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Linux gaming is faster because Windows APIs are becoming Linux kernel features

https://www.xda-developers.com/linux-gaming-is-getting-faster-because-windows-apis-are-becoming-l...
391•haunter•3d ago•253 comments

Setting up a free *.city.state.us locality domain (2025)

https://fredchan.org/blog/locality-domains-guide/
445•speckx•8h ago•142 comments

A History of IDEs at Google

https://laurent.le-brun.eu/blog/a-history-of-ides-at-google
217•laurentlb•4d ago•172 comments

Medicare's new payment model is built for AI. Most of the tech world has no idea

https://techcrunch.com/2026/05/12/medicares-new-payment-model-is-built-for-ai-and-most-of-the-tec...
24•brandonb•1h ago•13 comments

In-person examinations at Princeton will be proctored starting July 1

https://www.dailyprincetonian.com/article/2026/05/princeton-news-adpol-proctoring-in-person-exami...
180•bookofjoe•2h ago•234 comments

Chess puzzle I found in my dad's old book

https://ardoedo.it/kempelen/
46•Eswo•2d ago•8 comments

Tell HN: Dont use Claude Design, lost access to my projects after unsubscribing

60•pycassa•1h ago•12 comments

The Emacsification of Software

https://sockpuppet.org/blog/2026/05/12/emacsification/
147•rdslw•16h ago•100 comments

MacBook Neo Deep Dive: Benchmarks, Wafer Economics, and the 8GB Gamble

https://www.jdhodges.com/blog/macbook-neo-benchmarks-analysis/
126•tosh•4h ago•90 comments

Xs of Y – roguelike that names itself every run. Written in 4kLoC

https://github.com/nooga/xsofy
137•andsoitis•3d ago•60 comments

S-100 Virtual Workbench

https://grantmestrength.github.io/S100/
90•rbanffy•7h ago•19 comments

How can Apple deal with the memory shortage?

https://asymco.com/2026/05/11/the-great-memory-panic-of-2026/
58•tambourine_man•2d ago•26 comments

The US is winning the AI race where it matters most: commercialization

https://avkcode.github.io/blog/us-winning-ai-race.html
143•akrylov•9h ago•387 comments

Launch HN: Ardent (YC P26) – Postgres sandboxes in seconds with zero migration

https://www.tryardent.com/
55•vc289•6h ago•20 comments

Twin brothers wipe 96 government databases minutes after being fired

https://arstechnica.com/tech-policy/2026/05/drop-database-what-not-to-do-after-losing-an-it-job/
243•jnord•1d ago•168 comments

Reverting the incremental GC in Python 3.14 and 3.15

https://discuss.python.org/t/reverting-the-incremental-gc-in-python-3-14-and-3-15/107014
184•curiousgal•4d ago•71 comments

A sentimental tour of late 1990s and early 2000s hacking tools

https://andreafortuna.org/2026/05/13/amarcord/
34•speckx•4h ago•12 comments

"Not Medically Necessary": Helping America's Health Insurers Deny Coverage

https://www.propublica.org/article/evicore-health-insurance-denials-cigna-unitedhealthcare-aetna-...
137•ceejayoz•4h ago•94 comments

New stainless steel can survive conditions for hydrogen production in seawater

https://www.sciencedaily.com/releases/2026/05/260510030950.htm
279•HardwareLust•2d ago•135 comments

Making the news available at no cost is a victory

https://www.sltrib.com/opinion/commentary/2026/05/12/just-days-tribune-reporting/
90•danso•3h ago•101 comments

An idiot's guide to lead optimisation for proteins

https://magnusross.github.io/posts/protein-lead-optimisation-1/
131•magni121•2d ago•10 comments

Marco Polo: Finding a friend with only distance and motion

https://www.jackhogan.me/blog/marco-polo
3•jackhogan11•2d ago•0 comments

Meta won't let you block its AI account on Threads

https://www.theverge.com/tech/929091/meta-ai-threads-account-block
58•logickkk1•2h ago•22 comments

Leaving GitHub for Forgejo

https://jorijn.com/en/blog/leaving-github-for-forgejo/
507•jorijn•10h ago•268 comments

Comparing a 1980s memory map to the Raspi Pico

https://medium.com/@noborutakahashi/a-40-year-old-memory-map-comparable-to-todays-raspberry-pi-pi...
7•Schlagbohrer•3d ago•0 comments

Preserving Fisher-Price Pixter

https://dmitry.gr/?r=05.Projects&proj=37.%20Pixter
198•dmitrygr•2d ago•39 comments

Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

https://github.com/cactus-compute/needle
627•HenryNdubuaku•1d ago•180 comments

Substrate (YC S24) Is Hiring a Technical Success Manager

https://www.ycombinator.com/companies/substrate/jobs/T2fMBhD-technical-success-manager
1•kunle•11h ago

I moved my digital stack to Europe

https://monokai.com/articles/how-i-moved-my-digital-stack-to-europe/
850•monokai_nl•11h ago•522 comments

Exploring 8 Shaft Weaving

https://algorithmicpattern.org/2026/03/11/exploring-8-shaft-weaving/
16•surprisetalk•2d ago•0 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?