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NewPipe: YouTube client without vertical videos and algorithmic feed

https://newpipe.net/
138•nvader•2h ago•35 comments

uBlock filter list to hide all YouTube Shorts

https://github.com/i5heu/ublock-hide-yt-shorts/
674•i5heu•10h ago•220 comments

News publishers limit Internet Archive access due to AI scraping concerns

https://www.niemanlab.org/2026/01/news-publishers-limit-internet-archive-access-due-to-ai-scrapin...
436•ninjagoo•9h ago•283 comments

I love the work of the ArchWiki maintainers

https://k7r.eu/i-love-the-work-of-the-archwiki-maintainers/
82•panic•2h ago•9 comments

IBM tripling entry-level jobs after finding the limits of AI adoption

https://fortune.com/2026/02/13/tech-giant-ibm-tripling-gen-z-entry-level-hiring-according-to-chro...
300•WhatsTheBigIdea•1d ago•166 comments

My smart sleep mask broadcasts users' brainwaves to an open MQTT broker

https://aimilios.bearblog.dev/reverse-engineering-sleep-mask/
357•minimalthinker•12h ago•179 comments

Ooh.directory: a place to find good blogs that interest you

https://ooh.directory/
457•hisamafahri•14h ago•120 comments

Zvec: A lightweight, fast, in-process vector database

https://github.com/alibaba/zvec
86•dvrp•1d ago•16 comments

Instagram's URL Blackhole

https://medium.com/@shredlife/instagrams-url-blackhole-c1733e081664
106•tkp-415•1d ago•13 comments

5,300-year-old 'bow drill' rewrites story of ancient Egyptian tools

https://www.ncl.ac.uk/press/articles/latest/2026/02/ancientegyptiandrillbit/
83•geox•3d ago•5 comments

Can my SPARC server host a website?

https://rup12.net/posts/can-my-sparc-server-host-my-website/
38•e145bc455f1•4d ago•29 comments

Flood Fill vs. The Magic Circle

https://www.robinsloan.com/winter-garden/magic-circle/
45•tobr•3d ago•16 comments

Show HN: Off Grid – Run AI text, image gen, vision offline on your phone

https://github.com/alichherawalla/off-grid-mobile
54•ali_chherawalla•5h ago•22 comments

Amsterdam Compiler Kit

https://github.com/davidgiven/ack
102•andsoitis•11h ago•27 comments

Connes Embedding Problem

https://en.wikipedia.org/wiki/Connes_embedding_problem
6•jerlendds•2d ago•1 comments

Ask HN: How to get started with robotics as a hobbyist?

179•StefanBatory•6d ago•79 comments

The consequences of task switching in supervisory programming

https://martinfowler.com/fragments/2026-02-13.html
49•bigwheels•1d ago•25 comments

The Perfect Device

https://sometimes.digital/posts/the-perfect-device/
13•surprisetalk•3d ago•1 comments

Discord: A case study in performance optimization

https://newsletter.fullstack.zip/p/discord-a-case-study-in-performance
60•tylerdane•1d ago•34 comments

Show HN: Sameshi – a ~1200 Elo chess engine that fits within 2KB

https://github.com/datavorous/sameshi
202•datavorous_•14h ago•60 comments

Show HN: MOL – A programming language where pipelines trace themselves

https://github.com/crux-ecosystem/mol-lang
27•MouneshK•3d ago•9 comments

Colored Petri Nets, LLMs, and distributed applications

https://blog.sao.dev/cpns-llms-distributed-apps/
31•stuartaxelowen•6h ago•4 comments

A review of M Disc archival capability with long term testing results (2016)

http://www.microscopy-uk.org.uk/mag/artsep16/mol-mdisc-review.html
69•1970-01-01•11h ago•84 comments

Unicorn Jelly

https://unicornjelly.com/
47•avaer•14h ago•10 comments

Launching Interop 2026

https://hacks.mozilla.org/2026/02/launching-interop-2026/
52•linolevan•1d ago•3 comments

YouTube as Storage

https://github.com/PulseBeat02/yt-media-storage
179•saswatms•18h ago•130 comments

A header-only C vector database library

https://github.com/abdimoallim/vdb
69•abdimoalim•10h ago•25 comments

Descent, ported to the web

https://mrdoob.github.io/three-descent/
174•memalign•8h ago•36 comments

Windows NT/OS2 Design Workbook

https://computernewb.com/~lily/files/Documents/NTDesignWorkbook/
87•markus_zhang•4d ago•32 comments

Vim 9.2

https://www.vim.org/vim-9.2-released.php
359•tapanjk•12h ago•148 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?