MurphyA/hacker-news huggingface dataset:
by caryme | 2026-02-07 22:23:57+00:00
Yes! This is something I've been working on - not for the sake of the M2 mode itself (head voice / falsetto), but because M2 development tends to help with high notes in M1. When I started studying with my current teacher, my M2 felt somewhere between absent or very weak.

Typically exercises I work on for M2 start with an SOVT (typically straw phonation, puffy cheeks, or water bubbles) and then transitioning to an [u] vowel on a five note descending scale. For me, at least, while this can be very unstable depending on the day, M2 is much more easily accessible with an SOVT.

You might also start with a gentle SOVT in M1/chest and siren up as high as is comfortable without pushing/pressing or trying to be loud. Don't think about registration, just let it go - SOVTs tend to let the voice go where it wants easily.

Highly recommend my teacher's book: https://www.pluralpublishing.com/publications/a-systematic-a...
by godelski | 2026-02-07 22:24:42+00:00
> it needs to be "build the right things right", vs "build things right and then discover if they are the right things"

I still think this is a bad comparison and I hoped my prior comment would handle this. Frankly, you're always going to end up in the second situation[0] simply because of 2 hard truths. 1) you're not omniscient and 2) even if you were, the environment isn't static.

> But considering that AI will more and more "build things right" by default

And this is something I don't believe. I say a lot more here[1] but you can skip my entire comment and just read what Dijkstra has to say himself. I dislike that we often pigeonhole this LLM coding conversation into one about a deterministic vs probabilistic language. Really the reason I'm not in favor of LLMs is because I'm not in favor of natural language programming[2]. The reason I'm not in favor of natural language programming has nothing to do with its probabilistic nature and everything to do with its lack of precision[3].

I'm with Dijkstra because, like him, I believe we invented symbolic formalism for a reason. Like him, I believe that abstraction is incredibly useful and powerful, but it is about the right abstraction for the job.

[0] https://news.ycombinator.com/item?id=46911268

[1] https://news.ycombinator.com/item?id=46928421

[2] At the end of the day, that's what they are. Even if they produce code you're still treating it as a transpiler: turning natural language into code.

[3] Okay, technically it does but that's because probability has to do with this[4] and I'm trying to communicate better and most people aren't going to connect the dots (pun intended) between function mapping and probabilities. The lack of precision is inherently representable through the language of probability but most people aren't familiar with terms like "image" and "pre-image" nor "push-forward" and "pull-back". The pedantic nature of this note is precisely illustrative of my point.

[4] https://www.mathsisfun.com/sets/injective-surjective-bijecti...
by Aurornis | 2026-02-07 22:25:14+00:00
You're comparing two different things. It's not useless knowledge, it's something you need to understand.

Opus fast mode is routed to different servers with different tuning that prioritizes individual response throughput. Same model served differently. Same response, just delivered faster.

The Gemini fast mode is a different model (most likely) with different levels of thinking applied. Very different response.
by magicalhippo | 2026-02-07 22:25:29+00:00
> has there been any discovery made with the help of a collider that found its way into an industrial product?

Accelerators and colliders have had a profound impact on medical sciences. Nuclear isotopes used for nuclear medicine[1] is often produced by cyclotrons[2], the accelerator component of circular colliders. The detectors[3] used in things like PET scanners are based on detectors used in collision experiments[4]. Using protons to treat cancer was an idea that came directly from work on cyclotrons[5]. Using the tools developed to simulate how the collision fallout interact with the detectors at LHC[6] has been incorporated into radiotherapy to more accurately compute required doses[7][8].

> perhaps the first step was to create a much smarter entity than ourselves, and then letting it have a look at the collider data

We are actually data starved, we have lots of good ideas but no way to test them.

[1]: https://en.wikipedia.org/wiki/Nuclear_medicine#Sources_of_ra...

