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When OpenAI launched ChatGPT, people thought, ‘This is interesting.’ What’s blown me away is how quickly it’s evolved into something transformational.

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1func (agent *LLMAgent) StreamCompletion(
2	ctx context.Context,
3	request *CompletionRequest,
4	tokens chan<- Token,
5) error {
6	modelsToTry := agent.getModelsToTry()
7
8	for i, model := range modelsToTry {
9		hasReceivedFirstToken, err := agent.tryStreamSingleModel(ctx, request, model, tokens)
10		if err == nil {
11			return nil // Success
12		}
13
14		// Only retry if we haven't started streaming and have more models to try
15		if !hasReceivedFirstToken && i < len(modelsToTry)-1 {
16			continue
17		}
18
19		return err
20	}
21
22	return fmt.Errorf("all models failed to stream completion")
23}

Below the code from GitHub:

Table code is below:

Model	Forecasting model	Best for
N-Week Average	Averages past N weeks to project future demand.	Stable demand with minimal fluctuations
N-Week Average with Momentum	Applies a line of best fit to past N weeks, weighting recent weeks more heavily.	Gradual shifts in workload over time Seasonal Model
Seasonal Model	Analyzes yearly patterns to predict seasonal fluctuations.	Predictable high/low demand cycles
Prophet Model	Uses machine learning to model trends, seasonality, and events.	Dynamic environments with long-term demand shifts

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Below the code from GitHub:

Table code is below:

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