skysight-inc/HackerNews huggingface dataset:
If I may make a suggestion to @ianlancetaylor, I think using the ? for error checking is a fantastic idea, but I think a couple small changes would make this absolutely a game changer and even more Go-like:

To demonstrate my tweak to your idea, imagine this example code:

r, err := SomeFunction()
if err != nil {
return fmt.Errorf("something 1 failed: %v", err)
}

r2, err := SomeFunction2()
if err != nil {
return fmt.Errorf("something 2 failed: %v", err)
}

r3, err := SomeFunction3()
if err != nil {
return fmt.Errorf("something 3 failed: %v", err)
}

In the current proposal it would turn into this:

r := SomeFunction() ? {
return fmt.Errorf("something 1 failed: %v", err)
}

r2 := SomeFunction2() ? {
return fmt.Errorf("something 2 failed: %v", err)
}

r3 := SomeFunction3() ? {
return fmt.Errorf("something 3 failed: %v", err)
}

My first suggestion is to keep `err` variables visible. It ends up being not much longer, but it is much more readable and Go-like:

r, err := SomeFunction() ? {
return fmt.Errorf("something 1 failed: %v", err)
}

r2, err := SomeFunction2() ? {
return fmt.Errorf("something 2 failed: %v", err)
}

r3, err := SomeFunction3() ? {
return fmt.Errorf("something 3 failed: %v", err)
}

My second suggestion is to require ? to always have a block, and also allow them to "chain" so only the last statement needs a block:

r, err := SomeFunction() ?
r2, err := SomeFunction2() ?
r3, err := SomeFunction3() ? {
return fmt.Errorf("something 1, 2 or 3 failed: %v", err)
}

As you can see this is much shorter! Having the block is always required at the end of the "chain" of question mark statements is more consistent with how `if` statements require a block currently. It also makes the `return err` flow also always visible (no return magic). It also also has a huge advantage of it being much harder to miss a question mark syntactically. as a question mark without a block would be a syntax error.

For example, this is an error:

r, err := SomeFunction() ? // <-- compile error: missing block after ?

And also this is an error:

r, err := SomeFunction() ?
r2, err := SomeFunction2() // <-- compile error: missing block after ?
r3, err := SomeFunction3() ? {
return fmt.Errorf("something 1, 2 or 3 failed: %v", err)
}

Thanks for listening! Curious what folks think.
KIND OF

if you've tried to get o1 to give you outputs in a specific format, it often just tells you to take a hike. It's a stubborn model, which implies a lot

This is speculation, but it seems that the main benefit of reasoning models is that they provide a dimension along which RL can be applied to make them better at math and maybe coding, things with verifiable outputs.

Reasoning models likely don't learn better reasoning from their hidden reasoning tokens, they're 1) trying to find a magic token which when raised to its attention make it more effective (basically give it room to say something that jogs its memory) or 2) it is trying to find a series of steps which do a better job of solving a specific class of problem than a single pass does, making it more flexible in some senses but more stubborn along others

Reasoning data as training data is a poison pill, in all likelihood, and just makes a small window of RL vulnerable problems easier to answer (when we have systems that don't better). It doesn't really plan well, doesn't truly learn reasoning, etc

Maybe seeing the actual output of o3 will change my mind but I'm horrifically bearish on reasoning models
Hello...

Is it possible to use aider with a local model running in LMStudio (or ollama)?

From a quick glance i did not see an obvious way to do that...

Hopefully i am totally wrong!
>you should also make a rule saying astrology books aren't a legitimate source of sentence guidance

My example was absurd on purpose, I didn't want to bring an example where people could respond with "well, actually..."

But in the real world that is rarely the case, imagine substituting "astrology book" for "Bible/Quran/..." Would that be considered a legitimate source of sentence guidance? I'm sure people would spend years arguing about that...

