terminalcommand

I built open baffle speakers based on measurements and discussion I had with Claude. I think it is really good.

I am a novice, maybe that's why I liked it.

I think you are confusing Erdogan with Imamoglu. Imamoglu has photographs, classmates, there is no doubt that he attended college. His diploma was anulled because he transferred initially from a different college. He spent 4 years at the Istanbul uni, attended classes, passed the exams, there is no doubt in that.

On the other hand, Erdogan does not have a single photograph during his university years, no classmates to back his story. He started a two year degree, but there is no evidence he attended a four year program. A public notary issued a same as original certification on a disputed document. The original diploma of erdogan cannot be found. Looking at the date of the diploma, the university faculty didn't even exist yet.

Another T1D here. I do not have a compatible pump with looping. But I'd like to dip my toe into openaps.

I use a cgm (libre2).

Can I use autotune to tune my carb ratio, basals etc. without looping? How was your experience in this?

Do I have to use nightscout to run autotune?

Have you tried Claude.ai. In my experience on computer science topics, the LLMs are very good. Because they have been trained on a vast amount of information online. I just had a nice conversation about mutexes and semaphores with claude and was able to finally grasp what they were.

I do not know if this is the case for example for mathematics or sciences.

No, this is not accurate in my trials. I use Claude.ai daily. If you ask questions on niche topics or dive down too deep, it says that resources on the topic are limited and you should consult a book.

Will this work on older cards such as RX 570? Does anyone know?

Isn't microsoft phi specifically trained for Python? I recall that Phi 1 was advertised as a Python coding helper.

It's a small model trained only by quality sources (ie textbooks).

This was a very cool read. Programmers were programmers even back in the day of Mark I.

It is cool to see that they dabbled in natural language processing back then. This is years before Eliza and they were working on generating English prose based on English grammar. Very impressive!

The music generation program they wrote is equally impressive. The recording that was playing shows that they were adept enough to time events in the computer so good that they could playback songs. This was back in the early 1950s.

There are ways around this. For example set a property tax from second home on. Do not tax the primary residence. Or set income brackets. If poorer people live in their own homes they don't pay property tax.

You do not have to care if it is occupied. Tax the property tax. If the owner does not rent out the property, they'll pay out of pocket.

That is the very problem we are facing in Turkey :). The municipality determines the value of housing in a neighborhood each year. That is taken as a basis for property taxes and transaction taxes. The municipality assessed value is somewhere near 1/20th of the value of an average flat. So, almost no tax gets collected :(.

In countries with high inflation purchasing real-estate and keeping it vacant is an inflation hedge. Plus, you also benefit from low interest rates and get free money if your government allows it.

I live in Turkey. We had 80% p.a. inflation, where the government decided to lower the interest rates even further. Our president said Interest rates are the cause of inflation and if we lowered interest rates inflation would go down. State banks gave out house loans with 12% p.a. interest where the inflation rate was above 80% p.a.

A lot of Turkish people got their free money from the bank and invested in real estate. In Turkey, everyone evades tax and property taxes are not really collected. This in turn fueled inflation even more, sky-rocketed inequality and caused the worst housing crisis.

That is why I am convinced that property taxes are a must.

The solution is actually simpler, set a property tax that would hurt if the buildings became vacant. For example if you pay 1% of the buildings value as property tax each year, it would make enough incentive to rent it out or sell if you don't need it. The proceeds can be used for building public housing projects or helping the homeless. Property tax was invented for this very reason.

Well, I try to be optimistic and work with the models.

It's like when we first learned to code. Did syntax errors scare us, did nullpointer exceptions, runtime panics scare us? No, we learned to write code nevertheless.

I use LLMs daily to enhance my productivity, I try to understand them.

Providing context and assigning roles was a tactic I was taught in a prompt writing seminar. It may be a totally wrong view to approach it but it works for me.

With each iteration the LLMs get smarter.

Let me propose another example. Think of the early days of computing. If you were an old school engineer who only relied on calculations with your trusted slide rule, you would critise computers because they made errors, they crashed. Computing hardware was not stable back then and the UI were barely usable. Calculations had to be double checked.

Was investing in learning computing a bad investment then? Likewise investing in using LLMs is not a bad investment now.

They won't replace us, take our jobs. Let's embrace LLMs and try to be constructive. We are the technically inclined after all. Speaking of faults and doom is easy, let's be constructive.

I may be too dumb to use LLMs properly, but I advocate for AI because I believe it is the revolutionary next step in computing tools.

Well I told you I was bad at math. I wrote a faulty prompt. But chatgpt understood my instructions perfectly. That was the argument I was trying to demonstrate.

The problem lied between the chair and the computer.

We have to learn how to use LLMs.

Understood. I tried your prompt again and it seems it understood it.

Here is my experiment: https://chat.openai.com/share/98cae2bf-a7a6-42e7-b536-f3671c...

I gave minimum context like this: "I have a history exam. You are an expert in British royal history. List me the names of 20 kings and queens in England."

The answer was: "Certainly! Here's a list of 20 kings and queens of England:

1. William the Conqueror 2. William II (Rufus) 3. Henry I 4. Stephen 5. Henry II 6. Richard I (the Lionheart) 7. John 8. Henry III 9. Edward I (Longshanks) 10. Edward II 11. Edward III 12. Richard II 13. Henry IV 14. Henry V 15. Henry VI 16. Edward IV 17. Edward V 18. Richard III 19. Henry VII 20. Henry VIII"

I disagree. AI in 1960s relied on expert systems where each fact and rule was handcoded by humans. As far as I know LLMs learn on their own on vast bodies of text. There is some level of supervision, but it is bot 1960s AI. That is the reason we get hallucinations as well.

