--- title: "Link release builds with a faster linker" description: "When linking dominates the build, switch to a faster linker." url: https://starsling.dev/ci-playbooks/techniques/faster-release-linker canonicalUrl: https://starsling.dev/ci-playbooks/techniques/faster-release-linker --- # Link release builds with a faster linker When linking dominates the build, switch to a faster linker. ## How to choose an approach This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it. - **[OpenAI](https://starsling.dev/ci-playbooks/openai#faster-release-linker)**: Use it when A build profile shows linking or final code generation taking a large share of the build job. Check before adopting: Every linked binary must pass the same tests and signing steps. ## Apply this technique ````text Evaluate and apply this CI technique to my repository: Link release builds with a faster linker. When linking dominates the build, switch to a faster linker. Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/faster-release-linker.md Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence. Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured. Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo. Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES. Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch. Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge. ```` ## How OpenAI used this technique Read what OpenAI changed and follow the original sources. ### OpenAI: Link release builds with a faster linker Codex profiled its Windows x64 release build and found most of the time in final code generation and linking. The release workflow now configures the LLVM linker before the Cargo build. Evidence: Estimate Estimate: about four minutes off total release workflow time ([Source](https://github.com/openai/codex/pull/27315)) Use it when: A build profile shows linking or final code generation taking a large share of the build job. Before applying it: Every linked binary must pass the same tests and signing steps. [Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#faster-release-linker) [Primary source](https://github.com/openai/codex/pull/27315) ## CI optimization is continuous work. Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on. [Get self-improving CI](https://starsling.dev/products/runners) ## Related techniques ### [Let an agent iterate against one CI metric](https://starsling.dev/ci-playbooks/techniques/agent-metric-loop) Pick one measurable CI number, such as a build time, and let an agent keep each change that beats the baseline and discard each one that crashes or runs slower. 1 implementation · 1 company (Shopify) ### [Measure the cost of the release build profile](https://starsling.dev/ci-playbooks/techniques/profile-release-linking) Measure what the release build profile costs before keeping it. 1 implementation · 1 company (OpenAI) ### [Use build profiles to remove expensive work](https://starsling.dev/ci-playbooks/techniques/profile-build-hotspots) Measure where build time goes, then remove or replace the compiler work or dependencies responsible for the bottleneck. 2 implementations · 2 companies (OpenAI, Stripe) ## Company Playbooks ### [Anthropic](https://starsling.dev/ci-playbooks/anthropic) Run only affected tests ### [DoorDash](https://starsling.dev/ci-playbooks/doordash) Run build and test in parallel, sharded across CI agents ### [Linear](https://starsling.dev/ci-playbooks/linear) Benchmark the same job on faster hardware ### [Mastra](https://starsling.dev/ci-playbooks/mastra) StarSling runners for the slowest test workflow ### [OpenAI](https://starsling.dev/ci-playbooks/openai) Rank CI fixes by the minutes they save ### [Ramp](https://starsling.dev/ci-playbooks/ramp) Move a CPU-bound test suite to a faster CPU ### [Shopify](https://starsling.dev/ci-playbooks/shopify) Let an agent iterate against one CI metric ### [Spotify](https://starsling.dev/ci-playbooks/spotify) Build arm64 on native arm64 runners ### [Stripe](https://starsling.dev/ci-playbooks/stripe) Run only the tests a change can reach ### [Uber](https://starsling.dev/ci-playbooks/uber) Build the merge-queue speculations most likely to be needed