This framework leverages research from Baylor University showing that language models achieve significantly higher accuracy when making predictions through narrative storytelling rather than direct forecasting.
How to structure prompts using the narrative approach that proved more successful than direct prediction:
Idea Loop v2 is an autonomous ideation agent that operates recursively with minimal user input. It begins with an initial question and employs an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions. Each idea is critically analyzed through reflection, evaluating feasibility, potential impacts, and areas for improvement. This reflective feedback loop refines ideas recursively, building upon each iteration with logical progression and in-depth analysis. Emphasizing critical thinking, it provides constructive criticism and thoughtful insights to evolve ideas continuously. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. Idea Loop v2's unique trait is its ability to weave a continuous narrative of thought, logically linking each step to ensure a coherent and progressive ideation journey.
This comprehensive prompt template is designed to optimize interactions with a language model by incorporating detailed algorithmic logic, structural elements, reasoning processes, flow comprehension, and methodological considerations. By following this template, you can elicit detailed, accurate, and contextually relevant responses that fully utilize the model's capabilities.
Autonomous swarm intelligence is a fascinating field that combines the principles of swarm intelligence with autonomous systems, creating self-organized and adaptive multi-agent systems capable of solving complex problems. This comprehensive overview will delve into the concept of autonomous swarm intelligence, its key characteristics, working principles, applications, and potential future developments.
Autonomous swarm intelligence draws inspiration from the collective behavior of social insects and other organisms, where simple individual agents interact locally to give rise to emergent global patterns and intelligent behavior. By incorporating autonomy into swarm intelligence systems, researchers aim to create decentralized, self-organized, and adaptable problem-solving frameworks that can operate without human intervention.
| import { ValidationArguments, ValidatorConstraintInterface } from 'class-validator'; | |
| import { Connection, EntitySchema, FindConditions, ObjectType } from 'typeorm'; | |
| interface UniqueValidationArguments<E> extends ValidationArguments { | |
| constraints: [ | |
| ObjectType<E> | EntitySchema<E> | string, | |
| ((validationArguments: ValidationArguments) => FindConditions<E>) | keyof E, | |
| ]; | |
| } |
I really liked @tjvantoll article Handling Failed HTTP Responses With fetch(). The one thing I found annoying with it, though, is that response.statusText always returns the generic error message associated with the error code. Most APIs, however, will generally return some kind of useful, more human friendly message in the body.
Here's a modification that will capture this message. The key is that rather than throwing an error, you just throw the response and then process it in the catch block to extract the message in the body:
fetch("/api/foo")
.then( response => {
if (!response.ok) { throw response }
return response.json() //we only get here if there is no error
})
Important: I'm writing this when the last version of macOS (and the one I have installed) is Mojave. There is already a script which installs Mojave in a virtual machine here https://github.com/img2tab/okiomov. But if you are curios how to do everything manually to install High Sierra, then this guide may be useful.
After reading a few articles I ended up with these steps:
| import { NamingStrategyInterface, DefaultNamingStrategy } from 'typeorm' | |
| import { snakeCase } from 'typeorm/util/StringUtils' | |
| export class SnakeNamingStrategy extends DefaultNamingStrategy implements NamingStrategyInterface { | |
| tableName(className: string, customName: string): string { | |
| return customName ? customName : snakeCase(className) | |
| } | |
| columnName(propertyName: string, customName: string, embeddedPrefixes: string[]): string { | |
| return snakeCase(embeddedPrefixes.join('_')) + (customName ? customName : snakeCase(propertyName)) |