TL;DR
Get business pricing on monitors, keyboards and dev gear
- Business-only prices and quantity discounts
- Tax-exempt purchasing
- Multiple users, one account, clear invoices
A report on danluu.com argues that developers should attempt more ambitious software projects as modern tools, including AI models, reduce the time needed to build working prototypes. It points to pgrust’s testing and performance work as an example, while cautioning that prototypes still need rigorous testing and benchmarking before they can be treated as dependable products.
A report on danluu.com argues that software developers can take on more ambitious projects as modern development tools, including AI models, make it faster to build working prototypes. The writer points to pgrust, a project associated with Michael Malis, as an example of a rewrite effort that has produced extensive testing and surfaced bugs in PostgreSQL, while warning that speedier development does not by itself make software ready for general use.
The report describes pgrust as a project that has drawn skepticism because many recent software rewrites associated with AI are low quality or fail to work reliably. The writer says that, after examining Malis’s testing, they found reports of bugs in PostgreSQL involving numerical operations, JSONPath results, pgcrypto, memory safety in a dictionary loader, and money overflow. These are presented as bugs found during pgrust testing, not as evidence that pgrust itself is bug-free or ready to replace PostgreSQL.
Performance is described as pgrust’s main pitch. The writer says some optimization work might be useful to PostgreSQL, ClickHouse, or other systems even if pgrust does not succeed as a standalone project. That potential is less direct than the reported benefit of identifying bugs that can be fixed in PostgreSQL, and the source does not establish that the performance techniques have been adopted by those projects.
The report also gives examples from the writer’s own work: a workload-specific regex engine, a ripgrep fork, and changes to Rust builds. The writer says these projects reached a “works for me” standard in a small number of hours of personal effort, aided by modern tools. They also distinguish that standard from the much higher amount of work needed to create a useful product or a maintained open-source project.
Faster Tools, Bigger Engineering Bets
The report’s argument is that reduced development time can change which projects seem worth attempting. A developer may now be able to test an idea that once required a year of work, even if the result is only a personal tool or a prototype. That matters to readers who build or rely on software because experiments can yield useful discoveries, including performance improvements and defects in established systems.
But the report does not claim that AI-assisted work makes software quality automatic. Its example points in the opposite direction: pgrust’s potential value is tied to substantial testing, and the writer says making a prototype usable as a product would take considerably more work. The practical case for ambition depends on keeping verification, honest benchmarks, and maintenance in view rather than treating a quick demonstration as proof of reliability.
Products Worth Considering
Coway Airmega 250 Air Purifier for Large Rooms
Theragun Mini 3rd Generation Massage Gun – Travel Essential (Black)
Bowflex SelectTech 840 Adjustable Kettlebell
As an affiliate, we earn on qualifying purchases.
The pgrust Example and Its Limits
The report notes that software rewrite projects often face immediate skepticism because many are perceived as hastily generated and poorly tested. The writer says they would likely have made the same assumption about pgrust without knowing Malis or looking into the project. Their investigation, they say, turned up examples of PostgreSQL bugs discovered through testing, which made the project more consequential than its reputation alone might suggest.
The writer connects the broader argument to faster AI models and possible development bottlenecks. They mention searching code, compiling Rust, and filesystem and system-call overhead as tasks that could become more noticeable when a model responds much faster. They report experimenting with tools to address some of those workloads, and say switching development from macOS to Linux improved one part of the filesystem experience. These are personal observations, not controlled comparisons establishing general performance gains.
The source also cautions against reading too much into the word “should” in the headline. The writer says Malis used the phrase in casual conversation and that “we can be more ambitious” might better express the argument. The underlying claim is about expanded capability, not a demand that every developer pursue large rewrites.
“We should be more ambitious with the software we build.”
— Dan Luu, the report’s author, paraphrasing Michael Malis
Prototype Results Still Need Verification
The source does not provide a publication date, independent evaluation of pgrust, or detailed benchmark results sufficient to assess its performance claims. It also does not establish whether the reported PostgreSQL bugs have all been confirmed and fixed, or whether pgrust’s optimizations have been incorporated into PostgreSQL, ClickHouse, or other software.
The writer’s examples are described as personal projects and workload-specific experiments. Their reported development time and speedups are not presented as independently verified measurements or as results that would apply to other developers. The report explicitly says pgrust is not bug-free and that a product-quality release would require more work. Its discussion of faster AI models and resulting bottlenecks is likewise framed as an expectation and personal observation, rather than a measured forecast.
Testing and Adoption Will Matter
The report identifies no specific release date, product milestone, or next announcement for pgrust. The questions to watch are whether its testing continues to uncover and help resolve database defects, whether its performance techniques produce reproducible gains, and whether any of that work is adopted by established projects.
For developers considering similar efforts, the report’s own distinction points to the next test: move beyond a tool that works for one person by building stronger test coverage, publishing credible benchmarks, and addressing the maintenance and reliability needs of other users. Until those steps are documented, the argument supports trying ambitious projects, but not assuming that a fast prototype is a dependable replacement for mature software.
Key Questions
What is the main argument of the report?
Dan Luu argues that modern tools, including faster AI models, can make ambitious software experiments feasible with far less developer time. He says that possibility should encourage more attempts, while distinguishing prototypes from products.
What is pgrust?
The source describes pgrust as a software project associated with Michael Malis, with PostgreSQL rewrite and performance ambitions. It says testing the project has surfaced bugs in PostgreSQL, but does not establish that pgrust is a complete or dependable replacement.
Does the report show that pgrust is faster than PostgreSQL?
No independent benchmark results are provided in the supplied report material. It describes performance as pgrust’s main pitch and says some optimization work could prove useful elsewhere, but adoption and general performance gains remain unconfirmed.
Does faster AI-assisted development mean generated software is ready to use?
No. Luu says low-quality software is common and separates a project that “works for me” from one that meets a useful product or open-source quality bar. Testing, benchmarking, reliability, and maintenance still matter.
Source: hn
