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7.1 Earthquake in Japan

https://www.data.jma.go.jp/multi/quake/quake_detail.html?eventID=20260728163528&lang=en
539•krembo•7h ago•101 comments

New HIV vaccine shows unprecedented success in preclinical study

https://www.lji.org/news-events/news/post/new-hiv-vaccine-shows-unprecedented-success-in-preclini...
167•codebyaditya•1h ago•60 comments

Show HN: tale.fyi, we deserve a home for fiction

https://tale.fyi/@sam/announcing-tale-fyi-read-or-listen-to-an-entire-book-from-a-single-link
42•samuelcole•1h ago•26 comments

Kimi Linear: An Expressive, Efficient Attention Architecture

https://arxiv.org/abs/2510.26692
79•ronfriedhaber•3h ago•15 comments

Show HN: Formally verified 3D CSG: Trust 93 lines spec, not 1000 lines AI code

https://github.com/schildep/verified-3d-mesh-intersection
51•permute•1h ago•17 comments

Solving Fermat: Andrew Wiles

https://www.pbs.org/wgbh/nova/proof/wiles.html
24•1970-01-01•18h ago•3 comments

Google's Beyond Zero: Enterprise Security for the AI Era

https://spawn-queue.acm.org/doi/10.1145/3819083
75•jordigg•4h ago•40 comments

Show HN: Ctrlb-decompose: Strip the noise from logs before sending to LLMs

https://github.com/ctrlb-hq/ctrlb-decompose
33•ruhani_grover•1h ago•7 comments

DMARC Has Been Public Since 2012. 68.4% of Domains Still Don't Enforce It

https://ciphercue.com/blog/dmarc-enforcement-gap-rua-fragmentation-2026
56•adulion•4h ago•38 comments

Our position on open-weights models

https://www.anthropic.com/news/position-open-weights-models
1068•surprisetalk•16h ago•1538 comments

How to Survive Boiling Water

https://taxa.substack.com/p/how-to-survive-boiling-water
207•cainxinth•4d ago•30 comments

About the security content of macOS Tahoe 26.6

https://support.apple.com/en-us/128067
152•andor•5h ago•94 comments

What AI developers could learn from Charles Bukowski?

https://galjot.si/what-ai-developers-could-learn-from-charles-bukowski
28•sedovsek•1h ago•22 comments

Fast Remediation Is the New Trust Model (JFrog and OpenAI Zero-Day Findings)

https://jfrog.com/blog/jfrog-and-openai-collaboration-on-zero-day-security-findings/
27•882542F3884314B•2h ago•10 comments

Show HN: Scala Tutorials – interactive Scala 3 lessons in the browser

https://scalatutorials.com
39•eranation•3d ago•6 comments

A $500 RL fine-tune of a 9B open model beat frontier models on catalog review

https://fermisense.com/when-machines-take-the-wheel/
261•ilreb•12h ago•88 comments

Mondragon Corporation – a federation of co-operatives

https://en.wikipedia.org/wiki/Mondragon_Corporation
94•brnt•2h ago•12 comments

Usenet Archive Toolkit – process Usenet messages into a searchable archive

https://github.com/wolfpld/usenetarchive
15•bilegeek•4h ago•0 comments

Benchmarking Opus 5 on SlopCodeBench

https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/benchmarki...
355•dhorthy•16h ago•97 comments

Dolmenwood: Fantasy RPG built around the acclaimed Old-School Essentials rules

https://necroticgnome.com/collections/dolmenwood
18•doener•3d ago•7 comments

The Origins of Modern Mathematics in Russia

https://valeman.medium.com/the-origins-of-modern-mathematics-in-russia-from-peter-the-great-to-th...
11•ibobev•3d ago•0 comments

Ask HN: Crooked Timber showed showed me a virus captcha, What now?

10•Jgoauh•26m ago•5 comments

Ars Astronomica – English translations of rare Hebrew and Latin astronomy texts

https://arsastronomica.com/
94•sweisman•9h ago•29 comments

Vehicle Motion Cues

https://support.apple.com/guide/iphone/iphone-comfortably-riding-a-vehicle-iph55564cb22/ios
170•Austin_Conlon•13h ago•86 comments

Watching Go's new garbage collector move through the heap

https://theconsensus.dev/p/2026/07/19/observing-gos-garbage-collector-old-and-new.html
251•matheusmoreira•3d ago•36 comments

Great Mobile Apps Aren't Built Around Features

https://geekyants.com/case-studies/nowmatch-next-gen-social-and-dating-app-development
3•steve_7890•2h ago•0 comments

PyTorch: A Reference Language

https://docs.pytorch.org/devlogs/compiler/2026-07-25-pytorch-a-reference-language/
60•matt_d•10h ago•5 comments

TWC Classics

https://twcclassics.com/
30•stefanpie•5d ago•3 comments

Show HN: Segue – Save context in one AI, load it in another by a short handle

https://segue.ai/
10•csaguiar•2h ago•5 comments

Kimi K3 Now Available via Telnyx Inference API

https://telnyx.com/release-notes/kimi-k3-telnyx-inference
120•fionaattelnyx•16h ago•69 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?