skysight-inc/HackerNews huggingface dataset:
I know you're not supposed to feed the trolls but honestly I was so taken aback by this comment, no progress since ChatGPT? I just had to respond. Are you serious? From reasoning models to multimodal. From code assistants which are actually useful to AIs which will literally turn my silly scribbling into full songs which actually sound good!

I am completely blind and I used Gemini Live mode to help me change BIOS settings and reinstall Windows when the installer didn't pick up my USB soundcard. I spoke, with my own voice and completed a task with a computer which could only see my webcam stream. This, to me, is a heck of a lot more than ChatGPT was ever able to do in November 2022.

If you continue to insist that stuff isn't improving, well, you can in fact do that... But I don't know how much I can trust you in terms of overall situational awareness if you really don't think any improvements have been made at all in the previous two years of massive investment.
This is not because the models are better.
These services have unknown and opaque levels of shadow prompting[1] to tweak the behavior.
The subject article even mentions "tweaking their outputs to the liking of whoever pays the most".
The more I play with LLMs locally,
the more I realize how much prompting going on under the covers is shaping the results from the big tech services.

1 https://www.techpolicy.press/shining-a-light-on-shadow-promp...
I have yet to see a project where there have never been bugs due to someone forgetting to update the metadata attached with the enum when there's a change
Show HN: Detective Stories – A lateral thinking detective game
Interestingly, that very irritant is now the key to the widespread success of some pepper species by the way of a specific species of mammals.
Or they just read HN too much?
I was hoping this was an implementation of the schemaless indexing [1], which is the foundation for Azure DocumentDB.

That design allows arbitrary nested JSON data to be indexed using inverted indexes on top a variation of B-trees called Bw-trees, and seems like a nice way of indexing data automatically in a way that preserves the ability to do both exact and range matching on arbitrarily nested values.

[1] https://www.vldb.org/pvldb/vol8/p1668-shukla.pdf
Car Problems, Statistics, and Analysis Online Database Web Frontend (2022)
There's definitely demand for these long lifecycles. Red Hat (the company I work for) recently extended RHEL7 support until 2028, which will be about 14 years after it was first released: https://www.redhat.com/en/blog/announcing-4-years-extended-l...
In a similar situation at my workplace.

What models are you using that you feel comfortable trusting it to understand and operate on 10-20k LOC?

Using the latest and greatest from OpenAI, I've seen output become unreliable with as little as ~300 LOC on a pretty simple personal project. It will drop features as new ones are added, make obvious mistakes, refuse to follow instructions no matter how many different ways I try to tell it to fix a bug, etc.

Tried taking those 300 LOC (generated by o3-mini-high) to cursor and didn't fare much better with the variety of models it offers.

I haven't tried OpenAI's APIs yet - I think I read that they accommodate quite a bit more context than the web interface.

I do find OpenAI's web-based offerings extremely useful for generating short 50-200 LOC support scripts, generating boilerplate, creating short single-purpose functions, etc.

Anything beyond this just hasn't worked all that well for me. Maybe I just need better or different tools though?
But you would then need to bring it down to the low voltages required by the chips and that would greatly increased cost, volume, weight, electrical noise and heat of the device.
Some still are anxious about it, but others have seemingly stopped caring, as I think they believe their near monopoly positions means bad PR no longer matters. Looking at Comcast in particular.
I remember reading that rodents chewed car cables since the insulation was made using the same compound as soy and that attracted them, but in reality it was just they liked chewing on stuff.
https://www.caranddriver.com/news/a21933466/does-your-car-ha...
Deep Learning Runs on Floating-Point Math. What If That's a Mistake?
> CFPB is simply not delivering on its purpose

You can only throw such statement into the world if you would know what it would look like without the CFPB.
I did research on entirely unrelated NLP, actually. I worked on search for awhile (I am corecipient of SIGIR best paper ‘17) ad a bit in space- and memory- efficient nlp tasks.
> The new logic you'd want to add is likely something never done before.

