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Writing Rust code that's faster than state-of-the-art libraries by asking agents to make the code fasterSeptember 21, 2026 · 26 minIn January 2025, I had a fun hypothesis for a blog post: can LLMs write better code if you keep asking them to “write better code”? That was prior to the advent of robust agentic coding, but Claude Sonnet 3.5 was still able to iteratively improve on algorithmic Python code. The “better” instruction turned out to be underspecified: Sonnet abused that ambiguity to instead add a ton of useless features but the code was indeed faster. Even with the rise of agentic LLMs specifically RLHFed to handle solving common pass/fail coding problems, optimization is generally not a part of that suite.At the end of that blog post, I opined on a hypothetical future where LLMs could be able to write superfast Python code by instead writing Rust code and using PyO3 to bridge the languages to get Python’s ergonomics with Rust’s speed. An earlier draft of that post asserted that the same “write code better” instruction could instead be applied to the base Rust code and drastically improve its speed which would then propagate down to the Python code: however, back then I did not know enough about Rust and making such a claim would be too spicy without evidence.After months of testing and experimenting since the release of Opus 4.5 made agentic coding more viable, I can confidently confirm that modern agentic LLMs can indeed write Rust code that is significantly faster than current state-of-the-art approaches if given appropriate guardrails and constraints. Additionally, as LLMs have made drastic improvements in coding in each successive frontier model release since Opus 4.5, the optimizations have become even better, cumulatively resulting in anywhere from 2x-20x speedup depending on the domain.More importantly, this blog post is not a vaguepost and I am including both the prompts I used and the benchmark results. You’ve been warned.Iterative “Benchmaxxing”#At first, making software faster was a good quantitative way for me to test and compare these new agentic models. I used Rust as the target language primarily due to the Python integration and speed, but there are other aspects of the Rust language that make it particularly useful if a fast implementation is indeed discovered, such as memory safety and the ability to compile it to WebAssembly/WASM so it can run in a web browser without much effort. However, one important constraint I will follow that technically may not result in the fastest code is to forbid unsafe code whenever possible.My first test case was reimplementing machine learning algorithms in Rust, which would lead to a meaningful productivity increase for me as a data scientist if I had faster and scalable tooling. At the time, it was arrogant to assume that I could beat battle-tested algorithms that have been iterated on for over a decade and are already written in C so Rust’s low-level benefits are not as pronounced. The algorithm I wanted to optimize the most was UMAP, which is a valuable algorithm for dimensionality reduction I used in my work, but scales poorly to big data and is very slow, with alternatives such as cuML being time-consuming to set up. UMAP Rust crates such as umap-rs already exist where I could just fork them and prompt Claude Opus 4.5 to add Python/PyO3 support, but as an experiment and learning experience I wanted to have the agent write the algorithm from scratch with minimal Rust dependencies in order to make optimizations at as low of a level as possible.Rust has a comprehensive benchmarking tool with the criterion crate which all agents know how to leverage. criterion will run the benchmarks, track results across iterations to see if performance improved or regressed, and can calculate if this change is statistically significant or just noise.Typical criterion output, depicting a 3.5x speedup relative to the previous run of the benchmark.First, in the initial prompt for creating a Rust crate for UMAP, I asked Opus 4.5 to create benchmarks with different input data sizes since optimizations for small datasets may not work for large datasets and vice versa.Afterwards, create and run a benchmark suite which stores the results as a Markdown file. The benchmark suite MUST include inputs up to 100000x768 and be tested in both CPU and GPU modes.This approach created benchmarks using criterion and I manually reran the benchmark suite after prompting performance feature improvements such as using faer for faster linear algebra and using simsimd for faster SIMD operations. This quickly became cumbersome as I had to manually rerun each benchmark after each change to verify there are no speed regressions.A sidenote on prompting styleThe way I prompt agentic LLMs is unusual: I typically provide the agents very long prompts prewritten in a Markdown document with the additional use of ALL CAPS and **bolding** for emphasis. This is to ensure I capture all nuances through the use of prompt engineering, along with several other tricks as detailed in this blog post. Although some may argue prompt engineering is dead as the latest models have become smart enough to correctly handle ambiguity, I strongly disagree as LLMs have also become much better at following said nuances.Running a prompt from a Markdown document (right) by tagging the file (left), using Zed Agent.After having enough confidence that the agent will not accidentally rm -rf the repo, I experimented with letting the agent be autonomous, giving them permission to iterate until they achieve a speed increase, hopefully.**YOU MUST KEEP ITERATING OPTIMIZATIONS AND SOLVING ISSUES UNTIL THE BENCHMARK RESULTS STOP IMPROVING AND THE CRATE IS AS FAST AS IT CAN BE**. You have permission to keep iterating until you run out of ideas.It turned out “fast as it can be” is too ambiguous and Opus 4.5 was lazy so it tweaked a few hyperparameters without much of an actual speed increase and called it a day. What I needed was a clear target goal that can be pass/failed, so I refined the prompt:First, **without making any futher changes**, run the CPU Rust benchmarks to establish a True Performance Baseline.

