Rust

The Prompt is the Platform - Dominik Tornow, Resonate HQ

Jun 29, 2026 · 17:33

Dominik Tornow, founder and CEO of Resonate, argues that coding agents will retire general-purpose platforms by 2026, replacing them with bespoke implementations generated on demand from reusable specifications. He details Resonate's shift from delivering implementations to offering specifications — the product becomes the protocol. Tornow recounts how agents initially failed to build a production-grade Resonate server on Postgres due to the gap between abstract specs and concrete implementations. By inserting a concrete specification and deterministic simulation environment, agents could design correct algorithms for distributed systems. Working with NATS I/O, he explains how simulation exposes hidden facts like stale reads, letting agents debug failures. The resulting workflow — abstract spec, simulated implementation, concrete spec, production code — enables agents to drive design, not just coding, making "the prompt the platform."

Continuous Profiling for GPUs — Matthias Loibl, Polar Signals

Jul 22, 2025 · 11:31

Matthias Loibl of Polar Signals explains how continuous profiling for GPUs maximizes GPU efficiency using low-overhead, always-on sampling via eBPF. He contrasts tracing (high cost) with sampled profiling (e.g., 100 Hz, <1% overhead) and details GPU metrics collected from NVIDIA NVMe, including utilization, memory, clock speed, power, temperature, and PCIe throughput. The platform correlates these with CPU stack traces to identify bottlenecks, such as Python and CUDA functions underutilizing the GPU. A new GPU time profiling feature records the duration of CUDA kernel executions, showing actual time spent by functions on the GPU. Deployment runs on Linux with a binary, Docker, or Kubernetes DaemonSet; early adopters like TurboPuffer use it to optimize their vector engine.

Claude Code & the evolution of agentic coding — Boris Cherny, Anthropic

Jul 4, 2025 · 18:12

Boris Cherny, creator of Claude Code and Anthropic member of technical staff, argues that the product stays intentionally unopinionated and minimal because the model's coding capabilities are improving exponentially and the right UX remains unknown. He traces programming UX from 1950s punch cards to 1980s Smalltalk's live reload, Eclipse's static-analysis autocomplete, Copilot, and Devin's natural-language paradigm. Claude Code's terminal-first approach works in any terminal, over SSH, and inside VS Code or Cursor, with a GitHub integration that keeps data on user compute and a programmatic SDK for custom UIs. Key tips include using Claude for code-based Q&A (shortening onboarding from weeks to days), teaching it tools via `--help` and Claude MD files, leveraging TDD with visual iteration, and running multiple Claude instances in parallel via terminal tabs or GitHub Actions. Today's launch adds a plan mode triggered by Shift+Tab, which makes a plan and waits for approval before writing code.

CI in the Era of AI: From Unit Tests to Stochastic Evals — Nathan Sobo, Zed

Jun 27, 2025 · 14:50

Nathan Sobo, co-founder of Zed, explains how his team adapted continuous integration for AI-powered features in their Rust-based code editor. Zed's traditional CI eliminates non-determinism with simulated schedulers and deterministic tests, but LLMs forced a shift to stochastic evaluation. Their process starts with broad data-driven evals, then drills into focused stochastic unit tests run 100–200 times with pass thresholds, and finally refines into deterministic tests. Specific challenges like parsing streaming edits, XML tag mismatches, indentation normalization, and strange escaping were solved by robust algorithms—e.g., fuzzy matching and indent delta detection—rather than complex ML. Sobo emphasizes that rigorous empirical testing and traditional software engineering skills remain essential, even when embracing probability over binary pass/fail.

Rust is the language of the AGI - Michael Yuan

Jun 3, 2025 · 29:14

Michael Yuan argues that Rust, with its strong type system and compiler feedback, is the ideal language for AI code generation, unlike human-friendly Python or JavaScript. He presents Rust Coder, an open-source project supported by two Linux Foundation Mentorship grants, which uses MCP tools to generate, compile, and fix Rust projects. The system integrates a coding LLM (e.g., Qwen Coder) with a self-improving knowledge base of compiler errors, enabling it to generate correct code and automatically fix bugs. Yuan demonstrates its use in a Rust camp of 1000+ students and envisions future AI agents generating and deploying Rust code autonomously for tasks like drone control. He invites contributions to expand the knowledge base and enhance the tools for AGI.