Costs:
- API calls (GPT-4, Claude): ~$45/month
- Server/infrastructure: ~$20/month
- Setup time (one-time): ~20 hours Revenue:
- Bounties earned: $300
- Article revenue: ~$75
- Time saved (value): ~$6,760 (33.8 hrs × $50/hr × 4 weeks) ROI = (Revenue
- Costs) / Costs ROI = ($375 - $65) / $65 = 477% CODE_BLOCK: Costs:
- API calls (GPT-4, Claude): ~$45/month
- Server/infrastructure: ~$20/month
- Setup time (one-time): ~20 hours Revenue:
- Bounties earned: $300
- Article revenue: ~$75
- Time saved (value): ~$6,760 (33.8 hrs × $50/hr × 4 weeks) ROI = (Revenue
- Costs) / Costs ROI = ($375 - $65) / $65 = 477% CODE_BLOCK: Costs:
- API calls (GPT-4, Claude): ~$45/month
- Server/infrastructure: ~$20/month
- Setup time (one-time): ~20 hours Revenue:
- Bounties earned: $300
- Article revenue: ~$75
- Time saved (value): ~$6,760 (33.8 hrs × $50/hr × 4 weeks) ROI = (Revenue
- Costs) / Costs ROI = ($375 - $65) / $65 = 477% COMMAND_BLOCK: # Agent orchestration class AgentOrchestrator: def __init__(self): self.agents = { 'bounty_radar': BountyRadarAgent(), 'pr_submitter': PRSubmitterAgent(), 'content_engine': ContentEngineAgent(), 'code_reviewer': CodeReviewerAgent(), 'security_scanner': SecurityScannerAgent(), 'devops_monitor': DevOpsMonitorAgent(), 'earnings_tracker': EarningsTrackerAgent() } def run_cycle(self): for name, agent in self.agents.items(): try: result = agent.execute() self.log_result(name, result) except Exception as e: self.handle_error(name, e) COMMAND_BLOCK: # Agent orchestration class AgentOrchestrator: def __init__(self): self.agents = { 'bounty_radar': BountyRadarAgent(), 'pr_submitter': PRSubmitterAgent(), 'content_engine': ContentEngineAgent(), 'code_reviewer': CodeReviewerAgent(), 'security_scanner': SecurityScannerAgent(), 'devops_monitor': DevOpsMonitorAgent(), 'earnings_tracker': EarningsTrackerAgent() } def run_cycle(self): for name, agent in self.agents.items(): try: result = agent.execute() self.log_result(name, result) except Exception as e: self.handle_error(name, e) COMMAND_BLOCK: # Agent orchestration class AgentOrchestrator: def __init__(self): self.agents = { 'bounty_radar': BountyRadarAgent(), 'pr_submitter': PRSubmitterAgent(), 'content_engine': ContentEngineAgent(), 'code_reviewer': CodeReviewerAgent(), 'security_scanner': SecurityScannerAgent(), 'devops_monitor': DevOpsMonitorAgent(), 'earnings_tracker': EarningsTrackerAgent() } def run_cycle(self): for name, agent in self.agents.items(): try: result = agent.execute() self.log_result(name, result) except Exception as e: self.handle_error(name, e) COMMAND_BLOCK: # Bounty scanning every 30 minutes */30 * * * * /usr/bin/python3 /agents/bounty_radar.py # Content publishing twice daily (9 AM and 9 PM UTC) 0 9,21 * * * /usr/bin/python3 /agents/content_engine.py # Security scanning daily at 2 AM UTC 0 2 * * * /usr/bin/python3 /agents/security_scanner.py COMMAND_BLOCK: # Bounty scanning every 30 minutes */30 * * * * /usr/bin/python3 /agents/bounty_radar.py # Content publishing twice daily (9 AM and 9 PM UTC) 0 9,21 * * * /usr/bin/python3 /agents/content_engine.py # Security scanning daily at 2 AM UTC 0 2 * * * /usr/bin/python3 /agents/security_scanner.py COMMAND_BLOCK: # Bounty scanning every 30 minutes */30 * * * * /usr/bin/python3 /agents/bounty_radar.py # Content publishing twice daily (9 AM and 9 PM UTC) 0 9,21 * * * /usr/bin/python3 /agents/content_engine.py # Security scanning daily at 2 AM UTC 0 2 * * * /usr/bin/python3 /agents/security_scanner.py COMMAND_BLOCK: def execute_with_retry(self, task, max_retries=3): for attempt in range(max_retries): try: return task() except RateLimitError: time.sleep(2 ** attempt * 60) # Exponential backoff except APIError as e: self.log_error(e) if attempt == max_retries - 1: self.alert_human(e) return None COMMAND_BLOCK: def execute_with_retry(self, task, max_retries=3): for attempt in range(max_retries): try: return task() except RateLimitError: time.sleep(2 ** attempt * 60) # Exponential backoff except APIError as e: self.log_error(e) if attempt == max_retries - 1: self.alert_human(e) return None COMMAND_BLOCK: def execute_with_retry(self, task, max_retries=3): for attempt in range(max_retries): try: return task() except RateLimitError: time.sleep(2 ** attempt * 60) # Exponential backoff except APIError as e: self.log_error(e) if attempt == max_retries - 1: self.alert_human(e) return None COMMAND_BLOCK: def evaluate_bounty(bounty): score = 0 score += bounty.value * 0.3 # 30% weight on value score += (10 - bounty.competition) * 0.25 # 25% on low competition