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Networking and the Internet, from First Principles

https://fazamhd.com/mental-models/networking/
67•faza•1h ago•29 comments

Your code is fast – if you're lucky

https://tiki.li/blog/lucky_code.html
47•chrka•2h ago•9 comments

Einstein's relativity rules chemical bonds in heavy elements, new research shows

https://www.brown.edu/news/2026-07-09/chemical-bonds-relativity
301•hhs•15h ago•120 comments

Semantic/Hybrid Search in the Browser

https://bart.degoe.de/semantic-search-in-your-browser/
4•bartdegoede•40m ago•0 comments

QuadRF can spot drones and see WiFi through my wall

https://www.jeffgeerling.com/blog/2026/quadrf-can-spot-drones-and-see-wifi-through-my-wall/
637•speckx•21h ago•209 comments

Google Search lets creators know more about their reach

https://www.theverge.com/tech/961955/google-search-console-reach-platform-properties
40•herbertl•3d ago•21 comments

Otary – Image and Geometry Python Library Now Has Tutorials

https://alexandrepoupeau.com/otary/learn/
48•poupeaua•3d ago•1 comments

Apple sues OpenAI, accuses ex-employees of stealing trade secrets

https://9to5mac.com/2026/07/10/apple-sues-openai-trade-secret-theft/
1241•stock_toaster•16h ago•661 comments

FCC Approves Test of Space Mirror to Light Night Sky Despite Outcry

https://www.nytimes.com/2026/07/10/climate/fcc-space-mirror.html
25•reaperducer•1h ago•13 comments

An update on residential proxies and the scraper situation

https://lwn.net/SubscriberLink/1080822/990a8a5e2d379085/
243•chmaynard•18h ago•244 comments

An iroh powered smart fan

https://www.iroh.computer/blog/an-iroh-powered-smart-fan
131•surprisetalk•4d ago•35 comments

The mask that compiles to nothing: how HotSpots JIT learned to reason about bits

https://questdb.com/blog/jvm-jit-known-bits/
43•rowbin•5d ago•0 comments

SpaceX wants to launch 100k more Starlink satellites for 100x the bandwidth

https://www.zdnet.com/home-and-office/networking/spacex-wants-to-launch-100000-more-starlink-sate...
225•CrankyBear•19h ago•737 comments

Good Tools Are Invisible

https://www.gingerbill.org/article/2026/07/10/good-tools-are-invisible/
487•theanonymousone•1d ago•220 comments

The vintage beauty of Soviet control rooms (2018)

https://designyoutrust.com/2018/01/vintage-beauty-soviet-control-rooms/
149•mvdtnz•8h ago•49 comments

AI 2040: Plan A

https://ai-2040.com/
311•kschaul•1d ago•336 comments

Late Bronze Age Collapse

https://acoup.blog/2026/01/30/collections-the-late-bronze-age-collapse-a-very-brief-introduction/
387•dmonay•1d ago•267 comments

The tech of 'Terminator 2' – an oral history (2017)

https://vfxblog.com/2017/08/23/the-tech-of-terminator-2-an-oral-history/
233•markus_zhang•20h ago•80 comments

Silent speech with ultrasound

https://alephneuro.com/blog/silent-speech
72•chrwn•3d ago•15 comments

Combustion engine web-based simulator

https://combustionlab.net
200•mytuny•5d ago•70 comments

After 7 years in production, Scarf has reluctantly moved away from Haskell

https://avi.press/posts/2026-07-10-after-7-years-in-production-scarf-has-reluctantly-moved-away-f...
174•aviaviavi•1d ago•211 comments

Alternate clock designs and time systems

https://serialc.github.io/altClocks/
170•ethanpil•4d ago•92 comments

Inference Optimization for MiMo v2.5: Pushing Hybrid SWA Efficiency to the Limit

https://mimo.xiaomi.com/blog/mimo-v2-5-inference
94•theanonymousone•4d ago•37 comments

Snails' teeth beats spider silk as nature's strongest material (2015)

https://www.smithsonianmag.com/smart-news/spider-silk-loses-top-spot-natures-strongest-material-s...
208•simonebrunozzi•21h ago•156 comments

GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture [pdf]

https://cdn.openai.com/pdf/04d1d1e4-bc75-476a-97cf-49055cd98d31/cdc_proof.pdf
485•scrlk•19h ago•394 comments

Show HN: Getting GLM 5.2 running on my slow computer

https://github.com/JustVugg/colibri
871•vforno•2d ago•216 comments

New York City to ban deceptive subscription practices

https://www.theguardian.com/us-news/2026/jul/10/new-york-city-deceptive-subscriptions-ban
567•randycupertino•19h ago•277 comments

Show HN: Sdlc factory built on pi.dev:intent->DDD->architecture->tested code

https://github.com/arman-jalili/guardian-framework
3•arman-w-jalili•4d ago•0 comments

A love letter to flashcards

https://lesleylai.info/en/flashcards/
165•surprisetalk•22h ago•98 comments

Computation as a universal and fundamental concept

https://ergo.org/courses/computation-as-a-universal-and-fundamental-concept
145•simonpure•22h ago•114 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•1y 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•1y ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•1y ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•1y 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•1y ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•1y 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•1y 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•1y 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•1y 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•1y 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•1y 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•1y ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•1y 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•1y 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•1y 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•1y 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•1y ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•1y ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•1y 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•1y 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•1y 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•1y 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?