Friday, July 4, 2025

 This is a summary of the book titled “Predictable Winners” written by Ilya Trakhtenberg and Stuart Jackson and published by Stanford University Press in 2025. Launching a product and taking off from the runaway must be planned for says the author otherwise there are risks. “Predictable Winners” demonstrate patterns from a host of real-world case studies which maximize success at every stage and scope from testing prototypes to developing financial plans. It even calls for market research and revenue forecasting. As product owners articulate value propositions, they must manage risks by gauging interests, seeking improvements, and defining audience and market growth. It helps to understanding competition, building business case, and engage in launch planning. Winning companies often produce additional products and services.

In this book, the authors explore the art and science of launching successful innovations. Drawing from real-world examples, the book offers a comprehensive roadmap for navigating the uncertainty of product development—from prototype to launch—by prioritizing risk management, market insight, and strategic execution.

The authors argue that while innovation is often portrayed as a gamble, success can be engineered through disciplined, repeatable processes. Companies that manage risks effectively can push their success rates far beyond the typical 5% seen among new product launches. This begins with embracing the ”fail-fast” mindset—developing minimal prototypes, testing them rapidly, and using feedback to either pivot or proceed with confidence. Innovation, they emphasize, means accepting imperfection and using failure as an opportunity to learn and adapt.

A core theme of the book is deliberate market alignment. Breakthrough products succeed when they tap into underserved needs or enhance existing customer experiences. Case studies like Square (which empowered small businesses with affordable payment systems) and 3M (which leveraged material science expertise to create sticky notes) illustrate how applying unique strengths to familiar domains allows innovators to outpace competitors.

Feedback is another vital pillar. The authors advocate using analogs—similar existing products—as indicators of potential success. They also encourage social listening and direct user testing to gauge demand early. Entrepreneurs, for example, might use farmer’s markets or five-day design sprints to gather low-cost, actionable insights before investing heavily. The idea is simple: match the scale of your investment to your confidence in the product’s viability.

Identifying the right audience and scaling strategically is also key. Innovators must view early adopters not as an end goal but as a gateway to broader markets. Defining one’s serviceable addressable market (SAM) and segmenting potential customers by behavior, values, or financial status helps refine this focus. Intuitive Surgical’s initial success with urologists, for example, set the stage for wider adoption of its robotic surgery systems.

Market research, when done right, becomes a predictive tool. Interviews and surveys should uncover not just surface opinions but the true decision-making criteria of target customers. Observing what similar customers already buy can 44be more revealing than what they claim they might purchase. For example, evaluating streaming habits helped one sports entertainment firm model demand for a new service more accurately.

Understanding the competitive landscape is essential. Using methods like “wargaming” to simulate competitor responses helps anticipate threats before launch. Motorola’s Iridium satellite phone failure serves as a cautionary tale of underestimating market changes during prolonged development cycles. Instead of grand-scale surveys up front, the authors recommend iterative, year-over-year insights to stay nimble.

Another crucial aspect is building a credible business case. Innovators need to align pricing with perceived value and willingness to pay, especially among early adopters who may accept higher prices. Revenue forecasts should be built both bottom-up (from pricing and estimated units sold) and top-down (from projected market share). Stress-testing those forecasts through scenario planning or Monte Carlo simulations ensures realistic expectations.

Finally, the authors underscore the importance of thoughtful launch planning. Many innovations falter not because of design flaws, but due to poor rollout. Launch strategies should be drafted months in advance and continuously refined. They should address adoption barriers—like trust or usability—and map out team roles, deliverables, and market triggers. Uber’s strategy to mitigate user safety concerns, for instance, was a pivotal factor in its growth.

In conclusion, Predictable Winners debunks the myth of innovation as a guessing game. Instead, it presents innovation as an intentional, learnable discipline that rewards preparation, resilience, and empathy for customers. As companies scale, balancing breakthrough efforts with incremental improvements becomes the foundation of long-term success—just as Garmin did when it transitioned from GPS units to fitness trackers. Success isn’t just about launching; it’s about listening, learning, and continuously evolving.

#codingexercise: CodingExercise-07-04-2025.docx

Thursday, July 3, 2025

 #Codingexercise

Given a wire grid of size N * N with N-1 horizontal edges and N-1 vertical edges along the X and Y axis respectively, and a wire burning out every instant as per the given order using three matrices A, B, C such that the wire that burns is

(A[T], B[T] + 1), if C[T] = 0 or

(A[T] + 1, B[T]), if C[T] = 1

Determine the instant after which the circuit is broken

     public static boolean checkConnections(int[] h, int[] v, int N) {

        boolean[][] visited = new boolean[N][N];

        dfs(h, v, visited,0,0);

        return visited[N-1][N-1];

    }

    public static void dfs(int[]h, int[]v, boolean[][] visited, int i, int j) {

        int N = visited.length;

        if (i < N && j < N && i>= 0 && j >= 0 && !visited[i][j]) {

            visited[i][j] = true;

            if (v[i * (N-1) + j] == 1) {

                dfs(h, v, visited, i, j+1);

            }

            if (h[i * (N-1) + j] == 1) {

                dfs(h, v, visited, i+1, j);

            }

            if (i > 0 && h[(i-1)*(N-1) + j] == 1) {

                dfs(h,v, visited, i-1, j);

            }

            if (j > 0 && h[(i * (N-1) + (j-1))] == 1) {

                dfs(h,v, visited, i, j-1);

