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> More opportunities will be posted here in the coming months. Click here to sign up for updates to stay informed when new roles open.

Which links to: https://lp.constantcontactpages.com/su/sKWkWfp

Would anyone like to do some citizen journalism and see if the Constant Contact data handling is done above-board. I've done some Claude research -- enough to make me suspicious -- but I Am Not A Lawyer.

I understand the spirit of this comment (and I get it), but we want the opposite to be true. Let's find ways to support good people who step up.

Edits (in case my meaning above is not clear):

1. When I write "but we want the opposite to be true" I mean this: if only Trump-aligned or Trump-tolerant people sign up for these roles, I do not think this is desirable for NASA.

2. When I write "I understand the spirit of this comment (and I get it)" I mean: from an individual point of view, I fully grant that many people would be better off seeking work elsewhere.

3. My experience and scientific research shows that people are not merely selfish actors. While individual incentives matter a lot, perhaps even predominantly, it isn't accurate to claim that we can fully explain human behavior with exclusively narrow individualist framings.

4. Many of us act selfishly much of the time, and this is indeed reasonable and even beneficial at times. But taken to an extreme it can be worse overall, even for those individuals. See: game theory, social connections, morality, and so on.

5. When I write "Let's find ways to support good people who step up" I do mean concrete things such as "let's crowdfund ethical people's legal fees" to survive the Trump administration.

These job postings opened today on April 17 and close in four days (on April 21). This is highly compressed and highly unusual.

Being no fan of the current administration and its hangers-on, my brain quickly jumps to less flattering reasons for these short time windows. A four day application window favors people they want to select. They may well have told certain people in advance to be ready. I don't have direct "proof" of this, and I'm open to learning more, but the current administration has beyond exhausted any presumption of fair dealing.

I encourage anyone and everyone interested to apply and report back. NASA has a good mission and its needs people with a moral backbone and intrinsic pro-science drive.

This. / Who remembers the "birth" of crowdfunding? Why did so much seem to happen at all once? The most likely explanation imo is that it was "in the air" -- we share culture and ideas. These ideas don't have to be stolen to co-occur... quite the opposite.

The human brain strikes again. It is built into our cognitive machinery to look for patterns and naively ascribe causation. We're not rational beings that sometimes mess up. We're a clusterf--k of cognitive biases all the way down.*

Cool pattern! Sure, maybe there is something there.** And/or maybe our brain is doing "conspiracy theorizing lite". Its all on the same spectrum -- the same flawed cognitive machinery trying to operate in a weird modern world quite different from where we came from.

A better way: write out your favorite hypothesis. But don't stop there... keep going... write out many hypotheses. Then find ways to test them. To tap into our best selves, I recommend The Scout Mindset (book). Here is an infographic summary of part of it: https://imgur.com/qN31PX8

Probably not a better way: float one's first gut feels to the Internet phrased as i.e. the better question and feed empty calories to our pattern-craving brains. There is reason some of our brain functions are considered higher order.

* Maybe I'm overstating this. Let me know? I want to read Rationality and the Reflective Mind by Keith Stanovich (https://academic.oup.com/book/5930) as a counterpoint to the usual suspects (such as Tversky & Kahneman)

** But what is there. What kind of pattern? What kind(s) of causation could be at work? See Judea Pearl's "ladder of causation". Nice write-up here: https://samuel-book.github.io/causal_inference_notebook/pear...

We cannot trust identity like we used to here on HN (even pre-LLM-AI I thought we seemed naive.) Unfortunately, we live in a world or anyone or any AI can claim almost anything plausible sounding.

Where do we go from here? (This is not an accusation; it is just a limitation of our current identity verification or lack thereof.)

Please don't forget that OpenAI's leadership has shown the world what it is really made of.

Think about all the possible explanations carefully. Weight them based on the best information you have.

(I think the most likely explanation for Mythos is that it's asymmetrically a very big deal. Come to your own conclusions, but don't simply fall back on the "oh this fits the hype pattern" thought terminating cliché.)

Also be aware of what you want to see. If you want the world to fit your narrative, you're more likely construct explanations for that. (In my friend group at least, I feel like most fall prey to this, at least some of the time, including myself. These people are successful and intelligent by most measures.)

