Author: tongwing

New extension to MCP protocol enables support for interactive UI for MCP hosts. The initial extension specification supports only text/html content. Instead of just returning raw text, MCP servers can now deliver UI in the way that is intended to be visualized, eg. in the form of a HTML chart.

Today we’re introducing the proposal for the MCP Apps Extension (SEP-1865) to standardize support for interactive user interfaces in the Model Context Protocol.This extension addresses one of the most requested features from the MCP community and builds on proven work from MCP-UI and OpenAI Apps SDK – the ability for MCP servers to deliver interactive user interfaces to hosts.MCP Apps Extension introduces a standardized pattern for declaring UI resources, linking them to tools, and enabling bidirectional communication between embedded interfaces and the host application.

Source: MCP Apps: Extending servers with interactive user interfaces | mcp blog

In preparation for ERP 2.0, I recently changed my IU to the new OBU, but opted out of the touchscreen installation. LTA provides an option to download their ERP 2.0 app, which is supposed to function like the OBU screen to some extent. It includes functions like ability to view ERP charging details, traffic alerts, and manage payment options via your smartphone.

The ERP 2.0 app only runs in landscape mode and requires pairing with the OBU.

The app displays pretty much the same info as the physical touchscreen, which I thought is quite basic. I understand the need to not distract drivers with too much info, but surely it can provide more useful content than function as a glorified digital clock?

As I had a bit of time on a Saturday afternoon I decided to see what I can prototype using v0.dev, the AI-powered tool from Vercel. This is my first time using v0.dev and this is the result:

You can play around with the live app deployed to Vercel. This was done entirely in v0.dev in an hour with default settings with the free plan. It even works in portrait mode 🙂

You can see how I build the app by looking at the chat log. The app pretty much works without errors in each generation step. This is definitely a more polished way to develop quick frontend prototypes, compared to the early days of copy-pasting code, and asking it to make fixes.

Having said that, I do have to give quite specific instructions at some point to guide the model to make changes in the exact way that I want, for example to get the progress ring to start at the bottom instead of the top.

You can start building in Vercel for free, with USD5 of monthly credits. The app that I created cost me USD0.62. So I still have enough credit to play around before it runs out.

Coding agents or helpers have improved by leaps and bounds in the past few months. However, when people ask me whether they still need to learn coding, my answer is an unequivocal yes. Coding in the future – or even right now – is going to look very different from coding in the past. But it is still a skill that needs to be learnt. Think of it like photography: almost everyone carries a camera in their pocket today, but that doesn’t automatically make them a skilled photographer.

Everyone can generate a snake game in an instant nowadays. These are the low hanging fruit – much like taking a photo with your phone doesn’t require much skills. But as soon as you move up the rung to something more complex, the ability of an untrained person to do so breaks down rather quickly.

This quote from the article is particularly relevant today:

Without a solid foundation, you can’t distinguish between good AI suggestions and plausible-sounding nonsense. Therefore, a solid understanding of how to write and structure code remains really important to use this technology best.

Source: Coding agents have crossed a chasm // flurries of latent creativity

It is always good to have diversity in benchmarks, to avoid over-reliance and overfitting on one set of benchmarks. AWS just released SWE-PolyBench, their benchmark to evaluate AI coding agents’ ability to navigate and understand complex codebases.

Unlike SWE-Bench, which only works for Python code, SWE-PolyBench is designed to work for additional languages like Java, JavaScript and TypeScript.

Today, Amazon introduces SWE-PolyBench, the first industry benchmark to evaluate AI coding agents’ ability to navigate and understand complex codebases, introducing rich metrics to advance AI performance in real-world scenarios. SWE-PolyBench contains over 2,000 curated issues in four languages. In addition, it contains a stratified subset of 500 issues (SWE-PolyBench500) for the purpose of rapid experimentation. SWE-PolyBench evaluates the performance of AI coding agents through a comprehensive set of metrics: pass rates across different programming languages and task complexity levels, along with precision and recall measurements for code/file context identification. These evaluation metrics can help the community address challenges in understanding how well AI coding agents can navigate through and comprehend complex codebases

Perhaps unsurprisingly, Amazon Q Developer Agent is currently leading this benchmark in the leaderboard. It remains to be seen how well-adopted this new benchmark will be.