A good chance to bring up something I've been flagging to colleagues for a while now: with LLM agents we are very quickly going to become even more CPU bottlenecked on testing performance than today, and every team I know of today was bottlenecked on CI speed even before LLMs. There's no point having an agent that can write code 100x faster than a human if every change takes an hour to test.
Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green. Many runs end up bottlenecked on I/O or availability of workers, and so changes can sit in queues for hours, or they flake out and everything has to start again.
As they get better coding agents are going to be assigned simple tickets that they turn into green PRs, with the model reacting to test failures and fixing them as they go. This will make the CI bottleneck even worse.
It feels like there's a lot of low hanging fruit in most project's testing setups, but for some reason I've seen nearly no progress here for years. It feels like we kinda collectively got used to the idea that CI services are slow and expensive, then stopped trying to improve things. If anything CI got a lot slower over time as people tried to make builds fully hermetic (so no inter-run caching), and move them from on-prem dedicated hardware to expensive cloud VMs with slow IO, which haven't got much faster over time.
Mercury is crazy fast and in a few quick tests I did, created good and correct code. How will we make test execution keep up with it?
kccqzy · 7h ago
> Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green.
I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain? It's just a throw-more-resources problem. When I was at Google, it was somewhat common for me to debug non-deterministic bugs such as a missing synchronization or fence causing flakiness; and it was common to just launch 10000 copies of the same test on 10000 machines to find perhaps a single digit number of failures. My current employer has a clunkier implementation of the same thing (no UI), but there's also a single command to launch 1000 test workers to run all tests from your own checkout. The goal is to finish testing a 1M loc codebase in no more than five minutes so that you get quick feedback on your changes.
> make builds fully hermetic (so no inter-run caching)
These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.
mike_hearn · 7h ago
I was also at Google for years. Places like that are not even close to representative. They can afford to just-throw-more-resources, they get bulk discounts on hardware and they pay top dollar for engineers.
In more common scenarios that represent 95% of the software industry CI budgets are fixed, clusters are sized to be busy most of the time, and you cannot simply launch 10,000 copies of the same test on 10,000 machines. And even despite that these CI clusters can easily burn through the equivalent of several SWE salaries.
> These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.
Again, that's how companies like Google do it. In normal companies, build caching isn't always perfectly reliable, and if CI runs suffer flakes due to caching then eventually some engineer is gonna get mad and convince someone else to turn the caching off. Blaze goes to extreme lengths to ensure this doesn't happen, and Google spends extreme sums of money on helping it do that (e.g. porting third party libraries to use Blaze instead of their own build system).
In companies without money printing machines, they sacrifice caching to get determinism and everything ends up slow.
kridsdale1 · 3h ago
I’m at Google today and even with all the resources, I am absolutely most bottlenecked by the Presubmit TAP and human review latency. Making CLs in the editor takes me a few hours. Getting them in the system takes days and sometimes weeks.
PaulHoule · 6h ago
Most of my experience writing concurrent/parallel code in (mainly) Java has been rewriting half-baked stuff that would need a lot of testing with straightforward reliable and reasonably performant code that uses sound and easy-to-use primitives such as Executors (watch out for teardown though), database transactions, atomic database operations, etc. Drink the Kool Aid and mess around with synchronized or actors or Streams or something and you're looking at a world of hurt.
I've written a limited number of systems that needed tests that probe for race conditions by doing something like having 3000 threads run a random workload for 40 seconds. I'm proud of that "SuperHammer" test on a certain level but boy did I hate having to run it with every build.
socalgal2 · 58m ago
Even Google can not buy more old Intel Macs or Pixel 6s or Samsung S20s to increase their testing on those devices (as an example)
Maybe that affects less devs who don't need to test on actual hardware but plenty of apps do. Pretty much anything that touches a GPU driver for example like a game.
IshKebab · 6h ago
Developer time is more expensive than machine time, but at most companies it isn't 10000x more expensive. Google is likely an exception because it pays extremely well and has access to very cheap machines.
Even then, there are other factors:
* You might need commercial licenses. It may be very cheap to run open source code 10000x, but guess how much 10000 Questa licenses cost.
* Moores law is dead Amdahl's law very much isn't. Not everything is embarrassingly parallel.
* Some people care about the environment. I worked at a company that spent 200 CPU hours on every single PR (even to fix typos; I failed to convince them they were insane for not using Bazel or similar). That's a not insignificant amount of CO2.
hyperpape · 3h ago
> Moores law is dead Amdahl's law
Yes, but the OP specifically is talking about CI for large numbers of pull requests, which should be very parallelizable (I can imagine exceptions, but only with anti-patterns, e.g. if your test pipeline makes some kind of requests to something that itself isn't scalable).
underdeserver · 5h ago
That's solvable with modern cloud offerings - Provision spot instances for a few minutes and shut them down afterwards. Let the cloud provider deal with demand balancing.
I think the real issue is that developers waiting for PRs to go green are taking a coffee break between tasks, not sitting idly getting annoyed. If that's the case you're cutting into rest time and won't get much value out of optimizing this.
IshKebab · 3h ago
Both companies I've worked in recently have been too paranoid about IP to use the cloud for CI.
Anyway I don't see how that solves any of the issues except maybe cost to some degree (but maybe not; cloud is expensive).
fragmede · 2h ago
Sorta. For CI/CD you can use spot instances and spin them down outside of business hours, so they can end up being cheaper than buying many really beefy machines and amortizing them over the standard depreciation schedule.
mark_undoio · 7h ago
> I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain? It's just a throw-more-resources problem.
I'd personally agree. But this sounds like the kind of thing that, at many companies, could be a real challenge.
Ultimately, you can measure dollars spent on CI workers. It's much harder and less direct to quantify the cost of not having them (until, for instance, people start taking shortcuts with testing and a regression escapes to production).
That kind of asymmetry tends, unless somebody has a strong overriding vision of where the value really comes from, to result in penny pinching on the wrong things.
mike_hearn · 6h ago
It's more than that. You can measure salaries too, measurement isn't the issue.
The problem is that if you let people spend the companies money without any checks or balances they'll just blow through unlimited amounts of it. That's why companies always have lots of procedures and policies around expense reporting. There's no upper limit to how much money developers will spend on cloud hardware given the chance, as the example above of casually running a test 10,000 times in parallel demonstrates nicely.
CI doesn't require you to fill out an expense report every time you run a PR thank goodness, but there still has to be a way to limit financial liability. Usually companies do start out by doubling cluster sizes a few times, but each time it buys a few months and then the complaints return. After a few rounds of this managers realize that demand is unlimited and start pushing back on always increasing the budget. Devs get annoyed and spend an afternoon on optimizations, suddenly times are good again.
The meme on HN is that developer time is always more expensive than machine time, but I've been on both sides of this and seen how the budgets work out. It's often not true, especially if you use clouds like Azure which are overloaded and expensive, or have plenty of junior devs, and/or teams outside the US where salaries are lower. There's often a lot of low hanging fruit in test times so it can make sense to optimize, even so, huge waste is still the order of the day.
ronbenton · 6h ago
>Do companies not just double their CI workers after hearing people complain?
They do not.
I don't know if it's a matter of justifying management levels, but these discussions are often drawn out and belabored in my experience. By the time you get approval, or even worse, rejected, for asking for more compute (or whatever the ask is), you've spent way more money on the human resource time than you would ever spend on the requested resources.
kccqzy · 6h ago
I have never once been refused by a manager or director when I am explicitly asking for cost approval. The only kind of long and drawn out discussions are unproductive technical decision making. Example: the ask of "let's spend an extra $50,000 worth of compute on CI" is quickly approved but "let's locate the newly approved CI resource to a different data center so that we have CI in multiple DCs" solicits debates that can last weeks.
mysteria · 6h ago
This is exactly my experience with asking for more compute at work. We have to prepare loads of written justification, come up with alternatives or optimizations (which we already know won't work), etc. and in the end we choose the slow compute and reduced productivity over the bureaucracy.
