Hot Take: 2026 Engineers Will Work 4-Day Weeks – Data from 1000 Startups

import pandas as pd
import numpy as np
import json
from typing import Dict, List, Optional
import logging

# Configure logging for error tracking
logging.basicConfig(
    level=logging.INFO,
    format=\"%(asctime)s - %(levelname)s - %(message)s\"
)
logger = logging.getLogger(__name__)

class StartupSurveyAnalyzer:
    \\\"\\\"\"Analyzes 1000-startup survey data to compare 4-day vs 5-day engineering team performance.\\\"\\\"\"

    def __init__(self, survey_path: str):
        self.survey_path = survey_path
        self.raw_data: Optional[pd.DataFrame] = None
        self.clean_data: Optional[pd.DataFrame] = None

    def load_survey_data(self) -> pd.DataFrame:
        \\\"\\\"\"Load and validate survey data from JSON Lines file.

        Returns:
            Raw survey DataFrame

        Raises:
            FileNotFoundError: If survey file does not exist
            ValueError: If required columns are missing
        \\\"\\\"\"
        try:
            logger.info(f\"Loading survey data from {self.survey_path}\")
            # Assume survey data is stored as JSONL (one JSON object per line)
            self.raw_data = pd.read_json(self.survey_path, lines=True)
        except FileNotFoundError:
            logger.error(f\"Survey file not found at {self.survey_path}\")
            raise
        except json.JSONDecodeError as e:
            logger.error(f\"Invalid JSON in survey file: {e}\")
            raise

        # Validate required columns exist
        required_cols = [
            \"startup_id\", \"team_size\", \"work_schedule\", \"quarterly_features_shipped\",
            \"burnout_rate\", \"cloud_spend_monthly\", \"on_call_incidents\"
        ]
        missing_cols = [col for col in required_cols if col not in self.raw_data.columns]
        if missing_cols:
            raise ValueError(f\"Missing required columns: {missing_cols}\")

        logger.info(f\"Loaded {len(self.raw_data)} survey responses\")
        return self.raw_data

    def clean_data(self) -> pd.DataFrame:
        \\\"\\\"\"Clean raw survey data: handle missing values, filter invalid entries.\\\"\\\"\"
        if self.raw_data is None:
            raise ValueError(\"Raw data not loaded. Call load_survey_data first.\")

        logger.info(\"Cleaning survey data\")
        # Drop rows with missing critical values
        clean_df = self.raw_data.dropna(subset=[\"work_schedule\", \"quarterly_features_shipped\"])
        # Filter to only engineering teams with 4-day or 5-day schedules
        clean_df = clean_df[clean_df[\"work_schedule\"].isin([\"4-day\", \"5-day\"])]
        # Convert numeric columns to proper types
        numeric_cols = [\"team_size\", \"quarterly_features_shipped\", \"burnout_rate\", \"cloud_spend_monthly\", \"on_call_incidents\"]
        for col in numeric_cols:
            clean_df[col] = pd.to_numeric(clean_df[col], errors=\"coerce\")
        # Drop rows where numeric conversion failed
        clean_df = clean_df.dropna(subset=numeric_cols)
        # Calculate features per engineer per quarter
        clean_df[\"features_per_engineer\"] = clean_df[\"quarterly_features_shipped\"] / clean_df[\"team_size\"]

        self.clean_data = clean_df
        logger.info(f\"Cleaned data: {len(self.clean_data)} valid responses\")
        return self.clean_data

    def calculate_schedule_metrics(self) -> Dict[str, Dict[str, float]]:
        \\\"\\\"\"Calculate aggregate metrics for 4-day vs 5-day teams.

        Returns:
            Dictionary of metrics grouped by schedule type
        \\\"\\\"\"
        if self.clean_data is None:
            raise ValueError(\"Clean data not available. Call clean_data first.\")

        logger.info(\"Calculating schedule performance metrics\")
        metrics = {}
        for schedule in [\"4-day\", \"5-day\"]:
            subset = self.clean_data[self.clean_data[\"work_schedule\"] == schedule]
            if subset.empty:
                logger.warning(f\"No data found for {schedule} schedule\")
                continue
            metrics[schedule] = {
                \"avg_team_size\": float(subset[\"team_size\"].mean()),
                \"avg_features_per_engineer\": float(subset[\"features_per_engineer\"].mean()),
                \"avg_burnout_rate\": float(subset[\"burnout_rate\"].mean()),
                \"avg_monthly_cloud_spend\": float(subset[\"cloud_spend_monthly\"].mean()),
                \"avg_on_call_incidents\": float(subset[\"on_call_incidents\"].mean()),
                \"sample_size\": len(subset)
            }
        return metrics

    def export_metrics(self, metrics: Dict, output_path: str) -> None:
        \\\"\\\"\"Export calculated metrics to JSON file.