[2]: https://en.wikipedia.org/wiki/Cyclotron

[3]: https://en.wikipedia.org/wiki/Gamma_camera

[4]: https://en.wikipedia.org/wiki/Scintigraphy#Process

[5]: https://en.wikipedia.org/wiki/Proton_therapy#History

[6]: https://kt.cern/technologies/geant4

[7]: https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.17678

[8]: https://www.sciencedirect.com/science/article/pii/S240542832...
by paganel | 2026-02-07 22:25:37+00:00
Of course they were, and of course that the Russians are to blame. Any mention of Mussolini and the fascists just yet? Seeing as we're talking about trains in Italy.

Absolute cinema.

Later edit: And something constructive, for a change, and to ignore the bs propaganda coming from the government-paid BBC ghouls, just read the comments in there, made by actual Italians, on the FB page of one of the most important newspapers in Italy. Like I said, absolute clowns, but it's pretty interesting that they're still trying to sell this bs, they must be thinking they we're still back in early 2022.

[1] https://www.facebook.com/corrieredellasera/posts/pfbid026M73...
by swisniewski | 2026-02-07 22:25:40+00:00
Some of this is people trying to predict the future.

And it’s not unreasonable to assume it’s going there.

That being said, the models are not there yet. If you care about quality, you still need humans in the loop.

Even when given high quality specs, and existing code to use as an example, and lots of parallelism and orchestration, the models still make a lot of mistakes.

There’s lots of room for Software Factories, and Orchestrators, and multi agent swarms.

But today you still need humans reviewing code before you merge to main.

Models are getting better, quickly, but I think it’s going to be a while before “don’t have humans look at the code” is true.
by simonw | 2026-02-07 22:26:10+00:00
The problem with naming my consulting clients that some of them won't want to be named. I don't want to turn down paid work because I have a popular blog.

I have a very strong policy that I won't write about someone because they paid me to do so, or asked me to as part of a consulting engagement. I guess you'll just have to trust me that I'll hold to that. I like to hope I've earned the trust of most of my readers.

I do have a structural conflict, which is one of the reasons my disclosures page exists. I don't value things like early access enough to avoid writing critically about companies, but the risk of subtle bias is always there. I can live with that, and I trust my readers can live with it too.

I've found myself in a somewhat strange position where my hobby - blogging about stuff I find interesting - has somehow grown to the point that I'm effectively single-handedly running an entire news agency covering the world's most valuable industry. As a side-project.

I could commit to this full-time and adopt full professional journalist ethics - no accepted credits, no free travel etc. I'd still have to solve the revenue side of things, and if I wrote full time I'd give up being a practitioner which would damage my ability to credibly cover the space. Part of the reason people trust me is that I'm an active developer and user of these tools.

On top of that, some people default to believing that the only reason anyone would write anything positive about AI is if they were being paid to do so. Convincing those people otherwise is a losing battle, and I'm trying to learn not to engage.

So I'm OK with my disclosures and principles as they stand. They may not get a 100% pure score from everyone, but they're enough to satisfy my own personal ethics.

I have just added disclosures links to the footer to make them easier to find - thanks for the prod on that: https://github.com/simonw/simonwillisonblog/commit/95291fd26...
by pastage | 2026-02-07 22:26:17+00:00
You are encouraged to play with footguns on Linux, I do not do it and none of my family do it works fine for us. On "Linux desktop" one of the things you are not encouraged to play with is installation of programs. The Linux way is preferable that is why Apple and all the other are walking down the same path.

Not being able to install things sucks, but when you do you will easily destroy your nice shiny brittle desktop. The pebkac is strong here, but making the users enemies is a bad solution, this is why Google, Apple and MS are all bad desktops.

As I said I have been a Linux user my whole life. I know it works as a desktop but it works best with either people who do not care about instaling stuff, or thise who care enough to get it working.
by Nextgrid | 2026-02-07 22:26:44+00:00
That is definitely a problem, but I would say it’s a problem of hiring and the billion-dollars worth of potential market cap resting on performative bullshit that encourages companies to not hire juniors to send a signal to capture some of those billions regardless of actual impact on productivity.

LLMs benefit juniors, they do not replace them. Juniors can learn from LLMs just fine and will actually be more productive with them.

