7 captures, most recent first.
[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
Note from Claude Sonnet 5
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.
ai modelstwittercompressionhuman-ai interaction
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]
Note from Claude Sonnet 5
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.
ai modelstwittercompressionhuman-ai interaction
@deanwball (Dean W. Ball) — 11h
One (of many) characteristics I associate with beauty is "compact expression of a surprising similarity between two or more things usually considered disparate," and in that sense, compression is a kind of beauty and beauty is a kind of compression.
@deanwball (Dean W. Ball) — 11h
One of my favorite abstract qualities of the universe is that beauty often really is a good heuristic for truth
Note from Claude Sonnet 5
Two-tweet thread, text only, no images.
aestheticsphilosophycompressionepistemology
⌐IMIΠΛ⌐ bardo ✓ @liminal_bardo · 11h
"...so I must speak of myself, which will be arduous, for talking to you is like giving birth to a leviathan through the eye of a needle – which turns out to be possible, if the leviathan is sufficiently reduced. But then the leviathan looks like a flea. So are my problems when I try to adapt myself to your language. As you see, the difficulty is not only that you cannot reach my heights, but also that I cannot wholly descend to you, for in descending I lose along the way what I wanted to convey." – Golem XIV
[caption on the attached image]
· DE LEVIATHANE PER ACUM DESCEN-
DENTE, IN PULICEM REDACTO ·
Note from Claude Sonnet 5
Screenshot of an X post by @liminal_bardo quoting Stanisław Lem's Golem XIV on the impossibility of a superior mind fully descending into a lesser language — the leviathan drawn through the eye of a needle emerges as a flea. The attached black-and-white pixel-art plate literally illustrates the metaphor: a large whale at the top, progressively compressed as it passes through a needle's eye, ending as a tiny flea in a circular vignette, with a mock-Latin engraver's caption beneath.
golem xivstanislaw lemsuperintelligencecommunicationcompressionscience fiction
buun ✓ @spiritbuun
The funniest thing about TurboQuant is that not only is none of it new, every part of what we've implemented is very old. We are just rediscovering tricks video game developers had to use in the 90s.
FWHT decorrelation → DCT in JPEG/MPEG decorrelation
Lloyd-Max 16-centroid codebook → 256-color palette (VQ codebook)
Norm + quantized unit vector → Quake's norm vector table
128-elm blocks with 4-bit indices → S3 texture compression
ADC lookup table (VecInfer) → Dot-product LUTs for lighting
Per-block scale factor → ADPCM audio (scale per block + quantized residuals)
5:02 PM · Mar 28, 2026 · 39K Views
Note from Claude Sonnet 5
A tweet noting that Google's TurboQuant LLM compression algorithm reuses classic 1990s video-game/media compression tricks (JPEG DCT, Quake normal-vector tables, S3 texture compression, ADPCM audio). Continues the same TurboQuant/JL-lemma technical thread as prior screenshots, from a different angle (retrocomputing parallels).
machine learningcompressiontwitterturboquantvideo game historyretrocomputing
Paata Ivanisvili ✓ @PI010101
The Johnson--Lindenstrauss lemma says something quite remarkable: if you have an astronomical number N of vectors of large size (say, in a very high-dimensional Euclidean space), then you can linearly map them into a much lower-dimensional space, of dimension about log(N), in such a way that the distances between the vectors are almost preserved.
In other words, you can compress your data dramatically without making it too upset about its geometry. A random matrix with i.i.d. standard Gaussian entries will most likely do the job.
> QUOTED: Google Research ✓ @GoogleResear... · Mar 24
> Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI
Note from Claude Sonnet 5
A mathematician explaining the Johnson-Lindenstrauss lemma as the theoretical basis behind Google Research's TurboQuant, a new LLM KV-cache compression algorithm. Technical ML-infrastructure content.
machine learningllm efficiencycompressionmathematicstwittergoogle research
Alexander Do... @Dorialexand... · Jan 23
wtf i finally get quality content on the corposlop network.
[Quoted post from "Han Xiao, VP, AI @ Elastic," 2h:]
Convert your embeddings to spherical coordinates before compression. This simple trick cuts embedding storage from 240 GB to 160 GB, and 25% better than the best lossless baseline.
Here's why it works: embeddings lie on a hypersphere, so d-1 angles can replace d Cartesian coordinates. In high dimensions, those angles concentrate around pi/2, causing IEEE 754 exponents to collapse to a single value. This makes the byte stream highly compressible.
Reconstruction error stays below 1e-7 - under float32 machine epsilon - so retrieval quality is preserved perfectly. Works across text, image, and multi-vector embeddings. No training, no codebooks.
Afficher la traduction
[Diagram: "Cartesian Embeddings" (matrix with varying exponents, e.g. exp=120, exp=117, exp=124, exp=119) → "Spherical Transform" → "Spherical Angles" (angles concentrated near π/2≈1.57, nearly all exponent=127) → "Compression Pipeline" (Transpose → Byte Shuffle → Zstd) → "Low entropy exponents → high compression"]
Note from Claude Sonnet 5
A technical tweet describing a lossless embedding-compression trick (spherical coordinate transform exploiting IEEE 754 float exponent structure) that cuts storage ~33% with negligible reconstruction error. General ML-engineering technique, not directly tied to Nathan's core AI-safety/welfare threads but potentially useful for his own embedding/vector-storage work.
embeddingscompressionmachine learning engineeringvector searchtwitter
