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Git for US Legislation

https://lexifina.com/blog/git-for-the-us-code
99•alansaber•2h ago•44 comments

Genie 4

https://deepmind.google/blog/genie-4-a-new-generation-of-world-simulation/
792•acossta•12h ago•483 comments

Ink droplets in motion snag top prize in Nikon Small World contest

https://arstechnica.com/science/2026/10/meet-the-2026-winner-of-nikons-small-world-photomicrograp...
121•_Microft•2d ago•23 comments

Ghostty 1.4

https://ghostty.org/docs/install/release-notes/1-4-0
850•deaux•11h ago•265 comments

Tailscale SSH: Investigation and remediation update

https://tailscale.com/ssh-investigation-update
230•figmert•8h ago•116 comments

Early progress on accelerating drug development with Claude

https://www.anthropic.com/research/early-progress-drug-development
167•dataking•7h ago•155 comments

Survival LLM: AI assistant and emergency library that runs on your own computer

https://www.brangus.io/survival-llm
234•Luc•8h ago•116 comments

Assembly Nights

https://ratfactor.com/assembly-nights
27•andsoitis•2d ago•3 comments

Raspberry Pi Zero 3W and Pi 4 Model A spotted in device trees

https://bret.dk/raspberry-pi-zero-3-w-pi-4-model-a/
120•sthlmb•7h ago•52 comments

81 Jokes for the ZX81

https://spelk.press/gb/products/81-jokes-for-the-zx81
49•robin_reala•1d ago•3 comments

A Love Letter to the Super Dimension Fortress

https://brennan.day/a-love-letter-to-the-super-dimension-fortress/
12•jruohonen•3d ago•1 comments

New 14‑inch MacBook Pro with M6

https://www.apple.com/in/newsroom/2026/10/apple-introduces-the-new-14-inch-macbook-pro-with-m6/
307•beex•12h ago•394 comments

Python's experimental JIT to be removed

https://lwn.net/SubscriberLink/1102102/0c00e4d550f25f8c/
464•f311a•17h ago•252 comments

Tabletop Whale is an original science illustration blog

https://tabletopwhale.com
29•Levitating•4d ago•3 comments

Reviewers rarely talk about reliability. Owners bring it up twice as often

https://www.bettascore.ai/research/reliability-reviewers-vs-owners
84•tsxng•5h ago•75 comments

New iMac with M6

https://www.apple.com/imac/
319•notfried•15h ago•395 comments

Classification of Finite Simple Groups Formalized in Lean

https://cfsg.axiommath.ai/
187•pfdietz•11h ago•52 comments

An examination of lost firefly species of the US and Canada

https://journaloflostspecies.org/index.php/jls/article/view/37/33
103•bryanrasmussen•3d ago•23 comments

Radio-tagged Monarch butterflies

https://news.vt.edu/articles/2026/10/cmi-monarchs.html
39•rdmuser•10h ago•13 comments

SS United States to be sunk as largest artificial reef

https://divemagazine.com/scuba-diving-news/ss-united-states-to-be-sunk-as-worlds-largest-artifici...
107•geox•1d ago•76 comments

US Government is hiding a secret court ruling about how it spies on Americans

https://www.wired.com/story/the-us-government-is-hiding-a-secret-court-ruling-about-how-it-spies-...
853•helterskelter2•14h ago•305 comments

Four Months of Zig

https://gracefulliberty.com/articles/four-months-of-zig/
93•ibobev•8h ago•57 comments

Code-execution flaw in UEFI firmware threatens most servers on planet Earth

https://arstechnica.com/security/2026/10/code-execution-flaw-in-uefi-firmware-threatens-most-serv...
58•DamnInteresting•7h ago•10 comments

Moxnox 1.0: NT-Like, POSIX-Compatible OS Running Atop Btrfs

https://github.com/maharmstone/moxnox
29•SockThief•4h ago•4 comments

Why Micron Is Suddenly Ditching 4F² DRAM as Rivals Double Down

https://hothardware.com/news/why-micron-is-suddenly-ditching-4f-dram
151•rbanffy•1d ago•22 comments

Nvidia and Micron Make Up 34% of America's Earnings Boom

https://www.wsj.com/finance/stocks/the-earnings-boom-is-heavily-reliant-on-just-a-handful-of-stoc...
110•JumpCrisscross•2d ago•21 comments

Introducing LFM3: Every Modality for Specialized Intelligence

https://www.liquid.ai/blog/lfm3
128•Philpax•11h ago•51 comments

Expanding our partnership with GSA to bring Claude to all levels of government

https://www.anthropic.com/news/expanding-partnership-gsa
31•matthieu_bl•6h ago•23 comments

Land Value Capture in Practice: Ethiopia's Urban Land Lease System

https://blog.landeconomics.org/p/land-value-capture-in-practice-reflections
52•surprisetalk•1d ago•11 comments

Three TLS Bugs in GoAhead

https://foxio.io/blog/choose-your-backend-three-tls-bugs-in-goahead
64•owenshen24•9h ago•15 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?