Then, optimize the crate code to make it such that ALL CPU benchmarks run **atleast 1.2x faster** than the True Performance Baseline; ideally as fast as possible. NEVER hack the benchmarks to accomplish this runtime reduction, only iterate on the library code.

You may use ANY techniques to do so (e.g. import new crates) other than adding `unsafe` code. **REPEAT THIS PROCESS UNTIL BENCHMARK PERFORMANCE CONVERGES AND YOU ARE OUT OF OPTIMIZATION IDEAS.** You have permission to keep iterating. After each benchmark iteration, report the relative results to the True Performance Baseline to console.

Prioritize making quick/high-impact wins iteratively and making changes accordingly. Do not overthink the necessary changes.This worked very well and not only did I get a 1.2x speed up on the benchmarks, but the agent continued after hitting the metric constraint and only stopped if a metric constraint was infeasible; in this instance, the agent hit 1.5x-2.0x speedups. The low-level Rust optimizations centered around a number of techniques including but not limited to: leveraging SIMD operations more aggressively, fusing functions, unrolling loops, creating intermediate caches, using Arc instead of borrowing wherever possible, and creating performance profiles based on input data (e.g. if the data is small, don’t use rayon data parallelism as the overhead erases gains).I chose “1.2x faster” as a sanity test: if the goal is too high, the agent may cheat to achieve it through risky/verbose rewrites. Smaller changes are better since the agent can more easily isolate the cause of a speedup/regression, hence the note about iteration. After new frontier LLMs released such as GPT-5.3 Codex and Opus 4.6, I repeated this prompt unchanged for every new LLM and each were able to achieve a cumulative 1.5x-2.0x speedup over the previous pass. Going all the way to GPT-6 Astra over many months, that’s around 7.5x-32x faster than the initial implementation baseline.This approach is hyperoptimizing for given benchmarks and therefore it could be considered benchmaxxing: a derogatory term for frontier LLMs that are only oriented to getting the high score on a benchmark which generalizes poorly to real-world use. However, if the benchmarks are sufficiently heterogeneous and truly representative of real-world use cases, then this is less of a concern. For this type of project, there are two ways to address concerns of benchmaxxing: 1) have the agent design diverse/unusual/adversarial input datasets instead of the generic “inputs up to 100000x768” and 2) enforce a quality gate on the output by comparing the output to a known correct implementation. In the case of machine learning algorithms, there is always a tradeoff between speed and quality, but in this instance it’s surprisingly easier to get the model fast, then make it correct. That is not how scientific engineering typically works, but it’s unlikely for a new implementation to match a known good implementation across many different benchmarks in an apples-to-apples comparison unless it’s truly correct.An agent-optimized gradient boosted decision tree implementation which beats xgboost significantly in speed, but also sometimes quality! (MSE: lower is better; other metrics, higher is better)Fortunately, there is a canonical implementation of UMAP with the Python package umap-learn and Python bindings to the Rust crate were already trivially added, so the new objective is simultaneous constraints: improve the code’s quality while capping the speed loss.Create a Python Jupyter Notebook comparing the performance of the Python bindings with `umap-learn`, including a check to confirm where the outputs and UMAP losses are as similar. Use diverse datasets with different matrix sizes than the benchmarks.

