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Don't Make Me Talk to Your Chatbot

https://raymyers.org/post/dont-make-me-talk-to-your-chatbot/
163•pkilgore•2h ago•106 comments

Talos: Hardware accelerator for deep convolutional neural networks

https://talos.wtf/
31•llamatheollama•1h ago•6 comments

MacBook Pro with new M5 Pro and M5 Max

https://www.apple.com/newsroom/2026/03/apple-introduces-macbook-pro-with-all-new-m5-pro-and-m5-max/
627•scrlk•10h ago•612 comments

GPT‑5.3 Instant

https://openai.com/index/gpt-5-3-instant/
270•meetpateltech•6h ago•193 comments

Intel's make-or-break 18A process node debuts for data center with 288-core Xeon

https://www.tomshardware.com/pc-components/cpus/intels-make-or-break-18a-process-node-debuts-for-...
230•vanburen•5h ago•185 comments

Textadept

https://orbitalquark.github.io/textadept/
47•giancarlostoro•2d ago•8 comments

Claude's Cycles [pdf]

https://www-cs-faculty.stanford.edu/~knuth/papers/claude-cycles.pdf
423•fs123•13h ago•198 comments

Voxile: A ray-traced game made in its own engine and programming language

https://elbowgreasegames.substack.com/p/voxray-games-pushes-major-update
75•spacemarine1•3h ago•11 comments

Helsinki just went a full year without a single traffic death

https://www.politico.eu/article/helsinki-no-traffic-death-roads-eu-accident-finland-driving-trans...
81•mooreds•1h ago•39 comments

An Interactive Intro to CRDTs (2023)

https://jakelazaroff.com/words/an-interactive-intro-to-crdts/
87•evakhoury•5h ago•15 comments

Lenovo's New ThinkPads Score 10/10 for Repairability

https://www.ifixit.com/News/115827/new-thinkpads-score-perfect-10-repairability
38•wrxd•1h ago•6 comments

What's in a Name?..

https://sailsandcommas.com/2014/02/03/whats-in-a-name/
6•Curiositry•2d ago•0 comments

The Xkcd thing, now interactive

https://editor.p5js.org/isohedral/full/vJa5RiZWs
1116•memalign•13h ago•148 comments

We've freed Cookie's Bustle from copyright hell

https://gamehistory.org/cookies-bustle/
77•sb057•4h ago•9 comments

When AI writes the software, who verifies it?

https://leodemoura.github.io/blog/2026/02/28/when-ai-writes-the-worlds-software.html
117•todsacerdoti•7h ago•117 comments

Don't become an engineering manager

https://newsletter.manager.dev/p/dont-become-an-engineering-manager
289•flail•10h ago•205 comments

Launch HN: Cekura (YC F24) – Testing and monitoring for voice and chat AI agents

70•atarus•10h ago•19 comments

Physics Girl: Super-Kamiokande – Imaging the sun by detecting neutrinos [video]

https://www.youtube.com/watch?v=B3m3AMRlYfc
407•pcdavid•9h ago•63 comments

TorchLean: Formalizing Neural Networks in Lean

https://leandojo.org/torchlean.html
70•matt_d•2d ago•9 comments

Possible US Government iPhone-Hacking Toolkit in foreign spy and criminal hands

https://www.wired.com/story/coruna-iphone-hacking-toolkit-us-government/
165•alwillis•4h ago•50 comments

TV's TV (1987) & TV Games Encyclopedia (1988)

https://blog.gingerbeardman.com/2026/03/01/tvs-tv-1987-and-tv-games-encyclopedia-1988/
10•msephton•2d ago•0 comments

Disable Your SSH access accidentally with scp

https://sny.sh/hypha/blog/scp
93•zdw•3d ago•41 comments

I'm reluctant to verify my identity or age for any online services

https://neilzone.co.uk/2026/03/im-struggling-to-think-of-any-online-services-for-which-id-be-will...
865•speckx•10h ago•531 comments

MacBook Air with M5

https://www.apple.com/newsroom/2026/03/apple-introduces-the-new-macbook-air-with-m5/
354•Garbage•10h ago•409 comments

Cancel ChatGPT AI boycott surges after OpenAI pentagon military deal

https://www.euronews.com/next/2026/03/02/cancel-chatgpt-ai-boycott-surges-after-openai-pentagon-m...
8•nothrowaways•23m ago•0 comments

I'm losing the SEO battle for my own open source project

https://twitter.com/Gavriel_Cohen/status/2028821432759717930
430•devinitely•10h ago•221 comments

The Two Kinds of Error

https://evanhahn.com/the-two-kinds-of-error/
30•zdw•2d ago•18 comments

GitHub Is Having Issues

https://www.githubstatus.com/incidents/n07yy1bk6kc4
199•Simpliplant•5h ago•137 comments

Meta’s AI smart glasses and data privacy concerns

https://www.svd.se/a/K8nrV4/metas-ai-smart-glasses-and-data-privacy-concerns-workers-say-we-see-e...
1363•sandbach•1d ago•768 comments

Apple Studio Display and Studio Display XDR

https://www.apple.com/newsroom/2026/03/apple-unveils-new-studio-display-and-all-new-studio-displa...
207•victorbjorklund•10h ago•243 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•9mo 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•9mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•9mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•9mo 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•9mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•9mo 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•9mo 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•9mo 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•9mo 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•9mo 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•9mo 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•9mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•9mo 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•9mo 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•9mo 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•9mo 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•9mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•9mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•9mo 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•9mo 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•9mo 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•9mo 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?