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CODA: Rewriting Transformer Blocks as GEMM-Epilogue Programs

https://arxiv.org/abs/2605.19269
49•matt_d•3h ago•3 comments

Project Hail Mary – Stellar Navigation Chart

https://valhovey.github.io/gaia-mary/
840•speleo•15h ago•184 comments

The surprising story behind the first British person in space

https://www.bbc.com/culture/article/20260518-helen-sharman-the-story-behind-the-first-british-per...
40•xoxxala•1d ago•3 comments

Slumber a TUI HTTP Client

https://slumber.lucaspickering.me
49•jicea•3h ago•21 comments

Cleve Moler has died

https://www.mathworks.com/company/aboutus/founders/clevemoler.html
71•mychele•5h ago•4 comments

Blog ran on Ubuntu 16.04 for 10 years. I migrated it to FreeBSD

https://crocidb.com/post/this-blog-ran-on-ubuntu-16-04-for-10-years-i-migrated-it-to-freebsd/
263•speckx•13h ago•144 comments

The memory shortage is causing a repricing of consumer electronics

https://davidoks.blog/p/ai-is-killing-the-cheap-smartphone
166•d0ks•10h ago•168 comments

Was my $48K GPU server worth it?

https://rosmine.ai/2026/05/13/was-my-48k-gpu-worth-it/
423•apwheele•3d ago•308 comments

Uv is fantastic, but its package management UX is a mess

https://www.loopwerk.io/articles/2026/uv-ux-mess/
186•nchagnet•11h ago•98 comments

Using Kagi Search with Low Vision

https://veroniiiica.com/using-kagi-search-with-low-vision/
188•speckx•12h ago•59 comments

The death of the brick and mortar toy store

https://brainbaking.com/post/2026/05/the-death-of-the-brick-and-mortar-toy-store/
75•speckx•2d ago•67 comments

Mycorrhizal Fungi, Nature's Key to Plant Survival and Success

https://pacifichorticulture.org/articles/mycorrhizal-fungi-natures-key-to-plant-survival-and-succ...
87•mooreds•1d ago•12 comments

Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap)

https://blog.simbastack.com/indexed-a-year-of-video-locally/
368•asenna•18h ago•108 comments

Show HN: Freenet, a peer-to-peer platform for decentralized apps

https://freenet.org/
272•sanity•17h ago•170 comments

Python 3.15: features that didn't make the headlines

https://blog.changs.co.uk/python-315-features-that-didnt-make-the-headlines.html
367•rbanffy•20h ago•182 comments

Lost Images from the 1945 Trinity Nuclear Test Restored

https://spectrum.ieee.org/trinity-nuclear-test
339•pseudolus•21h ago•103 comments

Tristan Davey's Punch Card Archive

https://punchcards.tristandavey.com/
26•ohjeez•2d ago•5 comments

Flipper One – we need your help

https://blog.flipper.net/flipper-one-we-need-your-help/
1132•sandebert•21h ago•441 comments

Deciphering the Hashihara Castle Town Map

https://www.obayashi.co.jp/en/kikan_obayashi/detail/kikan_64_project.html
42•1970-01-01•2d ago•0 comments

The Hardware Lottery

https://hardwarelottery.github.io/
14•intelkishan•1d ago•2 comments

Launch HN: Runtime (YC P26) – Sandboxed coding agents for everyone on a team

https://www.runtm.com/
84•gustrigos•15h ago•22 comments

Deepfakes Tore a High School Apart

https://www.404media.co/radnor-high-school-pennsylvania-ai-deepfakes-child-sexual-abuse-material/
5•Brajeshwar•12m ago•0 comments

Spotify will start reserving concert tickets for fans

https://www.hollywoodreporter.com/music/music-industry-news/spotify-will-start-reserving-concert-...
140•elffjs•15h ago•288 comments

Waymo pauses Atlanta service as its robotaxis keep driving into floods

https://techcrunch.com/2026/05/21/waymo-pauses-atlanta-service-as-its-robotaxis-keep-driving-into...
304•mattas•15h ago•377 comments

Multi-Stream LLMs: new paper on parallelizing/separating prompts, thinking, I/O

https://arxiv.org/abs/2605.12460
100•atomicthumbs•12h ago•12 comments

Throwing AI-generated walls of text into conversations

https://noslopgrenade.com/
588•napolux•22h ago•346 comments

Google's Antigravity bait and switch

https://www.0xsid.com/blog/antigravity-bait-n-switch
654•ssiddharth•18h ago•298 comments

Show HN: KVBoost – chunk-level KV cache reuse for HuggingFace, 5–48x faster TTFT

https://pythongiant.github.io/KVBoost/
18•pythongiant•3h ago•12 comments

Seattle Shield, an intelligence-sharing network operated by the Seattle police

https://prismreports.org/2026/05/20/seattle-shield-private-companies-surveillance/
455•root-parent•14h ago•185 comments

We're testing new ad formats in Search and expanding our Direct Offers pilot

https://blog.google/products/ads-commerce/google-marketing-live-search-ads/
600•sofumel•22h ago•535 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•12mo 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?