Create social marketing package for Bazinga #273
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Create marketing/social-marketing-package.md with: - Brand positioning and elevator pitch - Twitter/X launch thread (6 tweets) + 6 standalone tweets - LinkedIn posts (launch, technical deep dive, use case) - Reddit posts (r/programming, r/MachineLearning, r/SideProject, r/ClaudeAI) - Hacker News Show HN post - Dev.to full article - Discord/Slack announcements - Visual asset descriptions for graphics - Hashtag strategy by platform - 7-day launch posting schedule
Marketing package v2.0: - New core message: "AI that codes like a professional engineering team" - Focus on mandatory quality gates, not time savings - Emphasize separation of concerns (writers don't review themselves) - Target: developers who care about code quality and compliance - Updated all platform content (Twitter, LinkedIn, Reddit, HN, Dev.to) README updates: - New tagline: enforced engineering practices over "3x faster" - "What Makes This Different" now explains the quality problem - Removed time comparisons from examples - "Performance" section renamed to "Quality Outcomes" - "Key Features" prioritizes security and review over parallelism - Updated "Before/After" to show quality gap, not coordination gap - Philosophy updated to emphasize engineering standards
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Pull request overview
This pull request creates a comprehensive social marketing package for the BAZINGA project and updates the README to align with a new engineering-focused positioning. The marketing shift emphasizes enforced engineering practices (security scanning, code review, test coverage) rather than parallel development speed.
Changes:
- Added a complete 1,117-line social marketing package with platform-specific content for Twitter, LinkedIn, Reddit, Hacker News, Dev.to, and Discord/Slack
- Repositioned README messaging from "parallel AI development teams" to "AI development with enforced engineering practices"
- Updated key features and benefits to emphasize quality gates and mandatory code review over development speed
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 5 comments.
| File | Description |
|---|---|
| marketing/social-marketing-package.md | Comprehensive marketing package with ready-to-use posts, articles, and messaging for multiple platforms emphasizing security and quality practices |
| README.md | Updated positioning, taglines, and feature descriptions to align with engineering practices focus; removed performance metrics in favor of quality outcomes |
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
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| ## Table of Contents | ||
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| 1. [Brand Positioning](#brand-positioning) | ||
| 2. [Twitter/X Posts](#twitterx-posts) | ||
| 3. [LinkedIn Posts](#linkedin-posts) | ||
| 4. [Reddit Posts](#reddit-posts) | ||
| 5. [Hacker News Post](#hacker-news-post) | ||
| 6. [Dev.to Article](#devto-article) | ||
| 7. [Discord/Slack Announcements](#discordslack-announcements) | ||
| 8. [Visual Assets Descriptions](#visual-assets-descriptions) | ||
| 9. [Hashtag Strategy](#hashtag-strategy) | ||
| 10. [Posting Schedule](#posting-schedule) |
There was a problem hiding this comment.
Inconsistent spacing in the table of contents. Items 1-9 use single digits without leading spaces, but item 10 requires proper alignment for consistent formatting.
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| ### r/programming | ||
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| **Title:** I built a framework that enforces code review, security scanning, and test coverage on AI-generated code |
There was a problem hiding this comment.
The Reddit post title uses "I built a framework" which is first-person perspective, but the framework appears to be for a team or organization based on the GitHub repository reference. Consider whether this first-person framing is appropriate for your marketing context.
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| I'm open-sourcing a framework that brings professional software engineering rigor to AI-assisted development. | ||
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| The Problem: | ||
| Current AI coding tools generate code without the safeguards we require from human developers. No security scanning. No mandatory code review. No test coverage requirements. We're essentially shipping unreviewed code to production. | ||
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| The Solution: | ||
| BAZINGA coordinates a complete AI engineering team that follows professional practices: | ||
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| Every change automatically receives: | ||
| ✅ Security scanning (SQL injection, XSS, hardcoded secrets) | ||
| ✅ Lint checking (code style, complexity, best practices) | ||
| ✅ Test coverage analysis (with 80% target) | ||
| ✅ Tech Lead code review (architecture, security, quality) | ||
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| The Workflow: | ||
| 1. Project Manager analyzes requirements | ||
| 2. Developer implements code + tests | ||
| 3. Security scan runs automatically | ||
| 4. Lint check enforces standards | ||
| 5. Tech Lead reviews all changes | ||
| 6. Only approved code is marked complete | ||
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| Key Principles: | ||
| • Writers don't review their own code | ||
| • Security scanning isn't optional | ||
| • Test coverage is measured, not assumed | ||
| • Every decision is logged for audit | ||
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| This isn't about speed—it's about quality. The same rigorous process professional engineering teams follow, enforced automatically. | ||
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| Quick Start: | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
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| MIT Licensed | Full Documentation | Audit Logging | ||
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| GitHub: github.com/mehdic/bazinga | ||
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| Feedback and contributions welcome. | ||
