Today I Learned

const guardrail = await bedrock.applyGuardrail({
  guardrailIdentifier: "gr-logistics-assistant",
  guardrailVersion: "3",
  source: "OUTPUT",
  content: [{ text: { text: modelResponse } }],
});

if (guardrail.action === "GUARDRAIL_INTERVENED") {
  return fallbackResponse;
}

const response = await anthropic.messages.create({
  model: "claude-sonnet-5",
  tools: [{ type: "skill", name: "logistics-triage" }],
  messages: [{ role: "user", content: "Reroute delayed shipment #4471" }],
});

async for result in deepgram_connection.transcribe_stream(audio):
    if result.is_final or result.speech_final:
        await handle_transcript(result.transcript)
    elif len(result.transcript.split()) > 4:
        await prefetch_intent(result.transcript)  # early, partial

{
  "response_format": {
    "type": "json_schema",
    "json_schema": { "name": "plan", "strict": true, "schema": { "$ref": "#/$defs/step" } }
  }
}

az ml online-deployment create \
  --name chatbot-v3 \
  --endpoint-name support-bot \
  --flow ./flows/triage-flow.yaml \
  --evaluation-gate groundedness>=0.85

graph = builder.compile(checkpointer=PostgresSaver(conn))
config = {"configurable": {"thread_id": "agent-4471"}}
async for event in graph.astream(input, config, stream_mode="values"):
    ...  # safe to crash/restart between events

response = agent.generate_content(
    query,
    tools=[Tool.from_retrieval(VertexAISearch(datastore=DATASTORE_ID))],
    grounding_config={"citation_granularity": "CHUNK"},
)

const stream = await elevenlabs.textToSpeech.stream(voiceId, {
  text: sentenceChunk,
  model_id: "eleven_turbo_v3",
  optimize_streaming_latency: 4,
});

await client.request(
  { method: "resources/subscribe", params: { uri: "logistics://shipments/4471" } },
  SubscribeResultSchema
);

{ "vectorSearchConfiguration": { "overrideSearchType": "HYBRID" } }

tools: [{ type: "computer_20260101", display_width: 1920, display_height: 1080 }]

az cognitiveservices account deployment create \
  --sku-name ProvisionedManaged --sku-capacity 100

def supervisor(state):
    return {"next": route_llm.invoke(state["messages"]).content}

graph.add_conditional_edges("supervisor", lambda s: s["next"], {
    "billing": "billing_agent", "shipping": "shipping_agent", "end": END,
})

response = model.generate_content(
    [Part.from_image(damage_photo), "File an incident report for this"],
    tools=[incident_report_tool],
)

if (frustrationScore(session) > ESCALATION_THRESHOLD) {
  await handOffToAgent(session, { reason: "low_confidence_or_frustration" });
}

export const handler = awslambda.streamifyResponse(async (event, responseStream) => {
  for await (const chunk of bedrockStream) {
    responseStream.write(chunk.delta.text);
  }
  responseStream.end();
});

SET hnsw.ef_search = 100;
SELECT id FROM documents ORDER BY embedding <=> $1 LIMIT 10;

room.on("activeSpeakersChanged", (speakers) => {
  if (speakers.includes(userParticipant) && bot.isSpeaking) {
    bot.cancelSpeech();
  }
});

{ "tool": "cancel_shipment", "requiredScopes": ["shipments:write"] }

POST /contentsafety/text:shieldPrompt?api-version=2026-02-01
{ "userPrompt": "...", "documents": ["<retrieved chunk>"] }

results = search_client.search(serving_config=CONFIG, query=user_query, page_size=5)

result = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_recall])

{ "Type": "Task", "Resource": "arn:aws:states:::bedrock:invokeModel", "Retry": [{ "ErrorEquals": ["Bedrock.ThrottlingException"], "MaxAttempts": 5 }] }

if not result.is_final and confidence(result.transcript) > 0.9:
    asyncio.create_task(classify_intent(result.transcript))

chain = prompt | model.bind_tools(tools) | parser
result = chain.invoke({"question": "Where is shipment 4471?"})

AI Foundry > Model catalog > Compare > gpt-4o-mini vs phi-4 vs mistral-small

paths:
  /reroute:
    post:
      description: Reroute a shipment to a new delivery address. Use only after address is confirmed.

const args = ToolArgsSchema.parse(rawArgs); // throws on unexpected fields

// Before: Manual optimization
const value = useMemo(() => expensive(a, b), [a, b]);

// After: Just write normal code
const value = expensive(a, b);
// Compiler handles memoization automatically!

const response = await anthropic.messages.create({
  model: "claude-3-5-sonnet-20241022",
  tools: [
    { type: "computer_20241022" },
    { type: "text_editor_20241022" },
    { type: "bash_20241022" }
  ],
  messages: [{ role: "user", content: "Set up a React project" }]
});

@import "tailwindcss";

