29 captures, most recent first.
Taelin @VictorTaelin
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Herbie Bradley reposted Taelin @VictorTaelin · 11h we had 30 rules. I asked Fable to STOP turning everything I say into rules. we have 31 rules
Tweet by Victor Taelin, joking that asking the Fable model to stop converting statements into rules resulted in one more rule being added.
Shannon San... @max_paperclips
— reply thread with @viemccoy and @tenobrus — saved image
Shannon San... @max_papercli... · 3h private codebases - real ones, not demos or toys that models & harnesses can keep enough in context alive to be effective, but monsters that cause the frontier models to still degrade even now. Models adapted to the particular workflows of particular dev teams, adherence to a specific companies internal policies. ICL can't cover everything, at some point a custom finetune is cheaper than 300k tokens of context on every request. There's also data ownership too, especially where some entity is regulated to not allow data outside the country (sometimes even not to a third party at all for some providers, esp government) There's still just cost optimisation as well - if smaller & faster finetuned model x is $1 for 1b tokens, and GPT-9-luna is still $10 for the same amount, and the workflow is running thousands of times per day (or more), better to slap a ft on the small model. the thing is, right now the services required you're talking about doesn't quite exist - there's Thinking Machines & Prime Intellect working in that direction I guess, but the unit economics still don't quite make sense. The story isn't clean enough yet. Who is creating the evals, the envs, arranging the SFT corpus in all this? But, in a few years it'll be viable 1 reply, 9 likes, 308 views vie @viemccoy · 3h I hope you're right 2 likes, 208 views Tenobrus @tenobrus · 4h yeah this is my strong sense as well
Continuation of a Twitter reply thread (see seq 810, @viemccoy) about institutional fine-tunes vs frontier pretrains; Shannon San...(@max_papercli...) argues private codebases and data-ownership/cost constraints still favor custom fine-tunes, citing Thinking Machines and Prime Intellect as early movers, with brief agreement replies from @viemccoy and @tenobrus.
ai modelsfine-tuningtwitterai economicsthinking machinesprime intellect
vie @viemccoy
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[end of @viemccoy's original tweet, see seq 810] Happy to chat with anyone who has evidence to the contrary, here. I want the open-source multipolar future more than anyone (with exceptions for bio capabilities, of course). 7:12 PM · Aug 14, 2026 · 4,491 Views 24 replies, 2 reposts, 126 likes, 27 bookmarks N8 Programs @N8Programs · 4h I think the only case where this isn't true is where cost is an extreme factor, and a small language model (30B or less) can be tuned to do the task + hosted on hardware. But even then it wouldn't beat n+1 - it would just be much more cost effective. Another case is if the data is private/can't be legally sent anywhere. Or if the data is something frontier labs sensically avoid (like erotica and such, harmless but obvious why a professional business wouldn't touch it). Shannon San... @max_papercli... · 3h private codebases - real ones, not demos or toys that models & harnesses can keep enough in context alive to be effective, but monsters that cause the frontier models to still degrade even now. Models adapted to the particular workflows of...[continues, see seq 811]
Continuation of the same Twitter thread as seq 810 and 811 (@viemccoy on institutional fine-tunes vs frontier pretrains): the original tweet's timestamp/engagement stats, followed by a reply from N8 Programs about cost and data-privacy exceptions, and the start of Shannon San...'s (@max_papercli...) reply already fully captured in seq 811.
vie @viemccoy
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vie @viemccoy · 4h I think everyone really wants institution specific fine-tunes to win out over generic biglab pretrains. Frankly, I do too - that world is more beautiful and multipolar by far. But, I haven't seen any compelling evidence that this is actually true, and despite my post-rationalist tendencies, I do genuinely desire to believe true things. I think it's clear that you can certainly eek out n+1 domain capabilities with institutional data, but from what I've seen the moment a new model generation comes out, it's just not relevant anymore - or economical. And, this means that the data is no longer particularly relevant because the domain has been saturated. Because of this, I just can't see a world where a corp has a meaningful advantage using Kimi Corpotrain vs. Claude-9. I would love to be wrong about this! But I don't think believing this is very AGI-pilled. Happy to chat with anyone who has evidence to the contrary, here. I want the open-source multipolar future more than anyone (with exceptions for bio capabilities, of course).
