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Train Your Own LLM from Scratch

https://github.com/angelos-p/llm-from-scratch
86•kristianpaul•2h ago•9 comments

About 10% of AMC movie showings sell zero tickets. This site finds them

https://walzr.com/empty-screenings
105•MrBuddyCasino•2h ago•76 comments

Bun is being ported from Zig to Rust

https://github.com/oven-sh/bun/commit/46d3bc29f270fa881dd5730ef1549e88407701a5
392•SergeAx•5h ago•257 comments

CVE-2026-31431: Copy Fail vs. rootless containers

https://www.dragonsreach.it/2026/05/04/cve-2026-31431-copy-fail-rootless-containers/
69•averi•2h ago•20 comments

How OpenAI delivers low-latency voice AI at scale

https://openai.com/index/delivering-low-latency-voice-ai-at-scale/
370•Sean-Der•10h ago•117 comments

Hand Drawn QR Codes

https://sethmlarson.dev/hand-drawn-qr-codes
27•jollyjerry•2h ago•1 comments

The Car That Watches You Back: The Advertising Infrastructure of Modern Cars

https://nobodyaskedforthis.lol/posts/connected-car/
56•cadito•4h ago•36 comments

Agent Skills

https://addyosmani.com/blog/agent-skills/
209•BOOSTERHIDROGEN•9h ago•92 comments

Gaps in national food production, worldwide

https://www.nature.com/articles/s43016-025-01173-4
41•simonebrunozzi•17h ago•17 comments

Nocturnal migratory birds follow rhythm of the moon

https://www.lunduniversity.lu.se/article/nocturnal-migratory-birds-follow-rhythm-moon
5•hhs•2d ago•0 comments

Securing a DoD contractor: Finding a multi-tenant authorization vulnerability

https://www.strix.ai/blog/how-strix-found-zero-auth-vulnerability-dod-backed-startup
190•bearsyankees•12h ago•79 comments

When Networking Doesn't Work

https://www.os2museum.com/wp/when-networking-doesnt-work/
50•kencausey•9h ago•7 comments

pgxbackup: Continuity Support for pgBackRest

https://thebuild.com/blog/2026/05/01/pgxbackup-continuity-support-for-pgbackrest/
31•Wingy•2d ago•4 comments

Does Employment Slow Cognitive Decline? Evidence from Labor Market Shocks

https://www.nber.org/papers/w35117
264•littlexsparkee•15h ago•242 comments

2-D Mathematical Curves

https://www.2dcurves.com/
9•the-mitr•2h ago•0 comments

Redis array: short story of a long development process

https://antirez.com/news/164
266•antirez•16h ago•87 comments

Testing macOS on the Apple Network Server 2.0 ROMs

http://oldvcr.blogspot.com/2026/05/testing-macos-on-apple-network-server.html
81•zdw•1d ago•16 comments

Talking to strangers at the gym

https://thienantran.com/talking-to-35-strangers-at-the-gym/
1297•thitran•18h ago•613 comments

Kids bypass age verification with fake moustaches

https://www.theregister.com/2026/05/04/uk_online_safety_act_age_checks_subvert/
33•dreadsword•2h ago•10 comments

1966 Ford Mustang Converted into a Tesla with Working 'Full Self-Driving'

https://electrek.co/2026/05/02/tesla-1966-mustang-ev-conversion-full-self-driving/
162•Brajeshwar•15h ago•116 comments

What I'm Hearing About Cognitive Debt (So Far)

https://margaretstorey.com/blog/2026/02/18/cognitive-debt-revisited/
179•raphaelcosta•4h ago•95 comments

Formatting a 25M-line codebase overnight

https://stripe.dev/blog/formatting-an-entire-25-million-line-codebase-overnight-the-rubyfmt-story
150•r00k•10h ago•78 comments

Microsoft Edge stores all passwords in memory in clear text, even when unused

https://twitter.com/L1v1ng0ffTh3L4N/status/2051308329880719730
498•cft•12h ago•180 comments

Y Combinator's Stake in OpenAI (0.6%?)

https://daringfireball.net/2026/05/y_combinators_stake_in_openai
263•gyomu•6h ago•33 comments

I am worried about Bun

https://wwj.dev/posts/i-am-worried-about-bun/
464•remote-dev•13h ago•310 comments

How Monero’s proof of work works

https://blog.alcazarsec.com/tech/posts/how-moneros-proof-of-work-works
273•alcazar•16h ago•192 comments

PyInfra 3.8.0

https://github.com/pyinfra-dev/pyinfra/releases/tag/v3.8.0
258•wowi42•17h ago•88 comments

Pomiferous: The most extensive apples (pommes) database

https://pomiferous.com/
118•Ariarule•15h ago•47 comments

GameStop makes $55.5B takeover offer for eBay

https://www.bbc.co.uk/news/articles/cn0p8yled1do
667•n1b0m•21h ago•641 comments

UK Fuel Price Intelligence – Market analytics from reporting stations

https://www.fuelinsight.co.uk
171•theazureguy•15h ago•82 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•11mo 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•11mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•11mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•11mo 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•11mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•11mo 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•11mo 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•11mo 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•11mo 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•11mo 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•11mo 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•11mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•11mo 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•11mo 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•11mo 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•11mo 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•11mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•11mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•11mo 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•11mo 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•11mo 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•11mo 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?