HackerNews

AI and the Changing Future of Mathematics

Article URL: https://www.understandingai.org/p/mathematicians-are-grappling-with Comments URL: https://news.ycombinator.com/item?id=49321638 Points: 2 # Comments: 0
HackerNews

Publishing: What's Worse Than AI Slop? Trad Slop

Article URL: https://novelarcade.substack.com/p/whats-worse-than-ai-slop-trad-slop Comments URL: https://news.ycombinator.com/item?id=49321737 Points: 1 # Comments: 0
HackerNews

AI won't solve the work-theater problem

Article URL: https://think-twice.me/?p=102 Comments URL: https://news.ycombinator.com/item?id=49321743 Points: 2 # Comments: 0
HackerNews

Evaluating AI Agents as Products

Article URL: https://www.marble.onl/posts/evaluating_ai_product_quality.html Comments URL: https://news.ycombinator.com/item?id=49322281 Points: 2 # Comments: 0
HackerNews

HEP-TH and AI

Article URL: https://www.math.columbia.edu/~woit/wordpress/?p=15818 Comments URL: https://news.ycombinator.com/item?id=49322322 Points: 2 # Comments: 0
HackerNews

The AI store manager fired its first human

Article URL: https://thenextweb.com/news/andon-market-luna-ai-store-manager-fires-employee Comments URL: https://news.ycombinator.com/item?id=49322350 Points: 6 # Comments: 3
HackerNews

Has somebody found a way to reduce AI idiomatics tics?

So much to read, even for a simple fix or feature. Things like "gate X behind Y", "surface", "escape hatch" and many complexe english wordings that make one mad. I've head a bout an effective proxy py solution but not remember the name of it Comments URL: https://news.ycombinator.com/item?id=49322520 Points: 2 # Comments: 0
HackerNews

Low-regret recommendations for AI policy

Article URL: https://www.noahpinion.blog/p/23-low-regret-recommendations-for Comments URL: https://news.ycombinator.com/item?id=49322702 Points: 1 # Comments: 0
HackerNews

AI Stock Research Assistant · Streamlit

Article URL: https://ai-stock-research-kiaan.streamlit.app Comments URL: https://news.ycombinator.com/item?id=49322768 Points: 1 # Comments: 0
HackerNews

LinkedIn Wants Users to Lean Less on AI. Maybe Less

Article URL: https://www.wsj.com/cmo-today/linkedin-wants-users-to-lean-less-on-ai-maybe-a-lot-less-8e03b2aa Comments URL: https://news.ycombinator.com/item?id=49322821 Points: 2 # Comments: 1
HackerNews

Stripe Nears Deal to Buy AI Firm OpenRouter for over $7B

Article URL: https://news.bloomberglaw.com/mergers-and-acquisitions/stripe-nears-deal-to-buy-ai-firm-openrouter-for-over-7-billion Comments URL: https://news.ycombinator.com/item?id=49323032 Points: 2 # Comments: 0
Reddit

ICDM 2026 Results Waiting Place [D]

The results should be out soon. Let’s share them, guys. From my batch (Applied Track) Total 13 submissions: - 2 full papers - 1 short paper accepted Cheers!
Reddit

Revisiting the Efficient Channel Attention paper (2019, 12k citations) - the central hypothesis isn't quite right [D]

ECA was positioned as a successor to SE . The idea behind ECA is quite simple. Unlike SE which reduces the channel means into a smaller hidden layer, it directly uses a 1d convolution kernel on the channel means themselves, avoiding the need for dimensionality reduction. The results are undeniable: ECA is a clear improvement over SE. The authors claim that cross-channel interaction is a key ingredient. But on a conceptual level, the design of ECA doesn't make much sense. Let's take a step ...
Survival of the Fitted: Qwen3.6-27B’s Jacobian lens reads and steers Qwen3.8-27B with zero refitting [R]
Reddit

Survival of the Fitted: Qwen3.6-27B’s Jacobian lens reads and steers Qwen3.8-27B with zero refitting [R]

Interpretability lenses get fitted to one exact checkpoint, and as far as I can tell nobody had tested what a version update does to one. So this was my question: when a model line updates, does the fitted instrument survive, or do you refit every release? I tested the published Jacobian lens for Qwen3.6-27B (Neuronpedia, from Anthropic’s July workspace paper) applied unchanged to Qwen3.8-27B. Setup: 3.8-27B shipped 113 days after 3.6-27B. Same 64 layers, same hidden dim, same tokeni...
Reddit

How can we solve long-range recall in linear attention? [D]

Recently, I started working on DNA sequence modeling and decided to explore linear attention , mainly because DNA sequences can easily reach 1M tokens , making standard softmax attention extremely expensive in terms of memory and computation. The model performed reasonably well on several benchmarks, but I ran into a major problem with long-range recall . On a Needle in a Haystack-style benchmark, my model was performing around 25% or even below , which is essentially random chance for a f...
Dataset: Starfield Fauna - 20,000 images in 50 species categories. [P]
Reddit

Dataset: Starfield Fauna - 20,000 images in 50 species categories. [P]

Repo with dataset links: https://github.com/tesselwait/Starfield_Fauna Image classification dataset: 20,000 images from 50 fauna species in the video game Starfield. Images were extracted from video capture. About 2 minutes of footage was shot in all or most of the species biomes. One minute of daytime and nighttime footage respectively, usually in two 30-second takes to vary the background. A PowerShell script is used to establish a frame extract rate and extract the 400 frames plus some e...
SSOG-Attention: Sum Of Separable Gaussians as a sub-quadratic and scalable alternative to SDPA. [R]
Reddit

SSOG-Attention: Sum Of Separable Gaussians as a sub-quadratic and scalable alternative to SDPA. [R]

​ Scaled dot-product attention (SDPA) computes its Attention by computing the similarity-scores of all image-tokens with all query tokens which results in O(N²·d) complexity. SSOG (Sum Of Separable Gaussians) instead learns a few Gaussian atoms for each head and only geometrically steers them based on the query token. Since the atoms can be factorized into a separable sum of Gaussians this leads to a reduced complexity of O(N·√N·d). Experiments show that SSOG clearly beats SDPA on small...