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Live: Artemis II Launch Day Updates

https://www.nasa.gov/blogs/missions/2026/04/01/live-artemis-ii-launch-day-updates/
893•apitman•15h ago•765 comments

Email obfuscation: What works in 2026?

https://spencermortensen.com/articles/email-obfuscation/
84•jaden•4h ago•18 comments

Steam on Linux Use Skyrocketed Above 5% in March

https://www.phoronix.com/news/Steam-On-Linux-Tops-5p
298•hkmaxpro•5h ago•118 comments

Quantum computing bombshells that are not April Fools

https://scottaaronson.blog/?p=9665
147•Strilanc•8h ago•46 comments

A new C++ back end for ocamlc

https://github.com/ocaml/ocaml/pull/14701
170•glittershark•8h ago•14 comments

EmDash – A spiritual successor to WordPress that solves plugin security

https://blog.cloudflare.com/emdash-wordpress/
554•elithrar•16h ago•399 comments

Mercor says it was hit by cyberattack tied to compromise LiteLLM

https://techcrunch.com/2026/03/31/mercor-says-it-was-hit-by-cyberattack-tied-to-compromise-of-ope...
29•jackson-mcd•1d ago•7 comments

Telli (YC F24) is hiring engineers, designers, and more [on-site, Berlin]

http://hi.telli.com/join-us
1•sebselassie•1h ago

DRAM pricing is killing the hobbyist SBC market

https://www.jeffgeerling.com/blog/2026/dram-pricing-is-killing-the-hobbyist-sbc-market/
457•ingve•10h ago•378 comments

Subscription bombing and how to mitigate it

https://bytemash.net/posts/subscription-bombing-your-signup-form-is-a-weapon/
128•homelessdino•4h ago•94 comments

AI Perfected Chess. Humans Made It Unpredictable Again

https://www.bloomberg.com/news/articles/2026-03-27/ai-changed-chess-grandmasters-now-win-with-unp...
29•GMoromisato•4d ago•16 comments

Fast and Gorgeous Erosion Filter

https://blog.runevision.com/2026/03/fast-and-gorgeous-erosion-filter.html
148•runevision•1d ago•14 comments

Show HN: NASA Artemis II Mission Timeline Tracker

https://www.sunnywingsvirtual.com/artemis2/timeline.html
43•AustinDev•4h ago•6 comments

Show HN: Git bayesect – Bayesian Git bisection for non-deterministic bugs

https://github.com/hauntsaninja/git_bayesect
269•hauntsaninja•4d ago•40 comments

AI for American-produced cement and concrete

https://engineering.fb.com/2026/03/30/data-center-engineering/ai-for-american-produced-cement-and...
189•latchkey•15h ago•110 comments

What Gödel Discovered (2020)

https://stopa.io/post/269
41•qnleigh•2d ago•7 comments

The future of code search is not regex – 100x faster than ripgrep

https://fff.dmtrkovalenko.dev/
42•neogoose•4h ago•18 comments

The Claude Code Leak

https://build.ms/2026/4/1/the-claude-code-leak/
136•mergesort•6h ago•110 comments

Ask HN: Who is hiring? (April 2026)

234•whoishiring•17h ago•196 comments

Signing data structures the wrong way

https://blog.foks.pub/posts/domain-separation-in-idl/
100•malgorithms•12h ago•44 comments

Weather.com/Retro

https://weather.com/retro/
165•typeofhuman•6h ago•26 comments

Show HN: Dull – Instagram Without Reels, YouTube Without Shorts (iOS)

https://getdull.app
78•kasparnoor•11h ago•62 comments

The Windows equivalents of the most used Linux commands

http://techkettle.blogspot.com/2026/04/the-windows-equivalents-of-most-used.html
54•elsadek•10h ago•38 comments

The revenge of the data scientist

https://hamel.dev/blog/posts/revenge/
138•hamelsmu•4d ago•27 comments

Built a cheap DIY fan controller because my motherboard never had working PWM

https://www.himthe.dev/blog/msi-forgot-my-fans
3•bobsterlobster•2d ago•1 comments

SpaceX files to go public

https://www.nytimes.com/2026/04/01/technology/spacex-ipo-elon-musk.html
303•nutjob2•14h ago•397 comments

Trinity Large Thinking

https://openrouter.ai/arcee-ai/trinity-large-thinking
34•kristianp•6h ago•13 comments

Reverse Engineering Crazy Taxi, Part 2

https://wretched.computer/post/crazytaxi2
30•wgreenberg•2d ago•2 comments

StepFun 3.5 Flash is #1 cost-effective model for OpenClaw tasks (300 battles)

https://app.uniclaw.ai/arena?tab=costEffectiveness&via=hn
158•skysniper•16h ago•72 comments

Set the Line Before It's Crossed

https://nomagicpill.substack.com/p/set-the-line-before-its-crossed
61•surprisetalk•2d ago•31 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•10mo 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•10mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•10mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•10mo 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•10mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo 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•10mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•10mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•10mo 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•10mo 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•10mo 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?