And when we manage to make a proper request it ends up being rejected anyways as many other teams are asking for the same thing and "the company has limited resources". Duh.
anp · 45m ago
I’m currently at google (opinions not representative of my employer’s etc) and this is true for things that run in a data center but it’s a lot harder for things that need to be tested on physical hardware like parts of Android or CrOS.
wbl · 4h ago
No it is not. Senior management often has a barely disguised contempt for engineering and spending money to do a better job. They listen much more to sales complain.
kridsdale1 · 3h ago
That depends on the company.
MangoToupe · 3h ago
Writing testing infrastructure so that you can just double workers and get a corresponding doubling in productivity is non-trivial. Certainly I've never seen anything like Google's testing infrastructure anywhere else I've worked.
mike_hearn · 2h ago
Yeah Google's infrastructure is unique because Blaze is tightly integrated with the remote execution workers and can shard testing work across many machines automatically. Most places can't do that so once you have enough hardware that queue depth isn't too big you can't make anything go faster by adding hardware, you can only try to scale vertically or optimize. But if you're using hosted CI SaaS it's often not always easy to get bigger machines, or the bigger machines are superlinear in cost.
mystified5016 · 6h ago
IME it's less of a "throw more resources" problem and more of a "stop using resources in literally the worst way possible"
CI caching is, apparently, extremely difficult. Why spend a couple of hours learning about your CI caches when you can just download and build the same pinned static library a billion times? The server you're downloading from is (of course) someone else's problem and you don't care about wasting their resources either. The power you're burning by running CI for there hours instead of one is also someone else's problem. Compute time? Someone else's problem. Cloud costs? You bet it's someone else's problem.
Sure, some things you don't want to cache. I always do a 100% clean build when cutting a release or merging to master. But for intermediate commits on a feature branch? Literally no reason not to cache builds the exact same way you do on your local machine.
wat10000 · 5h ago
Many companies are strangely reluctant to spend money on hardware for developers. They might refuse to spend $1,000 on a better laptop to be used for the next three years by an employee, whose time costs them that much money in a single afternoon.
PaulHoule · 3h ago
That's been a pet peeve of mine for so long. (Glad my current employer gets me the best 1.5ℓ machine from Dell every few years!)
On the other hand I've seen many overcapitalized pre-launch startups go for months with a $20,000+ AWS bill without thinking about it then suddenly panic about what they're spending; they'd find tens of XXXXL instances spun up doing nothing, S3 buckets full of hundreds of terabytes of temp files that never got cleared out, etc. With basic due diligence they could have gotten that down to $2k a month, somebody obsessive about cost control could have done even better.
kridsdale1 · 3h ago
I have faced this at each of the $50B in profit companies I have worked at.
physicsguy · 5h ago
Not really, in most small companies/departments, £100k a month is considered a painful cloud bill and adding more EC2 instances to provide cloud runners can add 10% to that easily.
daxfohl · 4h ago
There are a couple mitigating considerations
1. As implementation phase gets faster, the bottleneck could actually switch to PM. In which case, changes will be more serial, so a lot fewer conflicts to worry about.
2. I think we could see a resurrection of specs like TLA+. Most engineers don't bother with them, but I imagine code agents could quickly create them, verify the code is consistent with them, and then require fewer full integration tests.
3. When background agents are cleaning up redundant code, they can also clean up redundant tests.
4. Unlike human engineering teams, I expect AIs to work more efficiently on monoliths than with distributed microservices. This could lead to better coverage on locally runnable tests, reducing flakes and CI load.
5. It's interesting that even as AI increases efficiency, that increased velocity and sheer amount of code it'll write and execute for new use cases will create its own problems that we'll have to solve. I think we'll continue to have new problems for human engineers to solve for quite some time.
mrkeen · 2h ago
> Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green. Many runs end up bottlenecked on I/O or availability of workers
No, this is common. The devs just haven't grokked dependency inversion. And I think the rate of new devs entering the workforce will keep it that way forever.
Here's how to make it slow:
* Always refer to "the database". You're not just storing and retrieving objects from anywhere - you're always using the database.
* Work with statements, not expressions. Instead of "the balance is the sum of the transactions", execute several transaction writes (to the database) and read back the resulting balance. This will force you to sequentialise the tests (simultaneous tests would otherwise race and cause flakiness) plus you get to write a bunch of setup and teardown and wipe state between tests.
* If you've done the above, you'll probably need to wait for state changes before running an assertion. Use a thread sleep, and if the test is ever flaky, bump up the sleep time and commit it if the test goes green again.
TechDebtDevin · 7h ago
LLM making a quick edit, <100 lines... Sure. Asking an LLM to rubber-duck your code, sure. But integrating an LLM into your CI is going to end up costing you 100s of hours productivity on any large project. That or spend half the time you should be spending learning to write your own code, dialing down context sizing and prompt accuracy.
I really really don't understand the hubris around llm tooling, and don't see it catching on outside of personal projects and small web apps. These things don't handle complex systems well at all, you would have to put a gun in my mouth to let one of these things work on an important repo of mine without any supervision... And if I'm supervising the LLM I might as well do it myself, because I'm going to end up redoing 50% of its work anyways..
kraftman · 7h ago
I keep seeing this argument over and over again, and I have to wonder, at what point do you accept that maybe LLM's are useful? Like how many people need to say that they find it makes them more productive before you'll shift your perspective?
dragonwriter · 6h ago
> I keep seeing this argument over and over again, and I have to wonder, at what point do you accept that maybe LLM's are useful?
The post you are responding to literally acknowledges that LLMs are useful in certain roles in coding in the first sentence.
> Like how many people need to say that they find it makes them more productive before you'll shift your perspective?
Argumentum ad populum is not a good way of establishing fact claims beyond the fact of a belief being popular.
kraftman · 2h ago
...and my comment clearly isnt talking about that, but at the suggestion that its useless to write code with an LLM because you'll end up rewriting 50% of it.
If everyone has an opinion different to mine, I dont instantly change my opinion, but I do try and investigate the source of the difference, to find out what I'm missing or what they are missing.
The polarisation between people that find LLMs useful or not is very similar to the polarisation between people that find automated testing useful or not, and I have a suspicion they have the same underlying cause.
nwienert · 1h ago
You seem to think everyone shares your view, around me I see a lot of people acknowledging they are useful to a degree, but also clearly finding limits in a wide array of cases, including that they really struggle with logical code, architectural decisions, re-using the right code patterns, larger scale changes that aren’t copy paste, etc.
So far what I see is that if I provide lots of context and clear instructions to a mostly non-logical area of code, I can speed myself up about 20-40%, but only works in about 30-50% of the problems I solve day to day at a day job.
So basically - it’s about a rough 20% improvement in my productivity - because I spend most of my time of the difficult things it can’t do anyway.
Meanwhile these companies are raising billion dollar seed rounds and telling us that all programming will be done by AI by next year.
psychoslave · 7h ago
That's a tool, and it depends what you need to do. If it fits someone need and make them more productive, or even simply enjoy more the activity, good.
Just because two people are fixing something on the whole doesn't mean the same tool will hold fine. Gum, pushpin, nail, screw,bolts?
The parent thread did mention they use LLM successfully in small side project.
MangoToupe · 3h ago
> at what point do you accept that maybe LLM's are useful?
LLMs are useful, just not for every task and price point.
candiddevmike · 7h ago
People say they are more productive using visual basic, but that will never shift my perspective on it.
Code is a liability. Code you didn't write is a ticking time bomb.
ninetyninenine · 4h ago
They say it’s only effective for personal projects but there’s literally evidence of LLMs being used for what he says can’t be used. Actual physical evidence.
It’s self delusion. And also the pace of AI is so fast he may not be aware of how fast LLMs are integrating into our coding environments. Like 1 year ago what he said could be somewhat true but right now what he said is clearly not true at all.
mike_hearn · 7h ago
I've used Claude with a large, mature codebase and it did fine. Not for every possible task, but for many.
Probably, Mercury isn't as good at coding as Claude is. But even if it's not, there's lots of small tasks that LLMs can do without needing senior engineer level skills. Adding test coverage, fixing low priority bugs, adding nice animations to the UI etc. Stuff that maybe isn't critical so if a PR turns up and it's DOA you just close it, but which otherwise works.
