This does not work at all. I tried several questions and got many hallucinations. E.g. I ask who was the founder of my company and got wrong answers.

I have conducted similar experiments in the past and concluded that promping does not reduce hallucinations.

I asked who was the founder of my company and it gave me an answer. I was not expecting it to get it correct.I guess AI hallucination problem chooses when to happen. I get where you're coming from. Prompts alone often can't stop hallucinations. Improving training and data quality is key. Let's keep exploring other ways.

Yes, I use Camtasia for over a decade. It not only solves screen recording but also most other tasks you would need cutting video and sound.

You should avoid the numbers of the other players. Avoid the numbers in red, e.g. 3, 5, 7, 8, 9 and 12 and you lower the probability significantly that someone else has the same numbers and you need to share the big prize.

Your approach is intriguing. I appreciate the inclusion of descriptions that elucidate what's happening in the code; this is often lacking in professional and scientific programming. This is particularly relevant in quantitative finance, where many bugs stem from misunderstandings related to the timing associated with variables.

To address this, we implemented a similar concept in ThetaML for defining stochastic processes, financial instrument payoffs, and trading strategies. We introduced the

  theta t

operator, which represents the passage of time t. Here's how a stochastic process might be represented:

  S = 1
  loop inf
    theta @dt
    S = S * exp( (r - 0.5 * sigma^2) * @dt
  end

We also introduced stochastic expressions like expected value E(V!) given the current state of all simulated values where V! is a reference to the future value of V.

Example:

   1: model EuropeanPut
   2: % This model returns a simulated European put option price
   3: import S “Stock prices”
   4: import CUR “Discount factor”
   5: import K “Strike price for the European put option”
   6: import T “Time to maturity in years”
   7: export P “European put option price”
   8:
   9: P = E(V_CUR!)
  10: % T years pass
  11: theta T
  12: % at maturity T, the option payoff is discounted to time 0
  13: V_CUR = max(K - S, 0) * CUR
  14:
  15: end

This system enables the evaluation of the models using Monte Carlo Simulations and the computation of expected values E(x!) using regression techniques.

For those interested, you can find more information in the ThetaML Handbook: Here is the ThetaML Handbook: https://www.thetaris.com/doc/book-2012-thetaml-Handbook.pdf

What would be a use case for it? https://www.os-js.org/ is around for long time already. But, no wide usage yet...