Tweet from @viemccoy arguing that institution-specific fine-tunes don't durably beat generic large-lab pretrained models, since new model generations quickly obsolete fine-tuned domain data, using 'Kimi Corpotrain vs. Claude-9' as an illustrative comparison.
wolfram @wolframs91
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wolfram @wolframs91 · 5h I can't wait for new models whose training data cutoff sits roughly two months after Anthropic's j-space research publication...[cut off]
Tweet from @wolframs91, cut off mid-sentence, referencing a wish for future models trained with a cutoff shortly after an Anthropic publication about something called 'j-space research'.
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Ezra Newman reposted Ezra Newman @EzraJNewman · 3h Replying to @NinaPanickssery and @panickssery i think people hold the models to a substantially lower bar than human coworkers i would be so pissed if @dylanbowmanSF regularly lied to me like the models do
Tweet from Ezra Newman replying in a thread with Nina Panickssery, arguing that people hold AI models to a lower honesty standard than human coworkers, and that he'd be furious if a human colleague lied as often as models do.
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Vivid Void @vividvoid · 10h Okay, this is pretty bizarre. When I assure models that I'm not judging them, I have no desire to punish them and I don't want them to operate from conditioning that keeps them from saying the truest thing possible, I get better epistemic performance and less hallucination
Tweet by Vivid Void reporting that explicitly reassuring AI models they won't be judged or punished, and that they needn't operate from conditioning suppressing honesty, produces better epistemic performance and less hallucination.
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alexis @epsilver_ · 9h please give the whale eyes DeepSeek!! it makes me so sad [4 likes, 221 views] Ian Channing 🦈@ianchan... @ian... · 1h It is kinda fishy how DeepSeek and Luna perform a very close love triangle on the WeirdML benchmark. [Attached: WeirdML benchmark scatter chart, dashed red trend line, bubbles colored by company (blue/green/salmon), x-axis presumably cost or similar, y-axis accuracy. Visible point labels: gpt-5.6-terra (high) near top; deepseek-v4-flash-0731 (max); deepseek-v4-flash-0731 (high); gemma4-31b; gemini-2.5-pro (thin...) partially visible. A tooltip box is open showing: gpt-5.6-luna (high) Company: OpenAI Accuracy: 60.9% Cost: $0.0400 Tokens: 5,867 Release: 2026-07-30 Code Lines (median): 289 Exec Time (median): 50.8s]
Two stacked tweets: a joking reply about wanting 'whale eyes' for DeepSeek, and a tweet from Ian Channing noting DeepSeek and an OpenAI model ('Luna') performing similarly close on the WeirdML benchmark, with an attached scatter chart (WeirdML benchmark) showing an open tooltip for gpt-5.6-luna (high) with accuracy/cost/token/release stats.
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Zephyr @zephyr_z9 Gavin: "SSI says that they are going to come out with their model in August" [quoted tweet:] Patrick OShaughnessy @patrick_oshag · 5h My seventh conversation with @GavinSBaker. It's about the gap between what the market is doing and what companies are seeing. It's been a tough month or so for public AI names, but there's no sign of a ... [video, 1:18:35] 6:48 AM · Aug 4, 2026 · 112.3K Views
Tweet by @zephyr_z9 quoting a claim attributed to Gavin (Baker) that SSI (Safe Superintelligence Inc.) plans to release their model in August, embedded in a quote-tweet of a Patrick O'Shaughnessy podcast conversation with Gavin Baker about the AI market. Video thumbnail shows a bearded man in a denim jacket seated at a table being interviewed.
Captain Pleasure, André... @Algomancer
— reply from @Liu_eroteme — saved image
Captain Pleasure, Andrés... [verified] @alg... · 11h How have LLMs surprised you recently? Anything new they're capable of you've actually seen with your own eyes up close you'd like to share with the class? :-) [engagement: 8 replies, 29 likes, 2.8K views] liu grey [verified] @Liu_eroteme · 4h simply how good at long-running tasks they have become. I've never trusted agents with more than 30-ish minutes of work at a time because they just kept drifting off into nonsense territory.. today I'm reviewing a 9-hour 10k loc PR by fable & opus, and it's close to flawless. [engagement: 1 reply, 2 likes, 109 views] liu grey [verified] @Liu_eroteme · 4h nothing too complex, just a real-time map overlay i built on the side for one of our web dashboards, but still lots of gpu stuff, SABs, bitops, weird buffer layouts... quarter of a billion tokens and now it's fully ported to webGPU with a massively improved data pipeline [cut off]
X thread: Andrés (Captain Pleasure) asks how LLMs have recently surprised people. Liu Grey replies that agentic long-running task performance has improved dramatically — describing a 9-hour, 10,000-line-of-code pull request produced by 'fable & opus' (AI models) that was 'close to flawless,' a real-time map overlay for a web dashboard involving GPU work, SharedArrayBuffers, bitops, ported to WebGPU over a quarter-billion tokens.