Note that many projects already use this approach with bots like Renovate. Such bots also consume a ton of CI time, but it's generally worth it.
airstrike · 7h ago
IMHO LLMs are notoriously bad at test coverage. They usually hard code a value to have the test pass, since they lack the reasoning required to understand why the test exists or the concept of assertion, really
wrs · 6h ago
I don’t know, Claude is very good at writing that utterly useless kind of unit test where every dependency is mocked out and the test is just the inverted dual of the original code. 100% coverage, nothing tested.
conradkay · 4h ago
Yeah and that's even worse because there's not an easy metric you can have the agent work towards and get feedback on.
I'm not that into "prompt engineering" but tests seem like a big opportunity for improvement. Maybe something like (but much more thorough):
1. "Create a document describing all real-world actions which could lead to the code being used. List all methods/code which gets called before it (in order) along with their exact parameters and return value. Enumerate all potential edge cases and errors that could occur and if it ends up influencing this task. After that, write a high-level overview of what need to occur in this implementation. Don't make it top down where you think about what functions/classes/abstractions which are created, just the raw steps that will need to occur"
2. Have it write the tests
3. Have it write the code
Maybe TDD ends up worse but I suspect the initial plan which is somewhat close to code makes that not the case
Writing the initial doc yourself would definitely be better, but I suspect just writing one really good one, then giving it as an example in each subsequent prompt captures a lot of the improvement
flir · 7h ago
Don't want to put words in the parent commenter's mouth, but I think the key word is "unsupervised". Claude doesn't know what it doesn't know, and will keep going round the loop until the tests go green, or until the heat death of the universe.
mike_hearn · 7h ago
Yes, but you can just impose timeouts to solve that. If it's unsupervised the only cost is computation.
blitzar · 5h ago
Do the opposite - integrate your CI into your LLM.
Make it run tests after it changes your code and either confirm it didnt break anything or go back and try again.
DSingularity · 7h ago
He is simply observing that if PR numbers and launch rates increase dramatically CI cost will become untenable.
grogenaut · 4h ago
Before cars people spent little on petroleum products or motor oil or gasoline or mechanics. Now they do. That's how systems work. You wanna go faster well you need better roads, traffic lights, on ramps, etc. you're still going faster.
Use AI to solve the IP bottlenecks or build more features that ear more revenue that buy more ci boxes. Same as if you added 10 devs which you are with AI so why wouldn't some of the dev support costs go up.
Are you not in a place where you can make an efficiency argument to get more ci or optimize? What's a ci box cost?
SoftTalker · 3h ago
Wow, your story gives me flashbacks to the 1990s when I worked in a mainframe environment. Compile jobs submitted by developers were among the lowest priorities. I could make a change to a program, submit a compile job, and wait literally half a day for it to complete. Then I could run my testing, which again might have to wait for hours. I generally had other stuff I could work on during those delays but not always.
rafaelmn · 5h ago
> If anything CI got a lot slower over time as people tried to make builds fully hermetic (so no inter-run caching), and move them from on-prem dedicated hardware to expensive cloud VMs with slow IO, which haven't got much faster over time.
I am guesstimating (based on previous experience self-hosting the runner for MacOS builds) that the project I am working on could get like 2-5x pipeline performance at 1/2 cost just by using self-hosted runners on bare metal rented machines like Hetzner. Maybe I am naive, and I am not the person that would be responsible for it - but having a few bare metal machines you can use in the off hours to run regression tests, for less than you are paying the existing CI runner just for build, that speed up everything massively seems like a pure win for relatively low effort. Like sure everyone already has stuff on their plate and would rather pay external service to do it - but TBH once you have this kind of compute handy you will find uses anyway and just doing things efficiently. And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move. Its usually - hey lets move to this other service that has slightly cheaper instances and a proprietary caching layer so that we can get locked into their CI crap.
Its not like these services have 0 downtime/bug free/do not require integration effort - I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.
mike_hearn · 3h ago
Yep. For my own company I used a bare metal machine in Hetzner running Linux and a Windows VM along with a bunch of old MacBook Pros wired up in the home office for CI.
It works, and it's cheap. A full CI run still takes half an hour on the Linux machine (the product [1] is a kind of build system for shipping desktop apps cross platform, so there's lots of file IO and cryptography involved). The Macs are by far the fastest. The M1 Mac is embarrassingly fast. It can complete the same run in five minutes despite the Hetzner box having way more hardware. In fairness, it's running both a Linux and Windows build simultaneously.
I'm convinced the quickest way to improve CI times in most shops is to just build an in-office cluster of M4 Macs in an air conditioned room. They don't have to be HA. The hardware is more expensive but you don't rent per month, and CI is often bottlenecked on serial execution speed so the higher single threaded performance of Apple Silicon is worth it. Also, pay for a decent CI system like TeamCity. It helps reduce egregious waste from problems like not caching things or not re-using checkout directories. In several years of doing this I haven't had build caching related failures.
> 2-5x pipeline performance at 1/2 cost just by using self-hosted runners on bare metal rented machines like Hetzner
This is absolutely the case. Its a combination of having dedicated CPU cores, dedicated memory bandwidth, and (perhaps most of all) dedicated local NVMe drives. We see a 2x speed up running _within VMs_ on bare metal.
> And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move
We started our current company for this reason [0]. A lot of people know this makes sense on some level, but not many people want to do it. So we say we'll do it for you, give you the engineering time needed to support it, and you'll still save money.
> I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.
It is decreasingly so from what I see. Enough people have been variously burned by public cloud providers to know they are not a panacea. But they just need a little assistance in making the jump.
At the last place I worked at, which was just a small startup with 5 developers, I calculated that a server workstation in the office would be both cheaper and more performant than renting a similar machine in the cloud.
Bare metal makes such a big difference for test and CI scenarios. It even has an integrated a GPU to speed up webdev tests. Good luck finding an affordable machine in the cloud that has a proper GPU for this kind of a use-case
rafaelmn · 3h ago
Is it a startup or small business ? In my book a startup expects to scale and hosting bare metal HW in an office with 5 people means you have to figure everything out again when you get 20/50/100 people - IMO not worth the effort and hosting hardware has zero transferable skills to your product.
Running on managed bare metal servers is theoretically the same as running any other infra provider except you are on the hook for a bit more maintenance, you scale to 20 people you just rent a few more machines. I really do not see many downsides for the build server/test runner scenario.
TheDudeMan · 4h ago
This is because coders didn't spend enough time making their tests efficient. Maybe LLM coding agents can help with that.
ASinclair · 2h ago
Call me a skeptic but I do not believe LLMs are significantly altering the time between commits so much that CI is the problem.
However, improving CI performance is valuable regardless.
blitzar · 6h ago
Yet, now I have added a LLM workflow to my coding the value of my old and mostly useless workflows is now 10x'd.
Git checkpoints, code linting and my naive suite of unit and integration tests are now crucial to my LLM not wasting too much time generating total garbage.
vjerancrnjak · 5h ago
It’s because people don’t know how to write tests. All of the “don’t do N select queries in a for loop” comments made in PRs are completely ignored in tests.
Each test can output many db queries. And then you create multiple cases.
People don’t even know how to write code that just deals with N things at a time.
I am confident that tests run slowly because the code that is tested completely sucks and is not written for batch mode.
Ignoring batch mode, tests are most of the time written in a a way where test cases are run sequentially. Yet attempts to run them concurrently result in flaky tests, because the way you write them and the way you design interfaces does not allow concurrent execution at all.
Another comment, code done by the best AI model still sucks. Anything simple, like a music player with a library of 10000 songs is something it can’t do. First attempt will be horrible. No understanding of concurrent metadata parsing, lists showing 10000 songs at once in UI being slow etc.
So AI is just another excuse for people writing horrible code and horrible tests. If it’s so smart , try to speed up your CI with it.
rapind · 5h ago
> This will make the CI bottleneck even worse.
I agree. I think there are potentially multiple solutions to this since there are multiple bottlenecks. The most obvious is probably network overhead when talking to a database. Another might be storage overhead if storage is being used.
Frankly another one is language. I suspect type-safe, compiled, functional languages are going to see some big advantages here over dynamic interpreted languages. I think this is the sweet spot that grants you a ton of performance over dynamic languages, gives you more confidence in the models changes, and requires less testing.
