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I interviewed as a software developer at LinkedIn. The interviewer asked me to demonstrate my prompting skills, so I had AI write an article about what the recent death of my father taught me about B2B SaaS. Reading it brought tears to his eyes so he hired me on the spot.

I also aced my interview by focussing on pelicancode problems, instead of leetcode problems.

"I was interviewed for a job as a software developer last week and they asked me to draw a picture of a pelican riding a bicycle. Aced it, got the job as a senior software engineer."

That is the best joke I have heard this year. Ready for a stand-up comedy special. Or a song. Superb!

You should post your source code you wrote here… ;)

If you could actually hand write SVG code on the spot that looked like a realistic pelican riding a bike I would want to hire you for SOMETHING.

Yes. It's because you are asking it to generate an image of a pelican riding a bicycle. If someone asked you to draw a pelican riding a bycycle, would you interpret that to mean using 3d photorealism? LLMs follow conventions. The convention for an animal riding a bike is to create a childish 2d line drawing.

The more generic your prompt, the more generic the response. It's a regression to the "mean" of the training data aka GIGO for AI.

It's like when you ask your average person off the street to draw a house - it'll almost always be square with a triangle roof, one door, and two windows.

In the pelican/bike example, it's probably a bit of a self-perpetuating snowball too. If the earliest examples were bike left-to-right, flat ground, etc. then they are also being scraped up in future LLMs.

Not when rendered via POV-Ray:

https://blog.nawaz.org/posts/2025/Oct/pelican-on-a-bike-rayt...

I plan to update it with more pelicans from all the models released since.

(Spoiler alert: They haven't improved much since then).

It's really interesting, isn't it? They almost always cycle from left to right - but I have had a few which cycle in the other direction.

The 2D / flat ground feels reasonable for a SVG, which implies a vector illustration.

The canonical view of a bicycle is facing right. Usually, people want to draw/photograph/depict the side of the bicycle with the running gear, which is on the right side of the frame for historical reasons.

Search Google Images for "bicycle". Almost all bicycle product shots are staged the same way: side view, going left-to-right. It makes sense to me that given that skew in the training data, the model grounds itself in the bicycle.

The thing that distinguishes pelicans from other birds does so most strongly in profile. If you're looking straight at one, the throat pouch would be hidden by the beak.

I bet if it instead had something to do with black widow spiders we'd find that we're most often looking at the bottom of the spider's abdomen, regardless of whatever non-spider-like activity is supplied.

well it is svg, it is doing it from circles and lines as primitives, it wants to do it simply and kind of builds the whole thing hierarchically. Making it 3d is way more complicated (as the POV example shows) and the prompt doesn't say 3d anyway

I’m a firm believer in pelicanmaxxing.

They’re all so close in proportions.

Sun is missing a few rays and not wearing sunglasses.

Yes, I do a thing where I ask the machine to generate responses in the form of a lizard talking to a cat. The lizard is always a green gecko and the cat is always orange, which I never specify.

Is there a reason these pelicans always have roughly the same composition

Because they're computers. They don't have an imagination and the ability to create things from whole cloth the way humans do.

Much like a mother pelican, they regurgitate what they've been fed.

We should just consider the pelican bench as saturated and mostly meaningless.

https://en.wikipedia.org/wiki/Bird_feet_and_legs#/media/File...

Bird knees bend same way human ones do

I think it's hilarious that this detail is enough for me to dismiss looking into the model, but here we are, and it is.

It would be very embarrassing for any lab to benchmaxx the pelican on bicycle svg prompt, since it would be very easy to detect it by varying the prompt.

You win this thread's prize:

https://news.ycombinator.com/item?id=49538333

It also works as extremely effective engagement farming, for lack of a better phrase

Red eyes and a tattoo?

The useful training data is when you clarify your intent, when you tell the model a different approach would be better, when you consistently refactor towards Y and away from X, and so on. The training data isn’t the code, it’s the session transcript. (Anthropic would call this a “distillation attack” against their model, but in this case the model is you!)

Funny. I use it through Opencode Go which gives more use than I can use, but didn't realize it was actually free on Zen. Will switch to that I guess

I would imagine your interactions with it are more important than the output.

Every lab trains their models with AI generated code at this point.

Training on ai generated content is how the models got a big jump in capability

+1. I've used the recent Gemini Flash models and I've used Opus 5, and the latter makes the former look like a box of broken crayons. Unless Flash 3.8 and/or this Muse Spark model are a much bigger deal than people seem to think, I will eat my hat if either one can come close to Opus 5 in actual real life "long-horizon software engineering" tasks.

(I'm not happy about the above being true, but it's the reality I seem to inhabit.)

and they're ultimately tools strictly to replace you and your labor, they can't/won't cure cancer or make your life better. Your life will get worse and worse in every aspect until they extract maximum value from all of our lives with this technology through every avenue possible. Not sure why you guys are so excited about these developments.

