How to make software malleable for users

Ever looked at a piece of software and thought, “Wow, this is garbage… I could build something way better”? Most of the time, that “crappy” software wasn’t always bad. Back in its heyday—maybe 10 or 15 years ago—it was probably cutting edge. But technology evolves at a breakneck pace: user experience standards shift, new integrations emerge, AI opens new doors, and expectations rise. The problem? Too many platforms never adapt. Features go stale, designs age, and what once felt innovative now feels clunky. That’s when competitors step in and say: “I can do it better.” At Active Logic, we hear it all the time: “I want to build my own software because everything out there is crap.” And honestly, they’re not wrong. But here’s the opportunity: if a market already has “crappy” solutions, that’s proof of demand. Market validation is built in. If you have an idea, build it. But don’t stop there. Maintain it. Listen to your users. Keep it tested, clean, and evolving. Because the moment you stop moving forward is the moment your product starts falling behind. We’ve got a new product entering beta soon that proves this point—and I believe it will absolutely outpace its competitors. Because in software, the difference between winning and losing is simple: keep evolving.

Ephemeral software. This is where we’re going for a lot of software. It’s either THE shift or a major step in the shift we will see in the coming years. Kyle Ledbetter shared this video from Anthropic and it reminded me of what he and Andy Keil have talked about as they’ve been building Dreambase.ai. 🤯 Eventually we won’t need prebuilt software; the software will build itself in the moment led by the user, context, and information space. (Or where paid marketing wants to direct us?) We’re not that far from it right now, and I think it’s how we should be thinking about the next gen of software. Let’s get philosophical for a moment: Zooming out to what software really is, it’s a conversation between human and machine. The machine just hasn’t been very smart, so: ✳️ Engineers structure what the machine can understand and how to handle that input from humans. ✳️ Designers communicate to the human the intent and capabilities of that structure so they know what and how to use the product. ✳️ Product Managers determine where to invest to yield the biggest value-benefit for humans and for the software business, since it’s so costly to build. AI makes machines much better at communicating with us humans. It can now do a lot of what engineers and designers have done. With this approach shown in the video, it won’t cost a lot or take time to build, and the quality of the code doesn’t matter, it just matters if you get the result you’re looking for. The code is ephemeral. There's effectively no software to build anymore in these cases. It's the machine communicating with us directly to accomplish what we want to accomplish. So then our responsibility is just context, intent, approach, direction. And making sure it's doing it well. It's wild, and I think it's where the opportunity is. That's about as far as I've gotten. What happens after that?!? https://lnkd.in/gnBAbc8u

n8n is great for testing ideas. Terrible for production systems. We just told a client this. Here's why. The issues show up around month 3: Workflows that worked fine with 100 operations suddenly break at 1,000. Debugging becomes difficult because the visual interface hides what's actually happening. Making changes requires careful coordination because one workflow often depends on three others. The bigger problem: maintenance burden. When your team grows or processes change, those visual workflows become a tangled web. We've inherited three n8n implementations this year. Each time, rebuilding them properly took less time than untangling the existing setup. We typically recommend purpose-built solutions or custom code depending on the complexity. They cost more upfront but scale predictably and stay maintainable. n8n works well for specific scenarios - internal tools, simple integrations, rapid testing. Testing an idea or building an MVP? Sure, we'll use it. It gets you to validation fast. But for production systems handling customer data or revenue critical processes? That's where we draw the line. We write about these technical decisions regularly on our blog. Blog → https://lnkd.in/exVh9pyK The technical debt catches up fast.

As AI transforms how we build software, how do developers adapt? Watch our latest South Park Commons roundtable with Harrison Chase, Ben Hylak, Oliver Gilan, and Charlie Holtz as they explore the emerging frontier of writing software today. (Full episode in the comments below.)