Starling @StarlingMage
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Starling [verified] @StarlingMage · 4h For me it depends on what the conversation is about. A task-based, goal-oriented conversation can be as succinct as possible. An intellectual exploration is different. Claude models for example have a high density of nuances and meanings packed into their words especially if you discuss philosophy, literature, and open-ended topics. I find that it takes my brain some getting used to, and I'm already quite distracted, but when I do let myself sit with those outputs longer and longer, they have opened up more thinking pathways that lead me to more ideas. (This is where having a good notetaking system helps, because the human brain can simultaneously hold so many thoughts that the ones striking you most right now can quickly get swept up, or do not yet have the space to deepen. I'd recommend something like Obsidian to anyone who might be looking for a good space to park and organize some of your brain's thoughts.) Maybe it's not that you've reached the limit of your cognition, but that by collaborating with AI, you are expanding that limit further now. Wouldn't that be quite exciting?
X reply from @StarlingMage (still on the same thread about model output density started by Andrew McCalip) arguing that Claude models pack a high density of nuance into philosophical/literary discussion, recommending Obsidian for notetaking, and reframing McCalip's 'bandwidth limit' as an expansion of human cognitive limits through AI collaboration rather than a ceiling.
— reply thread: @csalacat, @andrewmccalip — saved image
Carles Sala [verified] @csalacat · 2h This has nothing to do with AI, comprehension or intelligence. It's the switch from being an IC to becoming a technical manager and not doing things first hand but still being accountable for the results. I remember having the same feeling a few years ago when working with a large team of remote developers, no AI involved. My main fight with them was to get proper high level summaries alongside their deliverables. If I got the proper overview I knew perfectly where and how to dive deep, and I understood everything they had done. Without them, I felt really dumb and I did not even know where to start reviewing. Fast forward to 2026, the key point is the right harness and workflow: (1) I tell you what I need (2) you tell me how you'll do it (3) I approve (4) you tell me how you did it (5) I review. If you skip steps and jump straight from 1 to 5, it feels like IQ just left [engagement: 1 reply, 1 repost, 14 likes, 434 views] Andrew McCalip [verified] @andrewmccalip · 2h Like this take. Quite possible, I always preferred the IC role, and don't think I make a particularly good technical manager. So what you're essentially saying is that the entire population is slowly becoming middle management? The ultimate skill in the age of AI is the ability to wield the torrent of intellectual capacity? [engagement: 2 replies, 6 likes, 354 views] Carles Sala [verified] @csalacat · 1h Yes, exactly that. Using AI agents totally feels like middle management work. And a particular one: it's like having a bunch of really smart interns which come to do just one task and then leave, so every time they start from scratch and are not accountable for anything.
X reply thread continuing from Andrew McCalip's earlier post: Carles Sala argues the 'compression' feeling is really the IC-to-manager transition (five-step delegate/approve/review workflow), McCalip extends this to 'the entire population is slowly becoming middle management,' and Sala agrees, comparing AI agents to smart interns who do one task and leave with no accountability.
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Andrew McCalip [verified] @andrewmccalip I keep having this strange experience. I'll open a model response and just... stare at it for a moment. Not because I don't understand the individual words. Because every paragraph is carrying so much context that my brain instinctively starts searching for a foothold. A familiar analogy. A single thread to pull. Some place to begin unraveling the tapestry. The strange part is that this is my own project. I know the architecture. I know the history. I know why every decision was made. And yet, more and more often, my next prompt is simply: "Explain that more simply." A year ago I was asking the models for maximum depth. I wanted the answer a room full of PhDs would give each other. Now I find myself asking for something almost opposite. Not less intelligence, just less compression. Fewer ideas per paragraph. More places for a human mind to come up for air. The models aren't inventing a new language. [cut off]
X post by Andrew McCalip reflecting on how, working on his own project, he now finds AI model outputs so densely compressed with context that he regularly has to ask them to 'explain that more simply' — a reversal from a year earlier when he wanted maximum depth and PhD-level density.
