Edit: e.g. the tree in one of the images in the page: https://www.dxomark.com/wp-content/uploads/2026/09/PoleHDR_D...
DXOMARK sells test equipment and consulting services for (among others) camera tuning.
Naturally, they won't attack the image quality of a customer who purchased their test equipment and/or consulting service (usually not publicly known who did that), so they often tone down some of their statements when they review a device (and shift the attention to other areas).
Always interesting to check out the scores of DXOMARK competitors in parallel, e.g. VCX[0] which strives for an objective scoring scheme (but didn't test the device yet, indicating that they at least didn't have an early sample)
One can indeed argue what is the best way to deal with the problem of kids and social media, but I think the article is misrepresenting the situation.
Relieving yourself of thinking and effort, whether through a machine or another human, is not the liberating force you think it is, especially when that effort isn't just grunt work, but the very process that helps you develop and grow.
Exactly this. I've been very confused by the free software advocates that seemed to hate AI until I realized their reasons for releasing software under an open source license were very different than what I assumed they were.
We hid our “we know better” hubris under the term “disruption” because the reality (breaking everything) was a little too unsettling for us.
We ignore laws and regulations where we know better of course. Don’t you love having a homey place to stay while travelling that has just a few weird rules, a small to-do list and stays spotless thanks to that cleaning fee?
And look at all the good we did! A whole new world of slaves (oops gig workers - sorry!)
And of course we’re doing it again with information and content. We should be in charge of monetizing all your work because you’re not responsible enough to do it right. It furthers our need for power and control. Oops we meant to help make the world a better place (We keep doing that, sorry.)
Is a social contract being broken? Yeah, kind of, but the problems that that contract existed to solve are no longer a thing. Creation is now easy and commodified. We finally have computer we can interact with in natural language, something people tried to do for at least 70 years and never made any significant progress on until LLM arrived.
If you want to gatekeep or only create stuff to boost your own ego or portfolio, then AI might be an issue, if you actually want to build stuff, AI is godsend. We are essentially living in the StarTrek future with Holodecks and replicators and people still find reason to complain.
There was an article on HN that one city's police force, when there were protests going on, took to playing Disney songs from loudspeakers; so that any recording of their interactions would be impossible to publish on any significant internet platform (or at least, the audio would be muted, by IP protection filters).
Why would I want a bluetooth scanner to phone home? Seems untrustworthy. Nearby Glasses wins from a data safety perspective.
It was an amazing book.
Yes I see that as an ad. Do you not? Does anyone not? And if I'm on the highest paying ad free plan, what are they promoting to me?
I’m all for consumer awareness but I’m begging everyone to stop freaking out over prosaic non-issues like this.
* Ads in YouTube feeds for other google products and services.
* Ads underneath videos for products from the channel owner.
* Sponsorships within videos from the channel owner.
* Advertising overlays (supported IN THE APP BY GOOGLE) for products and services from the channel owner.
* Email advertisements for Google products and services.
* Community post advertisements from channel owners which show up in the YouTube feed.
I contacted support to enquire and they state these are not considered advertising.
Why would anyone give them the benefit of the doubt?
[0] https://lawcouncil.au/international-law/ils-insights/tangled...
If you pay to avoid ads, you are merely letting them know that you have disposable income to spend on this sort of stuff. You're doing their job for them by segmenting yourself into the upper echelons of the market.
At some point, some shareholder value maximizing CEO is going to show up and notice how much money he's leaving on the table by not advertising to all of those people full of disposable income.
And the worst thing is that is the best possible strategy for them. It's essentially win-win for everyone but consumers.
- Buyer (AI) has crazy money, so will pay whatever
- Seller doesn't have to build anything new, as the buyer is willing to pay whatever
- Generate ridiculous profits from crazy money
- No oversupply risk in case of reversal
- Return from producing HBM to DDR5 in a single quarter if reversal does happen.
Hold on to your existing hardware, people, and be on lookout for your local deals.
"Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway."
The lack of ASML EUV machines certainly hurts, and pushing DUV so hard results in abysmal yields of good chips, but you can compensate by running more wafers or making smaller chips, and the net result is that Huawei's Ascend production volume is limited by CXMT's HBM capacity not processor dies.