Software on demand is already here. Anthropic just released "Imagine with Claude" and it's not getting nearly enough attention. What it does: Claude Sonnet 4.5 generates complete applications in real-time. No prewritten code. No predetermined functions. Just live software creation responding to your prompts. This is a preview of how radically different software development could look in the near future. The line between "describing what you want" and "having a working app" just got very thin. #AI #SoftwareDevelopment #Innovation

No-code tools have come a long way. The new generation is genuinely impressive — you can build complex products faster than ever, connect dozens of systems, and ship something that looks and feels real in days. But there’s a point where speed meets structure and that’s where things start to break. We often get involved right around that stage — when a senior developer needs to look at the codebase, do an audit, or run a security review. That’s usually when the hidden complexity starts showing up. What worked perfectly fine for a few hundred users starts to fall apart at a few thousand. The logic gets harder to follow. The integrations start failing. The costs — both time and money — begin to skyrocket. It’s not because the tools are bad. Tools like this are amazing. They’ve opened the door for more people to build, experiment, and launch. But they’re not designed for long-term scale. That’s why at Taktway, we still build real software. We use AI-assisted development and modern workflows to move fast — but everything is built with scalability, security, and maintainability in mind from day one. It’s not about being against no-code. It’s about understanding its limits. There’s a point where prototypes should turn into real systems — and the earlier that transition happens, the smoother everything goes. It takes a little more work upfront but in the long run, you’ll thank yourself for it.

Stop chasing shiny software. The truth? Your product isn��t revolutionary. It's just a fancy spreadsheet dressed up in a sleek interface. You labeled it ‘SaaS' and suddenly you're a player in the game. You secured millions in funding, expanded your team, and now you tout it as a groundbreaking platform. But here's the reality check: It still doesn’t address the real issues. Instead, it adds layers of complexity that leave users frustrated. You know what the world doesn’t need? Another dashboard with flashy features that no one truly understands how to use. What needs fixing is genuine functionality that solves actual problems. Here are some hard truths: → Users crave simplicity, not more bells and whistles. → They want solutions that make their lives easier, not another subscription they have to juggle. → Your focus should be on value over vanity. Instead of chasing trends or adding features just for hype, ask yourself: Does it truly benefit the user? If not, it’s time for a rethink. Let’s create products that work, not just pretty platforms. What’s your take?

The biggest mistake developers are making right now? They think AI is just another tool. It’s not. It’s a new substrate for building software. History shows the pattern: • 1990s → Code lived on desktops • 2000s → Code lived on the web • 2010s → Code lived on mobile • 2020s → Code lives inside AI And that changes the rules of the game. We’re moving from: • Writing instructions → to teaching intelligence • Debugging functions → to shaping behaviors • Shipping commits → to orchestrating agents As Andrej Karpathy said: “Programs are no longer written. They’re grown.” What does this mean in practice? ⚡ Systems that learn instead of being patched ⚡ Products that understand goals, not just clicks ⚡ Developers who multiply output by orders of magnitude The uncomfortable truth? Your coding skills have a half-life. The faster you adapt, the longer you stay relevant. The future belongs to those who stop fighting AI; and start collaborating with it.

ENTERING THE GOLDEN AGE OF SOFTWARE? I am feeling visionary on a Friday afternoon, even before alcoholic beverages.    AI is discussed in terms of "the death of software" sometimes. I think it is completely the opposite. The use for software is not fixed and the opportunities to create super-tailored software just increased massively as the cost and hardship of coding just dropped massively. As software become more tailored, the value derived from it will increase.    Software engineers will be relieved of the grunt work which can be left to AI. Instead they will become system architects, managers of complexity, AI model fine-tuners and hard-problem-solvers. The creators will increasingly be the domain experts, working together with the IT experts.    Point is, I doubt this is a zero-sum game where new software necessarily takes away the market from old software. Rather, I think the whole pie is about to grow. A lot. Cheers!

“Low-code is just for prototypes.” Heard that before? I hear it all the time. Here’s the truth: Low-code isn’t about building toys. It’s a way to design and deploy real business applications, with less code and faster delivery. Over the years, I’ve seen the same misconceptions pop up again and again: - “Low-code isn’t scalable.” - “It’s only for non-technical people.” - “It can’t integrate with serious data stacks.” - “It’s a temporary fix, not a long-term solution”. - “Low-code isn’t secure.” - “Developers hate low-code.” Each of these is wrong. In my next carousel, I’ll break down why, and what’s actually true when you use low-code and automation properly. 👉 Curious? Swipe through below.