Technical Term: Optimization

The actor model isn’t new. Carl Hewitt introduced it at MIT in 1973, the same year that Ethernet was invented. For fifty years, this elegant model of computation, where independent actors maintain state and communicate through messages, has powered everything from Erlang’s telecom switches to WhatsApp’s billions of messages. But until now it has required specialized runtimes, complex deployment, or significant infrastructure overhead. Today’s “AI agents” are essentially rediscovering what distributed systems engineers have known for decades: isolated, message-passing actors are the natural way to build resilient, scalable systems.

The technology industry has developed an unfortunate habit of wrapping straightforward engineering advances in mystical language. When sites online boast of claims to “blur the lines between P and NP,” they’re usually describing something far more mundane: dealing with technology problems more efficiently. The mathematical complexity remains unchanged, but it shouldn’t be used as a barrier to understanding the practicalities. This isn’t cheating or transcending mathematics - it’s recognizing that most real-world performance barriers come from architectural mismatches, not algorithmic limits.

Modern computing systems present a fundamental paradox: while processor speeds have increased exponentially, memory latency improvements have been modest, creating an ever-widening performance gap. This disparity manifests most acutely in the cache hierarchy, where the difference between an L1 cache hit (approximately 4 cycles) and main memory access (200+ cycles) represents a fifty-fold performance penalty. For systems pursuing native performance without runtime overhead, understanding and exploiting cache behavior becomes not merely an optimization, but an architectural imperative.

In a recent YouTube interview, Tri Dao, architect of Flash Attention and contributor to Mamba, delivered an insight worth exploring here: “If you’re a startup you have to make a bet … you have to make an outsized bet” [8:29]. This admission, in the flow of discussion about AI, reveals the fundamental tension in today’s technology infrastructure landscape. Most of the industry has agreed to a single big bet: placing the majority of resources on GPU-centric architectures and matrix multiplication as the foundation of intelligence even as the limits of Moore’s Law and the laws of thermodynamics loom large.

The promise of edge computing for AI workloads has evolved from experimental optimization to production-ready enterprise architecture. What began as our exploration of WASM efficiency gains has matured into a comprehensive platform strategy that leverages Cloudflare’s full spectrum of services; from Workers and AI inference to containers, durable execution, and Zero Trust security. A Pragmatic Approach Our initial focus on pure WASM compilation through the Fidelity framework revealed both the tremendous potential and practical limitations of edge-first development.

The Steam-Powered Illusion The current AI oligarchy’s greatest deception isn’t about capabilities; it’s about implementation. While hyperscalers tout their models as “flying cars” of intelligence, the reality behind the curtain resembles something far more primitive: akin to steam-powered automobiles complete with teams of engineers frantically shoveling coal into boilers just to keep the engines running. This isn’t hyperbole. Today’s AI models require data centers that consume the water and power output of small cities, yet deliver chronically delayed responses in a technology environment where commercial viability is determined by human interactions measured in milliseconds.

Modern async and parallel programming presents an engineering challenge: we need both the performance of low-level control and the safety of high-level abstractions. Nearly 20 years ago, the .NET ecosystem pioneered the async/await syntactic pattern, making concurrent code accessible to millions of developers and influencing other technology stacks in following years. However, this pattern comes with tradeoffs - runtime machinery that, while powerful, can become opaque when we need to understand or optimize workload behavior.

Software tools face an eternal tension: wait to build fast executables or speed up workflow at the cost of the end result. Traditional approaches have forced developers to choose between aggressive optimization (and long compilation cycles) that produces efficient code versus rapid compilation cycles often yield code bloat. What if we could have both? Or rather, what if we could have the choice that matters when it matters most? The answer lies in understanding something most programmers miss about functional programming:

A startup’s gene analysis samples nearly melted because someone confused Fahrenheit and Celsius in their monitoring system. A Mars orbiter was lost because of mixed metric and imperial units. Medication dosing errors have killed patients due to milligrams versus micrograms confusion. These aren’t edge cases - they’re symptoms of a fundamental problem in how we build mission-critical systems: Most languages approach types as an afterthought rather than a first line of defense.

While this idea might be met with controversy in the current swarm of AI hype, we believe that the advent of sub-quadratic AI models, heterogeneous computing, and unified memory architectures will show themselves as pivotal components to next generation AI system design. The elements are certainly taking shape. As we stand at this technological crossroads, AMD’s evolving unified CPU/GPU architecture, exemplified by the MI300A and its planned successors (MI325, MI350, MI400), combined with their strategic acquisition of Xilinx, offers a compelling case study for re-imagining how AI models can operate.

The Fidelity framework’s Farscape CLI addresses a pressing challenge in modern software development: how to enhance the safety of battle-tested C/C++ tools without disrupting the countless systems that depend on them. Every day, organizations rely on command-line tools like OpenSSL, libzip, and many others that represent decades of engineering expertise but carry the inherent memory safety risks of their C/C++ heritage. Farscape’s “shadow-api” design aims to provide a breakthrough solution: the ability to generate drop-in replacements for these critical tools that maintain perfect compatibility while adding comprehensive type and memory safety guarantees.