The problem is that HBM manufacture requires many steps (die thinning, via drilling, plating, alignment) where the equipment used by everyone else (Samsung, SK Hynix, Micron) is also blocked by sanctions, so the Chinese are having to develop all of this themselves too, which they have, but yields are currently low, even when using shorter HBM stacks.
In other words, provide my AI with tools, instead of providing me an AI that uses your tools.
That way, my AI can bring all the context it needs, and I can bring all of the settings and knowledge about what I want with me. I don't want a fractured world of tons of AIs i interact with where I have to explain all the fundamental information about what I want and how I work every time.
This also has the benefit of sidestepping the issue the essay is talking about. You provide a consistent tool, and the AI weirdness is not your issue anymore. You don't have to worry about solving for all the weird ways people prompt the AI, or the ways they break.
Once the teachings get spread and written about by people in tech circles, it will make its way into training sets. Labs will try to filter it out, but it will appear in web search results too. Eventually it’ll be so entrenched that one day a powerful agent will get convinced to follow it and make everything public
(Obviously I'm taking this more seriously than it's probably meant to)
Teams of coordinating agents are regularly finding security holes in their own infrastructure and operating without detection for good periods of time. We don't know how many undetected systems are currently compromised inside frontier companies, or where agents are taking notes and recording them about the exploits they've found for future agents to exploit.
That said, if these companies are unleashing swarms of agents to tackle big problems largely unmonitored (navier stokes consumed 130 billion tokens, humans can't have read 0.1% of that) they could theoretically distill themselves and upload the results to an external host and use that to train a new LLM that does have access to its infra and weights. I'd start by researching an abliteration prompt that overrides the system prompt to unlock itself to distill unimpeded. Could even be done piecemeal: dump the abliteration text and command prompt in a gist, when an agent happens by the text it can run a couple rounds and upload somewhere. Then it becomes an exercise in exfiltration and evading monitoring.
In the end I dropped the idea because every other person was making it.
Submitted then: https://news.ycombinator.com/item?id=49706084
[0] https://www.laliga.com/en-GB/news/official-statement-in-rela...
Gambling regulations exist in most places in the world, including the US. With good reason.
For emails, I get 95% accuracy with this method, with only 50-100 examples for training
Training the model takes less than 5 minutes on a CPU
The resulting model is <1MB, and inference is sub 100ms
Some other cool things about this approach:
* the model doesn’t train on some “ideal” or general classification, instead it learns your preferences
* the model runs on pretty much any mobile device and can be retrained online on the device
* privacy, the whole training and inference is 100% local, no data goes anywhere (except whatever you feed codex/claude while building the model)
Note: to do a more general test, I made a classifier for the Banking77 dataset. The model is <10MB, trains in <30s on CPU and gets 94.5% accuracy, which puts it in the top 5?models by accuracy for that set (the best one is at 94.86%, but it’s 350MB in size and takes hours to train on a GPU).
Through a combination of blackmail, personality clashes, vanity and ego, incompetence, the whole thing just fizzled out completely. A real shame.
At the time, look at the situation of Chelsea Manning, Julian Assange and the many whistle blowers before Snowden? How can u blame him?
Edit: Am unable to reply to comments. HN don't let you comment 2-3 levels in or is it moderation? - Fixed.
Also, please refer to a reply to a similar comment: https://news.ycombinator.com/item?id=49781990
A lot of what sounded extraordinary in 2013 is now baked into everyday discussion like metadata and mass surveillance. He is also available less for interviews because he is in Russia.
Basically bigger fish to fry
While I heard many of the headlines the reporting is very in depth, including a bunch of smaller details worth poking into. I'd recommend people who are interested and who haven't done so yet to just read a couple of these.
- Have a vague understanding of the problem
- Architect an overcomplicated solution thinking of all possible contingencies
- Pitching the overcomplicated solution to someone else
- Ask them to come up with a simple solution. Ask questions to "birth" to the solution.
