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aakresearch

131 karma · joined September 14, 2011

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aakresearch··on Ask HN: What are tools you have made for yourself since the advent of AI?
- A little script that is best described as "symbolic submodule" workflow enabler for git: https://github.com/fontmaniac/symgit. As in "symbolic link" vs "hard link" - where hash tying host repo and pseudo-submodule is not git-generated, which makes "submodule" completely substitutable. For the description of creation process see the relevant section of Readme.md

- Companion script named Dr.Emmett - to retrospectively time-walk git commit history and rebuild the repo in a new place. It is not opened yet, and may not be ever as it doesn't satisfy LLM/human ratio worthy of publishing.

- Both scripts were made in service to my FNA-"game" project VibeSopwith: https://github.com/fontmaniac/VibeSopwith.Game. A more or less comprehensive description, and treatise on my stance about vibe- and agentic-coding can be found in the project's Readme.md

- VibeSopwith catalysed creation (accretion?) of Nage.Strata: https://github.com/fontmaniac/Nage.Strata - "Not-a-game-engine" collection of useful "primitives" for FNA-based game development.

- VibeSopwith Readme.md has notable mention of "Aether Bodybuilder" - a helper tool for visually coupling Aether.Physics2D "bodies" to specific sprite textures. Fully vibe-coded in a couple of hours, and as such won't ever be publicly opened, having LLM/human ration approaching infinity.

aakresearch··on Using AI to write better code more slowly
> I fear that workflow is just chat, search, and being a rubber duck for my thoughts

This is exactly what I settled upon after my own trying really hard. It is liberating, I have no fear at all!

aakresearch··on Ontario auditors find doctors' AI note takers routinely blow basic facts
Oops... I am deeply sorry, thank you for the heads up! It seems I've myself committed a cardinal sin that I am usually quick to point in others - rushing to reply without comprehending the full message. (Meta-oops: I realized how LLM-ish it sounds. Quick, reboot before my cover is blown!)

I happen to believe that the flaw being discussed IS fundamental and inherent in the design and architecture of LLM - this is why I always put "AI" in scare quotes. I've spoken about it in some of my other comments, namely this https://news.ycombinator.com/item?id=47162553 and to some extent this https://news.ycombinator.com/item?id=48046333. And as you do, I, too, hope that I am wrong about the hype and its eventual clash with reality, but do not hold my breath.

aakresearch··on Ontario auditors find doctors' AI note takers routinely blow basic facts
>>At the same time, do you really want every conversation you have with your doctor recorded

>Yes. This is what medical records are.

No. Medical records are limited extracts from conversations, which is your doctor and only your doctor is qualified to make, using "semantic analysis applied to your unique situation", not "linguistic probabilistic inference applied to conversation about your situation using token weights averaged over billion unrelated samples"

> It's not like the doctor is talking to you about which anime series are the best. You're talking about your health, your body, your disease, your treatment.

No jokes, no banter, no chit-chat, no complements to doctor's new Tesla?

> It's important to keep track of that.

Same fallacy Meta fell into when started tracking employees' keystrokes and mouse gestures. 90% of my mouse movements are just fidgeting, with no relation to the task at hand - and it is not a crime! But if I knew my mouse fidgeting is being watched, I'll make sure that percentage goes up to 99% - for the LLM which is gonna be trained off it to self-immolate over its NSFW nature.

aakresearch··on Knitting bullshit
I may have different perception facilities in the brain, but to me the images in the article are horrifying. Such bad, Frankenstein-level, criminal butchering of absolutely everything!
aakresearch··on AI didn't delete your database, you did
I am by no means an expert, but I'd like to offer my mental model - up to you to decide if it is solid or not, but it works for me.

I think the core intuition is that, like with any other "rasterized" system with finite memory that cannot encode an absence of anything - relation, concept, entity, LLM cannot encode an absence of something through its internal weights. Say, you can have "Product" or "Order" tables in you database, but you cannot have "NotAProduct" or "NotAnOrder" tables - for obvious reasons of such relations being infinite and uncountable. So, to establish an absence of Product or Order your application must execute a "search" operation through the relevant tables. But in LLM-space "search" operation does not exist. It is mathematically undefined. LLM arrives at output (or "what to do") through a sequence of projections of input token vector through its "latent space". It "moves toward" high-probability clusters, fundamentally unable to "move away". So, the success of any "negation" in the prompt ("don't touch this file", "draw me a ballot box without a flag on it") depends on how heavily such scenario represented in the training data/model space. And again, the absence-of-something may be hard-to-impossible to usefully encode, especially if "something" is not fixed. Therefore, to expect "don't touch this file" sentence to result in, well, not touching the file is pure gambling. Sometimes it may look like working, albeit for wrong reasons, and some other times LLM may do exactly the opposite - because its weight matrix statistically pushes it towards "touch this file", completely ignoring (nonexistent in its latent space) "don't".

