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Native Instant Space Switching on macOS

https://arhan.sh/blog/native-instant-space-switching-on-macos/
155•PaulHoule•2h ago•73 comments

How Microsoft Abuses Its Users

https://lzon.ca/posts/other/microsoft-user-abuse/
57•jpmitchell•55m ago•22 comments

Reverse engineering Gemini's SynthID detection

https://github.com/aloshdenny/reverse-SynthID
60•_tk_•1h ago•19 comments

Charcuterie – Visual similarity Unicode explorer

https://charcuterie.elastiq.ch/
36•rickcarlino•1h ago•3 comments

PicoZ80 – Drop-In Z80 Replacement

https://eaw.app/picoz80/
82•rickcarlino•3h ago•16 comments

Hegel, a universal property-based testing protocol and family of PBT libraries

https://hegel.dev
60•PaulHoule•3h ago•22 comments

Research-Driven Agents: What Happens When Your Agent Reads Before It Codes

https://blog.skypilot.co/research-driven-agents/
87•hopechong•5h ago•37 comments

Unfolder for Mac – A 3D model unfolding tool for creating papercraft

https://www.unfolder.app/
86•codazoda•5h ago•18 comments

Old laptops in a colo as low cost servers

https://colaptop.pages.dev/
95•argentum47•3h ago•45 comments

How Do You Find an Illegal Image Without Looking at It?

https://mahmoud-salem.net/the-invisible-shield
17•danso•2d ago•4 comments

Bitmap fonts make computers feel like computers again

https://korigamik.dev/blog/bitmap_fonts/
71•speckx•2h ago•51 comments

Top laptops to use with FreeBSD

https://freebsdfoundation.github.io/freebsd-laptop-testing/
252•fork-bomber•12h ago•145 comments

Show HN: I built a Cargo-like build tool for C/C++

https://github.com/randerson112/craft
100•randerson_112•5h ago•90 comments

Moving from WordPress to Jekyll (and static site generators in general)

https://www.demandsphere.com/blog/rebuilding-demandsphere-with-jekyll-and-claude-code/
6•rgrieselhuber•1h ago•2 comments

Maine is about to become the first state to ban major new data centers

https://www.gadgetreview.com/maine-is-about-to-become-the-first-state-to-ban-major-new-data-centers
185•rmason•2h ago•254 comments

Reallocating $100/Month Claude Code Spend to Zed and OpenRouter

https://braw.dev/blog/2026-04-06-reallocating-100-month-claude-spend/
262•kisamoto•13h ago•179 comments

EFF is leaving X

https://www.eff.org/deeplinks/2026/04/eff-leaving-x
914•gregsadetsky•4h ago•790 comments

Progressive encoding and decoding of 'repeated' protobuffer fields

https://schilk.co/blog/protobuffer-repeat-append/
5•quarkz02•4d ago•0 comments

Instant 1.0, a backend for AI-coded apps

https://www.instantdb.com/essays/architecture
8•stopachka•3h ago•0 comments

Introduction to Nintendo DS Programming

https://www.patater.com/files/projects/manual/manual.html
196•medbar•1d ago•38 comments

Show HN: Druids – Build your own software factory

https://github.com/fulcrumresearch/druids
8•etherio•1d ago•1 comments

A WebGPU implementation of Augmented Vertex Block Descent

https://github.com/jure/webphysics
113•juretriglav•10h ago•14 comments

The Future of Everything Is Lies, I Guess: Part 3 – Culture

https://aphyr.com/posts/413-the-future-of-everything-is-lies-i-guess-culture
77•aphyr•8h ago•55 comments

Wit, unker, Git: The lost medieval pronouns of English intimacy

https://www.bbc.com/future/article/20260408-the-extinct-english-words-for-just-the-two-of-us
169•eigenspace•11h ago•105 comments

Meta removes ads for social media addiction litigation

https://www.axios.com/2026/04/09/meta-social-media-addiction-ads
505•giuliomagnifico•8h ago•205 comments

Show HN: CSS Studio. Design by hand, code by agent

https://cssstudio.ai
130•SirHound•10h ago•89 comments

Open source security at Astral

https://astral.sh/blog/open-source-security-at-astral
344•vinhnx•17h ago•95 comments

Doing Impressions: Monet's Early Caricatures (ca. late 1850s)

https://publicdomainreview.org/collection/claude-monet-caricatures/
39•prismatic•3d ago•1 comments

Netflix Prices Went Up Again – I Bought a DVD Player Instead

https://aywren.com/2026/04/09/netflix-prices-went-up-again-i-bought-a-dvd-player-instead/
159•speckx•1h ago•178 comments

Help Keep Thunderbird Alive

https://updates.thunderbird.net/en-US/thunderbird/140.0/apr26-1e/donate/
474•playfultones•14h ago•330 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•10mo 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•10mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•10mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•10mo 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•10mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•10mo 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•10mo 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•10mo 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•10mo 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•10mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•10mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•10mo 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•10mo 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•10mo 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•10mo 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?