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Dirtyfrag: Universal Linux LPE

https://www.openwall.com/lists/oss-security/2026/05/07/8
239•flipped•2h ago•104 comments

The Burning Man MOOP Map

https://www.not-ship.com/burning-man-moop/
486•speckx•8h ago•255 comments

Agents need control flow, not more prompts

https://bsuh.bearblog.dev/agents-need-control-flow/
239•bsuh•5h ago•135 comments

AlphaEvolve: Gemini-powered coding agent scaling impact across fields

https://deepmind.google/blog/alphaevolve-impact/
223•berlianta•7h ago•85 comments

Natural Language Autoencoders: Turning Claude's Thoughts into Text

https://www.anthropic.com/research/natural-language-autoencoders
134•instagraham•4h ago•41 comments

AI slop is killing online communities

https://rmoff.net/2026/05/06/ai-slop-is-killing-online-communities/
270•thm•3h ago•264 comments

DeepSeek 4 Flash local inference engine for Metal

https://github.com/antirez/ds4
225•tamnd•6h ago•69 comments

Colored Shadow Penumbra

https://chosker.github.io/blog/colored-shadow-penumbra
25•ibobev•3h ago•10 comments

I want to live like Costco people

https://tastecooking.com/i-want-to-live-like-costco-people/
160•speckx•6h ago•381 comments

Chrome removes claim of On-device Al not sending data to Google Servers

https://old.reddit.com/r/chrome/comments/1t5qayz/chrome_removes_claim_of_ondevice_al_not_sending/
377•newsoftheday•6h ago•148 comments

Principles for agent-native CLIs

https://twitter.com/trevin/status/2051316002730991795
40•blumpy22•4h ago•22 comments

PySimpleGUI 6

https://github.com/PySimpleGUI/PySimpleGUI
81•geophph•2d ago•35 comments

Creating for a niche

https://www.davesnider.com/posts/working-in-a-niche
11•snide•2h ago•0 comments

Show HN: Full Python GUI apps in the browser – no JavaScript, no server

https://github.com/pthom/imgui_bundle
20•pstomi•4h ago•7 comments

Child marriages plunged when girls stayed in school in Nigeria

https://www.nature.com/articles/d41586-026-00720-8
311•surprisetalk•8h ago•231 comments

RaTeX: KaTeX-compatible LaTeX rendering engine in pure Rust

https://ratex.lites.dev/
149•atilimcetin•3d ago•84 comments

Easy Random Trees

https://blog.wilsonb.com/posts/2026-02-27-easy-random-trees.html
8•aebtebeten•2d ago•1 comments

The Self-Cancelling Subscription

https://predr.ag/blog/the-self-cancelling-subscription/
128•surprisetalk•7h ago•58 comments

OpenBSD Stories: The closest thing to cute kittens (OpenBSD/zaurus)

http://miod.online.fr/software/openbsd/stories/zaurus1.html
55•zdw•1d ago•6 comments

Show HN: Kstack – Skill pack for monitoring/troubleshooting K8s in Claude Code

https://github.com/kubetail-org/kstack
11•andres•16h ago•3 comments

Show HN: TRUST – Coding Rust like it's 1989

https://github.com/wojtczyk/trust
104•wojtczyk•16h ago•66 comments

OurCar: What I learned making an app for my family

https://mendelgreenberg.com/posts/ourcar/
88•chabad360•1d ago•66 comments

Motherboard sales 'collapse' amid unprecedented shortages fueled by AI

https://www.tomshardware.com/pc-components/motherboards/motherboard-sales-collapse-by-more-than-2...
221•speckx•6h ago•259 comments

I switched from Mac to a Lenovo Chromebook

https://blog.johnozbay.com/i-left-apples-ecosystem-for-a-lenovo-chromebook-and-you-can-too.html
91•speckx•6h ago•129 comments

GovernGPT (YC W24) Is Hiring Engineers to Build Thinking Systems in Montreal

https://www.ycombinator.com/companies/governgpt/jobs/hRyltS0-backend-engineer-thinking-systems
1•owalerys•10h ago

Boris Cherny: TI-83 Plus Basic Programming Tutorial (2004)

https://www.ticalc.org/programming/columns/83plus-bas/cherny/
173•suoken•3d ago•79 comments

MPEG-2 Transport Stream Packaging for Media over QUIC Transport

https://www.ietf.org/archive/id/draft-gregoire-moq-msfts-00.html
54•mondainx•7h ago•18 comments

ZAYA1-8B matches DeepSeek-R1 on math with less than 1B active parameters

https://firethering.com/zaya1-8b-open-source-math-coding-model/
79•steveharing1•13h ago•50 comments

Indian matchbox labels as a visual archive

https://www.itsnicethat.com/features/the-view-from-mumbai-matchbook-graphic-design-130426
149•sahar_builds•3d ago•33 comments

Mozilla says 271 vulnerabilities found by Mythos and "almost no false positives"

https://arstechnica.com/information-technology/2026/05/mozilla-says-271-vulnerabilities-found-by-...
39•epistasis•2h ago•9 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•11mo 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•11mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•11mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•11mo 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•11mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•11mo 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•11mo 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•11mo 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•11mo 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•11mo 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•11mo 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•11mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•11mo 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•11mo 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•11mo 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•11mo 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•11mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•11mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•11mo 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•11mo 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•11mo 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•11mo 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?