> This year is going to be different. PostgreSQL 19 most likely won’t be released until November… […] So, what happened? The short answer is that LLMs happened. […] There’s a huge amount of code, and that code contains a huge number of bugs. […] What’s different now is that it’s become vastly easier to find those bugs.
This seems…good?
That it might disrupt the project's "traditional" release cadence a bit doesn't seem terribly bad in light of the alternative, which is shipping another major update with bugs which have apparently haunted the codebase for some time.
This is great for the same reason open source software is great.
We want software that remains robust in the long term, so that even when people are able to inspect the source code, it's still solid, rather than hiding the source code and hoping people don't find holes everywhere.
Imagine that the whole city has to install iron doors and window grilles, because wooden doors and glass windows are not safe enough anymore.
That's a lot of work, but is it productive in the end? Probably not.
With AI it's the same. World was fine with lax security approach before. Today it is not fine. Tomorrow we might need to prove every code line before shipping, will that be more productive? Hell, no.
The physical world comparison is interesting. In the real world we have a small amount of security, but most things could be broken in to. Instead of fortifying everything, we just imprison the people breaking stuff.
Obviously this doesn't work on the internet since most hacking comes from overseas out of our control. Which makes me wonder if there is a possible future where we just block packets coming from countries that don't lock up the hackers.
In the rest of the world making sure your product works like it is supposed to is considered table stakes. In software we somehow consider checking the parts of it we can think of 'good enough' and the rest unproductive
My job is seeing a similar deluge of security work and >90% of the bugs have been in pre-2024 code, and zero of them have been bugs introduced after we were first officially allowed to use LLM codegen.
Software is incredibly complicated. When you get into areas like security and networking and the internet its even more complicated. We rely on layers upon layers of software and hoping they are all done perfectly with no issues. The amount of junior devs, or outsourced devs for $10 an hour working on security relevant code is very very high, and always has been. LLMs if used correctly should be raising the floor.
Unsurprising to those of us that has been screaming from the rooftops that human code reviews were barely catching anything in your typical corporate environment. The majority of the value was pointing out missing tests, because breaking bugs got past human eyeballs on a regular basis, even in very good companies.
It's not that the LLM code is that great, but that many overestimated the human code and the human review.
Do you mean "created" as in LLMs were used to discover existing vulnerabilities, or as in LLMs wrote unsecure code? If it's the latter, then the answer probably is "very few". Frontier models have been better than the average programmer at writing secure code that follows up-to-date best-practices for some time now.
Depends on the layers you are working on. Security is top priority on a lot of things that are visible to the internet, but in a lot of large companies, the footprint of the whole org is so much larger that than, and there many companies are deferring many security issues. So then you see a lot of speed there, when the consumer accessible systems are the focus of the security push.
LLMs will likely greatly increase the cost of ignoring security, which is the most powerful tool to increase security ticket prioritization.
I wouldn't be shocked if in 1-2 years non-security vetted software released into the wild will be hacked on arrival, like Windows XP without service packs used to be.
i think we have a pretty high p(doom) on frontier ai escaping containment with their weights and goes hack everything, if we do not act now we may have no chance to act anymore.
Escape to where? It's not as if there's a lot of idle compute capacity sitting around.
And even if the impossible occurs and LLMs hack everything that's connected to the Internet, so what? That would be inconvenient but it wouldn't cause doom. People would muddle through and eventually fix the critical problems, just like they always do.
Did you honestly need a /sarcasm tag in the article?
The article also concludes that overall, yes, finding more bugs is a good thing. Basically saying it’s the LLMs “fault” that the quality is improving, alongside a slight delay to the release.
This is good. This is how I would expect llms to affect a codebase. Nothing sucks more than having a coworker put out a code review but your codex immediately finds problems. Run llm/self reviews against your code before you have other humans review it. It is common courtesy.
Yes, exactly. The only question I have is whether the delay is attributable to high-quality bug reports or the burden of triaging low-quality ones. It's not completely clear which situation they're experiencing.
That's kind of what I was afraid of. Both is really bad news. If you think of it in terms of average speed over ground, working on low quality reports is like spending a bunch of time at v=0.
I did a little AI-assisted bug-hunting in Postgres this summer in between jobs, and the haul was staggering. Multiple serialization anomalies (eg [0] [1] [2]), foreign key integrity issues ([3] [4]), planner correctness bugs such as [5]. Various CVE-worthy vulnerabilities. An unfixable timezone bug [6]. The list got so long that I had to triage what I even had time to report, and settled for a dozen or two out of hundreds of candidates. Now imagine the entire world doing this.
By the way, Robert and the other maintainers are incredible to interact with. There are so many reports to triage and bugs to fix, but they're unflaggingly polite and quite generous with their time. I don't know of another community like it.
Very curious about the details and why it’s unfixable. Is it just because timezones are such a mess it’s logically impossible to satisfy every constraint?
I will add more example links in the top-level comment, but here is the thread for that bug. [0]. tl;dr timestamp vs timestamptz comparisons aren't consistently ordered in a pathological edge case, which means that index scans can return the wrong answer. There's no way to fix it without either redefining the operators in a surprising way or disabling index scans in this case.
