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Old and new apps, via modern coding agents by Terry Tao

https://terrytao.wordpress.com/2026/07/11/old-and-new-apps-via-modern-coding-agents/
251•subset•5h ago•66 comments

How to Read More Books

https://scotto.me/blog/2026-07-12-how-to-read-more-books/
31•silcoon•50m ago•6 comments

AI Boosts Research Careers but Flattens Scientific Discovery

https://spectrum.ieee.org/ai-science-research-flattens-discovery
81•zaikunzhang•3h ago•61 comments

Understanding the Odin Programming Language

https://odinbook.com/
84•AlexeyBrin•4h ago•24 comments

Don't You Mean Extinct?

https://fabiensanglard.net/extinct/index.html
18•zdw•1h ago•5 comments

Ghostel.el: Terminal emulator powered by libghostty

https://dakra.github.io/ghostel/
136•signa11•7h ago•20 comments

A no-brainer for protecting your brain

https://www.economist.com/leaders/2026/07/09/a-no-brainer-for-protecting-your-brain
18•saikatsg•1h ago•5 comments

Unauthenticated RCE in Motorola's MR2600 Router

https://mrbruh.com/motorola/
54•MrBruh•4h ago•16 comments

Gina Gallery of International Naive Art

https://www.ginagallery.com/
22•o4c•3h ago•10 comments

Vint Cerf, a “father of the Internet”, is retiring

https://techcrunch.com/2026/06/30/the-father-of-the-internet-is-finally-retiring/
221•compiler-guy•2d ago•127 comments

The power of collaboration: How we can reduce traffic congestion

https://research.google/blog/the-power-of-collaboration-how-we-can-reduce-traffic-congestion/
8•raahelb•1h ago•4 comments

Why study Diophantine equations?

https://hidden-phenomena.com/articles/modular
5•mb1699•44m ago•1 comments

Satteri: A Markdown pipeline forged in Rust for the JavaScript world

https://satteri.bruits.org/
25•nateb2022•4d ago•5 comments

Lessons from the Vasa Shipwreck

https://www.ft.com/content/200a6c44-9b66-4af3-82eb-98acb53898e4
16•bookofjoe•3d ago•9 comments

Ditching Zotero for a Text File

https://atthis.link/blog/2026/57207.html
39•speckx•5d ago•27 comments

Show HN: Mindwalk – Replay coding-agent sessions on a 3D map of your codebase

https://github.com/cosmtrek/mindwalk
130•cosmtrek•10h ago•54 comments

Mesh LLM: distributed AI computing on iroh

https://www.iroh.computer/blog/mesh-llm
310•tionis•17h ago•72 comments

Protobuf-py: Protobuf for Python, without compromises

https://buf.build/blog/protobuf-py
110•ming13•4d ago•29 comments

Show HN: Skillscript – A declarative, sandboxed language for tool orchestration

https://github.com/sshwarts/skillscript
6•sshwarts•3h ago•6 comments

Nvidia, CoreWeave, and Nebius: Inside the Circular Financing of the GPU Boom

https://io-fund.com/ai-stocks/nvidia-coreweave-nebius-circular-financing-gpu-boom
338•adletbalzhanov•23h ago•144 comments

Xbox 'OG' Adventures

https://mamoniem.com/xbox-og-adventures/
34•davikr•5d ago•5 comments

An agent in 100 lines of Lisp

https://thebeach.dev/posts/lisp-agent/
210•jamiebeach•4d ago•61 comments

Autoresearch, Claude and Constrained Optimization

https://www.elliotcsmith.com/autoresearch-claude-and-constrained-optimization/
4•gmays•2h ago•0 comments

RISCBoy is an open-source portable games console, designed from scratch

https://github.com/Wren6991/RISCBoy
190•mariuz•18h ago•26 comments

Handsum: An LQIP Image File Format

https://nigeltao.github.io/blog/2026/handsum.html
38•dmit•4d ago•5 comments

Show HN: Ant – A JavaScript runtime and ecosystem

https://antjs.org
310•theMackabu•20h ago•142 comments

I Did Not Kill Stanley Lieber: How to Draw (With 9front)

https://triapul.cz/automa/i_did_not_kill_stanley_lieber
98•c-c-c-c-c•3d ago•37 comments

TK, or the secret to effortless writing (2024)

https://atthis.link/blog/2024/49629.html
5•Tomte•39m ago•0 comments

Relm – local LLMs as base-R objects, with interpretability

https://github.com/Vadale/R-ebirth
5•grauk•3d ago•0 comments

EF Core 11 makes your split queries faster

https://steven-giesel.com/blogPost/d4401fd0-805a-4703-9d9e-5fe3b57c25ea
66•rellem•1w ago•34 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?