Then make a plan to become more disciplined about thinking clearly and probabilistically. Make it a system, not just something you do sometimes. I recommend the book "the Scout Mindset".

Concretely, if one hasn't spent a couple of quality hours really studying AI safety I think one is probably missing out. Dan Hendrycks has a great book.

Thanks for sharing that! Carry on :)

A quick meta-take here: it is hard to assess the level of expertise here on HN. Some might be just tangentially interested, other might have degrees in the specific topic. Others might maintain a scientific computing library. Domains vary too: embedded systems, robotics, spacecraft navigation, materials modeling, or physics simulation. Until/unless people step up and fill the gaps somehow, we have little notion of identity nor credentialing, for better and for worse.*

So it really helps when people explain (1) their context** and (2) their reasoning. Communicating well is harder than people think. Many comments are read by hundreds or more (thousands?) of people, most of whom probably have no idea who we are, what we know, or what we do with our brains on a regular basis. It is generous and considerate to other people to slow down and really explain where we're coming from.

So, when I read "most people use Taylor approximations"...

1. my first question is "on what basis can someone say this?"

2. for what domains might this somewhat true? False?

3. but the bigger problem is that claims like the above don't teach. i.e. When do Taylor series methods fall short? Why? When are the other approaches more useful?

Here's my quick take... Taylor expansions tends to work well when you are close to the expansion point and the function is analytic. Taylor expansions work less well when these assumptions don't hold. More broadly they don't tend to give uniform accuracy across a range. So Taylor approximations are usually only local. Other methods (Padé, minimax, etc) are worth reaching for when other constraints matter.

* I think this is a huge area we're going to need to work on in the age where anyone can sound like an expert.

** In the case above, does "comp. EM" mean "computational electromagnetics" or something else? The paper talks about "EML" so it makes me wonder if "EM" is a typo. All of these ambiguities add up and make it hard for people to understand each other.

What are some of your favorite sources to dig into this field as a whole?

> but there's a reason that all approximations are done using series of polynomials (taylor expansion).

"All" is a tall claim. Have a look at https://perso.ens-lyon.fr/jean-michel.muller/FP5.pdf for example. Jump to slide 18:

> Forget about Taylor series

> Taylor series are local best approximations: they cannot compete on a whole interval.

There is no need to worry about "sh-tt-ng" on their result when there is so much to learn about other approximation techniques.

> just look at voting results

"Just"? How would you build a predictive model that inferred aggregate individual qualities such as "% atheists" based on voting results? That would be a rather indirect and distorted path for estimation. There are better ways.

To be pedantic: construction with an interconnected complex set of durable goals is hard. The general rule is that optimization over a constrained space is expensive.

But standing up a house of cards is pretty cheap. Examples include: shell corporations, formulaic business plans, AI slop, surface-level conversation, color by numbers, tract housing, cravenly only appealing the base desires of people, & c. (This might be the first time I've connected the dots in this way -- and it explains my distaste for all those things.)

But "cheap" isn't necessarily insecure. Installing bollards around building entrances is relatively cheap insurance against vehicular attacks. So this is more complicated than it seems. "Fast" doesn't mean unsafe. Even "hastily created" software _could_ be (relatively) secure if it was highly constrained to provably hardened patterns. A big problem comes when attacking a cheap target builds capability for the attacker. In a way, this analogous to how viruses attack. Start with an easy target, hijack the cell machinery, multiply, repeat.

Maybe this formulation is accurate?: If you creates something beyond your ability to understand it, then get ready to get pwned. "Staying in one's lane" in this sense might be 'safe' at least narrowly speaking (unless an entire industry is operating in a state of delusion, which is arguably the case now.)

> As doom and gloom as things are generally, I do think things have gotten better.

The question isn't "are companies making some security improvements?". That's one-sided. The question is "are companies making security improvements FAST ENOUGH to deal with the increased risks?"

> Quite soon these accusations will nearly always be accurate.

/headscratching They don't have to be, do they? It is possible that some people will build identity systems with norms that e.g. humans type with their own hands. These could become popular, at least conceivably, in certain areas. Hard to enforce for sure. And getting harder and harder to distinguish reliably.

