Ed Burns is a Principal Software Engineer focused on bringing idiomatic Java experiences to Microsoft and GitHub technologies. He has worked with Java since 1997 across client, server, cloud, and artificial intelligence applications.
## Professional Focus
- Works at Microsoft and GitHub.
- Concentrates on making Java development feel natural and idiomatic within their technologies.
## Experience
- Has used Java since 1997.
- His experience spans:
- Client-side development
- Server-side systems
- Cloud technologies
- Artificial intelligence
No specific blog topic or technical argument is included in the provided content.
GitHub’s legal team used Copilot CLI to turn repetitive legal work into customizable internal tools without relying on traditional software engineering. By expressing workflows, standards, and policies in plain language and Markdown, lawyers built systems that improved consistency, reduced drafting time, and preserved human oversight. The post argues that domain expertise can be operationalized into useful AI tools by anyone who can clearly define a process.
## Building a Contract Drafting Style Guide
- Principal Product Counsel Ngandu Kasuku created **terms-ai** to manage varied commercial agreements involving data, infrastructure, and product integrations.
- The tool stores instructions, drafting resources, workflows, and reference documents in a version-controlled repository.
- An internal style guide enforces plain-language drafting and replaces repetitive prompt copying with consistent guidance.
- A library of approved agreements lets the tool draw on prior work for addenda and new contracts.
- Sensitive agreements remain in a controlled internal environment rather than the open-source repository.
- Kasuku reports cutting drafting and review time roughly in half while producing more consistent provisions.
- The main insight was that AI could support a lawyer’s own judgment and working style, not merely perform isolated tasks.
## Turning Legal Workflows into Plain-Language Instructions
- Online Safety Counsel Jesse Geraci began with a workflow for analyzing source code in **DMCA** notices.
- Copilot instructions covered triage, code comparison, license checks, circumvention review, policy references, and report templates.
- Instead of traditional programming, the workflow encoded legal reasoning through structured instruction files.
- Different modes were created for clients and lawyers, including faster client analysis, escalation recommendations, deeper legal review, and arguments for both sides.
- The system later grew into a desktop application supporting contract review, NDA triage, risk assessment, compliance checks, and response drafting.
- Reusable skills and agents handle tasks such as intake, playbook alignment, risk scoring, evidence verification, escalation, and report assembly.
- Legal teams can still customize the system through readable Markdown, while human review remains essential.
## Broader Lessons for Nontechnical Teams
- Repetitive work in almost any profession can be a starting point for automation.
- Clear definitions of methodology, standards, and desired outputs can substitute for extensive programming knowledge.
- Teams should begin with one bottleneck, use Copilot CLI to prototype a solution, and expand based on real usage.
- These tools are decision-support systems—not replacements for professional judgment.
Teams can use Copilot CLI to turn their existing expertise into repeatable, transparent workflows while retaining control over sensitive data and final decisions.
Joseph is a cybersecurity and AI expert who creates software and educational content to help developers build more securely. His open-source game, videos, and international speaking engagements have reached a broad audience, combining practical security guidance with accessible explanations.
## Cybersecurity and AI Leadership
- Develops software and content focused on secure development.
- Helps shape how developers approach cybersecurity and AI.
## Open-Source Security Education
- Created the open-source game [gh.io/scg].
- More than 10,000 developers have used it to build future-proof security skills.
## Educational Videos
- His videos have received over 2.8 million views.
- Simplifies complex security topics into actionable advice for a global audience.
## International Speaking
- Delivered 79 talks across 25 countries in the past four years.
- Known for combining technical insight with energetic stage presence.
Overall, Joseph’s work spans hands-on tools, accessible education, and public speaking, making cybersecurity knowledge more practical and widely available to developers.
Open source security depends on supporting the maintainers who sustain critical software, not merely hosting their code. GitHub argues that funding, education, practical security tools, and AI assistance can reduce maintainer burnout while improving the broader software supply chain. Its new commitments focus on making security work more manageable as AI accelerates both vulnerability discovery and attacks.
## A $12.5 Million Open Source Security Commitment
- GitHub is joining Anthropic, AWS, Google, and OpenAI in committing $12.5 million to the Linux Foundation’s Alpha-Omega initiative.
- The funding will help integrate emerging AI security capabilities into existing open source workflows.
- The effort builds on GitHub’s broader role as a provider of security tools, education, and long-term maintainer support.