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| #SoftwareEngineering #CodeQuality #Security #OpenSource #BestPractices | ||
| ``` | ||
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| --- | ||
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| ### Follow-up Post (Engineering Practices Deep Dive) | ||
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| ``` | ||
| Why AI-Generated Code Needs the Same Review Process as Human Code | ||
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| Last week I shared BAZINGA, a framework for AI development with enforced engineering practices. Here's the engineering philosophy behind it. | ||
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| The Core Problem: | ||
| We've established rigorous practices for human-written code: peer review, security scanning, test requirements, coding standards. But we're shipping AI-generated code without these safeguards. | ||
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| This creates risk: | ||
| • Security vulnerabilities in unscanned code | ||
| • Technical debt from unreviewed architecture | ||
| • Bugs from untested edge cases | ||
| • Inconsistent code quality | ||
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| BAZINGA's Engineering Model: | ||
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| 1️⃣ Separation of Concerns | ||
| The developer who writes code doesn't review it. Independent Tech Lead agent evaluates architecture, security, and quality. Same principle as human code review. | ||
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| 2️⃣ Mandatory Quality Gates | ||
| Security scan, lint check, and coverage analysis run on EVERY change. Not optional. Not skippable. Built into the workflow. | ||
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| 3️⃣ Structured Problem-Solving | ||
| When issues arise, BAZINGA applies formal frameworks: | ||
| • Root Cause Analysis (5 Whys methodology) | ||
| • Architectural Decision Records | ||
| • Security Issue Triage | ||
| • Performance Investigation | ||
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| 4️⃣ Audit Trail | ||
| Every decision is logged with reasoning. Who reviewed what. What security issues were found. What coverage was achieved. Full traceability. | ||
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| 5️⃣ Escalation Paths | ||
| Complex problems get escalated to more capable models. Security-sensitive code goes to senior engineers. Clear escalation criteria. | ||
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| The Result: | ||
| Code that meets the same standards you'd expect from a professional engineering team. Every time. Automatically enforced. | ||
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| This matters for: | ||
| • Teams with compliance requirements | ||
| • Organizations caring about security posture | ||
| • Developers who value code quality | ||
| • Anyone shipping to production | ||
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| GitHub: github.com/mehdic/bazinga | ||
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| #SoftwareEngineering #CodeReview #SecurityFirst #Engineering #BestPractices | ||
| ``` | ||
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| --- | ||
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| ### Post (For Engineering Managers) | ||
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| ``` | ||
| Engineering Managers: AI Coding Tools Need Governance | ||
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| If your team uses AI coding assistants, consider this: | ||
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| What review process does AI-generated code go through before reaching production? | ||
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| In most cases: none. The developer accepts the suggestion and commits. | ||
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| This bypasses everything we've built: | ||
| • Code review requirements | ||
| • Security scanning pipelines | ||
| • Test coverage gates | ||
| • Architecture review | ||
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| BAZINGA addresses this by building governance into AI development: | ||
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| For Security Teams: | ||
| • Automatic vulnerability scanning (OWASP Top 10) | ||
| • Hardcoded secret detection | ||
| • Dependency audit | ||
| • Results logged for compliance | ||
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| For Quality Teams: | ||
| • Enforced test coverage (configurable thresholds) | ||
| • Lint checking against team standards | ||
| • Architecture review by Tech Lead agent | ||
| • Full audit trail of decisions | ||
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| For Engineering Managers: | ||
| • Same workflow for AI and human code | ||
| • No special exceptions for AI-generated code | ||
| • Consistent quality standards | ||
| • Traceable review process | ||
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| The Principle: | ||
| AI-generated code should meet the same bar as human-written code. BAZINGA enforces this automatically. | ||
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| Quick setup for evaluation: | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init test-project | ||
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| MIT Licensed. Full source available. | ||
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| GitHub: github.com/mehdic/bazinga | ||
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| #EngineeringLeadership #CodeGovernance #AIGovernance #Security #SoftwareQuality | ||
| ``` | ||
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| --- | ||
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| ## Reddit Posts | ||
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| ### r/programming | ||
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| **Title:** I built a framework that enforces code review, security scanning, and test coverage on AI-generated code | ||
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| ``` | ||
| Hey r/programming, | ||
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| I've been thinking about a gap in AI coding tools: they generate code without the safeguards we require from human developers. | ||
