howard's broader record includes parallel-computing work for NASA Ames, computational research at MIT, difficult distributed-systems diagnosis, enterprise Java integration, interactive graphics and technical-business consulting. In one production engagement, he resolved a nine-month intermittent caching-grid degradation and crash problem in a system handling tens of millions of pricing tasks per hour.

That experience supports investigation across implementation, timing, concurrency, infrastructure and business purpose. Recent AI-assisted development adds new tools and possibilities to a long-standing practice of understanding unfamiliar systems and making technical findings useful to decision-makers.