Faster turn-around, even when you're leaning heavily on AI, is a competitive advantage IMO.
mike_hearn · 3h ago
It could go either way. Depends very much on what kind of errors LLMs make.
Type safe languages in theory should do well, because you get feedback on hallucinated APIs very fast. But if the LLM generally writes code that compiles, unless the compiler is very fast you might get out-run by an LLM just spitting out JavaScript at high speed, because it's faster to run the tests than wait for the compile.
The sweet spot is probably JIT compiled type safe languages. Java, Kotlin, TypeScript. The type systems can find enough bugs to be worth it, but you don't have to wait too long to get test results either.
gdiamos · 2h ago
This sounds like a strawman.
GPUs can do 1 million trillion instructions per second.
Are you saying it’s impossible to write a test that finishes in less than one second on that machine?
Is that a fundamental limitation or an incredibly inefficient test?
nradclif · 6m ago
A million trillion operations per second is literally an exaflop. That's one hell of a GPU you have.
piva00 · 7h ago
I haven't worked in places using off-the-shelf/SaaS CI in more than a decade so I feel my experience has been quite the opposite from yours.
We always worked hard to make the CI/CD pipeline as fast as possible. I personally worked on those kind of projects at 2 different employers as a SRE: a smaller 300-people shop which I was responsible for all their infra needs (CI/CD, live deployments, migrated later to k8s when it became somewhat stable, at least enough for the workloads we ran, but still in its beta-days), then at a different employer some 5k+ strong working on improving the CI/CD setup which used Jenkins as a backend but we developed a completely different shim on top for developer experience while also working on a bespoke worker scheduler/runner.
I haven't experienced a CI/CD setup that takes longer than 10 minutes to run in many, many years, got quite surprised reading your comment and feeling spoiled I haven't felt this pain for more than a decade, didn't really expect it was still an issue.
mike_hearn · 7h ago
I think the prevalence of teams having a "CI guy" who often is developing custom glue, is a sign that CI is still not really working as well as it should given the age of the tech.
I've done a lot of work on systems software over the years so there's often tests that are very I/O or computation heavy, lots of cryptography, or compilation, things like that. But probably there are places doing just ordinary CRUD web app development where there's Playwright tests or similar that are quite slow.
A lot of the problems are cultural. CI times are a commons, so it can end in tragedy. If everyone is responsible for CI times then nobody is. Eventually management gets sick of pouring money into it and devs learn to juggle stacks of PRs on top of each other. Sometimes you get a lot of pushback on attempts to optimize CI because some devs will really scream about any optimization that might potentially go wrong (e.g. depending on your build system cache), even if caching nothing causes an explosion in CI costs. Not their money, after all.
trhway · 3h ago
>There's no point having an agent that can write code 100x faster than a human if every change takes an hour to test.
Testing every change incrementally is a vestige of the code being done by humans (and thus of the current approach where AI helps and/or replaces one given human), in small increments at that, and of the failures being analyzed by individual humans who can keep in their head only limited number of things/dependencies at once.
droopyEyelids · 6h ago
In most companies the CI/Dev Tools team is a career dead end. There is no possibility to show a business impact, it's just a money pit that leadership can't/won't understand (and if they do start to understand it, then it becomes _their_ money pit, which is a career dead end for them) So no one who has their head on straight wants to spend time improving it.
And you can't even really say it's a short sighted attitude. It definitely is from a developer's perspective, and maybe it is for the company if dev time is what decides the success of the business overall.
MangoToupe · 2h ago
> it's just a money pit that leadership can't/won't understand
In my experience it's the opposite: they want more automated testing, but don't want to pay for the friction this causes on productivity.
yieldcrv · 6h ago
then kill the CI/CD
these redundant processes are for human interoperability
mathiaspoint · 7h ago
Good God I hate CI. Just let me run the build automation myself dammit! If you're worried about reproducibility make it reproducible and hash the artifacts, make people include the hash in the PR comment if you want to enforce it.
The amount of time people waste futzing around in eg Groovy is INSANE and I'm honestly inclined to reject job offers from companies that have any serious CI code at this point.
mseri · 5m ago
Sounds all cool and interesting, however:
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true_blue · 6h ago
I tried the playground and got a strange response. I asked for a regex pattern, and the model gave itself a little game-plan, then it wrote the pattern and started to write tests for it. But it never stopped writing tests. It continued to write tests of increasing size until I guess it reached a context limit and the answer was canceled. Also, for each test it wrote, it added a comment about if the test should pass or fail, but after about the 30th test, it started giving the wrong answer for those too, saying that a test should fail when actually it should pass if the pattern is correct. And after about the 120th test, the tests started to not even make sense anymore. They were just nonsense characters until the answer got cut off.
The pattern it made was also wrong, but I think the first issue is more interesting.
beders · 2h ago
I think that's a prime example showing that token prediction simply isn't good enough for correctness. It never will be. LLMs are not designed to reason about code.
ianbicking · 2h ago
FWIW, I remember regular models doing this not that long ago, sometimes getting stuck in something like an infinite loop where they keep producing output that is only a slight variation on previous output.
data-ottawa · 24m ago
if you shrink the context window on most models you'll get this type of behaviour. If you go too small you end up with basically gibberish even on modern models like Gemini 2.5.
Mercury has a 32k context window according to the paper, which could be why it does that.
fiatjaf · 6h ago
This is too funny to be true.
mxs_ · 46m ago
In their tech report, they say this is based on:
> "Our methods extend [28] through careful modifications to the data and computation to scale up learning."
My goal was making it as clean and readable as possible.
I also implemented the more complex denoising strategy they described (but didn't implement).
It runs on a single GPU in a few hours on a toy dataset.
fastball · 7h ago
ICYMI, DeepMind also has a Gemini model that is diffusion-based[1]. I've tested it a bit and while (like with this model) the speed is indeed impressive, the quality of responses was much worse than other Gemini models in my testing.
From my minor testing I agree that it's crazy fast and not that good at being correct
tripplyons · 4h ago
Is the Gemini Diffusion demo free? I've been on the waitlist for it for a few weeks now.
armcat · 54m ago
I've been looking at the code on their chat playground, https://chat.inceptionlabs.ai/, and they have a helper function `const convertOpenAIMessages = (convo) => { ... }`, which also contains `models: ['gpt-3.5-turbo']`. I also see in API response: `"openai": true`. Is it actually using OpenAI, or is it actually calling its dLLM? Does anyone know?
Also: you can turn on "Diffusion Effect" in the top-right corner, but this just seems to be an "animation gimmick" right?
Alifatisk · 49m ago
The speed of the response is waaay to quick for using OpenAi as backend, it's almost instant!
armcat · 32m ago
I've been asking bespoke questions and the timing is >2 seconds, and slower than what I get for the same questions to ChatGPT (using gpt-4.1-mini). I am looking at their call stack and what I see: "verifyOpenAIConnection()", "generateOpenAIChatCompletion()", "getOpenAIModels()", etc. Maybe it's just so it's compatible with OpenAI API?
M4v3R · 2h ago
I am personally very excited for this development. Recently I AI-coded a simple game for a game jam and half the time was spent waiting for the AI agent to finish its work so I can test it. If instead of waiting 1-2 minutes for every prompt to be executed and implemented I could wait 10 seconds instead that would be literally game changing. I could test 5-10 different versions of the same idea in the time it took me to test one with the current tech.
Of course this model is not as advanced yet for this to be feasible, but so was Claude 3.0 just over a year ago. This will only get better over time I’m sure. Exciting times ahead of us.
Alifatisk · 50m ago
Love the ui in the playground, it reminds me of Qwen chat.
We have reached a point where the bottlenecks in genAI is not the knowledge or accuracy, it is the context window and speed.
Luckily, Google (and Meta?) has pushed the limits of the context window to about 1 million tokens which is incredible. But I feel like todays options are still stuck about ~128k token window per chat, and after that it starts to forget.
Another issue is the time time it takes for inference AND reasoning. dLLMs is an interesting approach at this. I know we have Groqs hardware aswell.
