MurphyA/hacker-news huggingface dataset:
by adverbly | 2025-06-11 01:54:27+00:00
> I enjoy technology but less and less so each year, because it increasingly feels like there’s some kind of disconnect with the real world that’s hard to put my finger on

Same here.

I want to be an engineer.

As a good engineer, I find a problem then recursively ask why.

I end up with a political root cause - not a technical one.

This makes me sad.

I wish I could just be an engineer.
by refurb | 2025-06-11 01:54:32+00:00
> The 2 explanations of why you think like this is either you are incapable of reading comprehension, or you are so ideologically driven that you basically make up reality in your head real time that fits your world view. And either reason is why conservatives should no longer be tolerated - because its not about my view vs yours, its about actual reality vs your insanity.

Did you not read the same thing I read? He held 4 different positions in the same interview - healthy people shouldn't wear masks, then masks actually make things worse, to "I don't care if you wear them", to "healthy people wearing them causes shortages".

Then add on top later on he demanded oppressive mask mandates for everyone.

You call that a public health care expert?

And that's not even getting into his political answers about the Wuhan lab and his involvement.

The guy is a terrible, terrible public health leader, yet because he's Team Blue(tm) you give it all a pass.
by pradn | 2025-06-11 01:55:02+00:00
It's easier to keep doing something incremental than to fundamentally change things.

One avenue these phone OS, and any consumer OS, can pursue is making it easy to string together app sub-steps. An app can be "cracked open" into sub-step a) either by developers themselves b) or by a backend process at app submission time. The backend process could look at the screens, and see what the user journeys are - this is within reach for current LLMs. An "sub-step" here is like taking a flights app and turning it into different types of search functions - search by date, location, points etc. So an app becomes a bunch of interfaces.

Once you have these "sub-steps" a local LLM can string them together, because it can understand their inputs, outputs, and behaviors.

Actually executing the sub-steps would require the OS to execute the app in the background and run the sub-steps for the user. This would be akin to what browser agents do right now

So this is a way of semantically extracting the "verbs" in existing apps.

With a library of these "sub-steps" in apps, combined with similar ones extracted from the internet - you could chain together the web and native worlds, in the service of the user.

It's easier on phone OSs because phone apps are usually already logged in. It's realistic for iOS to just do a bunch of stuff for you in the background. You really can probably get finance info from all your finance apps, for example.

I'm not saying this is necessarily the answer - but re-thinking the OS in this sort of what is what would be an actually ambitious thing for Apple or Google to do. All these small tweaks are opiates.
by GlenTheMachine | 2025-06-11 01:57:24+00:00
Also, for the type of work I'd do with an arm like this, I'd be more than happy to just have the follower arm. You need a leader arm to do some types of teleoperation or imitation learning, but not really to do reinforcement learning or learn about control theory.

What you do need is an articulated rigid body model that you can import into e.g. NVIDIA Isaac Lab or Gazebo. The availability of a good digital model is a HUGE selling point.
by shmerl | 2025-06-11 01:57:54+00:00
It's the truth standpoint. DRM is an overreaching preemptive policing, i.e. by its mere definition it's always aimed against the user, therefore it's always an anti-feature.

Things like fourth amendment exist for a simple reason that overreaching policing skews into being abusive. Police could always argue abusive policing "helps prevent crime" same as copyright maximalists could argue DRM "helps prevent piracy". But both would be invalid due to overreaching nature or such policing.

To put this concept into perspective. DRM runs on your personal device, in your personal digital space, for the benefit of someone who tries to police you, treating you as an a priory criminal. So conceptually it's not any better than what fourth amendment is aimed to prevent.

Excusing such concepts with "market forces" is simply cringe.
by RandyOrion | 2025-06-11 01:58:35+00:00
Below are my comments on Magistral small (not medium).

24B size is good for local inference.

As a model outputting long "reasoning" traces (~10k tokens), 40k context length is a little concerning.

Where are the results of normal benchmarks, e.g., MMLU/pro, IFEval and such.

Still, thank you Mistral team for releasing this model with Apache 2.0.
by cbsmith | 2025-06-11 01:59:50+00:00
Yeah, as soon as I read that I felt like the author was living in a very different context from mine. It's never even occurred to me that fine-tuning could be an effective method for injecting new knowledge.

