That has not been my experience.

If you expect it to give you 100% working code with minimal instruction, it might not work (although it might). But if you work with it just a little bit, it's pretty amazing.

Example: I was reading this article about text summarization with python. The article listed this code:

def summarize(text, per):
    nlp = spacy.load('en_core_web_sm')
    doc= nlp(text)
    tokens=[token.text for token in doc]
    word_frequencies={}
    for word in doc:
        if word.text.lower() not in list(STOP_WORDS):
            if word.text.lower() not in punctuation:
                if word.text not in word_frequencies.keys():
                    word_frequencies[word.text] = 1
                else:
                    word_frequencies[word.text] += 1
    max_frequency=max(word_frequencies.values())
    for word in word_frequencies.keys():
        word_frequencies[word]=word_frequencies[word]/max_frequency
    sentence_tokens= [sent for sent in doc.sents]
    sentence_scores = {}
    for sent in sentence_tokens:
        for word in sent:
            if word.text.lower() in word_frequencies.keys():
                if sent not in sentence_scores.keys():                            
                    sentence_scores[sent]=word_frequencies[word.text.lower()]
                else:
                    sentence_scores[sent]+=word_frequencies[word.text.lower()]
    select_length=int(len(sentence_tokens)*per)
    summary=nlargest(select_length, sentence_scores,key=sentence_scores.get)
    final_summary=[word.text for word in summary]
    summary=''.join(final_summary)
    return summary

I asked chat GPT what it did:

At a high level, this code defines a function that takes in a piece of text and a percentage value, and produces a summary of the text by:

1. Tokenizing the text and calculating the frequency of each word in the document
2. Normalizing the word frequencies to the range [0, 1]
3. Tokenizing the text into sentences and calculating a score for each sentence based on the normalized word frequencies of the words it contains
4. Selecting the highest-scoring sentences based on the specified percentage of the total number of sentences
5. Returning the selected sentences as a single summary string

I then asked it to write that same code as c# and it gave me the (nearly: I had to add one cast) perfect LINQ:

            var words = Regex.Split(text, @"\s+")
                .Where(x => !stopWords.IsStopWord(x.ToLower()) && !char.IsPunctuation(x[0]))
                .GroupBy(x => x) 
                .ToDictionary(g => g.Key, g => (double)g.Count());

            var maxCount = words.Max(x => x.Value);
            words = words.ToDictionary(x => x.Key, x => (double)x.Value / maxCount);

            var sentences = Regex.Split(text, @"(?<=[\.!\?])\s+")
                .Select(x => x.Trim())
                .ToArray();

            var scores = sentences
                .Select(x => x.Split(' ')
                    .Select(y => words.ContainsKey(y) ? words[y] : 0)
                    .Sum())
                .ToArray();

            var summary = string.Join(" ", sentences
                .Zip(scores, (s, c) => new { Sentence = s, Score = c })
                .OrderByDescending(x => x.Score)
                .Take((int)(per * sentences.Length))
                .Select(x => x.Sentence));

That's a pretty high level of complexity to me.

I’ve been using it to teach myself basic coding for Excel Macros, yeah nothing fancy but it gave me enough of a ground level understanding to get started super fast.

Overall I think peoples expectations are too high right now. Let’s see what GPT4 does next year.

Where it really shines is having it write something “in the style of x”. Have x be a famous author, historical figure, Homeric style, Shakespearean English, etc. Really interesting results if one plays with it a bit.

I can't believe how many people are trying to use a chat bot to write code and expect it to work. Use a code bot.

> I see it as a better google search.

Except it's not even that. It's a slightly smarter chatbot, google finds links you can use.

It is extremely impressive at translating code between languages, code it has never seen before. And obviously that is not data that has been scraped from a web page.

However, a lot of people point out it doesn't write complex code that well. The thing is, it's not advertised as a tool to help out software engineers. The fact that it can actually write code as well as it does I find impressive enough.