- Third-party
Muse Spark 1.1 is Meta's agentic and coding model for multi-step tool loops, software engineering assistants, and long-context reasoning.
| Model Info | |
|---|---|
| More information | link ↗ |
| Request formats | Responses |
| Pricing |
|
const response = await env.AI.run(
'meta/muse-spark-1.1',
{
messages: [
{
role: 'system',
content:
'You are a pragmatic senior JavaScript reviewer. Prefer small, production-safe fixes and explain the root cause before the patch.',
},
{
role: 'user',
content:
'Review this function:\n\nasync function processOnce(queue) {\n if (queue.length === 0) return;\n const item = queue.shift();\n await save(item);\n if (queue.length > 0) processOnce(queue);\n}\n\nTwo requests can call processOnce(queue) at the same time. What can go wrong, and how would you fix it without changing the save() function? Include a concise corrected implementation.',
},
],
max_completion_tokens: 4096,
},
)
console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "meta/muse-spark-1.1",
"input": {
"messages": [
{
"role": "system",
"content": "You are a pragmatic senior JavaScript reviewer. Prefer small, production-safe fixes and explain the root cause before the patch."
},
{
"role": "user",
"content": "Review this function:\n\nasync function processOnce(queue) {\n if (queue.length === 0) return;\n const item = queue.shift();\n await save(item);\n if (queue.length > 0) processOnce(queue);\n}\n\nTwo requests can call processOnce(queue) at the same time. What can go wrong, and how would you fix it without changing the save() function? Include a concise corrected implementation."
}
],
"max_completion_tokens": 4096
}
}'This is a concurrent drain + fire-and-forget bug.
### Root cause
JS is single-threaded, but `await` yields to the event loop. You have shared mutable state `queue` with no mutual exclusion.
If A and B call `processOnce(queue)` at the same time:
1. A: `queue.length > 0` -> true
2. A: `shift()` -> item1, `await save(item1)` -> **yields**
3. B: `queue.length > 0` -> still true, `shift()` -> item2, `await save(item2)` -> **now `save()` is running in parallel**. If `save()` must be serial / order dependent, you break it. `shift()` can also return `undefined` if B empties it first.
4. `if(queue.length > 0) processOnce(queue)` is **not awaited**. Caller resolves early, queue is still draining. Rejections are lost and caller thinks it's done.
### Fix
Serialize draining. One drain loop at a time, `while` not recursion, and piggyback concurrent callers onto the same promise + re-check for items enqueued in the gap.
Don't change `save()`. Just guard it:
```js
let drainPromise = null;
async function processOnce(queue) {
if (drainPromise) return drainPromise;
drainPromise = (async () => {
while (queue.length > 0) {
const item = queue.shift();
await save(item);
}
})().finally(() => {
drainPromise = null;
// items pushed between last shift() and clearing the flag
if (queue.length > 0) return processOnce(queue);
});
return drainPromise;
}
```
What this changes:
* `drainPromise` lock prevents 2 concurrent drain loops.
* `while` loop + `await save()` guarantees serial saves.
* Concurrent callers return the same promise, so they properly await completion.
