I believe OP's point is that for a given model quality, inference cost decreases dramatically over time. The article you linked talks about effective total inference costs which seem to be increasing.
Those are not contradictory: a company's inference costs can increase due to deploying more models (Sora), deploying larger models, doing more reasoning, and an increase in demand.
However, if we look purely at how much it costs to run inference on a fixed amount of requests for a fixed model quality, I am quite convinced that the inference costs are decreasing dramatically. Here's a model from late 2025 (see Model performance section) [1] with benchmarks comparing a 72B parameter model (Qwen2.5) from early 2025 to the late 2025 8B Qwen3 model.
The 9x smaller model outperforms the larger one from earlier the same year on 27 of the 40 benchmarks they were evaluated on, which is just astounding.
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.
I also enjoy watching Charles, a French-Canadian cyclist currently cycling from Canada to Europe. As a geologist he regularly explains rock formations and rock types he encounters.
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.
I agree with your premise that there is often an unproductive pendulum-like phenomenon in public debates where interpretations swing from one extreme to the other, making nuanced discussions difficult.
However I don't believe that PG's article meant to address the elephant, but rather was a meta-level thesis on how he sees debates being shut down by orthodoxy, and for that he does suggest what he thinks would be a possible solution.
Perhaps the thesis could have gained in being more balanced to as you say "avoid giving tacit permissions for the extremists on the other side"? On the other hand, does one always have to shield one's expressions with disclaimers and is one not free to share thoughts however raw in order to express, discuss and learn, update our beliefs?
There likely is a bigger responsibility when one has a larger audience to avoid misinterpretations, but ultimately I believe as long as there is a rational and nuanced discussion to take the good points and have a productive debate, it should be okay.
How can we create incentives to have a more nuanced discussion?
Some of the disagreement or confusion seems to stem from the definition of the word "woke" which means different things to different people?
Having read both essays I don't see them necessarily in disagreement. pg criticizes the performative and orthodox nature of some social justice activists' behavior, however it doesn't seem that the author's behavior here is performative at all.
Perhaps we should just avoid these terms like "woke" and just say what we mean to avoid this societal dissonance? I feel like decent rational people can talk past each other depending on how they have been exposed to the term.
Some high paying jobs also come with high pressure and little free time which could harm life satisfaction. It could be that high earners that are likely to participate in such a study are the ones that have more free time to dedicate to spontaneous endeavors, therefore might already have a higher life satisfaction.
This bias can also exist for lower-paying jobs, however I would guess proportionally there might be more 80-hour/week type high responsibility jobs in the higher paying brackets.
The high level API seems very smooth to quickly iterate on testing RAGs. It seems great for prototyping, however I have doubts whether it's a good idea to hide the LLM calling logic in a DB extension.
Error handling when you get rate limited, the token has expired or the token length is too long would be problematic, and from a security point of view it requires your DB to directly call OpenAI which can also be risky.
Personally I haven't used that many Postgres extensions, so perhaps these risks are mitigated somehow that I don't know?
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You can accumulate the gradients of multiple batches before doing the weight update step. This allows you to run effectively much larger batch sizes than your GPU would allow without it.
She says that the ROI probability calculation is wrong, which is the last part of her video and a separate topic.
The part about correlations has not been retracted AFAIK. I agree that there's a need for a baseline though, there is one example on her recent twitter feed but more samples are needed to get a better picture.
I occasionally read articles on Nate Silver's substack but I'm still missing the breadth of 538conbined with the trademark data-driven analysis.