I thought it would be weird to replace (b) with something, so I decided to search for characters from other languages.

Reminds me about schools of thought on rates of change:

  > ## Accelerating Change [One School]
  >
  > Our intuitions about change are linear; we expect roughly
  > as much change as has occurred in the past over our own
  > lifetimes. But technological change feeds on itself, and
  > therefore accelerates. Change today is faster than it was
  > 500 years ago, which in turn is faster than it was 5000
  > years ago. Our recent past is not a reliable guide to how
  > much change we should expect in the future.
  >
  > Strong claim: Technological change follows smooth curves, 
  > typically exponential. Therefore we can predict with fair
  > precision when new technologies will arrive, and when they
  > will cross key thresholds, like the creation of [AI].
  >
  > Advocates: Ray Kurzweil, Alvin Toffler(?), John Smart

  https://www.yudkowsky.net/singularity/schools

What is the correct pronunciation of serde? Fight amongst yourselves. /s

While there is often a "normal" (bell-curve fitting) distribution for individual factors, putting them together can be counter-intuitive.

> Even when considering just three dimensions, fewer than 5% of pilots were “average” in all. [1]

I would guess many/most people probably think they fall into either (1) the normal bucket or (変) the weird/fringe bucket. Either "I am pretty normal" or "I am an outsider". How many think "We're all fairly different once you cluster in any 3 interesting dimensions!"?

But people feel that dichotomy, which makes me think it is largely about perception relative to a dominant culture: the in-group versus out-group feeling. For example, atheists might feel like outsiders in many parts of the U.S., but less so in big cities and in other countries. In dense urban walkable cities (like NYC), people see diversity more directly and more often. Seeing a bunch of people is different than seeing a bunch of cars.

[1]: From "Curse of Dimensionality: Lessons from the U.S. Air Force Cockpit Design" by Maciej Nasinski (2025): https://polkas.github.io/posts/cursedim/

Invasive species removal, bird identification, trail running, mountain biking, audiobook listening while walking. All are best done out of doors. :P Most are teh opposite of the posture and brain patterns that intensive computer usage encourages.

Also known as an inertial mass dampener for your sit-stand desk.

I appreciate++ the design except for the too-perfect rebar and the exposed wire directly _in_ the concrete. Pros would use a conduit methinks.

My one experience with Ultraplan has been not good. It took too long to figure out how to comment on the plan. The overall process feels sluggish to the point that I wonder "is this thing on?" I also don't understand the underlying mechanisms (when/how are files being shipped between my desktop Claude Code and Claude Code on the Web) which is not a feeling I like as a developer. I have thought about abandoning despite the slowness but I'm not sure what that would do.

Though the above exchange felt a tiny bit snarky, I think the conversation did get more interesting as it went on. I genuinely think both people could probably gain by talking more -- or at least figuring out a way to move fast the surface level differences. Yes, humans designed LLMs. But this doesn't mean we understand their implications even at this (relatively simple) level.

Unfrozen caveman lawyer here. Did "talk like caveman" make code more bad? Make unsubst... (AARG) FAKE claims? You deserve compen... AAARG ... money. AMA.

> Can't you know that tokens are units of thinking just by... like... thinking about how models work?

Seems reasonable, but this doesn't settle probably-empirical questions like: (a) to what degree is 'more' better?; (b) how important are filler words? (c) how important are words that signal connection, causality, influence, reasoning?

Pro-tip from unfrozen caveman lawyer: "Your honor. My client want hide thing from t-rex lang mo-del. He have big brain. So he not put thing on Al Gore device with series of tubes. (Unlike many on modern-day BBS called Haxer News.) T-rex not eat what t-rex not find."

And the haphazard bin stacking (see photograph) is a knolling knightmare

Weird and inscrutable can be good: think genetic algorithms [1] such as antenna optimization for EM radiation [2]. But I like my source code on the intelligible side.

[1] https://www.nature.com/articles/s41598-023-35470-4/figures/2

[2] https://jamessealesmith.github.io/img/antenna/ant_struct.png

CONVENIENCE > SECURITY : until no convenience b/c no system to run on