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X5.1 solar flare, G4 geomagnetic storm watch

https://www.spaceweatherlive.com/en/news/view/593/20251111-x5-1-solar-flare-g4-geomagnetic-storm-...
217•sva_•6h ago•56 comments

.NET MAUI is coming to Linux and the browser, powered by Avalonia

https://avaloniaui.net/blog/net-maui-is-coming-to-linux-and-the-browser-powered-by-avalonia
107•vyrotek•5h ago•77 comments

I didn't reverse-engineer the protocol for my blood pressure monitor in 24 hours

https://james.belchamber.com/articles/blood-pressure-monitor-reverse-engineering/
150•jamesbelchamber•6h ago•62 comments

Laptops adorned with creative stickers

https://stickertop.art/main/
220•z303•1w ago•229 comments

Four strange places to see London's Roman Wall

https://diamondgeezer.blogspot.com/2025/11/odd-places-to-see-londons-roman-wall.html
85•zeristor•5h ago•23 comments

Heroku Support for .NET 10

https://www.heroku.com/blog/support-for-dotnet-10-lts-what-developers-need-know/
45•runesoerensen•5h ago•15 comments

A modern 35mm film scanner for home

https://www.soke.engineering/
161•QiuChuck•8h ago•113 comments

The terminal of the future

https://jyn.dev/the-terminal-of-the-future
139•miguelraz•7h ago•69 comments

The history of Casio watches

https://www.casio.com/us/watches/50th/Heritage/1970s/
176•qainsights•3d ago•95 comments

Pikaday: A friendly guide to front-end date pickers

https://pikaday.dbushell.com
147•mnemonet•13h ago•71 comments

My fan worked fine, so I gave it WiFi

https://ellis.codes/blog/my-fan-worked-fine-so-i-gave-it-wi-fi/
137•woolywonder•6d ago•51 comments

A catalog of side effects

https://bernsteinbear.com/blog/compiler-effects/
81•speckx•8h ago•5 comments

FFmpeg to Google: Fund us or stop sending bugs

https://thenewstack.io/ffmpeg-to-google-fund-us-or-stop-sending-bugs/
626•CrankyBear•9h ago•488 comments

Scaling HNSWs

https://antirez.com/news/156
159•cyndunlop•13h ago•32 comments

We ran over 600 image generations to compare AI image models

https://latenitesoft.com/blog/evaluating-frontier-ai-image-generation-models/
124•kalleboo•10h ago•71 comments

Modern Optimizers – An Alchemist's Notes on Deep Learning

https://notes.kvfrans.com/7-misc/modern-optimizers.html
18•maxall4•4d ago•1 comments

Collaboration sucks

https://newsletter.posthog.com/p/collaboration-sucks
329•Kinrany•7h ago•191 comments

Meticulous (YC S21) is hiring to redefine software dev

https://jobs.ashbyhq.com/meticulous/3197ae3d-bb26-4750-9ed7-b830f640515e
1•Gabriel_h•7h ago

The Department of War just shot the accountants and opted for speed

https://steveblank.com/2025/11/11/the-department-of-war-just-shot-the-accountants-and-opted-for-s...
137•ridruejo•13h ago•207 comments

iPhone Pocket

https://www.apple.com/newsroom/2025/11/introducing-iphone-pocket-a-beautiful-way-to-wear-and-carr...
454•soheilpro•17h ago•1126 comments

Adk-go: code-first Go toolkit for building, evaluating, and deploying AI agents

https://github.com/google/adk-go
53•maxloh•8h ago•18 comments

Terminal Latency on Windows (2024)

https://chadaustin.me/2024/02/windows-terminal-latency/
92•bariumbitmap•9h ago•71 comments

Why Nietzsche Matters in the Age of Artificial Intelligence

https://cacm.acm.org/blogcacm/why-nietzsche-matters-in-the-age-of-artificial-intelligence/
61•pseudolus•4h ago•40 comments

Array-programming the Mandelbrot set

https://jcmorrow.com/mandelbrot/
62•jcmorrow•4d ago•9 comments

Cache-friendly, low-memory Lanczos algorithm in Rust

https://lukefleed.xyz/posts/cache-friendly-low-memory-lanczos/
113•lukefleed•10h ago•18 comments

Text rendering and effects using GPU-computed distances

https://blog.pkh.me/p/47-text-rendering-and-effects-using-gpu-computed-distances.html
6•PaulHoule•3h ago•0 comments

Learning to Model the World with Language

https://dynalang.github.io/
21•jxmorris12•5d ago•0 comments

Agentic pelican on a bicycle

https://www.robert-glaser.de/agentic-pelican-on-a-bicycle/
59•todsacerdoti•8h ago•41 comments

Étude in C minor (2020)

https://zserge.com/posts/etude-in-c/
60•etrvic•1w ago•13 comments

Show HN: Cactoide – Federated RSVP Platform

https://cactoide.org/
56•orbanlevi•11h ago•22 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•5mo 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•5mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•5mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•5mo 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•5mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•5mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•5mo 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•5mo 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•5mo 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•5mo 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?