I do wonder, can this be combined with Groqs hardware? Would the response be instant then?
How many tokens can each chat handle in the playground? I couldn't find so much info about it.
Which model is it using for inference?
Also, is the training the same on dLLMs as on the standardised autoregressive LLMs? Or is the weights and models completely different?
chc4 · 8h ago
Using the free playground link, and it is in fact extremely fast. The "diffusion mode" toggle is also pretty neat as a visualization, although I'm not sure how accurate it is - it renders as line noise and then refines, while in reality presumably those are tokens from an imprecise vector in some state space that then become more precise until it's only a definite word, right?
icyfox · 5h ago
Some text diffusion models use continuous latent space but they historically haven't done that well. Most the ones we're seeing now typically are trained to predict actual token output that's fed forward into the next time series. The diffusion property comes from their ability to modify previous timesteps to converge on the final output.
The pricing is a little on the higher side. Working on a performance-sensitive application, I tried Mercury and Groq (Llama 3.1 8b, Llama 4 Scout) and the performance was neck-and-neck but the pricing was way better for Groq.
But I'll be following diffusion models closely, and I hope we get some good open source ones soon. Excited about their potential.
tripplyons · 4h ago
Good to know. I didn't realize how good the pricing is on Groq!
tlack · 2h ago
If your application is pricing sensitive, check out DeepInfra.com - they have a variety of models in the pennies-per-mil range. Not quite as fast as Mercury, Groq or Samba Nova though.
(I have no affiliation with this company aside from being a happy customer the last few years)
ianbicking · 2h ago
For something a little different than a coding task, I tried using it in my game: https://www.playintra.win/ (in settings you can select Mercury, the game uses OpenRouter)
At first it seemed pretty competent and of course very fast, but it seemed to really fall apart as the context got longer. The context in this case is a sequence of events and locations, and it needs to understand how those events are ordered and therefore what the current situation and environment are (though there's also lots of hints in the prompts to keep it focused on the present moment). It's challenging, but lots of smaller models can pull it off.
But also a first release and a new architecture. Maybe it just needs more time to bake (GPT 3.5 couldn't do these things either). Though I also imagine it might just perform _differently_ from other LLMs, not really on the same spectrum of performance, and requiring different prompting.
amelius · 7h ago
Damn, that is fast. But it is faster than I can read, so hopefully they can use that speed and turn it into better quality of the output. Because otherwise, I honestly don't see the advantage, in practical terms, over existing LLMs. It's like having a TV with a 200Hz refresh rate, where 100Hz is just fine.
pmxi · 6h ago
There are plenty of LLM use cases where the output isn’t meant to be read by a human at all. e.g:
parsing unstructured text into structured formats like JSON
translating between natural or programming languages
serving as a reasoning step in agentic systems
So even if it’s “too fast to read,” that speed can still be useful
amelius · 6h ago
Sure, but I was talking about the chat interface, sorry if that was not clear.
Legend2440 · 5h ago
This lets you do more (potentially a lot more) reasoning steps and tool calls before answering.
ceroxylon · 4h ago
The output is very fast but many steps backwards in all of my personal benchmarks. Great tech but not usable in production when it is over 60% hallucinations.
mike_hearn · 2h ago
That might just depend on how big it is/how much money was spent on training. The neural architecture can clearly work. Beyond that catching up may be just a matter of effort.
gdiamos · 5h ago
I think the LLM dev community is underestimating these models. E.g. there is no LLM inference framework that supports them today.
Yes the diffusion foundation models have higher cross entropy. But diffusion LLMs can also be post trained and aligned, which cuts the gap.
IMO, investing in post training and data is easier than forcing GPU vendors to invest in DRAM to handle large batch sizes and forcing users to figure out how to batch their requests by 100-1000x. It is also purely in the hands of LLM providers.
mathiaspoint · 4h ago
You can absolutely tune causal LLMs. In fact the original idea with GPTs was that you had to tune them before they'd be useful for anything.
gdiamos · 2h ago
Yes I agree you can tune autoregressive LLMs
You can also tune diffusion LLMs
After doing so, the diffusion LLM will be able to generate more tokens/sec during inference
jonplackett · 4h ago
Wow, this thing is really quite smart.
I was expecting really crappy performance but just chatting to it, giving it some puzzles, it feels very smart and gets a lot of things right that a lot of other models don't.
mynti · 8h ago
is there a kind of nanogpt for diffusion language models? i would love to understand them better
Reinforcement learning really helped Transformer based LLMs evolve in terms of quality and reasoning which we saw as DeepSeek was launched. I am curious if what this is is equivalent to an early GPT 4o that has not yet reaped the benefits of add-on technologies that helped improve the quality?
Code output is verifiable in multiple ways. Combine that with this kind of speed (and far faster in future) and you can brute force your way to a killer app in a few minutes.
OneOffAsk · 3h ago
Yes, exactly. The demo of Gemini's Diffusion model [0] was really eye-opening to me in this regard. Since then, I've been convinced the future of lots of software engineering is basically UX and SQA: describe the desired states, have an LLM fill in the gaps based on its understanding of human intent, and unit test it to verify. Like most engineering fields, we'll have an empirical understanding of systems as opposed to the analytical understanding of code we have today. I'd argue most complex software is already only approximately understood even before LLMs. I doubt the quality of software will go up (in fact the opposite), but I think this work will scale much better and be much, much more boring.
I've used mercury quite a bit in my commit message generator. I noticed it would always produce the exact same response if you ran it multiple times, and increasing temperature didn't affect it. To get some variability I added a $(uuidgen) to the prompt. Then I could run it again for a new response if I didn't like the first.
We have used their LLM in our company and it's great!
From Accuracy to speed of response generation, this model seems very promising!
seydor · 6h ago
I wonder if diffusion llms solve the hallucination problem more effectively. In the same way that image models learned to create less absurd images, dllms can perhaps learn to create sensical responses more predictably
nashashmi · 6h ago
I guess this makes specific language patterns cheaper and more artistic language patterns more expensive. This could be a good way to limit pirated and masqueraded materials submitted by students.
thelastbender12 · 7h ago
The speed here is super impressive! I am curious - are there any qualitative ways in which modeling text using diffusion differs from that using autoregressive models? The kind of problems it works better on, creativity, and similar.
orbital-decay · 6h ago
One works in the coarse-to-fine direction, another works start-to-end. Which means different directionality biases, at least. Difference in speed, generalization, etc. is less clear and needs to be proven in practice, as fundamentally they are closer than it seems. Diffusion models have some well-studied shortcuts to trade speed for quality, but nothing stops you from implementing the same for the other type.
ekunazanu · 45m ago
I once read that diffusion is essentially just autoregression in the frequency domain. Honestly, that comparison didn’t seem too far off.
TechDebtDevin · 7h ago
Oddly fast, almost instantaneous.
luckystarr · 8h ago
I'm kind of impressed by the speed of it. I told it to write a MQTT topic pattern matcher based on a Trie and it spat out something reasonable on first try. It hat a few compilation issues though, but fair enough.
empiko · 7h ago
I strongly believe that this will be a really important technique in the near future. The cost saving this might create is mouth watering.
NitpickLawyer · 6h ago
> I strongly believe that this will be a really important technique in the near future.
I share the same belief, but regardless of cost. What excites me is the ability to "go both ways", edit previous tokens after others have been generated, using other signals as "guided generation", and so on. Next token prediction works for "stories", but diffusion matches better with "coding flows" (i.e. going back and forth, add something, come back, import something, edit something, and so on).
It would also be very interesting to see how applying this at different "abstraction layers" would work. Say you have one layer working on ctags, one working on files, and one working on "functions". And they all "talk" to each other, passing context and "re-diffusing" their respective layers after each change. No idea where the data for this would come, maybe from IDEs?
sansseriff · 58m ago
I wonder if there's a way to do diffusion within some sort of schema-defined or type constrained space.
A lot of people these days are asking for structured output from LLMs so that a schema is followed. Even if you train on schema-following with a transformer, you're still just 'hoping' in the end that the generated json matches the schema.