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| When a human writes code, it goes through: | ||
| - Code review | ||
| - Security scanning | ||
| - Test coverage checks | ||
| - Lint validation | ||
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| When AI generates code, it usually goes straight to commit. | ||
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| So I built BAZINGA, which enforces professional engineering practices on AI development: | ||
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| **What it does:** | ||
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| Every change automatically gets: | ||
| - Security scan (bandit, npm audit, gosec, etc.) | ||
| - Lint check (ruff, eslint, golangci-lint, etc.) | ||
| - Test coverage analysis | ||
| - Tech Lead code review | ||
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| **Key principle:** Writers don't review their own code. The Developer agent writes code, a separate Tech Lead agent reviews it. Same separation of concerns as human teams. | ||
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| **The workflow:** | ||
| ``` | ||
| PM analyzes requirements | ||
| ↓ | ||
| Developer implements + writes tests | ||
| ↓ | ||
| Security scan runs (can't skip) | ||
| ↓ | ||
| Lint check runs (can't skip) | ||
| ↓ | ||
| Tech Lead reviews architecture + quality | ||
| ↓ | ||
| Approved → Complete | ||
| ``` | ||
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| **Why this matters:** | ||
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| AI-generated code has the same potential for: | ||
| - Security vulnerabilities | ||
| - Architectural problems | ||
| - Missing edge cases | ||
| - Technical debt | ||
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| It should go through the same review process. | ||
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| **Quick start:** | ||
| ``` | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
| ``` | ||
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| MIT licensed. Works with Claude Code. | ||
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| GitHub: github.com/mehdic/bazinga | ||
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| Interested in feedback on the approach. How do you handle code review for AI-generated code on your teams? | ||
| ``` | ||
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| --- | ||
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| ### r/ExperiencedDevs | ||
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| **Title:** Treating AI-generated code with the same rigor as human code | ||
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| ``` | ||
| Senior+ devs: how are you handling code quality for AI-assisted development? | ||
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| I noticed a pattern on my team: AI suggestions get accepted and committed without the review process we require for human code. No security scan. No architecture review. Just "looks good, ship it." | ||
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| This bothered me enough to build something about it. | ||
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| **The Problem:** | ||
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| We've spent years building engineering practices: | ||
| - Mandatory code review | ||
| - Security scanning in CI | ||
| - Test coverage requirements | ||
| - Architecture review for significant changes | ||
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| AI tools bypass all of this. The developer is the reviewer of AI-generated code, which violates separation of concerns. | ||
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| **My Approach:** | ||
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| I built BAZINGA, a framework that enforces professional practices on AI development: | ||
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| 1. **Separation of roles** - Developer agent writes code, Tech Lead agent reviews it (independent review) | ||
| 2. **Mandatory security scanning** - Every change scanned for OWASP Top 10, hardcoded secrets, etc. | ||
| 3. **Enforced test coverage** - Coverage measured, not assumed | ||
| 4. **Audit trail** - All decisions logged with reasoning | ||
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| **The workflow mirrors what good teams do:** | ||
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| Requirements → Development → Security scan → Lint → Code review → Approval | ||
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| No shortcuts. Can't skip the security scan. Can't skip review. | ||
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| **Questions for the community:** | ||
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| 1. How does your team handle AI-generated code review? | ||
| 2. Do you have different standards for AI vs human code? | ||
| 3. What security/quality checks do you run on AI output? | ||
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| GitHub if interested: github.com/mehdic/bazinga | ||
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| Genuinely curious how other experienced devs are thinking about this. |
There was a problem hiding this comment.
Inconsistent voice throughout the document. The LinkedIn post (line 253) says "I'm open-sourcing", the Reddit posts use "I built" and "I've been thinking", while other sections use third-person or imperative voice. Consider standardizing the voice/perspective for consistency across all marketing materials.
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| - 🚀 **[Get Started](#quick-start)** - Install and run your first orchestration | ||
| - 🔒 **[Quality Gates](#automatic-quality-gates)** - Security, lint, coverage details | ||
| - 📚 **[Examples](examples/EXAMPLES.md)** - See real usage patterns | ||
| - ⚙️ **[Advanced Features](docs/ADVANCED.md)** - Unlock more power | ||
| - 🏗️ **[Architecture](docs/ARCHITECTURE.md)** - How it works under the hood |
There was a problem hiding this comment.
A link to Advanced Features documentation has been removed from the Quick Links section. If the "docs/ADVANCED.md" file still exists in the repository, consider keeping this useful reference for users seeking advanced functionality.