If anything, I expect fine-tuning to destroy knowledge (and reasoning), which hopefully (if you did your fine-tuning right) is not relevant to the particular context you are fine-tuning for.
by stlava | 2025-06-11 02:00:42+00:00
I feel that if you need an LLM to help pivot between existing data it just means the operability tool has gaps in user functionality. This is by far my biggest gripe with DataDog today. All the data is there but going from database query to front end traces should be easy but is not.

Sure we can use an LLM but I can for now click around faster (if those breadcrumbs exist) than it can reason.

Also the LLM would only point to a direction and I’m still going to have to use the UI to confirm.
by voxl | 2025-06-11 02:00:49+00:00
It definitely tripped my "self-important bullshit author" detector, which doesn't happen to be a very useful heuristic since it requires me to read the whole article.

But anyway, the examples used are not in any way like another. The "futurist" in particular fundamentally lacked the "they do things well and then get sloppy" which leads me to believe the author has a particular axe to grind about the topic of tech progress.

For a lot of the other examples, the entire point is that the person knows they are being sloppy, they know they're letting issues slip through the cracks. If you know you're doing a bad thing, what use is the analogy to a heuristic? It's not a heuristic to be sloppy, it's just being sloppy.
by tptacek | 2025-06-11 02:01:07+00:00
Look, I like Honeycomb a lot and we're dependent on it for parts of our orchestrator. It's great; it accelerates investigations.

But even with Honeycomb, we are sitting on an absolute mountain of telemetry data, in logs, in metrics, and in our trace indices at Honeycomb.

We can solve problems by searching and drilling down into all those data sources; that's how everybody solves problems. But solving problems takes time. Just having the data in the graph does not mean we're near a solution!

An LLM agent can chase down hypotheses, several at a time, and present them with collected data and plausible narratives. It'll miss things and come up with wild goose chases, but so do people during incidents.
by NoahZuniga | 2025-06-11 02:01:10+00:00
I mean, its not that surprising. It seems most AI labs have seen that the value they will be able to extract from providing the models is going to be a small part of the value created by AI models.

Compared to the value extracted by selling models, a significant portion will go to data center operators, so it makes sense that Google wants some of the openai compute, and openai wants lower costs/more capacity than what they can get with azure.

Google doesn't think it will be damaging its own ai business that much with this.
by 0xCE0 | 2025-06-11 02:01:30+00:00
So, the design language of 2025+ is wobbly "organic" 3D'ishly morphing UI elements, either translucent or not. Surely it was hard work after engineering round-cornered windows and centered taskbars. Can't wait to see the future innovations of these $1T+ companies.

- https://www.apple.com/newsroom/2025/06/apple-introduces-a-de...

- https://m3.material.io/blog/building-with-m3-expressive

- https://fluent2.microsoft.design/design-principles
by edot | 2025-06-11 02:01:43+00:00
Have you tried 3D printing? It’s a pretty cheap and forgiving way to get into manufacturing. You’ll get a lot of practice with CAD. Autodesk Fusion 360 is free for hobby use and very good.

For machining, I’d start with wood (wood lathe or router, cheaper than a real metal lathe or mill). Very similar concepts as real machining (feeds and speeds, toolpaths, CAM). Up to you if you want to start manual or CNC. The Shapeoko routers are very nice and relatively affordable (in this domain). You can also find used manual machines locally, but it’s hard to know if they’re in good condition if you don’t know anything.

If you want to read, the Machinery’s Handbook, aka the Bible in any shop, is enormous and contains pretty much anything you’d want to know.

Oh, and get a set of Mitutoyo digital calipers. Expensive but the only way to go, in my opinion. Measuring is fundamental to manufacturing.
by bitpush | 2025-06-11 02:02:20+00:00
The first part of that statement is valid but the second one isn't.