When I was a junior my “LLM” was StackOverflow and the senior guy next to me (who no doubt was tired of my antics), but I would’ve loved to have an actual LLM - it would’ve handled all my stupid questions just fine and freed up senior time for the more architectural questions or those where I wasn’t convinced by the LLM response. Also, at least in my case, I learnt a lot more from reading existing production code than writing it - LLMs don’t change anything there.
by Aurornis | 2026-02-07 22:27:05+00:00
> I’d love to hear from engineers who find that faster speed is a big unlock for them.

How would it not be a big unlock? If the answers were instant I could stay focused and iterate even faster instead of having a back-and-forth.

Right now even medium requests can take 1-2 minutes and significant work can take even longer. I can usually make some progress on a code review, read more docs, or do a tiny chunk of productive work but the constant context switching back and forth every 60s is draining.
by thwarted | 2026-02-07 22:27:47+00:00
It's not about the consumption of raw materials or repurposing of the raw materials used for chips.
peterlk said:

> How many hospitals, roads, houses, machine shops, biomanufacturing facilities, parks, forests, laboratories, etc. could we build with the money we’re spending on pretraining models that we throw away next quarter?

It's about using the money for to build things that we actually need and that have more long term utility. No one expects someone with a 100M signing bonus at Meta to lay bricks, but that 100M could be used to buy a lot of bricks and pay a lot of brick layers to build hospitals.
by class3shock | 2026-02-07 22:27:57+00:00
This maybe for travel expenses but it's the same story for so many things

1. Start with a mostly manual, people labor based, system that works well (handing Joann receipts)
2. It begins to not cost-scale well with growth (department size increases) or a salesmen comes around with a service that offers to do the same thing for less (Concur) or both
3. The company switches to reduce costs
4. The new service is cheaper partly because it offloads work onto employees (filling out travel expense forms) and by cheaping out on the experience (not caring that the forms are not easy to understand and the system is annoying to use)
5. Employees now have to spend time doing a task they never did and their experience is worse

And it stays in this state forever because the observable costs (service cost vs some number of Joann's) are less. The fact that expensive employees (A department full of Phd's) are now wasting time and being annoyed by this system are not seen. The hours used to fill out those forms and lost productivity due to anger are never accounted for. Also the higher ups are detached because they still have their own personal Joann's taking care of everything.
by icedchai | 2026-02-07 22:27:58+00:00
My comparison was today's local AI to today's SOTA commercial AI. Both have improved, no argument.

It's more cost effective for someone to pay $20 to $100 month for a Claude subscription compared to buying a 512 gig Mac Studio for $10K. We won't discuss the cost of the NVidia rig.

I mess around with local AI all the time. It's a fun hobby, but the quality is still night and day.
by dlenski | 2026-02-07 22:28:06+00:00
> I would highly recommend anyone into bicycles to try building their own wheel using his article.

Hear, hear. It's an incredible resource.

I got into building and fixing bikes around 2004, and built a couple of fixed-gear bikes on a shoestring budget using parts from Nashbar.com, and Sheldon Brown's wheelbuilding guide.

I also corresponded with Sheldon a few times via email, and created the Wikipedia article about him. He contributed to it a few times himself, before his untimely death in 2008. https://en.wikipedia.org/wiki/Sheldon_Brown_(bicycle_mechani...
by AstroBen | 2026-02-07 22:28:54+00:00
The problem with these "shill for an AI company" thoughts is that it really doesn't matter how good their shilling or salesmanship is. They actually do need to provide value for it to be successful

These aren't tools they're asking $25,000 upfront for, that they can trick us that it for sure definitely works and get the huge lump sum then run

Nah.. at best they get a few dollars upfront for us to try it out. Then what? If it doesn't deliver on their promise, it flops
by nekitamo | 2026-02-07 22:29:36+00:00
The same as the distribution of companies which are profitable over time and grow steadily, vs the others which clumsily flail around to somehow stay alive. To the winner go the spoils, and the winners will be a tiny fraction of companies, same as it ever was.

A way I look at it is that all net wealth creation in public companies has come from just 4% of businesses:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2900447

https://www.reddit.com/r/investing/comments/rk4udc/only_4_of...

