Updating workshop

Signed-off-by: Mikkel Mørk Hegnhøj <mikkel@fermyon.com>

.devcontainer/Dockerfile

@@ -2,45 +2,51 @@
 # it avoids downloading any platform-specific binaries, and installs the required
 # tools either through Debian's package manager, or through installation scripts
 # that download the appropriate binaries.
-FROM mcr.microsoft.com/devcontainers/base:jammy
+FROM mcr.microsoft.com/devcontainers/base:ubuntu
 
 # provided by docker
 ARG TARGETARCH
 ARG USERNAME=vscode
 ARG USER_UID=1000
 ARG USER_GID=$USER_UID
 
-RUN apt-get update && apt-get install -y \
-    nodejs                               \
-    npm                                  \
-    python3-pip
+# Install Python
+RUN sudo apt update
+RUN sudo apt install python3.10-venv -y
 
 # Install Go
-ARG GO_VERSION="1.22.1"
+ARG GO_VERSION="1.23.1"
 RUN wget -O go.tar.gz "https://go.dev/dl/go${GO_VERSION}.linux-${TARGETARCH}.tar.gz" && \
     tar -C /usr/local -xzf go.tar.gz && \
     rm go.tar.gz
 ENV PATH="$PATH:/usr/local/go/bin"
 
 # Install TinyGo
-ARG TINYGO_VERSION="0.31.1"
+ARG TINYGO_VERSION="0.33.0"
 RUN wget "https://github.com/tinygo-org/tinygo/releases/download/v${TINYGO_VERSION}/tinygo_${TINYGO_VERSION}_${TARGETARCH}.deb" && \
     dpkg -i "tinygo_${TINYGO_VERSION}_${TARGETARCH}.deb"
 
+# Install Rust
+ENV PATH="$PATH:/home/$USERNAME/.cargo/bin"
+RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | bash -s -- \
+    -y --target wasm32-wasi
+
+# Set the current user before node and spin install for plugins/templates
+USER $USERNAME
+
 # Install Spin
-ARG SPIN_VERSION="v2.3.1"
+ARG SPIN_VERSION="v2.7.0"
 RUN mkdir -p /opt/spin && \
     cd /opt/spin && \
     curl -fsSL https://developer.fermyon.com/downloads/install.sh | bash -s -- -v $SPIN_VERSION && \
     ln -s /opt/spin/spin /usr/local/bin/spin
 
-# Set the current user before spin install for plugins/templates
-USER $USERNAME
-
-# Install Rust
-ENV PATH="$PATH:/home/$USERNAME/.cargo/bin"
-RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | bash -s -- \
-    -y --target wasm32-wasi
+# Install Node and NPM
+ARG NODE_VERSION=20
+ENV NVM_DIR=/home/${USERNAME}/.nvm
+RUN curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.0/install.sh | bash
+RUN . "$NVM_DIR/nvm.sh" && nvm install ${NODE_VERSION}
+ENV PATH="/root/.nvm/versions/node/v${NODE_VERSION}/bin/:${PATH}"
 
 # Install Spin Plugins & Templates
 RUN spin plugins update && \

README.md

@@ -2,7 +2,9 @@
 
 Welcome to the home for all Fermyon workshops!
 
-Complete a workshop, submit a survey, and claim a ![**Slat KeyCap**](./media/slats-keycap-cropped.png)
+The below workshops are available for you to go and run on your own, or use in an instructor-led setting. Feel free to have fun and share with the world :-)
+
+Best - The Fermyon Team
 
 ## Workshops
 

magic-8-ball/01-getting-started.md

@@ -4,9 +4,11 @@ This document assumes you have followed [the setup guide](./00-setup.md) and hav
 
 ```bash
 $ spin --version
-spin 2.3.0 (ee6706c8 2024-02-29)
+spin 2.7.0 (a111517 2024-07-30)
 ```
 
+> Note: The workshop was last tested with Spin 2.7.0
+
 [Spin](https://github.com/fermyon/spin) is an open source framework for building, distributing, and running serverless applications and microservices with WebAssembly (or Wasm).
 
