Vibe-Coding-Instruct

Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 732, in track_read
                  out = f_read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf2 in position 6: invalid continuation byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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instruction string	output string	prompt string
Create a coding assistant. Scenario 0: Provide a practical implementation plan.	Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Create a coding assistant. Scenario 0: Provide a practical implementation plan. ### Input: ### Response: Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy an AI application. Scenario 1: Provide a practical implementation plan.	Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy an AI application. Scenario 1: Provide a practical implementation plan. ### Input: ### Response: Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 2: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 2: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy an AI application. Scenario 3: Provide a practical implementation plan.	Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy an AI application. Scenario 3: Provide a practical implementation plan. ### Input: ### Response: Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Design a chatbot platform. Scenario 4: Provide a practical implementation plan.	Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a chatbot platform. Scenario 4: Provide a practical implementation plan. ### Input: ### Response: Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy a MERN application. Scenario 5: Provide a practical implementation plan.	Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy a MERN application. Scenario 5: Provide a practical implementation plan. ### Input: ### Response: Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling consideratio...
Integrate local LLMs. Scenario 6: Provide a practical implementation plan.	Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Integrate local LLMs. Scenario 6: Provide a practical implementation plan. ### Input: ### Response: Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Create a coding assistant. Scenario 7: Provide a practical implementation plan.	Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Create a coding assistant. Scenario 7: Provide a practical implementation plan. ### Input: ### Response: Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy an AI application. Scenario 8: Provide a practical implementation plan.	Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy an AI application. Scenario 8: Provide a practical implementation plan. ### Input: ### Response: Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy a MERN application. Scenario 9: Provide a practical implementation plan.	Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy a MERN application. Scenario 9: Provide a practical implementation plan. ### Input: ### Response: Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling consideratio...
Design a chatbot platform. Scenario 10: Provide a practical implementation plan.	Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a chatbot platform. Scenario 10: Provide a practical implementation plan. ### Input: ### Response: Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 11: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 11: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Add AI chat to an app. Scenario 12: Provide a practical implementation plan.	Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Add AI chat to an app. Scenario 12: Provide a practical implementation plan. ### Input: ### Response: Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 13: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 13: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Create a coding assistant. Scenario 14: Provide a practical implementation plan.	Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Create a coding assistant. Scenario 14: Provide a practical implementation plan. ### Input: ### Response: Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 15: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 15: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
API returns 500 errors. Scenario 16: Provide a practical implementation plan.	Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: API returns 500 errors. Scenario 16: Provide a practical implementation plan. ### Input: ### Response: Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Design a chatbot platform. Scenario 17: Provide a practical implementation plan.	Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a chatbot platform. Scenario 17: Provide a practical implementation plan. ### Input: ### Response: Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 18: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 18: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Design a chatbot platform. Scenario 19: Provide a practical implementation plan.	Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a chatbot platform. Scenario 19: Provide a practical implementation plan. ### Input: ### Response: Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Build an AI project manager. Scenario 20: Provide a practical implementation plan.	Create a full-stack application with planning, task tracking, AI suggestions, analytics, and collaboration tools. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Build an AI project manager. Scenario 20: Provide a practical implementation plan. ### Input: ### Response: Create a full-stack application with planning, task tracking, AI suggestions, analytics, and collaboration tools. Include milestones, technology choices, risks, testing strategy, and scaling c...
Integrate local LLMs. Scenario 21: Provide a practical implementation plan.	Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Integrate local LLMs. Scenario 21: Provide a practical implementation plan. ### Input: ### Response: Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy a MERN application. Scenario 22: Provide a practical implementation plan.	Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy a MERN application. Scenario 22: Provide a practical implementation plan. ### Input: ### Response: Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerati...
Add AI chat to an app. Scenario 23: Provide a practical implementation plan.	Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Add AI chat to an app. Scenario 23: Provide a practical implementation plan. ### Input: ### Response: Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
API returns 500 errors. Scenario 24: Provide a practical implementation plan.	Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: API returns 500 errors. Scenario 24: Provide a practical implementation plan. ### Input: ### Response: Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy a MERN application. Scenario 25: Provide a practical implementation plan.	Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy a MERN application. Scenario 25: Provide a practical implementation plan. ### Input: ### Response: Host frontend separately, deploy backend services, configure databases, secrets, monitoring, and backups. Include milestones, technology choices, risks, testing strategy, and scaling considerati...
Create a coding assistant. Scenario 26: Provide a practical implementation plan.	Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Create a coding assistant. Scenario 26: Provide a practical implementation plan. ### Input: ### Response: Use Electron, Monaco Editor, local LLM integration, chat memory, and project indexing. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 27: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 27: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Integrate local LLMs. Scenario 28: Provide a practical implementation plan.	Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Integrate local LLMs. Scenario 28: Provide a practical implementation plan. ### Input: ### Response: Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Add AI chat to an app. Scenario 29: Provide a practical implementation plan.	Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Add AI chat to an app. Scenario 29: Provide a practical implementation plan. ### Input: ### Response: Implement conversation memory, prompt templates, model routing, and streaming responses. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Build an AI project manager. Scenario 30: Provide a practical implementation plan.	Create a full-stack application with planning, task tracking, AI suggestions, analytics, and collaboration tools. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Build an AI project manager. Scenario 30: Provide a practical implementation plan. ### Input: ### Response: Create a full-stack application with planning, task tracking, AI suggestions, analytics, and collaboration tools. Include milestones, technology choices, risks, testing strategy, and scaling c...
React app re-renders infinitely. Scenario 31: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 31: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 32: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 32: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
API returns 500 errors. Scenario 33: Provide a practical implementation plan.	Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: API returns 500 errors. Scenario 33: Provide a practical implementation plan. ### Input: ### Response: Check logs, validate inputs, inspect database connectivity, and improve exception handling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Integrate local LLMs. Scenario 34: Provide a practical implementation plan.	Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Integrate local LLMs. Scenario 34: Provide a practical implementation plan. ### Input: ### Response: Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Integrate local LLMs. Scenario 35: Provide a practical implementation plan.	Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Integrate local LLMs. Scenario 35: Provide a practical implementation plan. ### Input: ### Response: Use Ollama or llama.cpp, manage context windows, and optimize inference performance. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Design a scalable SaaS. Scenario 36: Provide a practical implementation plan.	Use microservices, API gateway, PostgreSQL, Redis, monitoring, CI/CD, and container orchestration. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a scalable SaaS. Scenario 36: Provide a practical implementation plan. ### Input: ### Response: Use microservices, API gateway, PostgreSQL, Redis, monitoring, CI/CD, and container orchestration. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Deploy an AI application. Scenario 37: Provide a practical implementation plan.	Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Deploy an AI application. Scenario 37: Provide a practical implementation plan. ### Input: ### Response: Containerize services, configure GPUs if needed, add logging, metrics, and autoscaling. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
Design a chatbot platform. Scenario 38: Provide a practical implementation plan.	Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: Design a chatbot platform. Scenario 38: Provide a practical implementation plan. ### Input: ### Response: Use message queues, vector search, session storage, analytics, and model serving infrastructure. Include milestones, technology choices, risks, testing strategy, and scaling considerations.
React app re-renders infinitely. Scenario 39: Provide a practical implementation plan.	Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.	### Instruction: React app re-renders infinitely. Scenario 39: Provide a practical implementation plan. ### Input: ### Response: Inspect state updates, useEffect dependencies, memoization, and component hierarchy. Include milestones, technology choices, risks, testing strategy, and scaling considerations.