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Deno Desktop

https://docs.deno.com/runtime/desktop/
601•GeneralMaximus•7h ago•224 comments

GLM 5.2 vs. Opus

https://techstackups.com/comparisons/glm-5.2-vs-opus/
198•ritzaco•5h ago•161 comments

Codex logging bug may write TBs to local SSDs

https://github.com/openai/codex/issues/28224
192•vantareed•5h ago•99 comments

Help I accidentally a wigglegram

https://lmao.center/blog/wiggle-accidents/
335•gregsadetsky•2d ago•72 comments

Did my old job only exist because of fraud?

https://david.newgas.net/did-my-old-job-only-exist-because-of-fraud/
648•advisedwang•15h ago•280 comments

Investors get real-time view of UK bond market activity for the first time

https://www.fca.org.uk/news/press-releases/investors-get-real-time-view-uk-bond-market-activity-f...
55•monkeydust•5h ago•14 comments

Apertus – Open Foundation Model for Sovereign AI

https://apertvs.ai/
446•T-A•15h ago•147 comments

Munich 1991: The Roots of the Current AI Boom

https://people.idsia.ch/~juergen/ai-boom-roots-munich-1991.html
115•tosh•2d ago•46 comments

The Story of Semicolon

https://sheets.works/data-viz/semicolon
7•theanonymousone•2d ago•2 comments

There is minimal downside to switching to open models

https://www.marble.onl/posts/cancel_claude.html
278•amarble•15h ago•229 comments

Sakana Fugu

https://sakana.ai/fugu/
146•Finbarr•10h ago•86 comments

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/
11•snikolaev•2h ago•0 comments

Writing Postcards with a 3D Printer

https://severinbucher.com/posts/writing-postcards-with-a-3d-printer/
26•typesafeJ•3d ago•12 comments

Memory Safe Inline Assembly

https://fil-c.org/inlineasm
129•pizlonator•2d ago•29 comments

Everything is logarithms

https://alexkritchevsky.com/2026/05/25/everything-is-logarithms.html
247•E-Reverance•15h ago•50 comments

Identity verification on Claude

https://support.claude.com/en/articles/14328960-identity-verification-on-claude
805•bathory•1d ago•675 comments

Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions

https://www.teachmecoolstuff.com/viewarticle/fine-tuning-a-local-llm-to-categorize-questions
160•dev-experiments•13h ago•32 comments

Lisp in the Rust Type System

https://github.com/playX18/lisp-in-types/
82•quasigloam•2d ago•4 comments

Danish privacy activist Lars Andersen raided by police

https://twitter.com/LarsAnders1620/status/2068208864747540516#m
297•I_am_tiberius•8h ago•253 comments

JSON-LD explained for personal websites

https://hawksley.dev/blog/json-ld-explained-for-personal-websites/
235•ethanhawksley•18h ago•75 comments

My 1992 view of the problems of computer programming in 1992

https://blog.plover.com/prog/fortran-i.html
41•speckx•2d ago•12 comments

UTFS: A Tar-Like File System for Embedded Systems (2025)

https://clisystems.com/article-UTFS-intro/
11•zdw•4d ago•6 comments

How I play video games with spinal muscular atrophy

https://www.openassistivetech.org/how-i-actually-play-video-games-with-sma-the-tools-i-use-every-...
131•dannyobrien•3d ago•17 comments

Show HN: Teach your kids perfect pitch

https://github.com/paytonjjones/bsharp
161•paytonjjones•1d ago•104 comments

Japanese verb conjugation the simple hard way

https://underreacted.leaflet.pub/3mmevu6woys27
119•valzevul•13h ago•178 comments

Minecraft: Java Edition 26.2, the first version with Vulkan 1.2

https://www.minecraft.net/en-us/article/minecraft-java-edition-26-2
168•ObviouslyFlamer•5d ago•71 comments

Efficient C++ Programming for Modern C++ CPUs, Chapter 4/part 2

https://6it.dev/blog/infographics-operation-costs-in-cpu-clock-cycles-take-2-80736
72•birdculture•2d ago•17 comments

PowerFox Browser

https://powerfox.jazzzny.me/
149•thisislife2•15h ago•41 comments

Show HN: Criterion Closet as a website – pull any of 1,247 films off the shelf

https://the-criterion-closet.vercel.app
142•olievans•1d ago•43 comments

Rent collections are down in New York

https://www.politico.com/news/2026/06/21/rent-collections-are-down-in-new-york-and-no-ones-sure-w...
99•JumpCrisscross•14h ago•402 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?