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Ggml.ai joins Hugging Face to ensure the long-term progress of Local AI

https://github.com/ggml-org/llama.cpp/discussions/19759
204•lairv•1h ago•38 comments

I found a useful Git one liner buried in leaked CIA developer docs

https://spencer.wtf/2026/02/20/cleaning-up-merged-git-branches-a-one-liner-from-the-cias-leaked-d...
143•spencerldixon•1h ago•79 comments

Show HN: A native macOS client for Hacker News, built with SwiftUI

https://github.com/IronsideXXVI/Hacker-News
62•IronsideXXVI•1h ago•35 comments

Child's Play: Tech's new generation and the end of thinking

https://harpers.org/archive/2026/03/childs-play-sam-kriss-ai-startup-roy-lee/
20•ramimac•53m ago•9 comments

Trump's global tariffs struck down by US Supreme Court

https://www.bbc.com/news/live/c0l9r67drg7t
82•blackguardx•14m ago•30 comments

The path to ubiquitous AI (17k tokens/sec)

https://taalas.com/the-path-to-ubiquitous-ai/
408•sidnarsipur•5h ago•269 comments

Untapped Way to Learn a Codebase: Build a Visualizer

https://jimmyhmiller.com/learn-codebase-visualizer
104•andreabergia•6h ago•19 comments

PayPal discloses data breach that exposed user info for 6 months

https://www.bleepingcomputer.com/news/security/paypal-discloses-data-breach-exposing-users-person...
72•el_duderino•2h ago•13 comments

Gemini 3.1 Pro

https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/
871•MallocVoidstar•1d ago•860 comments

Mothers (YC X26) Is Hiring

https://jobs.ashbyhq.com/9-mothers?utm_source=x8pZ4B3P3Q
1•ukd1•1h ago

Minions – Stripe's Coding Agents Part 2

https://stripe.dev/blog/minions-stripes-one-shot-end-to-end-coding-agents-part-2
76•ludovicianul•4h ago•38 comments

The Rediscovery of 103 Hokusai Lost Sketches (2021)

https://japan-forward.com/eternal-hokusai-the-rediscovery-of-103-hokusai-lost-sketches/
24•debo_•4d ago•2 comments

Raspberry Pi Pico 2 at 873.5MHz with 3.05V Core Abuse

https://learn.pimoroni.com/article/overclocking-the-pico-2
78•Lwrless•6h ago•18 comments

Consistency diffusion language models: Up to 14x faster, no quality loss

https://www.together.ai/blog/consistency-diffusion-language-models
167•zagwdt•11h ago•57 comments

Web Components: The Framework-Free Renaissance

https://www.caimito.net/en/blog/2026/02/17/web-components-the-framework-free-renaissance.html
106•mpweiher•6h ago•68 comments

AI is not a coworker, it's an exoskeleton

https://www.kasava.dev/blog/ai-as-exoskeleton
373•benbeingbin•19h ago•398 comments

Nvidia and OpenAI abandon unfinished $100B deal in favour of $30B investment

https://www.ft.com/content/dea24046-0a73-40b2-8246-5ac7b7a54323
205•zerosizedweasle•3h ago•168 comments

Infrastructure decisions I endorse or regret after 4 years at a startup (2024)

https://cep.dev/posts/every-infrastructure-decision-i-endorse-or-regret-after-4-years-running-inf...
371•Meetvelde•3d ago•162 comments

Reading the undocumented MEMS accelerometer on Apple Silicon MacBooks via iokit

https://github.com/olvvier/apple-silicon-accelerometer
97•todsacerdoti•10h ago•52 comments

Notes on Clarifying Man Pages

https://jvns.ca/blog/2026/02/18/man-pages/
35•surprisetalk•1d ago•19 comments

The Popper Principle

https://theamericanscholar.org/the-popper-principle/
3•lermontov•1d ago•0 comments

I tried building my startup entirely on European infrastructure

https://www.coinerella.com/made-in-eu-it-was-harder-than-i-thought/
537•willy__•6h ago•290 comments

Show HN: Micasa – track your house from the terminal

https://micasa.dev
597•cpcloud•23h ago•190 comments

FreeCAD

https://www.freecad.org/index.php
287•doener•3d ago•110 comments

US plans online portal to bypass content bans in Europe and elsewhere

https://www.reuters.com/world/us-plans-online-portal-bypass-content-bans-europe-elsewhere-2026-02...
397•c420•1d ago•754 comments

Silicon Valley engineers were indicted for allegedly sending secrets to Iran

https://www.cnbc.com/2026/02/20/three-engineers-charged-stealing-google-trade-secrets-data-iran-s...
69•giuliomagnifico•4h ago•33 comments

Defer available in gcc and clang

https://gustedt.wordpress.com/2026/02/15/defer-available-in-gcc-and-clang/
227•r4um•4d ago•191 comments

A beginner's guide to split keyboards

https://www.justinmklam.com/posts/2026/02/beginners-guide-split-keyboards/
193•thehaikuza•4d ago•201 comments

Fast KV Compaction via Attention Matching

https://arxiv.org/abs/2602.16284
53•cbracketdash•10h ago•10 comments

An ARM Homelab Server, or a Minisforum MS-R1 Review

https://sour.coffee/2026/02/20/an-arm-homelab-server-or-a-minisforum-ms-r1-review/
99•neelc•14h ago•80 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?