#ai

Well, "one engineer one month one million lines of code" -> "who the hell knows what's in there" -> "you need some AI coder architect".

https://www.linkedin.com/posts/galenh_principal-software-engineer-coreai-microsoft-activity-7407863239289729024-WTzf

#ai

It's just conflicting thoughts patched together.



AI Is Changing How We Learn at Work
https://hbr.org/2025/12/ai-is-changing-how-we-learn-at-work

Harvard Business Review

AI Is Changing How We Learn at Work

As artificial intelligence rapidly transforms the workplace, it is also reshaping how people learn, develop expertise, and form their professional identities. Although gen AI promises to accelerate learning and boost productivity, it risks undermining the…

#visualization


Broken Chart: discover 9 visualization alternatives – Dr Dominic Royé
https://dominicroye.github.io/blog/2025-12-14-broken-charts/

dominicroye.github.io

Broken Chart: discover 9 visualization alternatives

Researcher in climate science at MBG-CSIC

#misc

There's always something more exciting to explore in our life. If not, we create new games with new rules. This is what we humans are really good at.


Kosmyna, Nataliya, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, and Pattie Maes. 2025. “Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task.” arXiv [Cs.AI]. arXiv. http://arxiv.org/abs/2506.08872.

#engineering

Clear and concise intro.

Making Sense of Memory in AI Agents – Leonie Monigatti
https://www.leoniemonigatti.com/blog/memory-in-ai-agents.html

Leonie Monigatti

Making Sense of Memory in AI Agents – Leonie Monigatti

Learn how to make stateless LLM agents remember conversations, manage context, and implement memory banks.

#misc

Quote from the following article:

> Scientists want AI partnership but can't yet trust it for core research




Introducing Anthropic Interviewer \ Anthropic https://www.anthropic.com/news/anthropic-interviewer

Anthropic

Introducing Anthropic Interviewer

What 1,250 professionals told us about working with AI

#ai

What GitHub imagined about everyone creating their own app is almost here. Github Spark is not that good, vercel ship 0 is also buggy. Cloude is doing a fairly good job here.

The other day I needed a special checklist and instead of writing the code myself, I asked Gemini to do it and it was surprisingly good.


---
Build interactive diagram tools | Claude | Claude
https://www.claude.com/resources/use-cases/build-interactive-diagram-tools

Claude

Build interactive diagram tools | Claude | Claude

Turn a detailed prompt into a working educational app with the data depth interactions and visual polish you specify.

#ai

Interesting... Going from finite Eucleadian space to Hilbert space

https://openreview.net/forum?id=1b7whO4SfY

openreview.net

Gated Attention for Large Language Models: Non-linearity, Sparsity,...

Gating mechanisms have been widely utilized, from early models like LSTMs and Highway Networks to recent state space models, linear attention, and also softmax attention.
Yet, existing literature...

#data

Last year a colleague introduced Marimo to me. It was surprisingly good after the first few days but then I was annoyed by the auto rerun on expensive computations.

Then I bailed out.

This morning I read a blog on this topic and realized there's a switch...

Time to jump in again.



https://docs.marimo.io/guides/configuration/runtime_configuration/#disable-autorun-on-cell-change-lazy-execution

#dl

Introducing more symmetries in attention

https://github.com/NVIDIA/torch-harmonics

https://neurips.cc/virtual/2025/loc/san-diego/poster/117783

GitHub

GitHub - NVIDIA/torch-harmonics: Differentiable signal processing on the sphere for PyTorch

Differentiable signal processing on the sphere for PyTorch - NVIDIA/torch-harmonics

#ai

Yeah, just import antigravity.

Google Antigravity
https://antigravity.google/

Google Antigravity

Google Antigravity - Build the new way

#fun

Lol citing the onion in a research paper...


https://ajba.um.edu.my/index.php/JAT/article/download/11954/7915

#ai

This is more than language models. Somehow many forecasting models also almost fall inside the realm. Somehow the root of this is the latent space. Time series models even classical ones, have enlarged latent space, which is more or less embedding patterns with higher dimensions.

However this paper is a bit fishy. I just can't trust the proof of theorem 2.2.

Language Models are Injective and Hence Invertible
https://arxiv.org/abs/2510.15511

arXiv.org

Language Models are Injective and Hence Invertible

Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exact recovery of...

#ai


I ran into this quite interesting paper when exploring embeddings of time series.

In the past, the manifold hypothesis has always been working quite well for me regarding physical world data. You just take a model, compress it, do something in the latent space, decode it, damn it works so well. The latent space is so magical.

To me, the hyperparameters for the latent space has always been some kind of battle between the curse of dimensionality and the Whitney embedding theorem.

Then there comes the language models. The ancient word2vec was already amazing. It brings in the questioin of why embeddings works unbelievably well in language models and it bugs me a lot. If you think about it, regardless of the model, embedding has been working so well. This hints that language embeddings might be universal. There is the linear representation hypothesis, but it is weird as it is missing the global structure. This paper provides a bit more clarity. The authors used a lot of assumptions but the proposal is interesting in the sense that the cosine similarity we used is likely a tool that depends on the distance on the manifold of the continuous features in the backstage.



https://arxiv.org/abs/2505.18235v1

arXiv.org

The Origins of Representation Manifolds in Large Language Models

There is a large ongoing scientific effort in mechanistic interpretability to map embeddings and internal representations of AI systems into human-understandable concepts. A key element of this...

#misc

https://www.reddit.com/r/singularity/s/n9msKlNeAo

Reddit

From the singularity community on Reddit: 1X Neo is here

Explore this post and more from the singularity community

#ai

AI to juniors is more or less a "fuck you in particular" thingy.

https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/

#ai

https://x.com/Google/status/1981403118803046686

X (formerly Twitter)

Google (@Google) on X

Google Earth AI brings together our geospatial models for floods, wildfires, cyclones, air quality and much more to help with everything from environmental monitoring to disaster response. Today, we’re advancing its capabilities and expanding access around…