The autocomplete era: late 2024 ⌨️
At LayerX, we started experimenting with Cursor at the tail end of 2024. Initially, it felt like a productivity boost smarter autocomplete, faster boilerplate generation, and the occasional snippet that saved us a few minutes of typing. We were impressed, sure, but it still felt like a tool that accelerated what we already knew how to do. The AI was a Capable Assistant, filling in the blanks, but we remained firmly in the driver's seat for every meaningful decision.
The reasoning revolution: When models learned to think 🧠
Then the reasoning models arrived, and everything shifted. When OpenAI released o1 and Anthropic introduced Claude Sonnet with its deeper contextual understanding, we weren't just getting code suggestions anymore, we were getting architecture and design advice. These models could reason through complex trade-offs, spot edge cases we'd missed, and propose solutions that required genuine understanding of our codebase and constraints.
Suddenly, our pair programming sessions weren't just faster; they were fundamentally different. The AI had evolved from a glorified autocomplete into something closer to a thoughtful collaborator, capable of tackling the kind of nuanced engineering challenges that previously demanded senior expertise and extended deliberation.
As our reliance on coding agents deepened, we noticed a subtle but profound transformation in how we approached our daily work. Co-programming with AI became our default mode, not as a novelty, but as a genuine force multiplier. Tasks that once stretched across days now wrapped up in hours. Bug fixes that required careful archaeology through legacy code became faster to diagnose and resolve. The precision improved too; with an AI partner that never tired and could hold vast coding and architecture context in memory, we caught edge cases earlier and wrote more robust and optimized implementations in from the start.
Guardrails against technical debt 🛡️
But we weren't naive about the risks. The conversation around AI-generated code often circles back to the same concern: Technical Debt.
Critics warn that AI produces "junior code", functional but shallow, riddled with shortcuts that accumulate into maintenance nightmares.
We took this seriously. Rather than blindly shipping whatever the agent produced, we transformed not just how we write code, but how we govern it. We introduced guardrails that prevented AI-generated code from touching production without rigorous review.
Every pull request went through deliberate refactoring sessions, where we questioned structure, naming, organization, design patterns , and long-term maintainability.
Perhaps most importantly, we learned to front-load our collaboration: long planning sessions with the agent before a single line was written,discussing architecture, exploring trade-offs, and aligning on design principles.
“In 2025 an Agentic AI Coder became a thinking partner first, a code generator second.”
Terminal-based agents: Claude Code 💻
The next leap came when we started experimenting with terminal-based coding agents like Claude Code.
Honestly, we were skeptical at first. After years of relying on the comfort of IDE-driven development, syntax highlighting, inline suggestions, visual debugging, and all the familiar affordances, dropping into a terminal felt like a step backward.
Why would we abandon the rich tooling we'd spent years mastering for a command-line interface? But after a few sessions, something clicked. The terminal stripped away distractions and forced a different kind of focus. Conversations with the agent became more deliberate, more structured.
We found ourselves articulating problems more clearly, because we had to. There was no UI to lean on, no quick mouse click to bail us out. The agent's responses felt more cohesive too, as if the constrained environment encouraged deeper reasoning rather than scattered suggestions. Within weeks, what had seemed like a regression became our preferred way of coding. The terminal wasn't limiting us; it was sharpening us. Claude Code became the default mode for how we interacted with our codebase, whether exploring unfamiliar modules, refactoring sprawling functions, or architecting new features from scratch.
Extensibility that scales with us
What truly set Claude Code apart was its extensible architecture. Unlike closed systems that forced us to work within rigid boundaries, Claude Code allowed us to create our own subagents, specialized assistants tailored to our specific workflows. We built custom tools for our daily rituals: agents that understood our deployment pipelines, others fine-tuned for our smart contract auditing process, and some dedicated to navigating the intricacies of our github repositories. The platform became a foundation we could mold, not a box we had to fit into. This extensibility meant that as our needs evolved, our tooling evolved with us, seamlessly integrating into the cadence of how we actually build and deliver software.
Beyond code: Planning, design, and security 🎯
But the transformation extended far beyond writing code. It reshaped how we plan software in the first place.
AI-Powered software planning 📋
We developed an agent capable of ingesting a product specification and decomposing it into a structured development roadmap. A complete plan with clearly described user stories, dependencies mapped between them, estimated story sizes, and recommended technical approaches for each phase. What used to require hours of grooming sessions and whiteboard debates now emerged in minutes, giving us a foundation to refine rather than build from scratch. The agent didn't replace our judgment; it accelerated our ability to reach clarity.