99% of software development jobs are not as groundbreaking as this. It’s mostly companies doing exactly what their competitors are doing. Very few places are actually doing things that an LLM model has truly never seen crawling through GutHub. Even new innovative products generally boil down to the same database fetches and CRUD glue and JSON parsing and front end form filling code.
I don’t know that it needs to experience the world in real-time, but when the brain thinks about things it’s updating its own weights. I don’t think attention is a sufficient replacement for that mechanism.

Reasoning LLMs feel like an attempt to stuff the context window with additional thoughts, which does influence the output, but is still a proxy for plasticity and aha-moments that can generate.
How do they know that a human is doing the translation? What's to stop someone from just c&ping the text into an LLM, giving it a quick proofread, then sending it back to the client and saying "I translated this"?

Sounds like easy money, maybe I should get into the translation business.
> I have been authoring the various Drive Stats reports for the past ten years and this will be my last one. I am retiring, or perhaps in Drive Stats vernacular, it would be “migrating.”

Thank you for all these reports over the years.
Nor Safari
More discussion: https://news.ycombinator.com/item?id=43004889
Siri will not even play my very specifically named Spotify playlist unless I prefix the name with “my”. It will rather just play something completely random public thing
Yea, i agree fully.

real programming of course wont go away. But in the public eye it lost its mysticism as seemingly anyone can code now. Of course that aint true and noone managed to create anything of substance by prompting alone.
On the other hand, every time I've been in an "update everything constantly" situation there have been extremely painful outages every 6 months because people put dramatically too much faith in their unit tests to make updating "quick and painless".

Personally I go for having a recurring calendar entry every few months that blocks off a few days to very carefully read all the patch notes before updating
I guess that depends on what you mean by equivalence between CPS and SSA.

My prefered form is basic blocks with arguments. Where the final jump has parameters it passes to it's destination blocks.

That form has a 1-1 equivalence with SSA and I find it much easier to reason about. Every SSA algorithm I have looked at so far works just fine on the representation. Plus there is none of the annoying transforming into and out of SSA.

If you omit the mucking about with return closures in CPS what is left is basic blocks with arguments.

To see basic blocks with arguments as a form of SSA just see every basic block argument as a phi function, and the callers parameters that feed into that argument as the other end of the sources of the phi function.

Just from your description I think your pizlo special SSA is actually a form of basic blocks with arguments. What I don't see from your description is why Upsilon and phi aren't combined into a single notion (What I would call an incoming block parameter).
Step the GPU voltage up to 48V. (anyway you make a new connector that's not compatible with existing PSUs. Why not actually fix a problem at the same time, once and for all! [48V should be enough for anybody, right?])
Recurring checklists using Org Mode in Emacs
Sorry; although I'm "on your side" in the sense that I want Rust in Linux and am bummed out that it seems increasingly like it's going to be a multigenerational slog, thanks to entrenched opposition from some of the C old guard, and which might even outlive Linux's window of viability.

But this is Linux kernel development. You just can't be such snowflakes, and so quick to be outraged. My mom died young, of cancer. I don't like cancer. I know that using "cancer" as a metaphor is a very negative way to characterize anything.

But the qualifier he added in the original statement is absolutely enough to make it a fair comment.

He really, really doesn't think using multiple programming languages is a sane approach. Would you really feel better if he said, "I think a multi-language approach is a very bad idea, it will inevitably spread, and it will end up causing systemic problems far away from this initial usage, and make the kernel increasingly frail and unhealthy over time" ?

If so, ... weird?

If not, then I think you are overreacting to the blunt (but technical, if perhaps motivated from some more emotional response) that he really doesn't like this idea at all.

"Cancer" is an appropriate shorthand to refer to a practice that you think causes systemic harm that compounds or grows worse over time. (I'm afraid I myself may have used it to describe PowerPoint presentations as the basis for meetings.)

I think his comment meets the minimal etiquette bar for a serious technical discussion. I mean, it wasn't nice. But the fact that he doesn't agree with you, or like your work, isn't an insult.

