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Using LLMs at Oxide

https://rfd.shared.oxide.computer/rfd/0576
383•steveklabnik•8h ago•157 comments

Kilauea erupts, destroying webcam [video]

https://www.youtube.com/watch?v=TK2N99BDw7A
326•zdw•10h ago•76 comments

Z2 – Lithographically fabricated IC in a garage fab

https://sam.zeloof.xyz/second-ic/
163•embedding-shape•7h ago•29 comments

Screenshots from developers: 2002 vs. 2015 (2015)

https://anders.unix.se/2015/12/10/screenshots-from-developers--2002-vs.-2015/
291•turrini•12h ago•109 comments

GrapheneOS is the only Android OS providing full security patches

https://grapheneos.social/@GrapheneOS/115647408229616018
594•akyuu•20h ago•270 comments

Eurydice: a Rust to C compiler (yes)

https://jonathan.protzenko.fr/2025/10/28/eurydice.html
90•todsacerdoti•8h ago•38 comments

The past was not that cute

https://juliawise.net/the-past-was-not-that-cute/
176•mhb•12h ago•225 comments

Discovering the indieweb with calm tech

https://alexsci.com/blog/calm-tech-discover/
81•todsacerdoti•6h ago•8 comments

Tiny Core Linux: a 23 MB Linux distro with graphical desktop

http://www.tinycorelinux.net/
433•LorenDB•19h ago•191 comments

Perl's decline was cultural

https://www.beatworm.co.uk/blog/computers/perls-decline-was-cultural-not-technical
259•todsacerdoti•16h ago•312 comments

Z-Image: Powerful and highly efficient image generation model with 6B parameters

https://github.com/Tongyi-MAI/Z-Image
300•doener•6d ago•119 comments

Zebra-Llama – Towards efficient hybrid models

https://arxiv.org/abs/2505.17272
97•mirrir•13h ago•46 comments

United States Antarctic Program Field Manual (2024) [pdf]

https://www.usap.gov/usapgov/travelAndDeployment/documents/Continental-Field-Manual-2024.pdf
92•SheinhardtWigCo•11h ago•17 comments

OMSCS Open Courseware

https://sites.gatech.edu/omscsopencourseware/
178•kerim-ca•14h ago•70 comments

Bikeshedding, or why I want to build a laptop

https://geohot.github.io//blog/jekyll/update/2025/11/29/bikeshedding-or-laptop.html
118•cspags•6d ago•107 comments

HTML as an Accessible Format for Papers (2023)

https://info.arxiv.org/about/accessible_HTML.html
234•el3ctron•19h ago•114 comments

Why does the Salish Sea glow in the dark?

https://www.atlasobscura.com/articles/untold-earth-105-salish-sea-bioluminescence
16•prismatic•2d ago•6 comments

'Vampire Squid from Hell' Reveals the Ancient Origins of Octopuses

https://www.sciencealert.com/vampire-squid-from-hell-reveals-the-ancient-origins-of-octopuses
23•6LLvveMx2koXfwn•5d ago•1 comments

What even is "literate programming"?

https://pqnelson.github.io/2024/05/29/literate-programming.html
8•joecobb•4d ago•3 comments

Saving Japan's exceptionally rare 'snow monsters'

https://www.bbc.com/future/article/20251203-japans-disappearing-snow-monsters
80•1659447091•11h ago•7 comments

Recreating the lost SDK for a 42-year-old operating system: VisiCorp Visi On

https://git.sr.ht/~nkali/vision-sdk/tree/main/item/note/index.md
68•nkali•2d ago•6 comments

Principles of Slack Maximalism

https://aelerinya.substack.com/p/the-10-principles-of-slack-maximalism
3•surprisetalk•1w ago•2 comments

Autism's confusing cousins

https://www.psychiatrymargins.com/p/autisms-confusing-cousins
276•Anon84•22h ago•271 comments

Trains cancelled over fake bridge collapse image

https://www.bbc.com/news/articles/cwygqqll9k2o
179•josephcsible•9h ago•134 comments

Oblast: A better Blasto game for the Commodore 64

http://oldvcr.blogspot.com/2025/12/oblast-better-blasto-game-for-commodore.html
21•todsacerdoti•8h ago•5 comments

Dhrystone

https://en.wikipedia.org/wiki/Dhrystone
22•krelian•4d ago•2 comments

Coffee linked to slower biological ageing among those with severe mental illness

https://www.kcl.ac.uk/news/coffee-linked-to-slower-biological-ageing-among-those-with-severe-ment...
151•bookofjoe•12h ago•84 comments

The unexpected effectiveness of one-shot decompilation with Claude

https://blog.chrislewis.au/the-unexpected-effectiveness-of-one-shot-decompilation-with-claude/
207•knackers•1w ago•113 comments

Mathematics Without Numbers (1959)

https://www.jstor.org/stable/20026529?seq=1
53•measurablefunc•5d ago•15 comments

What Is Generative UI?

https://tambo.co/blog/posts/what-is-generative-ui
32•grouchy•3d ago•35 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•6mo 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•6mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•6mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•6mo 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•6mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•6mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•6mo 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•6mo 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•6mo 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•6mo 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?