Tudor Girba

"Software engineering has changed radically. It is now mainly about reading code. But reading code does not scale. We need to delegate to AI." I am glad you now recognize code reading as a problem. However, the broader problem statement is not quite correct and neither is the conclusion.

However, getting accurate and representative answers quickly replaces beliefs with facts, and paves the way for real engineering to flourish. In doing so, we might discover that the gains are substantial, and that our own abilities are worth retaining and enhancing.

"Software engineering has changed radically. It is now mainly about reading code. But reading code does not scale. We need to delegate to AI." I am glad you now recognize code reading as a problem. However, the broader problem statement is not quite correct and neither is the conclusion.

But there is a way to augment our understanding by enhancing how we can get accurate, representative and explainable answers for questions about our systems. This can be achieved through contextual deterministic tools that are specifically built to provide answers for each question.

"Software engineering has changed radically. It is now mainly about reading code. But reading code does not scale. We need to delegate to AI." I am glad you now recognize code reading as a problem. However, the broader problem statement is not quite correct and neither is the conclusion.

It is rarely a subject of conversation, and, even when it is, the reading process itself is seldom challenged. It was writing that captured the attention while reading was silently accepted as costly and inevitable. Indeed, developers spent most of their time reading code since a long time.

"Software engineering has changed radically. It is now mainly about reading code. But reading code does not scale. We need to delegate to AI." I am glad you now recognize code reading as a problem. However, the broader problem statement is not quite correct and neither is the conclusion.

The U.S. act to selectively allow Fable access based on nationality should make it finally obvious that an overly deep dependency on foreign actors is a threat to digital sovereignty. Hopefully, this can act as a wake-up call for Europe 1/

While such an investment does not have a moat (nor should it), enhancing human abilities is very much in line with European values. U.S. could do the same, but given their current trajectory, they likely would consider it uninteresting and weak, which is a good premise for any asymmetric move. 5/

The U.S. act to selectively allow Fable access based on nationality should make it finally obvious that an overly deep dependency on foreign actors is a threat to digital sovereignty. Hopefully, this can act as a wake-up call for Europe 1/

The hype behind Mythos is based on the argument that we have reached the peak of human ability and that AI has already surpassed it. Yet, when it comes to software engineering, we are far from even knowing what the peak of human ability is. 3/

The U.S. act to selectively allow Fable access based on nationality should make it finally obvious that an overly deep dependency on foreign actors is a threat to digital sovereignty. Hopefully, this can act as a wake-up call for Europe 1/

Systems are often seen as a set of features. An alternative is to regard them as encoded knowledge. 1/

To guide the AI interaction, I script it contextually. I explore what the AI does and see the results live. The agent works with my object and changes the code based on that context. And it is treated as just another object viewed in the system in the same way as anything else. 8/

Systems are often seen as a set of features. An alternative is to regard them as encoded knowledge. 1/

But how does GenAI fit into the view of systems as knowledge work? Take a look here. I am exploring an API; the GitHub API in this case. I arrived at a contributor entity, and now I want to see what this contributor is linked to. 6/

Systems are often seen as a set of features. An alternative is to regard them as encoded knowledge. 1/

Elegance is often a guide, and every time a simplification happens, a leap becomes possible. Any system can be seen in terms of features, and any system can be viewed as encoded knowledge. It is up to us. The choice influences what we praise. 4/

Systems are often seen as a set of features. An alternative is to regard them as encoded knowledge. 1/

Features are to be produced. Productivity gets measured in throughput. It can be seen in tickets that materialize in code instructions that manipulate data. The faster the better. The more the better. Force is often preferred. 2/