The Expensive Fictions of Low-Level Programming Languages
Low-level languages like C and C++ impose high cognitive and security costs through undefined behavior and manual memory management. The article argues these languages' hardware abstractions are increasingly broken, advocating for safer alternatives like Rust that offer stronger guarantees without sacrificing performance.
Background
- The article challenges the assumption that low-level languages (C, C++, Rust) are inherently faster than high-level ones (Python, Java, Go). It argues this "fiction" leads to unnecessary complexity, security bugs, and wasted effort.
- Low-level languages give direct control over memory and hardware but require manual memory management, which causes buffer overflows, use-after-free errors, and other vulnerabilities behind countless real-world exploits.
- The author contrasts "performance theater" (premature optimization in low-level languages) with pragmatic engineering, arguing that high-level languages with optimized runtimes often match or beat hand-tuned low-level code for most applications.
- The piece joins a long-running debate in software engineering. Recent relevance comes from the rise of memory-safe languages like Rust and a 2024 U.S. government report urging a shift away from memory-unsafe languages.
Max Weinbach says he had early access to OpenAI's new model GPT-5.6 Sol, calling it his favorite model by far. He highlights that it never gives up and will keep reasoning until it's done. OpenAI announced that GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday, with preview access expanding globally now.
The US government ordered Anthropic to suspend access to its Fable 5 and Mythos 5 models for all customers, citing a potential jailbreak technique that involved asking the model to review a codebase for vulnerabilities—a capability Anthropic says is available in other public models. Access was abruptly cut off on June 12.
Andrej Karpathy announces the release of Claude Fable 5, the same underlying model as Mythos but with added safeguards. He calls it a major step forward, particularly for long problem-solving sessions on difficult tasks, and describes it as state-of-the-art on nearly all benchmarks with exceptional performance in software engineering, research, and vision.
Roman Storm warns that the legal theory in his case could set a precedent making open-source developers liable for how others use their code, potentially criminalizing the mere publication of privacy, messaging, or crypto tools. He notes that developer Michael Lewellen cannot publish lawful code due to prosecution fears, and argues this chilling effect extends beyond any single case.
Meta's engineering culture is deteriorating under Mark Zuckerberg and Scale AI CEO Alexandr Wang, who have introduced keyboard tracking, reassignments to data labeling, and AI-centric performance metrics. Critics argue this incentivizes performative AI use, drives away experienced engineers, and contributed to a major Instagram hijacking incident caused by AI-written and AI-reviewed code.