Drives me crazy that the engineers I work with are perfectly happy writing TZ-less datetime strings to the hundreds of tables we manage. Every user has to guess, or be informed, that they’re UTC.
I am pretty sure we have always been able to add holes faster than we can find them and fill them, and that this has not changed with the advent of faster hole finding tools that are also faster hole producing tools.
It feels like we need AI methods to write provably correct code, along with a way for AI to automatically translate existing code into corresponding provably correct code.
(And hope performance stays in the same ballpark as the old code.)
I feel like AI is amazing for read only “search” tasks (query this, look for bugs, find vulnerabilities) but it’s not quite as good for “write” tasks like “add this feature”. Search tasks are easily verified, code changes are much harder to verify
"Add this feature" is often too vague, unless it's yet another button to a list of buttons. If you want your feature in a particular way, you need to specify it. If you're not even sure what you want, it's granted you won't be satisfied by the result, so you need to spend time figuring that out first. In other words, they're definitely great for write tasks too, but it does require more effort from the operator to formulate the problem.
That problem is as old as commercial programming; it doesn't matter that now your actors are prompt engineer and LLM, the same story can be told with manager and programmer.
This feels like just another variation of “it’s not the tool it’s the user,” which is too frequently leveraged during any discussion about AI. It makes it impossible to honestly assess the quality of the tool. You can always just say “people are doing it wrong.”
I mean that's what you would expect from a new and rapidly-evolving tool. Being able to identify the kinds of work where people typically use it sub-optimally is a useful thing though!
I'm not sure about that. The most common error for a search task is omission, and that's really hard to spot - how do you verify that some answer that should be there wasn't included?
For things like "add this feature" you can verify by trying out the feature and seeing if it works. Of course, that won't tell you if there are edge-case bugs or security holes or other design flaws, but you can get to a positive confirmation of the happy case pretty quickly.
I've slowly been coming around to this viewpoint as well. It's interesting that there's so much focus on using AI to write code (which, don't get me wrong, does work well but makes you lose track of what you're building so quickly) and seemingly less on analyzing code, finding bugs, etc
The planning, which can also be done in concert with AI, before getting the AI to write the code helps with not losing track of what you're building. In my current workflow, I never tell AI to just code X, I always approach it in such a way to leave space for research and breaking it up into multiple units of work (tickets) if the scope of the feature or idea calls for it.
I wouldn't say it's "interesting", rather that it's a natural result of incentive structures. If the AI labs try to sell their product as being good at finding bugs, then their expected valuation is the entire market cap of the existing static analysis industry, which is not very high. If they instead sell their product as being good at writing software, then their expected valuation is the "market cap" of the code-writing industry, a.k.a. the total spent on software development labor, which is very high. It doesn't matter what's true, only what promises to make them the most money.
It is not in any way a “query thing” in the same manner as PageRank, it was based on prior work in deep learning and it is irrelevant that Deepmind was purchased by the same company that was behind Google Search.
I guess the real challenge is figuring out how to scale verification, cause discovery isn't the bottleneck anymore. Generating more bug reports doesn't necessarily translate into faster fixes if the same small group of maintainers still have to validate the reports, review fixes, etc.
Makes sense. Code base is over 30 years old. Lot's of hands went into shaping it. There will be bugs as not one contributor as the entire codebase in their head. The testing is where bugs should be caught. Not by ones knowledge. I actually think there will be more bugs that come out of nowhere either found by humans or automation.
Most of the parent comments in this thread did not seem to understand the article. So here’s the tl;dr
Postgres release is delayed due to the quality bar getting raised. New features are getting more eyeballs, and therefore leading to more rollbacks. Pre-existing releases are getting bug reports and backported fixes at much higher rate. Bug fixing might be taking away time from feature development, but does harden the codebase. LLM usage seems to have been a net positive.
I absolutely think that this is fantastic. LLMs are widely and so, everyone and their grandma has the capability to compromise on any system. We need to be more proactive on fixing bugs.
Most likely pretty bad. It's not all that difficult to sweet talk Fable into doing a security review of a code base. I've pointed it at a few of the major open source projects in the main tech stack I work with and it found dozens of bugs in each. Many were fairly minor, and some of the more significant issues relied on fairly narrow attack vectors (for example, a compromised DB server being used to attack a web server). But this was just a single search pass, literally a few lines of prompt and an hour's worth of agent time.
Postgres is old but a lot of its features are new. You can't really say it's mature software, as much as it has changed over its lifetime.
Truly mature software doesn't get used, because people see low activity on github and assume it's "dead" or see no new features in years and assume that it's stagnating.
I'm not very positive on the social, technical, or intellectual impact of AI on software development. Actually, that's understating it. I think AI is basically cancer for the mind. That said, this framing gave me pause:
> The long-term picture is less clear and in some ways more troubling: given that we have forty years of bugs that humans were unable to find but LLMs are quite capable of discovering, how are we ever going to catch up, and how will PostgreSQL continue to innovate? Right now, I’m not sure. What I do know is that I, and many others, are working really hard to figure it out.