If anything, most of big tech has shown exceptional humility against new threats

Instagram incorporating stories (Snapchat)

YouTube incorporating Shorts (tiktok)

Google search incorporating AI Mode (perplexity et al)

This is in stark contrast to Kodak and the likes who scoffed at digital camera and phone cameras as distraction. They were sure that their ways were superior, ultimately leading to their demise.
by reissbaker | 2025-06-11 02:02:45+00:00
Clickbait headline. "Fine-tuning LLMs for knowledge injection is a waste of time" is true, but IDK who's trying to do that. Fine-tuning is great for changing model behavior (i.e. the zillions of uncensored models on Hugging Face are much more willing to respond to... dodgy... prompts than any amount of RAG is gonna get you), and RAG is great for knowledge injection.

Also... "LoRA" as a replacement for finetuning??? LoRA is a kind of finetuning! In the research community it's actually referred to as "parameter efficient finetuning." You're changing a smaller number of weights, but you're still changing them.
by woopsn | 2025-06-11 02:03:11+00:00
Artificial intelligence is a nourished and well educated population. Plus some Adderall maybe. Those are the key insights which represent the only scientific basis for that term.

The crazy thing is that a well crafted language model is great product. A man should be content to say "my company did something akin to compressing the whole internet behind a single API" and take his just rewards. Why sully that reputation boasting to have invented a singularity that solves every (economically registerable) problem on Earth?
by tptacek | 2025-06-11 02:04:15+00:00
One of the interesting things an agent can do that no individual telemetry tool does effectively is make deductions and integrate information across data sources. It's a big open challenge for us here; in any given incident, we're looking at Honeycomb traces, OpenSearch for system logs, and Prometheus metrics in a VictoriaMetrics cluster. Given tool calls for each of these data sources, an agent can generate useful integrated hypotheses without any direct integration between the data sources. That's pretty remarkable.
by hug | 2025-06-11 02:05:11+00:00
The author would do well to check out Kagi -- the search results for all of the suggested queries are, to my eye, better than the results they found.

Given that Kagi's higher tier plans come with search-enabled LLM chat interfaces, and those searches use Kagi's results (which, again, appear to be superior) it seems to me that you get the best of both worlds: Better search, and better search results to feed into your search-enabled LLM queries.

I am not affiliated with Kagi or anything, it's just honestly that good a product.
by sothatsit | 2025-06-11 02:05:27+00:00
Recently, Sean Heelan wrote a post "How I used o3 to find CVE-2025-37899, a remote zeroday vulnerability in the Linux kernel’s SMB implementation". It might be what they are referring to.

Link: https://sean.heelan.io/2025/05/22/how-i-used-o3-to-find-cve-...
by genocidicbunny | 2025-06-11 02:07:36+00:00
When I was involved in the hiring pipeline, I absolutely felt a level of personal responsibility since I was directly contributing to the decision to hire or not hire an applicant. That's not to say I was willing to shoulder the entirety of the responsibility, but knowing that my decision would affect not only the applicant, but their potential future coworkers too, I did feel responsible for making sure I had as much information as I could get and that I was making the best decisions I could.
by therealdrag0 | 2025-06-11 02:07:47+00:00
What are you trying to get at?

Here’s some indicator of damages. 1500 damaged businesses in Minneapolis alone: https://www.newsweek.com/businesses-year-after-floyd-1596610

And typical estimates are a couple billion in insurance claims (I think nationally?)

Are you just arguing about if it’s “a lot” or not. Thats entirely subjective and not worth debating.
by yahoozoo | 2025-06-11 02:08:57+00:00
Perhaps Apple is being cautious about going super deep into AI like, quite literally, every other company? Maybe they see generative video/image/audio as slop, or maybe they don’t think LLMs are going to get much better? Didn’t some of their researchers just release a paper basically saying “LLMs don’t really do what you think they do”?
by AdieuToLogic | 2025-06-11 02:10:38+00:00
This post is a thinly veiled marketing promo. Here's why.

Skip to the summary section titled "Fast feedback is the only feedback" and its first assertion:

... the only thing that really matters is fast, tight feedback loops at every stage of development and operations.

This is industry dogma generally considered "best practice" and sets up the subsequent straw man:

AI thrives on speed—it'll outrun you every time.

False.

