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A “frozen” dictionary for Python

https://lwn.net/SubscriberLink/1047238/25c270b077849dc0/
79•jwilk•3h ago•46 comments

Size of Life

https://neal.fun/size-of-life/
2175•eatonphil•21h ago•241 comments

Meta shuts down global accounts linked to abortion advice and queer content

https://www.theguardian.com/global-development/2025/dec/11/meta-shuts-down-global-accounts-linked...
180•ta988•2h ago•102 comments

Show HN: Local Privacy Firewall-blocks PII and secrets before ChatGPT sees them

https://github.com/privacyshield-ai/privacy-firewall
18•arnabkarsarkar•1d ago•1 comments

The Cost of a Closure in C

https://thephd.dev/the-cost-of-a-closure-in-c-c2y
102•ingve•6h ago•33 comments

Getting a Gemini API key is an exercise in frustration

https://ankursethi.com/blog/gemini-api-key-frustration/
617•speckx•17h ago•247 comments

Patterns.dev

https://www.patterns.dev/
362•handfuloflight•12h ago•83 comments

Australia begins enforcing world-first teen social media ban

https://www.reuters.com/legal/litigation/australia-social-media-ban-takes-effect-world-first-2025...
832•chirau•1d ago•1268 comments

Why Startups Die

https://www.techfounderstack.com/p/why-startups-die
54•makle•3d ago•35 comments

Show HN: oeis-tui – A TUI to search OEIS integer sequences in the terminal

https://github.com/hako/oeis-tui
9•wesleyhill•1w ago•0 comments

Helldivers 2 on-disk size 85% reduction

https://store.steampowered.com/news/app/553850/view/491583942944621371
74•SergeAx•1w ago•53 comments

How the Brain Parses Language

https://www.quantamagazine.org/the-polyglot-neuroscientist-resolving-how-the-brain-parses-languag...
47•mylifeandtimes•3d ago•14 comments

Auto-grading decade-old Hacker News discussions with hindsight

https://karpathy.bearblog.dev/auto-grade-hn/
474•__rito__•20h ago•213 comments

Booting Linux in QEMU and Writing PID 1 in Go to Illustrate Kernel as Program

https://serversfor.dev/linux-inside-out/the-linux-kernel-is-just-a-program/
159•birdculture•6d ago•40 comments

South Korea – A Cautionary Tale for the Rest of Humanity

https://worksinprogress.co/issue/two-is-already-too-many/
6•barry-cotter•1h ago•4 comments

How Google Maps allocates survival across London's restaurants

https://laurenleek.substack.com/p/how-google-maps-quietly-allocates
307•justincormack•2d ago•151 comments

Python Workers redux: fast cold starts, packages, and a uv-first workflow

https://blog.cloudflare.com/python-workers-advancements/
77•dom96•2d ago•28 comments

Go's escape analysis and why my function return worked

https://bonniesimon.in/blog/go-escape-analysis
23•bonniesimon•6d ago•11 comments

VCMI: An open-source engine for Heroes III

https://vcmi.eu/
130•eamag•4d ago•15 comments

How can I read the standard output of an already-running process?

https://devblogs.microsoft.com/oldnewthing/20251204-00/?p=111841
4•ibobev•5d ago•0 comments

Rubio stages font coup: Times New Roman ousts Calibri

https://www.reuters.com/world/us/rubio-stages-font-coup-times-new-roman-ousts-calibri-2025-12-09/
317•italophil•1d ago•527 comments

Show HN: Wirebrowser – A JavaScript debugger with breakpoint-driven heap search

https://github.com/fcavallarin/wirebrowser
43•fcavallarin•23h ago•10 comments

Super Mario 64 for the PS1

https://github.com/malucard/sm64-psx
249•LaserDiscMan•18h ago•97 comments

Flow Where You Want – Guidance for Flow Models

https://drscotthawley.github.io/blog/posts/FlowWhereYouWant.html
27•rundigen12•5d ago•1 comments

Qwen3-Omni-Flash-2025-12-01:a next-generation native multimodal large model

https://qwen.ai/blog?id=qwen3-omni-flash-20251201
280•pretext•21h ago•95 comments

Show HN: Automated license plate reader coverage in the USA

https://alpranalysis.com
198•sodality2•19h ago•116 comments

Incomplete list of mistakes in the design of CSS

https://wiki.csswg.org/ideas/mistakes
141•OuterVale•9h ago•93 comments

Fossils reveal anacondas have been giants for over 12 million years

https://www.cam.ac.uk/stories/twelve-million-years-of-giant-anacondas
54•ashishgupta2209•1w ago•24 comments

Scientists create ultra fast memory using light

https://www.isi.edu/news/81186/scientists-create-ultra-fast-memory-using-light/
106•giuliomagnifico•6d ago•24 comments

Common Lisp, ASDF, and Quicklisp: packaging explained

https://cdegroot.com/programming/commonlisp/2025/11/26/cl-ql-asdf.html
92•todsacerdoti•1d ago•24 comments
Open in hackernews

Building an agentic image generator that improves itself

https://simulate.trybezel.com/research/image_agent
67•palashshah•6mo 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•6mo ago
Quite interesting, do you have some documentation of your platform and capabilities? Your landing page is quite synthetic
palashshah•6mo ago
hey! we're working with an initial set of customers, and plan to launch full capabilities soon. stay tuned :)
ramesh31•6mo 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•6mo ago
appreciate the compliment! yep, it's definitely necessary and is the bare minimum for building image generation systems in production.
shmoogy•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Why do you agree? I think we should outsource as much as we can to abstraction. We've been doing it forever.
dandelany•6mo 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•6mo 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•6mo 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•6mo 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•6mo ago
Palash this is a great post, I learnt a lot as an image gen noob! Keep writing more :)
palashshah•6mo ago
this is incredible to hear! i plan to keep writing on a weekly basis, and will be posting them on twitter.
t_mann•6mo 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•6mo 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•6mo 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•6mo 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?