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1-Bit Hokusai's "The Great Wave"

https://www.hypertalking.com/2023/05/08/1-bit-pixel-art-of-hokusais-the-great-wave-off-kanagawa/
107•stephen-hill•3d ago•12 comments

New 10 GbE USB adapters are cooler, smaller, cheaper

https://www.jeffgeerling.com/blog/2026/new-10-gbe-usb-adapters-cooler-smaller-cheaper/
330•calcifer•8h ago•174 comments

Google plans to invest up to $40B in Anthropic

https://www.bloomberg.com/news/articles/2026-04-24/google-plans-to-invest-up-to-40-billion-in-ant...
668•elffjs•22h ago•665 comments

Martin Galway's music source files from 1980's Commodore 64 games

https://github.com/MartinGalway/C64_music
36•ingve•3h ago•6 comments

Lambda Calculus Benchmark for AI

https://victortaelin.github.io/lambench/
33•marvinborner•2h ago•11 comments

How to Implement an FPS Counter

https://vplesko.com/posts/how_to_implement_an_fps_counter.html
74•vplesko•3d ago•15 comments

A Man Who Invented the Future

https://hedgehogreview.com/web-features/thr/posts/the-man-who-invented-the-future
36•apollinaire•3d ago•9 comments

A web-based RDP client built with Go WebAssembly and grdp

https://github.com/nakagami/grdpwasm
26•mariuz•3h ago•8 comments

Plain text has been around for decades and it’s here to stay

https://unsung.aresluna.org/plain-text-has-been-around-for-decades-and-its-here-to-stay/
165•rbanffy•13h ago•68 comments

A 3D Body from Eight Questions – No Photo, No GPU

https://clad.you/blog/posts/questionnaire-mlp/
104•arkadiuss•3d ago•18 comments

Humpback whales are forming super-groups

https://www.bbc.com/future/article/20260416-the-humpback-super-groups-swarming-the-seas
149•andsoitis•3d ago•77 comments

Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)

https://github.com/nex-crm/wuphf
130•najmuzzaman•5h ago•62 comments

Paraloid B-72

https://en.wikipedia.org/wiki/Paraloid_B-72
233•Ariarule•3d ago•42 comments

PCR is a surprisingly near-optimal technology

https://nikomc.com/2026/04/22/pcr/
60•mailyk•2d ago•7 comments

Replace IBM Quantum back end with /dev/urandom

https://github.com/yuvadm/quantumslop/blob/25ad2e76ae58baa96f6219742459407db9dd17f5/URANDOM_DEMO.md
218•pigeons•13h ago•32 comments

Sabotaging projects by overthinking, scope creep, and structural diffing

https://kevinlynagh.com/newsletter/2026_04_overthinking/
469•alcazar•23h ago•114 comments

The mail sent to a video game publisher

https://www.gamefile.news/p/panic-mail-arco-despelote-time-flies-thank-goodness-teeth
77•colinprince•3d ago•1 comments

My audio interface has SSH enabled by default

https://hhh.hn/rodecaster-duo-fw/
276•hhh•18h ago•83 comments

Iliad fragment found in Roman-era mummy

https://www.thehistoryblog.com/archives/75877
209•wise_blood•2d ago•66 comments

Panipat: The Rise of the Mughals

https://www.historytoday.com/archive/feature/panipat-rise-mughals
14•Thevet•3d ago•10 comments

Only One Side Will Be the True Successor to MS-DOS – Windows 2.x

https://blisscast.wordpress.com/2026/04/21/windows-2-gui-wonderland-12a/
15•keepamovin•3h ago•9 comments

Commenting and Approving Pull Requests

https://www.jakeworth.com/posts/on-commenting-and-approving-pull-requests/
4•jwworth•2d ago•0 comments

Open source memory layer so any AI agent can do what Claude.ai and ChatGPT do

https://alash3al.github.io/stash?_v01
95•alash3al•12h ago•45 comments

There Will Be a Scientific Theory of Deep Learning

https://arxiv.org/abs/2604.21691
287•jamie-simon•20h ago•122 comments

Education must go beyond the mere production of words

https://www.ncregister.com/commentaries/schnell-repairing-the-ruins
92•signor_bosco•13h ago•39 comments

Cosmology with Geometry Nodes

https://www.blender.org/user-stories/cosmology-with-geometry-nodes/
86•shankysingh•13h ago•1 comments

Email could have been X.400 times better

https://buttondown.com/blog/x400-vs-smtp-email
209•maguay•2d ago•172 comments

Turbo Vision 2.0 – a modern port

https://github.com/magiblot/tvision
166•andsoitis•9h ago•42 comments

Work with the garage door up (2024)

https://notes.andymatuschak.org/Work_with_the_garage_door_up
173•jxmorris12•3d ago•120 comments

DeepSeek v4

https://api-docs.deepseek.com/news/news260424
1981•impact_sy•1d ago•1512 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•11mo ago
Hey HN! We recently graduated from YC, and have been building customer personas for large e-commerce companies. We recently expanded into the image generation space, and have been working on research about how to automatically improve the quality of generated images.

