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The Free Universal Construction Kit

https://fffff.at/free-universal-construction-kit/
139•robinhouston•3d ago•24 comments

1-Bit Hokusai's "The Great Wave" (2023)

https://www.hypertalking.com/2023/05/08/1-bit-pixel-art-of-hokusais-the-great-wave-off-kanagawa/
441•stephen-hill•3d ago•80 comments

Using coding assistance tools to revive projects you never were going to finish

https://blog.matthewbrunelle.com/its-ok-to-use-coding-assistance-tools-to-revive-the-projects-you...
47•speckx•4h ago•27 comments

New 10 GbE USB adapters are cooler, smaller, cheaper

https://www.jeffgeerling.com/blog/2026/new-10-gbe-usb-adapters-cooler-smaller-cheaper/
487•calcifer•14h ago•285 comments

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

https://github.com/MartinGalway/C64_music
136•ingve•9h ago•17 comments

Desmond Morris has died

https://www.bbc.com/news/articles/c51y797v200o
57•martey•4d ago•9 comments

Show HN: Kloak, A secret manager that keeps K8s workload away from secrets

https://getkloak.io/
12•neo2006•1h ago•4 comments

Which one is more important: more parameters or more computation? (2021)

https://parl.ai/projects/params_vs_compute/
32•jxmorris12•1d ago•2 comments

AI agents that argue with each other to improve decisions

https://github.com/rockcat/HATS
15•rockcat12•2h ago•5 comments

GPT‑5.5 Bio Bug Bounty

https://openai.com/index/gpt-5-5-bio-bug-bounty/
98•Murfalo•6h ago•84 comments

Discret 11, the French TV encryption of the 80s

https://fabiensanglard.net/discret11/
110•adunk•9h ago•18 comments

Hokusai and Tesselations

https://dl.ndl.go.jp/pid/1899550/1/11/
64•srean•3h ago•12 comments

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

https://github.com/nakagami/grdpwasm
93•mariuz•9h ago•38 comments

Commenting and approving pull requests

https://www.jakeworth.com/posts/on-commenting-and-approving-pull-requests/
65•jwworth•2d ago•55 comments

What async promised and what it delivered

https://causality.blog/essays/what-async-promised/
85•zdw•3d ago•78 comments

Insights into firewood use by early Middle Pleistocene hominins

https://www.sciencedirect.com/science/article/pii/S0277379126001824
37•wslh•2d 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/
58•keepamovin•9h ago•41 comments

Mine, an IDE for Coalton and Common Lisp

https://coalton-lang.github.io/mine/
9•varjag•2h ago•0 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/
235•rbanffy•19h ago•123 comments

Lute: A Standalone Runtime for Luau

https://lute.luau.org/
17•vrn-sn•2d ago•5 comments

The Stanford Freshmen Who Want to Rule the World

https://www.theatlantic.com/ideas/2026/04/stanford-students-power/686920/
10•apparent•45m ago•1 comments

North American Millets Alliance(2023)

https://milletsalliance.org/
4•num42•2h ago•0 comments

Nicholas Carlini – Black-hat LLMs [video]

https://www.youtube.com/watch?v=1sd26pWhfmg
8•simonebrunozzi•32m ago•0 comments

Replace IBM Quantum back end with /dev/urandom

https://github.com/yuvadm/quantumslop/blob/25ad2e76ae58baa96f6219742459407db9dd17f5/URANDOM_DEMO.md
303•pigeons•19h ago•41 comments

Lambda Calculus Benchmark for AI

https://victortaelin.github.io/lambench/
109•marvinborner•9h ago•34 comments

Sabotaging projects by overthinking, scope creep, and structural diffing

https://kevinlynagh.com/newsletter/2026_04_overthinking/
501•alcazar•1d ago•127 comments

HEALPix

https://en.wikipedia.org/wiki/HEALPix
39•hyperific•7h ago•5 comments

Panipat: The rise of the Mughals

https://www.historytoday.com/archive/feature/panipat-rise-mughals
45•Thevet•3d ago•56 comments

America's Geothermal Breakthrough Could Unlock a 150-Gigawatt Energy Revolution

https://oilprice.com/Alternative-Energy/Geothermal-Energy/Americas-Geothermal-Breakthrough-Could-...
10•sleepyguy•58m ago•5 comments

Simulacrum of Knowledge Work

https://blog.happyfellow.dev/simulacrum-of-knowledge-work/
8•thehappyfellow•3h ago•0 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?