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Large Language Models Develop Novel Social Biases Through Adaptive Exploration

https://openreview.net/challenge?redirect=%2Fforum%3Fid%3Dpc7fqaOcAH
42•paimapi•1h ago•10 comments

Google DeepMind Releases AlphaGenome Atlas

https://blog.google/innovation-and-ai/models-and-research/google-deepmind/alphagenome-atlas/
467•utiiiD•8h ago•113 comments

Navier-Stokes – Tristan Buckmaster [pdf]

https://cims.nyu.edu/~tristanb/statement.pdf
1069•procedurecall•17h ago•469 comments

On the Navier–Stokes Millennium Prize Problem

https://openai.com/index/navier-stokes-solution/
1015•tedsanders•5h ago•846 comments

Kimi K3 (2.8T) at 1 token/s on a MacBook Pro, streamed from four SSDs

https://github.com/argonautlabsai/deltafin
183•Argonautlabs•2h ago•82 comments

DaVinci Resolve 21.1

https://www.blackmagicdesign.com/media/release/20260908-03
328•tosh•9h ago•146 comments

How to Build a Printer

https://nishantjosh.dev/blogs/how-to-build-a-fking-printer/
27•cat-whisperer•1h ago•1 comments

Benchmarking Qwen3.8 27B quantizations: 4-bit holds up, 1-bit collapses

https://quesma.com/blog/qwen38-27b-quantizations-benchmarked/
198•stared•8h ago•97 comments

Mercury 2.5

https://www.inceptionlabs.ai/blog/introducing-mercury-2-5
101•Topfi•2h ago•13 comments

Animation in Bevy: The Big Picture

https://glocq.com/en/blog/20260827/
30•ibobev•2h ago•1 comments

I-have-ADHD: A skill to stop coding agents from burying the answer

https://github.com/ayghri/i-have-adhd
269•domhudson•8h ago•213 comments

A Topological Picture Book, Rendered

https://e-infinity.space/picture-book/
4•mathgenius•38m ago•0 comments

Tao: Open math problems being non-renewably mined by AI

https://mathstodon.xyz/@tao/117237320796901560
22•_alternator_•2h ago•6 comments

Muse: Meta's personal AI agent, features and capabilities

https://ai.meta.com/muse/
207•yks•3h ago•197 comments

Implementation of GCC's Nested Functions (vs. C++ Lambdas)

https://uecker.codeberg.page/2026-09-05.html
47•uecker•3d ago•5 comments

Tracing np.add, all the way down

https://blog.veitheller.de/numpy.html
24•luu•4d ago•1 comments

Show HN: LLM Attention Visualization

https://ishamf.dev/p/llm-attention-visualizer/
102•ifz•6h ago•19 comments

Replacing a Rust Enum with a 64-Bit Word Made My Interpreter 17% Faster

https://pointersgonewild.com/2026-08-25-replacing-a-rust-enum-with-a-64-bit-word/
65•metrofun•3d ago•30 comments

The Helicopter with Radioactive Blades

https://hackaday.com/2026/09/07/the-helicopter-with-radioactive-blades/
138•zdw•1d ago•36 comments

Show HN: Copperhead – Hardware as Fast as Software

https://copperhead.sh/
194•animeshchouhan•9h ago•76 comments

The two Christian saints who are the Buddha

https://signoregalilei.com/2026/08/30/the-two-christian-saints-who-are-secretly-the-buddha/
198•surprisetalk•8h ago•138 comments

The 92-Year-Old Mathematician and the Teenage Apprentice

https://www.nytimes.com/2026/09/06/science/92-year-old-mathematician-apprentice.html
121•robinhouston•2d ago•10 comments

C*: Unifying Programming and Verification in C

https://arxiv.org/abs/2504.02246
65•rramadass•7h ago•39 comments

Connecting the Machines

https://herdr.dev/blog/connecting-the-machines/
69•collinmanderson•6h ago•24 comments

The Microeconomics of Artificial Intelligence (Open Access)

https://direct.mit.edu/books/oa-monograph/6067/The-Microeconomics-of-Artificial-Intelligence
4•neehao•2d ago•0 comments

AlphaGenome Atlas predictive map of every DNA letter change in the human genome

https://deepmind.google/blog/alphagenome-atlas-a-predictive-map-of-every-possible-dna-letter-chan...
76•fady0•8h ago•9 comments

ZX Spectrum: Experimenting with 1-Bit Sound

https://bumbershootsoft.wordpress.com/2026/09/05/zx-spectrum-experimenting-with-1-bit-sound/
89•ibobev•8h ago•26 comments

Function Arguments Are Not Function Colors

https://jerf.org/iri/post/2026/func_args_are_not_colors/
27•ingve•4h ago•10 comments

Reverse Engineering an ASIC

https://kjartanvandriel.github.io/asic/
10•burekqueen•1d ago•0 comments

FreeBSD 14.5-Release

https://www.freebsd.org/releases/14.5R/announce/
107•joshcsimmons•11h ago•21 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•1y 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•1y ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•1y ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•1y 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•1y ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•1y 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•1y 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•1y 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•1y 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•1y 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•1y 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•1y ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•1y 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•1y 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•1y 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•1y 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•1y ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•1y ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•1y 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•1y 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•1y 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•1y 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?