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Nano Banana can be prompt engineered for nuanced AI image generation

https://minimaxir.com/2025/11/nano-banana-prompts/
107•minimaxir•1h ago•27 comments

Zed is our office

https://zed.dev/blog/zed-is-our-office
256•sagacity•3h ago•110 comments

Launch HN: Tweeks (YC W25) – Browser extension to de-enshittify the web

https://www.tweeks.io/onboarding
71•jmadeano•2h ago•60 comments

GitHub Partial Outage

https://www.githubstatus.com/incidents/1jw8ltnr1qrj
124•danfritz•3h ago•52 comments

Checkout.com hacked, refuses ransom payment, donates to security labs

https://www.checkout.com/blog/protecting-our-merchants-standing-up-to-extortion
421•StrangeSound•9h ago•208 comments

Rand Paul: Congress bill destroys hemp farmer livelihoods

https://www.courier-journal.com/story/opinion/contributors/2025/11/13/rand-paul-congress-funding-...
90•bilsbie•1h ago•22 comments

SIMA 2: An agent that plays, reasons, and learns with you in virtual 3D worlds

https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d...
81•meetpateltech•3h ago•17 comments

Blender Lab

https://www.blender.org/news/introducing-blender-lab/
140•radeeyate•5h ago•39 comments

The Useful Personal Computer

https://technicshistory.com/2025/11/02/the-useful-personal-computer/
38•cfmcdonald•1w ago•2 comments

BAML is hiring compilers/rust engineers (YC W23)

https://github.com/BoundaryML/baml/tree/canary/jobs
1•hellovai•2h ago

We cut our Mongo DB costs by 90% by moving to Hetzner

https://prosopo.io/blog/we-cut-our-mongodb-costs-by-90-percent/
134•arbol•3h ago•90 comments

Kratos - Cloud native Auth0 open-source alternative (self-hosted)

https://github.com/ory/kratos
89•curtistyr•4h ago•60 comments

Denx (a.k.a. U-Boot) Retires

https://www.denx.de/
62•synergy20•4h ago•11 comments

Pebble: How to Build a Smartwatch: Software – Setting Expectations and Roadmap

https://ericmigi.com/blog/how-to-build-a-smartwatch-software-setting-expectations-and-roadmap/
42•teekert•4h ago•12 comments

Think in Math. Write in Code

https://www.jmeiners.com/think-in-math/
15•alabhyajindal•4d ago•1 comments

Heartbeats in Distributed Systems

https://arpitbhayani.me/blogs/heartbeats-in-distributed-systems/
58•sebg•5h ago•21 comments

Tesla Is Recalling Cybertrucks Again. Yep, More Pieces Are Falling Off

https://www.popularmechanics.com/cars/hybrid-electric/a69384091/cybertruck-lightbar-recall/
235•2OEH8eoCRo0•3h ago•203 comments

Android developer verification: Early access starts

https://android-developers.googleblog.com/2025/11/android-developer-verification-early.html
1243•erohead•18h ago•575 comments

Microsoft confirms Windows 11 is about to change gets enormous backlash – Neowin

https://www.neowin.net/news/microsoft-confirms-windows-11-is-about-to-change-massively-gets-enorm...
64•OptionOfT•59m ago•46 comments

Human Fovea Detector

https://www.shadertoy.com/view/4dsXzM
400•AbuAssar•18h ago•82 comments

A Challenge to Roboticists: My Humanoid Olympics

https://spectrum.ieee.org/humanoid-robot-olympics
34•quapster•1w ago•4 comments

Steam Machine

https://store.steampowered.com/sale/steammachine
2523•davikr•1d ago•1194 comments

Android 16 QPR1 is being pushed to the Android Open Source Project

https://grapheneos.social/@GrapheneOS/115533432439509433
217•uneven9434•15h ago•116 comments

COBOL to Kotlin via Formal Models (IR and Alloy and Golden Master)

https://marcoeg.medium.com/from-cobol-to-kotlin-795920b1f371
29•marcoeg•5d ago•8 comments

Cursor: Past, Present, and Future

https://cursor.com/blog/series-d
28•whizusukite•4h ago•11 comments

Reverse Engineering Yaesu FT-70D Firmware Encryption

https://landaire.net/reversing-yaesu-firmware-encryption/
116•austinallegro•11h ago•16 comments

Britain's railway privatization was an abject failure

https://www.rosalux.de/en/news/id/53917/britains-railway-privatization-was-an-abject-failure
415•robtherobber•5h ago•365 comments

GPT-5.1: A smarter, more conversational ChatGPT

https://openai.com/index/gpt-5-1/
512•tedsanders•23h ago•651 comments

Homebrew no longer allows bypassing Gatekeeper for unsigned/unnotarized software

https://github.com/Homebrew/brew/issues/20755
312•firexcy•21h ago•245 comments

Continuous Autoregressive Language Models

https://arxiv.org/abs/2510.27688
93•Anon84•1w ago•7 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?