Its a single player game. there is no such thing as "over powered". If there was such a thing then the other 1.25m engines would be better considered underpowered because the vector is logically just a fraction of a mammoth that in turn is simply a cluster of vectors no other 1.25m engine shares such a relationship to anchor its stats in and justify its numbers.

And the gimbal isn't even much of an advantage: Try launching a rocket with vectors and SAS, the'll regularly rip your rocket apart by oscillating widly without gimbal limit; which is already an issue for the MS.


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I'd say the vector is maybe 5% better than the mainsail. But it's super interesting and adds to the game cause of it's small size combined with high thrust+weight, strong vectoring, and free placement.

It's not 'free thrust'. Every bit of weight lowers D/V, which is everything. Hence it's stupid to go for a Vector when a skipper suffices. Latter has better VAC ISP btw. As for corrective thrust, that's already something the mainsail can do. If that kind of vectoring isn't enough, then you're in huge trouble. Seriously, I can't every imagine when a vector would have save me. Aside from a shuttle maybe.

However to get 100% recovery rate you need to land near KSC which is also quite difficult, so I would say that from recoverability perspective vectors are quite balanced since you need 100% recovery rate to make them competitive to other options.

My daughter also did a bit of painting - these are her first 28mm. She always loves 'girls' - and will dig through my boxes looking for female minis which are pretty under-represented, I do admit. I'd like to find some cheap female heads, as I'd prefer to convert normal male minis for her rather than have a constant stream of short skirts and chainmail bikinis which seems to be the majority of what is on offer.

Here is my final vectorizing of this motorcycle (Just the shapes with no colors put in.)


And here is what the final motorcycle looks like with no outline and each shape filled with the appropriate color.



First I outline the shape with large clumps of hair (fur).


Second I just draw lots of simple vector lines.


Third I create an art brush in the shape of a simple triangle. When it is applied to these curving lines it will look just like more hairs.


Fourth I will use the: Object/Expand Appearance to convert these brushes into solid vector shapes, then I will use the Pathfinder/Merge option to combine these hairs with the original shape.


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If you can abandon older compilers / STL implementations, return vectors freely (and make sure your own objects support it, too). If your code base needs to support "lesser" compilers, stick to the old style.

However, one should keep in mind that the cost of constructing the new vector (then destructing it) still exists, and using output parameters instead of returning by value is still useful when you desire to reuse the vector's capacity. This is documented as an exception in F.20 of the C++ Core Guidelines.

Not surprisingly, when numIter = 1 (i.e., mem(v) = 8GB), the times are perfectly identical. Indeed, in both cases we are only allocating once a huge vector of 8GB in memory. This also proves that no copy happened when using BuildLargeVector1(): I wouldn't have enough RAM to do the copy!

We can notice that time1 is pretty much constant from numIter = 1 to numIter = 256, which means that allocating one huge vector of 8GB is pretty much as costly as allocating 256 vectors of 32MB. However, allocating one huge vector of 8GB is definitly more expensive than allocating one vector of 32MB, so reusing the vector's capacity provides performance gains.

From numIter = 512 (mem(v) = 16MB) to numIter = 8M (mem(v) = 1kB) is the sweet spot: both methods are exactly as fast, and faster than all other combinations of numIter and vecSize. This probably has to do with the fact that the L3 cache size of my processor is 8MB, so that the vector pretty much fits completely in cache. I don't really explain why the sudden jump of time1 is for mem(v) = 16MB, it would seem more logical to happen just after, when mem(v) = 8MB. Note that surprisingly, in this sweet spot, not re-using capacity is in fact slightly faster! I don't really explain this.

When numIter > 8M things start to get ugly. Both methods get slower but returning the vector by value gets even slower. In the worst case, with a vector containing only one single int, reusing capacity instead of returning by value is 3.3x faster. Presumably, this is due to the fixed costs of malloc() which start to dominate.

Both are about embedding tokens (words or sub-words) in a vector space. Both rely on the same fundamental principle to learn this space: tokens that appear together end up close together in the embedding space. The distance function used to compare tokens is the same in both cases: cosine distance. Even the dimensionality of the embedding space is similar: on the order of 10e3 or 10e4.

They were building a model to embed words in a vector space \u2014 a problem that already had a long academic history at the time, starting in the 1980s. Their model used an optimization objective designed to turn correlation relationships between words into distance relationships in the embedding space: a vector was associated to each word in a vocabulary, and the vectors were optimized so that the dot-product (cosine proximity) between vectors representing frequently co-occurring words would be closer to 1, while the dot-product between vectors representing rarely co-occurring would be closer to 0. ff782bc1db

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