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Linus Torvalds used Claude Code as a directed coding assistant on an experimental Linux page-cache optimization. His laptop benchmark improved a narrow eight-thread read test from about 10 million to 30–33 million reads per second, but kernel reviewers found a race before the patch merged. The exact interleaving and the series’ final merge status remain unreported in the available accounts.
What Torvalds was trying to improve
The work targets a scalability bottleneck in Linux’s page cache: many threads performing small reads from the same file can contend on folio reference counting. A folio is the kernel’s unit for managing one or more physically contiguous pages. Taking and releasing a reference for every read helps keep cached memory alive, but that atomic bookkeeping can become the limiting factor when the same file is read concurrently at high frequency.
Torvalds’ branch is named noref-filemap-read-with-llm. It contains an eight-patch series based on Linux 7.3-rc6, plus fixes. The series is an experiment, not a released change in the mainline kernel.
How the no-reference design works
Readers avoid a reference operation on every read
The proposed approach lets readers copy data from the page cache without taking a folio reference for each individual read. That removes the hot reference-counting operation from the tested path and is intended to improve scalability when numerous threads access one file.
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Memory is held back to make that safe
Removing the per-read reference does not eliminate the lifetime problem. If a folio leaves the cache while a reader can still be using it, the memory could be reused too early. The patch series therefore holds memory removed from the cache out of reuse for a short period. The trade-off is temporary additional idle-memory consumption in exchange for less reference-count contention.
Linux’s readahead documentation describes readahead as bringing content into the page cache before an application explicitly asks for it. That background matters here: the optimization changes how already-cached data is kept alive and reclaimed, rather than changing the basic idea of loading data into the cache ahead of a request.
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What Claude Code did—and what Torvalds did
In the patch message reproduced by Phoronix, Torvalds wrote: “The code, the test programs, the review behind the list above and the first draft of this text were done with an LLM coding assistant (Claude Code) under my direction. The measurements were run on my machine.”
That attribution describes a division of labor, not an autonomous merge. Claude Code assisted with implementation, test programs, a review checklist and draft prose. Torvalds directed the work, evaluated the result and ran the measurements. Human kernel review still had the authority to challenge the design—and did so by identifying a race.
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It’s FOSS also reported Torvalds saying, “I actually really like using AI,” during a Prague conversation with Dirk Hohndel at Open Source Summit Europe. That comment reflects his broader view of AI-assisted development; it is not evidence that this particular patch is correct or ready for mainline.
How much faster was the test?
Torvalds measured the change on his own laptop with eight threads reading one file. The reported figures are workload-specific and should not be treated as a general Linux performance result.
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| Condition | Reported throughput | Qualification |
|---|---|---|
| Before the change | About 10 million reads per second | Torvalds’ laptop; eight threads, one file |
| After the change | About 30–33 million reads per second | Same described workload and machine |
| Implied improvement | Roughly threefold | Calculated from the specific 10-million to 30–33-million figures |
| Alternative description | “Up to 23x” | The New Stack’s presentation; it conflicts with the more specific figures and lacks the same detail |
The 10-million-to-30–33-million comparison is the better-defined result because it is tied directly to the workload and machine Torvalds described. It does not establish gains for different CPUs, storage devices, file sizes, thread counts, read patterns or production workloads.
What race did reviewers find?
The New Stack reported that kernel reviewers found a race before anything was merged. A race means that the outcome can depend on the timing or ordering of concurrent operations, which is especially serious in code managing memory lifetimes. A bug in this area could allow a reader to observe or copy from memory after it has been reclaimed or reused.
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The available reports do not identify the exact code path, schedule, reproducer, severity or whether the race was introduced specifically by the no-reference mechanism. They also do not establish whether a corrected series was later posted. Supplying those details would go beyond what has been documented.
Did the patch merge into Linux?
There is no established evidence in the reported accounts that this series reached the mainline Linux kernel. The branch and its eight patches should therefore be treated as experimental work based on Linux 7.3-rc6 plus fixes, not as a production-kernel feature that users can enable.
The race finding is the key status signal: a promising benchmark is not enough for a kernel change whose safety depends on precise object-lifetime guarantees. Until a reviewed, corrected series is accepted, distributions and kernel users should not assume that the optimization exists in their kernels or that its benchmark applies to them.
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What this experiment shows about AI-assisted kernel work
- AI can accelerate exploration. The assistant was credited with code, tests, review material and draft text under a maintainer’s direction.
- Benchmarks need context. The striking result came from one laptop and one narrow concurrent-read workload.
- Memory safety remains the hard gate. Avoiding reference operations creates a lifetime-management trade-off, including temporary memory retention.
- Review is still decisive. Human reviewers found a race before merge, so generated or AI-assisted code did not bypass kernel correctness review.
- Experimental status matters. Nothing in the reported evidence shows that the optimization is part of a released mainline kernel.
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