frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

O(x)Caml in Space

https://gazagnaire.org/blog/2026-05-14-borealis.html
64•yminsky•1h ago•2 comments

Show HN: Find the best local LLM for your hardware, ranked by benchmarks

https://github.com/Andyyyy64/whichllm
138•andyyyy64•3h ago•15 comments

Explore Wikipedia Like a Windows XP Desktop

https://explorer.samismith.com/
198•smusamashah•3h ago•47 comments

Steve Jobs Next Computer: His Forgotten Exile Years

https://spectrum.ieee.org/steve-jobs-next-computer
36•rbanffy•1h ago•25 comments

Welcome to the Strip Mining Era of OSS Security

https://www.metabase.com/blog/strip-mining-era-of-open-source-security
13•salsakran•42m ago•1 comments

Removing the modem and GPS from my 2024 RAV4 hybrid

https://arkadiyt.com/2026/05/13/removing-the-modem-and-gps-from-my-rav4/
920•arkadiyt•19h ago•478 comments

SigNoz (YC W21, open source Datadog) Is hiring for growth and engineering roles

https://signoz.io/careers
1•pranay01•19m ago

UK government replaces Palantir software with internally-built refugee system

https://www.bbc.com/news/articles/c2l2j1lxdk5o
307•cdrnsf•13h ago•105 comments

Show HN: GlycemicGPT – Open-source AI-powered diabetes management

https://github.com/GlycemicGPT/GlycemicGPT
46•jlengelbrecht•7h ago•30 comments

Where's Ed: Anthropic Told Court $5B but Public $19B

https://www.flyingpenguin.com/wheres-ed-anthropic-told-court-5-billion-but-public-19-billion/
23•jorisw•4h ago•19 comments

A few words on DS4

https://antirez.com/news/165
347•caust1c•13h ago•143 comments

Building ML framework with Rust and Category Theory

https://hghalebi.github.io/category_theory_transformer_rs/
52•adamnemecek•19h ago•14 comments

Details of the Daring Airdrop at Tristan Da Cunha

https://www.tristandc.com/government/news-2026-05-11-airdrop.php
174•kspacewalk2•8h ago•55 comments

RTX 5090 and M4 MacBook Air: Can It Game?

https://scottjg.com/posts/2026-05-05-egpu-mac-gaming/
621•allenleee•20h ago•145 comments

First public macOS kernel memory corruption exploit on Apple M5

https://blog.calif.io/p/first-public-kernel-memory-corruption
385•quadrige•17h ago•98 comments

Gyroflow: Video stabilization using gyroscope data

https://github.com/gyroflow/gyroflow
106•nateb2022•2d ago•18 comments

New Nginx Exploit

https://github.com/DepthFirstDisclosures/Nginx-Rift
397•hetsaraiya•19h ago•88 comments

Codex is now in the ChatGPT mobile app

https://openai.com/index/work-with-codex-from-anywhere/
362•mikeevans•16h ago•178 comments

UK sovereign LLM inference

https://relax.ai/docs
85•benjamintnorris•2h ago•84 comments

Mullvad exit IPs are surprisingly identifying

https://tmctmt.com/posts/mullvad-exit-ips-as-a-fingerprinting-vector/
437•RGBCube•9h ago•252 comments

Tesla Wall Connector bootloader bypasses the firmware downgrade ratchet

https://www.synacktiv.com/en/publications/exploiting-the-tesla-wall-connector-from-its-charge-por...
106•p_stuart82•15h ago•48 comments

Solar-based sleep patterns compared to modern norms

https://dylan.gr/1775146616
84•James72689•8h ago•73 comments

Claude for Legal

https://github.com/anthropics/claude-for-legal
112•Einenlum•15h ago•102 comments

HDD Firmware Hacking

https://icode4.coffee/?p=1465
197•jsploit•20h ago•26 comments

RISC-V Router

https://router.start9.com/
127•janandonly•16h ago•75 comments

Access to frontier AI will soon be limited by economic and security constraints

https://writing.antonleicht.me/p/cut-off
177•thoughtpeddler•11h ago•169 comments

Porting 3D Movie Maker to Linux

https://benstoneonline.com/posts/porting-3d-movie-maker-to-linux/
139•speckx•3d ago•31 comments

What's in a GGUF, besides the weights – and what's still missing?

https://nobodywho.ooo/posts/whats-in-a-gguf/
158•bashbjorn•18h ago•46 comments

New arXiv policy: 1-year ban for hallucinated references

https://twitter.com/tdietterich/status/2055000956144935055
543•gjuggler•15h ago•191 comments

Overseas fakers using AI videos to push a narrative of UK decline, BBC finds

https://www.bbc.co.uk/news/articles/ckgpyn30dp3o
38•dijksterhuis•2h ago•31 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?