Comments

average_r_user•11mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•11mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•11mo ago
This is a wonderful writeup of building a simple agentic system in general. What OP describes is more or less the bare minimum you should be doing at this point to get good (consistent) results from an LLM; single-shot prompting is a thing of the past.
palashshah•11mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•11mo ago
I'm surprised you landed on using o3 as the judge - we found it way too expensive. I use llm as a judge for generating color variations of products, definitely hoping for some improvements - it can be brutal to get non hallucinated features along with proper final rendering.
omneity•11mo ago
Have you tried open weights vision models such as Qwen VL, MiniCPM, PaliGemma...?

I'm also curious how usable are simpler vision models such as Florence in case you explored this direction.

palashshah•11mo ago
we're currently in the process of doing this. i think something that could potentially work is to iterate upon the initial image composition / structure using cheaper models, and then upscale at the end. this way you're saving on that iteration cost, but eventually land on a higher-scale image.
shmoogy•11mo ago
I actually haven't but nova from Amazon was surprisingly good at things like bounding boxes compared to some others You kind of have to test and measure so many different aspects to get the best at specific tasks Thanks for the idea
elif•11mo ago
This is great and provides a good starting point for any similar efforts.

However I think the temptation to lean all tasks on AI is perhaps a little naive if not lazy.

For mask generation, there is really not much reason to use AI. In this example, simple stochastic blob detection, a trivial function you could get from openCV or ask a college sophomore to write would generate much better quality masks.

palashshah•11mo ago
totally agreed here. i think my goal primarily with the mask generation was to test out how effective openai's capabilities were.

we're currently working on pipelines that limit the the involvement of AI to various tasks. for example, when generating an ad there's usually logo, some banner text, and background image.

we can use gpt-image-1 to generate the background image, another LLM to identify the coordinates of where we place the logo, and just add the logo onto the image. this is just one example!

jackphilson•11mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•11mo ago
"Simple stochastic blob detection" is an abstraction. You write (or import) a function where the the gnarly logic lives and call `detectBlobs()`. "Use an abstraction" doesn't mean you should use the same abstraction for every task, you should use the right tool for the job.
mentalgear•11mo ago
Again another example of "the unreasonable effectiveness of LLMs in a loop". At with time, the tasks for loop become bigger and more complex, until we find ourselves "outlooped" at least job wise.
ramoz•11mo ago
Nice retrospective but I guess this process is no longer needed as model's get better; esp as they start enabling features like consistent subjects. Seems like a lot of overhead to correct text for inspirational images, but I can imagine you need to always present some form of _quality_ to your clients.

Feel like control nets and some minimal photoshop work would've been better.

palashshah•11mo ago
totally. it got to a point where most of the text generated in our images was incorrect, and so it wasn't a great look showing that to our clients.

we're actually working on some form of what you described where we take images generated from LLMs + add consistent logos discretely rather than generatively.

abshkbh•11mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•11mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•11mo ago
I was kind of hoping this would be in the 'Dreambooth mold' of finetuning open weights models. I have used that with some success some ~2 years ago, does anyone know what improvements there have been in that direction since Dreambooth?
zahlman•11mo ago
It's frankly amazing to me that "ask another LLM to evaluate the image" actually produces useful feedback that results in actual improvement from the first LLM.

But then, I guess it's not much different of an idea from the earlier use of GANs, or of telling LLMs to "stop hallucinating", etc.

palashshah•11mo ago
totally. the way i think about it (purely based on intuition) is that asking an LLM to do understanding + image generation is too complex for it to be effective. if we separate out the tasks into discrete steps, the evaluation becomes better, and the generation simply becomes instruction following.
jacob019•11mo ago
This is all edited with gpt-image-1? The revised images are amazing. Were example logos provided or is it just working off of it's knowledge of a well known brand?