frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•7mo 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•7mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•7mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•7mo 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•7mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•7mo 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•7mo 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•7mo 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•7mo 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•7mo 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•7mo 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•7mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•7mo 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•7mo 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•7mo 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•7mo 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•7mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•7mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•7mo 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•7mo 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•7mo 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•7mo 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?

Cowork: Claude Code for the rest of your work

https://claude.com/blog/cowork-research-preview
554•adocomplete•5h ago•284 comments

TimeCapsuleLLM: LLM trained only on data from 1800-1875

https://github.com/haykgrigo3/TimeCapsuleLLM
464•admp•8h ago•192 comments

Fabrice Bellard's TS Zip (2024)

https://www.bellard.org/ts_zip/
95•everlier•4h ago•31 comments

Postal Arbitrage

https://walzr.com/postal-arbitrage
240•The28thDuck•7h ago•121 comments

Unauthenticated remote code execution in OpenCode

https://cy.md/opencode-rce/
210•CyberShadow•1d ago•53 comments

'I rarely get outside': scientists ditch fieldwork in the age of AI

https://www.nature.com/articles/d41586-025-04150-w
29•Growtika•4d ago•12 comments

The chess bot on Delta Air Lines will destroy you (2024) [video]

https://www.youtube.com/watch?v=c0mLhHDcY3I
139•cjaackie•4h ago•84 comments

Date is out, Temporal is in

https://piccalil.li/blog/date-is-out-and-temporal-is-in/
300•alexanderameye•9h ago•95 comments

LLVM: The bad parts

https://www.npopov.com/2026/01/11/LLVM-The-bad-parts.html
272•vitaut•10h ago•53 comments

Show HN: AI in SolidWorks

https://www.trylad.com
119•WillNickols•7h ago•59 comments

F2 (YC S25) Is Hiring

https://www.ycombinator.com/companies/f2/jobs/cJsc7Fe-product-designer
1•arctech•2h ago

Floppy disks turn out to be the greatest TV remote for kids

https://blog.smartere.dk/2026/01/floppy-disks-the-best-tv-remote-for-kids/
478•mchro•11h ago•286 comments

Show HN: Agent-of-empires: OpenCode and Claude Code session manager

https://github.com/njbrake/agent-of-empires
53•river_otter•10h ago•13 comments

Perlsecret – Perl secret operators and constants

https://metacpan.org/dist/perlsecret/view/lib/perlsecret.pod
52•mjs•6d ago•12 comments

What old tennis players teach us (2017)

https://www.raphkoster.com/2017/09/22/31098/
29•surprisetalk•4d ago•18 comments

Google removes AI health summaries after investigation finds dangerous flaws

https://arstechnica.com/ai/2026/01/google-removes-some-ai-health-summaries-after-investigation-fi...
35•barishnamazov•1h ago•12 comments

Apple picks Google's Gemini to power Siri

https://www.cnbc.com/2026/01/12/apple-google-ai-siri-gemini.html
623•stygiansonic•9h ago•346 comments

Message Queues: A Simple Guide with Analogies (2024)

https://www.cloudamqp.com/blog/message-queues-exaplined-with-analogies.html
71•byt3h3ad•7h ago•21 comments

Anthropic made a mistake in cutting off third-party clients

https://archaeologist.dev/artifacts/anthropic
207•codesparkle•13h ago•173 comments

GitHub: A case study in link maintenance and 404 pages (2013)

https://chrismorgan.info/blog/github-links-case-study/
12•roryokane•5d ago•2 comments

Ai, Japanese chimpanzee who counted and painted dies at 49

https://www.bbc.com/news/articles/cj9r3zl2ywyo
173•reconnecting•15h ago•60 comments

Show HN: Fall asleep by watching JavaScript load

https://github.com/sarusso/bedtime
44•sarusso•6h ago•15 comments

Zen-C: Write like a high-level language, run like C

https://github.com/z-libs/Zen-C
151•simonpure•11h ago•90 comments

Building a 25 Gbit/s workstation for the SCION Association

https://github.com/scionassociation/blog-25gbit-workstation
63•romshark•8h ago•24 comments

Using DistributedDataParallel to train a base model from scratch in the cloud

https://www.gilesthomas.com/2026/01/llm-from-scratch-29-ddp-training-a-base-model-in-the-cloud
3•ibobev•4d ago•0 comments

Ansible battle tested hardening for Linux, SSH, Nginx, MySQL

https://github.com/dev-sec/ansible-collection-hardening
46•walterbell•5d ago•10 comments

Launch a Debugging Terminal into GitHub Actions

https://blog.gripdev.xyz/2026/01/10/actions-terminal-on-failure-for-debugging/
132•martinpeck•12h ago•55 comments

Reproducing DeepSeek's MHC: When Residual Connections Explode

https://taylorkolasinski.com/notes/mhc-reproduction/
98•taykolasinski•10h ago•30 comments

Personal thoughts/notes from working on Zootopia 2

https://blog.yiningkarlli.com/2025/12/zootopia-2.html
298•pantalaimon•5d ago•62 comments

Interview Coder Just Leaked Full Names and Companies of All SWEs Who Cheated [video]

https://www.youtube.com/watch?v=8T1vW85xFiQ
21•mickle00•1h ago•2 comments