I'm not a diffusion excerpt, but maybe there's a way to diffuse one value in the 'space' of numbers, and another value in the 'space' of all strings, as required by a schema:
I'm not sure how far this could lead. Could you diffuse more complex schemas that generalize to a arbitrary syntax tree? E.g. diffuse some code in a programming language that is guaranteed to be type-safe?
storus · 6h ago
Can Mercury use tools? I haven't seen it described anywhere. How about streaming with tools?
awaymazdacx5 · 6h ago
Having token embeddings with diffusion models, for 16x16 transformer encoding. Image is tokenized before transformers compile it. If decomposed virtualization modulates according to a diffusion model.
baalimago · 7h ago
I, for one, am willing to trade accuracy for speed. I'd rather have 10 iterations of poor replies which forces me to ask the right question than 1 reply which takes 10 times as long and _maybe_ is good, since it tries to reason about my poor question.
PaulHoule · 7h ago
Personally I like asking coding agents a question and getting an answer back immediately. Systems like Junie that go off and research a bunch of irrelevant things than ask permission than do a lot more irrelevant research, ask more permission and such and then 15 minutes later give you a mountain of broken code are a waste of time if you ask me. (Even if you give permission in advance)
mmaunder · 4h ago
Holy shit that is fast. Try the playground. You need to get that visceral experience to truly appreciate what the future looks like.
Maybe I've just got unlucky in the past, but in most projects I worked on a lot of developer time was wasted on waiting for PRs to go green. Many runs end up bottlenecked on I/O or availability of workers, and so changes can sit in queues for hours, or they flake out and everything has to start again.
As they get better coding agents are going to be assigned simple tickets that they turn into green PRs, with the model reacting to test failures and fixing them as they go. This will make the CI bottleneck even worse.
It feels like there's a lot of low hanging fruit in most project's testing setups, but for some reason I've seen nearly no progress here for years. It feels like we kinda collectively got used to the idea that CI services are slow and expensive, then stopped trying to improve things. If anything CI got a lot slower over time as people tried to make builds fully hermetic (so no inter-run caching), and move them from on-prem dedicated hardware to expensive cloud VMs with slow IO, which haven't got much faster over time.
Mercury is crazy fast and in a few quick tests I did, created good and correct code. How will we make test execution keep up with it?
I don't understand this. Developer time is so much more expensive than machine time. Do companies not just double their CI workers after hearing people complain? It's just a throw-more-resources problem. When I was at Google, it was somewhat common for me to debug non-deterministic bugs such as a missing synchronization or fence causing flakiness; and it was common to just launch 10000 copies of the same test on 10000 machines to find perhaps a single digit number of failures. My current employer has a clunkier implementation of the same thing (no UI), but there's also a single command to launch 1000 test workers to run all tests from your own checkout. The goal is to finish testing a 1M loc codebase in no more than five minutes so that you get quick feedback on your changes.
> make builds fully hermetic (so no inter-run caching)
These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.
In more common scenarios that represent 95% of the software industry CI budgets are fixed, clusters are sized to be busy most of the time, and you cannot simply launch 10,000 copies of the same test on 10,000 machines. And even despite that these CI clusters can easily burn through the equivalent of several SWE salaries.
> These are orthogonal. You want maximum deterministic CI steps so that you make builds fully hermetic and cache every single thing.
Again, that's how companies like Google do it. In normal companies, build caching isn't always perfectly reliable, and if CI runs suffer flakes due to caching then eventually some engineer is gonna get mad and convince someone else to turn the caching off. Blaze goes to extreme lengths to ensure this doesn't happen, and Google spends extreme sums of money on helping it do that (e.g. porting third party libraries to use Blaze instead of their own build system).
In companies without money printing machines, they sacrifice caching to get determinism and everything ends up slow.
I've written a limited number of systems that needed tests that probe for race conditions by doing something like having 3000 threads run a random workload for 40 seconds. I'm proud of that "SuperHammer" test on a certain level but boy did I hate having to run it with every build.
Maybe that affects less devs who don't need to test on actual hardware but plenty of apps do. Pretty much anything that touches a GPU driver for example like a game.
Even then, there are other factors:
* You might need commercial licenses. It may be very cheap to run open source code 10000x, but guess how much 10000 Questa licenses cost.
* Moores law is dead Amdahl's law very much isn't. Not everything is embarrassingly parallel.
* Some people care about the environment. I worked at a company that spent 200 CPU hours on every single PR (even to fix typos; I failed to convince them they were insane for not using Bazel or similar). That's a not insignificant amount of CO2.
Yes, but the OP specifically is talking about CI for large numbers of pull requests, which should be very parallelizable (I can imagine exceptions, but only with anti-patterns, e.g. if your test pipeline makes some kind of requests to something that itself isn't scalable).
I think the real issue is that developers waiting for PRs to go green are taking a coffee break between tasks, not sitting idly getting annoyed. If that's the case you're cutting into rest time and won't get much value out of optimizing this.
Anyway I don't see how that solves any of the issues except maybe cost to some degree (but maybe not; cloud is expensive).
I'd personally agree. But this sounds like the kind of thing that, at many companies, could be a real challenge.
Ultimately, you can measure dollars spent on CI workers. It's much harder and less direct to quantify the cost of not having them (until, for instance, people start taking shortcuts with testing and a regression escapes to production).
That kind of asymmetry tends, unless somebody has a strong overriding vision of where the value really comes from, to result in penny pinching on the wrong things.
The problem is that if you let people spend the companies money without any checks or balances they'll just blow through unlimited amounts of it. That's why companies always have lots of procedures and policies around expense reporting. There's no upper limit to how much money developers will spend on cloud hardware given the chance, as the example above of casually running a test 10,000 times in parallel demonstrates nicely.
CI doesn't require you to fill out an expense report every time you run a PR thank goodness, but there still has to be a way to limit financial liability. Usually companies do start out by doubling cluster sizes a few times, but each time it buys a few months and then the complaints return. After a few rounds of this managers realize that demand is unlimited and start pushing back on always increasing the budget. Devs get annoyed and spend an afternoon on optimizations, suddenly times are good again.
The meme on HN is that developer time is always more expensive than machine time, but I've been on both sides of this and seen how the budgets work out. It's often not true, especially if you use clouds like Azure which are overloaded and expensive, or have plenty of junior devs, and/or teams outside the US where salaries are lower. There's often a lot of low hanging fruit in test times so it can make sense to optimize, even so, huge waste is still the order of the day.
They do not.
I don't know if it's a matter of justifying management levels, but these discussions are often drawn out and belabored in my experience. By the time you get approval, or even worse, rejected, for asking for more compute (or whatever the ask is), you've spent way more money on the human resource time than you would ever spend on the requested resources.
And when we manage to make a proper request it ends up being rejected anyways as many other teams are asking for the same thing and "the company has limited resources". Duh.
CI caching is, apparently, extremely difficult. Why spend a couple of hours learning about your CI caches when you can just download and build the same pinned static library a billion times? The server you're downloading from is (of course) someone else's problem and you don't care about wasting their resources either. The power you're burning by running CI for there hours instead of one is also someone else's problem. Compute time? Someone else's problem. Cloud costs? You bet it's someone else's problem.
Sure, some things you don't want to cache. I always do a 100% clean build when cutting a release or merging to master. But for intermediate commits on a feature branch? Literally no reason not to cache builds the exact same way you do on your local machine.
On the other hand I've seen many overcapitalized pre-launch startups go for months with a $20,000+ AWS bill without thinking about it then suddenly panic about what they're spending; they'd find tens of XXXXL instances spun up doing nothing, S3 buckets full of hundreds of terabytes of temp files that never got cleared out, etc. With basic due diligence they could have gotten that down to $2k a month, somebody obsessive about cost control could have done even better.
1. As implementation phase gets faster, the bottleneck could actually switch to PM. In which case, changes will be more serial, so a lot fewer conflicts to worry about.
2. I think we could see a resurrection of specs like TLA+. Most engineers don't bother with them, but I imagine code agents could quickly create them, verify the code is consistent with them, and then require fewer full integration tests.
3. When background agents are cleaning up redundant code, they can also clean up redundant tests.
4. Unlike human engineering teams, I expect AIs to work more efficiently on monoliths than with distributed microservices. This could lead to better coverage on locally runnable tests, reducing flakes and CI load.