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| **Repository:** https://github.com/mehdic/bazinga | ||
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| --- | ||
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| ## Table of Contents | ||
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| 1. [Brand Positioning](#brand-positioning) | ||
| 2. [Twitter/X Posts](#twitterx-posts) | ||
| 3. [LinkedIn Posts](#linkedin-posts) | ||
| 4. [Reddit Posts](#reddit-posts) | ||
| 5. [Hacker News Post](#hacker-news-post) | ||
| 6. [Dev.to Article](#devto-article) | ||
| 7. [Discord/Slack Announcements](#discordslack-announcements) | ||
| 8. [Visual Assets Descriptions](#visual-assets-descriptions) | ||
| 9. [Hashtag Strategy](#hashtag-strategy) | ||
| 10. [Posting Schedule](#posting-schedule) | ||
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| --- | ||
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| ## Brand Positioning | ||
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| ### Core Message | ||
| **"AI that codes like a professional engineering team—with mandatory code review, security scanning, and test coverage on every change."** | ||
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| ### Taglines (Pick Your Favorite) | ||
| - "Production-quality code, every time." | ||
| - "The AI dev team that follows engineering best practices." | ||
| - "Security scans. Code review. Test coverage. Automatic." | ||
| - "Stop shipping code without review." | ||
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| ### Elevator Pitch (30 seconds) | ||
| > BAZINGA is a multi-agent orchestration framework for Claude Code that enforces professional software engineering practices automatically. Instead of AI that just generates code, BAZINGA coordinates a full engineering team: developers write code and tests, security scans catch vulnerabilities before commit, Tech Lead reviews architecture and quality. Every change goes through the same rigorous process a professional team would follow. No shortcuts. No skipped reviews. | ||
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| ### Key Differentiators (Engineering Focus) | ||
| 1. **Mandatory Quality Gates** - Security scan, lint check, test coverage on EVERY change | ||
| 2. **Enforced Code Review** - Tech Lead reviews all code before completion | ||
| 3. **Professional Workflow** - Same process real engineering teams follow | ||
| 4. **No Shortcuts** - Can't skip security scans or tests | ||
| 5. **Separation of Concerns** - Writers don't review their own code | ||
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| ### Target Audience Keywords | ||
| - Professional developers, engineering teams | ||
| - Developers who care about code quality | ||
| - Teams with security and compliance requirements | ||
| - Engineers tired of AI-generated code without review | ||
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| --- | ||
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| ## Twitter/X Posts | ||
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| ### Launch Announcement Thread | ||
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| **Tweet 1 (Hook):** | ||
| ``` | ||
| Just open-sourced BAZINGA: AI development with enforced engineering practices. | ||
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| Every change gets: | ||
| • Security scanning | ||
| • Lint checking | ||
| • Test coverage | ||
| • Code review | ||
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| No shortcuts. No skipped reviews. Production-quality code. | ||
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| Here's why this matters: 🧵 | ||
| ``` | ||
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| **Tweet 2 (The Problem):** | ||
| ``` | ||
| The problem with AI coding today: | ||
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| AI generates code → you ship it → hope it's secure | ||
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| No security scan. | ||
| No code review. | ||
| No test coverage check. | ||
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| We'd never accept this from a human developer. Why accept it from AI? | ||
| ``` | ||
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| **Tweet 3 (The Solution):** | ||
| ``` | ||
| BAZINGA enforces what professional teams do: | ||
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| 1. Developer writes code + tests | ||
| 2. Security scan runs automatically | ||
| 3. Lint check enforces standards | ||
| 4. Tech Lead reviews architecture | ||
| 5. Only then: ready for merge | ||
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| Same rigor. Every time. No exceptions. | ||
| ``` | ||
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| **Tweet 4 (Quality Gates):** | ||
| ``` | ||
| Automatic quality gates on every change: | ||
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| 🔒 Security: bandit, npm audit, gosec, brakeman | ||
| 📏 Lint: ruff, eslint, golangci-lint, rubocop | ||
| 📊 Coverage: pytest-cov, jest, go test | ||
| 👀 Review: Tech Lead examines architecture + security | ||
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| Can't skip them. Can't bypass them. | ||
| ``` | ||
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| **Tweet 5 (Separation of Concerns):** | ||
| ``` | ||
| Key principle: writers don't review their own code. | ||
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| BAZINGA enforces role separation: | ||
| • Developer writes code | ||
