frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

More than 135 open hardware devices flashable with your own firmware

https://openhardware.directory
118•iosifnicolae2•4d ago•10 comments

A Decade of Slug

https://terathon.com/blog/decade-slug.html
519•mwkaufma•10h ago•43 comments

Microsoft's 'unhackable' Xbox One has been hacked by 'Bliss'

https://www.tomshardware.com/video-games/console-gaming/microsofts-unhackable-xbox-one-has-been-h...
609•crtasm•14h ago•218 comments

Python 3.15's JIT is now back on track

https://fidget-spinner.github.io/posts/jit-on-track.html
319•guidoiaquinti•10h ago•139 comments

Mistral AI Releases Forge

https://mistral.ai/news/forge
232•pember•8h ago•35 comments

Have a Fucking Website

https://www.otherstrangeness.com/2026/03/14/have-a-fucking-website/
38•asukachikaru•1h ago•16 comments

SSH has no Host header

https://blog.exe.dev/ssh-host-header
3•apitman•10m ago•0 comments

Get Shit Done: A meta-prompting, context engineering and spec-driven dev system

https://github.com/gsd-build/get-shit-done
261•stefankuehnel•9h ago•132 comments

The pleasures of poor product design

https://www.inconspicuous.info/p/the-pleasures-of-poor-product-design
57•NaOH•4h ago•17 comments

Show HN: Sub-millisecond VM sandboxes using CoW memory forking

https://github.com/adammiribyan/zeroboot
97•adammiribyan•15h ago•16 comments

Leviathan

https://www.gutenberg.org/files/3207/3207-h/3207-h.htm
15•mrwh•2d ago•5 comments

Why AI systems don't learn – On autonomous learning from cognitive science

https://arxiv.org/abs/2603.15381
65•aanet•7h ago•19 comments

It Took Me 30 Years to Solve This VFX Problem – Green Screen Problem [video]

https://www.youtube.com/watch?v=3Ploi723hg4
204•yincrash•4d ago•86 comments

Unsloth Studio

https://unsloth.ai/docs/new/studio
219•brainless•14h ago•48 comments

A tale about fixing eBPF spinlock issues in the Linux kernel

https://rovarma.com/articles/a-tale-about-fixing-ebpf-spinlock-issues-in-the-linux-kernel/
42•y1n0•4h ago•1 comments

Launch HN: Kita (YC W26) – Automate credit review in emerging markets

34•rheamalhotra1•9h ago•5 comments

Launch an autonomous AI agent with sandboxed execution in 2 lines of code

https://amaiya.github.io/onprem/examples_agent.html
20•wiseprobe•4h ago•4 comments

Electron microscopy shows 'mouse bite' defects in semiconductors

https://news.cornell.edu/stories/2026/03/electron-microscopy-shows-mouse-bite-defects-semiconductors
41•hhs•4d ago•9 comments

Forget Flags and Scripts: Just Rename the File

https://robertsdotpm.github.io/software_engineering/program_names_as_input.html
7•Uptrenda•1h ago•6 comments

Honda is killing its EVs

https://techcrunch.com/2026/03/14/honda-is-killing-its-evs-and-any-chance-of-competing-in-the-fut...
265•sylvainkalache•2d ago•558 comments

I Simulated 38,612 Countryle Games to Find the Best Strategy

https://stoffregen.io/posts/countryle/
3•st0ffregen•1d ago•0 comments

Ryugu asteroid samples contain all DNA and RNA building blocks

https://phys.org/news/2026-03-ryugu-asteroid-samples-dna-rna.html
215•bookofjoe•17h ago•120 comments

Edge.js: Run Node apps inside a WebAssembly sandbox

https://wasmer.io/posts/edgejs-safe-nodejs-using-wasm-sandbox
118•syrusakbary•11h ago•33 comments

Show HN: Fatal Core Dump – A debugging murder mystery played with GDB

https://www.robopenguins.com/fatal_core_dump/
44•axlan•4d ago•1 comments

Arno's Engram Keyboard Layouts

https://github.com/binarybottle/engram
9•so-cal-schemer•4d ago•7 comments

Show HN: I built an interactive 3D three-body problem simulator in the browser

https://structuredlabs.github.io/threebodyproblem/
39•amrutha_•4d ago•15 comments

Kagi Small Web

https://kagi.com/smallweb/
722•trueduke•19h ago•197 comments

The Starving Time in Jamestown

https://www.historytoday.com/archive/history-matters/starving-time-jamestown
4•samclemens•4d ago•1 comments

Spice Data (YC S19) Is Hiring a Product Specialist

https://www.ycombinator.com/companies/spice-data/jobs/P0e9MKz-product-specialist-new-grad
1•richard_pepper•12h ago

OpenSUSE Kalpa

https://kalpadesktop.org/
185•ogogmad•15h ago•82 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•10mo 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•10mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•10mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•10mo 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•10mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•10mo 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•10mo 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•10mo 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•9mo 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•10mo 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•10mo 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•10mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•10mo 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•10mo 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•10mo 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•10mo 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•10mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•10mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•10mo 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•10mo 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•10mo 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•10mo 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?