Sholto Maud sholtomaud

To deploy, create a Lambda function and enable function URL (no auth - yolo), then use the handler above in your function. That same implementation will also work with API Gateway HTTP (aka v2), if you want to use ALB or API Gateway REST (aka v1) you should swap the schema used for parsing.

Then you can test using a POST request with this body:

{
  "jsonrpc": "2.0",
  "method": "tools/list",
  "id": 2
}

Mixture of Reflection (MoR) Model: Detailed Implementation ## Forward: The Next Generation of AI Models

Reflection-based AI models are poised to redefine how AI is utilized, shifting from generating rapid, surface-level responses to producing thoughtful, in-depth analyses. These models emphasize self-evaluation and iterative improvement, leveraging internal feedback loops to refine outputs and enhance performance over multiple cycles.

This year has seen a marked shift toward reflection models, which differ from earlier Mixture of Experts (MoE) architectures. While MoE models efficiently handle specific tasks using specialized subnetworks, reflection-based models integrate iterative reasoning, enabling them to "think" before delivering results. This approach allows for evaluating and correcting reasoning pathways, ultimately improving performance through self-critique.

The proposed Mixture of Reflection (MoR) architecture builds on this foundation by combining the strengths of MoE with reflection-based re

	# Step 1: Represent Universe State
	Initialize Ψ(t) in Hilbert space H

	# Step 2: Define Field Configurations
	Define configuration space M with measure μ
	For each (g, φ) in M:
	Represent fields as algebraic structures (groups, rings, etc.)

	# Step 3: Complexity Operator
	Define operator T acting on Ψ(t) to extract complexity

	#!/usr/bin/env python

	import socket
	import threading
	import subprocess

	def handle_client(client_socket):
	request = client_socket.recv(8192).decode('utf-8', errors='ignore')
	first_line = request.split('\n')[0]

	#!/usr/bin/env python

	import boto3
	import argparse
	from operator import itemgetter
	from collections import defaultdict


	def nested_defaultdict():
	return defaultdict(nested_defaultdict)

	#cloud-config
	package_upgrade: true
	packages:
	- nfs-utils
	- httpd
	- php
	runcmd:
	- echo "$(curl -s http://169.254.169.254/latest/meta-data/placement/availability-zone).FILE_SYSTEM_ID.efs.us-west-2.amazonaws.com:/ /var/www/html/efs nfs4 defaults" >> /etc/fstab
	- mkdir /var/www/html/efs
	- mount -a

	#!/bin/bash

	usage() {
	echo "Usage: $0 [-v <Ruth\|Matthew\|Amy>]" 1>&2
	echo "Ruth : Female English (US) - Default" 1>&2
	echo "Matthew : Male English (US)" 1>&2
	echo "Amy : Female English (British)" 1>&2
	exit
	}

	import ec2 = require('@aws-cdk/aws-ec2');
	import cdk = require('@aws-cdk/core');

	import { Fn, Tag, Resource } from '@aws-cdk/core';
	import { AmazonLinuxImage, UserData, InstanceType } from '@aws-cdk/aws-ec2';
	import { Role, ServicePrincipal, ManagedPolicy, CfnInstanceProfile } from '@aws-cdk/aws-iam'

	/**
	* Create my own Ec2 resource and Ec2 props as these are not yet defined in CDK
	* These classes abstract low level details from CloudFormation

	<!DOCTYPE HTML>
	<html>
	<body>
	<svg width="600" height="600" vertion="1.1"
	id="svg" xmlns="http://www.w3.org/2000/svg">

	<defs>
	<clipPath id="angleClip">
	<polygon id="atriangle" />
	</clipPath>