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Defeating a 40-year-old copy protection dongle

https://dmitrybrant.com/2026/02/01/defeating-a-40-year-old-copy-protection-dongle
477•zdw•11h ago•139 comments

Apple's MacBook Pro DFU port documentation is wrong

https://lapcatsoftware.com/articles/2026/2/1.html
77•zdw•5h ago•15 comments

My iPhone 16 Pro Max produces garbage output when running MLX LLMs

https://journal.rafaelcosta.me/my-thousand-dollar-iphone-cant-do-math/
266•rafaelcosta•11h ago•122 comments

Show HN: NanoClaw – “Clawdbot” in 500 lines of TS with Apple container isolation

https://github.com/gavrielc/nanoclaw
336•jimminyx•9h ago•110 comments

Show HN: Wikipedia as a doomscrollable social media feed

https://xikipedia.org
148•rebane2001•8h ago•59 comments

Actors: A Model of Concurrent Computation [pdf] (1985)

https://apps.dtic.mil/sti/tr/pdf/ADA157917.pdf
68•kioku•7h ago•27 comments

Apple I Advertisement (1976)

http://apple1.chez.com/Apple1project/Gallery/Gallery.htm
222•janandonly•15h ago•127 comments

Contracts in Nix

https://sraka.xyz/posts/contracts.html
39•todsacerdoti•1d ago•9 comments

Adventure Game Studio: OSS software for creating adventure games

https://www.adventuregamestudio.co.uk/
308•doener•18h ago•61 comments

Treasures found on HS2 route stored in secret warehouse

https://www.bbc.com/news/articles/c93v21q5xdvo
56•breve•10h ago•20 comments

Leaked Chats Expose the Daily Life of a Scam Compound's Enslaved Workforce

https://www.wired.com/story/the-red-bull-leaks/
82•smurda•3h ago•15 comments

Time Machine-style Backups with rsync (2018)

https://samuelhewitt.com/blog/2018-06-05-time-machine-style-backups-with-rsync
70•accrual•8h ago•31 comments

Ian's Shoelace Site

https://www.fieggen.com/shoelace/
132•righthand•14h ago•21 comments

Building Your Own Efficient uint128 in C++

https://solidean.com/blog/2026/building-your-own-u128/
69•PaulHoule•11h ago•32 comments

Two kinds of AI users are emerging

https://martinalderson.com/posts/two-kinds-of-ai-users-are-emerging/
161•martinald•8h ago•139 comments

Efficient String Compression for Modern Database Systems

https://cedardb.com/blog/string_compression/
115•jandrewrogers•2d ago•29 comments

Rev Up the Viral Factories

https://www.science.org/content/blog-post/rev-viral-factories
5•etiam•2d ago•0 comments

Founding is a snowball

https://blog.bawolf.com/p/founding-is-a-snowball
72•bryantwolf•3d ago•26 comments

MicroPythonOS graphical operating system delivers Android-like user experience

https://www.cnx-software.com/2026/01/29/micropythonos-graphical-operating-system-delivers-android...
208•mikece•3d ago•70 comments

Stop Using Pseudo-Types

https://f2r.github.io/en/stop-using-pseudo-types.html
10•speckx•4d ago•0 comments

Netbird – Open Source Zero Trust Networking

https://netbird.io/
673•l1am0•22h ago•256 comments

FOSDEM 2026 – Open-Source Conference in Brussels – Day#1 Recap

https://gyptazy.com/blog/fosdem-2026-opensource-conference-brussels/
226•yannick2k•22h ago•145 comments

Towards a science of scaling agent systems: When and why agent systems work

https://research.google/blog/towards-a-science-of-scaling-agent-systems-when-and-why-agent-system...
78•gmays•14h ago•27 comments

A Crisis comes to Wordle: Reusing old words

https://forkingmad.blog/wordle-crisis/
88•cyanbane•14h ago•94 comments

Soldering Prototypes with Enamel Magnet Wire (2020)

https://tomverbeure.github.io/2020/02/22/In-The-Lab-Magnet-Wire-Soldering.html
17•hasheddan•2d ago•21 comments

Amiga Unix (Amix)

https://www.amigaunix.com/doku.php/home
121•donatj•21h ago•51 comments

Teaching my neighbor to keep the volume down

https://idiallo.com/blog/teaching-my-neighbor-to-keep-the-volume-down
672•firefoxd•13h ago•314 comments

Building a Telegram Bot with Cloudflare Workers, Durable Objects and Grammy

https://flashblaze.xyz/posts/cloudflare-workers-durable-objects-telegram-bot/
16•flashblaze•5h ago•2 comments

Clearspace (YC W23) Is Hiring an Applied Researcher (ML)

https://www.ycombinator.com/companies/clearspace/jobs/GOWiDwp-research-engineer-at-clearspace
1•anteloper•13h ago

Show HN: ÆTHRA – Writing Music as Code

77•CzaxTanmay•3d ago•18 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•8mo 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•8mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•8mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•8mo 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•8mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•8mo 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•8mo 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•8mo 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•8mo 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•8mo 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•8mo 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•8mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•8mo 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•8mo 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•8mo 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•8mo 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•8mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•8mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•8mo 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•8mo 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•8mo 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•8mo 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?