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Anthropic acquires Bun

https://bun.com/blog/bun-joins-anthropic
1711•ryanvogel•13h ago•816 comments

AI agents break rules under everyday pressure

https://spectrum.ieee.org/ai-agents-safety
93•pseudolus•5d ago•23 comments

IBM CEO says there is 'no way' spending on AI data centers will pay off

https://www.businessinsider.com/ibm-ceo-big-tech-ai-capex-data-center-spending-2025-12
448•nabla9•13h ago•523 comments

Interview with RollerCoaster Tycoon's Creator, Chris Sawyer (2024)

https://medium.com/atari-club/interview-with-rollercoaster-tycoons-creator-chris-sawyer-684a0efb0f13
38•areoform•3h ago•4 comments

Paged Out

https://pagedout.institute
363•varjag•11h ago•40 comments

Super fast aggregations in PostgreSQL 19

https://www.cybertec-postgresql.com/en/super-fast-aggregations-in-postgresql-19/
38•jnord•1w ago•1 comments

Quad9 DOH HTTP/1.1 Retirement, December 15, 2025

https://quad9.net/news/blog/doh-http-1-1-retirement/
26•pickledoyster•1h ago•1 comments

Understanding ECDSA

https://avidthinker.github.io/2025/11/28/understanding-ecdsa/
44•avidthinker•3h ago•5 comments

OpenAI declares 'code red' as Google catches up in AI race

https://www.theverge.com/news/836212/openai-code-red-chatgpt
610•goplayoutside•16h ago•672 comments

I designed and printed a custom nose guard to help my dog with DLE

https://snoutcover.com/billie-story
490•ragswag•3d ago•58 comments

Sending DMARC reports is somewhat hazardous

https://utcc.utoronto.ca/~cks/space/blog/spam/DMARCSendingReportsProblems
26•zdw•2h ago•6 comments

Japanese game devs face font dilemma as license increases from $380 to $20k

https://www.gamesindustry.biz/japanese-devs-face-font-licensing-dilemma-as-leading-provider-incre...
182•zdw•3h ago•78 comments

Counter Galois Onion: Improved encryption for Tor circuit traffic

https://blog.torproject.org/introducing-cgo/
64•wrayjustin•1w ago•6 comments

Learning music with Strudel

https://terryds.notion.site/Learning-Music-with-Strudel-2ac98431b24180deb890cc7de667ea92
467•terryds•1w ago•110 comments

Amazon launches Trainium3

https://techcrunch.com/2025/12/02/amazon-releases-an-impressive-new-ai-chip-and-teases-a-nvidia-f...
166•thnaks•12h ago•65 comments

Qwen3-VL can scan two-hour videos and pinpoint nearly every detail

https://the-decoder.com/qwen3-vl-can-scan-two-hour-videos-and-pinpoint-nearly-every-detail/
172•thm•3d ago•56 comments

Load ZX Spectrum – first Museum dedicated to our first personal computer

https://loadzx.com/en/
38•elvis70•6d ago•8 comments

Zig's new plan for asynchronous programs

https://lwn.net/SubscriberLink/1046084/4c048ee008e1c70e/
269•messe•17h ago•201 comments

All Sources of DirectX 12 Documentation

https://asawicki.info/news_1794_all_sources_of_directx_12_documentation
17•ibobev•1w ago•6 comments

All about automotive lidar

https://mainstreetautonomy.com/blog/2025-08-29-all-about-automotive-lidar/
142•dllu•1d ago•61 comments

School cell phone bans and student achievement

https://www.nber.org/digest/202512/school-cell-phone-bans-and-student-achievement
134•harias•13h ago•133 comments

What, if anything, is universal to music cognition? (2024)

https://www.nature.com/articles/s41562-023-01800-9
3•Hooke•1w ago•1 comments

Free static site generator for small restaurants and cafes

https://lite.localcafe.org/
123•fullstacking•11h ago•76 comments

Kohler Can Access Pictures from "End-to-End Encrypted" Toilet Camera

https://varlogsimon.leaflet.pub/3m6zrw6k2bs2p?interactionDrawer=quotes
152•TimDotC•5h ago•132 comments

DOOM could have had PC Speaker Music

https://lenowo.org/viewtopic.php?t=45
81•minki_the_avali•8h ago•55 comments

Delty (YC X25) Is Hiring

https://www.ycombinator.com/companies/delty/jobs/aPWMaiq-full-stack-software-engineer
1•lalitkundu•10h ago

100k TPS over a billion rows: the unreasonable effectiveness of SQLite

https://andersmurphy.com/2025/12/02/100000-tps-over-a-billion-rows-the-unreasonable-effectiveness...
341•speckx•13h ago•123 comments

Python Data Science Handbook

https://jakevdp.github.io/PythonDataScienceHandbook/
267•cl3misch•19h ago•49 comments

Practical Intro to Operational Transformation

https://archive.casouri.cc/note/2025/practical-intro-ot/
41•casouri•6d ago•3 comments

Addressing the adding situation

https://xania.org/202512/02-adding-integers
257•messe•20h ago•91 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?