| • QA validates behavior | ||
| • Tech Lead reviews quality | ||
| • PM validates requirements | ||
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| Same principle professional teams follow. | ||
| ``` | ||
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| **Tweet 6 (Quick Start):** | ||
| ``` | ||
| Get started: | ||
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| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
| cd my-project | ||
| /bazinga.orchestrate implement your feature here | ||
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| MIT licensed. Every change gets proper review. | ||
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| ⭐ github.com/mehdic/bazinga | ||
| ``` | ||
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| --- | ||
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| ### Standalone Tweets (Mix & Match) | ||
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| **Tweet A (Security Focus):** | ||
| ``` | ||
| AI-generated code without security scanning is a liability. | ||
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| BAZINGA runs security analysis on every change: | ||
| • SQL injection detection | ||
| • XSS vulnerabilities | ||
| • Hardcoded secrets | ||
| • Auth bypass patterns | ||
| • Insecure dependencies | ||
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| Before code is marked complete. Always. | ||
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| github.com/mehdic/bazinga | ||
| ``` | ||
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| **Tweet B (Code Review):** | ||
| ``` | ||
| Would you merge code without review? | ||
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| BAZINGA's Tech Lead agent reviews every change: | ||
| • Architecture alignment | ||
| • Security concerns | ||
| • Code quality | ||
| • Test coverage | ||
| • Edge cases | ||
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| AI shouldn't skip what humans require. | ||
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| github.com/mehdic/bazinga | ||
| ``` | ||
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| **Tweet C (Test Coverage):** | ||
| ``` | ||
| "It works on my machine" isn't enough. | ||
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| BAZINGA enforces test coverage: | ||
| • Unit tests written with implementation | ||
| • Coverage measured automatically | ||
| • Untested paths flagged | ||
| • 80% coverage target | ||
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| Tests aren't optional. They're required. | ||
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| github.com/mehdic/bazinga | ||
| ``` | ||
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| **Tweet D (Professional Workflow):** | ||
| ``` | ||
| How professional engineering teams work: | ||
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| 1. Ticket → Developer | ||
| 2. Developer → Code + Tests | ||
| 3. Code → Security scan | ||
| 4. Code → Lint check | ||
| 5. Code → Peer review | ||
| 6. Approved → Merge | ||
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| BAZINGA follows the same workflow. Automatically. | ||
| ``` | ||
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| **Tweet E (Role Separation):** | ||
| ``` | ||
| Why separate roles matter: | ||
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| Developer: "My code is secure" | ||
| Security scan: "Found 3 vulnerabilities" | ||
| Developer: "My tests cover everything" | ||
| Coverage tool: "47% coverage" | ||
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| BAZINGA uses independent verification. | ||
| Writers don't validate their own work. | ||
| ``` | ||
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| **Tweet F (No Shortcuts):** | ||
| ``` | ||
| "I'll add tests later" | ||
| "Security scan takes too long" | ||
| "Just ship it" | ||
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| BAZINGA doesn't allow shortcuts: | ||
| • Security scan runs every time | ||
| • Tests required before review | ||
| • Coverage checked automatically | ||
| • Review mandatory | ||
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| Professional standards. Enforced. | ||
| ``` | ||
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| **Tweet G (Compliance):** | ||
| ``` | ||
| For teams with compliance requirements: | ||
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| Every BAZINGA session logs: | ||
| • Security scan results | ||
| • Test coverage metrics | ||
| • Code review decisions | ||
| • Reasoning and rationale | ||
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| Full audit trail. Automatic documentation. | ||
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| github.com/mehdic/bazinga | ||
| ``` | ||
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| --- | ||
|
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| ## LinkedIn Posts | ||
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| ### Launch Announcement | ||
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| ``` | ||
| Announcing BAZINGA - AI Development with Enforced Engineering Practices | ||
|
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| I'm open-sourcing a framework that brings professional software engineering rigor to AI-assisted development. | ||
|
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| The Problem: | ||
| Current AI coding tools generate code without the safeguards we require from human developers. No security scanning. No mandatory code review. No test coverage requirements. We're essentially shipping unreviewed code to production. | ||
