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Nvidia Nemotron 3.5 Lightning and NeMo Switchyard

https://blogs.nvidia.com/blog/nemotron-lightning-switchyard-rtx-dgx/
142•droidjj•3h ago•73 comments

WorldClaw Agentic 3D open-world generation at scale

https://tencent-hunyuan.github.io/Hunyuan3D-WorldClaw/
29•EwanG•1h ago•10 comments

Compression is prediction

https://ngrok.com/blog/compression-is-prediction
178•nikolay•3h ago•75 comments

Mojo 1.0

https://www.modular.com/blog/modular-26-5-mojo-1-0-is-here
243•dayanruben•6h ago•108 comments

Stealing Reasoning Traces from Proprietary LLM APIs

https://stolen-thoughts.com/
444•quantumgarbage•9h ago•185 comments

Making holograms with a pen plotter

https://blog.jordan.matelsky.com/Penplotter-holography/
91•DemiGuru•4h ago•10 comments

Suzanne: AI tool for designing and manufacturing physical products

https://www.suzanne3d.com/
18•Samanthajeanneb•1h ago•9 comments

Show HN: iPhone app takes simultaneous images from 2 lenses, fuses into 1 photo

https://photosynthesis.camera
166•sajomes•3d ago•169 comments

OpenAI’s head of ethics leaves less than a year after joining

https://www.ft.com/content/e49dfb75-f841-4466-a577-f7aaff8779a0
228•ilamont•10h ago•299 comments

RSI Simulator

https://www.paradigm.xyz/writing/rsi-simulator
17•ckraeuter•6h ago•8 comments

How we used to get jobs: A newspaper classifieds story

https://ironicsans.ghost.io/how-we-used-to-get-jobs/
90•speckx•4h ago•73 comments

pg_clickhouse v0.10: Subquery pushdown and 1000x faster TPC-H queries

https://clickhouse.com/blog/pg_clickhouse-whats-new-july-2026
9•saisrirampur•1h ago•0 comments

Grok Bot

https://x.ai/bot
77•rvz•5h ago•76 comments

England set to be one of the first countries to eliminate hepatitis C

https://www.bbc.com/news/articles/c75gk620r22o
469•stevekemp•10h ago•335 comments

Jolt: Clojure compiler implemented with Chez Scheme

https://jolt-lang.github.io
134•mark_l_watson•3d ago•49 comments

Manus will return to operating as an independent company

https://manus.im/blog/a-note-to-our-users
122•thm•8h ago•65 comments

Nvidia's Risky Business

https://stratechery.com/2026/nvidias-risky-business/
272•jonbaer•13h ago•126 comments

Emergent Introspective Awareness in Large Language Models

https://arxiv.org/abs/2601.01828
10•doener•1h ago•2 comments

Show HN: Git-knife – edit commit messages, authors, and dates like a spreadsheet

https://github.com/TheRealYT/git-knife
115•YonathanTesfaye•7h ago•83 comments

OpenSSH 10.5/10.5p1

https://www.openssh.org/releasenotes.html#10.5
84•voxadam•5h ago•30 comments

CSS properties you should know for better text designs

https://master.dev/blog/typographic-css-tricks/
57•ibobev•5h ago•5 comments

London Underground begins scanning passengers' faces

https://www.btp.police.uk/news/btp/news/england/btp-expands-live-facial-recognition-lfr-trial-int...
183•BlueBerry2001•13h ago•212 comments

Apple Silicon and macOS VMs: Faster LLM Inference with llama.cpp

https://github.com/trycua/cua/blob/main/blog/gpu-passthrough-macos-vms.md
272•frabonacci•8h ago•43 comments

A new study of a bot running a store finds it is friendly but not very smart

https://www.nytimes.com/2026/08/04/us/ai-boss-san-francisco-andon-market.html
48•jjwiseman•1w ago•49 comments

The Hat and the Spectre – Recent Groundbreaking Discoveries in Mathematics

https://momath.org/the-hat/
5•vismit2000•3d ago•1 comments

What I learned by putting GitHub Copilot behind a MitM proxy

https://www.lighthousenewsletter.com/p/i-put-github-copilot-behind-a-mitm
143•j0selit0•12h ago•23 comments

Launch HN: Keet (YC S24) – An app to create video courses on anything

https://www.trykeet.com/
39•zackashen•8h ago•42 comments

Show HN: Write.md – A free, open-source, themeable Markdown editor for macOS

https://writemd.app/
68•danielbilekq•9h ago•62 comments

Archive of Animal Photography Reveals 18,000 Species and Counting

https://www.smithsonianmag.com/science-nature/this-amazing-archive-of-animal-photography-reveals-...
30•pseudolus•2d ago•9 comments

OpenAI launches ChatGPT desktop app for Linux

https://techcrunch.com/2026/08/11/openai-launches-chatgpt-desktop-app-for-linux/
27•ashurandi•2h ago•9 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?