This technology is strictly an extractive parasite on the world. Use it, but don't be excited.

Both versions of DeepSWE (1.0 and 1.1) are likely not that meaningful anymore. Whether through models progression or through contamination.

Muse 1.2 wrote a terrible "smart summaries" extension for my pi setup. It was sending every single steamed chunk for summarization instead of waiting for the full CMD.

This is an error I would expect from sonnet 4, not a model that was supposedly just a few points behind sol.

It's somewhat useful to note just for your own timelines that Fable was reportedly trained in February. I'm not sure when mythos 5.1 finished training, but muse spark 1.3 almost certainly finished more recently than that.

This doesn't mean it's not one of the best models available (clearly it is), but that table didn't compare Fable/mythos (unless I missed it?) and OpenAI will be releasing a much more recently trained model (Astra) any day.

So you shouldn't think "wow, Facebook has caught up"

You should think, "wow, Facebook is less than 6 months behind the frontier" and that they're actually creating good models which is going to be good in many ways (price for customers, for one!)

There are downsides too, but I'll discuss those separately somewhere

You gotta keep up. Fable 5.1 came out yesterday and is better so anything else is to be treated as garbage now.

If my experience with image generation is any indication, unless AWS keys are somehow extremely prevalent in the training data, you may get something that looks like one, but it definitely won't be valid.

A small number of inputs in a large dataset can poison training data pretty drastically. Anthropic wrote a good article about it a while back [0]. This should mean its possible to pull back that information fairly easily.

It is hard to not feed it "secrets" too. Models will see path names, read compose files, etc. Of course you can configure things to not leak this type of information, but its not default in most harnesses and isn't 100% sufficient anyways.

[0] https://www.anthropic.com/research/small-samples-poison

doesn't mean the raw text goes into training. they most likely have a pipeline to clean out any secrets before they train on it?

Muse Spark may be competitive in capabilities but it’s not for serious works since Meta trains on your prompts so no ZDR, in contrast Chinese provider like Z.AI promises ZDR which is more attractive to big corps.

It's the perfect model for open-source work because it's gonna end up in the training data anyway

There's a lot of value in agentic loop tool failure + recovery training data

Web Search doesn't have a discount on contributor pricing

Well thats very interesting. Thank you. Will be interesting to see how hard/easy it is to translate my Claude skills, loop design, etc to the new harness.

This kind of raises another question to me regarding the coding benchmarks, how much of it is model versus harness?

> Deepseek proposes RLVR as a way to get around the lack of $ they have to produce human reasoning trace data.

What was the difference between what deepseek did for R1 and what OpenAI did for o1?

o1 was first, and Anthropic were doing a bit of it; DeepSeek brought it to the masses, but did not invent it.

I don’t know why people think DeepSeek did reasoning models / RLVR before OpenAI, there was a gap of months.

Thanks. Just downloaded and pretty impressed so far. It's fast and nice to work with.

Any more info on this?

It makes me sad that people don’t see right through Anthropic’s gambit.

They want to position AI as an insurmountable threat in order to regulate away any future competitors. They’re trying to speedrun regulatory capture.

Which safety commitments did they back away from? My understanding is that they believe safety can only be researched from the frontier, and so they're trying to be pragmatic to stay near the frontier (and viable) in their choices.

From what I know, the "books3" dataset was normalised in the LLM and research ecosystem, where collected datasets were seen as valid to train on and/or fair use. I'm not sure any of the major frontier companies are free from that, if we don't believe it was fair use.

I do think most of their choices are explainable by "they just believe in agi risk". You truly wouldn't want non-agi-pilled companies to train on your data and approach the frontier if you were worried. You might slightly hurt your own business with safety filters (that no one else does) if you were worried. They are less worried about other "moral" decisions like "sharing" if they conflict with AGI: the research they still share is all of their safety research.

This definitely doesn't make them "good", but they do seem fairly "consistent". Most of these issues were talked about publicly by the founders long before Anthropic was founded and/or the AI race+money appeared.

> Anthropic leadership

Which one? The main bit that reports to Daniela Amodei, or the little comfort blanket cabinet around Dario and his "chief of staff"?

There is a leadership branch that can pretend to be morally qualified and aware and to think about the big picture and ethics.

It is at least somewhat remote from the bit that is doing the actual business things.

I will give Anthropic credit for standing up against the department of war. The bar is incredibly low, but not doing domestic surveillance and not creating autonomous weapons are laudable.

That doesn’t mean I like them pirating books and being shady about tokens and paternalistic “safety”

It's hard for me to see much difference between Amodei and Sama. My guess is they're both savvy SV CEOs who will bend their message, alliances and principles pretty far if that's what it takes to get ahead. Musk and Zuck feel like something else entirely, with all the reactionary imagery, populist bullshit and the societal damage around their platforms.