"AI thrives" on many things, but "speed" is not one of them. Note the false consequence ("it'll outrun you every time") used to set up the the epitome of vacuous sales pitch drivel:

To succeed, you need tools that move at the speed of AI as well.

I hope there's a way I can possibly "move at the speed of AI"...

Honeycomb's entire modus operandi is predicated on fast feedback loops, collaborative knowledge sharing, and treating everything as an experiment. We’re built for the future that’s here today, on a platform that allows us to be the best tool for tomorrow.

This is as subtle as a sledgehammer to the forehead.

What's even funnier is the lame attempt to appear objective after all of this:

I’m also not really in the business of making predictions.

Really? Did the author read anything they wrote before this point?
by jazzyjackson | 2025-06-11 02:10:44+00:00
(having no insider info:) Because it can be used as a primary key ID across aggregated marketing databases including your voting history / party affiliation, income levels, personality and risk profiles etc etc etc. If a company wants to, and your data hygiene hasn't been tip top, your phone number is a pointer to a ton of intimate if not confidential data. Twitter was fined $150 million for asking for phone numbers under pretense of "protecting your account" or whatever but they actually used it for ad targeting.

>> Wednesday's 9th Circuit decision grew out of revelations that between 2013 and 2019, X mistakenly incorporated users' email addresses and phone numbers into an ad platform that allows companies to use their own marketing lists to target ads on the social platform.

>> In 2022, the Federal Trade Commission fined X $150 million over the privacy gaffe.

>> That same year, Washington resident Glen Morgan brought a class-action complaint against the company. He alleged that the ad-targeting glitch violated a Washington law prohibiting anyone from using “fraudulent, deceptive, or false means” to obtain telephone records of state residents.

>> X urged Dimke to dismiss Morgan's complaint for several reasons. Among other arguments, the company argued merely obtaining a user's phone number from him or her doesn't violate the state pretexting law, which refers to telephone “records.”

>> “If the legislature meant for 'telephone record' to include something as basic as the user’s own number, it surely would have said as much,” X argued in a written motion.

https://www.mediapost.com/publications/article/405501/None
by lantry | 2025-06-11 02:11:40+00:00
> AI could make it happen if it manages to dispense with all these lousy proxies.

The thing that you, altman, and every other AI booster fail to explain is how AI will do this. Every argument is littered with "if"s and "could"s, and it's just taken for granted that AI will be smart enough to find a solution that solves all our problems. It's also taken for granted that we'll actually implement the solution! If AI said that curing cancer requires raising taxes, cancer would remain uncured.
by sanderjd | 2025-06-11 02:11:45+00:00
No, it's not what happened... The election was not won with 35% of the popular vote...

Again, if you insist on talking about "35% of people in the US" rather than "35% of voters", then fine, but I think it's a weird way to talk about it. We don't know what the people who didn't vote thought about the candidates. Voting is the way that we find that out!
by imgabe | 2025-06-11 02:12:28+00:00
USAID is not the sole source of administering AIDS and malaria treatments. No doubt many more people were successfully treated because those treatments exist.

I don't think I understand your point. We shouldn't develop new treatments because one country won't supply them to the entire world for free? Why is it the US's responsibility to treat every disease everywhere in the world? Do these countries not have their own governments?
by gklitt | 2025-06-11 02:12:39+00:00
Neat —- Scrappy looks like a lovely prototype! As the creators say in their writeup, it fits nicely into the lineage of HyperCard-style “media with optional scripting” editors, which provide a gentle slope into programming.

In the section on dynamic documents towards the end of our essay, we show several of our lab’s own takes on this category of tool, including an example of integrating AI as an optional layer over a live programmable document.
by techpineapple | 2025-06-11 02:14:55+00:00
Right, but the vision isn’t LLM troubleshooting, the title of the article isnt look at the way you can troubleshoot with an LLM, it’s “its the end of observability as we know it). he goes on to say it’s AI constantly analyzing data in a Unified sub-second database? Even without the AI that’s expensive.
by Nevermark | 2025-06-11 02:15:33+00:00
> My take: echo chambers have become mainstream (ironically, aided by technology).

Technology can scale up small conveniences into major economic and quality of life wins.