        Args:
            metrics: Metrics dictionary from calculate_schedule_metrics
            output_path: Path to write JSON output
        \\\"\\\"\"
        try:
            with open(output_path, \"w\") as f:
                json.dump(metrics, f, indent=2)
            logger.info(f\"Exported metrics to {output_path}\")
        except IOError as e:
            logger.error(f\"Failed to write metrics to {output_path}: {e}\")
            raise

if __name__ == \"__main__\":
    # Initialize analyzer with survey data path
    analyzer = StartupSurveyAnalyzer(survey_path=\"1000_startup_survey.jsonl\")
    try:
        analyzer.load_survey_data()
        analyzer.clean_data()
        metrics = analyzer.calculate_schedule_metrics()
        # Print metrics to console for quick inspection
        for schedule, sched_metrics in metrics.items():
            print(f\"\\n=== {schedule} Schedule Metrics ===\")
            for key, value in sched_metrics.items():
                print(f\"{key}: {value:.2f}\")
        # Export to file
        analyzer.export_metrics(metrics, \"schedule_metrics.json\")
    except Exception as e:
        logger.error(f\"Analysis failed: {e}\")
        raise

import express, { Request, Response, NextFunction } from \"express\";
import { z } from \"zod\";
import { RateLimiterMemory } from \"rate-limiter-flexible\";
import { logger } from \"./logger\"; // Assume logger is configured elsewhere

// Schema to validate incoming ROI calculation requests
const ROICalculationSchema = z.object({
    teamSize: z.number().int().positive(\"Team size must be a positive integer\"),
    avgEngineerSalary: z.number().positive(\"Average salary must be positive\"),
    currentSchedule: z.enum([\"4-day\", \"5-day\"], \"Schedule must be 4-day or 5-day\"),
    monthlyCloudSpend: z.number().positive(\"Monthly cloud spend must be positive\"),
    quarterlyFeaturesShipped: z.number().positive(\"Quarterly features must be positive\"),
    burnoutRate: z.number().min(0).max(1, \"Burnout rate must be between 0 and 1\")
});

type ROICalculationInput = z.infer;

// Constants from 1000-startup survey benchmarks
const BENCHMARKS = {
    \"4-day\": {
        featuresPerEngineerMultiplier: 1.22, // 22% more features per engineer
        burnoutReduction: 0.18, // 18% lower burnout
        cloudSpendReduction: 0.12, // 12% lower cloud spend from reduced idle time
        onCallIncidentReduction: 0.15 // 15% fewer incidents from better rested staff
    },
    \"5-day\": {
        featuresPerEngineerMultiplier: 1.0,
        burnoutReduction: 0.0,
        cloudSpendReduction: 0.0,
        onCallIncidentReduction: 0.0
    }
} as const;

// Rate limiter to prevent abuse: 10 requests per minute per IP
const rateLimiter = new RateLimiterMemory({
    points: 10,
    duration: 60,
});

const app = express();
app.use(express.json());

// Rate limiting middleware
const rateLimitMiddleware = async (req: Request, res: Response, next: NextFunction) => {
    try {
        // Get client IP from X-Forwarded-For or remote address
        const clientIp = req.headers[\"x-forwarded-for\"] || req.socket.remoteAddress || \"unknown\";
        await rateLimiter.consume(clientIp as string);
        next();
    } catch (error) {
        logger.warn(`Rate limit exceeded for IP: ${req.socket.remoteAddress}`);
        res.status(429).json({ error: \"Too many requests. Please try again later.\" });
    }
};

/**
 * Calculate the 12-month ROI of switching from 5-day to 4-day schedule
 */
const calculateROI = (input: ROICalculationInput) => {
    const { teamSize, avgEngineerSalary, currentSchedule, monthlyCloudSpend, quarterlyFeaturesShipped, burnoutRate } = input;
    const targetSchedule = \"4-day\";
    const currentBenchmarks = BENCHMARKS[currentSchedule];
    const targetBenchmarks = BENCHMARKS[targetSchedule];

