DLS 2023
who it's for
language designers, runtime engineers, compiler engineers, developer tooling engineers, debugging tool developers, cloud infrastructure engineers, developer experience engineers
topics
polyglot native bridges; CUDA/OpenCL kernel embedding; Alo memory runtime; sound dynamic gradual typing; like types (T|Dynamic); JIT warmup and memory tradeoffs; WebAssembly module instantiation; runtime snapshotting for cloud; multiverse time-travel debugging; AI agent grounding with TypeScript
| title | author | length |
|---|---|---|
| Is Polyglot Programming Really a Thing? | Walter Cazzola | 30m |
Polyglot code is common: examples include OpenJDK and TensorFlow mixing C, C++, Java and Python, and frameworks using CUDA/OpenCL kernels embedded as strings… | ||
| The importance of facing outwards: why dynamic languages can and should address th... | Stephen Kell | 30m |
Argues that dynamic languages should be outward-facing and demonstrates a working Python runtime that calls libpng without writing C wrappers by using an Alo… | ||
| Going Static, Gradually: Semantic Soundness and Telling the Truth at Scale | Maxwell Heiber | 28m |
Introduces Sound Dynamic as a semantics that makes Hack’s syntactic types correspond to semantic sets so types “don’t lie,” trading full deep checks for… | ||
| All Languages are Dynamic | James Noble | 32m |
Argues that languages are dynamic in multiple, concrete ways: implementations use ZX81 BASIC bytecodes and UCSD Pascal p‑code or JITs, IDEs enable Smalltalk‑78… | ||
| Are we Heading Towards a Dynamic Language Winter? | Maxime Chevalier Boisvert | 28m |
Argues that interest and funding for dynamic languages are softening while Rust and TypeScript climb popularity, yet Python and JavaScript remain dominant… | ||
| Prof. Strangelove or: How I Learned to Stop Worrying and Love Dynamic Languages | Jan Vitek | 34m |
Three decades of work yields concrete claims and tools: about 140 papers across language design, types, and implementations; an automated record-replay… | ||
| JITs are Nice, but Why Aren’t We Using Them? | Kevin Menard | 30m |
Companies running thousands of Ruby projects find JITs deliver big peak wins but fail operational constraints: Truffle Ruby and YJIT hit 100% speedups in some… | ||
| Keeping the asm in Wasm – Running high-level languages on a low-level VM | Andreas Rossberg | 33m |
WebAssembly remains a low-level VM that intentionally exposes hardware-like costs and forbids unsafe access to registers, stack or self-modifying code, so… | ||
| Language Runtimes for the New Cloud Era | Rodrigo Bruno | 33m |
High‑level language runtimes impose 10–100 MB footprints and 100–200 ms cold start penalties while many serverless functions run under one second and are… | ||
| Programming Languages for AI Programing Agents | Mark Marron | 34m |
Argues that languages and tools must expose concrete signals so LLM agents can be grounded: showing that about 5% of get/fetch methods are impure changes… | ||
| The Bright Future of Debuggers: Challenges and Opportunities | Christophe Scholliers | 37m |
Claims that debugging consumes ~50% of dev time costing $312B motivate concrete tools: a formal Multiverse debugging shows all interleavings for… | ||
| Most Notable Paper Award | Stefan Marr | 03m |
Awards a 10‑year retrospective selection that measures the runtime cost of gradual typing using micro and macro benchmarks, compares several cast insertion… | ||
| nothing matches | ||