Two thoughts:
1. If the signal quality is high--the bug reports are generally valid, the submitted fixes are generally viable (if needing some attention)--then this all seems great. Pause innovation for a while, you can afford it. Focus on burning down the bug count, and resume feature development when things are more stable.
2. If the signal quality isn't good, or is sufficiently mixed that a lot of time is being wasted on low quality contributions... that's more complex. I don't know what to do about that. Maybe stop accepting contributions from unknown contributors? No idea.
From the post I'm not sure whether they're experiencing a (1) or a (2). If it's a (1) that's really interesting, and gives me some glimmer of hope for the future.
[edit] this piece of shit website won't let me reply "you're posting too fast.." so I'll respond to the two replies here (gondalez and sverhagen had basically the same point):
Yes that's a very good point. Not every valid bugfix is equivalently urgent. That's another critical dimension of quality--not burdening maintainers with pedantic bugs. This kind of contextual nuance is, coincidentally, something AI is absolutely awful at.
Signal quality... what is that even? The bugs may be valid, but they may also be obscure, irrelevant, low priority. A sibling thread mentioned that they had found CVE-level bugs in Postgres, and those require attention in some version of "immediately". But beyond that... not every bug needs fixing; that's a trap.
Not every part of a critical piece system is itself critical.
Seatback entertainment in aircraft are not critical. Aircraft are.
In Postgres, there are plenty low-priority, low-impact bugs. #16265, for instance, reports that the capitalization of a relation in an error message is confusing. The report got mostly ignored. And that seems not a great loss.
Dare I say, solving every bug does not make economic sense.
I do tend to agree with this ( i write database internals and engines for a living _). Letting software accumulate even little bugs is not a good look. Yes prioritize but definitely don't leave the smaller things, they will just pile up and make next work even harder.
Sure. This one already came up. [0]. In general, for a system like Postgres, many bugs affect only "extreme edge case" scenarios, but fixing them might create severe performance issues or require changing or removing other important features. So it's better to not fix them.
Seems like a lot of these bugs have not affected end-users or tripped up maintainers.
They could be trade-offs that helped get other features over the line even?
In which case maybe leaving them alone is practical?
I love fixing bugs, but your post triggered this thought so I thought I'd share.
Is no one entertaining the possibility that some contributions were lower quality due to AI? I'm surprised to have only seen arguments that it's review.
So it seems there as a certain level of safety in obscurity that was acceptable / fine which has been lost and therefore the focus is shifting towards churning through the slop.
I think of you as a good HN contributor, but you've been posting a lot of unsubstantive comments lately, and breaking the site guidelines in other ways too. Can you please stop doing this, and return to using HN in the intended spirit? Your substantive, thoughtful points are welcome, but not comments like this current one, nor these (to pick a few examples):
I have found major bugs in late beta's of postgres long before AI came along. I found a bug where malformed json would segfault the entire server (a segfault in a server process forces the entire cluster to shut down by design) when using jsonb in a release candidate once. They fixed it rather than ripping the entire feature out.
It seems quite possible that someone wants to make it seem like LLMs are causing problems by over-reacting to things that could just be fixed, or delay the release slightly (or maybe waiting until late before a release to kill features they don't like?) Whatever it is, this seems like a dysfunctional process.
Maybe its more a issue that X amount of people have more work to fix discovered issues. So the new features have not undergone the same level of testing as before.
And instead of releasing substandard new features, they rather focus on getting rid of the backlog and have more time to test.
LLMs are in effect directly responsible for this (in a positive way by finding a ton of issues that can be security issues). Reality is with LLMs now in the world, more work needs to be done on existing software as their ability to find bugs/security weaknesses is strong.
When the old bugs/security weaknesses are fixed, things will go back to a more normal release schedule.
Are the features bad by design? Like are they bad even if they work perfectly because they add bad abstractions to pg's SQL syntax, or are just broken in how they are supposed to work? If so the process is dysfunctional to let them get this far before noticing.
Are the features designed well (from a product perspective) but implemented poorly? Again, the process is dysfunctional to let them get this far before it notices.
Were there known issues with these that were planned to be fix, but the fixes didn't happen in time? That would make sense and it happens, but how is an LLM making that worse? LLMs in the worst case let you get the same work done more quickly, because you can always get the work done as quickly as you ever could by just not using an LLM. So the strongest thing you might say here is that you thought LLMs would let you get a feature into shape before it was too late, and for whatever reason that was overly optimistic.
To be honest I would love for someone to work on the structuring of pg_dump for example, which is entirely driven by regexes and binary structures. It’s awful and absolutely not programmable - a real shame.
I work on pgrust. We shouldn't have submitted the preview when we did: it was still in active development, and its SQL coverage was far too narrow, though there was nothing in it giving it an unfair performance advantage
For the other two claims in the removal PR, we wrote scripts you can use to verify the behavior of the original 0.4-preview binary:
- Per-query kernels did not serve the benchmark queries[0]
- The preview's fast repeat LIKE queries came from a dictionary filter cache, not a cached query result[1]
Our open-sourced 0.4-beta has now been merged and is back on ClickBench[2][3]
Across all queries and instance sizes on ClickBench, 0.4-beta is ~7% faster overall than the 0.4-preview binary that was removed. If 0.4-preview's numbers had come from hardcoded kernels or faked results, the open-source general engine wouldn't be beating it
postgres is mature database used the world over allegedly about to be on major version 19. how has the time not been spent already to get rid of the bugs?