As a society we need to understand that LLM hallucination is no different than a bloom filter giving you a false-positive.
An SpO2 sensor can be nice to have but it's useless to most people. A healthy person near sea level will always have an SpO2 near 100%. It's only really useful to people with medical conditions like sleep apnea, or athletes at high altitude. And even then you won't be able to get continuous monitoring. Current wrist SpO2 sensors require the wearer to hold still for a while in order to get an accurate reading.

I turned mine off to save battery life.
Web: https://bookshop.org/ebooks

Android: https://play.google.com/store/apps/details?id=org.bookshop.a...

iOS: https://apps.apple.com/us/app/bookshop-org/id6465290096
Nice! Yeah, using protobufs seems reasonable. Re. GC, Racket has support for "freezing" values in place to prevent the GC from moving them, but freezing too many values can impact the GC's operation so I'd watch out for that if that's possible in OCaml.
Haha, cool, thank you! I had some notes ready but didn't get around to finishing it sooner. Besides, I'm sure the course slides were much better material for exam prep ;)
Omg, thanks!
I got incredible results in asking AIs for sql queries. I just enter my data and what I want the output to look like. Then I ask it to provide 10 different versions that might be faster. I test them all and tell it which is faster and then I ask it to make variations on this path. Then I ask it to add comments to the code which is the fastest. I verify the query, do some more test, and I'm good to go. I understand SQL pretty well but trying to make 10 different versions of one code would've took me at least an hour.
As a Van Halen fan, I think of it because of the album titled "5150". But it has other well known uses as well.
If AI increases the productivity of a single engineer between 10-100x over the next decade, there will be a seismic shift in the industry and the tech giants will not walk away unscathed.

There are coordination costs to organising large amounts of labour. Costs that scale non-linearly as massive inefficiencies are introduced. This ability to scale, provide capital and defer profitability is a moat for big tech and the silicon valley model.

If a team of 10 engineers become as productive as a team of 100-1000 today, they will get serious leverage to build products and start companies in domains and niches that are not currently profitable because the middle managers, C-Suite, offices and lawyers are expensive coordination overhead. It is also easier to assemble a team of 10 exceptional and motivated partners than 1000 employees and managers.

Another way to think about it is what happens when every engineer can marshal the AI equivalent of $10-100m dollars of labour?

My optimistic take is that the profession will reach maturity when we become aware of the shift in the balance of power. There will be more solo engineers and we will see the emergence of software practices like the ones doctors, lawyers and accountants operate.
I am considering this in the context of 1930's history.
Weird. I never ran the math.

Becoming a billionaire is actually more achievable than I would have predicted, by a lot.
If you drop a pin anywhere in south london, it takes several "unknown river"s which on the map are stated clearly as the River Wandle.

The US drops seemed a lot cleaner
Yeah, like 15th century old.
Sure. My sense is that the Unis have somewhat fallen out of love with MOOCs (as have students as they realize that they're not getting the certification value they have hoped for).
Not a very good car. :)

Some of us get crazy with our homelabs. The love of technology is enough of a motivator.

It's cold in my office today. I'm considering that I could use more cores, crunch some ML stuff locally instead of in the cloud, and solve two problems at once. The thermostat for the heater is allllll the way downstairs after all :D
Read Ebooks. Support Local Bookstores
How can I explain my position so that you understand it? Of course, the post office is undemocratic if voters cannot influence who is head of it.

Are we talking past each other with different meanings? Any organisation where voters don't have a say is undemocratic. That doesn't mean I put any value in it, it is just what it is according to the definition of the word.
When I need a pin, I use uuidgen and grab the first four decimal digits. (I guess that could potentially include the `4` but it hasn't happened yet and the odds are low.) I guess I'd better screen some of them out!

