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Keep Android Open

https://f-droid.org/2026/02/20/twif.html
1580•LorenDB•18h ago•579 comments

Andrej Karpathy talks about "Claws"

https://simonwillison.net/2026/Feb/21/claws/
88•helloplanets•2h ago•113 comments

I Verified My LinkedIn Identity. Here's What I Handed Over

https://thelocalstack.eu/posts/linkedin-identity-verification-privacy/
101•ColinWright•4h ago•23 comments

Turn Dependabot off

https://words.filippo.io/dependabot/
486•todsacerdoti•14h ago•139 comments

I found a Vulnerability. They found a Lawyer

https://dixken.de/blog/i-found-a-vulnerability-they-found-a-lawyer
636•toomuchtodo•16h ago•289 comments

Padlet (YC W13) Is Hiring in San Francisco and Singapore

https://padlet.jobs
1•coffeebite•1m ago

Facebook is cooked

https://pilk.website/3/facebook-is-absolutely-cooked
1145•npilk•17h ago•629 comments

Ggml.ai joins Hugging Face to ensure the long-term progress of Local AI

https://github.com/ggml-org/llama.cpp/discussions/19759
748•lairv•22h ago•185 comments

Wikipedia deprecates Archive.today, starts removing archive links

https://arstechnica.com/tech-policy/2026/02/wikipedia-bans-archive-today-after-site-executed-ddos...
464•nobody9999•17h ago•277 comments

EU mandates replaceable batteries by 2027 (2023)

https://environment.ec.europa.eu/news/new-law-more-sustainable-circular-and-safe-batteries-enters...
143•cyrusmg•3h ago•82 comments

Understanding Std:Shared_mutex from C++17

https://www.cppstories.com/2026/shared_mutex/
15•ibobev•3d ago•0 comments

CERN rebuilt the original browser from 1989 (2019)

https://worldwideweb.cern.ch
186•tylerdane•12h ago•66 comments

Lean 4: How the theorem prover works and why it's the new competitive edge in AI

https://venturebeat.com/ai/lean4-how-the-theorem-prover-works-and-why-its-the-new-competitive-edg...
44•tesserato•3d ago•21 comments

Acme Weather

https://acmeweather.com/blog/introducing-acme-weather
69•cryptoz•4h ago•47 comments

Coccinelle: The Linux kernel's source-to-source transformation tool

https://github.com/coccinelle/coccinelle
19•anon111332142•3h ago•2 comments

LibreOffice blasts OnlyOffice for working with Microsoft to lock users in

https://www.neowin.net/news/libreoffice-blasts-fake-open-source-onlyoffice-for-working-with-micro...
62•XzetaU8•3h ago•36 comments

What Is OAuth?

https://leaflet.pub/p/did:plc:3vdrgzr2zybocs45yfhcr6ur/3mfd2oxx5v22b
131•cratermoon•10h ago•41 comments

Gitas – A tool for Git account switching

https://github.com/letmutex/gitas
18•letmutex•4d ago•11 comments

Every company building your AI assistant is now an ad company

https://juno-labs.com/blogs/every-company-building-your-ai-assistant-is-an-ad-company
202•ajuhasz•17h ago•104 comments

Cord: Coordinating Trees of AI Agents

https://www.june.kim/cord
104•gfortaine•10h ago•46 comments

When etcd crashes, check your disks first

https://nubificus.co.uk/blog/etcd/
13•_ananos_•4h ago•4 comments

Index, Count, Offset, Size

https://tigerbeetle.com/blog/2026-02-16-index-count-offset-size/
93•ingve•3d ago•28 comments

The bare minimum for syncing Git repos

https://alexwlchan.net/2026/bare-git/
6•speckx•3d ago•2 comments

Large Language Model Reasoning Failures

https://arxiv.org/abs/2602.06176
12•T-A•3h ago•3 comments

Blue light filters don't work – controlling total luminance is a better bet

https://www.neuroai.science/p/blue-light-filters-dont-work
175•pminimax•17h ago•186 comments

Show HN: Mines.fyi – all the mines in the US in a leaflet visualization

https://mines.fyi/
85•irasigman•14h ago•42 comments

OpenScan

https://openscan.eu/pages/scan-gallery
167•joebig•15h ago•13 comments

The path to ubiquitous AI (17k tokens/sec)

https://taalas.com/the-path-to-ubiquitous-ai/
751•sidnarsipur•1d ago•417 comments

24 Hour Fitness won't let you unsubscribe from marketing spam, so I fixed it

https://ahmedkaddoura.com/projects/24hf-unsubscribe
57•daem•3h ago•13 comments

Continuous batching (2025)

https://huggingface.co/blog/continuous_batching
36•jxmorris12•5d ago•7 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•9mo 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•9mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•9mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•9mo 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•9mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•9mo 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•9mo 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•9mo 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•9mo 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•9mo 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•9mo 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•9mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•9mo 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•9mo 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•9mo 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•9mo 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•9mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•9mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•9mo 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•9mo 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•9mo 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•9mo 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?