TL;DR
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A report on Dan Luu’s essay argues that developers can now attempt software projects that once required far more time, pointing to Michael Malis’s pgrust and Luu’s own experimental tools. The essay stresses that faster development does not make projects reliable by itself: careful testing and benchmarking remain necessary, and the experiments described are not all production-ready.
Software developer and writer Dan Luu has argued that developers should attempt more ambitious projects as newer tools make some software experiments much faster to build. In an essay titled “We should be more ambitious with the software we build,” Luu points to Michael Malis’s pgrust project and several of his own prototypes, while stressing that the projects still require testing and that some are not polished products.
Luu says Malis has been showing him work on pgrust, a project aimed at implementing PostgreSQL in Rust, since early in the year. Luu says he found evidence of substantial testing: while testing pgrust, Malis identified bugs in PostgreSQL, including problems involving numeric operations, JSONPath results, pgcrypto, memory safety in a dictionary loader and money overflow. Luu’s account describes those as bugs found during testing; it does not establish that every issue has been fixed or independently verify the project’s current status.
The essay presents performance as pgrust’s main motivation. Luu says some demonstrated optimizations could potentially benefit PostgreSQL or other database systems even if pgrust itself does not succeed. That possible spillover is an expectation, not a reported result that the optimizations have already been adopted.
Luu also describes building a workload-tuned regex engine, a ripgrep fork and a modified Rust toolchain. He says these experiments took a low single-digit number of hours of his own time to reach a “works for me” level. He cautions that bringing them to the standard of a useful product or open-source project would take substantially more work. His examples illustrate faster prototyping, not proof that AI-assisted or rapid development reliably produces dependable software.
Faster Prototypes, Higher Expectations
The essay’s point is about what developers may now be able to attempt, not a claim that software quality has automatically improved. If it takes less time to test an idea, individuals can explore tools and system designs that previously seemed too large for a personal project. That could produce useful experiments and, as Luu argues in pgrust’s case, expose defects in established software.
There is also a practical reason for caution. Luu acknowledges that low-quality, AI-generated projects are common and that outsiders may have little time to investigate each new rewrite. A fast prototype is not the same as a dependable release: testing, benchmarks and maintenance still shape whether users can trust a tool. The essay’s argument is that skepticism about poor projects should not rule out careful work on difficult problems.
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The Projects Behind the Argument
Luu describes a climate in which software rewrites are often dismissed as low-quality projects, partly because many make performance claims based on weak benchmarks. He says he understands that reaction: without knowing Malis or examining the testing, he might have assumed pgrust was another project unlikely to work. His view changed after learning about the bugs uncovered during its tests.
The essay also connects development speed to faster coding models and the limits they can expose elsewhere in a computer system. Luu names search, Rust compilation, system calls and file-system operations as potential bottlenecks when coding tools become faster. He says his own experiments targeted some of those constraints, and that moving development from macOS to Linux improved file-system performance for his work. These are observations from his own workloads, not broad comparative benchmarks for all developers or machines.
Luu’s discussion of pgrust distinguishes an experiment from a finished product. He says the project may or may not succeed, and frames the potential value of its performance work as indirect if other systems later adopt similar optimizations. The essay does not report that pgrust has replaced PostgreSQL or is ready as a general-purpose substitute.
Prototype Quality and Adoption
The essay does not provide a full independent evaluation of pgrust’s performance, feature coverage, security or readiness for production use. It also does not establish whether the PostgreSQL bugs Luu lists have been fixed, how broadly they affect users, or whether pgrust’s optimizations will be adopted by PostgreSQL or another database.
Luu’s development-time estimates refer to his own projects and a “works for me” standard. The source does not give a controlled comparison of those projects against conventional development, nor does it specify which coding models or tools contributed to each result. The scope and reproducibility of the performance gains remain unclear from the essay.
Testing Will Determine the Value
The next meaningful indicators are whether pgrust continues to mature, whether its tests and performance results can be evaluated by other developers, and whether any identified PostgreSQL bugs or proposed optimizations lead to changes in maintained software. The essay does not announce a release date, adoption plan or follow-up milestone.
For readers considering similar projects, Luu’s account points to a distinction that will remain relevant as development tools improve: quicker construction can make more experiments practical, but the value of those experiments depends on reproducible tests, credible benchmarks and ongoing maintenance. Whether the projects described meet those standards for wider use is still to be shown.
Key Questions
What is pgrust?
pgrust is a project discussed by Dan Luu that aims to implement PostgreSQL in Rust. Luu says its testing has uncovered bugs in PostgreSQL, but the essay does not establish that pgrust is a complete or production-ready replacement.
Does the essay say AI-generated software is reliable?
No. Luu acknowledges that many low-quality, AI-generated projects are circulating. His argument is that developers can still pursue ambitious work when they test and benchmark it carefully; faster development alone does not guarantee quality.
How long did Luu’s experiments take?
Luu says several personal projects reached a “works for me” level in a low single-digit number of hours of his time. He adds that turning them into useful products or open-source projects would require much more work.
Has pgrust’s performance work been adopted elsewhere?
The essay does not report that PostgreSQL or another database has adopted pgrust’s optimizations. Luu says some ideas could potentially carry over even if pgrust itself does not succeed.
Source: danluu.com