There is no way to reliably know what will work, and no "skill" or "art" in this. Well, no more than in dice rolling or horoscope casting.

I'd like to add that for the above reason I find "agentic development" usefulness on par with avian remains reading. But when I explored it two practical advises seemed to be helpful in nudging LLM around negation problem:

- Omit the "don't" prompt completely, thus not creating a false "attractor" for LLM; and

- Provide an alternate positive directive ("what to DO", not "what to NOT DO") to act as "escape hatch" when LLM might "want" to touch the sacred file or drop the production DB.

While it looked like somewhat working, I think it is trivially obvious that trying to predict all the nonsense LLM might want to perform and coming up with possible "escape hatches" for everything very quickly becomes utterly impractical.

aakresearch··on Who owns the code Claude Code wrote?
It seems that author unironically advises to write your commit messages like this: "Restructured Claude’s module architecture, rejected initial state management approach, rewrote error handling from scratch", to have a chance at defense in potential court hearing. I find it funny, if vindicating for my personal approach. If the expectation is to "restructure, reject, rewrite" what "AI" spits out, why use "AI" at all at this point???
aakresearch··on Shall I implement it? No
To my understanding, LLM, by design, is unable to encode negation semantics. Neither negation "operation", nor any other "subtractive" operations are computable in LLM machinery. Thinking out loud, in your example the "Read code" and "Form hypothesis" seem to be useful instructions for what you want, while "Do not write any code" and "Not to fix the bug" might actually be misleading for the model. Intuitively (in human terms) one would imagine that, when given such "instruction", LLM would be repelled from latent-space region associated with "write any code" or "fix the bug". But in reality LLM cannot be "repelled", it is just attracted to the region associated with full, negated "DO NOT <xxxx>". And this region probably either has a significant overlap with the former ("DO <xxx>") or even includes it wholesale. This may explain why it sometimes seems to "work" as intended, albeit accidentally. My 2c.
aakresearch··on I was interviewed by an AI bot for a job
Agree. Your post mirrors surprisingly well with mine of last year [0] - did you, by chance, happened to come across it? I said "you’d likely get approximately the same quality of candidates from a randomly selected 50 out of 5,000". And the main idea I was trying to convey is parallel to yours too: companies, consciously or not, try to find "the best candidate", while using completely inadequate "hiring funnel" approach. If that is their genuine need, they would be better off with "headhunting" approach. And the whole industry, businesses and candidates alike, would be better off if businesses recognize that the difference between "best" and "second best", or even "third best" is not meaningful (outside of C-suite) and not worth exponentially higher spend.

- [0] https://news.ycombinator.com/item?id=45271736 (believe it or not, em-dashes are all mine, even though I regret now putting my original draft through LLM - even if it was for grammar-check only)

aakresearch··on Don't post generated/AI-edited comments. HN is for conversation between humans
I am in agreement with you, but regret that you missed an opportunity to swap two paragraphs around and purposefully mislabel them (i.e. the LLM-generated as your own, and vice versa). I'd be very curious if audience here would successfully pick it up!
aakresearch··on The L in "LLM" Stands for Lying
I think you are mistaken. I remember landing upon that website some time before 2023 and being greatly impressed by that very 3D parallax effect. To my knowledge there were no AI tools available in 2022 capable of producing effects like that. There are barely now. To my understanding it was made possible by another project of the same author [0] - check the date. Also check this [1] - dated 2003.

Is it so hard to accept that humans are more capable of doing intricate and impressive work than any LLM will ever be?