            }

        }

    }

    public static int burnout(int N, int[] A, int[] B, int[] C) {

        int[] h = new int[N*N];

        int[] v = new int[N*N];

        for (int i = 0; i < N*N; i++) { h[i] = 1; v[i] = 1; }

        for (int i = 0; i < N; i++) {

            h[(i * (N)) + N - 1] = 0;

            v[(N-1) * (N) + i] = 0;

        }

        System.out.println(printArray(h));

        System.out.println(printArray(v));

        for (int i = 0; i < A.length; i++) {

            if (C[i] == 0) {

                v[A[i] * (N-1) + B[i]] = 0;

            } else {

                h[A[i] * (N-1) + B[i]] = 0;

            }

            if (!checkConnections(h,v, N)) {

                return i+1;

            }

        }

        return -1;

    }

        int[] A = new int[9];

        int[] B = new int[9];

        int[] C = new int[9];

        A[0] = 0; B [0] = 0; C[0] = 0;

        A[1] = 1; B [1] = 1; C[1] = 1;

        A[2] = 1; B [2] = 1; C[2] = 0;

        A[3] = 2; B [3] = 1; C[3] = 0;

        A[4] = 3; B [4] = 2; C[4] = 0;

        A[5] = 2; B [5] = 2; C[5] = 1;

        A[6] = 1; B [6] = 3; C[6] = 1;

        A[7] = 0; B [7] = 1; C[7] = 0;

        A[8] = 0; B [8] = 0; C[8] = 1;

        System.out.println(burnout(9, A, B, C));

1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0

8

Alternatively,

    public static boolean burnWiresAtT(int N, int[] A, int[] B, int[] C, int t) {

        int[] h = new int[N*N];

        int[] v = new int[N*N];

        for (int i = 0; i < N*N; i++) { h[i] = 1; v[i] = 1; }

        for (int i = 0; i < N; i++) {

            h[(i * (N)) + N - 1] = 0;

            v[(N-1) * (N) + i] = 0;

        }

        System.out.println(printArray(h));

        System.out.println(printArray(v));

        for (int i = 0; i < t; i++) {

            if (C[i] == 0) {

                v[A[i] * (N-1) + B[i]] = 0;

            } else {

                h[A[i] * (N-1) + B[i]] = 0;

            }

        }

        return checkConnections(h, v, N);

    }

    public static int binarySearch(int N, int[] A, int[] B, int[] C, int start, int end) {

        if (start == end) {

            if (!burnWiresAtT(N, A, B, C, end)){

                return end;

            }

            return -1;

        } else {

            int mid = (start + end)/2;

            if (burnWiresAtT(N, A, B, C, mid)) {

                return binarySearch(N, A, B, C, mid + 1, end);

            } else {

                return binarySearch(N, A, B, C, start, mid);

            }

        }

    }

1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0

8

# another codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNZtJwEAAAABshhrvJLSFN5M5D4gUrCASQ?e=dOCJd9

#another https://1drv.ms/w/c/d609fb70e39b65c8/Echlm-Nw-wkggNZWJwEAAAABSyuJZ0hy_QwL1EyPKtXITw?e=uY5xGy


Wednesday, July 2, 2025

 The following sample is an addition to the Hungarian algorithm described in the previous article to allow a drone to calculate its flight path from the current to the next position using the shortest possible route. It uses the dubins package from PyPI — a lightweight and widely used implementation for shortest-path planning under curvature constraints. 

#! /usr/bin/python 

# pip install dubins matplotlib numpy 

import dubins 
import numpy as np 
import matplotlib.pyplot as plt 
 
def compute_dubins_path( 
    start_pos: tuple[float, float, float], 
    end_pos: tuple[float, float, float], 
    turning_radius: float = 10.0, 
    step_size: float = 1.0 
): 
    """ 
    Compute the shortest Dubins path between two configurations. 
 
    Parameters: 
        start_pos: (x, y, heading in radians) 
        end_pos: (x, y, heading in radians) 
        turning_radius: Minimum turning radius of the drone 
        step_size: Distance between sampled points along the path 
 
    Returns: 
        List of (x, y, heading) tuples along the path 
    """ 
    path = dubins.shortest_path(start_pos, end_pos, turning_radius) 
    configurations, _ = path.sample_many(step_size) 
    return configurations 
 
# Example usage 
if __name__ == "__main__": 
    # Initial and final positions: (x, y, heading in radians) 
    q0 = (0.0, 0.0, np.deg2rad(0))       # Start at origin, facing east 
    q1 = (50.0, 30.0, np.deg2rad(90))    # End at (50,30), facing north 
 
    path_points = compute_dubins_path(q0, q1, turning_radius=15.0, step_size=0.5) 
 
    # Plotting the path 
    x_vals, y_vals = zip(*[(x, y) for x, y, _ in path_points]) 
    plt.figure(figsize=(8, 6)) 
    plt.plot(x_vals, y_vals, label="Dubins Path", linewidth=2) 
    plt.scatter(*q0[:2], color='green', label="Start") 
    plt.scatter(*q1[:2], color='red', label="End") 
    plt.axis("equal") 
    plt.title("Dubins Path for Drone Flight") 
    plt.xlabel("X") 
    plt.ylabel("Y") 
    plt.legend() 
    plt.grid(True) 
    plt.show() 


#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EYzGgu5Fc4dEoCUHWQYxMbUBSfvC36iKh8ESBaLtozvdqA?e=uXE4Jm