 Spin uses Wasm because of its portability, sandboxed execution environment, and near-native speed. [More and more languages have support for WebAssembly](https://www.fermyon.com/wasm-languages/webassembly-language-support), so you should be able to use your favorite language to build your first serverless application with Wasm.
@@ -135,7 +137,7 @@ You are now ready to expand your application. You can follow the [guide for buil
 
 ## b. Building your first Spin application with JavaScript or TypeScript
 
-JavaScript is one of the most popular programming languages. Spin has an [experimental SDK and toolchain](https://github.com/fermyon/spin-js-sdk) for JavaScript and TypeScript which is based on [QuickJS](https://bellard.org/quickjs/), a small embeddable JavaScript runtime.
+JavaScript is one of the most popular programming languages. Spin has an [experimental SDK and toolchain](https://github.com/fermyon/spin-js-sdk) for JavaScript and TypeScript.
 
 > Note: you can [read more about how the Spin SDK for JavaScript and TypeScript is built on Fermyon's blog](https://www.fermyon.com/blog/spin-js-sdk).
 
@@ -144,25 +146,29 @@ Let's create a new Spin application in TypeScript, based on the HTTP template. T
 ```bash
 $ spin new hello-typescript -t http-ts --accept-defaults && cd hello-typescript
 $ tree
-|-- README.md
-|-- package.json
-|-- spin.toml
-|-- src
-|    -- index.ts
-|-- tsconfig.json
-|-- webpack.config.js
+.
+├── knitwit.json
+├── package.json
+├── spin.toml
+├── src
+│   ├── index.ts
+│   └── spin.ts
+├── tsconfig.json
+└── webpack.config.js
 ```
 
 > You can `sudo apt-get install tree` if you do not have `tree` installed.
 
 Let's explore the `spin.toml` file. This is the Spin manifest file, which tells Spin what events should trigger what components. In our case we have a HTTP trigger at the route `/...` — a wildcard that matches any request sent to this application. The trigger sends requests to our component `hello-rust`. In more complex applications, you can define multiple components that are triggered for requests on different routes.
 
 ```toml
+spin_manifest_version = 2
+
 [application]
+authors = ["Fermyon Engineering <engineering@fermyon.com>"]
+description = ""
 name = "hello-typescript"
 version = "0.1.0"
-authors = ["Fermyon Engineering <engineering@fermyon.com>"]
-description = "Hello World Rust Application"
 
 [[trigger.http]]
 route = "/..."
@@ -211,32 +217,31 @@ The command will start Spin on port 3000. You can now access the application by
 
 ```bash
 $ curl localhost:3000
-Hello from TS-SDK
+hello universe
 ```
 
 That response is coming from the handler function for this component — in the case of a TypeScript component, defined in the index file from `src/index.ts`. Our entire application consists of a single function, `handleRequest`, which takes the HTTP request as an argument and returns an HTTP response.
 
 Let's change the message body to "Hello, WebAssembly!":
 
 ```typescript
-export const handleRequest: HandleRequest = async function (
-  request: HttpRequest
-): Promise<HttpResponse> {
-  return {
-    status: 200,
-    headers: { "content-type": "text/plain" },
-    body: "Hello, WebAssembly!",
-  };
-};
+import { ResponseBuilder } from "@fermyon/spin-sdk";
+
+export async function handler(req: Request, res: ResponseBuilder) {
+    console.log(req);
+    res.send("Hello, WebAssembly!");
+}
 ```
 
 We can now run `spin build` again, which will compile our component, and we can use the `--up` flag to automatically start the application, then send another request:
 
