Roman Leventov leventov

Roman: Imagine an agent made of yourself (i.e., GPT-4 model) with some software harness around. This software harness models plan execution by keeping a hierarchical tree of goals and tasks and supplying it in the beginning of the GPT-4 context, where the GPT-4 model is repeatedly asked to either create or update a plan for achieving a specific goal or task in the hierarchy, or drill down and execute a sub-task, or assess whether a task or goal is achieved and move to the next step in the higher-level task.

With some periodicity, when the highest-level task in the tree is completed, GPT-4 is prompted with the question "What I should do next?", to create a new highest-level goal or task.

How would you answer this question if you was a part of such an agent?

GPT: As a part of such an agent, when prompted with the question "What should I do next?" after completing the highest-level task in the tree, I would analyze the current context, available resources, and any constraints or priorities that might

Keybase proof

I hereby claim:

I am leventov on github.
I am leventov (https://keybase.io/leventov) on keybase.
I have a public key ASDzs8Wl-lVqJjYp08P567ms5Hx5uQ9qFvxqOwkyfiQ3LQo

To claim this, I am signing this object:

	import (
	"log"
	"math/rand"
	"testing"

	"github.com/montanaflynn/stats"
	"gonum.org/v1/gonum/floats"
	)

	func TestMidCardinalitySimulation(t *testing.T) {


	# (Pseudocode)

	def shift_distance(chain_end, base):
	return (chain_end - base) & (capacity - 1)

	def next(index):
	return (index + 1) & (capacity - 1)

	def prev(index):

	package tests;

	import java.util.*;
	import java.util.Map.Entry;
	import java.util.concurrent.TimeUnit;

	import org.openjdk.jmh.annotations.Benchmark;
	import org.openjdk.jmh.annotations.BenchmarkMode;
	import org.openjdk.jmh.annotations.Fork;
	import org.openjdk.jmh.annotations.Measurement;

	package tests;

	import com.google.common.hash.HashFunction;
	import net.openhft.chronicle.hash.hashing.Accesses;
	import net.openhft.chronicle.hash.hashing.Hasher;
	import net.openhft.hashing.LongHashFunction;
	import org.openjdk.jmh.annotations.*;

	import java.util.concurrent.TimeUnit;

	package spoon.test;

	import spoon.reflect.declaration.*;

	import java.util.ArrayList;
	import java.util.IntSummaryStatistics;
	import java.util.TreeSet;
	import java.util.function.Consumer;
	import java.util.function.ToIntFunction;

	import json

	MIN_STARS = 700

	with open('{}.json'.format(MIN_STARS)) as json_file:
	repos = json.load(json_file)

	repos_by_language = {}
	by_top = {}
	for repo in repos:

	loadLevel collections overuseFactor
	1 hftc 7.226
	1 hftc 10.791
	1 hftc 7.470
	1 hftc 11.172
	1 hftc 7.748
	1 hftc 11.576
	1 hftc 8.039
	1 hftc 11.993
	1 hftc 8.339