|
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| The Solution: | ||
| BAZINGA coordinates a complete AI engineering team that follows professional practices: | ||
|
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| Every change automatically receives: | ||
| ✅ Security scanning (SQL injection, XSS, hardcoded secrets) | ||
| ✅ Lint checking (code style, complexity, best practices) | ||
| ✅ Test coverage analysis (with 80% target) | ||
| ✅ Tech Lead code review (architecture, security, quality) | ||
|
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| The Workflow: | ||
| 1. Project Manager analyzes requirements | ||
| 2. Developer implements code + tests | ||
| 3. Security scan runs automatically | ||
| 4. Lint check enforces standards | ||
| 5. Tech Lead reviews all changes | ||
| 6. Only approved code is marked complete | ||
|
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| Key Principles: | ||
| • Writers don't review their own code | ||
| • Security scanning isn't optional | ||
| • Test coverage is measured, not assumed | ||
| • Every decision is logged for audit | ||
|
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| This isn't about speed—it's about quality. The same rigorous process professional engineering teams follow, enforced automatically. | ||
|
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||
| Quick Start: | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
|
|
||
| MIT Licensed | Full Documentation | Audit Logging | ||
|
|
||
| GitHub: github.com/mehdic/bazinga | ||
|
|
||
| Feedback and contributions welcome. | ||
|
|
||
| #SoftwareEngineering #CodeQuality #Security #OpenSource #BestPractices | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ### Follow-up Post (Engineering Practices Deep Dive) | ||
|
|
||
| ``` | ||
| Why AI-Generated Code Needs the Same Review Process as Human Code | ||
|
|
||
| Last week I shared BAZINGA, a framework for AI development with enforced engineering practices. Here's the engineering philosophy behind it. | ||
|
|
||
| The Core Problem: | ||
| We've established rigorous practices for human-written code: peer review, security scanning, test requirements, coding standards. But we're shipping AI-generated code without these safeguards. | ||
|
|
||
| This creates risk: | ||
| • Security vulnerabilities in unscanned code | ||
| • Technical debt from unreviewed architecture | ||
| • Bugs from untested edge cases | ||
| • Inconsistent code quality | ||
|
|
||
| BAZINGA's Engineering Model: | ||
|
|
||
| 1️⃣ Separation of Concerns | ||
| The developer who writes code doesn't review it. Independent Tech Lead agent evaluates architecture, security, and quality. Same principle as human code review. | ||
|
|
||
| 2️⃣ Mandatory Quality Gates | ||
| Security scan, lint check, and coverage analysis run on EVERY change. Not optional. Not skippable. Built into the workflow. | ||
|
|
||
| 3️⃣ Structured Problem-Solving | ||
| When issues arise, BAZINGA applies formal frameworks: | ||
| • Root Cause Analysis (5 Whys methodology) | ||
| • Architectural Decision Records | ||
| • Security Issue Triage | ||
| • Performance Investigation | ||
|
|
||
| 4️⃣ Audit Trail | ||
| Every decision is logged with reasoning. Who reviewed what. What security issues were found. What coverage was achieved. Full traceability. | ||
|
|
||
| 5️⃣ Escalation Paths | ||
| Complex problems get escalated to more capable models. Security-sensitive code goes to senior engineers. Clear escalation criteria. | ||
|
|
||
| The Result: | ||
| Code that meets the same standards you'd expect from a professional engineering team. Every time. Automatically enforced. | ||
|
|
||
| This matters for: | ||
| • Teams with compliance requirements | ||
| • Organizations caring about security posture | ||
| • Developers who value code quality | ||
| • Anyone shipping to production | ||
|
|
||
| GitHub: github.com/mehdic/bazinga | ||
|
|
||
| #SoftwareEngineering #CodeReview #SecurityFirst #Engineering #BestPractices | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ### Post (For Engineering Managers) | ||
|
|
||
| ``` | ||
| Engineering Managers: AI Coding Tools Need Governance | ||
|
|
||
| If your team uses AI coding assistants, consider this: | ||
|
|
||
| What review process does AI-generated code go through before reaching production? | ||
|
|
||
| In most cases: none. The developer accepts the suggestion and commits. | ||
|
|
||
| This bypasses everything we've built: | ||
| • Code review requirements | ||
| • Security scanning pipelines | ||
| • Test coverage gates | ||
| • Architecture review | ||
|
|
||
| BAZINGA addresses this by building governance into AI development: | ||
|
|
||
| For Security Teams: | ||
| • Automatic vulnerability scanning (OWASP Top 10) | ||
| • Hardcoded secret detection | ||
| • Dependency audit | ||
| • Results logged for compliance | ||
|
|
||
| For Quality Teams: | ||
| • Enforced test coverage (configurable thresholds) | ||
| • Lint checking against team standards | ||
| • Architecture review by Tech Lead agent | ||
| • Full audit trail of decisions | ||
|
|
||
| For Engineering Managers: | ||
| • Same workflow for AI and human code | ||
| • No special exceptions for AI-generated code | ||
| • Consistent quality standards | ||
| • Traceable review process | ||
|
|
||
| The Principle: | ||
| AI-generated code should meet the same bar as human-written code. BAZINGA enforces this automatically. | ||