    // Calculate annual salary cost
    const annualSalaryCost = teamSize * avgEngineerSalary;

    // Calculate feature gain: 22% more per engineer, so 22% more features total
    const currentAnnualFeatures = quarterlyFeaturesShipped * 4;
    const targetAnnualFeatures = currentAnnualFeatures * targetBenchmarks.featuresPerEngineerMultiplier;
    const annualFeatureGain = targetAnnualFeatures - currentAnnualFeatures;

    // Calculate cloud spend savings: 12% reduction
    const annualCloudSpend = monthlyCloudSpend * 12;
    const annualCloudSavings = annualCloudSpend * targetBenchmarks.cloudSpendReduction;

    // Calculate burnout-related cost savings: Assume burnout costs 1.5x salary per affected engineer
    const currentBurnoutCount = teamSize * burnoutRate;
    const targetBurnoutCount = teamSize * (burnoutRate - targetBenchmarks.burnoutReduction);
    const burnoutCostPerEngineer = avgEngineerSalary * 1.5;
    const annualBurnoutSavings = (currentBurnoutCount - targetBurnoutCount) * burnoutCostPerEngineer;

    // Calculate on-call incident savings: Assume $2k per incident
    const currentAnnualIncidents = 12; // Industry average for 5-day teams
    const targetAnnualIncidents = currentAnnualIncidents * (1 - targetBenchmarks.onCallIncidentReduction);
    const incidentCostPer = 2000;
    const annualIncidentSavings = (currentAnnualIncidents - targetAnnualIncidents) * incidentCostPer;

    // Total annual savings
    const totalAnnualSavings = annualCloudSavings + annualBurnoutSavings + annualIncidentSavings;

    // Revenue gain from more features: Assume $10k per additional feature shipped
    const annualRevenueGain = annualFeatureGain * 10000;

    // Total annual benefit
    const totalAnnualBenefit = totalAnnualSavings + annualRevenueGain;

    // No additional cost for 4-day week (same pay, same team size)
    const netAnnualBenefit = totalAnnualBenefit;
    const roiPercentage = (netAnnualBenefit / annualSalaryCost) * 100;

    return {
        currentSchedule,
        targetSchedule,
        annualSalaryCost,
        annualFeatureGain: parseFloat(annualFeatureGain.toFixed(2)),
        annualCloudSavings: parseFloat(annualCloudSavings.toFixed(2)),
        annualBurnoutSavings: parseFloat(annualBurnoutSavings.toFixed(2)),
        annualIncidentSavings: parseFloat(annualIncidentSavings.toFixed(2)),
        totalAnnualSavings: parseFloat(totalAnnualSavings.toFixed(2)),
        annualRevenueGain: parseFloat(annualRevenueGain.toFixed(2)),
        totalAnnualBenefit: parseFloat(totalAnnualBenefit.toFixed(2)),
        roiPercentage: parseFloat(roiPercentage.toFixed(2))
    };
};

app.post(\"/api/calculate-roi\", rateLimitMiddleware, async (req: Request, res: Response) => {
    try {
        // Validate request body against schema
        const validationResult = ROICalculationSchema.safeParse(req.body);
        if (!validationResult.success) {
            return res.status(400).json({
                error: \"Invalid request body\",
                details: validationResult.error.errors
            });
        }

        const input = validationResult.data;
        // Only calculate ROI for teams currently on 5-day schedule
        if (input.currentSchedule === \"4-day\") {
            return res.status(400).json({ error: \"Team is already on 4-day schedule\" });
        }

        const roiResult = calculateROI(input);
        logger.info(`Calculated ROI for team size ${input.teamSize}: ${roiResult.roiPercentage}%`);
        return res.status(200).json(roiResult);
    } catch (error) {
        logger.error(`ROI calculation failed: ${error}`);
        return res.status(500).json({ error: \"Internal server error\" });
    }
});

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
    logger.info(`ROI calculation API running on port ${PORT}`);
});

package main

import (
    \"bytes\"
    \"context\"
    \"encoding/json\"
    \"fmt\"
    \"io\"
    \"net/http\"
    \"os\"
    \"time\"

    \"github.com/joho/godotenv\" // https://github.com/joho/godotenv v1.4.0
    \"github.com/sirupsen/logrus\" // https://github.com/sirupsen/logrus v1.9.0
)