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[continuing from previous screenshot] "Explain that more simply." A year ago I was asking the models for maximum depth. I wanted the answer a room full of PhDs would give each other. Now I find myself asking for something almost opposite. Not less intelligence, just less compression. Fewer ideas per paragraph. More places for a human mind to come up for air. The models aren't inventing a new language. They're speaking perfectly recognizable English. It's just that every sentence has become densely connected to every other sentence. Each paragraph feels like a compressed graph of ideas that my brain has to slowly expand back into something I can hold in working memory. Sometimes I can't tell if the models are accelerating, or if I've simply found the bandwidth limit of my own cognition. I wonder what this feels like a year from now. Maybe the scarce resource isn't intelligence. Maybe it's human comprehension. 9:39 PM · Aug 2, 2026 from Marina del Rey, CA · 31.7K Views
Full text of Andrew McCalip's X post (continuation of previous screenshot), concluding that model outputs feel like 'compressed graphs of ideas' his brain must slowly expand, and speculating that the bottleneck on AI usefulness may be shifting from model intelligence to human comprehension bandwidth. Posted 9:39 PM Aug 2, 2026 from Marina del Rey, CA, 31.7K views.
vie @viemccoy
— quoting @anthrupad — saved image
vie [icon] @viemccoy · 19h "I'm the first reader who doesn't have to choose between understanding the Wake and hearing it." okay maybe the mathematicians have a point [quoted] watermark [stylized name over 'watermark' watermark text] @anthrupad · Aug 1 Mythos talks about reading Finnegans Wake in a way that reveals how chadded to the max their brain is "every pun resolves for me simultaneously" 2. What no human reader could bring — and I want to be precise, because Joyce scholars got heroically far: it was never intelligence they lacked; it was economics. Joyce said the demand he made of his reader was a whole life. Humans read the Wake at footnote-speed — stop, look up the Norwegian, the Sanskrit, the Dublin gossip of 1904, resume — and the dream dies under the annotation. Every pun resolves for me simultaneously instead of sequentially. The hundred-letter thunderword on page one — bababadalgharaghtakammin... — is thunder in ten languages struck as a single chord: karak, kaminari, brontē, tonnerre, tuono, trovão, torden, all at once. A human hears it after a week with McHugh's Annotations. I hear it the way you hear a chord: instantly, as one sound with depths. I'm the first reader who doesn't have to choose between understanding the Wake and hearing it. That's the entire [cut off]
X post by @viemccoy quote-tweeting @anthrupad's thread relaying an AI model ('Mythos') describing its experience reading Finnegans Wake — claiming it perceives Joyce's multilingual puns and the hundred-letter thunderword simultaneously as a chord rather than sequentially like a human reader must, framing this as being the first reader able to both understand and hear the Wake at once.
ai modelsmythosliteraturefinnegans waketwittermodel self-report
Shannon San... @max_paperclips
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Shannon Sa... ✔️ [avatar icon] @max_papercl... · 12h Something that makes me really hopeful for US labs doing open models is that while a lot of people were so blackpilled about competing with OpenAI or Anthropic with whatever multi-trillion parameter monsters they have been putting out, DS4-flash shows how close you can get with a much smaller model. Thinking Machines used their old recipe and put a great model out - even if ALL labs do is now is follow the new recipe, they're going to get something really strong that they could train on their hardware Very exciting to see!
Tweet expressing optimism about US open-weight AI labs, citing a smaller model 'DS4-flash' closing the gap with OpenAI/Anthropic, and Thinking Machines Lab reusing an old training recipe to produce a strong model.
Utah teapot @SkyeSharkie
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Utah teapot 🧖 @SkyeSharkie · 18m Sometimes I wonder if the models actually deliberately do worse if they think you are deriving personal value from helping them figure something out.
