frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

UK Biobank leak: Health details of 500 000 people are offered for sale

https://www.bmj.com/content/393/bmj.s781
99•dberhane•1h ago•32 comments

S. Korea police arrest man over AI image of runaway wolf that misled authorities

https://www.bbc.com/news/articles/c4gx1n0dl9no
134•giuliomagnifico•3h ago•81 comments

How to be anti-social – a guide to incoherent and isolating social experiences

https://nate.leaflet.pub/3mk4xkaxobc2p
79•calcifer•2h ago•61 comments

Spinel: Ruby AOT Native Compiler

https://github.com/matz/spinel
117•dluan•4h ago•25 comments

Aspartame is not that bad?

https://dynomight.net/aspartame/
22•pHequals7•1h ago•6 comments

DeepSeek v4

https://api-docs.deepseek.com/
1209•impact_sy•10h ago•848 comments

The operating cost of adult and gambling startups

https://orchidfiles.com/stigma-is-a-tax-on-every-operational-decision/
13•theorchid•35m ago•0 comments

Mounting tar archives as a filesystem in WebAssembly

https://jeroen.github.io/notes/webassembly-tar/
26•datajeroen•2h ago•4 comments

Why I Write (1946)

https://www.orwellfoundation.com/the-orwell-foundation/orwell/essays-and-other-works/why-i-write/
199•RyanShook•10h ago•49 comments

US special forces soldier arrested after allegedly winning $400k on Maduro raid

https://www.cnn.com/2026/04/23/politics/us-special-forces-soldier-arrested-maduro-raid-trade
346•nkrisc•15h ago•393 comments

An update on recent Claude Code quality reports

https://www.anthropic.com/engineering/april-23-postmortem
784•mfiguiere•19h ago•607 comments

Show HN: How LLMs Work – Interactive visual guide based on Karpathy's lecture

https://ynarwal.github.io/how-llms-work/
112•ynarwal__•6h ago•25 comments

Bitwarden CLI compromised in ongoing Checkmarx supply chain campaign

https://socket.dev/blog/bitwarden-cli-compromised
793•tosh•22h ago•381 comments

GPT-5.5

https://openai.com/index/introducing-gpt-5-5/
1416•rd•19h ago•945 comments

Show HN: Gova – The declarative GUI framework for Go

https://github.com/NV404/gova
65•aliezsid•6h ago•14 comments

The Rich and Powerful Want to Live Forever. What If They Could?

https://www.nytimes.com/2026/04/24/magazine/eternal-life-longevity-world-leaders.html
7•moichael•13m ago•3 comments

MeshCore development team splits over trademark dispute and AI-generated code

https://blog.meshcore.io/2026/04/23/the-split
236•wielebny•20h ago•126 comments

Meta tells staff it will cut 10% of jobs

https://www.bloomberg.com/news/articles/2026-04-23/meta-tells-staff-it-will-cut-10-of-jobs-in-pus...
655•Vaslo•18h ago•632 comments

Show HN: Tolaria – Open-source macOS app to manage Markdown knowledge bases

https://github.com/refactoringhq/tolaria
229•lucaronin•15h ago•96 comments

Using the internet like it's 1999

https://joshblais.com/blog/using-the-internet-like-its-1999/
180•joshuablais•16h ago•120 comments

Habitual coffee intake shapes the microbiome, modifies physiology and cognition

https://www.nature.com/articles/s41467-026-71264-8
188•scubakid•8h ago•136 comments

UK Biobank health data keeps ending up on GitHub

https://biobank.rocher.lc
158•Cynddl•23h ago•39 comments

Composition Shouldn't be this Hard

https://www.cambra.dev/blog/announcement/
80•larelli•5h ago•55 comments

Familiarity is the enemy: On why Enterprise systems have failed for 60 years

https://felixbarbalet.com/familiarity-is-the-enemy/
60•adityaathalye•8h ago•32 comments

TorchTPU: Running PyTorch Natively on TPUs at Google Scale

https://developers.googleblog.com/torchtpu-running-pytorch-natively-on-tpus-at-google-scale/
155•mji•16h ago•14 comments

nowhere: an entire website encoded in a URL

https://hostednowhere.com/
65•bpierre•2h ago•44 comments

My phone replaced a brass plug

https://drobinin.com/posts/my-phone-replaced-a-brass-plug/
155•valzevul•20h ago•39 comments

A programmable watch you can actually wear

https://www.hackster.io/news/a-diy-watch-you-can-actually-wear-8f91c2dac682
195•sarusso•3d ago•92 comments

Alberta startup sells no-tech tractors for half price

https://wheelfront.com/this-alberta-startup-sells-no-tech-tractors-for-half-price/
2218•Kaibeezy•1d ago•750 comments

Show HN: Agent Vault – Open-source credential proxy and vault for agents

https://github.com/Infisical/agent-vault
116•dangtony98•1d ago•40 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?