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Dav2d

https://code.videolan.org/videolan/dav2d
194•dabinat•3h ago•76 comments

VS Code inserting 'Co-Authored-by Copilot' into commits regardless of usage

https://github.com/microsoft/vscode/pull/310226
21•indrora•55m ago•3 comments

Inventions for battery reuse and recycling increase seven-fold in last decade

https://www.epo.org/en/news-events/news/inventions-battery-reuse-and-recycling-increase-more-seve...
112•JeanKage•2d ago•5 comments

Do_not_track

https://donottrack.sh/
76•RubyGuy•3h ago•37 comments

NetHack 5.0.0

https://nethack.org/v500/release.html
266•rsaarelm•2h ago•58 comments

California to begin ticketing driverless cars that violate traffic laws

https://www.bbc.com/news/articles/clypjx3rg2go
133•geox•2h ago•154 comments

Flue is a TypeScript framework for building the next generation of agents

https://flueframework.com/
57•momentmaker•3h ago•23 comments

Little Magazines Are Back

https://wsjfreeexpression.substack.com/p/little-magazines-are-back
26•prismatic•2d ago•2 comments

Why does it take so long to release black fan versions?

https://www.noctua.at/en/expertise/blog/how-can-it-take-so-long-to-release-black-fan-versions
651•buildbot•16h ago•271 comments

Welcome to Hell Developer

https://noahclements.com/Wahoo-Bolt-Hidden-Debug-Mode/
28•denysvitali•3h ago•5 comments

How fast is a macOS VM, and how small could it be?

https://eclecticlight.co/2026/05/02/how-fast-is-a-macos-vm-and-how-small-could-it-be/
204•moosia•11h ago•77 comments

Barman – Backup and Recovery Manager for PostgreSQL

https://github.com/EnterpriseDB/barman
119•nateb2022•3d ago•22 comments

Roblox shares plummet 18% as child safety measures weigh on bookings

https://www.cnbc.com/2026/05/01/roblox-rblx-stock-child-safety-earnings.html
122•1vuio0pswjnm7•3h ago•70 comments

Unsigned Sizes: A Five Year Mistake

https://c3-lang.org/blog/unsigned-sizes-a-five-year-mistake/
44•lerno•2h ago•45 comments

Uber wants to turn its drivers into a sensor grid for self-driving companies

https://techcrunch.com/2026/05/01/uber-wants-to-turn-its-millions-of-drivers-into-a-sensor-grid-f...
101•nickvec•5h ago•113 comments

I Do Not Recommend Bitwarden

https://xn--gckvb8fzb.com/i-do-not-recommend-bitwarden/
35•maxloh•49m ago•26 comments

Refusal in Language Models Is Mediated by a Single Direction

https://arxiv.org/abs/2406.11717
69•fagnerbrack•7h ago•24 comments

Why IPv6 is so complicated

https://github.com/becarpenter/book6/blob/main/01.%20Introduction%20and%20Foreword/Why%20IPv6%20i...
62•speckx•3d ago•120 comments

The USB Situation

https://randsinrepose.com/archives/the-usb-situation/
64•herbertl•3d ago•67 comments

Why are there both TMP and TEMP environment variables? (2015)

https://devblogs.microsoft.com/oldnewthing/20150417-00/?p=44213
178•ankitg12•12h ago•83 comments

Open Design: Use Your Coding Agent as a Design Engine

https://github.com/nexu-io/open-design
147•steveharing1•8h ago•78 comments

Canonical Under Attack

https://status.canonical.com
28•ta988•1h ago•4 comments

Dotcl: Common Lisp Implementation on .NET

https://github.com/dotcl/dotcl
138•reikonomusha•2d ago•31 comments

Ti-84 Evo

https://education.ti.com/en/products/calculators/graphing-calculators/ti-84-evo
566•thatxliner•1d ago•454 comments

Show HN: Pollen – distributed WASM runtime, no control plane, single binary

https://github.com/sambigeara/pollen
90•sambigeara•2d ago•41 comments

America's Expanding Domestic Surveillance

https://www.wsj.com/articles/americas-expanding-domestic-surveillance-08b73187
152•Brajeshwar•5h ago•92 comments

Show HN: DAC – open-source dashboard as code tool for agents and humans

https://github.com/bruin-data/dac
89•karakanb•3d ago•30 comments

Modern C++ Programming: Busato

https://github.com/federico-busato/Modern-CPP-Programming
14•KnuthIsGod•4h ago•0 comments

Artemis II Photo Timeline

https://artemistimeline.com/#artemis-ii-walkout-nhq202604010003
333•geerlingguy•3d ago•26 comments

DeepSeek V4—almost on the frontier

https://simonwillison.net/2026/Apr/24/deepseek-v4/
443•indigodaddy•1d ago•287 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?