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Smart Web of Things (WoT) Thing Description (TD) Generator & RAG Search System
An intelligent, full-stack application designed to generate, validate, and search W3C-compliant Web of Things (WoT) Thing Descriptions (TDs).
This project integrates state-of-the-art Large Language Models (LLMs) with a robust self-correction validation loop and a highly optimized two-stage Retrieval-Augmented Generation (RAG) search pipeline to provide a seamless developer experience for WoT devices.
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WIP: Omnisearch prototype for a client cooked in @Framer as part of a larger simulation engine
Had Claude cook up some overrides to help with up/down key nav through the search results
Goal here is to surface any searchable object type in the platform using a single result list item component so its extensible for future query types.
Wow.... This design Got my goat
Things I believe after building with AI:
- If you do something manually more than twice, automate it.
- AI agents are the most fun thing you can build right now.
- A demo that works beats a pitch deck every time.
- The best engineering is invisible. You only notice when it's missing.
Hi, I'm Ayesha, a Full-stack developer in Berlin. I spend my days in TypeScript, React and Python, mostly teaching LLMs to do real work. Think agents that plan code changes and push them to GitHub on their own, RAG assistants, CRM automation that cut response times in half.
I'm new here on Contra so I put together a quick tour of my portfolio and what I build. My favorite part is the AI agent that runs a Kanban board by itself. Full breakdown coming in my next post.
Your turn. What's rule #1 in your work?
Rule #1: "There has to be a better way," you've just found your next project.
Most of the things I've built started with frustration, not inspiration.
AI allows us to write code faster. But that doesn’t necessarily mean we are delivering software faster.
This is one of the most interesting contradictions emerging in software development.
According to GitLab’s 2026 AI Accountability Report, conducted by The Harris Poll among 1,528 developers and technology decision-makers across six countries:
• 78% say developers are writing and committing code faster since adopting AI tools.
• 79% believe individual developer productivity has improved.
• However, 85% agree that the bottleneck has shifted from writing code to reviewing and validating it.
• Additionally, 82% believe AI-generated code could create a new form of technical debt that their organizations are not yet prepared to manage.
GitLab calls this the “AI Paradox”: individual productivity increases, but the overall software delivery process does not accelerate at the same pace.
Research from DORA, Google Cloud’s software delivery performance research program, points in a similar direction.
Its findings show that a 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability.
This does not mean that AI always harms software development. These results show an association, not a universal rule or a causal relationship that applies to every team.
The problem is not using AI.
The problem begins when an organization accelerates code generation while leaving the rest of its development process unchanged:
• Slow manual reviews.
• Limited automated testing.
• Ambiguous requirements.
• Oversized pull requests.
• Lack of architectural criteria.
• Poor traceability.
• Unclear ownership of AI-generated code.
If we double the speed of one stage without improving the next ones, we simply move the bottleneck somewhere else.
Manav Khurana, Chief Product and Marketing Officer at GitLab, summarized it clearly:
“Speed without control is a liability, not an advantage.”
As a full-stack developer, I believe AI can genuinely accelerate tasks such as creating components, writing documentation, exploring solutions, refactoring code, or generating an initial set of tests.
But producing more code should never be the main measure of success.
The result should be measured in software that:
• Solves a real need correctly.
• Remains maintainable over time.
• Includes testing and security controls.
• Can be understood by other developers.
• Reaches production without multiplying incidents.
AI does not eliminate the need for architecture, testing, and technical judgment. On the contrary, the faster we can generate code, the more important these elements become.
The next competitive advantage will not be generating more code with AI. It will be verifying it, governing it, and turning it into reliable software.
Has AI actually reduced delivery time for your team, or has it simply increased the amount of code waiting to be reviewed?
And remember: good software starts with good decisions. See you in the next one.
The distinction between writing code faster and delivering software faster is exactly where many teams get stuck. Which non-coding bottleneck do you think AI-assisted teams underestimate most?
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