Blog posts exploring the concept "Type-Safety"
← Back to all tagsBlog posts exploring the concept "Type-Safety"
← Back to all tagsEvery software engineering team knows the testing treadmill. Write code, write tests, run tests, fix failures, write more tests to catch what you missed, maintain those tests forever. We’ve accepted this as the calendar and staffing cost multiplication demanded by standard approaches to quality software. But what if this entire cycle represents a fundamental inefficiency; a workaround in the absence of something better? The Fidelity framework’s proof-aware compilation offers a well established garden path: mathematical certainty at compile time, eliminating entire categories of tests while actually increasing safety.
Read MoreSoftware verification has always forced a cruel choice: accept runtime overhead for safety checks, or trust that your optimizing compiler won’t break critical invariants. Traditional compilers treat proofs as obstacles to optimization, while proof assistants have a reputation (earned or not) that they generate code too conservative for production use. But what if verification and optimization weren’t opposing forces but complementary dimensions of the same compilation process? The Fidelity Framework’s hypergraph architecture makes this vision real by treating proof obligations as first-class hyperedges that guide, not hinder, aggressive optimization.
Read MoreA 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.
Read MoreThe debate over higher-kinded types (HKTs) in F# reveals fundamental tensions between theoretical elegance and practical accessibility. This analysis examines these tensions through two lenses: first, Don Syme’s philosophical stance that has shaped standard F#, and second, how the Fidelity framework’s unique position as a native F# compiler might change this calculus. The goal is to understand both perspectives while considering whether Fidelity’s different constraints warrant a different approach. For readers from different language communities, this analysis offers distinct insights.
Read MoreCreating 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.
Read MoreThe 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?
Read MoreThe computing world has fragmented into specialized ecosystems - embedded systems demand byte-level control, mobile platforms enforce strict resource constraints, while server applications require elasticity and parallelism. Traditionally, these environments have forced developers to choose between conflicting approaches: use a high-level language with garbage collection and accept the performance overhead, or drop down to systems programming with manual memory management and lose expressiveness. Beyond Runtime Boundaries The Fidelity Framework represents a fundamental rethinking of this dichotomy.
Read MoreAt SpeakEZ, we are working on transformative approaches to transfer learning that combine convolutional neural networks (CNNs) with Topological Object Classification (TopOC) methods. This memo outlines our design approach to creating dimensionally-constrained models that maintain representational integrity throughout the transfer learning process while enabling deployment to resource-constrained hardware through our Fidelity Framework compilation pipeline. By leveraging F#’s Units of Measure (UMX) system to enforce dimensional constraints across the entire model architecture, we achieve not only safer and more reliable models but also significantly more efficient computational patterns that can be directly compiled to FPGAs and custom ASICs.
Read MoreThe embedded systems industry has operated under a fundamental assumption for decades: achieving hardware control requires sacrificing high-level abstractions and type safety. This assumption has created a divide between embedded development and modern software engineering practices, forcing developers to choose between expressiveness and efficiency. The Fidelity Framework challenges this paradigm through a revolutionary approach that delivers hardware type safety with truly zero runtime cost, a breakthrough in hardware/software co-design methodology.
Read MoreThe 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.
Read MoreIn the world of distributed systems, trust is fundamentally a mathematical problem. For decades, organizations have relied on single points of failure: a master key, a root certificate, a privileged administrator. But what if we told you that the mathematics of secure multi-party computation, pioneered by Adi Shamir in 1979 and refined through Schnorr signatures, has reached a point where distributed trust is not just theoretically possible, but practically superior to centralized approaches?
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