I sometimes think that the world would need to do a "snow leopard" release of everything... to unit test, to fix... and then we will be able to move forward... but AiMaxSlopIt will result in those situations where you have to rollback or redo from scratch parts... because it's unmantainable because it's bad... it's spread everywhere and "the map" is not there.
Anecdote, I did a travel across USA and when I was looking for a hotel, if you go from apple maps, to website, to try to book, the "load browser" in the maps... could end in states where you could not go back because back is get out of where you are and go back to the start of the map... and was a terrible experience... then you turbo charge it with "implement game changer UI improvements for the new bar on UI design" goal of some guy with enough power to make damage... and you end with softslop (or is it sloprosoft?)
> This year is going to be different. PostgreSQL 19 most likely won’t be released until November… […] So, what happened? The short answer is that LLMs happened. […] There’s a huge amount of code, and that code contains a huge number of bugs. […] What’s different now is that it’s become vastly easier to find those bugs.
This seems…good?
That it might disrupt the project's "traditional" release cadence a bit doesn't seem terribly bad in light of the alternative, which is shipping another major update with bugs which have apparently haunted the codebase for some time.
This is great for the same reason open source software is great.
We want software that remains robust in the long term, so that even when people are able to inspect the source code, it's still solid, rather than hiding the source code and hoping people don't find holes everywhere.
At work we have put a full halt on feature work because the number of security related tasks has become staggering and urgent.
We will hopefully get over this wave and be able to move forward with much more secure software, but it's crazy right now.
Interesting that this could result in net negative software productivity world wide in terms of features right now.
LLMs finding unpatched vulnerabilities faster than they can be reasonably patched, bringing all other software development to a halt.
That's only if you consider security to be unproductive!
If you consider it to be productive work, it's a massive productivity boon happening right now. Of course, product managers might disagree...
Security work is not productive.
Imagine that the whole city has to install iron doors and window grilles, because wooden doors and glass windows are not safe enough anymore.
That's a lot of work, but is it productive in the end? Probably not.
With AI it's the same. World was fine with lax security approach before. Today it is not fine. Tomorrow we might need to prove every code line before shipping, will that be more productive? Hell, no.
The physical world comparison is interesting. In the real world we have a small amount of security, but most things could be broken in to. Instead of fortifying everything, we just imprison the people breaking stuff.
Obviously this doesn't work on the internet since most hacking comes from overseas out of our control. Which makes me wonder if there is a possible future where we just block packets coming from countries that don't lock up the hackers.
In the rest of the world making sure your product works like it is supposed to is considered table stakes. In software we somehow consider checking the parts of it we can think of 'good enough' and the rest unproductive
When I had a brilliant stop on defense, a few savvy fans would appreciate it. When I scored goals, everyone knew.
Some people will much appreciate the holes in Postgres that are being plugged. Many more will be bored with patch releases until new features rollout.
A software affecianado appreciates patches, and might do well to teach laypeople the benefits of a good defense.
How many of the security issues found were created by LLMs in the first place, though?
My job is seeing a similar deluge of security work and >90% of the bugs have been in pre-2024 code, and zero of them have been bugs introduced after we were first officially allowed to use LLM codegen.
Considering the software is much older than LLMs. Not many.
Also, in the article they mention a significant number of patches for older versions as well.
Software is incredibly complicated. When you get into areas like security and networking and the internet its even more complicated. We rely on layers upon layers of software and hoping they are all done perfectly with no issues. The amount of junior devs, or outsourced devs for $10 an hour working on security relevant code is very very high, and always has been. LLMs if used correctly should be raising the floor.
An uncomfortable truth about the era of LLM code is that machine produced code is, on average, quite a bit more secure than human code.
Unsurprising to those of us that has been screaming from the rooftops that human code reviews were barely catching anything in your typical corporate environment. The majority of the value was pointing out missing tests, because breaking bugs got past human eyeballs on a regular basis, even in very good companies.
It's not that the LLM code is that great, but that many overestimated the human code and the human review.
Do you mean "created" as in LLMs were used to discover existing vulnerabilities, or as in LLMs wrote unsecure code? If it's the latter, then the answer probably is "very few". Frontier models have been better than the average programmer at writing secure code that follows up-to-date best-practices for some time now.
Depends on the layers you are working on. Security is top priority on a lot of things that are visible to the internet, but in a lot of large companies, the footprint of the whole org is so much larger that than, and there many companies are deferring many security issues. So then you see a lot of speed there, when the consumer accessible systems are the focus of the security push.
Hopefully with the amount of visibility LLMs are bringing to the situation, security will finally be considered a first-class feature
LLMs will likely greatly increase the cost of ignoring security, which is the most powerful tool to increase security ticket prioritization.
