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Project Gutenberg – keeps getting better

https://www.gutenberg.org/
336•JSeiko•2h ago•111 comments

Bun Rust rewrite: "codebase fails basic miri checks, allows for UB in safe rust"

https://github.com/oven-sh/bun/issues/30719
138•ndiddy•2h ago•77 comments

A 0-click exploit chain for the Pixel 10

https://projectzero.google/2026/05/pixel-10-exploit.html
234•happyhardcore•5h ago•95 comments

I designed a nibble-oriented CPU in Verilog to build a scientific calculator

https://github.com/gdevic/FPGA-Calculator
34•gdevic•1h ago•4 comments

Image-blaster: Creates 3D environments, SFX, and meshes from a single image

https://github.com/neilsonnn/image-blaster
50•MattRogish•3h ago•10 comments

U.S. DOJ demands Apple and Google unmask over 100k users of car-tinkering app

https://macdailynews.com/2026/05/15/u-s-doj-demands-apple-and-google-unmask-over-100000-users-of-...
116•tencentshill•1h ago•61 comments

Show HN: Watch a neural net learn to play Snake

https://ppo.gradexp.xyz/
61•c1b•1d ago•13 comments

O(x)Caml in Space

https://gazagnaire.org/blog/2026-05-14-borealis.html
199•yminsky•8h ago•44 comments

I built Zenith: a live local-first fixed viewport planetarium

https://smorgasb.org/zenith-tech/
42•surprisetalk•3h ago•6 comments

Hightouch (YC S19) Is Hiring

https://hightouch.com/careers
1•joshwget•1h ago

Explore Wikipedia Like a Windows XP Desktop

https://explorer.samismith.com/
407•smusamashah•10h ago•104 comments

ASCII by Jason Scott

https://ascii.textfiles.com/
97•bookofjoe•4h ago•19 comments

Radicle: Sovereign {code forge} built on Git

https://radicle.dev/
171•KolmogorovComp•6h ago•44 comments

High dimensional geometry is transforming the MRI industry (2017) [pdf]

https://www.ams.org/government/DonohoPresentation06-28-17Final.pdf
64•nill0•5h ago•21 comments

Aperio Lang

https://aperio-lang.github.io/aperio/introduction.html
23•mmcclure•1h ago•12 comments

Feedr v0.8.0 – a TUI RSS reader, now read the full article from your terminal

https://github.com/bahdotsh/feedr
9•bahdotshxx•1h ago•4 comments

A new book on Steve Jobs at NeXT

https://spectrum.ieee.org/steve-jobs-next-computer
129•rbanffy•8h ago•112 comments

Waymo recalls 3,800 robotaxis after they drive into flood waters

https://www.cnbc.com/2026/05/12/waymo-recalls-3800-robotaxis-after-able-drive-into-standing-water...
27•drob518•58m ago•26 comments

Amazon workers under pressure to up their AI usage are making up tasks

https://www.fastcompany.com/91541586/amazon-workers-pressured-to-up-ai-use-extraneous-tasks
235•hackernj•5h ago•231 comments

Show HN: Sx – an open-source package manager for AI skills, MCPs, and commands

https://github.com/sleuth-io/sx
12•detkin•1h ago•4 comments

A few words on DS4

https://antirez.com/news/165
399•caust1c•20h ago•167 comments

We don't know why Malawi is poor

https://newsletter.deenamousa.com/p/we-dont-know-why-malawi-is-poor
63•alphabetatango•2h ago•72 comments

Ask HN: How to be SOC2 Type 2 compliant as a solo-entreprenuer?

91•sochix•11h ago•87 comments

NanoTDB – Golang Append-Only Time Series DB

https://github.com/aymanhs/nanotdb
44•aymanhs72•8h ago•6 comments

Details of the Daring Airdrop at Tristan Da Cunha

https://www.tristandc.com/government/news-2026-05-11-airdrop.php
238•kspacewalk2•15h ago•90 comments

Building ML framework with Rust and Category Theory

https://hghalebi.github.io/category_theory_transformer_rs/
86•adamnemecek•1d ago•19 comments

First public macOS kernel memory corruption exploit on Apple M5

https://blog.calif.io/p/first-public-kernel-memory-corruption
424•quadrige•1d ago•115 comments

Codex is now in the ChatGPT mobile app

https://openai.com/index/work-with-codex-from-anywhere/
449•mikeevans•22h ago•225 comments

Travelers on Air Force One ordered to throw away gifts, phones after China trip

https://techcrunch.com/2026/05/15/us-orders-travelers-on-air-force-one-to-throw-away-gifts-pins-a...
9•leopoldj•48m ago•1 comments

Trade Dollars with other startups. Book it as revenue

https://www.revswap.ai/
160•tormeh•5h ago•121 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?