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Linux Sandboxes and Fil-C

https://fil-c.org/seccomp
164•pizlonator•7h ago•43 comments

Recovering Anthony Bourdain's (really) lost Li.st's

https://sandyuraz.com/blogs/bourdain/
166•thecsw•8h ago•54 comments

Using E-Ink tablet as monitor for Linux

https://alavi.me/blog/e-ink-tablet-as-monitor-linux/
69•yolkedgeek•4d ago•25 comments

An Implementation of J

https://www.jsoftware.com/ioj/ioj.htm
43•ofalkaed•5h ago•17 comments

I fed 24 years of my blog posts to a Markov model

https://susam.net/fed-24-years-of-posts-to-markov-model.html
142•zdw•9h ago•63 comments

Closures as Win32 Window Procedures

https://nullprogram.com/blog/2025/12/12/
54•ibobev•6h ago•5 comments

Lean Theorem Prover Mathlib

https://github.com/leanprover-community/mathlib4
20•downboots•4h ago•0 comments

I tried Gleam for Advent of Code

https://blog.tymscar.com/posts/gleamaoc2025/
255•tymscar•13h ago•143 comments

VPN location claims don't match real traffic exits

https://ipinfo.io/blog/vpn-location-mismatch-report
318•mmaia•10h ago•185 comments

If a Meta AI model can read a brain-wide signal, why wouldn't the brain?

https://1393.xyz/writing/if-a-meta-ai-model-can-read-a-brain-wide-signal-why-wouldnt-the-brain
32•rdgthree•4h ago•15 comments

Cat Gap

https://en.wikipedia.org/wiki/Cat_gap
81•Petiver•4d ago•11 comments

Therapeutic Use of Cannabis and Cannabinoids: A Review

https://jamanetwork.com/journals/jama/fullarticle/2842072?guestAccessKey=a368e622-e374-4a0c-8d3b-...
27•bookofjoe•4h ago•7 comments

The Rise of Computer Games, Part I: Adventure

https://technicshistory.com/2025/12/13/the-rise-of-computer-games-part-i-adventure/
70•cfmcdonald•9h ago•25 comments

Heavy metal is healing teens on the Blackfeet Nation

https://www.hcn.org/issues/57-11/heavy-metal-is-healing-teens-on-the-blackfeet-nation/
25•cdrnsf•2h ago•2 comments

No-Tifier (2017)

https://subject.space/projects/no-tifier/
4•aebtebeten•3d ago•0 comments

Dhtml Lemmings (2004)

https://www.elizium.nu/scripts/lemmings/index.php
16•tetris11•5d ago•8 comments

Why Twilio Segment moved from microservices back to a monolith

https://www.twilio.com/en-us/blog/developers/best-practices/goodbye-microservices
208•birdculture•9h ago•166 comments

Useful patterns for building HTML tools

https://simonwillison.net/2025/Dec/10/html-tools/
263•simonw•3d ago•77 comments

Awesome-Jj: Jujutsu Things

https://github.com/Necior/awesome-jj
35•n3t•5h ago•4 comments

Cryptids

https://wiki.bbchallenge.org/wiki/Cryptids
107•frozenseven•1w ago•15 comments

From Azure Functions to FreeBSD

https://jmmv.dev/2025/12/from-azure-functions-to-freebsd.html
86•todsacerdoti•5d ago•14 comments

Mystery Science Theater 3000: The Definitive Oral History of a TV Masterpiece

https://www.wired.com/2014/04/mst3k-oral-history/
29•indigodaddy•6d ago•4 comments

Ask HN: How can I get better at using AI for programming?

283•lemonlime227•14h ago•309 comments

Go Proposal: Secret Mode

https://antonz.org/accepted/runtime-secret/
179•enz•4d ago•78 comments

Free Software Awards Winners Announced: Andy Wingo, Alx Sa, Govdirectory

https://www.fsf.org/news/2024-free-software-awards-winners
30•pseudolus•4h ago•3 comments

Using Python for Scripting

https://hypirion.com/musings/use-python-for-scripting
113•birdculture•5d ago•85 comments

What is the nicest thing a stranger has ever done for you?

https://louplummer.lol/nice-stranger/
350•speckx•2d ago•254 comments

Some surprising things about DuckDuckGo

https://gabrielweinberg.com/p/some-surprising-things-about-duckduckgo
97•ArmageddonIt•8h ago•74 comments

EasyPost (YC S13) Is Hiring

https://www.easypost.com/careers
1•jstreebin•13h ago

Researchers seeking better measures of cognitive fatigue

https://www.nature.com/articles/d41586-025-03974-w
121•bikenaga•3d ago•32 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•6mo 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•6mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•6mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•6mo 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•6mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•6mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•6mo 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•6mo 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•6mo 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•6mo 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?