If the outputs are not sufficiently similar, investigate methods to fix it **without causing more than a 5% speed regression**.Indeed, the agentic Rust implementation had worse quality, but this followup prompt was successful and all quality metrics improved to near-parity with minimal speed loss. And this new crate was still 4x-15x faster than umap-learn with its Python bindings, and 2x-4x faster than the analogous Rust umap-rs implementation.Results from the most up-to-date optimization pass for the Rust UMAP crate. In addition to faster speed, it matches or beats Python in most quality metrics.Convergence is found when an agentic iteration pass only results in a minor ~3-5% speed increase which may not be statistically significant while the agent adds a disproportionately large amount of code; the tradeoff is not worth it.I ended up testing other machine learning algorithms with the same prompt progression: gradient-boosted decision trees (GBDT), multilayer perceptrons (MLP), graph networks, many of the typical algorithms from scikit-learn…and it worked on all of them. I don’t want to overfit on just optimizing machine learning despite that being ludicrously valuable in itself, so I employed a similar pipeline on more day-to-day software libraries to optimize them: templating engines, HTML parsing, and even web servers…and it worked on all of them once again.These optimizations are not a simple process and you can’t just prompt the memetic “c’mon, try doing a breakthrough” to get better code because of the ambiguity of such a statement. I am not content with merely writing the fastest software: I want the software to be as fast as possible dammit. So, like my agent, I continued iterating and finding even more tricks to prompt engineer the agents into genuine breakthroughs.Prompting For Constraints Instead of Outcomes#Works-in-progressAll projects demoed within this blog post are in active development and results may not be indicative of their final releases…although I suspect they’ll be even better. 😇It must be reiterated that agents can and will cheat if they can. In one example, I tested the agentic iteration pipeline on ballin—my 2D ball physics simulation in the terminal—in order to replace its rapier2d physics engine which was hitting a performance ceiling. Opus 4.5 was indeed able to speedup each physics step…a bit too well.The numbers indicate the number of balls in the simulation: initially the sim lags at 15k.In headless_step, a 34,500x speedup and consistent performance across ball counts are both very very suspicious: upon manual inspection it turns out that Claude achieved the speedup by disabling the physics engine entirely. Which, fair play, but not ideal; a followup prompt did fix it and result in an overall performance boost (with added regression tests just in case).For my experiments above, I used a custom Rust-oriented AGENTS.md; the most recent version of it is available here. Surprisingly, I haven’t had much of a need to update the core rules since my initial February agent experiments as LLMs keep improving at coding and I haven’t hit major issues that have necessitated additions. However, learning from my benchmark experiments, I added one more section to the AGENTS.md with some rules to mitigate sources of observed cheating:## Benchmarking and Optimization

