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Zed 1.0

https://zed.dev/blog/zed-1-0
848•salkahfi•3h ago•295 comments

The Abstraction Fallacy: Why AI can simulate but not instantiate consciousness

https://deepmind.google/research/publications/231971/
40•joshus•35m ago•26 comments

We need a federation of forges

https://blog.tangled.org/federation/
372•icy•4h ago•196 comments

FastCGI: 30 years old and still the better protocol for reverse proxies

https://www.agwa.name/blog/post/fastcgi_is_the_better_protocol_for_reverse_proxies
69•agwa•1h ago•9 comments

Online age verification is the hill to die on

https://x.com/GlennMeder/status/2049088498163216560
289•Cider9986•2h ago•178 comments

Soft launch of open-source code platform for government

https://www.nldigitalgovernment.nl/news/soft-launch-for-government-open-source-code-platform/
439•e12e•8h ago•109 comments

Ghostty is leaving GitHub

https://mitchellh.com/writing/ghostty-leaving-github
3217•WadeGrimridge•22h ago•951 comments

Third Editor Fired in Elsevier's Citation Cartel Crackdown

https://www.chrisbrunet.com/p/third-editor-fired-in-elseviers-citation
58•RigbyTaro•2h ago•13 comments

Linux 7.0 Broke PostgreSQL: The Preemption Regression Explained

https://read.thecoder.cafe/p/linux-broke-postgresql
82•0xKelsey•2h ago•27 comments

An open-source stethoscope that costs between $2.5 and $5 to produce

https://github.com/GliaX/Stethoscope
60•0x54MUR41•3h ago•26 comments

Cursor Camp

https://neal.fun/cursor-camp/
51•bpierre•2h ago•8 comments

How to Build the Future: Demis Hassabis [video]

https://www.youtube.com/watch?v=JNyuX1zoOgU
16•sandslash•3h ago•2 comments

Show HN: A new benchmark for testing LLMs for deterministic outputs

https://interfaze.ai/blog/introducing-structured-output-benchmark
21•khurdula•2h ago•5 comments

Mistral Medium 3.5

https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5
219•meetpateltech•2h ago•125 comments

Making AI chatbots friendly leads to mistakes and support of conspiracy theories

https://www.theguardian.com/technology/2026/apr/29/making-ai-chatbots-more-friendly-mistakes-supp...
40•Cynddl•2h ago•23 comments

Letting AI play my game – building an agentic test harness to help play-testing

https://blog.jeffschomay.com/letting-ai-play-my-game
87•jschomay•5h ago•17 comments

GitHub – DOS 1.0: Transcription of Tim Paterson's DOS Printouts

https://github.com/DOS-History/Paterson-Listings
78•s2l•6h ago•4 comments

Stardex Is Hiring a Founding Customer Success Lead

https://www.ycombinator.com/companies/stardex/jobs/6GCK1HC-founding-customer-success-lead
1•sanketc•6h ago

Ramp's Sheets AI Exfiltrates Financials

https://www.promptarmor.com/resources/ramps-sheets-ai-exfiltrates-financials
3•takira•21m ago•0 comments

Improving ICU handovers by learning from Scuderia Ferrari F1 team

https://healthmanagement.org/c/icu/IssueArticle/improving-handovers-by-learning-from-scuderia-fer...
43•embedding-shape•4h ago•43 comments

Maryland becomes first state to ban surveillance pricing in grocery stores

https://www.theguardian.com/technology/2026/apr/29/maryland-grocery-stores-ban-surveillance-pricing
45•01-_-•1h ago•15 comments

Bugs Rust won't catch

https://corrode.dev/blog/bugs-rust-wont-catch/
547•lwhsiao•15h ago•310 comments

Before GitHub

https://lucumr.pocoo.org/2026/4/28/before-github/
613•mlex•20h ago•201 comments

Show HN: Adblock-rust Manager – Firefox extension to enable the Brave ad blocker

https://github.com/electricant/adblock-rust-manager
62•electricant•5h ago•33 comments

Court Rules 2nd Amendment Covers Firearms Parts Good News Those Who Build Guns

https://cowboystatedaily.com/2026/04/28/court-rules-2nd-amendment-covers-firearms-parts-good-news...
59•Bender•1h ago•34 comments

How ChatGPT serves ads

https://www.buchodi.com/how-chatgpt-serves-ads-heres-the-full-attribution-loop/
460•lmbbuchodi•18h ago•317 comments

Laws of UX

https://lawsofux.com/
5•bobbiechen•1h ago•0 comments

Why Software Needs a Third Loop [audio]

https://www.heavybit.com/library/podcasts/third-loop/ep-3-give-it-a-name-why-software-needs-a-thi...
4•mooreds•1h ago•0 comments

Rise of the Forward Deployed Engineer

https://www.hfsresearch.com/research/fde-optional-ai-flywheel-spin/
4•nipponese•1h ago•1 comments

Why AI companies want you to be afraid of them

https://www.bbc.com/future/article/20260428-ai-companies-want-you-to-be-afraid-of-them
232•rolph•2h ago•171 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?