> He said "you are cancer, go away".

I find this absurd mischaracterization of what he said a lot more unacceptable than what he actually said. He's bluntly stating his technical opinion (which, again, I don't agree with). But you're not paraphrasing; you're basically lying.
Yes I see your point, correlation is not causation. Again, this is my best guess and observation based on my personal view of the world and my understanding of data on hands at t0 (today). This doesn't spare if from being incorrect or extremely wrong, as always when dealing with predictions of a future outcome.
The title should be "Elon Musk's Team Creates Another Mess to Be Cleaned Up by the Judiciary."

The question we should all be asking is whether Congress has any intentions whatsoever of doing their actual job? So far, they haven't been doing jack squat. Wonder if DOGE will suspend their salaries?! :)
Did anyone else read this as "Firing programmers for (AI will destroy everything)" or have I been reading too much Yudkowsky
Bucket can evolve to red/black tree, if there are too many entries given O(logK) (collisions) for lookups. Still bucket based ones tend to be outclasses (both latency and memory footprint) by the dumbest linear probe.
Gumption was branded "cowboy coding," then dragged out back and shot.

I like where you're coming from, but micromanagement is still the flavor of the times.
Checking with GLTR:

- 6 of the tokens were within the top 10 predictions
- 8 of the tokens were in the #10-100 range
- 3 of the tokens were in the #100-1000 range
- 3 of the tokens weren't in any of the top 1000 predictions
From an old fan of Starcraft: No.

Civ 6 was on sale on the Switch for like $2 a few months ago, and I'd heard so much about the series in general I decided to see what the hype was about and if it could fill that RTS gap despite being turn based.

Shockingly clunky controls for being 6 and I had to turn on auto-turn-advancement because otherwise I'd be sitting there most turns just hitting next... next... next... waiting for things to complete. Even then the bot players took a while so I couldn't do anything while waiting for them.

After the tutorial I did one full game, won it with a religious victory (even after fooling around for 100 turns not sure what I should be going for), and haven't touched it again.
How to Do an AI Risk Assessment
That's an assumption. Most/all neural network based tech faces a similar problem of exponentially diminishing returns. You get from 0 to 80 in no time. A bit of effort and you eventually ramp it up to 85, and it really seems the goal is imminent. Yet suddenly each percent, and then each fraction of a percent starts requiring exponentially more work. And then you can even get really fun things like you double your training time and suddenly the resultant software starts scoring worse on your metrics, usually due to overfitting.

And it seems, more or less, clear that the rate of change in the state of the art has already sharply decreased. So it's likely LLMs have already entered into this window.
MongoDB imitators? Wasn't CouchDB before MongoDB? CouchDB also stores JSON documents. They did create an imitation of the Mongo query syntax, but the document model doesn't seem to originate from Mongo, as far as I can tell.
I agree but too many serious people are hinting we are very close I can't ignore it anymore.
Sure, when Sam Altman / Zuckerberg say we're close I don't know if I can believe him because obviously the dudes will say anything to sell/pump the stock price.
But how about Demis Hassabis ? He doesn't strike me like that at all. Same for Geoff Hinton, Bengio and a couple of others.
As in writing test cases? I’ve seen devs write (heavily mocked) unit tests using only AI, but these are worse than no tests for a variety of reasons. Our company also used to contract for these tests…but only because they wanted to make the test coverage metric to up. They didn’t add any value but the contractor was offshore and cheap.

If you’re able to have AI generate integration level tests (ie call an API then ensure database or external system is updated correctly - correctly is doing a lot of heavy lifting here) that would be amazing! You’re sitting on a goldmine, and I’d happily pay for these kind of tests.
Many, after doing a review of Rust, say they don't like or will stop using it. It's very premature to declare it has "won", whatever that can be said to mean. Example, ThePrimeTime[1] (famous YouTube programmer) is another stating he does not like Rust anymore, and rather use some other language.