|
|
||
| Quick setup for evaluation: | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init test-project | ||
|
|
||
| MIT Licensed. Full source available. | ||
|
|
||
| GitHub: github.com/mehdic/bazinga | ||
|
|
||
| #EngineeringLeadership #CodeGovernance #AIGovernance #Security #SoftwareQuality | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ## Reddit Posts | ||
|
|
||
| ### r/programming | ||
|
|
||
| **Title:** I built a framework that enforces code review, security scanning, and test coverage on AI-generated code | ||
|
|
||
| ``` | ||
| Hey r/programming, | ||
|
|
||
| I've been thinking about a gap in AI coding tools: they generate code without the safeguards we require from human developers. | ||
|
|
||
| When a human writes code, it goes through: | ||
| - Code review | ||
| - Security scanning | ||
| - Test coverage checks | ||
| - Lint validation | ||
|
|
||
| When AI generates code, it usually goes straight to commit. | ||
|
|
||
| So I built BAZINGA, which enforces professional engineering practices on AI development: | ||
|
|
||
| **What it does:** | ||
|
|
||
| Every change automatically gets: | ||
| - Security scan (bandit, npm audit, gosec, etc.) | ||
| - Lint check (ruff, eslint, golangci-lint, etc.) | ||
| - Test coverage analysis | ||
| - Tech Lead code review | ||
|
|
||
| **Key principle:** Writers don't review their own code. The Developer agent writes code, a separate Tech Lead agent reviews it. Same separation of concerns as human teams. | ||
|
|
||
| **The workflow:** | ||
| ``` | ||
| PM analyzes requirements | ||
| ↓ | ||
| Developer implements + writes tests | ||
| ↓ | ||
| Security scan runs (can't skip) | ||
| ↓ | ||
| Lint check runs (can't skip) | ||
| ↓ | ||
| Tech Lead reviews architecture + quality | ||
| ↓ | ||
| Approved → Complete | ||
| ``` | ||
|
|
||
| **Why this matters:** | ||
|
|
||
| AI-generated code has the same potential for: | ||
| - Security vulnerabilities | ||
| - Architectural problems | ||
| - Missing edge cases | ||
| - Technical debt | ||
|
|
||
| It should go through the same review process. | ||
|
|
||
| **Quick start:** | ||
| ``` | ||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
| ``` | ||
|
|
||
| MIT licensed. Works with Claude Code. | ||
|
|
||
| GitHub: github.com/mehdic/bazinga | ||
|
|
||
| Interested in feedback on the approach. How do you handle code review for AI-generated code on your teams? | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ### r/ExperiencedDevs | ||
|
|
||
| **Title:** Treating AI-generated code with the same rigor as human code | ||
|
|
||
| ``` | ||
| Senior+ devs: how are you handling code quality for AI-assisted development? | ||
|
|
||
| I noticed a pattern on my team: AI suggestions get accepted and committed without the review process we require for human code. No security scan. No architecture review. Just "looks good, ship it." | ||
|
|
||
| This bothered me enough to build something about it. | ||
|
|
||
| **The Problem:** | ||
|
|
||
| We've spent years building engineering practices: | ||
| - Mandatory code review | ||
| - Security scanning in CI | ||
| - Test coverage requirements | ||
| - Architecture review for significant changes | ||
|
|
||
| AI tools bypass all of this. The developer is the reviewer of AI-generated code, which violates separation of concerns. | ||
|
|
||
| **My Approach:** | ||
|
|
||
| I built BAZINGA, a framework that enforces professional practices on AI development: | ||
|
|
||
| 1. **Separation of roles** - Developer agent writes code, Tech Lead agent reviews it (independent review) | ||
| 2. **Mandatory security scanning** - Every change scanned for OWASP Top 10, hardcoded secrets, etc. | ||
| 3. **Enforced test coverage** - Coverage measured, not assumed | ||
| 4. **Audit trail** - All decisions logged with reasoning | ||
|
|
||
| **The workflow mirrors what good teams do:** | ||
|
|
||
| Requirements → Development → Security scan → Lint → Code review → Approval | ||
|
|
||
| No shortcuts. Can't skip the security scan. Can't skip review. | ||
|
|
||
| **Questions for the community:** | ||
|
|
||
| 1. How does your team handle AI-generated code review? | ||
| 2. Do you have different standards for AI vs human code? | ||
| 3. What security/quality checks do you run on AI output? | ||
|
|
||
| GitHub if interested: github.com/mehdic/bazinga | ||
|
|
||
| Genuinely curious how other experienced devs are thinking about this. | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ### r/softwaredevelopment | ||
|
|
||
| **Title:** Framework for enforcing engineering best practices on AI coding | ||
|
|
||
| ``` | ||
| Built something to address a concern: AI coding assistants that skip the engineering practices we've established. | ||
|
|
||
| **The concern:** | ||
|
|
||
| Professional development has evolved these practices for good reasons: | ||
| - Code review catches bugs and knowledge-shares | ||
| - Security scanning prevents vulnerabilities | ||
| - Test coverage ensures reliability | ||
| - Lint checks maintain consistency | ||
|
|
||
| AI coding tools typically skip all of these. Generate → Accept → Commit. | ||
|
|
||
| **BAZINGA's approach:** | ||
|
|
||
| Enforce the same workflow for AI that we use for humans: | ||
|
|
||
| 1. **Mandatory code review** - Tech Lead agent reviews all code (writers don't review themselves) | ||
| 2. **Automatic security scanning** - SQL injection, XSS, secrets, dependencies | ||