But when it's tolerated, technology also ramps up seemingly small conflicts of interest into economic and society-degrading monsters.

Our legal intolerance for conflicts of interest as business models needs to go up a lot. No amount of strongly worded letters, uncomfortable senate interviews, or unplug-the-system theater, are going to discourage billionaires farming people's behavior, attention and psychology from continuing to farm people's behavior, attention and psychology.
by deadbabe | 2025-06-11 02:16:57+00:00
These days chatbots are good enough that when I do use a search engine, I really just want pure search results, I’m not interested in getting another AI opinion. I am sick and tired of getting AI overviews for a google search. What’s a better search engine? Heck, it doesn’t even have to be “better”, I’m looking for different results, not just perfect matches.
by rezatabrizi | 2025-06-11 02:17:13+00:00
If you've ever looked for a recipe, you know the pain of digging through ads just to find the ingredients. Then you have to make a grocery list and run to the store, hoping you don't forget anything (and if you're like me, you definitely will). That might be fine… until you try tracking your macros. Then it becomes a nightmare of trying to find a recipe that fits your macros and doing the math (9 * fat + 4 * protein … you get the idea). It's tedious, boring, and exhausting (which is why I end up running to Chipotle).

But I also love cooking, so I wanted to make cooking healthy, macro-friendly meals frictionless. Skip the scrolling, skip the macro math, skip the grocery store. Just tell the AI what kind of recipe you want—it’ll generate not 1, but 3 that fit your macros and send you to an Instacart page with all the ingredients pre-filled based on your desired servings.
by bitmasher9 | 2025-06-11 02:17:38+00:00
I do wonder what would be an acceptable level of guarantee to trigger a “human written” bit.

I actually think a video of someone typing the content, along with the screen the content is appearing on, would be an acceptably high bar at this present moment. I don’t think it would be hard to fake, but I think it would very rarely be worth the cost of faking it.

I think this bar would be good for about 60 days, before someone trains a model that generates authentication videos for incredibly cheap and sells access to it.
by miki123211 | 2025-06-11 02:18:00+00:00
If somebody writes in a foreign language and asks Chat GPT to translate to English, is that AI generated content? What about if they write on paper and use an LLM to OCR? What if they give the AI a very detailed outline, constantly ask for rewrites and are ruthless in removing any facts they're not 100% sure of if they slip in? What if they only use AI to fix the grammar and rewrite bad English into a proper scientific tone?

My answer would be a clear "no" to all of these, even though the content ultimately ends up fully copy-pasted from an LLM in all those cases.
by Vetch | 2025-06-11 02:19:25+00:00
I think the point remains that few have been able to catch up to OpenAI. For a while it was just Anthropic. Then Google after failing a bunch of times. So, if we relax this to LLMs not by OpenAI, Anthropic or Google, then Deepseek is really the only one that's managed to reach their quality tier (even though many others have thrown their hat into the ring). We can also get approximate glimpses into which models people use by looking at OpenRouter, sorted by Top Weekly.

In the top 10, are models by OpenAI (gpt4omini), Google (gemini flashes and pros), Anthropic (Sonnets) and Deepseeks'. Even though the company list grows shorter if we instead look at top model usage grouped by order of magnitude, it retains the same companies.

Personally, the models meeting my quality bar are: gpt 4.1, o4-mini, o3, gpt2.5pro, gemini2.5flash (not 2.0), claude sonnet, deepseek and deepseek r1 (both versions). Claude Sonnet 3.5 was the first time I found LLMs to be useful for programming work. This is not to say there are no good models by others (such as Alibaba, Meta, Mistral, Cohere, THUDM, LG, perhaps Microsoft), particularly in compute constrained scenarios, just that only Deepseek reaches the Quality tier of the big 3.
by chiefalchemist | 2025-06-11 02:19:30+00:00
Re: sold access to them to Customs and Border Protection (CBP), and then as part of the contract told CBP to not reveal where the data came from, according to internal CBP documents obtained by 404 Media.

Irrelevant and pointless. If CBP had such data where else could it come from?

Uncle Sam, who could legislate privacy protections, loves the fact there is so little protection. No need for legal channels and approvals when the data can be purchased directly or indirectly.