patresh

Technically yes, but has been reported to be quite benchmaxxed. In practice Deepseek Flash 4.1 and GLM 5.3 might therefore still outperform Mimo 2.6 pro.

Where did you all go to once FiveThirtyEight died down?

I occasionally read articles on Nate Silver's substack but I'm still missing the breadth of 538conbined with the trademark data-driven analysis.

They're likely of limited use for someone looking for introductory material to ML, but for someone having done some computer vision and used various types convolution layers, it can be useful to see a summary with visualizations.

Does anyone have experience with longer DeepResearch tasks with Mammouth? How does it compare to using Gemini's / ChatGPT's DeepResearch or GPTResearcher + API-based alternatives?

For standard questions I feel like it doesn't matter too much what you use. When it comes to multi-step searching + reasoning flows like look for alternatives, fetch pricing, feature lists, compare etc, the differences are larger because of the engineering glue and prompting around the pure LLM inference which makes the tools more or less powerful.

I've had no issues with the app lately, but it's still missing the feature of building a local search index to do searches based on e-mail content, like the web client can do.

Yes, I don't mean HN doesn't experience toxicity, but putting things in context, if you read random posts on X versus HN there is no comparison.

Moderation for sure helps, would there be ways to make it scalable with less manual supervision? Or a system that would organize people with certain rule-sets to distribute them into suitable sized groups?

I do agree with your statement that "Good discussions evolve naturally and also randomly", let's say now your platform becomes popular. It will attract players that will want to exploit that either to sway opinions for their own gain, and I believe that this is becoming increasingly cheaper to game and simulate whole crowds. So the limits are mostly with this in mind.

Indeed perhaps the term social platform is vague and different "optimal rules" could be different for social platforms that is a mega-forum, a network for friends, or just generic post sharing.

I'm wondering if there is some sort of taxonomy of these rulesets or levers that exist? Or a review paper on what has been tried and what effects they had? There are so many possible ways to structure online social interactions.

Indeed, there are different societal structures that would attract more one or the other type of person.

I wonder if it would be possible to simulate this to understand what behaviors will emerge if you set certain types of rules. It is certainly difficult to create coherent personalities with LLMs that act in realistic ways but I wonder if one could get an approximation.

Perhaps what I have in mind is also not best described as "pleasant", but also something that is net-positive for society, where as a whole society is better off having that than not. This is arguably the case for HN but not necessarily for some of the bigger ones out there.

If the diagram is representative of what is happening, it would seem that each cluster is represented as a hypersphere, possibly using the cluster centroid and max distance from the centroid to any cluster member as radius. Those hyperspheres can then overlap. Not sure if that is what is actually happening though.

What is the clustering performed on? Is another embedding model used to produce the embeddings or do they come from the LLM?

Typically LLMs don't produce usable embeddings for clustering or retrieval and embedding models trained with contrastive learning are used instead, but there seems to be no mention of any other models than LLMs.

I'm also curious about what type of clustering is used here.