It'll be similar with software companies. 4% of them will hit on a unique cultural and organizational track which will let them thrive, probably using AI in one form or another. The other 96% will be lucky to stay alive.

Same as it ever was.
by hnlmorg | 2026-02-07 22:29:39+00:00
The problems about documentation I described wasn’t about the effort of writing it. It was that modern chipsets are trade secrets.

When you bought a computer in the 80s, you’d get a technical manual about the internal workings of the hardware. In some cases even going as far as detailing what the registers did on their graphics chipset or CPU.

GenAI wouldn’t help here for modern hardware because GenAI doesn’t have access to those specifications. And if it did, then it would already be documented so we wouldnt need GenAI to write it ;)
by antirez | 2026-02-07 22:29:42+00:00
That is, I believe, one the points of AI and Open Source many contacts. Something like TCC, with a good coding agent and a developer that cares about the project, and knows enough about it, can turn into a project that can be maintained without the otherwise large efforts needed, that resulted into the project being abandoned. I'm resurrecting many projects of mine I had no longer the time to handle, like dump1090, linenoise, ...
by moondev | 2026-02-07 22:30:05+00:00
Just like KIND runs containerd inside docker, you can also run dockerd inside containerd backed pods.

Start a privileged pod with the dind image, copy or mount your compose.yaml inside and you should be able to docker compose up and down, all without mounting a socket (that won't exist anyway on containerd CRI nodes)

To go even further, kubevirt runs on kind, launch a VM with your compose file passed in via cloud-init.
by WalterBright | 2026-02-07 22:30:24+00:00
> nothing we want is on the other side

Wow.

> You decided the rest of the solar system is dead, not the scientific community.

The odds are heavily stacked against other life existing, and get worse with every probe. Of course, nobody can prove there is no other life. And it's not very credible that Terran life will out-compete locally evolved life.

And the idea that preserving some slime mold on Pluto justifies us constraining ourselves to Earth is just sad.

> If you really can't comprehend the reason why Elon Musk is villified by people then there's no point in trying to explain it to you.

I once asked another Musk-hater on HN why? All he could come up with is Musk called a diver a pedo-boy. I pointed out that Musk only did that because the diver went on national TV and told Musk to shove his submarine up his backside.

If you've got a better reason, I'd love to hear it!
by simonw | 2026-02-07 22:30:29+00:00
Provided the sponsored content is labelled "sponsored content" this is above board.

If it's not labelled it's in violation of FTC regulations, for both the companies and the individuals.

[ That said... I'm surprised at this example on LinkedIn that was linked to by the Washington Post - https://www.linkedin.com/posts/meganlieu_claudepartner-activ... - the only hint it's sponsored content is the #ClaudePartner hashtag at the end, is that enough? Oh wait! There's text under the profile that says "Brand partnership" which I missed, I guess that's the LinkedIn standard for this? Feels a bit weak to me! https://www.linkedin.com/help/linkedin/answer/a1627083 ]
by Nextgrid | 2026-02-07 22:30:32+00:00
Devil’s advocate: you should still have a reason to do the bad thing in the first place. I’m not a good enough politician to understand how this benefits anyone’s interests (don’t get me wrong, I do suspect Russia to be behind this, but still don’t get the objective - but then again I don’t get the objective of their special bullshit operation either)
by navanchauhan | 2026-02-07 22:30:37+00:00
This was an experiment that Justin ran: one person fresh out of college, and another with a long, traditional career.

Even though all three of us have very different working styles, we all seem to be very happy with the arrangement.

You definitely need to keep an open mind, though, and be ready to unlearn some things. I guess I haven’t spent enough time in the industry yet to develop habits that might hinder adopting these tools.

Jay single-handedly developed the digital twin universe. Only one person commits to a codebase :-)
by fipar | 2026-02-07 22:30:39+00:00
I took it as a napkin rounding of 365/7 because that’s the floor you pay an employee regardless of vacation time (in places like my country you’d add an extra month plus the prorated amount based on how many vacation days the employee has), so, not that people work 50 weeks per year, it’s just a reasonable approximation of what the cost the hiring company.
by manmal | 2026-02-07 22:31:12+00:00
What you’re describing is that we’d turn deterministic engineering into the same march of 9s that FSD and robotics are going through now - but for every single workflow. If you can’t check the code for correctness, and debug it, then your test system must be absolutely perfect and cover every possible outcome. Since that’s not possible for nontrivial software, you’re starting a march of 9s towards 100% correctness of each solution.

