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Show HN: Brutalist Concrete Laptop Stand (2024)

https://sam-burns.com/posts/concrete-laptop-stand/
410•sam-bee•5h ago•149 comments

Good Taste the Only Real Moat Left

https://rajnandan.com/posts/taste-in-the-age-of-ai-and-llms/
38•speckx•32m ago•21 comments

Cloudflare targets 2029 for full post-quantum security

https://blog.cloudflare.com/post-quantum-roadmap/
63•ilreb•2h ago•23 comments

Moving fast in hardware: lessons from lab to $100M ARR

https://blog.zacka.io/p/simplify-then-add-lightness-bc4
28•rryan•1h ago•4 comments

We found an undocumented bug in the Apollo 11 guidance computer code

https://www.juxt.pro/blog/a-bug-on-the-dark-side-of-the-moon/
280•henrygarner•6h ago•145 comments

Dropping Cloudflare for Bunny.net

https://jola.dev/posts/dropping-cloudflare
230•shintoist•3h ago•109 comments

Show HN: A cartographer's attempt to realistically map Tolkien's world

https://www.intofarlands.com/atlasofarda
95•intofarlands•4h ago•16 comments

Every GPU That Mattered

https://sheets.works/data-viz/every-gpu
241•jonbaer•7h ago•132 comments

Google open-sources experimental agent orchestration testbed Scion

https://www.infoq.com/news/2026/04/google-agent-testbed-scion/
8•timbilt•2h ago•0 comments

Claude Code is locking people out for hours

https://github.com/anthropics/claude-code/issues/44257
153•sh1mmer•1h ago•170 comments

9 Mothers (YC P26) Is Hiring – Lead Robotics and More

https://jobs.ashbyhq.com/9-mothers?utm_source=x8pZ4B3P3Q
1•ukd1•2h ago

You can't cancel a JavaScript promise (except sometimes you can)

https://www.inngest.com/blog/hanging-promises-for-control-flow
40•goodoldneon•2h ago•26 comments

Global Physics Photowalk: 2025 winners revealed

https://www.quantamagazine.org/global-physics-photowalk-2025-winners-revealed-20260401/
13•ibobev•3d ago•1 comments

Identify a London Underground Line just by listening to it

https://tubesoundquiz.com/
133•nelson687•6h ago•41 comments

SQLite in Production: Lessons from Running a Store on a Single File

https://ultrathink.art/blog/sqlite-in-production-lessons
82•thunderbong•3d ago•61 comments

My Experience as a Rice Farmer

https://xd009642.github.io/2026/04/01/My-Experience-as-a-Rice-Farmer.html
293•surprisetalk•5d ago•137 comments

Wi-Fi That Can Withstand a Nuclear Reactor: This receiver chip can take it

https://spectrum.ieee.org/robotics-in-nuclear-industry
55•voxadam•4d ago•3 comments

An AI robot in my home

https://allevato.me/2026/04/07/an-ai-robot-in-my-home
4•kukanani•3h ago•0 comments

12k Tons of Dumped Orange Peel Grew into a Landscape Nobody Expected (2017)

https://www.sciencealert.com/how-12-000-tonnes-of-dumped-orange-peel-produced-something-nobody-im...
29•pulisse•39m ago•4 comments

Haunting Photos Show the Aftermath of the Kursk Submarine Disaster in 2000

https://rarehistoricalphotos.com/kursk-submarine-disaster-photos/
99•mooreds•5d ago•23 comments

Show HN: Stop paying for Dropbox/Google Drive, use your own S3 bucket instead

https://locker.dev
184•Zm44•5h ago•148 comments

DeiMOS – A Superoptimizer for the MOS 6502

https://aransentin.github.io/deimos/
51•Aransentin•5h ago•15 comments

Show HN: Pion/handoff – Move WebRTC out of browser and into Go

https://github.com/pion/handoff
66•Sean-Der•4h ago•11 comments

Blackholing My Email

https://www.johnsto.co.uk/blog/blackholing-my-email/
124•semyonsh•7h ago•13 comments

Show HN: Finalrun – Spec-driven testing using English and vision for mobile apps

https://github.com/final-run/finalrun-agent
4•ashish004•1h ago•2 comments

AI may be making us think and write more alike

https://dornsife.usc.edu/news/stories/ai-may-be-making-us-think-and-write-more-alike/
174•giuliomagnifico•4h ago•168 comments

Breaking the console: a brief history of video game security

https://sergioprado.blog/breaking-the-console-a-brief-history-of-video-game-security/
66•sprado•6h ago•19 comments

Running Out of Disk Space in Production

https://alt-romes.github.io/posts/2026-04-01-running-out-of-disk-space-on-launch.html
110•romes•4d ago•52 comments

Sam Altman may control our future – can he be trusted?

https://www.newyorker.com/magazine/2026/04/13/sam-altman-may-control-our-future-can-he-be-trusted
1798•adrianhon•1d ago•735 comments

Show HN: Ghost Pepper – Local hold-to-talk speech-to-text for macOS

https://github.com/matthartman/ghost-pepper
435•MattHart88•20h ago•190 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?