It appears part of the controversy surrounding Rust, is that many are of the opinion that it's not worth it because of the limited use case, poor readability, complexity, long compile times, etc... and that appears to be what certain advocates of Rust are not understanding or appreciating the difference in opinions. Rust is fine for them, specifically, but not for everyone.

[1]: https://www.youtube.com/watch?v=1Di8X2vRNRE
The question isn't whether the code is complex algorithmically, the question is whether the code is:

* Too large to fit in the useful context window of the model,

* Filled with bunch of warts and landmines, and

* Connected to external systems that are not self-documenting in the code.

Most stuff that most of us are working on meets all three of these criteria. Even microservices don't help, if anything they make things worse by pulling the necessary context outside of the code altogether.

And note that I'm not saying that the tools aren't useful, I'm saying that they're nowhere near good enough to be threatening to anyone's job.
being out of date on dependencies isn't the same thing as using mature tech. I sure hope you're updating on a regular schedule, for the bug and security fixes at the very least
I was wondering if anyone here has experimented with running a cluster of SBC for LLM inference? Ex. the Radxa ROCK 5C has 32GB of memory and also a NPU and only costs about 300 euros. I'm not super up to date on the architecture on modern LLMs, but as far as I understand you should be able to split the layers between multiple nodes? It is not that much data the needs to be sent between them, right? I guess you won't get quite the same performance as a modern mac or nvidia GPU, but it could be quite acceptable and possibly a cheap way of getting a lot of memory.

On the other hand I am wondering about what is the state of the art in CPU + GPU inference. Prompt processing is both compute and memory constrained, but I think token generation afterwards is mostly memory bound. Are there any tools that support loading a few layers at a time into a GPU for initial prompt processing and then switches to CPU inference for token generation? Last time I experimented it was possible to run some layers on the GPU and some on the CPU, but to me it seems more efficient to run everything on the GPU initially (but a few layers at a time so they fit in VRAM) and then switch to the CPU when doing the memory bound token generation.
It's nice when bigquery and python together for data analysis that little things like that are congruent
The bottleneck for single batch inference is memory bandwidth. The M4 Pro has less memory bandwidth than the P40, so it would be slower. Also, the setup presented in the OP has system RAM, allowing you to run models than what fits in 48GB of VRAM (and with good speeds too if you offload with something like ktransformers).
How does the US administration undermine the framing of democracy versus authoritarianism? They were democratically elected. Their actions have at least a simple majority of support among voters according to polls. The US still has all the fundamentals of a democracy such as free speech, and strong individual rights in the first and second and other amendments. People aren’t getting tossed out of windows or sent to labor camps or controlled by social credit scores. In my opinion, there is no equivalency between America (whether under Biden or Trump) and an authoritarian regime like in Russia or China.
Remember when Seagate sponsored the /r/datahoarder subreddit instead of making better hard drives?
What you aren't including is the chronic cost of maintaining the status quo, which is the entire reason for making the change. Sometimes making no decision costs you more than making a risky one - and if the decision is risky, you'll want to investigate it first.

During that investigation you discover things (like having to implement your own components) and try to account for how much they'll subtract from the time and energy you save. If the calculus works out, you keep going. If it doesn't, you stop. We kept going, and now we can ship complex features and optimizations to both UIs within a day or two.
The issue people have is that those effects draw attention to themselves (be it “charm” or dismay) and away from the actual content.
I’m not sure that the AI itself has many ethical complications. Many terms of service grant AI companies ownership of the data you supply to them though and that very problematic for many tribes which consider the language sacred and not to be shared.
Yea, every progeammer should write at least a cpu emulator in their language of choice, its such a undervalued exercise that will teach you so much about how stuff really works.
When I started my current 24-bay NAS more than 10 years ago, I specifically looked at the Backblaze drive stats (which were a new thing at that time) to determine which drives to buy (I chose 4TB 7200rpm HGST drives).

My Louwrentius stats are: zero drive failures over 10+ years.