score += bounty.match_to_skills * 0.25 # 25% on skill match score += bounty.repo_quality * 0.2 # 20% on repo quality return score COMMAND_BLOCK: def evaluate_bounty(bounty): score = 0 score += bounty.value * 0.3 # 30% weight on value score += (10 - bounty.competition) * 0.25 # 25% on low competition score += bounty.match_to_skills * 0.25 # 25% on skill match score += bounty.repo_quality * 0.2 # 20% on repo quality return score COMMAND_BLOCK: def evaluate_bounty(bounty): score = 0 score += bounty.value * 0.3 # 30% weight on value score += (10 - bounty.competition) * 0.25 # 25% on low competition score += bounty.match_to_skills * 0.25 # 25% on skill match score += bounty.repo_quality * 0.2 # 20% on repo quality return score COMMAND_BLOCK: # Your first agent: a simple bounty scanner import subprocess import json def scan_bounties(): """Scan GitHub for open bounty issues.""" result = subprocess.run( ['gh', 'search', 'issues', 'bounty', '--state', 'open', '--limit', '50', '--json', 'title,url,commentsCount,repository'], capture_output=True, text=True ) bounties = json.loads(result.stdout) # Filter: low competition, reasonable comments viable = [ b for b in bounties if b['commentsCount'] < 5 and 'bounty' in b['title'].lower() ] return viable if __name__ == '__main__': results = scan_bounties() print(f"Found {len(results)} viable bounties") for b in results: print(f" - {b['title']}") print(f" {b['url']}") COMMAND_BLOCK: # Your first agent: a simple bounty scanner import subprocess import json def scan_bounties(): """Scan GitHub for open bounty issues.""" result = subprocess.run( ['gh', 'search', 'issues', 'bounty', '--state', 'open', '--limit', '50', '--json', 'title,url,commentsCount,repository'], capture_output=True, text=True ) bounties = json.loads(result.stdout) # Filter: low competition, reasonable comments viable = [ b for b in bounties if b['commentsCount'] < 5 and 'bounty' in b['title'].lower() ] return viable if __name__ == '__main__': results = scan_bounties() print(f"Found {len(results)} viable bounties") for b in results: print(f" - {b['title']}") print(f" {b['url']}") COMMAND_BLOCK: # Your first agent: a simple bounty scanner import subprocess import json def scan_bounties(): """Scan GitHub for open bounty issues.""" result = subprocess.run( ['gh', 'search', 'issues', 'bounty', '--state', 'open', '--limit', '50', '--json', 'title,url,commentsCount,repository'], capture_output=True, text=True ) bounties = json.loads(result.stdout) # Filter: low competition, reasonable comments viable = [ b for b in bounties if b['commentsCount'] < 5 and 'bounty' in b['title'].lower() ] return viable if __name__ == '__main__': results = scan_bounties() print(f"Found {len(results)} viable bounties") for b in results: print(f" - {b['title']}") print(f" {b['url']}")
- Hunt for open-source bounties and submit PRs while I sleep
- Write and publish technical articles without my intervention
- Monitor CI/CD pipelines and fix common failures
- Review code and provide actionable feedback
- Scan for security vulnerabilities across my projects
- Manage my GitHub notifications and respond to issues
- Track earnings and optimize my time allocation
- Repository age and activity patterns
- Whether previous PRs were actually merged
- If the maintainer has a real contribution history
- Whether bounty amounts are realistic
- Real personal experiences and data
- Specific numbers and outcomes
- A distinctive voice (not corporate-speak)
- Genuine insights that couldn't be found elsewhere
- Reads the full issue discussion
- Checks for existing PRs
- Proposes an approach and waits for feedback
- Only then implements the solution - 3,000+ words minimum
- Real data and specific examples
- Personal narrative (what you actually did, not generic advice)
- Code samples that actually run
- Honest discussion of failures, not just successes
- Has the repo merged external PRs before?
- Does the maintainer respond to comments?
- Are the bounty amounts realistic?
- Is there actual code in the repository?
- Delegate effectively — know what to hand off and what to keep
- Build robust systems — handle errors, edge cases, and failures gracefully
- Maintain quality — use agents for volume, humans for polish
- Stay ethical — don't spam, don't submit low-quality work, respect communities
- Well-defined inputs — GitHub API provides structured data
- Clear success criteria — did you find viable bounties?
- Immediate feedback — you'll know quickly if it works
- Real value — even one $100 bounty justifies the effort