True story: friend had a bank (in the 1990s) randomly generate a PIN of 2222 for him. He got it reset.
In the ever-evolving world of web development, frameworks like React and Angular have become synonymous with robust, feature-rich applications. However, they come with their own set of challenges, from steep learning curves to complex setup processes. Enter HTMX — a lightweight, modern library that offers a refreshing alternative. In this post, we’ll explore what HTMX is, the problems it solves, its limitations, and why you might want to consider giving up on React (or Angular) for your next project.
Hey y’all, I wrote about my 6-year tenure as a CTO across two fintech startups, specifically around securing banking systems.

I was particularly proud of the GraphQL/Redis solution I came up with which enabled the part "This means there were zero database IDs exposed in any UI or API response"
DeepSeekMath: Pushing Limits of Mathematical Reasoning in Open Language Models
Why are some camera shots so dull? Is that a HDR problem?
$6k Complete hardware and software setup for running DeepSeek-R1 locally
Complete hardware + software setup for running Deepseek-R1 locally. The actual model, no distillations, and Q8 quantization for full quality. Total cost, $6,000. All download and part links below:
Relevant link (2023): https://www.lesswrong.com/posts/T5RzkFcNpRdckGauu/link-a-com...

The top comment has an interesting take: "Unless Ziz gets back in the news, there’s not much reason for someone in 2025 or later to be reading this."
Of course, lower cost is almost always better.

But just because advanced devices with (currently) costly components have higher costs is no reason to not create them.

If something works and meets a need, the costs of components and manufacture usually come down as engineering and manufacturing progresses on the learning curve and competition comes into play. (Not true when there is a captured market where extractive pricing becomes the norm, but those are the exception in consumer goods)
Challenge: I would really like somebody that has experience in LLM based coding tools to try and fix gnome-terminal:

https://news.ycombinator.com/item?id=42814509
An app so broken that can tell you lost access to the Chat... WHILE...you are in a meeting hearing and seeing a visual presentation...Open bug for at least 6 years...

https://answers.microsoft.com/en-us/msteams/forum/all/in-my-...

Unable to pause screen during a presentation, one of the most basic features of a professional platform. A feature requested here by more than 2460 users, for at least the last five years:

https://feedbackportal.microsoft.com/feedback/idea/a5063e83-...

Microsoft is fast-food not a Michelin restaurant...
I asked a physicist friend about this. At 6x mass, it would only be 6x the gravity if it was the same size as Earth. Depending on the density, the gravity could actually be weaker than Earth!
FTC Releases Summary of Key Accomplishments
The Little Learner A straight line to deep learning
Not before AGI and I still see no signs of it.
It's certainly true that there is plenty of science in cooking and plenty of art in baking. My comment is very reductive.

That said, cooking is generally far more forgiving that baking. If you put some amount of chicken in an oven set to a temperature of "very hot", you'll eventually have cooked chicken.

If you mix some amount of flour, water, and yeast, let it sit for some amount of time, and then put it in a "very hot" oven, you're unlikely to end up with what anyone would call "bread". It may not even really be that edible unless you were to grind it back into a power and mix it with water.
A new life for empty offices: Growing kale and cucumbers
The LinkedIn profile is not for the hiring manager. Why would you look at it? It’s just a regurgitation of what’s on your resume.

The purpose of LinkedIn for most people is to keep their business contacts and to look for jobs. Of course some use it to appear to be a “thought leader”.

Do you think just because you don’t use LinkedIn to apply for jobs no one does?

This is like the old Slashdot meme “I haven’t owned a TV in over a decade. Do people still watch TV”?
To be fair that was decades ago. The mini-storage place I use now asks for your unit # AND your PIN. So it would be a lot harder to guess like described above.
> crashes occur with much higher probability than a normal distribution would assume.

This is probably the most intuitive way to reason about it, but there's a subtlety there that relates to the magnitude of the crashes and their probability of happening. Intuitively, if the normal distribution was correct in estimating market behavior, you'd not expect to see 10 sigma events ever (we are looking at something like 1/10^20 of this happening). And yet, these events happen with some regularity when you try to use normal distributions for market returns.

