Rodolfo Rodriguez Girbes OctAg0nO

Cursor's Memory Bank

I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

flowchart TD

A distilled list of open-source CRM software

Source: https://opensource.com/business/16/2/top-6-open-source-crm-tools-2016 + community contributions

EspoCRM `github.com/espocrm/espocrm`_ PHP https://www.espocrm.com

This is not working complete code.

This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

Stay focused, keep building.

	import ast
	import logging
	import inspect

	from typing import Type, TypeVar
	from dspy import Assert, Module, ChainOfThought, Signature, InputField, OutputField
	from pydantic import BaseModel, ValidationError

	logger = logging.getLogger(__name__)
	logger.setLevel(logging.ERROR)

	# Synthetic data generation using DSPy
	from typing import List
	import dspy

	llm = dspy.OpenAI(model='gpt-3.5-turbo',api_key=openai_key)

	dspy.settings.configure(lm=llm)

	class FactGeneration(dspy.Signature):
	"""Generate facts and their veracity, it should be different than old data"""

	# Synthetic data generation using DSPy
	from typing import List
	import dspy

	llm = dspy.OpenAI(model='gpt-3.5-turbo',api_key=openai_key)

	dspy.settings.configure(lm=llm)

	class FactGeneration(dspy.Signature):
	"""Generate facts and their veracity"""

	1. # create new .py file with code found below
	2. # install ollama
	3. # install model you want “ollama run mistral”
	4. conda create -n autogen python=3.11
	5. conda activate autogen
	6. which python
	7. python -m pip install pyautogen
	7. ollama run mistral
	8. ollama run codellama
	9. # open new terminal

	import os
	import autogen
	import memgpt.autogen.memgpt_agent as memgpt_autogen
	import memgpt.autogen.interface as autogen_interface
	import memgpt.agent as agent
	import memgpt.system as system
	import memgpt.utils as utils
	import memgpt.presets as presets
	import memgpt.constants as constants
	import memgpt.personas.personas as personas

	import { BrowserModule } from '@angular/platform-browser';
	import { NgModule } from '@angular/core';
	import { HttpClientModule, HTTP_INTERCEPTORS } from '@angular/common/http';

	import { ApplicationHttpClient, applicationHttpClientCreator } from './http-client';

	import { AppComponent } from './app.component';
	import { HttpClient } from '@angular/common/http';

	@NgModule({

	import {HttpClient, HttpErrorResponse, HttpHeaders, HttpParams} from '@angular/common/http';
	import {Injectable} from '@angular/core';
	import {Observable} from 'rxjs/Observable';

	export interface IRequestOptions {
	headers?: HttpHeaders;
	observe?: 'body';
	params?: HttpParams;
	reportProgress?: boolean;
	responseType?: 'json';