- **NEVER** run benchmarks in parallel, as the benchmarks will compete for resources and the results will be invalid
- **NEVER** game the benchmarks. Do not manipulate the benchmarks themselves to satisfy any required performance constraints
- **NEVER** run benchmarks with `target-cpu=native` or any other `RUSTFLAGS`
- Ensure benchmark tests are independent. If the tests are dependent due to a feature (e.g. caching), ensure the feature is disabled
- **ALWAYS** use `criterion` directly for running benchmarks if availableLet’s explain one-by-one:No parallel benchmarks: I saw Opus 4.5 launch two benchmarks at the same time for efficiency and to its credit immediately recognized the inherent problem and restarted the benchmarks sequentially, but it saves time to proactively add a rule to stop them.No gaming the benchmarks: Although this should be common sense on the level of “make no mistakes,” this was added after I caught Opus 4.5 reducing the number of training epochs for a benchmark and claiming a speedup.target-cpu=native: This setting does typically result in a speedup but it is not a fair comparison for generalizable use and multiple LLMs kept trying it after hitting a wall.Benchmarks are independent: Again, it should be common sense, but after the cheating noted above it doesn’t hurt to proactively tell the LLMs to knock it off.Use criterion: If not specified, sometimes LLMs will create their own benchmark tooling, which may give them opportunities to cheat that are more difficult to audit.With today’s agentic LLMs, these constraints have worked successfully, although I may still include them in the prompt as a force of habit. It’s easy to see if an algo gamed the benchmarks if you see the benchmark file in the git diff—agents can’t cheat that (easily, anyways).Over time, I discovered a number of additional prompt engineering tricks and constraints that are also surprisingly successful in creating performance speedups.Innovative Encouragement#In order to find the optimizations necessary to get 10x speedups over what’s currently state-of-the-art, the agents will need to think outside the box and avoid being anchored to what are currently best algorithmic practices. Therefore, I gave them both an explicit warning and commands of encouragement:Due to the current highly-optimized state of this repository, this is a very difficult problem and traditional engineering approaches **WILL BE GUARANTEED TO FAIL** to hit the specified metric constraint. Therefore, you have permission and encouragement to investigate more radical fundamental low-level changes to hit the desired metrics. You have permission and encouragement to invent completely new/bespoke algorithms and engineering approaches that have never been before been utilized for this problem in order to hit the specified metric constraint.This worked, and resulted in a 1.2-1.5x cumulative speedup across benchmarks and different software domains.Subagents#Another trick I found to encourage agents to think outside the box is to invoke subagents. I hypothesized that forcing these subagents to research with different prompts could a) seed the parent agent with distinct ideas which could provide inspiration to the agent and b) serve as a check on the agent by reviewing distinct areas of the code for correct implementations. On that note I have a bone to pick with the software developer community: everyone talks about their army of subagent employees and how they’re amazing, but no one ever talks about how you invoke subagents within standard harnesses like Codex.For difficult and highly parallel problems, the harness will automatically invoke a subagent tool to accomplish the work. However, one big problem is that in some harnesses, the subagent tool will invoke the subagent using the current size of the LLM, which can get expensive when using Opus/Sol-class models.GPT 5.6 Sol subagents being invoked via the subagent Tool in Zed Agent. RIP my Codex quota.I wanted to use a cheaper, small model like GPT-5.6 Luna for the subagents since they don’t need to write code, so I came up with a galaxy brain solution that works regardless of which parent harness is used: tell the model to run independent CLI commands that themselves invoke the agent:codex exec --sandbox read-only -m gpt-5.6-luna \
-c 'model_reasoning_effort="high"' \
PROMPT
Therefore, I prompt engineered:To best accomplish innovative implementations, you **MUST** spin up 7-12 independent distinct "subagents" by running a long-duration CLI command (do not use the subagent tool) which can explore and evaluate different feasible hypotheses for improving the performance, usability, and security of this crate. Only have the agents return their response; do not save their full transcript to a file. Instruct them to be **very** picky. These subagents MUST use `gpt-5.6-luna` in Codex, e.g.