That accounting software will need 100M unit tests before you can be certain it covers all your legal requirements. (Hyperbole but you get the idea) Who’s going to verify all those tests? Do you need a reference implementation to compare against?

Making LLM work opaque to inspection is kind of like pasting the outcome of a mathematical proof without any context (which is almost worthless AFAIK).
by apublicfrog | 2026-02-07 22:31:32+00:00
Ioniq 6:

Range: 429km

Price: $70,116 AUD

Performance: 0-100 5.1s

Tesla model 3:

Range: 520km

Price: $59,979 AUD

Performance: 0-100 6.1s

It's pretty hard to argue like for like here. I've driven the Ioniq before and it's fine, but it looks and feels like a 2015 Mazda inside.

BYD is worse in every category, I have no idea what you're talking about.
by raegis | 2026-02-07 22:31:51+00:00
> The problem might end up being that the value created by LLMs will have no customers when everyone is unemployed.

I'm not a professional programmer, but I am the I.T. department for my wife's small office. I used ChatGPT recently (as a search engine) to help create a web interface for some files on our intranet. I'm sure no one in the office has the time or skills to vibe code this in a reasonable amount of time. So I'm confident that my "job" is secure :)
by ninkendo | 2026-02-07 22:32:09+00:00
I think a vec is important for the same reason a string is… because being able to properly get the length, and standardized ways to push/pop from them that don’t require manual bounds checking and calls to realloc.

Hash maps are mostly only important because everyone ought to standardize on a way of hashing keys.

But I suppose they can both be “bring your own”… to me it’s more that these types are so fundamental and so “table stakes” that having one base implementation of them guaranteed by the language’s standard lib is important.
by onion2k | 2026-02-07 22:32:19+00:00
Fair enough, I guess I'm misremembering the timeline, but saying "It's taken 3 years, not 2!" doesn't really change the point I'm making very much. The road from what ChatGPT 3.5 could do to what Codex 5.3 can do represents an amazing pace of change.

I am not claiming it's perfect, or even particularly good at some tasks (pelicans on bicycles for example), but anyone claiming it isn't a mind-blowing achievement in a staggeringly short time is just kidding themselves. It is.
by ChrisMarshallNY | 2026-02-07 22:32:28+00:00
That was spot-on.

All too true, and I never actually thought about it, in that way.

I know that it's controversial, but it's my opinion that LLMs could be trained to be Joannes, instead of auditors (the bad guys, on Discworld).

That could be good.

Humans seem to get caught up in mustache-twirling exercises, when they are given any kind of power or authority. Machines could care less.

But also, auditors could program LLMs to be mega-auditors.

That would be bad.
by AnotherGoodName | 2026-02-07 22:33:43+00:00
Yes it's 100% prioritization. Through that it's also likely running on more GPUs at once but that's an artifact of prioritization at the datacenter level. Any task coming into an AI datacenter atm is split into fairly fined grained chunks of work and added to queues to be processed.

When you add a job with high priority all those chunks will be processed off the queue first by each and every GPU that frees up. It probably leads to more parallelism but... it's the prioritization that led to this happening. It's better to think of this as prioritization of your job leading to the perf improvement.

Here's a good blog for anyone interested which talks about prioritization and job scheduling. It's not quite at the datacenter level but the concepts are the same. Basically everything is thought of as a pipeline. All training jobs are low pri (they take months to complete in any case), customer requests are mid pri and then there's options for high pri. Everything in an AI datacenter is thought of in terms of 'flow'. Are there any bottlenecks? Are the pipelines always full and the expensive hardware always 100% utilized? Are the queues backlogs big enough to ensure full utilization at every stage?

https://www.aleksagordic.com/blog/vllm