My Lazer Genesis Helmet is a MIPs and it’s the lightest helmet Lazer made at the time.

Much more breathable than my previous helmets too.

1. https://www.rbogash.com/B-52/Carls_Letter.html

If I were a lawyer responsible for defending Trump in the Hague, I'd argue that the tweet was actually an abbreviated way of saying "If Iran does not comply, we will destroy all military assets, including but not limited to their ICBMs, Bridges, and Power Stations, such that we have total military dominance."

Now very obviously (to me at least) this was not the intent of the message, but I don't know whether you could prove that in a hypothetical war crimes trial.

Indeed, this seems to be what supporters of Trump are leaning on, as you can make the argument that _any_ bridge, or _any_ powerplant could hypothetically be used by the military, and that this conflict is sufficiently important for the livelihood of people in America/"The West" that doing anything that even slightly helps tips the odds is justifiable.

37 miles?!? Why??

A little side project I've been working on is to train a model that sits on top of the LLM, looks at each key and determines whether it's needed after a certain lifespan, and evicts it if possible (after the lifespan is expired). Still working on it, but my first pass test has a reduction of 90% of the keys!

https://github.com/enjeyw/smartkv

If you want to go be pedantic about it and select one metric, markets are evaluated on their Brier Score or some other Proper Scoring Rule, not accuracy.

However, I prefer calibration as a high level way to explain prediction market performance to people, as it’s more intuitive.

In general prediction markets can’t be “correct” or “incorrect” - for instance if a prediction market says there’s a 60% chance of an event occurring, and it doesn’t occur, was the market right or wrong? Well it’s hard to say - certainly the market said the event was more likely to occur than not, but only just, and who knows? Maybe the event _only just_ occurred, and very nearly didn’t!

So generally we say a prediction market is “correct” if it is “well calibrated”, which is to say that if we took all the events that the market said had a 60% chance of occurring, then approximately 60% percent of these events occurred (with the same holding true for all other percentages).

On this note, an interesting phenomenon that used to occur was “favorite-longshot bias”, where markets would consistently overestimate the likelihood of longshot events occurring - so events that the market predicted would occur 10% of the time would only occur 5% of the time. What’s fascinating is that once people realized that this bias exited, they began to exploit it by making bets against longshots, which had the effect of moving the market and removing the biases, making the markets well calibrated. It’s a pretty neat example of the efficient market hypothesis in action!

I know one anecdote is not data, but his investment in BYD all the way back in 2008 does counter that viewpoint somewhat - his investment success in the BYD case isn’t from other investors following him in, it’s from him identifying BYD as a successful company far before any other major investors did.

The vast majority of tokens in a sequence will be irrelevant to an attention mechanism outside of a very small window. Right now however we tend to either keep all cache values forever, or dump them all once they hit a certain age.

My theory is that you can train model to look at the key vectors and from that information alone work out how long to keep a the token in the cache for. Results so far look promising and it’s easy to add after the fact without retraining the core model itself.

I haven't got any beyond my own working notes and some basic plots, but I've unceremoniously dumped them into a document here incase anyone else finds them interesting. If so I'd _love_ to chat with you. enjeyw @ google's email provder.

https://thealephengine.substack.com/p/67e3786f-8e84-41bd-888...

If lots of smart people have thought about something and still disagree on the correct approach, pick one and move one.

Comments URL: https://news.ycombinator.com/item?id=45367158

Points: 4

# Comments: 2

About 5 years ago I became more aware that reducing my consumption of ultra processed food was good for me. This was very bad for Beyond Meat’s prospects.

I suspect this experience generalizes.