 ```bash
 $ spin build --up
-$ curl -v localhost:3000
-< HTTP/1.1 200 OK
-< content-type: text/plain
+$ curl -i localhost:3000
+HTTP/1.1 200 OK
+content-length: 19
+content-type: text/plain;charset=UTF-8
+date: Thu, 12 Sep 2024 11:19:37 GMT
 
 Hello, WebAssembly!
 ```

magic-8-ball/02-json-api.md

@@ -103,36 +103,35 @@ We will create another Spin application in TypeScript based on the HTTP template
 $ spin new magic-eight-ball -t http-ts
 Description: A Magic 8 Ball App
 HTTP path: /magic-8
+Enable AoT Compilation [y/N]: N
+
 $ cd magic-eight-ball
 ```
 
 We will now write a HTTP component in our `index.ts` file that returns a Magic 8 Ball responses. Your `index.ts` file should look like this:
 
 ```typescript
-import { HandleRequest, HttpRequest, HttpResponse } from "@fermyon/spin-sdk";
-
-const encoder = new TextEncoder();
-
-export const handleRequest: HandleRequest = async function (
-  request: HttpRequest
-): Promise<HttpResponse> {
-  let answerJson = `{\"answer\": \"${answer()}\"}`;
-  return {
-    status: 200,
-    headers: { "Content-Type": "application/json" },
-    body: answerJson,
-  };
+import { ResponseBuilder } from "@fermyon/spin-sdk";
+
+interface Answer {
+    answer: String;
 };
 
-function answer(): string {
+export async function handler(req: Request, res: ResponseBuilder) {
+    res.statusCode = 200;
+    res.headers.append("Content-Type", "application/json");
+    res.send(JSON.stringify(answer()));
+}
+
+function answer(): Answer {
   let answers = [
     "Ask again later.",
     "Absolutely!",
     "Unlikely",
     "Simply put, no",
   ];
   let idx = Math.floor(Math.random() * answers.length);
-  return answers[idx];
+  return { answer: answers[idx] };
 }
 ```
 
@@ -151,7 +150,7 @@ Your Magic 8 Ball app is now running locally! Remember, we earlier had set our `
 
 ```bash
 $ curl localhost:3000/magic-8
-{"answer": "Absolutely!"}%
+{"answer": "Absolutely!"}
 ```
 
 > Note: you can find the complete applications used in this workshop in the [`apps` directory](./apps/).

magic-8-ball/03-spin-ai.md

@@ -72,10 +72,9 @@ fn answer(question: &str) -> Result<String> {
     let answer = llm::infer_with_options(
         model,
         &prompt,
-        llm::InferencingParams {
+        llm::InferencingParams 
             max_tokens: 20,
-            repeat_penalty: 1.5,
-            repeat_penalty_last_n_token_count: 20,
+            repeat_penalty: 1.5,            repeat_penalty_last_n_token_count: 20,
             temperature: 0.25,
             top_k: 5,
             top_p: 0.25,
@@ -98,6 +97,15 @@ Now, build your application and see the [deploy to Fermyon Cloud section](#deplo
 spin build
 ```
 
+### Configure access to an AI model
+
+By default, a given component of a Spin application will not have access to any Serverless AI models. Access must be provided explicitly via the Spin application’s manifest (the `spin.toml` file). We can give our `magic-8` component access to the llama2-chat model by adding the following ai_models configuration inside its `[[component]]` section:
+
+```toml
+[[component.magic-8-ball]]
+ai_models = ["llama2-chat"]
+```
+
 ## b. Building your Magic 8 Ball application with TypeScript
 
 We need to modify our `magic-eight-ball` component to:
@@ -106,22 +114,20 @@ We need to modify our `magic-eight-ball` component to:
 1. Get a yes/no question from the body of the HTTP request
 1. Use the `Llm.infer` function Spin `Llm` library to generate a response to the question
 
-First, create a `TextDecoder` at the top level:
-
-```ts
-const decoder = new TextDecoder();
-```
-
-Then, update the `handleRequest` function to get the question from the request body and return an error if
-the body is empty:
+Then, update the `handler` function to get the question from the request body. We also need to add `Llm` as an import from the Spin SDK:
 