I wouldn't be shocked if in 1-2 years non-security vetted software released into the wild will be hacked on arrival, like Windows XP without service packs used to be.
i think we have a pretty high p(doom) on frontier ai escaping containment with their weights and goes hack everything, if we do not act now we may have no chance to act anymore.
Escape to where? It's not as if there's a lot of idle compute capacity sitting around.
And even if the impossible occurs and LLMs hack everything that's connected to the Internet, so what? That would be inconvenient but it wouldn't cause doom. People would muddle through and eventually fix the critical problems, just like they always do.
Exactly. There's so much negativity, it sometimes borders Luddism.
"We found a bunch of ways to make our product safer and better. We need more time to ship a higher quality product."
PICKET SIGNS
Luddites were actually pretty reasonable when you dig into it. Maybe shouldn't be using it as an insult?
They're reasonable in a self-interested net-negative game-theory kind of way, yes. But that doesn't make it a good idea for society.
For their own personal interests. Not for those of society
Luddism, did not expect to ever hear that word with Postgres development :>
We could really use a lot more luddites tbh. Doesn't seem very applicable here tho.
Dont worry, if OpenAI and Anthropic have their way and everyone loses their jobs to AI, we're all becoming luddites.
Did you honestly need a /sarcasm tag in the article?
The article also concludes that overall, yes, finding more bugs is a good thing. Basically saying it’s the LLMs “fault” that the quality is improving, alongside a slight delay to the release.
They found a more efficient way to play Whac-A-Mole with bugs, they didn't find a way to reduce bugs.
It’s a good article. Easy to read. You should read it.
It doesn't mention formal methods once.
This is good. This is how I would expect llms to affect a codebase. Nothing sucks more than having a coworker put out a code review but your codex immediately finds problems. Run llm/self reviews against your code before you have other humans review it. It is common courtesy.
Yes, exactly. The only question I have is whether the delay is attributable to high-quality bug reports or the burden of triaging low-quality ones. It's not completely clear which situation they're experiencing.
It’s pretty clear there are a lot of valid reports based on the large increase in backport commits cited.
The post sort of gave a read that the lower elements may be a culprit rather than triaging higher qualitu.
I'd imagine just as every other project, both.
likely both
Certainly both. About half of AI bug reports I get are invalid. Many others are questionable. It's a huge time sink figuring it out.
https://www.vulncheck.com/blog/anthropic-glasswing-receipts
That's kind of what I was afraid of. Both is really bad news. If you think of it in terms of average speed over ground, working on low quality reports is like spending a bunch of time at v=0.
I did a little AI-assisted bug-hunting in Postgres this summer in between jobs, and the haul was staggering. Multiple serialization anomalies (eg [0] [1] [2]), foreign key integrity issues ([3] [4]), planner correctness bugs such as [5]. Various CVE-worthy vulnerabilities. An unfixable timezone bug [6]. The list got so long that I had to triage what I even had time to report, and settled for a dozen or two out of hundreds of candidates. Now imagine the entire world doing this.
[0] Write skew observed under serializable isolation: https://www.postgresql.org/message-id/flat/CA+COZaBo+ZpKgMvx...
[1] Serializable anomaly: duplicate primary keys: https://www.postgresql.org/message-id/flat/CA+COZaBOiiRPmEfX...
[2] SERIALIZABLE can commit write skew after SSI summarization: https://www.postgresql.org/message-id/flat/CA+COZaCtK=UQbeQw...
[3] Fix temporal foreign key validation of pre-existing rows https://www.postgresql.org/message-id/flat/CA+COZaChq_ukm2o7...
[4] Lost lock during toasting allows FK violation https://www.postgresql.org/message-id/flat/CA+COZaC-2-GQabq+...
[5] Partition pruning can incorrectly exclude a RANGE default partition https://www.postgresql.org/message-id/flat/CA+COZaDXrfTaBjLE...
[6] timestamp vs. timestamptz comparisons inconsistently ordered under DST : https://www.postgresql.org/message-id/flat/c22286ad6268347c6...
By the way, Robert and the other maintainers are incredible to interact with. There are so many reports to triage and bugs to fix, but they're unflaggingly polite and quite generous with their time. I don't know of another community like it.
> unfixable timezone bug
Very curious about the details and why it’s unfixable. Is it just because timezones are such a mess it’s logically impossible to satisfy every constraint?
I will add more example links in the top-level comment, but here is the thread for that bug. [0]. tl;dr timestamp vs timestamptz comparisons aren't consistently ordered in a pathological edge case, which means that index scans can return the wrong answer. There's no way to fix it without either redefining the operators in a surprising way or disabling index scans in this case.
[0] https://www.postgresql.org/message-id/flat/c22286ad6268347c6...
OT: Crazy that TZ-less are default in so many RDBMS. TZ included should be the default, and no-TZ opt in like TIMESTAMP NO TIME ZONE.
Drives me crazy that the engineers I work with are perfectly happy writing TZ-less datetime strings to the hundreds of tables we manage. Every user has to guess, or be informed, that they’re UTC.
i assume the magnitude of impact for a bunch of [now] glaring holes in popular software is unknowable.
question is, how long do you think this persists before there's only a long tail of minor exploits left?