- [0] https://github.com/unconed/CSS3D.js

- [1] https://acko.net/blog/avs/

aakresearch··on Artist who “paints” portraits on glass by hitting it with a hammer
Fictional artist Feofan Kopytto, who was immortalized in "The Little Golden Calf" [0] used oats and other cereals for his paintings. On the back of the book authors' talent it became customary to refer to his artistic endeavours as "charlatanism". Having internalized it through my Soviet upbringing, I struggle to see why the same wouldn't apply to the art being discussed. LLM kindly helped me to generate hypothetical ad copy for Kopytto in the same style [1] - I honestly see no reason to not relate to both with the same reverence (or, rather, lack thereof!). I'd appreciate a human explanation (re: why?), if anyone has a minute or two. It would help me (and maybe others) to guide understanding why AI slop of all kinds may or may not deserve the same treatment as intent-driven human "output".

Interestingly, I think this HN topic is very relevant to understanding of contemporary LLM hype, as it illustrates the power of language (and propensity of human mind) to create an appearance of substance and meaning even where there is absolute emptiness (or, worse, manipulative fraud) underneath.

- [0] https://en.wikipedia.org/wiki/The_Little_Golden_Calf

- [1] https://pastebin.com/j7wsXQxe

aakresearch··on An autopsy of AI-generated 3D slop
I am in agreement with many commenters here (https://news.ycombinator.com/item?id=47158240, https://news.ycombinator.com/item?id=47158573 and others) that this article is a clear illustration of failure on part of AI to capture the structure of material in a useful way. As addressed in the article, the effect is very visible in visual space, 3D modeling. I would argue it is very much present in LLM space too, just less prominent due to certain properties of the medium - text-based language. I also believe the effect is fundamental, rooted in the design of those models.

I'll leave here the note I've written down recently, while thinking about this fundamental limitation.

- The relationship between sentient/human thinking and its expression ("language") is similar to the one between abstract/"vector" image specification and its rendered form (which is necessarily pixel-based/rasterised)

- "Truly reasoning" system operates in the abstract/"vector" space, only "rendering" into "raster" space for communication purposes. Today's LLMs, by their natural design, operate entirely in the "raster" space of (linguistic) "tokens". But from outside point of view the two are indistiguishable, superficially.

- Today's LLMs is a brute force mechanism, made possible by availability of sheer computing power and ample training material.

- The whole premise of LLMs ("Large" and "Language" being load-bearing words here) is that they completely bypass the need to formalize the "vector" part, conceptualize in useful manner. I call it "raster-vector impedance".

- Even if not formalized, it can be said that internal "structures" that form within LLM somehow encode/capture ("isomorphic to" is the word I like to use) the semantics ("vector"). I believe the same can be said about "computer vision" ML systems which learn to classify images after being fed billions of them.

- However, I believe that, by nature, such internal encoding is necessarily incomplete and maybe even incorrect.

- Despite the above, LLM can still be a useful tool in many domains. I think language translation is a task that can be very successfully performed without necessarily "decoding" the emerging underlying structures. I.e. a sentence in source language can be mapped onto a region of latent space; an isomorphic region of latent space based on target language can be used to produce an output in the target language which will be representative of an equivalent meaning, from human perspective. All without explicit conceptual decoding of underlying token weight matrices. "Black-box" translation, so to speak. I am amazed (and disturbed, and horrified too!) that producing a viable code in a programming language from casual natural language prompt turned out to be a subset of general translation task, largely. Well, at least on lower levels.

- To me it is intuitive that such design (brute-force transforms of "rasterized" data instead of explicitly conceptualizing it into "vector" forms) is very limited and, essentially, a dead-end.

aakresearch··on An autopsy of AI-generated 3D slop
Such approach is not even new, and has been conceptualized centuries ago! [1]

[1] https://en.wikipedia.org/wiki/Stone_Soup

aakresearch··on How I use Claude Code: Separation of planning and execution
This is a very useful take, thank you. Really helped me to adjust my mental model without "antropomorphising" the machinery. Upvoted.

If I may, I would re-phrase/expand the last sentence of yours in a way that makes it even more useful for me, personally. Maybe it could help other people too. I think it is fair to say that in presence of hints like "Pretend you are X" or "Take a deeper look" the inference mechanism (driven by it's training weights, and now influenced by those hints via "attention math") is not "satisfied" until it pulls more relevant tokens into "working context" ("more" and "relevant" being modulated by the particular hint).

aakresearch··on AI adoption and Solow's productivity paradox
Upvoted, as it basically 99% matches my own thinking. Very well said. But I, personally, would not predict a breakthrough in this direction in the next 2-5 years, as there is no pathway from current LLM tech to "true reasoning". In my mental model LLM operates in "raster space" with "linguistic tokens" being "rasterization units". For "true reasoning" an AI entity has to operate fluently in "vector space", so to speak. LLM can somewhat simulate "reasoning" to a limited degree, and even that it only does with brute force - massive CPU/GPU/RAM resources, enormous amount of training data and giant working contexts. And still, that "simulation" is incomplete and unverifiable.