| 3. **Required test coverage** - Tests written with code, coverage measured | ||
| 4. **Lint enforcement** - Code meets team standards | ||
|
|
||
| **What makes this different from "just run security tools":** | ||
|
|
||
| - Review is independent (separate agent, not the writer) | ||
| - Can't skip or bypass quality gates | ||
| - Structured problem-solving frameworks for complex issues | ||
| - Full audit logging for compliance | ||
|
|
||
| **Example workflow:** | ||
| ``` | ||
| /bazinga.orchestrate implement user authentication | ||
|
|
||
| PM: Analyzes requirements, assigns to developer | ||
| Developer: Implements auth + writes tests | ||
| Security: Scans for auth vulnerabilities, injection, etc. | ||
| Lint: Checks code quality | ||
| Tech Lead: Reviews architecture, security, edge cases | ||
| PM: Validates requirements met | ||
| → Complete (only after all gates pass) | ||
| ``` | ||
|
|
||
| MIT licensed: github.com/mehdic/bazinga | ||
|
|
||
| Would appreciate thoughts from the community on this approach to AI code quality. | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ### r/ClaudeAI | ||
|
|
||
| **Title:** Multi-agent system that enforces code review and security scanning | ||
|
|
||
| ``` | ||
| Built a framework for Claude Code that enforces professional engineering practices. | ||
|
|
||
| **Why:** | ||
|
|
||
| Claude is great at generating code, but like all AI, it can produce: | ||
| - Security vulnerabilities | ||
| - Architectural issues | ||
| - Missing test coverage | ||
| - Edge case bugs | ||
|
|
||
| The solution isn't to avoid AI—it's to apply the same review process we use for human code. | ||
|
|
||
| **BAZINGA coordinates multiple Claude agents:** | ||
|
|
||
| - **Project Manager** - Analyzes requirements | ||
| - **Developer** - Implements code + tests | ||
| - **QA Expert** - Validates behavior | ||
| - **Tech Lead** - Reviews code quality + security | ||
|
|
||
| **Key principle:** The agent that writes code doesn't review it. Independent review, same as human teams. | ||
|
|
||
| **Automatic on every change:** | ||
| - Security scan (language-specific tools) | ||
| - Lint check (configurable per project) | ||
| - Test coverage analysis | ||
| - Architecture review | ||
|
|
||
| **Example:** | ||
| ``` | ||
| /bazinga.orchestrate implement password reset functionality | ||
|
|
||
| → Developer implements + writes tests | ||
| → Security scan checks for vulnerabilities | ||
| → Tech Lead reviews auth logic | ||
| → Only approved code completes | ||
| ``` | ||
|
|
||
| Works as a Claude Code extension. All agents enhanced with 72 technology specializations. | ||
|
|
||
| GitHub: github.com/mehdic/bazinga | ||
|
|
||
| Interested in how others are handling code quality with Claude. | ||
| ``` | ||
|
|
||
| --- | ||
|
|
||
| ## Hacker News Post | ||
|
|
||
| **Title:** Show HN: BAZINGA – Enforced engineering practices for AI coding | ||
|
|
||
| ``` | ||
| Hi HN, | ||
|
|
||
| I'm sharing BAZINGA, a framework that applies professional software engineering practices to AI development. | ||
|
|
||
| The observation: AI coding tools generate code without the safeguards we require from human developers. No mandatory code review. No security scanning. No test coverage requirements. | ||
|
|
||
| BAZINGA addresses this by coordinating multiple AI agents that follow a professional workflow: | ||
|
|
||
| ## The Workflow | ||
|
|
||
| 1. PM analyzes requirements | ||
| 2. Developer implements + writes tests | ||
| 3. Security scan runs (mandatory) | ||
| 4. Lint check runs (mandatory) | ||
| 5. Tech Lead reviews code (independent reviewer) | ||
| 6. Only approved code completes | ||
|
|
||
| ## Key Principles | ||
|
|
||
| **Separation of concerns:** Writers don't review their own code. Developer agent writes, Tech Lead agent reviews. Same principle as human teams. | ||
|
|
||
| **Mandatory quality gates:** Security scanning, lint checking, and coverage analysis run on every change. Not optional. | ||
|
|
||
| **Structured problem-solving:** Complex issues get formal analysis: | ||
| - Root Cause Analysis (5 Whys) | ||
| - Architectural Decision Records | ||
| - Security Issue Triage | ||
| - Performance Investigation | ||
|
|
||
| **Audit trail:** Every decision logged with reasoning. Full traceability for compliance. | ||
|
|
||
| ## What It Catches | ||
|
|
||
| - SQL injection, XSS, auth vulnerabilities (via bandit, npm audit, gosec, etc.) | ||
| - Code style violations (via ruff, eslint, golangci-lint, etc.) | ||
| - Missing test coverage (via pytest-cov, jest, etc.) | ||
| - Architectural concerns (via Tech Lead review) | ||
|
|
||
| ## Quick Start | ||
|
|
||
| uvx --from git+https://github.com/mehdic/bazinga.git bazinga init my-project | ||
|
|
||
| MIT licensed. Works with Claude Code. | ||
|
|
||
| ## Technical Approach | ||
|
|
||
| Built on research from Google's ADK and Anthropic's context engineering principles. Uses role-based separation with 6-layer drift prevention to ensure agents stay in their designated roles. | ||
|
|
||
| GitHub: https://github.com/mehdic/bazinga |
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Inconsistent URL formatting throughout the document. Some instances use "github.com/mehdic/bazinga" while line 678 uses "https://github.com/mehdic/bazinga" and line 5 uses "https://github.com/mehdic/bazinga". For consistency and clarity in marketing materials, standardize whether to include the "https://" protocol prefix across all references.
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