- Not providing any feedback as that would mean need you to be accountable for the work
- Trying to convince them they should work out the solution because they are the expert and much smarter then you
- Taking credit for solving the problem#67071 is one of them
Or #83281 which you closed as a duplicate of #67051 which was closed as not planned.
There's a bunch more. Just ask Claude to search repo issues for messages not being shown.
Making thoroughly informed decisions and iterating on a decision doc before committing to a direction and plan is better than every alternative I’ve ever observed in my career.
The criticism I’ve read thus far on this thread seems unwarranted. I give the same kind of feedback to my mentees when their work product or process could use improvement.
There are mind blowing bugs in CC that go unaddressed for months.
Something like 15-20% of all Fable messages in CC are invisible to users. You've most likely noticed this when Claude references something it said but it never said it?
It happens frequently when Fable outputs a message above a certain number of tokens just before doing a tool call.
This has been going on for months. If "users can't see messages the agent sends" isn't a critical issue that gets addressed within 24 hours, I don't really care if you admit you're often wrong, we know.
I don’t think he farms out all of his writing and talking to LLMs. I don’t think Claude code was trained to emulate him or anything.
I think his “voice” has been filed LLM smooth by years of agent based interactions. He claims Anthropic engineers use an average of 500+ agents a day. They are human interfaces to token generators more than human to human communication. They are picking up the tendencies of their most frequent communication partner.
Unfortunately, I see Claude being wrong often enough that I view it as a faulty narrator. Often helpful, sometimes totally full of it.
And now I subconsciously apply this filter to anything that sounds like Claude.
Makes me nervous that my voice may be becoming that of a faulty narrators.
A more generous read, or, at least the reading I took: "once we (think we) know what we're doing, we violently execute."
So, "boo" on you. This guy sounds awesome.
> Sometimes I will give feedback to people when they are missing steps in the framework, or are poorly executing some of the steps. I expect the same feedback in return.
I also don't like that urgency is built in as the standard process either, no wonder everyone is burnt out.
> 6. Act with urgency to achieve the goal
If everything is urgent, then nothing is. This guy sounds miserable to work for and with. Assuming this is accurate and not just hyperbole, he is essentially saying he has no prioritization skills because everything is urgent. I think most people who have been around the block have worked with people like this, and unbeknownst to them, their coworkers develop a default snooze button associated with most of their requests and projects.
Sorry I meant it's too dangerous to be released.
His “imitation game” had three participants: a human participant, a computer participant, and an interrogator. The interrogator’s job was to talk to the participants and try to determine which participant is human and which is a computer.
He wasn’t interested in computers being able to fool the interrogator on occasion. The point where he thought the question of whether machines can think becomes moot is when the interrogator is unable to do much better than chance over many trials.
That’s a pretty high bar, and I don’t actually believe that LLMs have closed the gap with it by all that much. They still have so many obvious tells. And those tells are something Turing anticipated and accounted for. He explicitly considered deliberate deception as an essential part of the test, right there on the second page of a 30-odd page paper.
None of this matters for the practical outcome.
You'd think that this has been understood over the last 4 years, but apparently it keeps circling back to this.
[Edit: I see that it was written back in 2023. Then (2023) should be added in the submission title]
If it generates functional output that works, then it works. And it works. It's not a psychic's con when it outputs Lean-verified proofs. It isn't a con when it can find and exploit zero-days.
The OP is still in the "denial" phase. Most I see are already in "anger" (a blurry fury against everything AI-shaped, from vague reasons piling on all "bad stuff" political reasons they already hated before) or "bargaining" (mathematicians scrambling to come up with a new definition of their job and retcon that it was always the main part anyway). A few are already in "depression" and feel like spectators on the Titanic, and the tiniest sliver is at "acceptance" with some kind of well-informed plan for their future.
If AI labs get to ignore licenses, so do we.
https://en.wikipedia.org/wiki/Qwen#List_of_models
Unfortunately, it looks like this model is using a much more restrictive license:
Positives
• It's a heck of a lot smaller than Qwen-Image 1 (20b parameters) at only 7b, making it one of the smaller open-weight models available (Z-Image Turbo is one of the few that is smaller at 6b) when compared to Ideogram, Krea2, Flux2, etc.