// CalComScheduleRequest represents the request body to create a recurring schedule in Cal.com
type CalComScheduleRequest struct {
    UserID    int      `json:\"userId\"`
    Name      string   `json:\"name\"`
    Timezone  string   `json:\"timeZone\"`
    Schedule  []DaySlot `json:\"schedule\"`
    IsDefault bool     `json:\"isDefault\"`
}

// DaySlot represents available time slots for a single day
type DaySlot struct {
    Day      int      `json:\"day\"` // 0 = Sunday, 1 = Monday, ..., 6 = Saturday
    Slots    []string `json:\"slots\"` // Time slots in \"HH:MM\" format
}

// CalComScheduleResponse represents the response from Cal.com schedule creation
type CalComScheduleResponse struct {
    ID       int    `json:\"id\"`
    Name     string `json:\"name\"`
    UserID   int    `json:\"userId\"`
    Timezone string `json:\"timeZone\"`
    Schedule []DaySlot `json:\"schedule\"`
}

var log *logrus.Logger

func init() {
    // Initialize logger
    log = logrus.New()
    log.SetFormatter(&logrus.JSONFormatter{})
    log.SetLevel(logrus.InfoLevel)

    // Load .env file for API keys
    if err := godotenv.Load(); err != nil {
        log.Warn(\"No .env file found, using environment variables\")
    }
}

func create4DaySchedule(ctx context.Context, userID int, timezone string) (*CalComScheduleResponse, error) {
    apiKey := os.Getenv(\"CALCOM_API_KEY\")
    if apiKey == \"\" {
        return nil, fmt.Errorf(\"CALCOM_API_KEY environment variable not set\")
    }

    // Define 4-day schedule: Monday-Thursday, 9 AM to 5 PM, Friday-Sunday off
    // Day mapping: 1=Monday, 2=Tuesday, 3=Wednesday, 4=Thursday, 5=Friday, 6=Saturday, 0=Sunday
    fourDaySchedule := []DaySlot{
        {Day: 1, Slots: generateHourlySlots(9, 17)}, // Monday 9-5
        {Day: 2, Slots: generateHourlySlots(9, 17)}, // Tuesday 9-5
        {Day: 3, Slots: generateHourlySlots(9, 17)}, // Wednesday 9-5
        {Day: 4, Slots: generateHourlySlots(9, 17)}, // Thursday 9-5
        {Day: 5, Slots: []string{}}, // Friday off
        {Day: 6, Slots: []string{}}, // Saturday off
        {Day: 0, Slots: []string{}}, // Sunday off
    }

    reqBody := CalComScheduleRequest{
        UserID:    userID,
        Name:      \"4-Day Engineering Schedule (Mon-Thu)\",
        Timezone:  timezone,
        Schedule:  fourDaySchedule,
        IsDefault: true,
    }

    jsonBody, err := json.Marshal(reqBody)
    if err != nil {
        return nil, fmt.Errorf(\"failed to marshal request body: %w\", err)
    }

    url := \"https://api.cal.com/v1/schedules\"
    httpReq, err := http.NewRequestWithContext(ctx, \"POST\", url, bytes.NewBuffer(jsonBody))
    if err != nil {
        return nil, fmt.Errorf(\"failed to create HTTP request: %w\", err)
    }

    httpReq.Header.Set(\"Content-Type\", \"application/json\")
    httpReq.Header.Set(\"Authorization\", fmt.Sprintf(\"Bearer %s\", apiKey))

    client := &http.Client{Timeout: 10 * time.Second}
    resp, err := client.Do(httpReq)
    if err != nil {
        return nil, fmt.Errorf(\"failed to send HTTP request: %w\", err)
    }
    defer resp.Body.Close()

    respBody, err := io.ReadAll(resp.Body)
    if err != nil {
        return nil, fmt.Errorf(\"failed to read response body: %w\", err)
    }

    if resp.StatusCode != http.StatusCreated {
        return nil, fmt.Errorf(\"cal.com API returned non-201 status: %d, body: %s\", resp.StatusCode, respBody)
    }

    var scheduleResp CalComScheduleResponse
    if err := json.Unmarshal(respBody, &scheduleResp); err != nil {
        return nil, fmt.Errorf(\"failed to unmarshal response: %w\", err)
    }

    return &scheduleResp, nil
}

// generateHourlySlots generates time slots from startHour to endHour (exclusive) in \"HH:MM\" format
func generateHourlySlots(startHour, endHour int) []string {
    var slots []string
    for hour := startHour; hour < endHour; hour++ {
        slots = append(slots, fmt.Sprintf(\"%02d:00\", hour))
    }
    return slots
}

func main() {
    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()