Meanwhile, the author (Andy Klein) of Backblaze Drive Stats mentions he is retiring, I wish him well and thanks!

PS. The data on my 24-drive NAS would fit on two modern 32TB drives. Crazy.
Because programmers know the right questions to ask

AIs have been shown to have confirmation bias depending on what you ask them. They won't question your assumptions on non-obvious subjects. Like "why should this application be written in Python and not C++". You could ask it the opposite, and it will provide ample justification for either position.
Nit: they don't do that anymore, because cross-origin caches cause privacy problems. But they used to, and this news is still percolating through the industry.
Pizlo form is not basic block with arguments, because basic blocks don't have arguments in Pizlo form.

I don't think basic block with arguments is CPS because CPS, in practice, involves return closures.
I'm thinking there is a lot of ''prosperity gospel'' rationalization going on.

https://en.wikipedia.org/wiki/Prosperity_theology
yeah pretty cool to see how they can better use a by-product of lithium extraction
Lessons from a Founder Growing into a Product Manager
Germany doesn’t have hurricanes or wildfires. Take those out and I’d bet the grids are much more comparable.

PGE has to de-energize lines to prevent fires. Hurricanes just blow them down.
Market hype is all it is.
Software: PHP, MySQL, HTML and CSS. And RSS of course.
Hardware: desktop computer with wired mouse and keyboard.
Full Prompt Developer
I think we who are already in tech have this gleeful fantasy that new tools impair newcomers in a way that will somehow serve us, the incumbents, in some way.

But in reality pretty much anyone who enters software starts off cutting corners just to build things instead of working their way up from nand gates. And then they backfill their knowledge over time.

My first serious foray into software wasn't even Ruby. It was Ruby on Rails. I built some popular services without knowing how anything worked. There was always a gem (lib) for it. And Rails especially insulated the workings of anything.

An S3 avatar upload system was `gem install carrierwave` and then `mount_uploader :avatar, AvatarUploader`. It added an avatar <input type="file"> control to the User form.

But it's not satisfying to stay at that level of ignorance very long, especially once you've built a few things, and you keep learning new things. And you keep wanting to build different things.

Why wouldn't this be the case for people using LLM like it was for everyone else?

It's like presuming that StackOverflow will keep you as a question-asker your whole life when nobody here would relate to that. You get better, you learn more, and you become the question-answerer. And one day you sheepishly look at your question history in amazement at how far you've come.
I myself am very conflicted. I will always do what the elders say but opening things up greatly enhances the chance of survival.
Until the US changes their AC power connectors, we just don’t have a use case for it frankly. When the entire system is going to always top out at 1200W or so (so you have an extra few hundred watts for monitors and such), we’re pretty limited to maximum amperage.
Yes.
I'm not suggesting they increase the voltage, as i said, there are lots of tradeoffs.

But i'll also say - outside of heat, all of the things you listed are not safety concerns (obviously, electrical noise can be if it's truly bad enough, but let's put that one mostly aside).

Having a small, cost efficient, low weight device that has no electrical noise is still not safe if it starts fires.
It is new. Elon has intentionally redesigned Twitter to surface right-wing and extremist opinions, which no longer get banned, in order to purge the "woke mind-virus", and the platform is flooded with bots.

Claiming that the platform is exactly the same as it's always been and then pointing out one substantial change from the way it's always been, is an insane degree of cognitive dissonance.
This is from Reddit, it should be taken with a grain of salt. Most people in the thread don't even trust it.
Any sufficiently advanced technology is indistinguishable from magic

~ Arthur C. Clarke

People who make decisions got bamboozled by the ads and marketing of AI companies. They failed to detect a lack of intelligence and got deceived, that they have magical golems for a fraction of the price but eventually, they will get caught with their pants down.
Yet, memory access can be limited by latency and bandwidth. Predictable access patterns (e.g. linear scan) tend to enjoy prefetech reduces the latency of accessing random parts.
$53 million in 2012 and $62.36 billion in profit last year…