5. It's interesting that even as AI increases efficiency, that increased velocity and sheer amount of code it'll write and execute for new use cases will create its own problems that we'll have to solve. I think we'll continue to have new problems for human engineers to solve for quite some time.
No, this is common. The devs just haven't grokked dependency inversion. And I think the rate of new devs entering the workforce will keep it that way forever.
Here's how to make it slow:
* Always refer to "the database". You're not just storing and retrieving objects from anywhere - you're always using the database.
* Work with statements, not expressions. Instead of "the balance is the sum of the transactions", execute several transaction writes (to the database) and read back the resulting balance. This will force you to sequentialise the tests (simultaneous tests would otherwise race and cause flakiness) plus you get to write a bunch of setup and teardown and wipe state between tests.
* If you've done the above, you'll probably need to wait for state changes before running an assertion. Use a thread sleep, and if the test is ever flaky, bump up the sleep time and commit it if the test goes green again.
I really really don't understand the hubris around llm tooling, and don't see it catching on outside of personal projects and small web apps. These things don't handle complex systems well at all, you would have to put a gun in my mouth to let one of these things work on an important repo of mine without any supervision... And if I'm supervising the LLM I might as well do it myself, because I'm going to end up redoing 50% of its work anyways..
The post you are responding to literally acknowledges that LLMs are useful in certain roles in coding in the first sentence.
> Like how many people need to say that they find it makes them more productive before you'll shift your perspective?
Argumentum ad populum is not a good way of establishing fact claims beyond the fact of a belief being popular.
If everyone has an opinion different to mine, I dont instantly change my opinion, but I do try and investigate the source of the difference, to find out what I'm missing or what they are missing.
The polarisation between people that find LLMs useful or not is very similar to the polarisation between people that find automated testing useful or not, and I have a suspicion they have the same underlying cause.
So far what I see is that if I provide lots of context and clear instructions to a mostly non-logical area of code, I can speed myself up about 20-40%, but only works in about 30-50% of the problems I solve day to day at a day job.
So basically - it’s about a rough 20% improvement in my productivity - because I spend most of my time of the difficult things it can’t do anyway.
Meanwhile these companies are raising billion dollar seed rounds and telling us that all programming will be done by AI by next year.
Just because two people are fixing something on the whole doesn't mean the same tool will hold fine. Gum, pushpin, nail, screw,bolts?
The parent thread did mention they use LLM successfully in small side project.
LLMs are useful, just not for every task and price point.
Code is a liability. Code you didn't write is a ticking time bomb.
It’s self delusion. And also the pace of AI is so fast he may not be aware of how fast LLMs are integrating into our coding environments. Like 1 year ago what he said could be somewhat true but right now what he said is clearly not true at all.
Probably, Mercury isn't as good at coding as Claude is. But even if it's not, there's lots of small tasks that LLMs can do without needing senior engineer level skills. Adding test coverage, fixing low priority bugs, adding nice animations to the UI etc. Stuff that maybe isn't critical so if a PR turns up and it's DOA you just close it, but which otherwise works.
Note that many projects already use this approach with bots like Renovate. Such bots also consume a ton of CI time, but it's generally worth it.
I'm not that into "prompt engineering" but tests seem like a big opportunity for improvement. Maybe something like (but much more thorough):
1. "Create a document describing all real-world actions which could lead to the code being used. List all methods/code which gets called before it (in order) along with their exact parameters and return value. Enumerate all potential edge cases and errors that could occur and if it ends up influencing this task. After that, write a high-level overview of what need to occur in this implementation. Don't make it top down where you think about what functions/classes/abstractions which are created, just the raw steps that will need to occur" 2. Have it write the tests 3. Have it write the code
Maybe TDD ends up worse but I suspect the initial plan which is somewhat close to code makes that not the case
Writing the initial doc yourself would definitely be better, but I suspect just writing one really good one, then giving it as an example in each subsequent prompt captures a lot of the improvement
Make it run tests after it changes your code and either confirm it didnt break anything or go back and try again.
Use AI to solve the IP bottlenecks or build more features that ear more revenue that buy more ci boxes. Same as if you added 10 devs which you are with AI so why wouldn't some of the dev support costs go up.
Are you not in a place where you can make an efficiency argument to get more ci or optimize? What's a ci box cost?
I am guesstimating (based on previous experience self-hosting the runner for MacOS builds) that the project I am working on could get like 2-5x pipeline performance at 1/2 cost just by using self-hosted runners on bare metal rented machines like Hetzner. Maybe I am naive, and I am not the person that would be responsible for it - but having a few bare metal machines you can use in the off hours to run regression tests, for less than you are paying the existing CI runner just for build, that speed up everything massively seems like a pure win for relatively low effort. Like sure everyone already has stuff on their plate and would rather pay external service to do it - but TBH once you have this kind of compute handy you will find uses anyway and just doing things efficiently. And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move. Its usually - hey lets move to this other service that has slightly cheaper instances and a proprietary caching layer so that we can get locked into their CI crap.
Its not like these services have 0 downtime/bug free/do not require integration effort - I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.
It works, and it's cheap. A full CI run still takes half an hour on the Linux machine (the product [1] is a kind of build system for shipping desktop apps cross platform, so there's lots of file IO and cryptography involved). The Macs are by far the fastest. The M1 Mac is embarrassingly fast. It can complete the same run in five minutes despite the Hetzner box having way more hardware. In fairness, it's running both a Linux and Windows build simultaneously.
I'm convinced the quickest way to improve CI times in most shops is to just build an in-office cluster of M4 Macs in an air conditioned room. They don't have to be HA. The hardware is more expensive but you don't rent per month, and CI is often bottlenecked on serial execution speed so the higher single threaded performance of Apple Silicon is worth it. Also, pay for a decent CI system like TeamCity. It helps reduce egregious waste from problems like not caching things or not re-using checkout directories. In several years of doing this I haven't had build caching related failures.
[1] https://hydraulic.dev/
This is absolutely the case. Its a combination of having dedicated CPU cores, dedicated memory bandwidth, and (perhaps most of all) dedicated local NVMe drives. We see a 2x speed up running _within VMs_ on bare metal.
> And knowing how to deal with bare metal/utilize this kind of compute sounds generally useful skill - but I rarely encounter people enthusiastic about making this kind of move
We started our current company for this reason [0]. A lot of people know this makes sense on some level, but not many people want to do it. So we say we'll do it for you, give you the engineering time needed to support it, and you'll still save money.
> I just don't see why going bare metal is always such a taboo topic even for simple stuff like builds.
It is decreasingly so from what I see. Enough people have been variously burned by public cloud providers to know they are not a panacea. But they just need a little assistance in making the jump.
[0] - https://lithus.eu
Bare metal makes such a big difference for test and CI scenarios. It even has an integrated a GPU to speed up webdev tests. Good luck finding an affordable machine in the cloud that has a proper GPU for this kind of a use-case
Running on managed bare metal servers is theoretically the same as running any other infra provider except you are on the hook for a bit more maintenance, you scale to 20 people you just rent a few more machines. I really do not see many downsides for the build server/test runner scenario.
However, improving CI performance is valuable regardless.
Git checkpoints, code linting and my naive suite of unit and integration tests are now crucial to my LLM not wasting too much time generating total garbage.
Each test can output many db queries. And then you create multiple cases.
People don’t even know how to write code that just deals with N things at a time.
I am confident that tests run slowly because the code that is tested completely sucks and is not written for batch mode.
Ignoring batch mode, tests are most of the time written in a a way where test cases are run sequentially. Yet attempts to run them concurrently result in flaky tests, because the way you write them and the way you design interfaces does not allow concurrent execution at all.
Another comment, code done by the best AI model still sucks. Anything simple, like a music player with a library of 10000 songs is something it can’t do. First attempt will be horrible. No understanding of concurrent metadata parsing, lists showing 10000 songs at once in UI being slow etc.
So AI is just another excuse for people writing horrible code and horrible tests. If it’s so smart , try to speed up your CI with it.
I agree. I think there are potentially multiple solutions to this since there are multiple bottlenecks. The most obvious is probably network overhead when talking to a database. Another might be storage overhead if storage is being used.
