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Blender's UI and Runtime: Much Faster Than GPUI

https://github.com/ppmpreetham/BUI
179•ppmpreetham•2h ago•67 comments

Claude Code sends your email address to websites

https://github.com/anthropics/claude-code/issues/107757
1166•motoroco•12h ago•359 comments

Kugelblitz – Mac-Native Google Earth Alternative

https://apps.metaebene.me/en/kugelblitz
72•luoc•2h ago•11 comments

sshole

https://sshhole.com
2719•josemrb•22h ago•652 comments

App Store 2026 in numbers: AI, pricing, abandoned apps

https://obelisk.club/research/app-store-2026
37•Peter216•2h ago•29 comments

Multiplayer tanks that demolish real buildings from OpenStreetMap

https://worldstrike.fun/
46•n4ze3m•2h ago•23 comments

MS Paint Remote Code Execution Vulnerability

https://www.cve.org/CVERecord?id=CVE-2026-83844
553•kuberwastaken•13h ago•200 comments

Dutch 'idealist' bank ASN blocks non-Google Android, including GrapheneOS

https://github.com/PrivSec-dev/banking-apps-compat-report/issues/105
13•microtonal•1h ago•1 comments

Pwn.college

https://pwn.college/
156•tripdout•7h ago•21 comments

Hardcoded credentials and other issues in Tata Motors App

https://www.cert-in.org.in/s2cMainServlet?pageid=PUBVLNOTES01&VLCODE=CIVN-2026-0537
26•qtqtqt•3h ago•6 comments

Nvidia Kubernetes Engine (NVKE)

https://github.com/dsx-ai-factory/nvke
80•gkapur•7h ago•21 comments

From Linux to Wikipedia: How Volunteers Build the Internet

https://thereader.mitpress.mit.edu/from-linux-to-wikipedia-how-volunteers-build-the-internet/
89•jonbaer•6h ago•34 comments

The Hackintosh Reborn

https://www.naut.ca/blog/2026/11/05/the-hackintosh-reborn-with-ai/
312•rubatuga•15h ago•177 comments

Inkling 2 Small

https://thinkingmachines.ai/news/inkling-2-small/
199•Philpax•10h ago•68 comments

Window Sweaters

https://github.com/saragordic/window-sweaters
351•nickswalker•17h ago•107 comments

Britain to make salary disclosure in job ads mandatory

https://www.reuters.com/business/world-at-work/britain-make-salary-disclosure-job-ads-mandatory-2...
182•1659447091•3h ago•184 comments

Show HN: Firenook – Firebase emulators in Rust: 40× faster writes, 10× less RAM

https://firenook.dev/
118•sanjevirau•21h ago•17 comments

Launch HN: Rivet (YC W23) – Scale and Orchestrate Agents on S3

https://github.com/rivet-dev/rivet
97•NathanFlurry•17h ago•51 comments

Motorola Signature 27 with GrapheneOS support

https://motorolanews.com/motorola-signature-27-2/
486•monneyboi•18h ago•345 comments

MoofTCP – A modern replacement for MacTCP on 68k Macs

https://github.com/matthewmrichter/mooftcp
49•marcusbasant•9h ago•20 comments

Cuban: Facebook knowingly runs AI deepfake scam ads; docs say $16B fraud revenue

https://finance.yahoo.com/technology/ai/articles/mark-cuban-says-facebook-knowingly-213000020.html
341•randycupertino•11h ago•161 comments

Show HN: A decent text-to-image model trained for ~$20K (Apache 2.0 Weights)

https://www.linum.ai/field-notes/pjit-21k
232•schopra909•1d ago•21 comments

September in Servo: <progress>, rounded rectangles, and more

https://servo.org/blog/2026/11/05/september-in-servo/
242•iamnothere•19h ago•61 comments

I wish tech companies knew more about me

https://rakhim.exotext.com/i-wish-tech-companies-knew-more-about-me
424•freetonik•20h ago•358 comments

Download your favourite YouTube videos

https://criminallyvulgar.dev/you-should-absolutely-download-your-favourite-youtube-videos/
346•surprisetalk•21h ago•162 comments

Pangram Image 1.0

https://www.pangram.com/blog/introducing-pangram-image-1-0
109•gmays•15h ago•49 comments

Living Maps: Bay Area tech since 1960s

https://tomorrowasusual.com/living-maps/bay-area-tech/
6•tudell•2h ago•5 comments

MCP Explained

https://mcp-explained.com/
302•rwilinski•20h ago•127 comments

Goodbye from Salon

https://www.salon.com/2026/11/05/goodbye-from-salon/
584•mkeeter•14h ago•230 comments

I struggle to believe that AI R&D is ingenious

https://www.lesswrong.com/posts/DALNiKBjMGMF8grme/arjun-panickssery-s-shortform?commentId=fJtaHHH...
92•porridgeraisin•5h ago•76 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•1y 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•1y ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•1y ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•1y 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•1y ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•1y 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•1y 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•1y 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•1y 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•1y 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•1y 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•1y ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•1y 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•1y 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•1y 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•1y 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•1y ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•1y ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•1y 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•1y 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•1y 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•1y 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?