frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

Reinventing how .NET builds and ships (again)

https://devblogs.microsoft.com/dotnet/reinventing-how-dotnet-builds-and-ships-again/
62•IcyWindows•2h ago•25 comments

A new bridge links the math of infinity to computer science

https://www.quantamagazine.org/a-new-bridge-links-the-strange-math-of-infinity-to-computer-scienc...
114•digital55•5h ago•25 comments

What They Don't Tell You About Maintaining an Open Source Project

https://andrej.sh/blog/maintaining-open-source-project/
55•andrejsshell•3h ago•23 comments

Unifying our mobile and desktop domains

https://techblog.wikimedia.org/2025/11/21/unifying-mobile-and-desktop-domains/
87•todsacerdoti•8h ago•19 comments

Show HN: We built an open source, zero webhooks payment processor

https://github.com/flowglad/flowglad
216•agreeahmed•7h ago•140 comments

The fall of Labubus and the mush of modern internet trends

https://www.michigandaily.com/arts/digital-culture/the-fall-of-labubus-and-the-mush-of-modern-int...
38•gnabgib•2d ago•33 comments

A DOOM vector engine for rendering in KiCad, and over an audio jack

https://www.mikeayles.com/#kidoom
45•mikeayles•3h ago•3 comments

Google Antigravity exfiltrates data via indirect prompt injection attack

https://www.promptarmor.com/resources/google-antigravity-exfiltrates-data
550•jjmaxwell4•6h ago•148 comments

LLVM Adds Constant-Time Support for Protecting Cryptographic Code

https://blog.trailofbits.com/2025/11/25/constant-time-support-lands-in-llvm-protecting-cryptograp...
16•birdculture•2h ago•4 comments

FLUX.2: Frontier Visual Intelligence

https://bfl.ai/blog/flux-2
231•meetpateltech•9h ago•68 comments

How to repurpose your old phone into a web server

https://far.computer/how-to/
166•louismerlin•3d ago•71 comments

The Generative Burrito Test

https://www.generativist.com/notes/2025/Nov/25/generative-burrito-test.html
69•pathdependent•2h ago•39 comments

Trillions spent and big software projects are still failing

https://spectrum.ieee.org/it-management-software-failures
300•pseudolus•13h ago•271 comments

Ilya Sutskever: We're moving from the age of scaling to the age of research

https://www.dwarkesh.com/p/ilya-sutskever-2
181•piotrgrabowski•8h ago•151 comments

Jakarta is now the biggest city in the world

https://www.axios.com/2025/11/24/jakarta-tokyo-worlds-biggest-city-population
236•skx001•19h ago•145 comments

Launch HN: Onyx (YC W24) – Open-source chat UI

165•Weves•11h ago•117 comments

Constant-time support coming to LLVM: Protecting cryptographic code

https://blog.trailofbits.com/2025/11/25/constant-time-support-coming-to-llvm-protecting-cryptogra...
41•ahlCVA•12h ago•16 comments

Notes on the Troubleshooting and Repair of Computer and Video Monitors

https://www.repairfaq.org/sam/monfaq.htm
14•WorldPeas•2h ago•1 comments

1,700-year-old Roman sarcophagus is unearthed in Budapest

https://apnews.com/article/hungary-roman-sarcophagus-discovery-budapest-77a41fe190bbcc167b43d0514...
16•gmays•1d ago•8 comments

Someone at YouTube Needs Glasses: The Prophecy Has Been Fulfilled

https://jayd.ml/2025/11/10/someone-at-youtube-needs-glasses-prophecy-fulfilled.html
276•jaydenmilne•3h ago•171 comments

3 things to know about Ironwood, our latest TPU

https://blog.google/products/google-cloud/ironwood-google-tpu-things-to-know/
12•zdw•3h ago•1 comments

Python is not a great language for data science

https://blog.genesmindsmachines.com/p/python-is-not-a-great-language-for
127•speckx•8h ago•139 comments

What Now? Handling Errors in Large Systems

https://brooker.co.za/blog/2025/11/20/what-now
4•thundergolfer•58m ago•0 comments

The 101 of analog signal filtering (2024)

https://lcamtuf.substack.com/p/the-101-of-analog-signal-filtering
119•harperlee•4d ago•9 comments

The gruesome new data on tech jobs

https://www.businessinsider.com/gruesome-tech-jobs-data-scientists-analytics-indeed-2025-11
27•pseudolus•1h ago•12 comments

Google steers Americans looking for health care into "junk insurance"

https://pluralistic.net/2025/11/25/open-season/
68•hn_acker•3h ago•21 comments

Inflatable Space Stations

https://worksinprogress.co/issue/inflatable-space-stations/
62•bensouthwood•4d ago•26 comments

Unison 1.0

https://www.unison-lang.org/unison-1-0/
200•pchiusano•5h ago•65 comments

Making Crash Bandicoot (2011)

https://all-things-andy-gavin.com/video-games/making-crash/
199•davikr•13h ago•33 comments

Proton Meet: Secure, end-to-end encrypted video conferencing – Proton

https://proton.me/meet
11•absqueued•32m ago•2 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•6mo 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•6mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•6mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•6mo 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•6mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•6mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•6mo 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•6mo 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•6mo 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•6mo 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?