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745•47thpresident•17h ago•178 comments

Trump says Venezuela’s Maduro captured after strikes

https://www.reuters.com/world/americas/loud-noises-heard-venezuela-capital-southern-area-without-...
408•jumpocelot•6h ago•906 comments

Daft Punk Easter Egg in the BPM Tempo of Harder, Better, Faster, Stronger?

https://www.madebywindmill.com/tempi/blog/hbfs-bpm/
551•simonw•16h ago•94 comments

Tally – A tool to help agents classify your bank transactions

https://tallyai.money/
63•ahmedatia•2h ago•48 comments

Of Boot Vectors and Double Glitches: Bypassing RP2350's Secure Boot

https://streaming.media.ccc.de/39c3/relive/2149
89•aberoham•6d ago•9 comments

IPv6 just turned 30 and still hasn't taken over the world

https://www.theregister.com/2025/12/31/ipv6_at_30/
409•Brajeshwar•22h ago•792 comments

A Beginner's Two-Component Crystal-Style Wi-Fi Detector

https://siliconjunction.wordpress.com/2025/12/12/a-beginners-two-component-crystal-style-wi-fi-de...
55•jensgk•3d ago•19 comments

2026 will be my year of the Linux desktop

https://xeiaso.net/notes/2026/year-linux-desktop/
598•todsacerdoti•13h ago•441 comments

Clicks Communicator

https://www.clicksphone.com/en/communicator
349•microflash•20h ago•215 comments

IQuest-Coder: A new open-source code model beats Claude Sonnet 4.5 and GPT 5.1 [pdf]

https://github.com/IQuestLab/IQuest-Coder-V1/blob/main/papers/IQuest_Coder_Technical_Report.pdf
105•shenli3514•9h ago•33 comments

Ask HN: Who is hiring? (January 2026)

301•whoishiring•21h ago•185 comments

GitHub – tomasf/Cadova: Swift DSL for parametric 3D modeling

https://github.com/tomasf/Cadova
24•bdcravens•3d ago•4 comments

A Basic Just-In-Time Compiler (2015)

https://nullprogram.com/blog/2015/03/19/
73•ibobev•12h ago•16 comments

How Smell Guides Our Inner World

https://www.quantamagazine.org/how-smell-guides-our-inner-world-20250703/
17•anarbadalov•5d ago•1 comments

Linux kernel security work

http://www.kroah.com/log/blog/2026/01/02/linux-kernel-security-work/
125•chmaynard•15h ago•56 comments

UK company sends factory with 1,000C furnace into space

https://www.bbc.co.uk/news/articles/c62vx0pgyrgo
71•vekerdyb•3d ago•27 comments

Jank Lang Hit Alpha

https://github.com/jank-lang/jank
193•makemethrowaway•17h ago•27 comments

The Cost of a Closure in C: The Rest

https://thephd.dev/the-cost-of-a-closure-in-c-c2y-followup
49•ingve•3d ago•21 comments

Show HN: uvx ptn, scan a QR, get a terminal in your phone

https://github.com/lyehe/porterminal
38•yxl448•9h ago•8 comments

Adventure 751 (1980)

https://bluerenga.blog/2026/01/01/adventure-751-1980/
35•quuxplusone•10h ago•3 comments

Fighting Fire with Fire: Scalable Oral Exams

https://www.behind-the-enemy-lines.com/2025/12/fighting-fire-with-fire-scalable-oral.html
175•sethbannon•19h ago•227 comments

Unix v4 (1973) – Live Terminal

https://unixv4.dev/
153•pjmlp•18h ago•73 comments

Ask HN: Who wants to be hired? (January 2026)

121•whoishiring•21h ago•225 comments

Einstein Probe detects an X-ray flare from nearby star

https://phys.org/news/2025-12-einstein-probe-ray-flare-nearby.html
40•wglb•12h ago•9 comments

Why 451 Is Good for You – Greylisting Perspectives from the Early Noughties

https://bsdly.blogspot.com/2025/12/why-451-is-good-for-you-greylisting.html
20•zdw•5d ago•17 comments

Accounting for Computer Scientists (2011)

https://martin.kleppmann.com/2011/03/07/accounting-for-computer-scientists.html
122•tosh•19h ago•49 comments

The rsync algorithm (1996) [pdf]

https://www.andrew.cmu.edu/course/15-749/READINGS/required/cas/tridgell96.pdf
158•vortex_ape•20h ago•21 comments

Punkt. Unveils MC03 Smartphone

https://www.punkt.ch/blogs/news/punkt-unveils-mc03
151•ChrisArchitect•21h ago•139 comments

Show HN: Website that plays the lottery every second

https://lotteryeverysecond.lffl.me/
193•Loeffelmann•13h ago•119 comments

TinyTinyTPU: 2×2 systolic-array TPU-style matrix-multiply unit deployed on FPGA

https://github.com/Alanma23/tinytinyTPU-co
115•Xenograph•18h ago•49 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?