Ivan Reznikov IvanReznikov

## langchain_table.csv

          
            Feature
            LangChain
            LLM
            ChatGPT

            
              Type
              Framework
              Model
              Model

            
              Purpose
              Build applications with LLMs
              Generate text
              Generate chat conversations

            
              Key features
               Chains prompts memory
              Large dataset of text and code
              Large dataset of chat conversations

            
              Advantages
              Can combine LLMs with other programming techniques
              Generates human-quality text
              Generates realistic chat conversations

            
              Disadvantages
              Requires some programming knowledge
              Not as easy to use for specific tasks
              Not as versatile as LangChain

## is_nan.py
def pandas_nan(x):
    import pandas as pd
    return pd.isna(x)

def numpy_nan(x):
    import numpy as np
    return np.isnan(x)

def math_nan(x):
    import math

## Nsum-fast.go
import (
    "sort"
)

//In this solution you'll see some uglyness. Mostly because I don't know go well, particularly how to create a nested recursion function.
//For this reason I have a global var here
var results [][]int

func NSum(N int, left_index int, right_index int, target int, result []int, nums []int) int{
    //cases, where calculations are not necessary

## 3sum-closest-fast.go
import(
    "sort"
    "math"
)

func threeSumClosest(nums []int, target int) int {
    sort.Ints(nums)
    var current_min int
    var current_dif float64 = -1.0
    for i, n := range nums {

## 3sum-closest-slowpoke.go
import (
	"math"
)

func threeSumClosest(nums []int, target int) int {
	var current_min int
	var current_dif float64 = -1.0
	for i, n := range nums {
		for i2, n2 := range nums[i+1:] {
			for _, n3 := range nums[i2+i+2:] {

## three-sum-fast-solution.go
import (
    "sort"
)
func threeSum(nums []int) [][]int {
    sort.Ints(nums)
    var res [][]int
    left_index := 0
    right_index := len(nums) - 1
    for i, n := range nums{
        //skipping value if i>0, meaning all int left are possitive and if previous one is the same

## three-sum-slowpoke-solution.go
import (
	"sort"
)

func threeSum(nums []int) [][]int {
	var res [][]int
	sum_map := make(map[string]int)
	for i, n := range nums {
		for i2, n2 := range nums[i+1:] {
			for _, n3 := range nums[i2+i+2:] {

## two-sum-fast-solution.go
func twoSum(nums []int, target int) []int {
    dif_map := make(map[int]int)
    for i, n := range nums{
        if i2, ok := dif_map[target - n]; ok {
            return []int{i2, i}
        }
        dif_map[n] = i
    }
    return nil
}

## two-sum-slowpoke-solution.go
func twoSum(nums []int, target int) []int {
	for i, n := range nums{
		for i2, n2 := range nums[i+1:]{
			if n+n2 == target{
				return []int{i, i2+i+1}
			}
		}
	}
	return nil
}

## keaybase.md

      
        
          
            
              
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                IvanReznikov
                / keaybase.md
            
            
              Created
              July 3, 2020 17:13
            
          
        
      
        
  
      
    Keybase proof

I hereby claim:

I am ivanreznikov on github.
I am ivanreznikov (https://keybase.io/ivanreznikov) on keybase.
I have a public key ASBmauybB17mDJXQ37zE1GprifR1Z9aC5oSkrQJcpCABJQo

To claim this, I am signing this object:
Feature	LangChain	LLM	ChatGPT
Type	Framework	Model	Model
Purpose	Build applications with LLMs	Generate text	Generate chat conversations
Key features	Chains prompts memory	Large dataset of text and code	Large dataset of chat conversations
Advantages	Can combine LLMs with other programming techniques	Generates human-quality text	Generates realistic chat conversations
Disadvantages	Requires some programming knowledge	Not as easy to use for specific tasks	Not as versatile as LangChain
	def pandas_nan(x):
	import pandas as pd
	return pd.isna(x)

	def numpy_nan(x):
	import numpy as np
	return np.isnan(x)

	def math_nan(x):
	import math
	import (
	"sort"
	)

	//In this solution you'll see some uglyness. Mostly because I don't know go well, particularly how to create a nested recursion function.
	//For this reason I have a global var here
	var results [][]int

	func NSum(N int, left_index int, right_index int, target int, result []int, nums []int) int{
	//cases, where calculations are not necessary
	import(
	"sort"
	"math"
	)

	func threeSumClosest(nums []int, target int) int {
	sort.Ints(nums)
	var current_min int
	var current_dif float64 = -1.0
	for i, n := range nums {
	import (
	"sort"
	)
	func threeSum(nums []int) [][]int {
	sort.Ints(nums)
	var res [][]int
	left_index := 0
	right_index := len(nums) - 1
	for i, n := range nums{
	//skipping value if i>0, meaning all int left are possitive and if previous one is the same
	func twoSum(nums []int, target int) []int {
	dif_map := make(map[int]int)
	for i, n := range nums{
	if i2, ok := dif_map[target - n]; ok {
	return []int{i2, i}
	}
	dif_map[n] = i
	}
	return nil
	}
	func twoSum(nums []int, target int) []int {
	for i, n := range nums{
	for i2, n2 := range nums[i+1:]{
	if n+n2 == target{
	return []int{i, i2+i+1}
	}
	}
	}
	return nil
	}