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Nano Banana can be prompt engineered for nuanced AI image generation

https://minimaxir.com/2025/11/nano-banana-prompts/
484•minimaxir•9h ago•130 comments

Apple Mini Apps Partner Program

https://developer.apple.com/programs/mini-apps-partner/
70•soheilpro•2h ago•43 comments

Zed is our office

https://zed.dev/blog/zed-is-our-office
492•sagacity•11h ago•246 comments

Why Fei-Fei Li and Yann LeCun Are Both Betting on "World Models"

https://entropytown.com/articles/2025-11-13-world-model-lecun-feifei-li/
6•signa11•20m ago•1 comments

650GB of Data (Delta Lake on S3). Polars vs. DuckDB vs. Daft vs. Spark

https://dataengineeringcentral.substack.com/p/650gb-of-data-delta-lake-on-s3-polars
95•tanelpoder•5h ago•28 comments

OpenMANET Wi-Fi HaLow open-source project for Raspberry Pi–based MANET radios

https://openmanet.net/
74•hexmiles•5h ago•23 comments

Launch HN: Tweeks (YC W25) – Browser extension to deshittify the web

https://www.tweeks.io/onboarding
182•jmadeano•11h ago•147 comments

How to fix subsystem request failed on channel 0

https://blog.x-way.org/Linux/2025/11/06/How-to-fix-subsystem-request-failed-on-channel-0.html
22•speckx•1w ago•7 comments

How to Get a North Korea / Antarctica VPS

https://blog.lyc8503.net/en/post/asn-5-worldwide-servers/
8•uneven9434•1h ago•1 comments

Blue Origin lands New Glenn rocket booster on second try

https://techcrunch.com/2025/11/13/blue-origin-lands-new-glenn-rocket-booster-on-second-try/
265•perihelions•5h ago•133 comments

I Built a One File Edge Probe to Tell Me When Time Is Lying

https://physical-ai.ghost.io/a-one-file-pwa-to-tell-you-when-time-is-lying/
15•boulevard•1w ago•1 comments

Kubernetes Ingress Nginx is retiring

https://www.kubernetes.dev/blog/2025/11/12/ingress-nginx-retirement/
36•TheApplicant•4h ago•3 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...
175•meetpateltech•11h ago•69 comments

Think in math, write in code (2019)

https://www.jmeiners.com/think-in-math/
118•alabhyajindal•4d ago•45 comments

Blender Lab

https://www.blender.org/news/introducing-blender-lab/
206•radeeyate•13h ago•44 comments

SlopStop: Community-driven AI slop detection in Kagi Search

https://blog.kagi.com/slopstop
321•msub2•8h ago•153 comments

Itiner-E – The Digital Atlas of Ancient Roads

https://itiner-e.org/
22•beatthatflight•1w ago•1 comments

Piramidal (YC W24) Hiring: Front End Engineer

https://www.ycombinator.com/companies/piramidal/jobs/i9yNX5s-front-end-engineer-user-interface
1•dsacellarius•6h ago

Show HN: DBOS Java – Postgres-Backed Durable Workflows

https://github.com/dbos-inc/dbos-transact-java
52•KraftyOne•6h ago•29 comments

The emergence and diversification of dog morphology

https://www.science.org/doi/10.1126/science.adt0995
26•Marshferm•4h ago•11 comments

The Eggstraordinary Fortress

https://ahmed1011001.github.io/Notes/stories/eggstrodinary.html
44•tippa123•8h ago•17 comments

The Useful Personal Computer

https://technicshistory.com/2025/11/02/the-useful-personal-computer/
87•cfmcdonald•1w ago•28 comments

Remind: A sophisticated calendar and alarm program

https://dianne.skoll.ca/projects/remind/
40•n3t•1w ago•5 comments

Disrupting the first reported AI-orchestrated cyber espionage campaign

https://www.anthropic.com/news/disrupting-AI-espionage
169•koakuma-chan•8h ago•116 comments

Heartbeats in Distributed Systems

https://arpitbhayani.me/blogs/heartbeats-in-distributed-systems/
111•sebg•13h ago•41 comments

Rust in Android: move fast and fix things

https://security.googleblog.com/2025/11/rust-in-android-move-fast-fix-things.html
309•abraham•8h ago•227 comments

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

https://www.checkout.com/blog/protecting-our-merchants-standing-up-to-extortion
554•StrangeSound•17h ago•244 comments

Android developer verification: Early access starts

https://android-developers.googleblog.com/2025/11/android-developer-verification-early.html
1290•erohead•1d ago•613 comments

Steam Machine

https://store.steampowered.com/sale/steammachine
2661•davikr•1d ago•1289 comments

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

https://www.denx.de/
98•synergy20•12h ago•30 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?