There's a specific kind of frustration when code looks correct, passes syntax checks, and then crashes the moment you run it. After more than a decade of solo app development — with apps that have surpassed 50 million downloads in total — I can say that AI-generated runtime errors have their own distinct fingerprint.
Gemma 4 is remarkably good at producing syntactically valid code. The issue is that runtime correctness requires something different: awareness of actual data shapes, library versions, and async execution contexts. Antigravity's IDE makes the feedback loop tight, but knowing which pattern you're dealing with shortens the diagnostic process considerably.
Here are the four patterns that account for most of the runtime errors I encounter with Gemma 4-generated code, along with how to fix each.
Pattern 1: TypeError — Type Mismatch
Symptom: TypeError: unsupported operand type(s) for +: 'int' and 'str'
Gemma 4 infers types statically while generating code, but Python's dynamic typing means those inferences don't always hold at runtime. When a function can return either a string or an integer — or when an external data source returns unexpected types — the generated code often handles only the happy path.
# Gemma 4-generated (problematic)
def fetch_user_age(user_id: str) -> int:
result = db.query(f"SELECT age FROM users WHERE id = '{user_id}'")
return result[0] # some DB drivers return strings
# Caller
total = fetch_user_age("u001") + 5 # TypeError if result[0] is "30" not 30Fix:
def fetch_user_age(user_id: str) -> int:
result = db.query(f"SELECT age FROM users WHERE id = '{user_id}'")
return int(result[0]) # explicit castIn Antigravity, use Cmd+K inline edit and ask Gemma 4 to "add explicit type casts for this function's return value." It usually resolves this in one step.
Pattern 2: AttributeError — None Access
Symptom: AttributeError: 'NoneType' object has no attribute 'xxx'
Gemma 4 tends to generate optimistic code — it assumes data exists. API responses and database queries that return None on missing records are the most common trigger.
# Gemma 4-generated (problematic)
user = db.find_user(email=email)
print(user.name) # AttributeError if user is NoneFix — add a guard clause:
user = db.find_user(email=email)
if user is None:
raise ValueError(f"User not found: {email}")
print(user.name)Ask Antigravity's Chat: "Add a None check for the return value of find_user and raise a descriptive error." Providing the filename with @ context makes this faster and more accurate.
Pattern 3: asyncio Errors — Missing await or Event Loop Conflicts
Symptom: RuntimeWarning: coroutine 'xxx' was never awaited or RuntimeError: This event loop is already running
Gemma 4 handles async code generation well in isolation, but when it generates async def functions to drop into existing synchronous code, await sometimes gets left off. Jupyter Notebook environments are a common trigger for the double-loop error.
# Gemma 4-generated (problematic)
async def fetch_data(url: str) -> dict:
async with aiohttp.ClientSession() as session:
response = await session.get(url)
return await response.json()
def process():
data = fetch_data("https://api.example.com/data") # missing await
print(data) # prints coroutine object, not dataFixes by context:
# Script context — use asyncio.run()
def process():
data = asyncio.run(fetch_data("https://api.example.com/data"))
print(data)
# Jupyter / nested loop context
import nest_asyncio
nest_asyncio.apply()
loop = asyncio.get_event_loop()
data = loop.run_until_complete(fetch_data("https://api.example.com/data"))Antigravity's editor sometimes underlines missing await calls. When it doesn't catch it, referencing the file with @ and asking "check for missing await in async calls" will catch it reliably.
Pattern 4: ImportError — API Changes Across Library Versions
Symptom: ImportError: cannot import name 'xxx' from 'yyy' or AttributeError: module 'xxx' has no attribute 'yyy'
Gemma 4's training data has a cutoff, which means it may generate import paths that were valid in older library versions but have since been moved or removed.
# Gemma 4-generated (outdated import)
from langchain.chat_models import ChatOpenAI # deprecated in v0.1+
# Current correct import
from langchain_openai import ChatOpenAIDiagnostic steps:
- Note the module name and class in the error message
- Run
pip show [package-name]to check installed version - Check the library's migration guide or changelog
- Ask Antigravity Chat: "What's the current import path for
ChatOpenAIin langchain v0.3?"
If you have Context7 MCP connected to Antigravity, it will pull the actual current documentation and suggest the correct import path — this is the fastest resolution route for version-related errors.
A Systematic Debug Workflow in Antigravity
With those four patterns in mind, here's an efficient triage flow:
Step 1: Paste the full traceback into Antigravity Chat. Gemma 4 is good at reading stack traces — the error context alone often produces an accurate diagnosis.
Step 2: Follow up with "List three possible reasons this error is occurring." Getting multiple hypotheses surfaces edge cases that a single explanation might miss.
Step 3: Once you have a leading hypothesis, reference the file with @filename and ask for a targeted fix. The added context makes generated fixes more accurate and less likely to introduce new issues.
For more on systematic debugging approaches, see Debugging Mindset in the AI Era and Antigravity AI Debugging Guide. For Python SDK-specific errors, Python SDK Error Diagnosis and Fix covers additional patterns.
The Takeaway
Gemma 4's code generation is strong. Runtime errors from it tend to cluster around four predictable failure modes: type assumptions, None optimism, async context mismatches, and version drift. Recognizing the pattern cuts debugging time significantly.
Start by pasting your current error traceback into Antigravity Chat. In most cases, that single step will surface a clear path forward.