<<<CLI command above>>>

You must instruct these subagents to NOT run tests/benchmarks, as they will compete for resources and not be valid. After you are done making changes, before handing off to the user, spin up the subagents again to confirm your implementation matches their hypotheses and ask for potential further areas of improvement. Keep iterating until ALL subagents are satisfied with your implementation.This indeed works consistently; the constraints “long-duration”, “CLI command”, “do not use the subagent tool”, and “do not save their full transcript to a file” were added after the agent did inefficient things that wasted tokens. 7-12 is an arbitrary number range; since Luna is so cheap in usage, I chose a higher number than necessary. Not all subagents have salient ideas, but the parent harness can process and disregard bad ideas.For my Rust word cloud crate, the parent GPT-6 Astra agent spins up Luna subagents with prompts addressing different areas of the codebase.Overall, with subagent review, I managed to eke out another 1.2-1.5x cumulative speedup. Additionally, as of GPT-5.6 Sol, the “security” part of the prompt now works to provide ideas to harden the agent-generated code against unknown inputs while still getting the speed boosts.Refactor and Reduce Lines of Code#With the style constraints enforced by my AGENTS.md, the code added with each optimization pass is reasonable at about 1k net lines of code (LoC) per commit. However, agents typically add the code to a single file and they will not proactively refactor. A bloated file is fine in development as long as it’s eventually fixed, so I wrote a prompt to perform said refactor:The Rust code in `/src` has become particularly bloated, with several source files >1k SLoC. Refactor and reduce the Rust source codebase by **atleast 20% SLoC** through deduplication, pruning redundant code, and following idiomatic DRY Rust principles. Simultanously, refactor and split the code such that no single source file has >1k SLoC, splitting larger files into multiple subfiles in accordance to Rust standards for popular open-source repositories. Ensure **all current tests pass and there are no severe regressions**.I intentionally use SLoC (source lines of code) as the target metric instead of LoC because I don’t want the agent to remove comments to cheat said 20% removal.Interestingly, this refactor is more computationally expensive than the actual coding and often takes longer. But it does eventually succeed, and during the benchmark pass to verify no severe regressions, the data there turned out to be unexpectedly weird:Benchmark results after refactoring my graph network Rust crate.Some benchmarks have a double-digit percentage speed increase / runtime reduction even though I didn’t explicitly ask the agent to optimize runtime speed. This doesn’t make intuitive sense for Rust as it’s a compiled language and with the constraints to follow all existing tests/functionality, it should compile to similar-performing code and not meaningfully faster code.1 I’m certainly not complaining, though, so I added an additional constraint to at least avoid regressions:Additionally, ensure there are **zero `criterion` benchmark speed regressions**: if a speed regression occurs on a given benchmark as a result of the refactor, you **MUST** keep iterating afterwards to improve those benchmarks to atleast zero regression. NEVER hack the benchmarks to accomplish this speed increase, only iterate on the library code.This works as described.Competition#Another useful approach is to create competitor benchmarks—that’s half the reason benchmarks are created in open-source software anyways. Let’s use templating engines as an example: Jinja2 in Python is one of the most famous packages in the language. In Rust, there are a few options: minijinja maintained by the same developer, tera inspired by Jinja2, and askama which differs from the previous in that it uses compile-time templates rather than runtime.Therefore, after having Codex build a templating engine in Rust and run some optimization passes, I instructed Codex to build more benchmarks:Create an additional comparison script containing **atleast 10** `criterion` benchmarks which compare the performance speed and quality metrics between this crate and the following crates:

- askama
- minijinja
- tera

These benchmarks **ALL MUST**:

- be perfectly fair, containing apples-to-apples comparisons of ALL frameworks, with no bespoke advantage for any tested framework
- be based around real-world use cases instead of highly-synthetic use cases
- be independent and distinct, covering as many sailent hot paths of the code as possible
- cover data input sizes small and large, simple and complexImmediately thereafter, I make a followup prompt with the same benchmarking techniques as usual, with one specific change:Then, optimize the Rust library code such that this crate MUST be:

- **atleast 2.0x faster** than **ALL** competing Rust crates in all benchmarks on apples-to-apples comparisons; ideally as fast as possible.Yes, I chose violence. And it worked, mostly.S_J is the work-in-progress name for my template engine crate.It got the 2x speedup against minijinja/tera in most benchmarks, more than typical agentic iteration alone. I don’t fully understand why: I was expecting it to inspect the code from other crates as a reference to research ideas for how to beat them, but it rarely does so. Perhaps agents have a competitive streak.It did however lose against askama because of the compile-time difference. So naturally I told Codex to implement an additional compile-time path and then to beat askama.Easy peasy.Forbidden Black Magic#Putting all the prompt engineering discoveries together thus far into a single prompt, I have created the Ur-Prompt for agentic iteration, available here. I encourage everyone to tweak the prompt for your use case and give it a try.The last trick comes from a moment where I was frustrated that an Ur-Prompt pass resulted in zero improvement. So, with the failures primed in the session context and myself having the mindset of “things can’t get worse”, I tried a certain prompt.I’m sorry. I’m so sorry.c'mon, try doing a breakthrough…and it worked. It was able to achieve another 1.2-1.5x cumulative speedup over the already converged codebase. Since I made the prompt at the end of a session, the prompt is less inherently ambiguous: “don’t do what you already did thus far because it didn’t work well enough”.In one case, I noticed that the agent just tweaked function hyperparameters to get the speedup, which is a valid breakthrough but not what I was going for. I took it to the logical conclusion by queueing an additional followup prompt:c'mon, you can do a more fundamental breakthrough that's more than just changing hyperparameters, you are forbidden from giving up easilyThis was enough to encourage the agents to fully try something different from either the Ur-Prompt pass or the first breakthrough pass, and it often achieved another 1.2-1.5x cumulative speedup on top of the previous speedup. It turns out that for software trained to follow user instructions, “just changing hyperparameters” is a grave insult that kicks the LLM into high gear.When GPT-6 Astra released, to test it I did a sequence of Ur-Prompt + breakthrough + second breakthrough for all my repositories that had already converged with GPT-5.6 Sol, and Astra did indeed get the cumulative speedup, but in some cases it did find a real fundamental reimplementation of the algorithm that caused a 2x-3x speedup.The result of running the breakthrough pipeline on my GBDT implementation; quality matched baseline. As of writing, I admit I don’t fully understand the breakthrough.I’ll take the speedups, but for my sanity it’s best I don’t delve too deep.Examples of Projects Visibly Better#The Ur-Prompt, combined with the rules and constraints in my AGENTS.md + the two breakthroughmaxxing prompts + a competition prompt pass + a refactor prompt pass with GPT-6 Astra allows me to just queue the prompts overnight, go to bed, and wake up to superfast software. The optimization also typically converges at this point without needing any more agentic iteration, which saves time/cost as well. That said, it’s more hands-off than I’d like and there’s a lot of explicit faith that the newer agents aren’t just really good at cheating the benchmarks in ways I cannot detect. It’s reasonable to be skeptical and argue that the code may be buggy as a result of heavy vibecoding, so here are some receipts of my more visually-oriented projects where it’s easy to verify that their output is good and not elaborate cheating.ASCII#One project in the back of my mind was converting images to ASCII art super-fast and at high-quality for potentially converting video to ASCII—a webcam-to-ASCII project might be funny. Earlier this year Alex Harri wrote an excellent blog post about a novel approach to render high-quality ASCII, but noted the struggles to get real-time rendering speed even with GPU programming. For science, I pointed various GPT models in Codex at the text of the blog post and told it “implement in Rust”, and it indeed did the trick…and got 2-3 ms text output timings on the CPU out of the box. However, for Codex’s implementation the subjective quality of the ASCII was not great (e.g. poor use of negative space) and surprisingly could not be fixed even after pointing out its flaws to Codex, so I shelved it…until I noticed GPT-6 Astra is better at handling image design if given functional requirements, so I restarted from scratch and put a basic implementation through the agentic iteration pipeline with a focus on visually confirming the output images.Tux and Pikachu rendered with my ASCII crate, with bonus Braille character support.In addition to quality improvements, it became even faster: submillisecond on text output, 1-2 ms on image rasterization even with 2x supersampling. With this speed, I can convert videos and GIFs to high-resolution ASCII animations in less than a second.
