👩💻Move Over Vibe Coding "Agentic engineering" is here.Plus: My conversation with Caltech's Data Architect about coding, SQL, and Humble engineers.
Here is what we have today, team:
📰 AI News and Trends
Other Tech News
My Conversations with Caltech Data Architecture.Vibecoding, future-proofing your career, data, SQL, humble engineers and more.
I had the pleasure to sit down with and interview, Armando Plasencia, Caltech Data Architect with 25 years of experience in the Data and Tech world. While chatting, he shared that long-term success in tech will be driven less by mastering specific tools and more by curiosity, humility, and community. He stresses that engineers must keep learning daily as AI automates routine coding, while strong collaboration and openness to feedback now outperform solo expertise. Plasencia highlights open-source as a fast-track career path, noting that contributors gain real-world experience with software used by hundreds of thousands of companies and build trusted professional networks. He also points to low-code and no-code platforms that can reduce development from months to minutes, shifting software creation toward business logic rather than syntax, while emphasizing SQL and data sovereignty as critical skills in an AI-driven era. This is the entire conversation, and we will be uploading shorts on our Substack Notes account.  Move Over Vibe Coding Agentic engineering is here.
A year ago, although it seems many more than that, Andre Karpathy coined the term Vibecoding. During that year we have seen apps like cursor more than 10x in value and usage, and other apps like lovable and repplit become darlings for any one wanting to build apps but lacking coding knowledge. The hype of prompting AI to build Apps and create code is now in the past and we are moving to Agentic Engineering, a new term coined by Andre as well, which is the act of AI coding itself. Researchers have demonstrated that large multi-agent AI systems can now run for days with minimal human input, generating ~1,000 software commits per hour and executing 10+ million actions while building and maintaining complex products like a web browser. This signals the rise of “self-driving codebases,” where AI increasingly designs, writes, tests, and fixes software on its own, a trend that could automate 80%+ of enterprise development within five years and cut software costs by 50–70%. While this does not yet qualify as full AGI, it shows early AGI-like behavior in narrow domains such as engineering, with systems capable of long-term planning, self-correction, and collaboration. As technology begins to build itself, human value will shift toward problem framing, system design, and governance rather than manual coding, making these skills essential for today’s youth. At the same time, cybersecurity risks will rise sharply, as autonomous systems can discover and exploit vulnerabilities in hours, enable self-evolving malware, and amplify supply-chain attacks, meaning future digital security will depend as much on controlling AI behavior as on defending traditional infrastructure. 📚Learning CornerReinforcement Learning: An Introduction – Sutton & Barto
What is the Key to Physical AGI?
Robots Will Learn Like Humans. Data, Not Hardware, Is the Key to Physical AGI Researchers argue that the main barrier to achieving Physical AGI in robotics is not hardware or algorithms, but data, as today’s robots are trained on only thousands of hours of controlled demonstrations compared to the billions of human-years behind modern language and vision models. The only scalable solution is capturing massive amounts of human egocentric video, potentially 100+ million hours, equivalent to 150 lifetimes of experience, and using it to train “world models” that learn physics, cause-and-effect, and task dynamics by predicting how scenes evolve over time. By learning from human experience first and then transferring that knowledge to robots, early systems like DreamZero have already shown 40%+ performance gains from just minutes of video data, suggesting that robots can acquire general physical intelligence with minimal retraining. This approach favors humanoid or human-like machines, which reduce the gap between human and robot movement, and points to a future where robots learn primarily by watching people rather than being manually programmed. If successful, this paradigm could enable robots to perform complex real-world tasks with little supervision, marking a realistic path toward Physical AGI driven by large-scale human experience rather than handcrafted robotics data. 🧰 AI Tools of The DayGenerative Video & World-Model Architectures
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Monday, February 9, 2026
👩💻Move Over Vibe Coding "Agentic engineering" is here.
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