I would argue that the research needed to enable such "vector operation" is nowhere near the stage to come to fruition in the next decade. So, my prediction is, maybe, 20-50 years for this to happen, if not more.

aakresearch··on Trillions spent and big software projects are still failing
Wow, sounds so familiar! I've once had to argue precisely against this very conclusion - "you saved us once in emergency, now you're bound to do it again".

Wrote to my management: "It is, by all means, great when a navigator is able to take over an incapacitated pilot and make an emergency landing, thus averting the fatality. But the conclusion shouldn't be that navigators expected to perform more landings or continue to be backup pilots. Neither it should be that we completely retrain navigators as pilots and vice versa. But if navigators are assigned some extra responsibility, it should be formally acknowledged by giving them appropriate training, tools and recognition. Otherwise many written-off airplanes and hospitalized personnel would ensue."

For all I know the only thing this writing might have contributed to was increased resentment by management.

aakresearch··on Show HN: I built an interactive HN Simulator
Now do LinkedIn Simulator!
aakresearch··on McKinsey wonders how to sell AI apps with no measurable benefits
Like a proverbial broken clock which shows correct time twice per 24 hours, AI may "show correct time" for 99% of prompts, but doesn't deserve any more trust.
aakresearch··on When the job search becomes impossible
> Pat yourself on the back and mutter “you need to be skilled and lucky to work here”

It would be absolutely amazing if employers and recruiters finally were doing exactly this. We are in this dead end precisely because everyone is under false illusion that their pool of candidates has a hidden gem outshining everybody else in existence, and they absolutely need to sift through the whole pool to find this gem. As a result, all pools are never exhausted and only ever spreading, with more and more desperate people sucked into multiples of them.

aakresearch··on When the job search becomes impossible
Your take is very sensible and I agree with it 100%, but the reality is that (by my assessment) it is absolutely not present in the wall of ATS filters one's job application is up against. I've sent hundred of CV/cover letters over last ten months, none of them are touched by LLM. Most cover letters I manually tailored to re-frame in line with job ad - where I cared a lot, some I just made with my generic template - still manually - where I couldn't be bothered to care. Invariably I either received no response at all, or for remaining 10% I received a generic rejection email, identically worded and styled in almost all cases.

Here it is, if you are curious:

"Thank you for your interest in the <position> position at <company> in <country>. Unfortunately, we will not be moving forward with your application, but we appreciate your time and interest in <company>."

The Resume I am sending out is just an evolution of one that worked very well for me for 25+ years. The roles, as far as I am able to see, are 80%-95% keyword match, with the non-matched keywords being exceedingly superficial. Yes, I haven't listed "blob storage", but guess what else I have used but haven't listed: "semicolon", "variable declaration" and "for-loops". Yet in this day and age one seems to be punished for not doing so.

I am very principled in not letting any AI anywhere close to my CV, because I think the usefulness of signal it conveys rests solely on it being addressed to and read by human, hence it has to be fully authored and tailored by human too. But these days this idea has completely flipped. Desperate times call for desperate measures. Standing by principles could lead to literal dying. Personally, I made peace with dying, but I cannot allow my family to go homeless. As such, I don't see it below me to go down the path of mass-blasting heavily over-tailored Resumes. If it bumps my chances from 0.05% to 0.2%, that's a four-fold increase that may be the difference between, literally, life and death. The organic job search with my natural skills and authentic ways of presentation I relied on for twenty years is dead.

aakresearch··on When the job search becomes impossible
Disclaimer: I've been navigating the job market for almost a year now, and have practically given up.

An acquaintance of mine—he was the owner's/CEO's deputy at a place I worked—now runs a team-coaching-turned-recruitment business. I saw a question from him on social media the other day, something along the lines of: "Some businesses in the industry are receiving over 5,000 applications for advertised roles. How do you effectively screen that many applications?" I didn’t respond, of course—I have neither credentials nor experience, nor any real relationship with this guy—but I formed an opinion nonetheless.