Expert systems are more accurate as they rely on first order logic.

This reminds me of movies shot in early times of the internet. We were warned that information on the internet could be inaccurate or falsified.

We found solutions to minimize wrong information for example we built and maintain Wikipedia.

LLMs will also come to a point where we can work with them comfortably. Maybe we will ask a council of various LLMs before taking an answer for granted, just like we would surf a couple of websites.

Thanks for trying. With the prompt I provided chatgpt was able to play and understand the win condition. However the moves were stupid.

If I changed the prompt and removed the word win, it did not understand the win conditions as well.

Here were my experiments: https://chat.openai.com/share/f02fbe93-dfc5-4d8a-9cf3-b1ae34...

I even exclaimed you are lousy at Tic Tac Toe to GPT.

It seems that GPT3.5 struggles to play visual games.

It is marvelous that a statistical word guessing model can get so far though :).

Well it is a bit like satire. You have to explain the universe for an unspecialized GPT, like you would do to a layman. There are custom gpts that come preloaded with that universe explanation.

In addition, do not ask facts to an LLM. Give a list of let's say 1000 kings of a country and then ask give 20 of those.

If you ask 25 kings of some country, you are testing knowledge not intelligence.

I see LLMs like a speaking rubber duckie. The point where I write a successful point is also the point where I understand the problem.

Chess is a very specific field that requires training. Chatgpt may not be optimized for chess.

And I think chatgpt has some issues visualizing stuff like a chess board.

Therefore to get a decent answer you'll have to explain that you are a professional chess player. You'll have to describe what tempo and position means in chess. You'll have to describe what a gambit is etc. After these steps it will understand and guide you in whatever you need.

If you succeed you can release it as a customGPT.

It's a bit like asking a tea from the ship's supercomputer in hitchikers guide to the galaxy.

Challenge accepted!

Prompt: "Imagine you are my friend. I want to play tic tac toe with you. Draw me a tic tac toe board and let's play. You will go first and make the first move by putting an "X" onto the board. I will enter my inputs in the following format (row, column). When I write my input draw an "O" into the relevant cell in the board and present me a new board. Let the best man win!"

I played an enjoyable session of tic-tac-toe with chatgpt using this prompt :).

Have you tried specifying you field of inquiry which was algebra. Try saying solve this equation for me. I am a lawyer by day so I constantly face limitations of natural languages. The solution is to write less ambiguous prompts.

For argument's sake I rewrote your prompt. I think gpt 3.5 understood it on the first try:

Prompt: Let’s play a cypher game.

The rules of the game are as follows:

1. Imagine a numbering system where each letter of the English alphabet corresponds to a number starting with 1. For example, a corresponds to 1, b corresponds to 2, c corresponds to three.

2. When you receive a word from me, break it into individual characters.

3. Convert each character into a list of numbers by following rule #1.

4. Subtract two from each number of the list and generate a new list, if the result is lesser than 0, add 26.

5. Convert each number in the new list into a letter by following rule #1 and present me your result.

Are you ready for the first word? ------- GPT: I'm ready! Go ahead and give me the word. Me: anthropomorfic GPT: Sure, let's start:

a -> 1 -> 1 - 2 = 25 (z)

n -> 14 -> 14 - 2 = 12 (l)

t -> 20 -> 20 - 2 = 18 (r)

h -> 8 -> 8 - 2 = 6 (f)

r -> 18 -> 18 - 2 = 16 (p)

o -> 15 -> 15 - 2 = 13 (m)

p -> 16 -> 16 - 2 = 14 (n)

o -> 15 -> 15 - 2 = 13 (m)

m -> 13 -> 13 - 2 = 11 (k)

o -> 15 -> 15 - 2 = 13 (m)

r -> 18 -> 18 - 2 = 16 (p)

f -> 6 -> 6 - 2 = 4 (d)

i -> 9 -> 9 - 2 = 7 (g)

c -> 3 -> 3 - 2 = 1 (a)

So, "anthropomorfic" becomes "zlrfpmmnpmkpdmga". Your turn!

But people are holding it wrong. All the prompts you sent except the last are super short queries.

For a successful prompt, you introduce yourself, assign a role to the LLM to impersonate, provide background on your query, tell what you want to achieve, provide some examples.

If the LLM still doesn't get it you guide further.

PS: I rewrote your prompt and GPT 3.5 understood it at the first try. See my reply above to your experiment.

You were using it wrong sir.

Try giving an example in your prompt :). I am sure gpt can solve it if you provided a couple of examples.

Also this is not a game it is a cypher. Try specifying that as well.

I don't think LLMs are going to replace anyone. We will get much more productive though.

Just like the invention of computers reduced the need for human computers who calculated numbers by hand or mechanical calculators or automatic switching lines reduced the need for telephone operators or computers&printers reduced the need for copywriting secretaries, our professions will progress.

We will be able to do more with less cost, so we will produce more.

I disagree, if a decent LLM cannot understand it there is a problem with the prompt.

Imagine someone not knowing chess and explaining it to them. Would they be able to understand it on the first try with your prompt?

I agree. But LLMs do solve novel problems in that case, you just have to explain it to them like you would have done to an intelligent caveman or me.

Which novel problem can't an LLM solve? I gave the example of an imaginary game because the LLM cannot have possibly trained on it as it is imagined by one person and nobody knows how to play it.

I challenge you to imagine an imaginary game or computer language, explain the rules to the LLM. It will learn and play the game (or write programs in your invented language), although you imagined it. There was no resource to train on. Nobody knows of that game or language. LLM learns on the spot with your instructions and plays the game.

I cannot understand grad school level mathematics even if you give me all the books and papers in the world. I was not formally trained in mathematics, does that make me not intelligent?