UI/UX Design to code pipeline 🎨
Our product design workflow underwent a similar revolution. Moving from Figma sketches to functional code once meant painstaking translation. Designers handing off specs, developers interpreting margins and spacing, endless back-and-forth over pixel-perfect details. With our AI-boosted workflows, that gap collapsed. Designs became living prototypes in minutes, allowing us to iterate on real implementations rather than static mockups. The feedback loop between design and engineering tightened dramatically.
The Solidity preliminary auditor🔒
Perhaps one of the most impactful additions was our Solidity preliminary auditor.
In Ethereum development, security isn't a nice to have, it's existential 🔐. Traditionally, vulnerabilities were caught late in the cycle, often during expensive external audits. Our custom agent changed that calculus entirely. It analyzed smart contracts as we wrote them, flagging potential attack vectors, reentrancy risks, and gas inefficiencies before code ever left our local environment. Catching issues early didn't just save money; it shifted our entire security posture from reactive to proactive.
The coding models evolution of 2025 📈
If 2024 was the year AI learned to code, 2025 was the year it learned to think.
The evolution of reasoning models throughout the year felt less like incremental updates and more like watching a new species of intelligence emerge in real-time.
Sonnet 4.0 set the tone early in the year, striking a remarkable balance between capability and efficiency. It became our workhorse, fast enough for real-time pair programming, sharp enough to handle the majority of our daily engineering tasks without breaking stride.
Then came GPT-5. OpenAI's flagship model arrived with capabilities that forced us to rethink our mental models. Its reasoning chains were longer, more coherent, and startlingly self-aware of their own limitations. It could plan multi-step implementations, anticipate where its own suggestions might fall short, and proactively flag areas requiring human oversight.
Sonnet 4.5 pushed the frontier further still. The leap in contextual understanding was palpable. It grasped nuance better, hallucinated less, and demonstrated an almost intuitive sense for codebases it had never seen before. Refactoring suggestions became more idiomatic. Architectural advice felt more grounded in real-world trade-offs.
And then Anthropic released Opus 4.5 ⚡, the culmination of everything we'd seen throughout the year. This wasn't just a smarter model; it was a qualitatively different experience. Anthoropic Opus 4.5 could hold entire system architectures in its context, reason through sprawling dependency graphs, and execute complex refactors that previously demanded days of careful, manual work. We handed it to our old codebases like TAIKAI, our hackathon Platform with years of accumulated decisions, and it untangled them with surgical precision. Opus was able to help us circumvent cyber attacks and protect our platform with highly tailored security advice.
In essence, a general IT engineer with full understanding of all engineering topics, augmenting the team's capabilities and decision power 💪.
Looking ahead: what's next 🔮
Looking back, the velocity of progress in 2025 was staggering. Each model release didn't just add features; it expanded what we believed was possible. Our workflows evolved in lockstep, constantly adapting to harness capabilities that, just months earlier, would have seemed like science fiction.
As we close out 2025, it's impossible to predict exactly where the next generation of coding models will take us. The pace of change has been so relentless that any forecast feels obsolete before it's written. But one thing is certain: whatever emerges next will only deepen our capabilities, allowing us to tackle challenges we haven't yet imagined with greater precision, speed, and confidence. The tools will grow sharper, the collaboration more seamless, and the boundaries of what a small team can achieve will continue to expand.
We've gone from tentative experimentation to full embrace, from skeptics to believers. And if this year has taught us anything, it's that the sky truly is the limit.
Key takeaways 💡
- Reasoning models changed the game: The shift from autocomplete to genuine reasoning transformed AI from a typing accelerator into an architectural thinking partner capable of handling complex engineering challenges.
- Guardrails are non-negotiable: AI velocity without governance leads to technical debt. Front-load planning, enforce review gates, and refactor deliberately, treat the AI as a thinking partner first, code generator second.
- Terminal-based agents sharpen focus: Stripping away IDE affordances forced clearer communication and produced more cohesive solutions. The constrained environment encouraged deeper reasoning rather than scattered suggestions.
- Extensibility unlocks real value: Custom subagents tailored to your workflows deployments such as our smart contract auditing subagent, multiply the benefits and evolve with your needs.
- Security can shift left: AI-powered preliminary auditors catch vulnerabilities during development, not after expensive external audits. For web3 development, this proactive security posture is existential, not optional.
This post is my own reflection on how we at LayerX evolved our engineering workflows throughout 2025, leveraging proprietary AI models from OpenAI and Anthropic and tools like Cursor. Next year, we're eager to explore smaller, open-weights models, more efficient alternatives to truly understand their potential on our workflows.
Building in web3? I'd love to hear how AI tools are changing your workflow. Find me on X (Twitter) or explore what we're building at LayerX.