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Residential proxies detected on White House network

https://layer3intel.com/blog/residential-proxies-white-house-network
1042•Rasbora•13h ago•330 comments

Lemmings CSS Animation

https://markblah.github.io/lemmings/
172•MarkBlah•5h ago•35 comments

Images 2.6

https://openai.com/index/introducing-images-2-6/
295•davidbarker•10h ago•174 comments

A thousand fewer paths to root

https://openai.com/index/a-thousand-fewer-paths-to-root/
331•dangelosaurus•10h ago•238 comments

Partial Operator Precedence

https://ziglang.org/devlog/2026/#2026-10-24
36•tosh•2d ago•14 comments

Kimi K3.1 Pro model weights are out

https://huggingface.co/moonshotai/Kimi-K3.1-Pro
531•abrarthought•18h ago•147 comments

Baidu dropped ERNIE 5.5 Flash Base

https://ernie.baidu.com/release/ernie-5-5-flash-base/
5•pretext•32m ago•2 comments

TextEdit vs. Pages

https://leancrew.com/all-this/2026/10/textedit-vs-pages/
9•surprisetalk•1d ago•0 comments

Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding

https://www.ricursive.com/ripple
291•Philpax•15h ago•107 comments

Spreading Liquid with Three.js

https://mofu-dev.com/en/blog/fluid-foundation/
100•mooreds•2d ago•15 comments

A new Go API for JSON

https://go.dev/blog/jsonv2
313•olexsmir•16h ago•145 comments

Insects don't swarm at LED lights

https://www.birdsoutsidemywindow.org/2026/11/03/insects-dont-swarm-at-led-lights/
255•speckx•14h ago•127 comments

Altera Announces Agilex 2X, a Complete FPGA and SoC Portfolio

https://www.altera.com/newsroom/agilex-2x
27•gmays•4h ago•11 comments

Future Maintenance of Mbed TLS

https://lists.trustedfirmware.org/archives/list/mbed-tls@lists.trustedfirmware.org/thread/YPIGCB7...
42•jonesjohnson•2d ago•10 comments

Conductor is joining Meta

https://www.conductor.build/announcement
176•kenrose•15h ago•187 comments

Hacking the Xbox 360 Hypervisor Part 3: Bad Update Improvements

https://icode4.coffee/?p=1746
113•muricula•11h ago•12 comments

Ask HN: What's your current AI agentic setup?

330•eggbrain•19h ago•352 comments

AI is forcing us to pay off 40 years of technical debt

https://spectrum.ieee.org/ai-in-cybersecurity-php2shell-vulnerability
1182•pseudolus•22h ago•694 comments

Show HN: After a decade of reversing, I got co-op working in DOS Wing Commander

https://fireflyk64.github.io/wcmulti/
182•daniel_rh•15h ago•24 comments

Qualcomm Cloud AI 100 Ultra – 150W AI Accelerator Card with 128GB at 548GB/s

https://www.qualcomm.com/data-center/products/cloud-ai-100-ultra
146•peter_d_sherman•14h ago•57 comments

Progress on provable inference for large language models

https://www.anthropic.com/research/provable-inference
221•ltononro•16h ago•105 comments

Launch HN: Kino AI (YC S23) – AI media manager and video editor

87•lukeigel•16h ago•34 comments

Starfall – Affordable, routine access to microgravity

https://www.spacex.com/starfall
219•gmays•9h ago•153 comments

Guerrilla bike lanes: San Francisco makes illicit infrastructure permanent (2016)

https://99percentinvisible.org/article/guerrilla-bike-lanes-san-francisco-makes-illicit-infrastru...
307•zenincognito•18h ago•234 comments

So my ISP was hijacking my DNS

https://www.karthihegde.dev/posts/dns_tampering
78•frostbyte7•21h ago•48 comments

OpenAI to acquire Pierre Computer Company

https://openai.com/index/openai-to-acquire-pierre/
238•itunpredictable•18h ago•73 comments

A wave of plastic-free coffee pods and machines

https://www.cnn.com/2026/11/02/food/coffee-machine-plastic-free
94•thunderbong•18h ago•117 comments

Being a kid making games in the early 2000s

https://jaytholen.net/teengamedev/
8•farmerbb•4h ago•0 comments

Tencent Hy4

https://hy.tencent.ai/research/hy4
244•Philpax•19h ago•89 comments

Basically no one is using AI agents for their DoorDash orders

https://arstechnica.com/ai/2026/10/doordash-says-outside-ai-agents-have-only-led-to-hundreds-of-o...
137•PaulHoule•2d ago•264 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?