## Expanding Maintainer Resources
- More than 280,000 GitHub maintainers are eligible for free access to:
- Core GitHub services
- GitHub Copilot Pro
- GitHub Actions
- Code scanning and Autofix
- Secret scanning and push protection
- Dependency alerts
- GitHub’s Secure Open Source Fund is adding $5.5 million in Azure credits and funding for training, expertise, community support, and new partners such as Datadog, Open WebUI, the Atlantic Council, and OWASP.
- GitHub Security Lab is improving security advisories and Private Vulnerability Reporting to reduce low-quality reports and ease the burden on maintainers.
## Results from Security-Focused Funding
- Previous Secure Open Source Fund programs supported 138 projects and more than 200 maintainers across 38 countries.
- Participating projects produced:
- 191 new CVEs
- More than 250 prevented secret leaks
- More than 600 detected and resolved leaked secrets
- These projects collectively affect billions of monthly software downloads.
- GitHub concludes that security improves when maintainers receive dedicated time, funding, education, and tools that fit naturally into their workflows.
## Using AI to Reduce Maintainer Burden
- AI has increased the speed and scale of vulnerability discovery for both attackers and defenders.
- Maintainers are facing more automated pull requests and security reports, often with poor signal-to-noise ratios, contributing to burnout.
- GitHub’s goal is to use AI for triage, pull request review, vulnerability identification, and remediation—not simply to generate more findings.
- GitHub has open sourced an AI-powered security research framework so maintainers, rather than only specialized security teams, can benefit from it.
- Copilot Pro provides eligible maintainers with AI-assisted code review, agentic security remediation workflows, and access to multiple leading models.
GitHub’s overall recommendation is to treat AI as a force multiplier and pair it with sustained funding, education, and workflow-integrated security tools. Supporting maintainers directly is presented as the most effective way to protect the wider software ecosystem.
GitHub and Andela argue that AI opportunity should not depend on geography or employer resources. Their AI Academy trained 3,000 engineers by embedding GitHub Copilot into real production work rather than isolated exercises. The approach improved developers’ ability to understand unfamiliar systems, work with legacy code, and focus more time on higher-value decisions—while preserving human review and accountability.
## Unequal Access to AI Skills
- Developers across Africa, South America, and Southeast Asia have substantial technical talent but uneven access to:
- Emerging AI tools
- Mentorship and structured training
- Reliable connectivity and high-performance computing
- Affordable cloud services and data
- Much existing training assumes constant internet access, well-resourced environments, and localized content.
- Contract-based or informal work can leave developers with limited time and financial capacity for reskilling.
- Without affordable access and regionally relevant learning communities, AI could deepen existing technology inequalities.
## Learning AI Within Production Work
- Mid-career developers generally cannot leave live systems and deadlines to experiment with new tools.
- Simply giving teams access to AI does not guarantee adoption; organizations also need:
- Clear role and use-case definitions
- Training tied to actual responsibilities
- Updated review and quality standards
- Andela selected developers whose work involved complex production systems and incorporated Copilot into:
- IDE workflows
- Pull request reviews
- Refactoring and maintenance
- This made training practical and exposed AI tools to legacy code, architectural complexity, and real production risks.
## Faster Orientation in Unfamiliar Systems
- One of the first benefits was not raw code-generation speed but faster understanding of existing systems.
- Developers used AI to:
- Generate unit tests before changing legacy code
- Reveal system behavior and architectural patterns
- Draft refactors and clarify control flow
- Sketch diagrams of system boundaries
- Tests provided safer boundaries for modifying poorly covered legacy code.
- AI suggestions still required cleanup and could introduce subtle errors, making disciplined review essential.
## Confidence and Productivity Gains
- After several weeks, developers reported:
- Faster onboarding
- Greater confidence handling ambiguous work
- Less time spent on setup and more on business and engineering decisions
- Senior engineer Daniel Nascimento estimated that Copilot increased his productivity by about 50%.
- The main value was not merely completing tasks faster, but freeing time to understand business needs and focus on meaningful impact.
## Practical Model for AI Adoption
- AI training is most effective when it is:
- Embedded in everyday development
- Based on real systems and responsibilities
- Supported by structured guidance
- Evaluated through production-quality standards
- Organizations should treat AI as a capability developed through practice, not as a standalone certification or experiment.
The GitHub–Andela experience suggests that inclusive AI adoption requires more than tool access. Pairing affordable, structured training with real production work can help developers worldwide build confidence, improve productivity, and participate more fully in the AI-driven future.