I am pretty sure we have always been able to add holes faster than we can find them and fill them, and that this has not changed with the advent of faster hole finding tools that are also faster hole producing tools.
It feels like we need AI methods to write provably correct code, along with a way for AI to automatically translate existing code into corresponding provably correct code.
(And hope performance stays in the same ballpark as the old code.)
I feel like AI is amazing for read only “search” tasks (query this, look for bugs, find vulnerabilities) but it’s not quite as good for “write” tasks like “add this feature”. Search tasks are easily verified, code changes are much harder to verify
"Add this feature" is often too vague, unless it's yet another button to a list of buttons. If you want your feature in a particular way, you need to specify it. If you're not even sure what you want, it's granted you won't be satisfied by the result, so you need to spend time figuring that out first. In other words, they're definitely great for write tasks too, but it does require more effort from the operator to formulate the problem.
That problem is as old as commercial programming; it doesn't matter that now your actors are prompt engineer and LLM, the same story can be told with manager and programmer.
This feels like just another variation of “it’s not the tool it’s the user,” which is too frequently leveraged during any discussion about AI. It makes it impossible to honestly assess the quality of the tool. You can always just say “people are doing it wrong.”
Idk, I think it pretty clear to me that
“Find the issues in this code” vs “write this feature” are different problems.
The first one is easier because the feedback loop is llm to code/test and trivially scalable.
The second one is llm to human which is going to be slower because it can’t read your mind.
The tighter and more deterministic the loop the more llms excel.
its certainly benchmarkable.
make a bunch of "build the feature" vs "heres a detailed spec" prompts, and see which one does better.
if you take out the AI boogieman, and instead go with "next token prediction" then "people are doing it wrong" is much more reasonable
I mean that's what you would expect from a new and rapidly-evolving tool. Being able to identify the kinds of work where people typically use it sub-optimally is a useful thing though!
I'm not sure about that. The most common error for a search task is omission, and that's really hard to spot - how do you verify that some answer that should be there wasn't included?
For things like "add this feature" you can verify by trying out the feature and seeing if it works. Of course, that won't tell you if there are edge-case bugs or security holes or other design flaws, but you can get to a positive confirmation of the happy case pretty quickly.
> The most common error for a search task is omission, and that's really hard to spot
NP-Hard, even[1]!
[1] https://www.i-programmer.info/news/112-theory/3830-physics-i...
We're never solving nphard. Just like we'll never extract all of the coal/oil/natural gas from earth
I've slowly been coming around to this viewpoint as well. It's interesting that there's so much focus on using AI to write code (which, don't get me wrong, does work well but makes you lose track of what you're building so quickly) and seemingly less on analyzing code, finding bugs, etc
The planning, which can also be done in concert with AI, before getting the AI to write the code helps with not losing track of what you're building. In my current workflow, I never tell AI to just code X, I always approach it in such a way to leave space for research and breaking it up into multiple units of work (tickets) if the scope of the feature or idea calls for it.
I wouldn't say it's "interesting", rather that it's a natural result of incentive structures. If the AI labs try to sell their product as being good at finding bugs, then their expected valuation is the entire market cap of the existing static analysis industry, which is not very high. If they instead sell their product as being good at writing software, then their expected valuation is the "market cap" of the code-writing industry, a.k.a. the total spent on software development labor, which is very high. It doesn't matter what's true, only what promises to make them the most money.
But it DIDNT “write that feature”. They made it extremely clear on the delay announcement the code was written without AI.
AI’s only part of this was helping review things and finding bugs. More bugs, bugs that would have been found later, whatever.
Postgres did not have a slop issue and it’s wrong for people to insinuate it.
You showed that strawman.
If the entire project is developed with AI it can add features effectively but for large old codebases it becomes an issue
No offense but this is not exactly a groundbreaking statement. We've known about this verifiable tasks thing for a while.
I mean the transformer model came out of Google, and it's literally a query thing so yeah.
any kind of pattern matching / transformation is going to be an ideal task for LLMs
It is not in any way a “query thing” in the same manner as PageRank, it was based on prior work in deep learning and it is irrelevant that Deepmind was purchased by the same company that was behind Google Search.
What? AI can't easily implement features. Just write a spec, ask if to expand it and then ask it to implement it
I guess the real challenge is figuring out how to scale verification, cause discovery isn't the bottleneck anymore. Generating more bug reports doesn't necessarily translate into faster fixes if the same small group of maintainers still have to validate the reports, review fixes, etc.
Probably true if development accelerates because of AI, but if it's reasonably stable then hopefully new bug reports will plateau.
Then just have the same AI bug checks for new PRs and automatically reject any that fail.
Makes sense. Code base is over 30 years old. Lot's of hands went into shaping it. There will be bugs as not one contributor as the entire codebase in their head. The testing is where bugs should be caught. Not by ones knowledge. I actually think there will be more bugs that come out of nowhere either found by humans or automation.