Frankly another one is language. I suspect type-safe, compiled, functional languages are going to see some big advantages here over dynamic interpreted languages. I think this is the sweet spot that grants you a ton of performance over dynamic languages, gives you more confidence in the models changes, and requires less testing.
Faster turn-around, even when you're leaning heavily on AI, is a competitive advantage IMO.
Type safe languages in theory should do well, because you get feedback on hallucinated APIs very fast. But if the LLM generally writes code that compiles, unless the compiler is very fast you might get out-run by an LLM just spitting out JavaScript at high speed, because it's faster to run the tests than wait for the compile.
The sweet spot is probably JIT compiled type safe languages. Java, Kotlin, TypeScript. The type systems can find enough bugs to be worth it, but you don't have to wait too long to get test results either.
GPUs can do 1 million trillion instructions per second.
Are you saying it’s impossible to write a test that finishes in less than one second on that machine?
Is that a fundamental limitation or an incredibly inefficient test?
We always worked hard to make the CI/CD pipeline as fast as possible. I personally worked on those kind of projects at 2 different employers as a SRE: a smaller 300-people shop which I was responsible for all their infra needs (CI/CD, live deployments, migrated later to k8s when it became somewhat stable, at least enough for the workloads we ran, but still in its beta-days), then at a different employer some 5k+ strong working on improving the CI/CD setup which used Jenkins as a backend but we developed a completely different shim on top for developer experience while also working on a bespoke worker scheduler/runner.
I haven't experienced a CI/CD setup that takes longer than 10 minutes to run in many, many years, got quite surprised reading your comment and feeling spoiled I haven't felt this pain for more than a decade, didn't really expect it was still an issue.
I've done a lot of work on systems software over the years so there's often tests that are very I/O or computation heavy, lots of cryptography, or compilation, things like that. But probably there are places doing just ordinary CRUD web app development where there's Playwright tests or similar that are quite slow.
A lot of the problems are cultural. CI times are a commons, so it can end in tragedy. If everyone is responsible for CI times then nobody is. Eventually management gets sick of pouring money into it and devs learn to juggle stacks of PRs on top of each other. Sometimes you get a lot of pushback on attempts to optimize CI because some devs will really scream about any optimization that might potentially go wrong (e.g. depending on your build system cache), even if caching nothing causes an explosion in CI costs. Not their money, after all.
Testing every change incrementally is a vestige of the code being done by humans (and thus of the current approach where AI helps and/or replaces one given human), in small increments at that, and of the failures being analyzed by individual humans who can keep in their head only limited number of things/dependencies at once.
And you can't even really say it's a short sighted attitude. It definitely is from a developer's perspective, and maybe it is for the company if dev time is what decides the success of the business overall.
In my experience it's the opposite: they want more automated testing, but don't want to pay for the friction this causes on productivity.
these redundant processes are for human interoperability
The amount of time people waste futzing around in eg Groovy is INSANE and I'm honestly inclined to reject job offers from companies that have any serious CI code at this point.
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The pattern it made was also wrong, but I think the first issue is more interesting.
Mercury has a 32k context window according to the paper, which could be why it does that.
> "Our methods extend [28] through careful modifications to the data and computation to scale up learning."
[28] is Lou et al. (2023), the "Score Entropy Discrete Diffusion" (SEDD) model (https://arxiv.org/abs/2310.16834).
I wrote the first (as far as I can tell) independent from-scratch reimplementation of SEDD:
https://github.com/mstarodub/dllm
My goal was making it as clean and readable as possible. I also implemented the more complex denoising strategy they described (but didn't implement).
It runs on a single GPU in a few hours on a toy dataset.
[1] https://deepmind.google/models/gemini-diffusion/
Also: you can turn on "Diffusion Effect" in the top-right corner, but this just seems to be an "animation gimmick" right?
Of course this model is not as advanced yet for this to be feasible, but so was Claude 3.0 just over a year ago. This will only get better over time I’m sure. Exciting times ahead of us.
We have reached a point where the bottlenecks in genAI is not the knowledge or accuracy, it is the context window and speed.
Luckily, Google (and Meta?) has pushed the limits of the context window to about 1 million tokens which is incredible. But I feel like todays options are still stuck about ~128k token window per chat, and after that it starts to forget.
Another issue is the time time it takes for inference AND reasoning. dLLMs is an interesting approach at this. I know we have Groqs hardware aswell.
I do wonder, can this be combined with Groqs hardware? Would the response be instant then?
How many tokens can each chat handle in the playground? I couldn't find so much info about it.
Which model is it using for inference?
Also, is the training the same on dLLMs as on the standardised autoregressive LLMs? Or is the weights and models completely different?
I have an explanation about one of these recent architectures that seems similar to what Mercury is doing under the hood here: https://pierce.dev/notes/how-text-diffusion-works/
However, is this what arXiv is for? It seems more like marketing their links than research. Please correct me if I'm wrong/naive on this topic.
US$0.000001 per output token ($1/M tokens)
US$0.00000025 per input token ($0.25/M tokens)
https://platform.inceptionlabs.ai/docs#models
But I'll be following diffusion models closely, and I hope we get some good open source ones soon. Excited about their potential.
(I have no affiliation with this company aside from being a happy customer the last few years)
At first it seemed pretty competent and of course very fast, but it seemed to really fall apart as the context got longer. The context in this case is a sequence of events and locations, and it needs to understand how those events are ordered and therefore what the current situation and environment are (though there's also lots of hints in the prompts to keep it focused on the present moment). It's challenging, but lots of smaller models can pull it off.
But also a first release and a new architecture. Maybe it just needs more time to bake (GPT 3.5 couldn't do these things either). Though I also imagine it might just perform _differently_ from other LLMs, not really on the same spectrum of performance, and requiring different prompting.
parsing unstructured text into structured formats like JSON
translating between natural or programming languages
serving as a reasoning step in agentic systems
So even if it’s “too fast to read,” that speed can still be useful
Yes the diffusion foundation models have higher cross entropy. But diffusion LLMs can also be post trained and aligned, which cuts the gap.
IMO, investing in post training and data is easier than forcing GPU vendors to invest in DRAM to handle large batch sizes and forcing users to figure out how to batch their requests by 100-1000x. It is also purely in the hands of LLM providers.
You can also tune diffusion LLMs
After doing so, the diffusion LLM will be able to generate more tokens/sec during inference
I was expecting really crappy performance but just chatting to it, giving it some puzzles, it feels very smart and gets a lot of things right that a lot of other models don't.
News coverage from February: https://techcrunch.com/2025/02/26/inception-emerges-from-ste...
[0] https://simonwillison.net/2025/May/21/gemini-diffusion/
The linked whitepaper is pretty useless, and I am saying as a big fan of diffusion-transformers-for-not-just-images-or-videos approach.
Also, Gemini Diffusion ([1]) is way better at coding than Mercury offering.
1. https://deepmind.google/models/gemini-diffusion/
I share the same belief, but regardless of cost. What excites me is the ability to "go both ways", edit previous tokens after others have been generated, using other signals as "guided generation", and so on. Next token prediction works for "stories", but diffusion matches better with "coding flows" (i.e. going back and forth, add something, come back, import something, edit something, and so on).
It would also be very interesting to see how applying this at different "abstraction layers" would work. Say you have one layer working on ctags, one working on files, and one working on "functions". And they all "talk" to each other, passing context and "re-diffusing" their respective layers after each change. No idea where the data for this would come, maybe from IDEs?
A lot of people these days are asking for structured output from LLMs so that a schema is followed. Even if you train on schema-following with a transformer, you're still just 'hoping' in the end that the generated json matches the schema.
I'm not a diffusion excerpt, but maybe there's a way to diffuse one value in the 'space' of numbers, and another value in the 'space' of all strings, as required by a schema:
{ "type": "object", "properties": { "amount": { "type": "number" }, "description": { "type": "string" } }, "required": ["amount", "description"] }
I'm not sure how far this could lead. Could you diffuse more complex schemas that generalize to a arbitrary syntax tree? E.g. diffuse some code in a programming language that is guaranteed to be type-safe?