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Valve releases Steam Controller CAD files under Creative Commons license

https://www.digitalfoundry.net/news/2026/05/valve-releases-steam-controller-cad-files-under-creat...
1390•haunter•17h ago•425 comments

How Unsloth and Nvidia made LLM training 25% faster on consumer GPUs

https://unsloth.ai/blog/nvidia-collab
10•segmenta•1h ago•0 comments

Appearing productive in the workplace

https://nooneshappy.com/article/appearing-productive-in-the-workplace/
1109•diebillionaires•16h ago•418 comments

Permacomputing Principles

https://permacomputing.net/principles/
132•andsoitis•6h ago•61 comments

SQLite Is a Library of Congress Recommended Storage Format

https://sqlite.org/locrsf.html
228•whatisabcdefgh•11h ago•62 comments

Diskless Linux boot using ZFS, iSCSI and PXE

https://aniket.foo/posts/20260505-netboot/
82•stereo-highway•5h ago•40 comments

Vibe coding and agentic engineering are getting closer than I'd like

https://simonwillison.net/2026/May/6/vibe-coding-and-agentic-engineering/
581•e12e•17h ago•631 comments

RSS Feeds Send Me More Traffic Than Google

https://shkspr.mobi/blog/2026/05/rss-feeds-send-me-more-traffic-than-google/
90•SpyCoder77•8h ago•16 comments

Show HN: Agent-skills-eval – Test whether Agent Skills improve outputs

https://github.com/darkrishabh/agent-skills-eval
15•darkrishabh•2h ago•0 comments

ProgramBench: Can Language Models Rebuild Programs from Scratch?

https://arxiv.org/abs/2605.03546
39•jonbaer•5h ago•22 comments

Chevrolet Performance eCrate package (400v/200hp)

https://www.chevrolet.com/performance-parts/crate-engines/ecrate
36•mindcrime•2d ago•15 comments

Photoshop's challenges with focus, pt. 2

https://unsung.aresluna.org/photoshops-challenges-with-focus-pt-2/
18•frizlab•2d ago•4 comments

The Mathematical Dance Inside Plant Cells

https://www.quantamagazine.org/the-hidden-mathematical-dance-inside-plant-cells-20260504/
38•isaacfrond•2d ago•2 comments

From Supabase to Clerk to Better Auth

https://blog.val.town/better-auth
254•stevekrouse•15h ago•176 comments

Google Cloud fraud defense, the next evolution of reCAPTCHA

https://cloud.google.com/blog/products/identity-security/introducing-google-cloud-fraud-defense-t...
295•unforgivenpasta•15h ago•298 comments

What I Learned Making an App for My Family

https://mendelgreenberg.com/posts/ourcar/
50•chabad360•19h ago•10 comments

The Vatican's Website in Latin

https://www.vatican.va/latin/latin_index.html
141•ks2048•7h ago•79 comments

Pen pal programs endure in a digital age

https://apnews.com/article/pen-pals-letters-comeback-bc87e1b9c229665bafd368e19751d6ca
46•petethomas•1d ago•7 comments

Show HN: Hallucinopedia

http://halupedia.com/
215•bstrama•16h ago•197 comments

SingleRide: Longest route on NYC Subway without visiting the same station twice

https://singleride.nyc/
6•TMWNN•1d ago•1 comments

Community firmware for the Xteink X4 e-paper reader

https://github.com/crosspoint-reader/crosspoint-reader
100•dmos62•1d ago•28 comments

Show HN: Tilde.run – Agent sandbox with a transactional, versioned filesystem

https://tilde.run/
161•ozkatz•17h ago•116 comments

Building my own Vi text editor in BASIC

https://leetusman.com/nosebook/yvi
57•zeech•1d ago•27 comments

Learning the Integral of a Diffusion Model

https://sander.ai/2026/05/06/flow-maps.html
132•benanne•14h ago•21 comments

Building the TD4 4-Bit CPU

https://jayakody2000lk.blogspot.com/2026/05/building-td4-4-bit-cpu.html
16•zdw•4h ago•6 comments

SoundOff: Low-Cost Passive Ultrasound Tags

https://yibo-fu.com/SoundOff-Low-cost-Passive-Ultrasound-Tags-for-Non-invasive-and-Non
67•jonbaer•15h ago•1 comments

A Theory of Deep Learning

https://elonlit.com/scrivings/a-theory-of-deep-learning/
185•elonlit•1d ago•42 comments

Finding the differences in a series of power supplies

https://www.lttlabs.com/articles/2026/05/05/testing-psu-series
47•LabsLucas•1d ago•6 comments

Show HN: Trust – Coding Rust like it's 1989

https://github.com/wojtczyk/trust
5•wojtczyk•3h ago•1 comments

Ted Turner has died

https://www.cnn.com/2026/05/06/us/ted-turner-death
271•pseudolus•18h ago•217 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?