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Native all the way, until you need text

https://justsitandgrin.im/posts/native-all-the-way-until-you-need-text/
36•dive•53m ago•13 comments

Apple Silicon costs more than OpenRouter

https://www.williamangel.net/blog/2026/05/17/offline-llm-energy-use.html
27•datadrivenangel•33m ago•10 comments

I don't think AI will make your processes go faster

https://frederickvanbrabant.com/blog/2026-05-15-i-dont-think-ai-will-make-your-processes-go-faster/
18•TheEdonian•29m ago•2 comments

Zerostack – A Unix-inspired coding agent written in pure Rust

https://crates.io/crates/zerostack/1.0.0
449•gidellav•14h ago•223 comments

Mozilla to UK regulators: VPNs are essential privacy and security tools

https://blog.mozilla.org/netpolicy/2026/05/15/mozilla-to-uk-regulators-vpns-are-essential-privacy...
333•WithinReason•6h ago•131 comments

Prolog Basics Explained with Pokémon

https://unplannedobsolescence.com/blog/prolog-basics-pokemon/
44•birdculture•2d ago•5 comments

Every AI Subscription Is a Ticking Time Bomb for Enterprise

https://www.thestateofbrand.com/news/ai-subscription-time-bomb
3•mooreds•53m ago•1 comments

A nicer voltmeter clock

https://lcamtuf.substack.com/p/a-nicer-voltmeter-clock
229•surprisetalk•13h ago•29 comments

Colossus: The Forbin Project

https://en.wikipedia.org/wiki/Colossus:_The_Forbin_Project
131•doener•2d ago•43 comments

Hosting a website on an 8-bit microcontroller

https://maurycyz.com/projects/mcusite/
159•zdw•11h ago•13 comments

Moving away from Tailwind, and learning to structure my CSS

https://jvns.ca/blog/2026/05/15/moving-away-from-tailwind--and-learning-to-structure-my-css-/
583•mpweiher•1d ago•327 comments

Playing Atari ST Music on the Amiga with Zero CPU

https://arnaud-carre.github.io/2026-05-15-ym-fast-emu/
64•z303•4h ago•21 comments

OpenAI and Government of Malta partner to roll out ChatGPT Plus to all citizens

https://openai.com/index/malta-chatgpt-plus-partnership/
226•bookofjoe•16h ago•273 comments

How Diamonds Are Made

https://diamond.jaydip.me/
14•lemonberry•1d ago•2 comments

SANA-WM, a 2.6B open-source world model for 1-minute 720p video

https://nvlabs.github.io/Sana/WM/
357•mjgil•1d ago•140 comments

Twilight of the Velocipede: Typesetting Races Before the Age of Linotype

https://publicdomainreview.org/essay/twilight-of-the-velocipede/
25•benbreen•15h ago•0 comments

Mado: Fast Markdown linter written in Rust

https://github.com/akiomik/mado
7•nateb2022•2d ago•2 comments

Illusions of understanding in the sciences

https://link.springer.com/article/10.1007/s42113-026-00271-1
58•sebg•2d ago•28 comments

We've made the world too complicated

https://user8.bearblog.dev/the-world-is-too-complicated/
323•James72689•1d ago•323 comments

Accelerando (2005)

https://www.antipope.org/charlie/blog-static/fiction/accelerando/accelerando.html
307•eamag•1d ago•174 comments

The Third Hard Problem

https://mmapped.blog/posts/48-the-third-hard-problem
101•surprisetalk•2d ago•48 comments

Frontier AI has broken the open CTF format

https://kabir.au/blog/the-ctf-scene-is-dead
391•frays•1d ago•404 comments

MCP Hello Page

https://www.hybridlogic.co.uk/blog/2026/05/mcp-hello-page
111•Dachande663•14h ago•36 comments

Halt and Catch Fire

https://unstack.io/halt-and-catch-fire
153•ScottWRobinson•18h ago•80 comments

Why did Clovis toolmakers choose difficult quartz crystal?

https://phys.org/news/2026-04-clovis-toolmakers-difficult-quartz-crystal.html
31•PaulHoule•2d ago•19 comments

Roman Letters

https://romanletters.org/
45•diodorus•2d ago•9 comments

δ-mem: Efficient Online Memory for Large Language Models

https://arxiv.org/abs/2605.12357
225•44za12•1d ago•58 comments

Unknowable Math Can Help Hide Secrets

https://www.quantamagazine.org/how-unknowable-math-can-help-hide-secrets-20260511/
58•Xcelerate•3d ago•12 comments

C++26 Shipped a SIMD Library Nobody Asked For

https://lucisqr.substack.com/p/c26-shipped-a-simd-library-nobody
155•signa11•2d ago•117 comments

A molecule with half-Möbius topology

https://www.science.org/doi/10.1126/science.aea3321
101•bryanrasmussen•4d ago•7 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•12mo 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•12mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•12mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•12mo 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•12mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•12mo 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•12mo 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•12mo 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•12mo 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•12mo 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•12mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•12mo 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•12mo 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•12mo 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•12mo 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•12mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•12mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•12mo 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•12mo 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•12mo 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•12mo 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?