Feddits appear to be a stable currency at first until the chief administrator disappears in the neutral zone and is rumoured to be a prisoner on Romulus. Then the federation needs to restart feddits as feddit.org.

There are tons of statistical methods to get reasonable conclusions without an RCT. Some things can not be detected with an RCT, because the experiment is just impossible to run, so sometimes you need methods to do causal identification with observable data. Here you do not even need causal identification methods for observational data. You just need to do some descriptive statistics for a large group of people well to find interesting patterns. Whether this aging pattern in the mid-40s is causal it coincidental is not important at first. The pattern itself is interesting.

For a user without much technical experience using a ready-made gui like Jan.ai with automatic model download and ability to run models with the ggml library on consumer grade hardware like mac M-series chips or cheap GPUs by either Nvidia or AMD is probably a good start.

For a little bit more technically proficient users Ollama is probably a great choice to start to host your own OpenAI-like API for local models. I mostly run gemma2 or small llama 3.1 like models with that.

Is that the lawyer from silicon valley?

The market will segment away from the current tech anyway. CATL Sodium-ion with comparatively low densities but also extremely low prices per kWh will likely win the low-end market and the market for stationary solutions. This is just due to the much lower resource costs. The high-end will be up for things like this battery by Samsung (or other comparable pilot products). The current technology will likely be in a weird middle spot.

Depends on what you do with it. Synthetic data seems to be really powerful if it’s human controlled and well built. Stuff like tiny stories (simple llm-generated stories that only use the complexity of a 3-year olds vocabulary) can be used to make tiny language models produce sensible English output. My favourite newer example is the base data for AlphaProof (llm-generated translations of proofs in Math-Papers to the proof-validation system LEAN) to teach an LLM the basic structure of Mathematics proofs. The validation in LEAN itself can be used to only keep high-quality (i.e. correct) proofs. Since AlphaProof is basically a reinforcement learning routine that uses an llm to generate good ideas for proof steps to reduce the size of the space of proof steps, applying it yields new correct proofs that can be used to further improve its internal training data.

No idea how Fortnite works. But can you (realistically) use the Cybertruck to cut off your enemies fingers with it’s automatic doors.

Na SpaceX would just use his neuralink chip it to automate the team that keeps Musk distracted from messing with important things in the company with a simple AI

Now that sounds dystopian as fuck. Because at scale this will involve human workers being tracked all the time and limited in their freedom. Ironically an AI might be used to track what workers do in such a scenario.

Yeah, TNGs third episode made sure to show that he is quite proficient at laying pipe

As R programmer I know that fully functional data analysis is just too hot for this community.

Im sorry, but as a language model, I don’t have the capability to perceive or assess physical appearances. I am however sure you have other desirable qualities.

Thanks. Now it’s right