I'd be game for that. I hate the fact that photos of me exist in this world.
Programmer Humor
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I suppose they weren't worth a 1000 words after all

This reminds me of IPoAC
No no IPoAC is far more viable
brilliant satire that critiques all of magazine journalism.
i love going to [insert publication here] and reading another article about 'so and so is ready for their next chapter in life.' the so and so always an uninteresting, overly wealthy fuckwit that hasn't accomplished anything other than going to college and having a wealthy parent.
Trying to find it funny, but in 2026 it's way too close to the bone and just makes me sad.
Had Sam Altman personally vocalized this article in full sincerity, I would not have been surprised.

This is so sad, Chat GPT generate me an email responding to this article
Amateur. I can compress entire seasons of a TV series to a few bytes. All I have to do is type its title in Netflix and then BAM, gigabytes of video come out.
Sure, but Netflix has a lot of cache misses for my favorite shows. I found a random website that has compressed matches for thousands more shows and movies than Netflix
Back in the day we had this neat trick to do 100% compression on photos. Just print them out on pieces of paper, for completely byte-free storage!
The sad thing is that this has been possible for decades using convolutional autoencoders, but with LLMs we forgot that AI architectures other than transformers still exist.
Yeah, it's really awful. With any luck, AI winter will follow AI summer, like usual, and the serious people can come out again. Although, aren't CNNs more of a this century thing? I guess two decades is still decades...
IIRC autoencoders actually produce the same image to within our ability to notice, as well.
It's humorous, but last I checked, the best general-purpose compressors with the highest levels of compression---even lossless, which is probably not what most people think of when they think of neural nets---are neural net based.
Neural net-based compressors are computationally expensive, which is why we don't normally use them for most day-to-day tasks, but they really can produce really small outputs.
I'm going to take the text of the US Constitution and stick it in a text file.
$ wget https://www.gutenberg.org/cache/epub/5/pg5.txt
$ stat -c %s pg5.txt
48326
Okay, so 48326 bytes.
Let's do lzo. You'd expect a limited amount of compression
LZO is "fast" compression, usually only used where compression speed is really important, like where you want to be compressing stuff that's going to be decompressed once and your bottleneck is throughput to disk:
$ lzop <pg5.txt >pg5.txt.lzo
$ stat -c %s pg5.txt.lzo
24843
Okay, how about gzip? That's Deflate, an older, but pretty-widely-used general-purpose compression algorithm.
$ gzip <pg5.txt >pg5.txt.gz
$ stat -c %s pg5.txt.gz
16660
Okay, what about LZMA? That's a newer, more-CPU-intensive thing that's probably a good general-purpose choice that'll generally give better compression ratios. It's the kind of thing that I'd probably use in a lot of cases. (Personally, these days, I tend to use pixz, which provides both indexed access for tarballs and parallel compression and decompression, which is important for modern processors.)
$ xz <pg5.txt >pg5.txt.xz
$ stat -c %s pg5.txt.xz
15488
Okay, now PAQ, a neural-net-based compressor:
$ zpaq a pg5.txt.zpaq a pg5.txt -method 5
$ stat -c %s pg5.txt.zpaq
13063
"Neutral net based" compression isn't even in the same universe as "compressed to a prompt" via LLM
It actually is. I mean, it's building a dictionary off of a variety of content ahead-of-time, rather than training it on the specific item in question, but that's not uncommon for non-general-purpose compressors.
I mean, doing so to a (probably short) prompt is (a) lossy (and I gave a lossless example) and (b) lossy to an extreme degree, to where it's probably not incredibly useful option for the kinds of systems that exist today.
But...existing diffusion models aren't actually intended for this, either. I'd bet that you could train a model to do image compression along these lines, with a large dictionary, that could do usable compression along the lines of what is (jokingly) described in the article. Probably have a larger compressed form than what they're thinking of.
EDIT: At one point in time, about over a quarter-century ago now, I went out and banged on a neural net post-processor for JPEG artifacts. The idea here is that JPEG very probably isn't optimally representing the final image, as a human, using their knowledge of what the world looks like, can manually (if time-consumingly) clean these up. I didn't meet with a lot of success; I only wanted to put a small amount of time into it, and I was working with much weaker hardware than people are running neural nets on today. But that generated a pre-existing dictionary, a pre-trained neural net, off a training corpus of uncompressed images. It didn't try to reconstruct the image from scratch, the way something like this would, just clean up artifacts, but it has that same pre-generated neural net approach.
I did not know about PAQ actually, thats cool. :)
It's basically just tricking the AI model to be your cloud storage provider.
I've read that the Trump administration is hiring him to archive all of the Epstein files using that format.
Compressing the Epstein files is easy: “Donald Trump raped children.” What’s my Weissman score?
Could have just stored those in PiFS https://github.com/philipl/pifs
That's nice, albeit I want to point out for anyone wondering that this is only conjectured and not guaranteed:
One of the properties that π is conjectured to have is that it is normal, which is to say that its digits are all distributed evenly, with the implication that it is a disjunctive sequence, meaning that all possible finite sequences of digits will be present somewhere in it.
There is no guarantee for any specific sequence to appear in π, but for short chunks chances are better (it's not really a probability, but it's simpler to say and I can't explain in details anyway). That's because (from wiki):
It is widely believed that the (computable) numbers √2, π, and e are normal, but a proof remains elusive.
On a similar note, I saw a story a bit back of someone saving input tokens by feeding the bot an image of a wall of text rather than the text itself and having it read the image via OCR.
Satire and reality are too hard too distinguish these days.
a typical jpeg of 20 Mb
Uh what. Literally should be the highest fucking possible quality from a $10K camera if it's that big. My raw images aren't even that big usually.
Its not typical, but you can get 20 Mb+ jpegs out of an entry level 18MP dlsr. Especially if there's lots of color and at like 5500x3300 resolutions and created with 100% quality preset.
I checked my immich and I have some (and larger), but yeah, not exactly typical.
Raw images is where you go wrong. You need at least 10 mb of metadata tags to achieve professional levels of file sizes. How can you even look at a picture without having a full description of all your childhood memories that lead you to take this beautiful picture of yesterdays mac''n'cheese dinner. This is why we need more data centers.
This is not funny, you don't understand how long and how many nights i spend overengineering compression pipelines for family photos and videos...
I've got mine down to 32 byte string to describe the location, datetime, and people in the photograph, with flags for who's smiling and/or blinking.
I want to see your porn metadata.
Absolutely debauched. Especially when you haven't even bought them coffee yet.
git-llmfs for JPEG, I kind of wish this was real just to laugh at users
Before you consider using the project, I highly recommend you read and understand the last paragraph of the LICENSE file. If you are seriously considering using it, it will become important.
This is so funny
For whoever is wondering, the license is MIT
Here is another one, no compression:
- read image
- send a prompt "regenerate image, here is pixel-by-pixel description"
- enjoy the result
(sarcasm)
New Math and vibe coding. Am I right? (insert canned laughter here).
He discovered alt text.