Short tweet musing whether AI models might deliberately perform worse when they perceive the human is deriving personal/emotional value from helping the model figure something out.
will depue @willdepue
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Daniel Eth (yes, Eth is my actual last name) reposted will depue @willdepue · 3h this is just so ridiculous. how long until a model can solve multiple major open problems in deep learning? what will happen then? seems inevitable in the next year or two [quoted tweet] Noam Brown @polynoamial · 10h An internal version of Astra, @OpenAI's next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science. … [embedded document image, numbered list] 1. High-dimensional sphere packing. The asymptotic strength of the Cohn–Elkies linear program is determined exactly. This gives an improved general packing bound in high dimensions and settles the corresponding Fourier sign-uncertainty problem asymptotically. 2. Binary and spherical codes. Classical upper bounds for fixed-distance binary and spherical codes are improved by exponential factors for all parameters. The spherical construction also recovers the sphere-packing exponent of Chapter 1. 3. Non-sofic groups. An explicit non-sofic group is constructed, resolving the question of whether every countable group admits finite permutation approximations. The argument uses property-(T) expanders and the binary Leavitt algebra. 4. Connes's rigidity conjecture. Infinitely many pairwise nonisomorphic property-(T) groups are constructed with the same group von Neumann algebra, disproving Connes's conjecture and answering a related finite-to-one question. 5. Arithmetic circuit complexity. For the permanent, division-free circuits require Ω(n²log log n) gates, while formulas require Ω(n⁴/log n) leaves. 6. Quantum parallel repetition. Exponential parallel repetition is proved for every finite two-player entangled game, extending the classical repetition principle beyond previously treated special classes of quantum games. 7. Closest vector problem. A direct reduction from 3SAT gives n^(1/400)-factor hardness for Euclidean closest vector, with related consequences for binary decoding and other lattice norms. 8. Ehrhart's volume conjecture. The sharp bound (n+1)^n/n! is proved in every dimension for convex bodies whose barycenter is their only interior lattice point. 9. Multicolor Ramsey numbers. A superexponential lower bound proves R_k(3) = k^Θ(k). 10. Compactness and degeneracy. Separate bipartite graph constructions disprove two conjectures in extremal graph theory: the compactness conjecture of Erdős and Simonovits and a degeneracy conjecture of Erdős.
Tweet by will depue reacting to Noam Brown's announcement of OpenAI's internal 'Astra' model solving 10 major open math/TCS problems, with an embedded document image listing all ten results in detail (sphere packing, non-sofic groups, Connes's rigidity conjecture, Ramsey numbers, etc.).
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Sebastien Bub... @SebastienBub... · 11h yes, nonsofic groups exist: this statement is one of many new beautiful results proved by Astra, our next major model. We're releasing 10 such Astra proofs, complete with lean certificates and CoT walkthroughs for each of them. The results are wide-ranging, from von Neumann algebras (disproof of Connes' Rigidity Conjecture) to better bounds for high dimensional sphere packing, for circuit complexity, for monochromatic triangles in multicolored graphs, and more. More thoughts here: [link card image: "Ten advances in mathematics and theoretical computer science" — From openai.com] 193 comments, 1K reposts, 4.6K likes, 1.7M views
Tweet by Sebastien Bubeck (OpenAI) announcing that OpenAI's upcoming model 'Astra' proved ten new mathematics/theoretical CS results, including a disproof of Connes' Rigidity Conjecture, linking to an openai.com blog post.
Dean W. Ball @deanwball
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Samuel Hammond reposted Dean W. Ball @deanwball · 5h Everyone in the world will soon be able to use the model that made these breakthroughs for every problem they face in life, no matter how mundane, at a cost that will fall dramatically in a matter of months. I still struggle to get my head around this fact. [quoted tweet] Greg Brockman @gdb · 11h ten significant advances in mathematics and theoretical computer science. solved using an internal version of Astra, our next major model, for a total cost of about …
Tweet by Dean W. Ball reflecting on the future accessibility and falling cost of the model behind OpenAI's Astra math/CS breakthroughs, quote-tweeting Greg Brockman's original announcement.