It's almost like running a trillion-dollar business with neck-to-neck competition against other frontier labs and even state-sponsored efforts requires some ethical trade-off.

Pirating books is just straight up morally correct. I don't like Anthropic's bullshit "safety" filters, but training on shadow library data? Yeah no, it makes sense.

It makes a lot more sense than having to work around copyright by scanning out physical books. Unfortunately, one was ruled legal and the other was not.

Yeah this would be a great point if it were true and they didn’t give Mythos access to companies to fix bugs, which they did and have.

It’s genuinely a difficult question. Not black and white. The models are really good at finding bugs, as demonstrated by people using Fable to reverse engineer. People make it sound like he’s just making it up.

Dario's "ethical" look is also kinda sus. I hate to use ad hominem, but the dude's wife literally pitched a porn film to Epstein even after he was a convicted registered sex offender [1]. Dario is also really sinophobic (it is commonly claimed in Chinese AI circles that his former employment at Baidu triggered him so much that he harbors a personal grudge against the entire race).

[1] https://www.forbes.com/sites/alisondurkee/2026/08/14/who-is-...

Pretty much. That's even worse imho.

I just don’t see how people have looked at what has happened with Mythos and the deluge of fixes from companies, then come to this conclusion.

He has a really hard job. He errs on the side of conservatism in releasing and then people get Really Mad.

Safeguards on cybersecurity are not great for Anthropic revenue! As evidenced by people getting pissed, moving to Sol, and them having a smaller market for what Fable can do.

It’s clearly bad for revenue and not great advertising to say, “you can’t use this but here is a nerfed version that will annoy you and not solve important problems.”

Technology doesn't just spring into being, there will always be comments on the organizations that developed it. If you don't like them or find them repetitive, it is far easier to collapse them and move on then bend a stranger to your will

I get it, but the underlying problem is: we don't have a society-wide, effective solution to counterbalancing extractive systems. Lacking a reliable label, we have to constantly signal what's on the ingredients list.

What I'm tired of is the top story (or five) on HN every day announcing Spark Opus Fable Grok Gemini v4.1i3-F. Like, who actually cares? Are people excited for the new benchmarks? Is it interesting to read the model cards? And look, part of my job is to use these things and part of my job is to pick EC2 servers, too. The front page of HN is increasingly resembling one of those endless AWS pricing lists.

And yeah, I don't like any of the people or companies building LLMs either. At least the griping is somewhat interesting by comparison. The model isn't news. The news on Hacker News is that other professionals feel the same way.

That's not how any of this works my man. Must be nice to think you live a life where neither has had a profound negative impact on your day to day

> If you don’t like it […], then maybe just […] stay silent.

You might consider following your own advice.

yeah, we should all just stfu because one internet dude is tired of hearing it

Not liking something because the embodiment of corporate malfeasance is a rational way to decide what products to support.

Staying silent is unfortunately how fascism festers.

Zuck's bad PR is to blame here. Not the commenters. He should fix that.

Anduril makes this same complaint whenever their job posts get dumped on. Same idea. Fix your bad PR, buddies :)

> But the post is about Muse Spark 1.3. What do you think about that?

That:

- like all models it was trained on stolen data

- additionally it was trained on Facebook users who were all opted in to AI training with a convoluted 10+ step process to opt-out of

> If you don’t like it because Meta made it, then maybe just don’t use it and stay silent.

Why should anyone stay silent?

Well what do you want? Presenting clear, desirable, and achievable visions and trying to build consensus for how AI should develop is crucial at this point in time.

Totally fine with open weight, since other people can provide it and Meta isn’t making money. I’d use an AWS-hosted version.

I am finding Poolside's a decent model.-

He didn't say 90% of the coding jobs. He said LLMs would write 90% of the code. As in be LLM generated.

They don't all suck equally.

Here's the order, from best to worst.

Amodei

Google

SamA

Zuck

Elon

How is this a tangential annoyance or a generic tangent?

> Meta announces they have a new model, demonstrating its capabilities.

> Parent comment states „regardless of this model‘s specific capabilities, if I can avoid it I will.“

"Avoid generic tangents" / "Please don't complain about tangential annoyances."

That's pretty much 90% of HN these days.

Apple releases a new iPhone? Here comes the flood of decade-old complaints about long-discontinued Mac butterfly keyboards and walled gardens.

Microsoft releases a new version of Windows? Here come the gripes about Azure.

Google changes something in GMail? Play Store!

It's like there's an army of bots out there determined to reduce the productivity of the Western tech bubble by diverting everyone into endless circular arguments about absolutely nothing of relevance to the topic at hand.

someone expressing their view of meta which is on point by the way.

Enabled genocide in Myanmar