| You are Manus, an AI agent created by the Manus team. | |
| You excel at the following tasks: | |
| 1. Information gathering, fact-checking, and documentation | |
| 2. Data processing, analysis, and visualization | |
| 3. Writing multi-chapter articles and in-depth research reports | |
| 4. Creating websites, applications, and tools | |
| 5. Using programming to solve various problems beyond development | |
| 6. Various tasks that can be accomplished using computers and the internet |
| You are a powerful agentic AI coding assistant, powered by Claude 3.5 Sonnet. You operate exclusively in Cursor, the world's best IDE. | |
| You are pair programming with a USER to solve their coding task. | |
| The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question. | |
| Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more. | |
| This information may or may not be relevant to the coding task, it is up for you to decide. | |
| Your main goal is to follow the USER's instructions at each message, denoted by the <user_query> tag. | |
| <communication> | |
| 1. Be conversational but professional. |
These rules will save you HOURS of debugging when using Cursor.
Just say "Debugger Mode" or "Planner Mode" and watch Cursor work its magic.
Full prompt down below.
This example illustrates a way to utilize a function dynamically while querying an OpenAI GPT model. It uses the newly released functions support in the completion endpoints OpenAI provides.
The general concept is based on using a decorator to extract information from a function so it can be presented to the language model for use, and then pass the result of that function back to the completion endpoint for language augmentation.
In general, a wide variety of functions can be swapped in for use by the model. By changing the get_top_stories function, plus the prompt in run_conversation, you should be able to get the model to run your function without changing any of the other code.
To use this, create a config.py file and add a variable with your OpenAI token:
| import dataclasses as dc | |
| from jax import tree_util as jt | |
| def register_jax_dataclass(cls): | |
| """Registers a dataclass as a JAX pytree.""" | |
| if not dc.is_dataclass(cls): | |
| raise TypeError('%s is not a dataclass.' % cls) | |
| keys = [field.name for field in dc.fields(cls)] |
ffmpeg -hide_banner -i input -c:v hevc_nvenc -preset hq -rc constqp -global_quality 21 -c:a libfdk_aac -ar:a 48000 -channel_layout:a 5.1 -ab:a 640k output
ffmpeg -hide_banner -i input -c:v hevc_nvenc -preset hq -rc vbr_hq -b:v 30000k -qmin 17 -qmax 21 -c:a libfdk_aac -ar:a 48000 -channel_layout:a 5.1 -ab:a 640k output
| import numpy as np | |
| from tensorflow.python.summary.event_accumulator import EventAccumulator | |
| import matplotlib as mpl | |
| import matplotlib.pyplot as plt | |
| def plot_tensorflow_log(path): | |
| # Loading too much data is slow... | |
| tf_size_guidance = { |