So there are two aspects to this: one is that the normal distribution and people underestimate how often crashes can happen, and they also underestimate how big those crashes can be. It's the latter that is arguably more dangerous, because if you lose money more frequently than expected, you might end up earning less, but if you underestimate how bad a drawdown can be, you can be easily wiped out before you can react.

So why do we use the normal distribution so often in finance? Because it is convenient. It works fine for 99.99% of the cases, and it is easier to deal with the tails as a special beast rather than always having a complicated model to look at. There's also an element of lottery. Significant crashes happen once every 10 years roughly, so you don't need a great deal of luck to make a lot of money without seeing one :D

> managing options is hard. when do you close the hedge after it gives you a profit? when do you put it back on.
It is definitely quite hard. Options are nonlinear instruments with a significant complexity to their behavior. This is why people pay Universa and other, lesser known tail risk funds to handle this complexity. You also have instruments like Variance Swaps, which allow one to easily lock in convexity pnl - considerably removing the timing aspect. You can construct these instruments synthetically using options, but this is not for the faint of heart.

It is doable on your own, but not without understanding options in depth and having tools that allow you to manage an option portfolio. These days, a person who is decent at freshman math and knows how to use python+pandas can easily manage this entirely on their own with a handful of scripts, but they'll likely need to spend a few months learning the theory and then it can take a year to build the neccessary intuition.
How does not printing money make any difference here? It's not like people put trillions of cash into nVidia. The cap is just outstanding shares times what they were last sold for, if someone buys at a higher price suddenly lots of phantom money appears and everybody who owns shares gets richer on paper.
https://aider.chat/docs/llms/ollama.html
Thanks for your interest in aider.

Yes, absolutely you can work with local models. Here are the docs for working with lmstudio and ollama:

https://aider.chat/docs/llms/lm-studio.html

https://aider.chat/docs/llms/ollama.html
They profit a lot from uninformed CTOs and founders just going for whatever they heard of, instead of looking into whether it is a good provider

If it wasn't for those old Super Bowl ads, GoDaddy wouldn't exist today.

Sex sells.
> I 100% expect some downvotes from the ccp.

Always happy to oblige when someone insinuates that any critics must be government agents
Perennial reminder that we do not have any real evidence that we are anywhere close to AGI, or that "throwing more resources at LLMs" is even theoretically a possible way to get to an AGI.

"Lots of people with either a financial motivation to say so or a deep desire for AGI to be real Soon™ said they can do it" is not actual evidence.

We do not know how to make an AGI. We do not know how to define an AGI. It is hypothetically possible that we could accidentally stumble into one, but nothing has actually shown that, and counting on it is a fool's bet.
We are creating the fire equivalent of an Ice Age
The thing is, that's not true at all. AI is great for some tasks, and poor for other tasks. That's the reason to break it down like this, because people are trying to explain where AI will and won't revolutionise things, instead of following along with the already-popping AI bubble uncritically

For example: AI's smash translation. They won't ever beat out humans, but as an automated solution? They rock. Natural language processing in general is great. If you want to smush in a large amount of text, and smush out a large amount of other text that's 98% equivalent but in a different structure, that's what AI is good for. Same for audio, or picture manipulation. It works because it has tonnes of training data to match your input against

What AI cannot do, and will never be able to do, is take in a small amount of text (ie a prompt), and generate a large novel output with 100% accuracy. It simply doesn't have the training data to do this. AI excels in tasks where it is given large amounts of context and asked to perform a mechanistic operation, because its a tool which is designed to extract context and perform conversions based on that context due to its large amounts of training data. This is why in this article the author was able to get this to work: they could paste in a bunch of examples of similar mechanical conversions, and ask the AI to repeat the same process. It has trained on these kinds of conversions, so it works reasonably well

Its great at this, because its not a novel problem, and you're giving it its exact high quality use case: take a large amount of text in, and perform some kind of structural conversion on it

