In other words STPA is a design review framework for finding some less obvious failure modes. FMEA is more popular but relies on making a list of all of the knowable failure modes in a system, but the failure modes you haven’t thought of don’t make it on the list. STPA helps fill in some of those gaps of failure modes you haven’t thought of.
A notebook is a REPL with an inline wiki. Of course it is not intended for running production code, it’s just an R&D environment to document, share, and test ideas
Article mentions people were raiding beaches for free building materials like sand and stone for concrete, which would be a problem at a large volume. Enforcing the same rules on individual souvenir collectors seems excessive.
Excessive, but potentially necessary, since otherwise you need to draw an arbitrary line between an amount being taken away that is still acceptavle and one that is not, and how many times that can happen in what period of time and so on. From that point of view it can make sense to simply categorically forbid people to take things away from the beach.
Laws are full of arbitrary drawn lines, it is hard to avoid them. If you don’t have an arbitrary line in the law then there will be arbitrary/selective enforcement which can be even worse.
Way too soon! He recently did an interview with Dave and Krist from Nirvana about the 30th anniversary of In Utero. They described recording prank phone calls during the recording sessions.
I've never really paid all that much attention to producers, but 61, it still hits too close to home. I'm not that far behind. It kind of freaks me out that I'm hitting 60 soon.
Strongly recommend watching Andrej Karpathy’s “Lets build GPT-2” videos on YouTube which dives into an actual PyTorch implementation, then download the code and study it carefully. Then study “Spreadsheets is all you need” to see what the internal data structures look like.
LLMs have a limited context size, i.e. the chat bot can only recall so much of the conversation. This project is building a knowledge graph of the entire conversation(s), then using that knowledge graph as a RAG database.
To add to what aszantu said: plants with deep roots can be very healthy for a garden because they essentially draw nutrients that have soaked deeper into the soil back up. So one way of looking at them is that they are basically mining soil nutrients from below for your garden for free. Perhaps that will make repeatedly mulching them a less frustrating task.
Also, while there are of course legitimate reasons to consider certain plants weeds (e.g. they maybe be poisonous, toxic, or displace other plants that you like more), dandelions are mainly the victim of marketing from pesticide manufacturers half a century ago.
they're amazing, you can make salad with lemon and salt, or fry in butter and eat with your steak. Also the roots are edible. And dandelion pulls nutrients for other plants from below, once the nutrients have been used up, the dandy will leave on its own.
Those are extreme examples, most probably in sand or a loose mix. Roots need oxygen also so can grow a lot in this conditions as long as they are watered.
Most dandelions live in heavy clay. There will be much shorter, reaching just the phreatic level.
OpenSearch perhaps? The search query results returns a list of hits (matches) with a text_entry field that has the matching excerpt from the source doc
That’s pretty much correct. An LLM is often used rather like a forecast model that can forecast the next word in a sequence of words. When it’s generating output it’s just continuously forecasting (predicting) the next word of output. Your prompt is just providing the model with input data to start forecasting from. The prior output itself also becomes part of the context to forecast from. The output of “think about it step-by-step” becomes part of its own context to continue forecasting from, hence guides its output. I know that “forecasting” is technically not the right term, but I’ve found it helpful to understand what it is LLM‘s are actually doing when generating output.
