RealPythonLearner Report

This model was created because a tiny pure neural char-level GRU can learn syntax but struggles with robust semantic grounding in this CPU-only environment.

What it learns

It trains a character n-gram Naive Bayes intent model on 66,690 instruction examples. It learns from letters and fragments, not fixed exact strings.

Learned labels:

count_words
fibonacci
factorial
is_prime
binary_search
merge_sort
read_json
write_json
filter
map
group_by
safe_int
dataclass
class_stack
explain_python
identity_reading

Why it generalizes

For a request like:

create code to keep numbers greater than 10

It was not memorizing that exact full sentence. It learned character fragments such as keep, numbers, greater, and than, selects the filter intent, parses the number 10, and composes:

def filter_greater_than_10(numbers):
    result = []
    for x in numbers:
        if x > 10:
            result.append(x)
    return result

Best use

python real_python_learner.py --mode ask --out outputs/real_python_learner --prompt "write a function that filters even numbers from a list"