Same frustration with frameworks like Langchain and Llama_Index let me to build a simple UI base Agentic freamwork that runs locally. https://github.com/ranjanprj/agentollama
Agile/Scrum and all forms of project management is a scam you should work in layers serving layer above, have wbs and tasks assigned out of it in your layer, justifying urgency by impact on business/users, reward people who accept and close most tasks by appreciation and break. And fire anyone who misbehaves.
I believe, they are describing real world consequences of scrum/agile and how real management uses it, which might be different from what the manifesto actually hoped.
I find it impossible to believe that Azure as a whole organisation takes security seriously. There might be individuals that do, but definitely nobody with decision making power. Half of the above described exploits are trivial and should have never passed any sort of competent review process.
Micro services is a deployment pattern and not a development pattern you could build monolith and expose various services to and various parts with an Ingress and point of to the same monalic and for example in java project these various end points of the services inside the same on it would only load up the classes/objects which are relevant to that service. There is no overhead in terms of memory or CPU by placing monolith as micro services exposed by end points
Micro services is a team organization pattern, emulating the software service model, except within a micro economy (i.e. a single business). In practice, this means that teams limit communication to the sharing of documentation and established API contracts, allowing people to scale without getting bogged down in meetings.
Conway might find some correlation, but strictly speaking, no. A service is not bound to any particular deployment pattern. Consider services in the macro economy. Each individual business providing a service is bound to do things differently. As micro services are merely emulation of macro services in the micro, each team is equally free to do things however they wish.
I just ran a 2006 Java code to detect and read car license plate, and it ran in the first run on Java 17. The code is 17 years old and runs just fine without any issues. I think Java folks messed up AI/ML space due to licensing. But I still think it's best PL for AI/ML.
What makes it the best language for AI and ML? I've never heard that take so just curious your thoughts. It's nice that legacy code still runs but that's usually not people's concern in ML.
I (and my colleagues) have worked on statistical, data analysis, and ML in Java since before data science was even a career field, and before Python became popular. In my opinion Java has better IDE support, more stable and proven libraries, and high performance.
Java does occasionally require that a person might have to implement their own code after reading a research paper, but I've always enjoyed that part of the job.
I've never understood Python's popularity except that I've heard some people say that it's used at Google.
