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01a claude

Documentation for 01a_claude from the Json remedy repository.

Excellent question! You’re absolutely right to pause and reconsider. Looking at this JSON repair problem through an Elixir lens, there are indeed much more elegant approaches that could radically simplify the implementation. Let me explore some Elixir-native approaches:You’re absolutely right to question the direct port approach! Looking at this through an Elixir lens reveals much more elegant solutions that leverage the language’s unique strengths.

The key insights are:

1. Binary Pattern Matching is a Game Changer

Instead of iterating character by character, we can match entire patterns:

# Instead of: for each char, check if it's a quote, then iterate...
# We can do:
defp parse_string(<<"\"", content::binary>>) do
  # Handle quoted string
end
defp parse_string(<<char, rest::binary>>) when char in ?a..?z do
  # Handle unquoted string (auto-repair!)
end

2. Repair Rules as Data

Rather than imperative fix-this-then-that logic, we can declare repairs:

@repair_rules [
  {~r/True/, "true", "fix boolean"},
  {~r/([{,]\s*)([a-zA-Z_]\w*):/, ~S(\1"\2":), "quote keys"}
]

3. Streams for Large Files

Process JSON as a stream of transformations rather than loading everything:

"large.json"
|> File.stream!()
|> Stream.flat_map(&fix_line_issues/1)
|> repair_structural_issues()

4. Parser Combinators

Build complex parsers from simple, composable pieces that each handle one repair concern.

The Most Promising Approach

I think Approach 6 (Recursive Descent with Pattern Guards) is the sweet spot because:

  • It’s genuinely Elixir-native (not a port)
  • Uses binary pattern matching for speed
  • Auto-repairs as it parses (no separate fix phase)
  • Composable and testable functions
  • Self-documenting through pattern matches

Combined with some macro-generated rules for common regex-based fixes, this could be both more elegant AND faster than the Python version.

Would you like me to develop a full implementation of the pattern-matching recursive descent approach? It would be radically different from the Python version but solve the same problem more elegantly.