🚀 How Our AI Accelerated 50 Businesses in Less Than a Year Since January 2025, our AI has been transforming software development across industries. It generates high-quality code in languages like Go and Python — complete with automated tests — and integrates directly with GitHub for seamless review, versioning, and deployment. Here’s what we’ve delivered: ⏱️ Up to 70% reduction in development time 🧪 Automated testing from the first commit 🔁 Less rework, more consistency across squads 📈 Real acceleration in over 50 businesses served If you're a CEO or CTO looking to scale your tech operations without expanding your team, this might be the right time to talk. 📩 Let’s connect. #artificialintelligence #ceo #cto #github #automation #techleadership #devops #innovation #digitaltransformation

🚀 How Our AI Accelerated 50 Businesses in Less Than a Year Since January 2025, our AI has been transforming software development across industries. It generates high-quality code in languages like Go and Python — complete with automated tests — and integrates directly with GitHub for seamless review, versioning, and deployment. Here’s what we’ve delivered: ⏱️ Up to 70% reduction in development time 🧪 Automated testing from the first commit 🔁 Less rework, more consistency across squads 📈 Real acceleration in over 50 businesses served If you're a CEO or CTO looking to scale your tech operations without expanding your team, this might be the right time to talk. 📩 Let’s connect. #artificialintelligence #ceo #cto #github #automation #techleadership #devops #innovation #digitaltransformation

As engineering leaders, our next design challenge is clear - How do we architect systems that think, act, and collaborate like engineers themselves? None of us can wear every hat - designer, developer, tester, DevOps, and SRE - all at once. But with Goose, we might come close. If you haven’t come across Goose yet, it’s worth a look 👇 🔗 github.com/block/goose It’s an open-source framework that turns any LLM into a Smart AI Engineer — one that understands requirements, builds, tests, deploys, and learns inside your environment. Example: - Open a ticket: “Add an API to calculate transaction fees.” - Goose reads the spec, scaffolds the endpoint, writes tests, fixes build issues, commits code, triggers CI/CD, and validates deployment. - No hand-offs. No context lost. Just flow. We now have the opportunity to move beyond automation - toward systems that think, act, and collaborate with us. Enabling smarter collaboration, where engineers and AI work hand-in-hand. #GooseAI #SmartAIEngineer #AIinEngineering #EngineeringLeadership #FromSilosToStreams #OpenSourceAI #DevOps #QualityEngineering

Still vetting dev teams like it's 2010? It's 2025. Everyone has Cursor and Copilot now. What used to matter: Bring them in for a whiteboard session. Ask them to reverse a linked list. Quiz them on algorithm complexity. They solve it perfectly. Recite the answer. Get hired. How it's shaping up now: Can they spot when AI code mostly works but quietly breaks edge cases? Do they catch the LLM hallucinations in code before it ships? Do they ask clarifying questions before building anything? When they explain their solution, are you hearing memorized patterns, or actual thinking? If your selection process isn't watching out for these? You're not getting their best engineers. You're inheriting technical debt faster. But hey, at least you'll ship fast. For a while.

Especially for those that have been doing manual implementation, you can teach the new people the way you know from a notepad/vs studio, but let’s be honest, that way is on its way out. Tech evolves, methods improve, data flow and fundamentals are the same, but instead of applying it to slower manual implementation, conversion to automated solutions is way superior

"Do you train dev teams in AI-assisted software engineering?" Yes, absolutely. I train teams in the most important AI-assisted software engineering skills of all: how to be high-performing /without/ "A.I.". This technology has proven to be an amplifier of dev team capability, but that's only good news if your team was already high-performing in terms of their ability to rapidly, reliably and sustainably iterate working software to meet changing business needs. And that's not true of the large majority, because most businesses didn't invest in building that capability. If your dev teams can't already rapidly, reliably and sustainably iterate working software without any drama, then attaching a code-generating firehose to their process is going to make the /real/ bottlenecks and leaks *worse*. In that sense, you could say I help dev teams become "AI-ready". And, very presciently of me, I was doing it decades before LLMs came along. Cool, right? Like... How did I know? ;-)

I used to think “real engineers” had to do everything manually. No shortcuts. No help. Just grind. But here’s the truth I’ve learned in DevOps: Great engineers don’t waste time reinventing the wheel — they move faster, learn deeper, and deliver better. That’s exactly what GitHub Copilot unlocked for me: ✅ Scripts and configs built faster than I could Google them ✅ Repetitive boilerplate replaced by real learning ✅ Terraform, Bash, and Linux commands that taught me why as I typed ✅ Confidence to start new projects without staring at a blank screen This isn’t cheating. It’s acceleration. It’s how I practice DevOps at the speed of today. 💡 The future engineer isn’t the one who types the most commands. It’s the one who learns, builds, and adapts the fastest. And AI is how we get there. #Copilot #AI #DevOps #CloudEngineer #MyDevOpsBlueprint

Coding in 2025: Easier than ever, harder than ever. When I first started coding, I remember spending nights debugging a single issue. Stack Overflow was my lifeline, and frameworks were limited. Fast forward to today ⏩ AI copilots write boilerplate in seconds. Libraries solve complex problems out of the box. Cloud services handle what once took teams weeks. 👉 Writing code is easy. But here’s the catch 👇 While syntax has become simpler, the real challenges have grown: 🔹 Designing scalable architectures in a distributed world 🔹 Keeping systems secure against AI-driven threats 🔹 Optimizing cloud costs while delivering fast 🔹 Learning new stacks at a speed never seen before 🔹 Collaborating across remote, diverse teams So yes—coding is easier, but software engineering is harder. It’s no longer just about writing code. It’s about solving the right problem, securely, at scale, and with people. I’m curious: Do you feel coding today is actually “easy,” or have the challenges just shifted into new areas?

Share my Tech journey. Working on catch up this fast-changing, ever-improving tech world. AI won't replace me! Topic 01: Docker What: A platform to build packages and distribute apps in isolated environments. When it shines: reproducible dev/test, microservices isolation, faster onboarding, CI/CD pipelines. My context: I don’t see an immediate need at my company right now, but I see why many teams pick Docker over VMs: faster startup, lower overhead, and predictable environments. Topic 02 (next): I might dive into LLMs next—curating fundamentals, tooling, and practical use cases. If you have must-read guides, please drop them below! #LearningJourney #Docker #Containers #DevOps #SoftwareEngineering #LLM

Context Engineering is redefining Software Engineering. We’re moving from writing logic to designing reasoning systems. From syntax to semantics. From code to context. Here’s the new paradigm: 🧠 Context → the new source code 💾 Memory → the new database 💬 Prompts → the new functions ⚙️ Agents → the new microservices 🚀 Agent orchestration → the new DevOps 🪄 AI-native IDEs → the new engineering copilots We’re entering an agentic era where software learns, adapts, and collaborates. Those who master both traditional code and intelligent systems will lead the next wave. The ones who don’t? They’ll be left debugging the past. 🔥 The shift is real. The pace is fast.