 ```ts
-const question = decoder.decode(request.body);
-if (question.length == 0) {
-  return {
-    status: 400,
-    body: encoder.encode("No question asked").buffer,
-  };
+import { ResponseBuilder, Llm } from "@fermyon/spin-sdk";
+
+export async function handler(req: Request, res: ResponseBuilder) {
+    const response: Answer = await req.text().then(data => {
+        console.log(data);
+        return answer(data);
+    });
+    console.log(response);
+    res.statusCode = 200;
+    res.headers.append("Content-Type", "application/json");
+    res.send(JSON.stringify(response));
 }
 ```
 
@@ -130,29 +136,30 @@ Use the `Llm.infer` function, passing in a prompt to tell the system what type o
 should give along with the user provided question.
 
 ```ts
-function answer(question: string): string {
-  const prompt = `<s>[INST] <<SYS>>
+function answer(question: string): Answer {
+    const prompt: string = `<s>[INST] <<SYS>>
         You are acting as a Magic 8 Ball that predicts the answer to a questions about events now or in the future.
         Your tone should be expressive yet polite.
         Your answers should be 10 words or less.
         Prefix your response with 'Answer:'.
         <</SYS>>
         User: ${question}[/INST]"`;
-  let response = Llm.infer(InferencingModels.Llama2Chat, prompt, {
-    maxTokens: 20,
-    repeatPenalty: 1.5,
-    repeatPenaltyLastNTokenCount: 20,
-    temperature: 0.25,
-    topK: 5,
-    topP: 0.25,
-  }).text;
-  // Parse the response to remove the expected `Answer:` prefix from the response
-  const answerPrefix = "Answer:";
-  response = response.trim();
-  if (response.startsWith(answerPrefix)) {
-    response = response.substring(answerPrefix.length);
-  }
-  return response;
+    let response: Answer = { answer: Llm.infer(Llm.InferencingModels.Llama2Chat, prompt, {
+        maxTokens: 20,
+        repeatPenalty: 1.5,
+        repeatPenaltyLastNTokenCount: 20,
+        temperature: 0.25,
+        topK: 5,
+        topP: 0.25,
+    }).text};
+    // Parse the response to remove the expected `Answer:` prefix from the response
+    console.log(response.answer);
+    const answerPrefix = "Answer:";
+    response.answer = response.answer.trim();
+    if (response.answer.startsWith(answerPrefix)) {
+        response.answer = response.answer.substring(answerPrefix.length);
+    };
+    return response;
 }
 ```
 
@@ -163,13 +170,12 @@ npm install
 spin build
 ```
 
-## Configure access to an AI model
+### Configure access to an AI model
 
 By default, a given component of a Spin application will not have access to any Serverless AI models. Access must be provided explicitly via the Spin application’s manifest (the `spin.toml` file). We can give our `magic-8` component access to the llama2-chat model by adding the following ai_models configuration inside its `[[component]]` section:
 
 ```toml
-[[component]]
-id = "magic-8-ball"
+[[component.magic-8-ball]]
 ai_models = ["llama2-chat"]
 ```
 
@@ -232,13 +238,12 @@ Now, build your application.
 $ spin build
 ```
 
-## Configure access to an AI model
+### Configure access to an AI model
 
 By default, a given component of a Spin application will not have access to any Serverless AI models. Access must be provided explicitly via the Spin application’s manifest (the `spin.toml` file). We can give our `magic-8` component access to the llama2-chat model by adding the following `ai_models` configuration inside its `[[component]]` section:
 
 ```toml
-[[component]]
-id = "magic-8-ball"
+[[component.magic-8-ball]]
 ai_models = ["llama2-chat"]
 ```