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CSS Grid Lanes

https://webkit.org/blog/17660/introducing-css-grid-lanes/
152•frizlab•1h ago•41 comments

Mistral OCR 3

https://mistral.ai/news/mistral-ocr-3
346•pember•1d ago•56 comments

Garage – An S3 object store so reliable you can run it outside datacenters

https://garagehq.deuxfleurs.fr/
420•ibobev•8h ago•86 comments

A Better Zip Bomb

https://www.bamsoftware.com/hacks/zipbomb/
57•kekqqq•2h ago•18 comments

TP-Link Tapo C200: Hardcoded Keys, Buffer Overflows and Privacy

https://www.evilsocket.net/2025/12/18/TP-Link-Tapo-C200-Hardcoded-Keys-Buffer-Overflows-and-Priva...
195•sibellavia•5h ago•56 comments

We ran Anthropic’s interviews through structured LLM analysis

https://www.playbookatlas.com/research/ai-adoption-explorer
22•jp8585•1h ago•15 comments

8-bit Boléro

https://linusakesson.net/music/bolero/index.php
143•Aissen•12h ago•25 comments

Amazon will allow ePub and PDF downloads for DRM-free eBooks

https://www.kdpcommunity.com/s/article/New-eBook-Download-Options-for-Readers-Coming-in-2026?lang...
516•captn3m0•14h ago•273 comments

GotaTun – Mullvad's WireGuard Implementation in Rust

https://mullvad.net/en/blog/announcing-gotatun-the-future-of-wireguard-at-mullvad-vpn
523•km•12h ago•109 comments

Graphite is joining Cursor

https://cursor.com/blog/graphite
154•fosterfriends•8h ago•182 comments

Qwen-Image-Layered: transparency and layer aware open diffusion model

https://huggingface.co/papers/2512.15603
46•dvrp•20h ago•5 comments

NOAA deploys new generation of AI-driven global weather models

https://www.noaa.gov/news-release/noaa-deploys-new-generation-of-ai-driven-global-weather-models
69•hnburnsy•2d ago•40 comments

Performance Hints (2023)

https://abseil.io/fast/hints.html
40•danlark1•6h ago•23 comments

Brown/MIT shooting suspect found dead, officials say

https://www.washingtonpost.com/nation/2025/12/18/brown-university-shooting-person-of-interest/
69•anigbrowl•20h ago•71 comments

Show HN: TinyPDF – 3kb pdf library (70x smaller than jsPDF)

https://github.com/Lulzx/tinypdf
97•lulzx•1d ago•12 comments

Rust's Block Pattern

https://notgull.net/block-pattern/
98•zdw•19h ago•38 comments

At least $9B billed across 14 Medicaid services in Minnesota may be fraudulent

https://www.cbsnews.com/minnesota/news/billions-paid-out-by-medicaid-in-minnesota-may-be-fraudule...
24•mhb•48m ago•3 comments

Believe the Checkbook

https://robertgreiner.com/believe-the-checkbook/
108•rg81•8h ago•46 comments

The FreeBSD Foundation's Laptop Support and Usability Project

https://github.com/FreeBSDFoundation/proj-laptop
127•mikece•9h ago•41 comments

Buteyko Method

https://en.wikipedia.org/wiki/Buteyko_method
23•rzk•2h ago•9 comments

Monumental snake engravings of the Orinoco River (2024)

https://www.cambridge.org/core/journals/antiquity/article/monumental-snake-engravings-of-the-orin...
11•bryanrasmussen•1w ago•1 comments

The pitfalls of partitioning Postgres yourself

https://hatchet.run/blog/postgres-partitioning
43•abelanger•3d ago•5 comments

Show HN: Misata – synthetic data engine using LLM and Vectorized NumPy

https://github.com/rasinmuhammed/misata
9•rasinmuhammed•3d ago•0 comments

Response Healing: Reduce JSON defects by 80%+

https://openrouter.ai/announcements/response-healing-reduce-json-defects-by-80percent
32•numlocked•1d ago•25 comments

Lite^3, a JSON-compatible zero-copy serialization format

https://github.com/fastserial/lite3
123•cryptonector•6d ago•32 comments

The scariest boot loader code

http://miod.online.fr/software/openbsd/stories/boot_hppa.html
17•todsacerdoti•3h ago•1 comments

Reverse Engineering US Airline's PNR System and Accessing All Reservations

https://alexschapiro.com/security/vulnerability/2025/11/20/avelo-airline-reservation-api-vulnerab...
79•bearsyankees•5h ago•39 comments

LLM Year in Review

https://karpathy.bearblog.dev/year-in-review-2025/
21•swyx•3h ago•9 comments

History LLMs: Models trained exclusively on pre-1913 texts

https://github.com/DGoettlich/history-llms
747•iamwil•1d ago•367 comments

Show HN: I Made Loom for Mobile

https://demoscope.app
57•admtal•6h ago•34 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•7mo 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?