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Muse Glimmer: 30B-parameter model optimized for always-on local agent workflows

https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
972•riordan•12h ago•549 comments

Rust SIMD on the GPU

https://www.vectorware.com/blog/simd-on-gpu/
96•sagacity•4h ago•47 comments

Mark Zuckerberg attacks 'closed' AI rivals as Meta returns to open models

https://www.ft.com/content/4e3957f8-ea7c-4c46-a3de-cdce8e526878
297•root-parent•8h ago•339 comments

Sonic Pi v5

https://www.patreon.com/samaaron/posts/sonic-pi-v5-166001392
271•samaaron•3d ago•72 comments

Publishing Schematics Before "Open Source" Was a Word

https://fabscene.medium.com/publishing-schematics-before-open-source-was-a-word-55-years-of-akizu...
29•extralongdivisi•3d ago•7 comments

Illinois Just Passed a Law That Puts Linux on the Hook for Age Verification

https://linuxstans.com/illinois-hb5511-operating-system-age-verification/
237•speckx•2h ago•286 comments

Confessions of a Long-Distance Sailor

https://arachnoid.com/lutusp/sailbook.html
21•AntiRush•2h ago•4 comments

Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots

https://cactuscompute.com/needle
86•HenryNdubuaku•5h ago•52 comments

Amazon backs power plant that may become top source of US climate pollution

https://arstechnica.com/tech-policy/2026/08/amazon-funds-biggest-gas-power-plant-in-us-despite-cl...
81•pjmlp•1h ago•41 comments

The Psychedelic Toad of the Sonoran Desert

https://en.wikipedia.org/wiki/Bufo_Alvarius:_the_Psychedelic_Toad_of_the_Sonoran_Desert
63•simonebrunozzi•6d ago•45 comments

Exploiting System Management Mode with a very long interrupt

https://github.com/xoreaxeaxeax/smiiiiiiiiiiiiiiii
108•WhiteDawn•6h ago•34 comments

Launch HN: Stoa Markets (YC S26) – A Marketplace for GPUs and AI Servers

https://www.stoaexchange.com
59•erenberke•6h ago•39 comments

Squeak 6.1

https://squeak.org/release_notes/6.1/
202•fniephaus•10h ago•101 comments

Stop Killing Games: It's time to sue Sony, join us

https://www.massaschadeconsument.nl/collectieve-acties/playstation/
70•EDM115•2h ago•26 comments

Humanising LLM Outputs Is Dumb

https://kuber.studio/blog/Reflections/Humanising-LLM-Outputs-is-Actually-Dumb
116•kuberwastaken•9h ago•69 comments

Stowaway – take the window seat on any plane or satellite overhead

https://stowaway.live/
39•thunderbong•3d ago•4 comments

Ask HN: In your experience, what are sound conventions for e-ink UI development?

123•BoxOfRain•3d ago•42 comments

Parametron: 50s Japanese computer that uses neither transistors nor vacuum tubes

https://ethw.org/Milestones:Parametron,_1954
167•xeonmc•12h ago•45 comments

Letter to Governor Abbott on responsible AI infrastructure in Texas

https://openai.com/index/responsible-ai-infrastructure-texas/
77•hackerBanana•8h ago•144 comments

Exploring Claude/GPT Knowledge Cutoffs and Pre-Training Timelines

https://blog.sshh.io/p/exploring-claudegpt-knowledge-cutoffs
86•sshh12•8h ago•12 comments

Magnitude 7.4 Earthquake – 5 km S of San José del Palmar, Colombia

https://earthquake.usgs.gov/earthquakes/eventpage/us6000tjl2/executive
146•Bender•7h ago•58 comments

Google Search Is Dying. What Comes Next Is Worse

https://thewalrus.ca/google-search-is-dying/
8•awnird•22m ago•1 comments

Mistral Patent for “Code implemented tool calls”

https://patentsgazette.uspto.gov/week26/OG/html/1547-5/US12670045-20260630.html
201•theanonymousone•9h ago•169 comments

Tail-call optimization in C is relatively recent (2025)

https://lwn.net/Articles/1034703/
111•prakashqwerty•11h ago•106 comments

Show HN: Higher-dimensional lattices unfolded into the 2D plane

https://number-garden.com/?@THLP@
9•unitX•4d ago•1 comments

Extreme 220GHz+Broadband Silicon Capacitor X2SC 0201M 22nF BV11

https://pim.murata.com/asset/pim4/siliconCapacitor/SICAP_X2SC422522_PDF_SILICONCAPACITOR
56•peter_d_sherman•7h ago•19 comments

Tl;dv: Over 180k meetings left wide open

https://bobdahacker.com/blog/tldv-hack
506•colesantiago•10h ago•171 comments

50k Boat Names

https://www.beautifulpublicdata.com/boat-names/
142•jonathanmkeegan•10h ago•102 comments

Mars Bar from 1991 found – and it's 20g bigger than today's

https://www.bbc.com/news/articles/c1j1kjy7gewo
274•RickJWagner•7h ago•422 comments

Back to the Future of Handwriting Recognition (2016)

https://jackschaedler.github.io/handwriting-recognition/
32•at1as•7h ago•8 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?