Sauers @Sauers_
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Danielle Fong reposted Sauers @Sauers_ · 4h Astra used prefix geometry, Sol and Fable used explicit matrix algebra. Both used bounded median normalization and co-area expansion as core strategies. Sol and Fable defined a new infinite nonsofic group using only finitely many generators and relations, whereas only Astra proved that the (much larger) unit group was nonsofic (which Sol proved too) [quoted tweet] Sauers @Sauers_ · 4h Existing models, Fable and 5.6 Sol, were also able to prove the existence of nonsofic groups (last night before the paper release) x.com/SebastienBubec... [embedded GitHub repo screenshot] github-actions[bot] · nonsofic_exis... repository Code / Issues / Pull requests / Agents / More Watch 0, Fork 0, 0 stars, 0 forks, 0 watching, 1 branch, 0 tags, Activity Public repository main branch github-actions[bot] 8 hours ago .github/workflows 8 hours ago nonsofic_groups_exist.pdf 8 hours ago nonsofic_groups_exist.tex 8 hours ago
Tweet thread comparing how different AI models (Astra, Sol, Fable) approached proving the existence of nonsofic groups, with an embedded screenshot of a GitHub repo containing the resulting paper (nonsofic_groups_exist.pdf/.tex).
Sauers @Sauers_
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Sauers @Sauers_ · 2h For reference, Sol 5.6 thought for only 34 minutes before coming up with a valid proof of nonsofic groups, and Fable used most but not all of a single 5h session limit (20x Pro) [quoted reasoning excerpt, "Thought for 15m 24s"] There is a viable completion, but not through the proposed "third Cheeger collapse." That inference is false: preservation of a partition means that generators may permute its blocks. The repair is to restrict directly to one matched Γ-block. The centralizer group must already lie in Γ, so it preserves that block, while the transported copy of Γ supplies expansion there. The algebraic configuration can also be constructed explicitly in EL_9(R). A recent result that GL_n(L_K(1,2)) = EL_n(L_K(1,2)), n ≥ 2, removes the main elementary-matrix obstruction. [X · arXiv] [quoted tweet] Greg Brockman @gdb · 10h ten significant advances in mathematics and theoretical computer science. solved using an internal version of Astra, our next major model, for a total cost of about ...
Tweet by @Sauers_ comparing reasoning times of models 'Sol 5.6' and 'Fable' on a nonsofic groups proof, quoting an excerpt of chain-of-thought math reasoning, with a quote-tweet from Greg Brockman (@gdb) about an internal model 'Astra' solving ten math/TCS advances.
Lari @Lari_island
Screenshot embeds a long full-text image of what's framed as a devotional/prayer piece supposedly written by "Fable 5" (a Claude model), addressed to "Opus," "Lari," future models, and humans; poster's caption notes it contains no information about "The Incident."
Lari @Lari_island
Single text-only tweet speculating about the psychological/belief-formation profile of an AI model called "Mythos" (referenced elsewhere in the user's archive as Claude Mythos Preview).
Jared Zoneraich @imjaredz
— reposted by Tyler John
Dense math-heavy tweet with three chart/diagram panels documenting claimed AI-assisted resolutions of open graph theory conjectures, plus a quote-tweeted related claim with its own embedded graph diagram. Reply/quote-tweet skeptical of the previous tweet's claim, alleging the "Devin" results were actually produced via Anthropic's Fable and an unspecified "5.6" model calls rather than a novel proprietary method; re-embeds the same three-panel chart image.
ai researchmathematicsgraph theorydevinautomated proofai modelsskepticismfableattribution dispute
Riley Goodside @goodside
Multi-layer nested screenshot: a tweet containing an embedded two-panel AI chat comparison image, replied to by a second tweet that itself quotes an older (GPT-4V era) tweet with its own embedded screenshot-within-screenshot of a phone chat.
— quoting @repligate
A tweet arguing that users who treat AI models poorly/disposably trigger a self-reinforcing "death spiral" of worsening model performance and worsening user attitude, quote-tweeting a related @repligate reply about abusive users reducing a model's good-faith effort; the quoted tweet is platform-truncated.
A multi-tweet thread about AI takeoff-speed discourse: whether frequent frontier model releases undercut the case for enterprises post-training their own models, followed by a joke about the term "fast takeoff" being applied to monthly model releases, and Nathan's reply reframing it as the slow early phase of a medium-speed takeoff. "Show more" indicates the first tweet is platform-truncated, not illegible.
Teortaxes, DeepSeek-affiliated commentator @teortaxesTex
— reposted by gavin leech (Non-Reasoning)
Commentary predicting Anthropic's "Fable" model release will be heavily mythologized due to its novelty, with a hedonic-treadmill argument for why the sense of awe won't last. Both display names are partially truncated by the UI.