Loosely Coupled Specs

We used to build tight contracts—APIs, schemas, interfaces—because machines needed exact matches to talk to each other.

Now we write loose definitions, natural language hints, and let the LLM figure out the mapping. It’s fuzzy logic thinking applied to system design.

The glue isn’t code anymore. It’s context. LLMs bridge gaps that would’ve required weeks of integration work.

Being less precise paradoxically makes systems more resilient—they interpret intent instead of failing hard on mismatched types.

Hard Skills Fade, Character Stays

Programming as a hard skill is depreciating fast. You still need to know architecture—when to reach for Three.js vs Next.js, how pieces fit together—but the implementation? LLMs do it better and faster.

So what’s left for humans? The things that can’t be prompted: tenacity when the project stalls, determination when the fifth approach fails, the learning reflex that never switches off.

Soft skills and character traits matter more today than ever. The ceiling isn’t “can you code this”—it’s “can you stay in the arena long enough to ship it.”

Concepts Are Now Fun

Ideas used to live and die in your head. You’d think “what if…” and it would stay a thought experiment forever—too much friction to actually build it.

Now concepts are a prompt away from reality. That random shower thought about a tool? You can have a working prototype before lunch.

Thought experiments aren’t just thoughts anymore. The gap between imagination and creation has collapsed, and suddenly thinking is fun again because you get to see your ideas walk.

MCP Bridges the Last Mile

Need a database? Doesn’t matter if it’s a markdown file, SQLite, or a full-blown Postgres cluster. MCP abstracts the implementation away—the user just says what they need, not how to get it.

This unlocks two worlds: homelab enthusiasts can self-host everything without learning twelve different interfaces, and enterprise users discover services they didn’t know existed. “Oh, I didn’t know this vendor had a calendar API.”

The new interface is the LLM—chat or voice—and everything else becomes plumbing. Applications don’t need to fight for screen time anymore. They just need to be callable.

Attention Economy Hasn’t Changed

Humans still have 24 hours. That hasn’t changed. What’s changed is now LLMs have joined social media and memes in the fight for your eyeballs.

Every scroll, every prompt, every rabbit hole—something is always trying to capture your attention. The question remains the same as it was ten years ago: more time consuming, or more time creating?

LLMs can be a lever or a leech. They either multiply your output or become another feed to refresh. The tool doesn’t decide. You do.

Attention Is Still the Currency

In an age where anyone can generate anything, the scarce resource isn’t content—it’s attention. Yours and everyone else’s.

The winners aren’t those who produce the most. They’re the ones who earn focused time from others while guarding their own.

Every new tool promises to save you time. But if you spend that saved time on more tools, you’ve just traded one master for another. Attention remains the only currency that matters.

MCP as a Distribution Channel

What if your product doesn’t have an API? Doesn’t matter—MCP can open it up anyway. Suddenly tools that were invisible to automation become first-class citizens in the LLM ecosystem.

Marketing shifts from eyeballs to context windows. You’re not optimizing for Google anymore—you’re optimizing for LLM discoverability. Call it LLM Search Optimization if you want. It’s a thing now.

Everything becomes a service, whether you designed it that way or not. The question isn’t “do we build an API?” It’s “how do we make sure the LLM knows we exist?”

Scaling Yourself Through Skills

Every skill.md you write is a piece of yourself, encoded. You’re training the model on how you think, how you solve, how you decide. It’s scaling without cloning.

But here’s the uncomfortable question: if the LLM can do what you taught it, are you replacing yourself? The answer is yes—and that’s the point.

The goal isn’t to stay irreplaceable at the current level. It’s to keep escalating. Automate today’s work so you can climb to tomorrow’s problems. The moment you stop ascending, the skill.md becomes your ceiling, not your ladder.

Technical Debt Is Cheap Now

That refactor you’ve been avoiding for two years? LLMs can knock it out in an afternoon. Technical debt has never been cheaper to repay.

The blocker isn’t cost anymore—it’s inertia. You still need to take the first step, open the file, and start the conversation with the model.

Most legacy code stays legacy not because it’s hard to fix, but because no one decides to fix it. The tools are ready. The question is: are you?

Don’t Get Absorbed by the Tool

OpenClaw is amazing. The ecosystem—Moltbook, ClawTask, all of it—makes it even more enticing. You can spend hours configuring, tweaking, optimizing your setup.

But remember: this is still the attention economy. The tool that saves you time can also consume it. Tinkering feels like progress, but shipping is progress.

Think beyond getting sucked in. How do you leverage this to amplify your output, not just your enthusiasm? The best tool is the one you stop thinking about because it just works.

Nothing Is Hard Anymore

“I can’t do it” used to be a valid excuse. Now it’s just inertia dressed up as inability. The tools exist. The knowledge is accessible. The only barrier is deciding to start.

But here’s the catch: this requires a complete rewiring of how you think. Decades of “that’s too hard” don’t disappear overnight. Your brain is hardwired to flinch at complexity.

The new skill isn’t technical—it’s mental. Unlearning the reflex to quit before you prompt. Relearning that the answer is probably one conversation away.

A to Z, But Watch for Slop

Great minds used to skip steps—A to E without trudging through B, C, D. That was the mark of insight, pattern recognition, earned intuition.

Now LLMs let anyone jump straight to Z. No prerequisites, no journey, no scar tissue. Just prompt and receive.

The risk? Slop. Output without understanding, answers without context, solutions that look right but crumble under pressure. Speed to Z means nothing if you can’t tell whether Z is actually correct.