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Pebble Production: February Update

https://repebble.com/blog/february-pebble-production-and-software-updates
81•smig0•2h ago•23 comments

C++26: Std:Is_within_lifetime

https://www.sandordargo.com/blog/2026/02/18/cpp26-std_is_within_lifetime
25•ibobev•1h ago•12 comments

Don't Trust the Salt: AI Summarization, Multilingual Safety, and LLM Guardrails

https://royapakzad.substack.com/p/multilingual-llm-evaluation-to-guardrails
111•benbreen•2d ago•32 comments

Show HN: Mini-Diarium - An encrypted, local, cross-platform journaling app

https://github.com/fjrevoredo/mini-diarium
64•holyknight•2h ago•38 comments

Paged Out Issue #8 [pdf]

https://pagedout.institute/download/PagedOut_008.pdf
67•SteveHawk27•2h ago•9 comments

Bridging Elixir and Python with Oban

https://oban.pro/articles/bridging-with-oban
51•sorentwo•3h ago•7 comments

The Mongol Khans of Medieval France

https://www.historytoday.com/archive/feature/mongol-khans-medieval-france
56•Thevet•2d ago•15 comments

Coding Tricks Used in the C64 Game Seawolves

https://kodiak64.co.uk/blog/seawolves-technical-tricks
27•atan2•2h ago•3 comments

Show HN: A physically-based GPU ray tracer written in Julia

https://makie.org/website/blogposts/raytracing/
60•simondanisch•3h ago•24 comments

Famous Signatures Through History

https://signatory.app/#famous-signatures
11•elliotbnvl•1h ago•8 comments

-fbounds-safety: Enforcing bounds safety for C

https://clang.llvm.org/docs/BoundsSafety.html
23•thefilmore•3d ago•12 comments

Sizing chaos

https://pudding.cool/2026/02/womens-sizing/
694•zdw•17h ago•375 comments

27-year-old Apple iBooks can connect to Wi-Fi and download official updates

https://old.reddit.com/r/MacOS/comments/1r8900z/macos_which_officially_supports_27_year_old/
392•surprisetalk•17h ago•222 comments

Voith Schneider Propeller

https://en.wikipedia.org/wiki/Voith_Schneider_Propeller
41•Luc•3d ago•11 comments

Old School Visual Effects: The Cloud Tank (2010)

http://singlemindedmovieblog.blogspot.com/2010/04/old-school-effects-cloud-tank.html
55•exvi•8h ago•7 comments

15 years of FP64 segmentation, and why the Blackwell Ultra breaks the pattern

https://nicolasdickenmann.com/blog/the-great-fp64-divide.html
153•fp64enjoyer•13h ago•53 comments

Cosmologically Unique IDs

https://jasonfantl.com/posts/Universal-Unique-IDs/
432•jfantl•20h ago•129 comments

Step 3.5 Flash – Open-source foundation model, supports deep reasoning at speed

https://static.stepfun.com/blog/step-3.5-flash/
146•kristianp•12h ago•56 comments

Ask HN: How do you employ LLMs for UI development?

21•jensmtg•47m ago•16 comments

DOGE Track

https://dogetrack.info/
133•donohoe•2h ago•48 comments

Tailscale Peer Relays is now generally available

https://tailscale.com/blog/peer-relays-ga
437•sz4kerto•22h ago•214 comments

Anthropic officially bans using subscription auth for third party use

https://code.claude.com/docs/en/legal-and-compliance
496•theahura•11h ago•594 comments

Lilush – LuaJIT static runtime and shell

https://lilush.link/
33•ksymph•2d ago•7 comments

Zero-day CSS: CVE-2026-2441 exists in the wild

https://chromereleases.googleblog.com/2026/02/stable-channel-update-for-desktop_13.html
355•idoxer•22h ago•201 comments

How to choose between Hindley-Milner and bidirectional typing

https://thunderseethe.dev/posts/how-to-choose-between-hm-and-bidir/
121•thunderseethe•3d ago•39 comments

Virgins, Unicorns and Medieval Literature (2017)

https://www.bowdoin.edu/news/2017/11/virgins-unicorns-and-medieval-literature.html
7•mooreds•2d ago•4 comments

A word processor from 1990s for Atari ST/TOS is still supported by enthusiasts

https://tempus-word.de/en/index
76•muzzy19•2d ago•38 comments

ShannonMax: A Library to Optimize Emacs Keybindings with Information Theory

https://github.com/sstraust/shannonmax
21•sammy0910•3h ago•4 comments

DNS-Persist-01: A New Model for DNS-Based Challenge Validation

https://letsencrypt.org/2026/02/18/dns-persist-01.html
294•todsacerdoti•20h ago•133 comments

Visualizing the ARM64 Instruction Set (2024)

https://zyedidia.github.io/blog/posts/6-arm64/
57•userbinator•3d ago•11 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?