Most of the parent comments in this thread did not seem to understand the article. So here’s the tl;dr
Postgres release is delayed due to the quality bar getting raised. New features are getting more eyeballs, and therefore leading to more rollbacks. Pre-existing releases are getting bug reports and backported fixes at much higher rate. Bug fixing might be taking away time from feature development, but does harden the codebase. LLM usage seems to have been a net positive.
I absolutely think that this is fantastic. LLMs are widely and so, everyone and their grandma has the capability to compromise on any system. We need to be more proactive on fixing bugs.
If software as mature and widely-used as Postgres has this level of hidden bugs, what about the average software product?
Most likely pretty bad. It's not all that difficult to sweet talk Fable into doing a security review of a code base. I've pointed it at a few of the major open source projects in the main tech stack I work with and it found dozens of bugs in each. Many were fairly minor, and some of the more significant issues relied on fairly narrow attack vectors (for example, a compromised DB server being used to attack a web server). But this was just a single search pass, literally a few lines of prompt and an hour's worth of agent time.
The average software product is worse. Lots worse.
And half the software products are worse than that.
Any organisation that said "let's move fast and break things" is now busy finding out what's actually broken.
Postgres is old but a lot of its features are new. You can't really say it's mature software, as much as it has changed over its lifetime.
Truly mature software doesn't get used, because people see low activity on github and assume it's "dead" or see no new features in years and assume that it's stagnating.
"it's AI's fault"
I missed when AI became accountable for Postgres releases, when did that happen?
It’s a tongue in cheek statement.
“A wizard is never late, nor is he early; he arrives precisely when he means to” -- Gandalf
Now, riddle me this. Does this mean productivity is up or down for the Postgres team?
I'm not very positive on the social, technical, or intellectual impact of AI on software development. Actually, that's understating it. I think AI is basically cancer for the mind. That said, this framing gave me pause:
> The long-term picture is less clear and in some ways more troubling: given that we have forty years of bugs that humans were unable to find but LLMs are quite capable of discovering, how are we ever going to catch up, and how will PostgreSQL continue to innovate? Right now, I’m not sure. What I do know is that I, and many others, are working really hard to figure it out.
Two thoughts:
1. If the signal quality is high--the bug reports are generally valid, the submitted fixes are generally viable (if needing some attention)--then this all seems great. Pause innovation for a while, you can afford it. Focus on burning down the bug count, and resume feature development when things are more stable.
2. If the signal quality isn't good, or is sufficiently mixed that a lot of time is being wasted on low quality contributions... that's more complex. I don't know what to do about that. Maybe stop accepting contributions from unknown contributors? No idea.
From the post I'm not sure whether they're experiencing a (1) or a (2). If it's a (1) that's really interesting, and gives me some glimmer of hope for the future.
[edit] this piece of shit website won't let me reply "you're posting too fast.." so I'll respond to the two replies here (gondalez and sverhagen had basically the same point):
Yes that's a very good point. Not every valid bugfix is equivalently urgent. That's another critical dimension of quality--not burdening maintainers with pedantic bugs. This kind of contextual nuance is, coincidentally, something AI is absolutely awful at.
Signal quality... what is that even? The bugs may be valid, but they may also be obscure, irrelevant, low priority. A sibling thread mentioned that they had found CVE-level bugs in Postgres, and those require attention in some version of "immediately". But beyond that... not every bug needs fixing; that's a trap.
"But beyond that... not every bug needs fixing; that's a trap"
this doesn't seem good attitude for critical software like database
Not every part of a critical piece system is itself critical.
Seatback entertainment in aircraft are not critical. Aircraft are.
In Postgres, there are plenty low-priority, low-impact bugs. #16265, for instance, reports that the capitalization of a relation in an error message is confusing. The report got mostly ignored. And that seems not a great loss.
Dare I say, solving every bug does not make economic sense.
I do tend to agree with this ( i write database internals and engines for a living _). Letting software accumulate even little bugs is not a good look. Yes prioritize but definitely don't leave the smaller things, they will just pile up and make next work even harder.
Old bugs are sometimes undesirable to fix because other systems/users have come to rely on their behavior.
That's a weird take. Do you have an example tibbar?
Sure. This one already came up. [0]. In general, for a system like Postgres, many bugs affect only "extreme edge case" scenarios, but fixing them might create severe performance issues or require changing or removing other important features. So it's better to not fix them.
[0] https://www.postgresql.org/message-id/flat/c22286ad6268347c6...
the optimal amount of fraud for a fintech is non-zero
why would the optimal amount of bugs be anything less for a database?
Seems like a lot of these bugs have not affected end-users or tripped up maintainers. They could be trade-offs that helped get other features over the line even?
In which case maybe leaving them alone is practical?
I love fixing bugs, but your post triggered this thought so I thought I'd share.
Is no one entertaining the possibility that some contributions were lower quality due to AI? I'm surprised to have only seen arguments that it's review.
If you look at the kind of bugs coming in, it's from all corners of the codebase, not just things changed in the last 1-2 years.
So it seems there as a certain level of safety in obscurity that was acceptable / fine which has been lost and therefore the focus is shifting towards churning through the slop.
