SigmaNEST’s Post

Nesting has been hailed as one of the most important technologies of the last 30 years for fabricators worldwide. And of course, it's true. Nesting software, with its automatic processing and ease of manual placement, has made it easier to obtain better material yields than ever before. While true, there are big tradeoffs to nesting. The article, "Think It Through", written by our own Kevin Keane, Product Strategy Manager, was published in Modern Metals magazine. Click the link below to read the full article. https://okt.to/fEsJ6x

What does sovereign manufacturing capability look like in a world of automated manufacturing processes? Motors and microcontrollers. Step 1: Building the machines that make the machines. Developing silicon manufacture and electric motor production using parts from international suppliers. Step 2: Replacing the existing tool-chain with locally made silicon and motors. Step 3: Use locally produced micros and motors to develop commercial and industrial robotics to deliver high production rates with low wage costs. (Russell Mineral Equipment, Crest Robotics, Automated Solutions Australia P/L, Max Morin ) Step 4 (optional): Let's stop exporting lithium to china and then re-importing it as batteries. I bet there's a robot that could do that job... Also, I hear Advanced Composite Design Corporation are developing breakthrough battery technologies that don't rely on lithium, so this may be a non-issue. https://lnkd.in/g3hp_U5Y)

An optimization problem is a type of problem that requires finding either a minimum or maximum result. For example, consider the task of traveling from one place to another. You can travel by bicycle, by bus, by train, or by airplane. If you need to reach your destination within 12 hours, you might choose to travel by train or by airplane, as these are feasible solutions that meet the time constraint. However, if you need to travel within 12 hours at the minimum cost, you would choose the train. This is the optimal solution. In optimization problems, there can be multiple feasible solutions, which are solutions that satisfy the given constraints or limitations. However, there will be exactly one optimal solution, which is the solution that achieves the objective of the problem in the best possible way. Several strategies can be used to solve optimization problems, including: 1. Greedy Method: This approach makes a series of choices, each of which looks the best at the moment, with the hope of finding the global optimum. 2. Dynamic Programming: This method involves breaking down a problem into simpler subproblems and solving each subproblem just once, storing the solutions to avoid redundant work. 3. Branch and Bound: This technique systematically explores all possible solutions by dividing them into smaller subproblems, which are then solved independently. We will learn about those strategies later 👋

Compressing ideas is loss-y. There’s no way to convey a complex idea and maintain fidelity. To work with an idea amongst abstractions is to accept that rabbit holes will develop. And, that problems will hide in those rabbit holes.

Evolved Messes can be solved via decoupling Thesis: 1. Evolved problems are mostly messes 2. Most problems are evolved problems (rather than designed) 3. Decoupling is the solution (in IT) Ackoff defines a mess as: A system of interconnected problems where one cannot address a problem in isolation without negatively impacting the problem and the other problems it's connected with. This definition puts it near Wicked Problems, but less horrid. Yudkowsky, in his book Inadequate Equilibria, extends the idea to a set of mutually reinforcing feedback systems. I forget what he calls it, but it's good framing. --------- How do systems come about? Mostly, they come about organically -- they evolve. We have problem 1, and we build a solution. Then we have problem 2, and we build a solution. Eventually problems 30, 400, and 9742 come up, and those get addressed too. Evolved systems, despite being pseudo-teleological, are actively NOT forward looking...which makes a lot of evolutionary paths dead ends in nature, and makes a lot of evolved IT systems eventually crumble under the weight of the cost of change. Without forward-looking (and active cleanup) in your system (system of systems?) evolution, you are near guaranteed to get a mess. --- If you have forward-looking in your system evolution, you have the capability to unwind your messes. Problems that are messy are usually messy because the systems are (as we say in software development) tightly coupled. Fortunately, it's possible in most organizational cases to decouple the pieces. And if you do, you can often reach a state where the system isn't (very) messy.

What is reverse engineering and introduction to reverse engineering? #reverseengineering #malwareanalays What is reverse engineering and introduction to reverse engineering https://lnkd.in/dxfWXUvy

Many people think making faster software will make it harder to understand and change and won't pay off. In my experience, the opposite is true. In Making Faster Systems, I explore the context behind "premature optimization is the root of all evil" and dive into three techniques we can all use to make faster, more understandable systems for everyone's benefit.