Stop paying enterprise prices for features you can build yourself. Here's a cybersecurity hack most engineers are sleeping on: Take an open-source vulnerability management platform. Connect an AI coding agent. Identify the features locked behind expensive enterprise licenses. Build your own versions of those capabilities. Custom dashboards. AI-powered vulnerability prioritization. Automated remediation workflows. Executive reporting. The gap between open-source and enterprise software is shrinking FAST. What used to require a dedicated development team can now be prototyped in days. And the best part? You control the infrastructure, the data, and the roadmap. Just respect the project's license and don't copy proprietary code. Stop thinking like a software consumer. Start thinking like a software builder. Quick win. Massive leverage.

Claude really cooked with this

Companies literally getting hacked daily at this point, everyday its something new

Unpopular opinion: Breaking into cybersecurity is MUCH easier than people think. Especially if you're already have experience as a DevOps Engineer, Data Engineer, Software Developer, or AI Engineer. The biggest mistake? Thinking you need to start your career over to get into cybersecurity. You don't. You're probably already doing 60–70% of the technical work that overlaps with security. You just haven't learned how to position it. Here's the play most engineers overlook: 1. Stop building projects. Start securing them. Already have an application? Perform threat modeling, identify vulnerabilities, implement authentication and authorization, and document your security controls. 2. DevOps Engineers → DevSecOps / Cloud Security Take your existing CI/CD pipelines and integrate SAST, DAST, container scanning, secrets management, and Infrastructure as Code security checks. 3. Data Engineers → Data Security Engineering Take your ETL pipelines and implement data classification, encryption, access controls, audit logging, and data loss prevention. 4. Software Engineers → Application Security Take applications you've already built and perform code reviews, dependency scanning, API security testing, and OWASP Top 10 assessments. 5. AI Engineers → AI Security Take your existing LLM applications and implement prompt injection testing, agent permission boundaries, model red teaming, and threat modeling using OWASP's LLM security guidance. Now here's where things get interesting... Document EVERYTHING. Create architecture diagrams, threat models, security findings, remediation reports, and before-and-after assessments. Turn your existing engineering projects into cybersecurity case studies. Now instead of telling a recruiter you're "trying to transition into cybersecurity," you can demonstrate actual security engineering work. The cybersecurity industry doesn't just need people who understand vulnerabilities. It needs engineers who understand how the systems being attacked are actually built. And if you've spent years building infrastructure, applications, data pipelines, or AI systems... You already have an advantage. You just need to weaponize your existing experience. Don't start over. Build on what you already know.

Knocking out a migration from GitHub to Gitea today

Your college network might be worth more than your degree. Years after graduating from Bowie State, me and my bros from the Computer Science department are STILL finding ways to make money together. We went from studying for exams together to creating opportunities together. Everybody talks about networking. We actually built a network.

The engineers who used to be the most valuable because they knew every command by heart are about to have a serious identity crisis. I legit remember being in college, driving all the way to Herndon, VA, walking into data centers to do rack and stacks. The field leads would hand us these thick packets with step-by-step instructions for configuring servers, installing software, setting up networks, and getting everything operational. Page after page. Command after command. And back then, knowing how to execute those instructions made you valuable. Fast-forward to today. You can literally connect an LLM, give it access to the environment, provide the right context, and have it automate nearly the entire configuration process in one shot. Something that used to take hours of manual configuration can now take minutes, with the right guardrails and validation. That's insane to me. But here's what should make a lot of engineers uncomfortable: The industry isn't paying a premium for your ability to follow instructions forever. It's increasingly paying for your ability to design systems, understand tradeoffs, solve complex problems, and know when the AI is wrong. The same technical knowledge that once made you indispensable is becoming accessible to anyone who knows how to ask the right questions. The barrier to execution is collapsing. The barrier to good judgment isn't. And I think a lot of people are seriously underestimating how quickly that changes the economics of engineering. The question isn't whether AI will replace engineers. It's whether the skills you've spent the last decade perfecting are still the skills the market will pay a premium for over the next five years. That's a much more uncomfortable conversation.

Picking up a M5 Max today

ProphetX finally giving public API key access 🔥

So glad my new spot isn’t an hour away from my Muay Thai gym

Decided to put Muse to the test as an AI SDR. The goal? Turn LinkedIn leads into booked sales calls without spending hours on manual outreach. Running the experiment this week. By Friday, I'll have a better idea of whether AI can actually deliver results or if it's just another productivity promise. I'll share the numbers.