Is there a framework for generating ideas? Do ideas just happen? Today I struggled to come up with ideas for my next blog post. None of my initial ideas truly resonated with me. Next I just started thinking about what my process for coming up with ideas looks like. Usually it is just writing down a bunch of random thoughts until one of those sparks interest in me. But then a realization came to me:

Sometimes ideas come as visualization at a later time – the other day I could not finish a bouldering (climbing) problem, always falling at the last move. I was walking and visualizing myself climbing and different moves and could not solve it. I was walking later in the day and a ‘toe hook’ idea came to me. The next day I went to the gym and simply flawlessly executed.

Similarly today I was looking at my keyboard and struggling to come up with ideas and then it came to me: ideas are not some kind of “magic” that appear from nowhere, but rather they are processes and you can make them happen and come to you. I don’t pretend to have a patent on generating ideas, otherwise I would sit on a pile of billions of dollars, but, anyway,  some thoughts:

Step 1: Input Loading. Any idea out there is some variation of previously existing idea, some synthesis of multiple existing ideas, or some application to the new context. We can say that Idea: Idea = Input A + Input B (in a new context). For example my blog post about Global Maximum was a combination of computer science “hill climbing” and my career story.

Step 2: The Constraint. Staring at a blank page is very intimidating because there are way too many degrees of freedom, but having constraints is helpful. One constraint I’m operating under right now is time (it is 6:00AM, I’m at a coffee place, and I have 1h20m to write my blog post). Another type of constraint is topic based (like how I connect athletic performance to software careers, blog post). There are more constraints to come up with “artificially”.

Step 3: Diffuse mode thinking. Everyone knows the cliche about the best ideas coming in the shower. This is actually a known phenomenon called Diffuse Mode thinking. It is a key step in James Webb Young’s technique for producing ideas:

• Gather new material.
• Work over the materials in your mind.
• Put the problem completely out of your mind.
• Your idea will come back to you with a flash of insight.
• Shape and develop your idea into practical usefulness.

Conclusion
This “framework” might not be complete but I think next time around I will open this blog post to remind me that this is simply a process and I can be in control of it and use techniques.

Have you ever thought about large software systems as organic living organisms? This sounds a bit odd and, maybe, philosophical. Starting with first principles: everything in the world is based on fundamental physical laws. When we think about the organic world we often imagine fuzzy, imprecise, evolving and reproducing things. But when we think about a software program, we often imagine strictly deterministic outputs to given inputs with no typical attributes of living organisms, though on a completely fundamental level both are build with the same basic physical particles and follow same laws. At the level of modelules of even proteins organic world is fairly deterministic similarly to small software systems, but when we get to a system that is built by hundreds of software engineers over a decade that serves millions or billions of requests per day it has more resemblance of a living organism than of that exact deterministic machinery.

I could think of multiple similarities, for instance:

• Growth and evolution. A codebase is constantly adapting to a changing environment, engineers constantly modify code. Some sub-systems die-off and some new ones are being added.
• Metabolism. The system consumes resources (CPU, ram, gpu, storage, network, or whatever other capacity) in order to operate (live). It has a “metabolic rate” (your cloud bill) and produces “waste” (logs, error reports, heat).
• Homeostasis. The system attempts to maintain a stable internal state. Monitoring, alerting, and auto-scaling are all of the things to keep it healthy and in balance. At work people even say “system health”.
• New Behaviors. This is a bit interesting. In any system with enough complexity, the interactions between individual parts sometimes create unpredictable, emergent behaviors that no single person designed. This sometimes results in extremely difficult to debug bugs or system behaviours that are challenging to explain.

Counterarguments could be things like:

• Replication. Arguably complex systems are not that advanced in self-replication, at least this isn’t their primary goal (unless it is computer virus). We have some data replication and scaling as some analogies, but maybe not quite enough.
• Consciousness. Although large systems don’t have “consciousness” we can argue that encoded adoptions, distributed algorithms, automatic maintenance, state monitoring are partially related to this. This becomes more blurry in the world of AI.
• Non-determinism. Systems are often designed to be deterministic. When they are large and complex enough randomness is necessarily introduced and ML models leverage randomness in principle.

Working for multiple very large companies serving billions of users definitely made me think that large software systems grow beyond human-scale comprehension. Sometimes I think these large systems are organisms and software engineers are part of this organism working on making parts of this organism alive and evolving. Also thinking of an analogy of software engineers looking after a large system as gardeners looking after a living garden. If it’s not looked after properly it becomes messy and bushy.

What does this mean practically? Well, this was just a thought I typed in, so not 100% sure but, maybe, it could mean:

• Stop trying to fit the entire system in our heads and instead build world-class observability (metrics, distributed traces, and logs) and keep parts of the system you are responsible for healthy.
• Embrace a bit of chaos. The system would sometimes behave unexpectedly. Some practices to stress test the system are good (chaos engineering) the same as organisms are sometimes tested with adverse environments or viruses/infections.
• Design with evolution in mind and do not expect your solution will be final or will never be pruned by other engineers.
• The more parts of the system are built with AI assistance the more the system becomes organic. We should focus on ‘trunk’ of trees while we might allow AI to build leaves and small branches.