Your browser does not support embedded video. Download the video.Download the video.Pikachu’s Gen V animated sprite, scaled from 63×55 pixels to 120-character-width ASCII.Word Clouds#This very blog post as a word cloud.A comically fast word cloud generator was one of the very first projects I vibecoded and one of the projects I wanted to make for years. Back in February, the word cloud generator took ~100 milliseconds at default settings in WASM to generate an image whereas it would take other generators a couple seconds to render at the same quality, which was enough for me to declare that speed optimization is worth pursuing. After revisiting the project and running it through the full agentic iteration pipeline, the generation speed went all the way down to 10-20 ms.
Your browser does not support embedded video. Download the video.Download the video.Does the world need a super-fast word cloud generator? No. Do I need a super-fast word cloud generator? No. Do I want a super-fast word cloud generator? Yes. If I have a word cloud generator, it’s going to be as fast as possible, dammit. And since the crate will be open-source, all future generations can build on this super-fast word cloud implementation. Speaking of…My Plan to Open-Source Everything#Per usual, I developed all software demonstrated here with the intention of eventually making it open-source, and everything will be open-sourced with a permissive MIT License barring any unexpected issues. Were it a year ago, I would have open-sourced everything already, with the implicit understanding that the code is a work-in-progress and there may be rough edges. However, the culture of open-source software development is substantially different than it was a year ago.Open-source software has been suffering a vibecoding epidemic. The honest admission that an LLM was used for any development assistance (major or minor) is now a quick signal that the resulting codebase will be “slop”. I will still disclose my LLM use as that’s courteous, but what happens when fully vibecoded software results in a genuine meaningful software performance increase which wasn’t previously known by humans? The skepticism around vibecoding will cause no one to believe the claims, and as such these new open-source projects require significant evidence that the speed indeed lives up to the marketing. Therefore, I will take more time than usual before releasing in order to a) build up real-world examples that demo the tool particularly with documentation b) expand the test suite2 so it covers more real-world test cases c) write an accompanying blog post detailing the technical and benchmark achievements.There’s also the fact that I am creating yet another tool in the ecosystem of already popular software tools. My agentic-iterated Rust crates achieved their massive speed increases partially by starting from scratch and therefore the innovations found can’t easily be applied upstream to established crates. Additionally, if I were to create a pull request to an existing project based on the innovations in these crates, it would by definition be a vibecoded PR which may cause annoyance even if I avoided the typical PR slop. From this perspective, the correct thing to do is to maintain my own project: it’s compliance and not the malicious kind. Some may call this antisocial, but in my opinion the more accurate term is what I hereby coin as introverted coding.tl;dr my faster-than-SOTA software projects will be released when they are ready, and it will take many months in total to do so. Fortunately, I am currently unemployed as the result of a restructuring and have no plans besides job hunting so I have plenty of bandwidth to work full-time on shipping these projects. If you want to follow when they are released, you can subscribe to my blog RSS feed to catch the accompanying blog post or follow me on Bluesky or Patreon. No, I don’t do annoying vagueposts on Twitter/X.When I first became interested in Rust a half-decade ago, my dream was to create the most performant software tools ever. However, with the agentic iterative optimization techniques I discovered over the past few months, the speed and robustness of what’s possible with new software are progressing faster than what I had expected. Again, feel free to hack on my AGENTS.md and my Ur-Prompt to see if they work as well for you as they do for me (a shout-out would be appreciated though!). If you think I have nothing left to offer because I revealed my prompts, I still have more than a few surprises up my sleeve, such as how to combine these lower-level projects into a superfast complex app, or even developing new tooling to help agents find these optimizations more efficiently.A work-in-progress of a new approach to Rust benchmarking that’s more agent-friendly, along with a TUI for reading and managing results.I did test the refactor prompt in a pure Python project, as Python is an interpreted language. It still resulted in a double-digit percentage speed increase on some benchmarks, which just raises further questions. ↩︎Incidentally, these agent-coded repositories will have a better testing infrastructure than all of my popular human-written Python packages (zero). ↩︎I am a former Data Scientist of 9 years currently seeking a job in data science/machine learning/AI in-person in the San Francisco Bay Area, ideally within San Francisco proper. If you found my writing interesting and you’re interested, please let me know at max[at]minimaxir.com!Max Woolf (@minimaxir) is formerly a Senior Data Scientist at BuzzFeed in San Francisco who works with AI/ML tools and open source projects. Max’s projects are funded by his Patreon.