Where AI fails is when being asked to invent whole cloth solutions to new problems. This is where its very bad. So for example, if you ask an AI tool to solve your business problem via code, its going to suck. Because unless your business problem is something where there are literally 1000s examples of how to solve it, the AI simply lacks the training data to do what you ask it, it'll make gibberish

It isn't the nature of the power of the AI, its that its inherently good for solving certain kinds of problems, vs other kinds of problems. It can't be solved with more training. The OPs problem is a decent use case for it. Most coding problems aren't. That's not that it isn't useful - people have already been successfully using them for tonnes of stuff - but its important to point out that its only done so well because of the specific nature of the use case

Its become clear that AI requires someone of equivalent skill as the original use case to manage its output if 100% accuracy is required, which means that it can only ever function as an assistant for coders. Again, that's not to say it isn't wildly cool, its just acknowledging what its actually useful for instead of 'waiting to have my mind blown'
It really doesn't lol. Those laws are like Moore's law, an observation rather than something Fundamental like laws in physics

The scaling has been plateauing, and half that equation is quality training data which is totally out at this point.

Maybe reasoning models will help produce synthetic data but that's still to be seen. So far the only benefit reasoning seems to bring is fossilizing the models and improving outputs along a narrow band of verifiable answers that you can do RL on to get correct

Synthetic data maybe buys you time, but it's one turn of the crank and not much more
There's a meaningful operative definition and you're muddying the waters over that definition on the grounds that, hey, who knows, maybe it's different somewhere in some way.

I just think you're confused if you think that observing a specific definition of habitable zone is tantamount to a specific denial of that possibility.
if this is what they're releasing to the public, I wonder what they are withholding

but then I remember, they're Chinese... and coming up with a novelty and then making damned sure nobody else can get it is a way to be characteristic of the west... or so I wished were true
1) OpenAI isn't public, so not possible.
2) MS is one of the most well diversified tech companies, so, if anything, this will be a positive.
Is there a way to download it? It would be nice to decide to play even while offline.
Like most people, I’ve been using Google Drive for years to store my files. It’s convenient, but recently, I’ve started to feel uneasy about it. With AI becoming more powerful and data being such a valuable commodity, it’s hard not to wonder:

• Who really owns my files?

• How is my data being used behind the scenes?

• What happens if there’s a breach, or the platform decides to change its terms?

The more I think about it, the more I realize that centralized platforms like Google Drive and Dropbox have too much control over something that should belong to me—and only me.

That’s why I’m building Vaulted. It’s a simple, decentralized cloud storage platform designed to give you:

• Full ownership of your data leveraging Cere Network —no tracking, no ads, and no third-party access.

• True privacy—everything is encrypted and stored on a decentralized network.

• A clean, easy-to-use interface—just upload your files and know they’re safe.

Right now, Vaulted is still in its early stages. The initial version is simple: you can upload and store files privately, securely, and completely under your control. It’s not fancy, but it solves the problem of data misuse and centralization.

I’d love to know what you think. If you’ve ever felt frustrated with how your data is handled by big tech,

As a thank-you, you’ll get $10 in free storage credit when we launch if you would like to fulfill a form
A more sensible way to break it down is by "something you have" (phone or YubiKey), "something you know" (password) and "something you are" (face or fingerprint).

Username and password are both something you know, so they count as just one factor. A card on the other hand is something you have, so combined with the PIN that's two factors.
Glowing molecule, by AI, would have taken 500M years to evolve in nature
Will bird flu spark a human pandemic? Scientists say the risk is rising
"But most of the AI-generated code I interact with is a hot unmaintainable mess of very verbose code"

That sounds like you're working with unskilled developers who are landing bad code.
OpenAI isn't publicly traded and MSFT's stake is so minor compared to their other business that it will have a negligible impact on their stock price.
I don’t think your point disagrees with my point. The better aligned your perception is with management on where/how value is getting delivered, the better you will judge how to invest your time. You might be right and they might be wrong. But if you know the business better than your bosses, then you might be working for the wrong people.