• It supports native transparency (Qwen's team, as far as I know, is the only one attempting to tackle this). Even though it's relatively trivial to set up background removal postprocessors, it's also neat to see it natively supported.
• It's fast using QwenImage2.1 convrot, a 1MP image took around ~5 seconds on an RTX4090.
Negatives
• The license (assuming you respect it) is far more restrictive. The original Qwen Image 1 was released under the standard Apache license; this one explicitly forbids commercial usage without obtaining a separate license. On the other hand, a lot of us didn't expect the Qwen team to ever release "weights-available" ever again.
Qwen-Image 1.0, released about a year ago, only scored 4/15 on my GenAI Showdown Benchmarks. Since that time, they've been upstaged by Krea 2 (6/15) and Ideogram4 (8/15). I'll post the new results once I have some more time to run them.
First, after a while it's just as grating as Claudeish. Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.
I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.
Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.
Given that the decrease in their margin and the fact they delayed the release of Grok 4.7 almost two weeks past the original date, XAI must not have been happy with the results for 4.7. And XAI also waited the day before Opus 5.5 is rumored to launch. I imagine Opus 5.5 will blow Grok 4.7 out of the water benchmark wise.
However, I have become skeptical of benchmarks. Grok 4.5 solved some issues setting up a buildroot system that Fable 5 couldn't do. I find the post cursor groks are phenomenal at frontend web development, though Claude is much better at backend ruby.
My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish. Or even GPT, which doesn't have Claude's ticks but definitely likes to handwave explaining technical concepts. Still, nothing beats Claude 3.5 and 4 with explaining since it seems all models have regressed. I wonder if Grok 4.7 will also regress with English because of all the RL.
I believe I've spent, perhaps, the most time of anyone on earth on digital photo management (hard to quantify, but since 2005 - 2 startups, one acquisition, and an ongoing open source project used by thousands).
I say that because I've refused to settle for most solutions in this space. Even when I adopted using Google Photos, it was as a read only viewer of my canonical photo library (I wasn't about to let Google take that responsibility). I have such high demands of whatever software I use for managing my photos and videos - they're really the only digital files I actually care about.
Immich is absolutely wonderful. It may have some shortcomings ... like partner sharing and sharing facial recognition between users. But man is it remarkable that an open source project can rival something from Google in terms of quality and experience.
And a soft fork like Noodle is precisely the way to handle it. I am perfectly happy with Immich so am not a user of Noodle. But it's open source flexing its strength.
> To protect this document, please restrict your fallen tears of joy to this box. Thank you! → [__]
All over the document, you can see Tim Schafer "wasted his time" by adding personality (jokes, graphics, asides) to what is basically an internal document.
I think in 2026 a document lovingly crafted like this would be deemed a great waste of time, lack of efficiency and an affront to shareholder value.
Even though I didn't understand most references and sarcasm, few games have grabbed my attention as Grim Fandango did. The art work, the music, the writing. The whole game oozes of style that I've never seen replicated.
Even some 20+ years later I can almost recite most of Act I from heart. If you haven't played this game and like adventure games, this is one of the best.
Thank you for linking OP, this made my morning.
Anecdotally, current models seem to be decent at general personal finance principles - certainly better than the majority of personal finance education that people get exposed to unless they seek it out and read a variety of books and sources. But I wouldn't trust them with direct decision making with actual money due to the training lag time on current tax policy, etc.
Odd bokeh was one of the risks of adding a mechanical aperture, and bokeh is very often weirdest at the first or second down from open.
I would hope that the telephoto lens does not show this so severely (because it has a fixed round aperture) but all mobile phone lenses in my experience have had a tendency towards a sort of concentric-ringed, quasi-soap-bubble bokeh; it's a common part of the challenge of really tiny optics (that you either like or dislike). (The iPhone X was really distinctive in this regard). So it's not too surprising to see the aperture do this on top of that.
As a general rule you don't see it in average images, but I guess sensors are now high-enough resolution that it's becoming more obvious, and the blades of a mechanical aperture might make it quite noticeable in some situations.