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Claude Opus 4.7

https://www.anthropic.com/news/claude-opus-4-7
1382•meetpateltech•9h ago•1005 comments

Codex for almost everything

https://openai.com/index/codex-for-almost-everything/
628•mikeevans•6h ago•349 comments

A Better R Programming Experience Thanks to Tree-sitter

https://ropensci.org/blog/2026/04/02/tree-sitter-overview/
57•sebg•2h ago•1 comments

Guy builds AI driven hardware hacker arm from duct tape, old cam and CNC machine

https://github.com/gainsec/autoprober
60•scaredpelican•2h ago•9 comments

Official Clojure Documentary page with Video, Shownotes, and Links

https://clojure.org/about/documentary
77•adityaathalye•4h ago•16 comments

Android CLI: Build Android apps 3x faster using any agent

https://android-developers.googleblog.com/2026/04/build-android-apps-3x-faster-using-any-agent.html
90•ingve•5h ago•24 comments

New unsealed records reveal Amazon's price-fixing tactics, California AG claims

https://www.theguardian.com/us-news/ng-interactive/2026/apr/16/amazon-price-fixing-california-law...
55•kmfrk•1h ago•9 comments

Qwen3.6-35B-A3B: Agentic coding power, now open to all

https://qwen.ai/blog?id=qwen3.6-35b-a3b
865•cmitsakis•10h ago•406 comments

Qwen3.6-35B-A3B on my laptop drew me a better pelican than Claude Opus 4.7

https://simonwillison.net/2026/Apr/16/qwen-beats-opus/
267•simonw•6h ago•61 comments

Cloudflare's AI Platform: an inference layer designed for agents

https://blog.cloudflare.com/ai-platform/
223•nikitoci•10h ago•57 comments

Show HN: Marky – A lightweight Markdown viewer for agentic coding

https://github.com/GRVYDEV/marky
26•GRVYDEV•7h ago•6 comments

Join Akkari's Founding Team (YC P26) as an Engineer

1•michael_moore•3h ago

The future of everything is lies, I guess: Where do we go from here?

https://aphyr.com/posts/420-the-future-of-everything-is-lies-i-guess-where-do-we-go-from-here
478•aphyr•10h ago•512 comments

Launch HN: Kampala (YC W26) – Reverse-Engineer Apps into APIs

https://www.zatanna.ai/kampala
66•alexblackwell_•8h ago•61 comments

IBM AP-101 general-purpose computer [pdf]

https://gandalfddi.z19.web.core.windows.net/Shuttle/IBM%20AP-101S%20General%20Purpose%20Computer%...
15•__patchbit__•3d ago•4 comments

GPT‑Rosalind for life sciences research

https://openai.com/index/introducing-gpt-rosalind/
41•babelfish•4h ago•8 comments

Artifacts: Versioned storage that speaks Git

https://blog.cloudflare.com/artifacts-git-for-agents-beta/
146•jgrahamc•10h ago•15 comments

Python Package Compiler:Package Matlab Programs for Deployment as Python Package

https://www.mathworks.com/help/compiler_sdk/ml_code/pythonpackagecompiler-app.html
6•teleforce•3d ago•0 comments

George Orwell Predicted the Rise of "AI Slop" in Nineteen Eighty-Four (1949)

https://www.openculture.com/2026/04/how-george-orwell-predicted-the-rise-of-ai-slop.html
13•doener•43m ago•3 comments

Playdate’s handheld changed how Duke University teaches game design

https://news.play.date/news/duke-playdate-education/
41•Ivoah•4h ago•18 comments

Circuit Transformations, Loop Fusion, and Inductive Proof

https://natetyoung.github.io/carry_save_fusion/
20•matt_d•3d ago•1 comments

The "Passive Income" trap ate a generation of entrepreneurs

https://www.joanwestenberg.com/the-passive-income-trap-ate-a-generation-of-entrepreneurs/
107•devonnull•3h ago•88 comments

"Wretches, Speak Evil of Me": Goethe and Schiller's Xenions (1896 Edition)

https://publicdomainreview.org/collection/xenions/
3•benbreen•2d ago•0 comments

Show HN: CodeBurn – Analyze Claude Code token usage by task

https://github.com/AgentSeal/codeburn
69•agentseal•3d ago•14 comments

Show HN: MacMind – A transformer neural network in HyperCard on a 1989 Macintosh

https://github.com/SeanFDZ/macmind
110•hammer32•10h ago•31 comments

Codex Hacked a Samsung TV

https://blog.calif.io/p/codex-hacked-a-samsung-tv
201•campuscodi•13h ago•116 comments

AI cybersecurity is not proof of work

https://antirez.com/news/163
193•surprisetalk•13h ago•78 comments

Cloudflare Email Service

https://blog.cloudflare.com/email-for-agents/
397•jilles•10h ago•188 comments

European civil servants are being forced off WhatsApp

https://www.politico.eu/article/european-civil-servants-new-messaging-services/
83•aa_is_op•4h ago•49 comments

Six Characters

https://ajitem.com/blog/iron-core-part-2-six-characters/
85•Airplanepasta•3d ago•13 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•10mo 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•10mo 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•10mo 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?