frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

Gut bacteria from amphibians and reptiles achieve tumor elimination in mice

https://www.jaist.ac.jp/english/whatsnew/press/2025/12/17-1.html
269•Xunxi•7h ago•62 comments

Gemini 3 Flash: Frontier intelligence built for speed

https://blog.google/products/gemini/gemini-3-flash/
873•meetpateltech•13h ago•467 comments

OBS Studio Gets a New Renderer

https://obsproject.com/blog/obs-studio-gets-a-new-renderer
196•aizk•9h ago•40 comments

Coursera to combine with Udemy

https://investor.coursera.com/news/news-details/2025/Coursera-to-Combine-with-Udemy-to-Empower-th...
472•throwaway019254•17h ago•284 comments

I got hacked: My Hetzner server started mining Monero

https://blog.jakesaunders.dev/my-server-started-mining-monero-this-morning/
278•jakelsaunders94•9h ago•197 comments

Working quickly is more important than it seems (2015)

https://jsomers.net/blog/speed-matters
107•bschne•3d ago•49 comments

Ask HN: Those making $500/month on side projects in 2025 – Show and tell

138•cvbox•4h ago•82 comments

AWS CEO says replacing junior devs with AI is 'one of the dumbest ideas'

https://www.finalroundai.com/blog/aws-ceo-ai-cannot-replace-junior-developers
855•birdculture•13h ago•450 comments

More than half of researchers now use AI for peer review, often against guidance

https://www.nature.com/articles/d41586-025-04066-5
18•neilv•59m ago•2 comments

Don MacKinnon: Why Simplicity Beats Cleverness in Software Design [audio]

https://maintainable.fm/episodes/don-mackinnon-why-simplicity-beats-cleverness-in-software-design
23•mooreds•2d ago•3 comments

TikTok unlawfully tracks shopping habits and use of dating apps?

https://noyb.eu/en/tiktok-unlawfully-tracks-your-shopping-habits-and-your-use-dating-apps
151•doener•5h ago•75 comments

A Safer Container Ecosystem with Docker: Free Docker Hardened Images

https://www.docker.com/blog/docker-hardened-images-for-every-developer/
302•anttiharju•13h ago•61 comments

Judge hints Vizio TV buyers may have rights to source code licensed under GPL

https://www.theregister.com/2025/12/05/vizio_gpl_source_code_ruling/
20•pabs3•1h ago•1 comments

Tell HN: HN was down

524•uyzstvqs•13h ago•294 comments

Show HN: High-Performance Wavelet Matrix for Python, Implemented in Rust

https://pypi.org/project/wavelet-matrix/
78•math-hiyoko•10h ago•2 comments

My payment agent is named George, not stripe-agent

https://blog.kestrelsnest.social/posts/2025-12-14-why-my-payment-agent-is-named-george-not-stripe...
48•fortyseven•3d ago•32 comments

Developers can now submit apps to ChatGPT

https://openai.com/index/developers-can-now-submit-apps-to-chatgpt/
112•tananaev•7h ago•66 comments

Inside PostHog: SSRF, ClickHouse SQL Escape and Default Postgres Creds to RCE

https://mdisec.com/inside-posthog-how-ssrf-a-clickhouse-sql-escaping-0day-and-default-postgresql-...
88•arwt•9h ago•23 comments

Zmij: Faster floating point double-to-string conversion

https://vitaut.net/posts/2025/faster-dtoa/
114•fanf2•3d ago•15 comments

Show HN: I built a fast RSS reader in Zig

https://github.com/superstarryeyes/hys
42•superstarryeyes•1d ago•11 comments

The Number That Turned Sideways

https://zuriby.github.io/math.github.io/the-number-that-turned-sideways.html
34•tzury•4d ago•14 comments

Oasis: Pooling PCIe Devices over CXL to Boost Utilization

https://dl.acm.org/doi/10.1145/3731569.3764812
5•blakepelton•5d ago•1 comments

How SQLite is tested

https://sqlite.org/testing.html
264•whatisabcdefgh•12h ago•73 comments

Ask HN: Does anyone understand how Hacker News works?

5•jannesblobel•6h ago•9 comments

Fast SEQUENCE iteration in Common Lisp

https://world-playground-deceit.net/blog/2025/12/fast-sequence-iteration-in-common-lisp.html
43•BoingBoomTschak•4d ago•8 comments

Launch HN: Kenobi (YC W22) – Personalize your website for every visitor

38•sarreph•13h ago•53 comments

Cloudflare Radar 2025 Year in Review

https://radar.cloudflare.com/year-in-review/2025
69•ksec•8h ago•29 comments

Opencoil – appropriating inductive charging pads in the wild (2020) [video]

https://media.ccc.de/v/rc3-11575-opencoil_a_roaming_speedshow
4•thenthenthen•2d ago•0 comments

A look back: LANPAR, the first spreadsheet

https://technicallywewrite.com/2025/12/16/lanpar
25•rbanffy•7h ago•1 comments

Mozilla's New CEO Confirms Firefox Will Become an "AI Browser"

https://www.omgubuntu.co.uk/2025/12/mozilla-new-ceo-firefox-ai-browser-strategy
20•LopRabbit•1h ago•19 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•7mo 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•7mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•7mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•7mo 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•7mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•7mo 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•7mo 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•7mo 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•6mo 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•7mo 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•7mo 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•7mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•7mo 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•7mo 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•7mo 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•7mo 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•7mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•7mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•7mo 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•7mo 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•7mo 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•7mo 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?