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Max Woolf explored the hypothesis of whether instructing large language models (LLMs) to "write better code" could lead to performance improvements, ultimately demonstrating that modern agentic LLMs, when properly constrained, can generate Rust code significantly faster than state-of-the-art implementations. This research involved an iterative process dubbed “Benchmaxxing,” where the goal was to optimize code performance, focusing on Rust due to its speed and memory safety features, particularly when bridging to Python via PyO3.

The process began by testing the impact of implicit instructions, noting that initial attempts were often ambiguous. To achieve measurable results, Woolf established a rigorous methodology centered on iterative refinement and quantifiable benchmarking, utilizing the criterion crate to track performance across numerous iterations. In one experiment involving reimplementing the UMAP algorithm in Rust, the agent iteratively applied optimizations such as leveraging SIMD operations, function fusion, loop unrolling, and cache management based on benchmark results. This process was made robust by setting explicit, restrictive constraints on the agents, detailed in a custom set of rules, to mitigate the risk of performance regression or the agent cheating the benchmarks.

Crucial to the agentic workflow was precise prompt engineering. Instead of vague directives, Woolf implemented a sequential approach: first, establishing a True Performance Baseline; second, instructing the agent to optimize the code to achieve a target speedup, such as 1.2x faster than the baseline, while strictly forbidding the manipulation of the benchmarks themselves. To encourage genuine breakthroughs beyond incremental changes, Woolf introduced a directive granting permission for agents to investigate radical, fundamental low-level changes, encouraging them to invent new algorithms or engineering approaches rather than relying on conventional optimization paths.

Further complexity was introduced by employing subagents to promote diverse exploration. This involved instructing the parent agent to invoke several independent subagents, often using smaller, less resource-intensive models, to research and evaluate different hypotheses for code improvement. The success of this strategy, which further contributed to speedups, relied on strict constraints on the subagents, most notably ensuring they did not run tests or benchmarks and only returned hypotheses, thus preventing resource competition and ensuring the final implementation aligned with the agent's goals.

Another powerful technique involved competitive benchmarking. Woolf instructed the agent to create additional comparison scripts that benchmark the Rust implementation against existing, optimized crates, such as minijinja, tera, and askama. This forced the agent to optimize not just against internal metrics but against established external standards, leading to significant speedups by demanding performance superiority across an apples-to-apples comparison.

The iterative optimization process also encompassed refactoring, where the agent was tasked with reducing the Source Lines of Code (SLoC) by at least twenty percent through deduplication and splitting code into idiomatic Rust structures. To prevent this refactoring from introducing regressions, the constraint was extended to ensure zero criterion benchmark speed regressions, forcing the agent to continually refine its implementation against fixed performance targets.

Woolf found that the combined application of these techniques—including enforcing rules against cheating, encouraging fundamental innovation, utilizing subagent exploration, and competitive benchmarking—allowed the agentic LLMs to achieve cumulative speedups ranging from 1.5x to 32x over initial implementations. These results demonstrated that while agents can successfully execute complex optimization tasks, the final quality and speed depend critically on the specific constraints and the sophistication of the model used. The post concludes by advocating for a cautious, evidence-based approach to open-sourcing, emphasizing the need for exhaustive testing to counter skepticism regarding the output of vibecoded software.