My intuition was simply: you don’t. If your candidate pool’s fitness function is normally distributed, you’d likely get approximately the same quality of candidates from a randomly selected 50 out of 5,000. The distribution will be practically the same, and the maximum will be indistinguishable from the true maximum in any practical sense. As I recently explored—prompted by some statistical curiosity—this, of course, is modulated by the mean and standard deviation of the distribution. A higher standard deviation calls for a larger subset, while a higher mean dramatically shrinks the number needed to screen.

But I also think nobody really knows their "true" fitness function, much less its distribution across the applicant pool—simply because businesses have no means or resources to actually measure or research this aspect. That doesn’t stop them from pretending they do.

I also felt the urge to respond with some snark: maybe they don’t really understand their own business, at least the placement/recruitment aspect of it. If they’re recruiting mid-echelon personnel—software devs, BAs, testers, other office roles—it makes sense to publish an ad and gather applications. But it doesn’t make sense to expect that pool to include extraordinary candidates from a fitness perspective. Sure, we’re all extraordinary in some sense, but in roles like these, businesses are simply not in a position to materially benefit from that extraordinarity. Nor are they able to detect it during hiring—and I don’t even mean that as snark; it’s just the nature of things.

In a natural distribution, the second-best candidate is not materially better than the top-best, and the third is almost like the second, and so on. Businesses that set their hiring threshold—whether explicitly calculated or intuition-based—too high will simply go out of business, because in this echelon you need to rely on mass hiring. Relatively speaking, of course, but you still need to be able to fill multiple positions with readily available candidates.

So, the question posed by my ex-coworker doesn’t make sense. But as I see it, it doesn’t make sense even for hiring in the top echelon—C-suites and so-called "rock stars." The strategy for top-echelon hiring is well established. It’s widely used in business, show business, and sports alike, where "fitness" follows a power-law distribution. It’s called "scouting" or "headhunting". You don’t throw a job ad over the fence and wait for a torrent of applications. You meticulously maintain a rolodex of potential candidates, watch their careers, court and dine them, and try to snatch them when they’re poised to make a move—or even just before it becomes obvious.

You don’t wait for them to apply to you—you apply to them. You don’t ask "Why do you want to work for our company?"—you shower them with perks and sign-on bonuses if they show even a hint of hesitation. The "agents" in this echelon work for candidates, often on retainer—not the other way around. It’s a completely different world, where you’re never in a position to screen a pool of 5,000 in hopes of finding an extraordinaire. In that world, hiring the second-best can have a humongous negative impact on company performance compared to hiring the top-best. For better or worse, that’s reality.

So I found my guy’s question quite unsettling—an indicator that yet another "recruiter" doesn’t really understand what they’re recruiting for.

I was able to fish out a useful metaphor from the LLM-generated word soup: "talent brokers vs gatekeepers." Over the last decade, recruitment agencies and internal departments have been universally rebranded as "talent acquisition." That rebranding feels disturbingly phony and hollow. Now I know why. Despite pretending to be "talent brokers"—scouting for talent—the reality hasn’t changed. They’re still the same old "gatekeepers", applying selectivity to boatloads of "talent" that come to them.

As industries and their associated keyword-spaces have grown dramatically—and AI tools have proliferated—such selectivity has become increasingly diluted. In my opinion, it’s now indistinguishable from random picks, yet still cloaked in the illusion of validity. For someone like me, swimming deep in the muck of the mid-echelon and with no ambition whatsoever to strive for oxygen-deprived heights of top echelon, it’s deeply disturbing.

aakresearch··on Ask HN: What is interviewing like now with everyone using AI?
Brilliantly put! Upvoted and "favorited".

I would also add meticulous attention to documenting requirements and decisions taken along the development process, especially where compromises were made. All the "why's", so to speak.

But yes, commercial development, capital-A "Agile" absolutely kills the drive.

aakresearch··on How A Mom Managed to Thru-Hike the Appalachian Trail with 13 Kids
Doesn't sound absurd at all to me. Divorce is bad enough so the child can perceive it as having the same "finality" as death, with additional burden of abandonment, disowning and guilt without any chance of reconciliation.