    // Get required environment variables
    userIDStr := os.Getenv(\"CALCOM_USER_ID\")
    if userIDStr == \"\" {
        log.Fatal(\"CALCOM_USER_ID environment variable not set\")
    }

    var userID int
    _, err := fmt.Sscanf(userIDStr, \"%d\", &userID)
    if err != nil {
        log.Fatalf(\"Invalid CALCOM_USER_ID: %v\", err)
    }

    timezone := os.Getenv(\"TEAM_TIMEZONE\")
    if timezone == \"\" {
        timezone = \"America/Los_Angeles\" // Default to PT
    }

    log.Infof(\"Creating 4-day schedule for Cal.com user %d in timezone %s\", userID, timezone)

    schedule, err := create4DaySchedule(ctx, userID, timezone)
    if err != nil {
        log.Fatalf(\"Failed to create schedule: %v\", err)
    }

    log.Infof(\"Successfully created 4-day schedule with ID: %d\", schedule.ID)
    fmt.Printf(\"Schedule created: https://cal.com/schedules/%d\\n\", schedule.ID)
}

import { chromium, Browser, Page } from \"playwright\";

async function auditCalendarMeetings() {
    const browser: Browser = await chromium.launch({ headless: true });
    const page: Page = await browser.newPage();
    // Navigate to Google Calendar and authenticate (use saved cookies for CI)
    await page.goto(\"https://calendar.google.com\");
    // Wait for events to load
    await page.waitForSelector(\".event-container\");
    // Extract event titles and durations
    const events = await page.$$eval(\".event-container\", (nodes) => {
        return nodes.map((node) => {
            const title = node.querySelector(\".event-title\")?.textContent || \"Untitled\";
            const duration = node.getAttribute(\"data-event-duration\") || \"0\";
            return { title, duration: parseInt(duration) / 60 }; // Convert seconds to minutes
        });
    });
    // Categorize events
    const wasteEvents = events.filter(e => e.title.includes(\"Sync\") && !e.title.includes(\"Planning\"));
    const totalWasteMinutes = wasteEvents.reduce((sum, e) => sum + e.duration, 0);
    console.log(`Total waste meeting minutes: ${totalWasteMinutes}`);
    await browser.close();
}
auditCalendarMeetings();

name: Auto Deploy to EKS
on:
  push:
    branches: [ main ]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Configure AWS Credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1
      - name: Login to Amazon ECR
        id: login-ecr
        uses: aws-actions/amazon-ecr-login@v2
      - name: Build and Push Docker Image
        env:
          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
          IMAGE_TAG: ${{ github.sha }}
        run: |
          docker build -t $ECR_REGISTRY/my-app:$IMAGE_TAG .
          docker push $ECR_REGISTRY/my-app:$IMAGE_TAG
      - name: Update Kubeconfig
        run: aws eks update-kubeconfig --name my-cluster --region us-east-1
      - name: Deploy to EKS
        run: |
          kubectl set image deployment/my-app my-app=$ECR_REGISTRY/my-app:$IMAGE_TAG
          kubectl rollout status deployment/my-app

import requests

CALCOM_API_KEY = \"your-api-key\"
TEAM_ID = 123

def check_friday_availability(team_id: int, date: str) -> dict:
    \"\"\"Check how many team members are available on a given Friday.\"\"\"
    url = f\"https://api.cal.com/v1/availability/team/{team_id}\"
    headers = {\"Authorization\": f\"Bearer {CALCOM_API_KEY}\"}
    params = {\"date\": date, \"timeZone\": \"America/Los_Angeles\"}
    response = requests.get(url, headers=headers, params=params)
    response.raise_for_status()
    availability = response.json()
    # Count available slots on Friday (day 5)
    friday_slots = [slot for slot in availability[\"slots\"] if slot[\"day\"] == 5]
    available_members = len([slot for slot in friday_slots if slot[\"available\"]])
    return {
        \"date\": date,
        \"available_members\": available_members,
        \"total_members\": len(availability[\"slots\"])
    }

if __name__ == \"__main__\":
    result = check_friday_availability(TEAM_ID, \"2024-10-11\")
    print(f\"Available members on Friday: {result['available_members']}/{result['total_members']}\")