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Agents can now create Cloudflare accounts, buy domains, and deploy

https://blog.cloudflare.com/agents-stripe-projects/
251•rolph•4h ago•140 comments

CARA 2.0 – “I Built a Better Robot Dog”

https://www.aaedmusa.com/projects/cara2
97•hakonjdjohnsen•2d ago•7 comments

StarFighter 16-Inch

https://us.starlabs.systems/pages/starfighter
247•signa11•5h ago•140 comments

.de TLD offline due to DNSSEC?

https://dnssec-analyzer.verisignlabs.com/nic.de
644•warpspin•11h ago•314 comments

.de domains were 'down' for 2 hours

https://status.denic.de/pages/incident/592577eab611ce1e0d00046f/69fa60ef9d12f5057a974f38
9•riedel•1h ago•1 comments

Telus Uses AI to Alter Call-Agent Accents

https://letsdatascience.com/news/telus-uses-ai-to-alter-call-agent-accents-a3868f63
133•debo_•6h ago•85 comments

Accelerating Gemma 4: faster inference with multi-token prediction drafters

https://blog.google/innovation-and-ai/technology/developers-tools/multi-token-prediction-gemma-4/
545•amrrs•15h ago•251 comments

245TB Micron 6600 ION Data Center SSD Now Shipping

https://investors.micron.com/news-releases/news-release-details/industry-leading-245tb-micron-660...
65•neilfrndes•4h ago•45 comments

YouTube, your RSS feeds are broken

https://openrss.org/blog/youtube-your-feeds-are-broken
99•veeti•6h ago•34 comments

Write some software, give it away for free

https://nonogra.ph/write-some-software-give-it-away-for-free-05-05-2026
251•nohell•10h ago•164 comments

Computer Use is 45x more expensive than structured APIs

https://reflex.dev/blog/computer-use-is-45x-more-expensive-than-structured-apis/
383•palashawas•15h ago•221 comments

Three Inverse Laws of AI

https://susam.net/inverse-laws-of-robotics.html
430•blenderob•16h ago•292 comments

Ombudsman column: The Pentagon is trying to silence me

https://www.stripes.com/opinion/2026-04-23/stripes-former-ombudsman-pentagon-trying-to-silence-21...
193•petethomas•4h ago•32 comments

EEVblog: The 555 Timer is 55 years old [video]

https://www.youtube.com/watch?v=6JhK8iCQuqI
277•brudgers•16h ago•68 comments

Why most product tours get skipped

https://productonboarding.com/articles/why-product-tours-get-skipped
141•pancomplex•10h ago•110 comments

Make some art with your phone sensors

https://tautme.github.io/phone-sensors/sensor-etch.html
31•adm4•2d ago•6 comments

Wiki Builder: Skill to Build LLM Knowledge Bases

https://academy.dair.ai/blog/wiki-builder-claude-code-plugin
51•omarsar•2d ago•6 comments

Google Chrome silently installs a 4 GB AI model on your device without consent

https://www.thatprivacyguy.com/blog/chrome-silent-nano-install/
1411•john-doe•1d ago•922 comments

Reverse-engineering the 1998 Ultima Online demo server

https://draxinar.github.io/articles/2026-05-01-uodemo-reverse-engineering.html
7•notsentient•1h ago•0 comments

Today I've made the difficult decision to reduce the size of Coinbase by ~14%

https://twitter.com/brian_armstrong/status/2051616759145185723
352•adrianmsmith•19h ago•535 comments

Knitting Bullshit

https://katedaviesdesigns.com/2026/04/29/knitting-bullshit/
8•ColinEberhardt•2h ago•0 comments

Show HN: Airbyte Agents – context for agents across multiple data sources

117•mtricot•16h ago•29 comments

I'm scared about biological computing

https://kuber.studio/blog/Reflections/I%27m-Scared-About-Biological-Computing
202•kuberwastaken•15h ago•166 comments

Show HN: Explore color palettes inspired by 3000 master painter artworks

https://paletteinspiration.com/
157•ouli•13h ago•59 comments

Agents for financial services and insurance

https://www.anthropic.com/news/finance-agents
235•louiereederson•16h ago•169 comments

When everyone has AI and the company still learns nothing

https://www.robert-glaser.de/when-everyone-has-ai-and-the-company-still-learns-nothing/
355•youngbrioche•22h ago•234 comments

Should I run plain Docker Compose in production in 2026?

https://distr.sh/blog/running-docker-in-production/
396•pmig•5d ago•275 comments

How to organize 3 acquired companies into one coherent website

https://littlelanguagemodels.com/how-to-structure-your-sites-after-a-big-acquisition/
5•mooreds•2d ago•0 comments

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

https://arxiv.org/abs/2604.26752
140•gmays•14h ago•28 comments

I completed 100 Days of Java over 5 years and mapped the journey as a graph

https://mohibulsblog.netlify.app/java/100daysofjava/graph/
53•celurian92•2d ago•23 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?