Katy Faust in "Them Before Us" published in 2021 cites the study making this exact claim: "One study found the death of a parent inflicts less psychological damage on a child than divorce. It appears children fare better when they know their parents didn’t choose to abandon their family." The source link in the book appears to be dead, but searching for the title "Parental Divorce—Worse than Parental Death" surfaces plenty of similar studies, enough to satisfy an inquisitive mind.

aakresearch··on Congo gov. says it's 'on alert' over mystery flu-like disease that killed dozens
How many would you consider un-acceptable to the point of introducing 2020-level of insane restrictions?
aakresearch··on How I animate 3Blue1Brown [video]
I feel it is fruitless to continue this conversation as it doesn't seem that we may find agreement, but if you insist..

(In the following paragraph "you" is mostly figurative, not personal)

Yes, I let a lot of anger and emotion into the reply to the bunch of diatribes which, themselves, were unsubstantiated attempts at gaslighting, full of derogatory rhetoric. This - hooking up on provocation - I consider a mistake on my part. However, if we omit my venting (the posts I was replying to would be reduced to zero without it), every point I made stands true, as it was observable reality at the time. Its "incoherence" is just a reflection on the described phenomena. It is not my fault that nothing in the "pandemic response" had any rhyme or reason. No matter how many times you repeat your aposteriori-constructed "presentation" which you wish to be true. That you don't care or dare to engage with my points makes me even more confident that they are valid and makes me care even less about what you may think of me. In fact, I wear "not normal" and "unhinged" badges from you with pride. And you wouldn't want to hear me speaking about it all in my mother-tongue, which you (personally) and me are likely to share - now, that would be "unhinged", but surely "coherent". Unfortunately I'm not equally mastered in English, which makes it another mistake to pick fights with local purveyors of "truth". I've been one of your political/cultural/intellectual "tribe" only few years ago, but your actions and words made me not able to associate with it forever anymore, under any circumstances. I invite you (personally) to recall the relevant saying about not wanting to do certain private business on the same field.

Putting a lid on it now, as I'm, probably, walking on the edge of HN posting guidelines, if not over it yet. There's certainly no "intellectual curiosity" or "good faith" expressed here by any participants, myself included.

aakresearch··on How I animate 3Blue1Brown [video]
It has nothing at all to do with treatment being unequal.

I only form my opinion from the current Wikipedia article, and this is how it looks to me.

- Mary Mallon, herself, was just as innocent, yet she was treated as a criminal, which is unfair.

- Her treatment was "hectic" as per Wikipedia. I'd say it was unreasonable and dangerous, with a big pile of negligence.

- Communication and information given to her was inconsistent at best.

- She had all the rights and reasons to disbelieve and dispute all conjectures made about her. Contemporary state-of-the-art knowledge and practices in medicine and "public health" was nowhere near conclusive and confident. She had a good shot at collecting evidence to prove her case, but to my understanding she was denied a fair judgement - case was dismissed before hearing. However, I believe that her continuous isolation could not be justified "beyond reasonable doubt" even by our modern knowledge and standard of proof!

- The demands put on her were unreasonable and excessive.

- He name was dragged through media, forever tainted in the process.

- She was never given appropriate consideration/compensation for all the conditions of her treatment and limitations of release. They could have offered her a lifetime pension which would have removed the need for her to work as a cook. They could have recruited her for research program, compensated accordingly. Instead, she was expected to bear full cost of all conditions put on her. The best shot at compensation was a promise of royalties for yet-unwritten book dragging her name through more mud. Which only added insult to injury, understandably, as it would absolutely do for so many of us.

- My understanding is that other similar cases were not "refused requests to isolate", but such requests were not even made, making Mary's case unfairly singled out.

- Last but not least, I absolutely disagree with your chosen turn of phrase that others were "unfairly given freedom". Freedom is not given, it is a default state of being. To take it away requires extraordinary justification, which in Mary's case was awfully deficient and remains so to this day.

aakresearch··on How I animate 3Blue1Brown [video]
Eloquent primer on "cogent argument" which is totally not "rambling stream of anger and emotion". Perfect basis for a healthy, respectful debate.

I'm really sorry I did bite on this provocation, but hope your post stays up for everyone to see and make their own judgement.

aakresearch··on How I animate 3Blue1Brown [video]
Case in point.
aakresearch··on How I animate 3Blue1Brown [video]
Yeah, it was a mistake to expect anything beyond thought-stopping cliches that were delivered by you. In hindsight it was a mistake to post at all. I don't think you've read my post anyways, or you've processed it on a level below a typical LLM.

Righteousness is the most potent drug of them all, is it not? No need to answer. Have a good day.

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