@theme {
  --color-brand: oklch(65% 0.25 250);
  --font-display: "Cal Sans", system-ui;
}

# Install on Windows
powershell -c "irm bun.sh/install.ps1 | iex"

# 7.5x faster than npm install
bun install

// Native AI streaming
import { streamText } from 'next/ai';

// Component-level caching
export default async function Analytics() {
  'use cache';
  'use revalidate 3600';
  return <Dashboard data={await fetchData()} />;
}

.trigger {
  anchor-name: --tooltip-anchor;
}

.tooltip {
  position: absolute;
  position-anchor: --tooltip-anchor;
  bottom: anchor(top);
  left: anchor(center);
}

const component = await v0.generate({
  prompt: "Create a pricing page",
  designSystem: "./design-tokens.json",
  componentLibrary: "shadcn",
  framework: "next"
});

User: "Add a comments table with user references"

AI generates:
- SQL migration
- RLS policies
- TypeScript types
- Edge function for notifications

// Use npm packages directly
import express from "npm:express";
import { PrismaClient } from "npm:@prisma/client";

const app = express();
const prisma = new PrismaClient();

@view-transition {
  navigation: auto;
}

.hero-image {
  view-transition-name: hero;
  view-transition-class: image-transition;
}

// Inferred type predicates
function isString(x: unknown) {
  return typeof x === "string";
}
// Now automatically infers: x is string

// Path aliases in package.json
// No more tsconfig paths!

npx shadcn@latest add drawer
npx shadcn@latest add carousel
npx shadcn@latest add chart
npx shadcn@latest add kanban

// Enable PPR in next.config.js
experimental: {
  ppr: true
}

// Use Suspense for dynamic parts
<Suspense fallback={<Skeleton />}>
  <DynamicComponent />
</Suspense>

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "user_info",
      schema: { type: "object", properties: { name: { type: "string" } } }
    }
  }
});

const channel = supabase.channel('room-1')

// Subscribe
channel.on('broadcast', { event: 'cursor' }, (payload) => {
  console.log(payload)
}).subscribe()

// Send
channel.send({ type: 'broadcast', event: 'cursor', payload: { x: 100, y: 200 } })

node --experimental-strip-types app.ts

export const handler = awslambda.streamifyResponse(
  async (event, responseStream) => {
    responseStream.write("Starting...");
    // Process data
    responseStream.write("Done!");
    responseStream.end();
  }
);

// Instead of sending full query
// Send hash: "abc123"

// Server looks up query from hash
const query = persistedQueries.get("abc123");

const [optimisticLikes, addOptimisticLike] = useOptimistic(
  likes,
  (state, newLike) => [...state, newLike]
);

async function handleLike() {
  addOptimisticLike({ id: Date.now() });
  await likeAction(); // Server Action
}

// supabase/functions/hello/index.ts
Deno.serve(async (req) => {
  const { name } = await req.json();
  return new Response(JSON.stringify({ message: `Hello ${name}!` }));
});

const response = await openai.chat.completions.create({
  model: "gpt-4o",
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Identify UI bugs in this screenshot" },
      { type: "image_url", image_url: { url: screenshotUrl } }
    ]
  }]
});

import { BedrockRuntimeClient, InvokeModelCommand } from "@aws-sdk/client-bedrock-runtime";

const client = new BedrockRuntimeClient({ region: "us-east-1" });
const response = await client.send(new InvokeModelCommand({
  modelId: "anthropic.claude-3-sonnet",
  body: JSON.stringify({ prompt: "Hello" })
}));

subscription {
  messagesCollection(filter: { room_id: { eq: "123" } }) {
    edges {
      node {
        id
        content
      }
    }
  }
}

import { test } from 'node:test';
import assert from 'node:assert';

test('snapshot test', (t) => {
  t.assert.snapshot({ foo: 'bar' });
});

const prompt = `Analyze this FHIR Patient resource and summarize:
- Active conditions
- Current medications
- Recent lab results

${JSON.stringify(fhirPatient)}`;

// Now you must explicitly opt-in to caching
fetch(url, { cache: 'force-cache' });

// Or at route level
export const dynamic = 'force-static';

// Create directory bucket
aws s3api create-bucket \
  --bucket my-bucket--use1-az4--x-s3 \
  --create-bucket-configuration '{"Bucket":{"Type":"Directory"}}'

-- Enable vector extension
create extension vector;

-- Create table with embedding column
create table documents (
  id serial primary key,
  content text,
  embedding vector(1536)
);

-- Similarity search
select * from documents
order by embedding <=> '[0.1, 0.2, ...]'
limit 5;

const assistant = await openai.beta.assistants.create({
  model: "gpt-4o",
  tools: [
    { type: "code_interpreter" },
    { type: "file_search" }
  ],
  tool_resources: {
    file_search: { vector_store_ids: ["vs_123"] }
  }
});