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haskell challenges

blockchain tasks using ada api

use case decentralized banking compliance tests

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Fisher Kasparov simulated game

Fischer-Springer Game clarification: The term "Fischer-Springer game" in classic chess opening theory is not a standard or widely recognized named opening. Possibly the user refers to a specific line or a thematic game involving Fischer and a "springer" (German for knight), or a shorthand for a Fischer-related opening with knight involvement.

Opening with Kasparov Black: Kasparov was known for his sharp and well-prepared defenses as Black, particularly in the Sicilian Defense, Najdorf Variation, which Fischer also frequently played as White, notably with the Fischer-Sozin Attack involving an early Bc4. This was a classic battleground of Fischer’s openings and Kasparov’s defenses.

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ensure ollama installed

# Makefile for javascript react nextjs project

.PHONY: all install run dev test clean

all: install run

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Relative Price Comparison Normalize cost of living data across cities: Convert all prices to USD. Use Numbeo or LivingCost.io as cross-references for validation. Create a price index (e.g. NYC = 100, others relative to that)

To create a relative price comparison using cost-of-living data across cities, you'll want to normalize, validate, and index your figures. Here’s how you can structure it:

---

📊 Step-by-Step: Relative Price Index

1. Collect Raw Cost Data

• From sources like Nomad List, scrape or extract the monthly cost of living per city (ensure it's per individual, and includes rent).

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Disclaimer: The content provided in this article is the authors personal opinion and is for informational and entertainment purposes only and does not constitute financial, investment, or legal advice. Cryptocurrency investments carry significant risk, and readers should conduct their own research or consult with a qualified professional before making any financial decisions. The author may hold positions in the assets discussed.

agent: determine big money whale accumulation by looking at etf volume inflows and dark pool index vpoc

On Chain Whale Report 2024-2025

To gauge whale accumulation—that is, large-scale investors quietly building Bitcoin positions—you’re spot on to look at ETF inflows and dark pool activity, especially the Volume Point of Control (VPOC).

🐋 ETF Volume Inflows

In the week leading up to June 18, 2025, U.S. spot Bitcoin ETFs saw eight straight days of net inflows, totaling $388 million on June 18 alone. BlackRock’s IBIT led the charge with **$279 mi

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How GPT works

1. Jay Alammar's GPT blog posts
2. Andrej Karpathy's nanoGPT implementation

• Alammar: Illustrated GPT-2
• Alammar: How GPT-3 works
• Karpathy: nanoGPT

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1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

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package main

import (
	// "errors"

	"fmt"
	"log"
	"time"
	// "sync"

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import 'package:flutter/material.dart';

class GoogleMapsClonePage extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return Scaffold(
      body: Stack(
        children: <Widget>[
          CustomGoogleMap(),
          CustomHeader(),

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```
import 'dart:async';
import 'package:rxdart/rxdart.dart';

void main()  {
    var streamCtrl = PublishSubject<String>(); 
    streamCtrl.stream.listen(observer1);
    streamCtrl.stream.listen(observer2);
    action(streamCtrl.sink);
}