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Frontier AI agents violate ethical constraints 30–50% of time, pressured by KPIs

https://arxiv.org/abs/2512.20798
336•tiny-automates•9h ago•221 comments

Discord will require a face scan or ID for full access next month

https://www.theverge.com/tech/875309/discord-age-verification-global-roll-out
1751•x01•21h ago•1703 comments

Rust implementation of Mistral's Voxtral Mini 4B Realtime runs in your browser

https://github.com/TrevorS/voxtral-mini-realtime-rs
280•Curiositry•10h ago•28 comments

Show HN: Pipelock – All-in-one security harness for AI coding agents

https://github.com/luckyPipewrench/pipelock
3•pipejosh•21m ago•0 comments

.Beat Swatch Internet Time

https://beats.wiki/
51•Deprogrammer9•5d ago•37 comments

Pure C, CPU-only inference with Mistral Voxtral Realtime 4B speech to text model

https://github.com/antirez/voxtral.c
192•Curiositry•11h ago•16 comments

Discord Alternatives, Ranked

https://taggart-tech.com/discord-alternatives/
333•pseudalopex•17h ago•191 comments

Why is the sky blue?

https://explainers.blog/posts/why-is-the-sky-blue/
641•udit99•20h ago•225 comments

Converting a $3.88 analog clock from Walmart into a ESP8266-based Wi-Fi clock

https://github.com/jim11662418/ESP8266_WiFi_Analog_Clock
532•tokyobreakfast•19h ago•165 comments

Hard-braking events as indicators of road segment crash risk

https://research.google/blog/hard-braking-events-as-indicators-of-road-segment-crash-risk/
311•aleyan•19h ago•433 comments

LiftKit – UI where "everything derives from the golden ratio"

https://www.chainlift.io/liftkit
209•peter_d_sherman•14h ago•104 comments

Is particle physics dead, dying, or just hard?

https://www.quantamagazine.org/is-particle-physics-dead-dying-or-just-hard-20260126/
143•mellosouls•12h ago•222 comments

Luce: First Electric Ferrari

https://www.ferrari.com/en-US/auto/ferrari-luce
207•kaizenb•17h ago•227 comments

Show HN: Total Recall – write-gated memory for Claude Code

https://github.com/davegoldblatt/total-recall
22•davegoldblatt•4d ago•11 comments

Sandboxels

https://neal.fun/sandboxels/
316•2sf5•20h ago•41 comments

Zulip.com Values

https://zulip.com/values/
103•nothrowaways•11h ago•22 comments

Qwen-Image-2.0: Professional infographics, exquisite photorealism

https://qwen.ai/blog?id=qwen-image-2.0
108•meetpateltech•3h ago•71 comments

Show HN: Elysia JIT "Compiler", why it's one of the fastest JavaScript framework

https://elysiajs.com/internal/jit-compiler
14•saltyaom•2d ago•0 comments

AI doesn’t reduce work, it intensifies it

https://simonwillison.net/2026/Feb/9/ai-intensifies-work/
161•walterbell•7h ago•150 comments

Eight more months of agents

https://crawshaw.io/blog/eight-more-months-of-agents
151•arrowsmith•2d ago•157 comments

MIT Technology Review has confirmed that posts on Moltbook were fake

https://www.technologyreview.com/2026/02/06/1132448/moltbook-was-peak-ai-theater/
121•helloplanets•2d ago•58 comments

Upcoming changes to Let's Encrypt and how they affect XMPP server operators

https://blog.prosody.im/2026-letsencrypt-changes/
131•zaik•15h ago•147 comments

UEFI Bindings for JavaScript

https://codeberg.org/smnx/promethee
232•ananas-dev•22h ago•111 comments

America has a tungsten problem

https://www.noleary.com/blog/posts/1
180•noleary•15h ago•173 comments

Thoughts on Generating C

https://wingolog.org/archives/2026/02/09/six-thoughts-on-generating-c
231•ingve•22h ago•80 comments

Ask HN: What are you working on? (February 2026)

286•david927•1d ago•972 comments

Game Theory Patterns at Work (2016)

https://daeus.blog/2026/01/18/game-theory-patterns-at-work/
96•kurinikku•15h ago•7 comments

Generative Pen-Trained Transformer

https://theodore.net/projects/Polargraph/
52•Twarner•4d ago•1 comments

Corruption Perceptions Index 2025

https://www.transparency.org/en/cpi/2025
22•tosh•1h ago•2 comments

The Abstraction Rises

https://cyber-omelette.com/posts/the-abstraction-rises.html
58•birdculture•2d ago•14 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?