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Open-source Zig book

https://www.zigbook.net
229•rudedogg•3h ago•76 comments

Tracking users with favicons, even in incognito mode

https://github.com/jonasstrehle/supercookie
128•vxvrs•3h ago•29 comments

Heretic: Automatic censorship removal for language models

https://github.com/p-e-w/heretic
353•melded•8h ago•135 comments

The fate of "small" open source

https://nolanlawson.com/2025/11/16/the-fate-of-small-open-source/
112•todsacerdoti•3h ago•72 comments

Peter Thiel sells off all Nvidia stock, stirring bubble fears

https://www.thestreet.com/investing/peter-thiel-dumps-top-ai-stock-stirring-bubble-fears
121•hypeatei•1h ago•90 comments

Dark Pattern Games

https://www.darkpattern.games
58•robotnikman•3h ago•27 comments

What if you don't need MCP at all?

https://mariozechner.at/posts/2025-11-02-what-if-you-dont-need-mcp/
77•jdkee•4h ago•33 comments

The Pragmatic Programmer: 20th Anniversary Edition (2023)

https://www.ahalbert.com/technology/2023/12/19/the_pragmatic_programmer.html
38•ahalbert2•2h ago•4 comments

Z3 API in Python: From Sudoku to N-Queens in Under 20 Lines

https://ericpony.github.io/z3py-tutorial/guide-examples.htm
68•amit-bansil•4h ago•2 comments

I have recordings proving Coinbase knew about breach months before disclosure

https://jonathanclark.com/posts/coinbase-breach-timeline.html
231•jclarkcom•2h ago•83 comments

I finally understand Cloudflare Zero Trust tunnels

https://david.coffee/cloudflare-zero-trust-tunnels
80•eustoria•5h ago•26 comments

FPGA Based IBM-PC-XT

https://bit-hack.net/2025/11/10/fpga-based-ibm-pc-xt/
123•andsoitis•7h ago•24 comments

Decoding Leibniz Notation (2024)

https://www.spakhm.com/leibniz
26•coffeemug•4h ago•1 comments

Fourier Transforms

https://www.continuummechanics.org/fourierxforms.html
88•o4c•1w ago•13 comments

Linux mode setting, from the comfort of OCaml

https://roscidus.com/blog/blog/2025/11/16/libdrm-ocaml/
31•ibobev•3h ago•4 comments

How Your Brain Creates 'Aha' Moments and Why They Stick

https://www.quantamagazine.org/how-your-brain-creates-aha-moments-and-why-they-stick-20251105/
4•wjb3•1h ago•0 comments

Your Land, My Land (Offrange) – Lithium vs. Lettuce in the Imperial Valley, CA

https://ambrook.com/offrange/photo-essay/lithium-v-lettuce
17•mfburnett•1d ago•2 comments

Brimstone: ES2025 JavaScript engine written in Rust

https://github.com/Hans-Halverson/brimstone
180•ivankra•11h ago•87 comments

Shell Grotto, Margate

https://en.wikipedia.org/wiki/Shell_Grotto,_Margate
17•Michelangelo11•1w ago•2 comments

Why Bcrypt Can Be Unsafe for Password Hashing?

https://blog.enamya.me/posts/bcrypt-limitation
8•enamya•1w ago•8 comments

Anthropic’s paper smells like bullshit

https://djnn.sh/posts/anthropic-s-paper-smells-like-bullshit/
789•vxvxvx•11h ago•246 comments

Garbage collection is useful

https://dubroy.com/blog/garbage-collection-is-useful/
106•surprisetalk•9h ago•33 comments

Waiting for SQL:202y: Group by All

http://peter.eisentraut.org/blog/2025/11/11/waiting-for-sql-202y-group-by-all
34•ingve•5d ago•12 comments

The Man Who Keeps Predicting the Web's Death

https://tedium.co/2025/10/25/web-dead-predictions-george-colony/
33•thm•5h ago•12 comments

De Bruijn Numerals

https://text.marvinborner.de/2023-08-22-22.html
59•marvinborner•7h ago•7 comments

Measuring the doppler shift of WWVB during a flight

https://greatscottgadgets.com/2025/10-31-receiving-wwvb-with-hackrf-pro/
112•Jyaif•1w ago•0 comments

Holes (1970) [pdf]

https://rintintin.colorado.edu/~vancecd/phil375/Lewis1.pdf
29•miobrien•2d ago•7 comments

Goldman Sachs asks in biotech Report: Is curing patients a sustainable business?

https://www.cnbc.com/2018/04/11/goldman-asks-is-curing-patients-a-sustainable-business-model.html
4•randycupertino•7m ago•0 comments

Vintage Large Language Models

https://owainevans.github.io/talk-transcript.html
58•pr337h4m•9h ago•19 comments

Running the "Reflections on Trusting Trust" Compiler (2023)

https://research.swtch.com/nih
108•naves•9h ago•5 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•5mo 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•5mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•5mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•5mo 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•5mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•5mo 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•5mo 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•5mo 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•5mo 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•5mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•5mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•5mo 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•5mo 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•5mo 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•5mo 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?