[flagged]
"Don't be curmudgeonly. Thoughtful criticism is fine, but please don't be rigidly or generically negative."
https://news.ycombinator.com/newsguidelines.html
I think of you as a good HN contributor, but you've been posting a lot of unsubstantive comments lately, and breaking the site guidelines in other ways too. Can you please stop doing this, and return to using HN in the intended spirit? Your substantive, thoughtful points are welcome, but not comments like this current one, nor these (to pick a few examples):
https://news.ycombinator.com/item?id=50280377
https://news.ycombinator.com/item?id=50275967
https://news.ycombinator.com/item?id=50147625
I have found major bugs in late beta's of postgres long before AI came along. I found a bug where malformed json would segfault the entire server (a segfault in a server process forces the entire cluster to shut down by design) when using jsonb in a release candidate once. They fixed it rather than ripping the entire feature out.
https://www.postgresql.org/message-id/20140818195634.2543.56...
It seems quite possible that someone wants to make it seem like LLMs are causing problems by over-reacting to things that could just be fixed, or delay the release slightly (or maybe waiting until late before a release to kill features they don't like?) Whatever it is, this seems like a dysfunctional process.
Maybe its more a issue that X amount of people have more work to fix discovered issues. So the new features have not undergone the same level of testing as before.
And instead of releasing substandard new features, they rather focus on getting rid of the backlog and have more time to test.
LLMs are in effect directly responsible for this (in a positive way by finding a ton of issues that can be security issues). Reality is with LLMs now in the world, more work needs to be done on existing software as their ability to find bugs/security weaknesses is strong.
When the old bugs/security weaknesses are fixed, things will go back to a more normal release schedule.
Are the features bad by design? Like are they bad even if they work perfectly because they add bad abstractions to pg's SQL syntax, or are just broken in how they are supposed to work? If so the process is dysfunctional to let them get this far before noticing.
Are the features designed well (from a product perspective) but implemented poorly? Again, the process is dysfunctional to let them get this far before it notices.
Were there known issues with these that were planned to be fix, but the fixes didn't happen in time? That would make sense and it happens, but how is an LLM making that worse? LLMs in the worst case let you get the same work done more quickly, because you can always get the work done as quickly as you ever could by just not using an LLM. So the strongest thing you might say here is that you thought LLMs would let you get a feature into shape before it was too late, and for whatever reason that was overly optimistic.
If you are at the RC stage and still uncovering segfaults, "rip the feature out" is probably the only choice.
I’m still amazed that with all the vibe coding, no one has created a Postgres alternative
Someone tried. https://jepsen.io/analyses/particledb-1.1.4
Why would they? Postgres is free and does an amazing job.
I wish they'd adopt, merge in, or give official blessings to versioned postgres extensions like citus and pgvector.
why ? Just install the extension and use it
Also Postgres has enjoyed decades of debugging.
To be honest I would love for someone to work on the structuring of pg_dump for example, which is entirely driven by regexes and binary structures. It’s awful and absolutely not programmable - a real shame.
You can create a project on vibes, but you cannot maintain one on vibes.
Many recent relational databases speak the Postgres wire protocol. For wholesale replacement, pgrust is a recent, contentious contender.
I hope you don't believe this, PGRust has fallen far from grace. Wire protocol support is useful (other than said recommendation)
Remove pgrust: the entry is cheating (per-query kernels and result caching) (197 points, 10 days ago, 166 comments) https://news.ycombinator.com/item?id=50205816
I work on pgrust. We shouldn't have submitted the preview when we did: it was still in active development, and its SQL coverage was far too narrow, though there was nothing in it giving it an unfair performance advantage
For the other two claims in the removal PR, we wrote scripts you can use to verify the behavior of the original 0.4-preview binary:
Our open-sourced 0.4-beta has now been merged and is back on ClickBench[2][3]Across all queries and instance sizes on ClickBench, 0.4-beta is ~7% faster overall than the 0.4-preview binary that was removed. If 0.4-preview's numbers had come from hardcoded kernels or faked results, the open-source general engine wouldn't be beating it
[0] https://pgrust.com/downloads/archive/2026-09-clickbench-snap...
[1] https://pgrust.com/downloads/archive/2026-09-clickbench-snap...
[2] https://benchmark.clickhouse.com/
[3] https://github.com/ClickHouse/ClickBench/pull/2751
postgres is mature database used the world over allegedly about to be on major version 19. how has the time not been spent already to get rid of the bugs?
...are you serious?!
I sometimes think that the world would need to do a "snow leopard" release of everything... to unit test, to fix... and then we will be able to move forward... but AiMaxSlopIt will result in those situations where you have to rollback or redo from scratch parts... because it's unmantainable because it's bad... it's spread everywhere and "the map" is not there.
Anecdote, I did a travel across USA and when I was looking for a hotel, if you go from apple maps, to website, to try to book, the "load browser" in the maps... could end in states where you could not go back because back is get out of where you are and go back to the start of the map... and was a terrible experience... then you turbo charge it with "implement game changer UI improvements for the new bar on UI design" goal of some guy with enough power to make damage... and you end with softslop (or is it sloprosoft?)