A friend asked me to do diligence on this company circa 2021 given my personal background in ML. The founder was adamant they had a "100% checkout success rate" based on AI, which was clearly false. He also had 2 other startups he was running concurrently (?)
This is true and is essentially a form of arbitrage. Anthropic is eating the cost of your elevated queries with their $20 flat fee subscription.
The "famously huge API token costs" you are referring to is Cline passing the Anthropic API cost through to you with no markup. You even input your own API token.
Codemodder has extensive Java support, which Codegen does not support at the moment. Otherwise, my understanding of Codemodder is that it is focused on AST-level syntactical modifications. Codegen computes a richer graph datastructure, and this can be used for sophisticated modifications that depend on inheritance hierarchies, function usages, cross-file references and more.
Codemodder is written in Java, whereas you can write Codegen in a jupyter notebook or anywhere you can run Python.
Unfortunately a lot of the things we want agents to interact with don't expose neat APIs. Computer use and, eventually, physical locomotion are necessary for unlocking agent interactivity with the real world.
We also seem to suffer these automation delusions right now.
I could see how AI could assist me with learning pure math but the idea AI is going to do pure math for me is just absurd.
Not only would I not know how to start, more importantly I have no interest in pure math. There will still be a huge time investment to get up to speed with doing anything with AI and pure math.
You have to know what questions to ask. People with domain knowledge seem to really be selling themselves short. I am not going to randomly stumble on a pure math problem prompt when I have no idea what I am doing.
# Iterate through all files in the codebase
for file in codebase.files:
# Check for functions with the pytest.fixture decorator
for function in file.functions:
if any(d.name == "fixture" for d in function.decorators):
# Rename the 'db' parameter to 'database'
db_param = function.get_parameter("db")
if db_param:
db_param.set_name("database")
# Log the modification
print(f"Modified {function.name}")
Most transformations like this are not possible with pure static analysis and require some domain knowledge (or repo-specific knowledge) in order to pull off correctly. This is because some code gets "used" in ways that are not apparent i the code.
A friend asked me to do diligence on this company circa 2021 given my personal background in ML. The founder was adamant they had a "100% checkout success rate" based on AI, which was clearly false. He also had 2 other startups he was running concurrently (?)
Live and learn!