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AI World Clocks

https://clocks.brianmoore.com/
775•waxpancake•9h ago•277 comments

SSL Configuration Generator

https://ssl-config.mozilla.org/
93•smartmic•5h ago•27 comments

Has Google solved two of AI’s oldest problems?

https://generativehistory.substack.com/p/has-google-quietly-solved-two-of
202•scrlk•3d ago•139 comments

No Leak, No Problem – Bypassing ASLR with a ROP Chain to Gain RCE

https://modzero.com/en/blog/no-leak-no-problem/
37•todsacerdoti•4h ago•1 comments

HipKittens: Fast and furious AMD kernels

https://hazyresearch.stanford.edu/blog/2025-11-09-hk
95•dataminer•1d ago•34 comments

Structured outputs on the Claude Developer Platform

https://www.claude.com/blog/structured-outputs-on-the-claude-developer-platform
106•adocomplete•8h ago•50 comments

'No One Lives Forever' turns 25 and you still can't buy it legitimately

https://www.techdirt.com/2025/11/13/no-one-lives-forever-turns-25-you-still-cant-buy-it-legitimat...
182•speckx•11h ago•102 comments

A race condition in Aurora RDS

https://hightouch.com/blog/uncovering-a-race-condition-in-aurora-rds
197•theanomaly•9h ago•65 comments

Async Mutexes

https://matklad.github.io/2025/11/04/on-async-mutexes.html
10•ingve•1w ago•1 comments

All praise to the lunch ladies

https://bittersoutherner.com/issue-no-12/all-praise-to-the-lunch-ladies
145•gmays•7h ago•74 comments

GEN-0 / Embodied Foundation Models That Scale with Physical Interaction

https://generalistai.com/blog/nov-04-2025-GEN-0
27•jackdoe•1w ago•1 comments

Manganese is Lyme disease's double-edge sword

https://news.northwestern.edu/stories/2025/11/manganese-is-lyme-diseases-double-edge-sword
129•gmays•10h ago•71 comments

Show HN: Tiny Diffusion – A character-level text diffusion model from scratch

https://github.com/nathan-barry/tiny-diffusion
107•nathan-barry•4d ago•13 comments

The disguised return of EU Chat Control

https://reclaimthenet.org/the-disguised-return-of-the-eus-private-message-scanning-plot
542•egorfine•9h ago•232 comments

Winamp clone in Swift for macOS

https://github.com/mgreenwood1001/winamp
192•hyperbole•14h ago•122 comments

Go's Sweet 16

https://go.dev/blog/16years
107•0xedb•5h ago•55 comments

Xqerl – Erlang XQuery 3.1 Processor

https://zadean.github.io/xqerl/
34•smartmic•3d ago•7 comments

Awk Technical Notes (2023)

https://maximullaris.com/awk_tech_notes.html
108•signa11•1w ago•41 comments

Mentra (YC W25) Is Hiring: Head of Growth to Make Smart Glasses Mainstream

https://www.ycombinator.com/companies/mentra/jobs/2YbQCRw-make-smart-glasses-mainstream-head-of-g...
1•caydenpiercehax•6h ago

Raycore: GPU accelerated and modular ray intersections

https://makie.org/website/blogposts/raycore/
13•simondanisch•4d ago•3 comments

Bitchat for Gaza – messaging without internet

https://updates.techforpalestine.org/bitchat-for-gaza-messaging-without-internet/
381•ciconia•9h ago•207 comments

AI note-taking startup Fireflies was really two guys typing notes by hand

https://www.pcgamer.com/software/ai/usd1-billion-ai-company-co-founder-admits-that-its-usd100-a-m...
30•thomassmith65•1h ago•11 comments

Being poor vs. being broke

https://blog.ctms.me/posts/2025-11-14-being-poor-or-being-broke/
414•speckx•10h ago•502 comments

Honda: 2 years of ml vs 1 month of prompting - heres what we learned

https://www.levs.fyi/blog/2-years-of-ml-vs-1-month-of-prompting/
292•Ostatnigrosh•4d ago•99 comments

Moving Back to a Tiling WM – XMonad

https://wssite.vercel.app/blog/moving-back-to-a-tiling-wm-xmonad
62•weirdsmiley•10h ago•59 comments

Linear algebra explains why some words are effectively untranslatable

https://aethermug.com/posts/linear-algebra-explains-why-some-words-are-effectively-untranslatable
119•mrcgnc•12h ago•93 comments

Magit manuals are available online again

https://github.com/magit/magit/issues/5472
115•vetronauta•15h ago•46 comments

Incus-OS: Immutable Linux OS to run Incus as a hypervisor

https://linuxcontainers.org/incus-os/
145•_kb•1w ago•55 comments

AGI fantasy is a blocker to actual engineering

https://www.tomwphillips.co.uk/2025/11/agi-fantasy-is-a-blocker-to-actual-engineering/
564•tomwphillips•14h ago•556 comments

Minisforum Stuffs Entire Arm Homelab in the MS-R1

https://www.jeffgeerling.com/blog/2025/minisforum-stuffs-entire-arm-homelab-ms-r1
70•kencausey•8h ago•39 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•5mo 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•5mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•5mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•5mo 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•5mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•5mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•5mo 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•5mo 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•5mo 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•5mo 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?