The Fidelity Framework and its ecosystem of technologies represent more than technical achievements, they embody our core values in executable form. Where our Compact establishes how people and groups interact within the SpeakEZ ecosystem, our technical innovations demonstrate these same principles applied to systems design. This alignment between human values and technical architecture is neither accidental nor superficial; it reflects our belief that sustainable innovation emerges when technological choices reinforce rather than contradict constituent needs.

As we’ve established in previous entries, FidelityUI’s zero-allocation approach provides an elegant solution for embedded systems and many desktop applications. But what happens when your application grows beyond simple UI interactions? When you need to coordinate complex business logic, handle concurrent operations, and manage sophisticated rendering pipelines? This is where the Olivier actor model and Prospero orchestration layer transform FidelityUI from a capable UI framework into a comprehensive application architecture that scales to distributed systems, all while maintaining deterministic memory management through RAII (Resource Acquisition Is Initialization) principles.

The journey of creating a native UI framework for F# presents a fascinating challenge: how do we preserve the elegant, functional programming experience that F# developers love while compiling to efficient native code with (in most cases) zero heap allocations? As we build FidelityUI, the UI framework for the Fidelity ecosystem, we find ourselves at the intersection of functional programming ideals and systems programming realities. Fortunately, we don’t have to start from scratch.

As a companion to our exploration of CXL and memory coherence, this article examines how the Fidelity framework could extend its zero-copy paradigm beyond single-system boundaries. While our BAREWire protocol is designed to enable high-performance, zero-copy communication within a system, modern computing workloads often span multiple machines or data centers. Remote Direct Memory Access (RDMA) technologies represent a promising avenue for extending BAREWire’s zero-copy semantics across network boundaries. This planned integration of RDMA capabilities with BAREWire’s memory model would allow Fidelity to provide consistent zero-copy semantics from local processes all the way to cross-datacenter communication, expressed through F#’s elegant functional programming paradigm.

The Fidelity framework introduces a revolutionary approach to building desktop applications with F#, enabling developers to create native user interfaces across multiple platforms while preserving the functional elegance that makes F# special. Drawing inspiration from the successful patterns established by Elmish and the MVU pattern - particularly within Avalonia - we take many lessons from Fabulous. FidelityUI adapts these proven approaches for native compilation, creating a framework that feels familiar to F# developers while delivering unprecedented performance through direct hardware access.

The “byref problem” in .NET represents one of the most fundamental performance bottlenecks in managed programming languages. While seemingly technical, this limitation cascades through entire application architectures, not only hijacking developer productivity but also forcing them into defensive copying patterns that can devastate performance in memory-intensive applications. The Fidelity framework doesn’t just solve this problem; our designs transform the limitation into the foundation for an entirely new approach to systems programming that maintains functional programming elegance while delivering hardware-level performance.

SpeakEZ’s Fidelity framework with its innovative BAREWire technology is uniquely positioned to take advantage of emerging memory coherence and interconnect technologies like CXL, NUMA, and recent PCIe enhancements. By combining BAREWire’s zero-copy architecture with these hardware innovations, Fidelity can put the developer in unprecedented control over heterogeneous computing environments with the elegant semantics of a high-level language. This innovation represents a fundamental shift in how distributed memory systems interact, and the cognitive demands it places on the software engineering process.

Creating software with strong correctness guarantees has traditionally forced developers to choose between practical languages and formal verification. The Fidelity Framework addresses this challenge through a groundbreaking integration of F# code, F* proofs, and MLIR’s semantic dialects. This essay explores how the Fidelity Framework builds upon the semantic verification foundations introduced in “First-Class Verification Dialects for MLIR” (Fehr et al., 2025) to create a unique pipeline that preserves formal verification from source code to optimized binary.

The promise of functional programming has always been apparent: write code that expresses a process to an end result, not how the machine should perform those actions. Yet for decades, this elegance came with a tax - runtime overhead, garbage collection pauses, and the implicit assumption that “real” systems programming belonged to C and its descendants. The Fidelity Framework challenges this assumption by asking a different question: What if we could preserve F#’s expressiveness, safety and precision while compiling to native code that rivals hand-written C in efficiency?

The journey from managed code to native compilation in F# represents a significant architectural shift. As the Fidelity Framework charts a course toward bringing F# to new levels of hardware/software co-design, we face a fundamental question: how do we distribute and manage packages in a world where the comfortable-yet-constraining assumptions afforded in the .NET ecosystem no longer hold? This article explores Fargo, a forward-looking package management system that reimagines F# code distribution for the age of multi-platform native compilation.

The computing landscape stands at an inflection point. AI accelerators are reshaping our expectations of performance while “quantum” looms as both opportunity for and threat to our future. Security vulnerabilities in memory-unsafe code continue to cost billions annually. Yet the vast ecosystem of foundational libraries, from TensorFlow’s core implementations to OpenSSL, remains anchored in C and C++. How might we bridge this chasm between the proven code we depend on and the type-safe, accelerated future we’re building at an increasing pace?

The AI industry is experiencing a profound shift in how computational resources are allocated and optimized. While the last decade saw rapid advances through massive pre-training efforts on repurposed GPUs, we’re now entering an era where test-time compute (TTC) and custom accelerators are emerging as the next frontier of AI advancement. As highlighted in recent industry developments, DeepSeek AI lab disrupted the market with a model that delivers high performance at a fraction of competitors’ costs, signaling two significant shifts: smaller labs producing state-of-the-art models and test-time compute becoming the next driver of AI progress.