examples: drop eslint

Author: tharvik

.github/workflows/lint-test-build.yml

@@ -36,25 +36,6 @@ jobs:
       - run: npm --workspaces run build
       - run: npm run lint
 
-  lint-docs-examples:
-    needs: [build-lib, build-lib-node, build-server]
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v6
-      - uses: actions/setup-node@v6
-        with:
-          node-version-file: .nvmrc
-      - uses: actions/cache@v5
-        with:
-          path: |
-            ~/.npm
-            ~/.cache/Cypress
-          key: npm-${{ runner.os }}-${{ hashFiles('package-lock.json') }}
-      - run: npm ci
-      - run: npm --workspace={discojs,discojs-node,server} run build
-      - run: npm run lint
-        working-directory: docs/examples
-
   build-lib:
     runs-on: ubuntu-latest
     steps:

docs/examples/eslint.config.js

@@ -1,26 +1,23 @@
 {
-  "name": "examples",
-  "private": true,
-  "type": "module",
-  "description": "In `training.ts` we give a brief example of discojs, in it we run two clients training jointly via federated learning. It trains  on a few examples of the [face task](https://www.kaggle.com/datasets/frabbisw/facial-age), the samples are already stored in the  repo and so it is not necessary to download any additional data.",
-  "scripts": {
-    "train": "npm run build && node dist/training.js",
-    "custom_task": "npm run build && node dist/custom_task.js",
-    "language_model": "npm run build && node dist/wikitext.js",
-    "benchmark_gpt": "npm run build && node dist/benchmark_gpt.js",
-    "build": "tsc",
-    "lint": "npx eslint .",
-    "test": "npm run train"
-  },
-  "license": "ISC",
-  "dependencies": {
-    "server": "*",
-    "@epfml/discojs": "*",
-    "@epfml/discojs-node": "*"
-  },
-  "devDependencies": {
-    "eslint": "10",
-    "typescript": "5",
-    "typescript-eslint": "8"
-  }
+	"name": "examples",
+	"private": true,
+	"type": "module",
+	"description": "In `training.ts` we give a brief example of discojs, in it we run two clients training jointly via federated learning. It trains  on a few examples of the [face task](https://www.kaggle.com/datasets/frabbisw/facial-age), the samples are already stored in the  repo and so it is not necessary to download any additional data.",
+	"scripts": {
+		"train": "npm run build && node dist/training.js",
+		"custom_task": "npm run build && node dist/custom_task.js",
+		"language_model": "npm run build && node dist/wikitext.js",
+		"benchmark_gpt": "npm run build && node dist/benchmark_gpt.js",
+		"build": "tsc",
+		"test": "npm run train"
+	},
+	"license": "ISC",
+	"dependencies": {
+		"server": "*",
+		"@epfml/discojs": "*",
+		"@epfml/discojs-node": "*"
+	},
+	"devDependencies": {
+		"typescript": "5"
+	}
 }