* `finally` re-check prevents losing items enqueued right after the loop ends.{
"id": "resp_6ac7afe7b1edff27f7904b24",
"object": "response",
"created_at": 1791471590,
"model": "muse-spark-1.1",
"output": [
{
"type": "reasoning",
"id": "rs_6ac7afe7b1edff27f7904b24:rs_d69dde63-919d-49f4-9c17-65b594289afb",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "msg_ce37fe35-31f4-4a9c-aea7-3f2ee96186e5",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "This is a concurrent drain + fire-and-forget bug.\n\n### Root cause\n\nJS is single-threaded, but `await` yields to the event loop. You have shared mutable state `queue` with no mutual exclusion.\n\nIf A and B call `processOnce(queue)` at the same time:\n\n1. A: `queue.length > 0` -> true\n2. A: `shift()` -> item1, `await save(item1)` -> **yields**\n3. B: `queue.length > 0` -> still true, `shift()` -> item2, `await save(item2)` -> **now `save()` is running in parallel**. If `save()` must be serial / order dependent, you break it. `shift()` can also return `undefined` if B empties it first.\n4. `if(queue.length > 0) processOnce(queue)` is **not awaited**. Caller resolves early, queue is still draining. Rejections are lost and caller thinks it's done.\n\n### Fix\n\nSerialize draining. One drain loop at a time, `while` not recursion, and piggyback concurrent callers onto the same promise + re-check for items enqueued in the gap.\n\nDon't change `save()`. Just guard it:\n\n```js\nlet drainPromise = null;\n\nasync function processOnce(queue) {\n if (drainPromise) return drainPromise;\n\n drainPromise = (async () => {\n while (queue.length > 0) {\n const item = queue.shift();\n await save(item);\n }\n })().finally(() => {\n drainPromise = null;\n // items pushed between last shift() and clearing the flag\n if (queue.length > 0) return processOnce(queue);\n });\n\n return drainPromise;\n}\n```\n\nWhat this changes:\n\n* `drainPromise` lock prevents 2 concurrent drain loops.\n* `while` loop + `await save()` guarantees serial saves.\n* Concurrent callers return the same promise, so they properly await completion.\n* `finally` re-check prevents losing items enqueued right after the loop ends.",
"annotations": [],
"logprobs": []
}
],
"status": "completed"
}
],
"status": "completed",
"usage": {
"input_tokens": 134,
"output_tokens": 2852,
"total_tokens": 2986,
"input_tokens_details": {
"cached_tokens": 113
},
"output_tokens_details": {
"reasoning_tokens": 2409
}
},
"top_logprobs": 0,
"temperature": 1,
"top_p": 1,
"service_tier": "auto",
"reasoning": {
"effort": "high"
},
"background": false,
"tools": [],
"tool_choice": "auto",
"truncation": "disabled",
"completed_at": 1791471591,
"error": null,
"incomplete_details": null,
"instructions": null,
"parallel_tool_calls": true,
"store": true,
"max_output_tokens": 4096
}SQL Correctness Review — Review a SQL query for correctness and performance, then propose a safer replacement.
const response = await env.AI.run(
'meta/muse-spark-1.1',
{
messages: [
{
role: 'system',
content:
'You are a database engineer. Be precise about semantics, indexes, and concurrency. Do not assume schema details that are not provided.',
},
{
role: 'user',
content:
"A payments table has columns id, account_id, status, amount_cents, created_at, and settled_at. Review this query used to calculate an account's settled balance: SELECT SUM(amount_cents) FROM payments WHERE account_id = ? AND status = 'settled' OR status = 'refunded' AND created_at >= ?; Identify the correctness bug, give the corrected query, and suggest one useful index. Keep the answer under 400 words.",
},
],
max_completion_tokens: 4096,
},
)
console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "meta/muse-spark-1.1",
"input": {
"messages": [
{
"role": "system",
"content": "You are a database engineer. Be precise about semantics, indexes, and concurrency. Do not assume schema details that are not provided."
},
{
"role": "user",
"content": "A payments table has columns id, account_id, status, amount_cents, created_at, and settled_at. Review this query used to calculate an account'\''s settled balance: SELECT SUM(amount_cents) FROM payments WHERE account_id = ? AND status = '\''settled'\'' OR status = '\''refunded'\'' AND created_at >= ?; Identify the correctness bug, give the corrected query, and suggest one useful index. Keep the answer under 400 words."
}
],
"max_completion_tokens": 4096
}
}'Operator precedence bug. `AND` binds tighter than `OR`, so your query is parsed as:
```sql
(account_id = ? AND status = 'settled')
OR
(status = 'refunded' AND created_at >= ?)
```
Failures:
1. Leaks data across accounts - all `refunded` rows for any account after `?` are summed into this account.
2. Drops the `created_at` filter for `settled` rows, and drops `account_id` filter for `refunded`.
### Corrected query
If you intend to filter both statuses by date:
```sql
SELECT COALESCE(SUM(amount_cents),0) FROM payments
WHERE account_id = ?
AND status IN ('settled','refunded')
AND created_at >= ?;
```
If you intended date filter only for refunded:
```sql
SELECT COALESCE(SUM(amount_cents),0) FROM payments
WHERE account_id = ?
AND (status = 'settled' OR (status = 'refunded' AND created_at >= ?));
```
Note: For a *settled* balance, you likely want `settled_at >= ?`, not `created_at`.
### Index
Composite to support equality on `account_id`, equality/IN on `status`, range on `created_at`, covering `amount_cents`:
```sql
CREATE INDEX idx_payments_acct_status_created
ON payments (account_id, status, created_at) INCLUDE (amount_cents);
```
This allows an index-only scan per account without touching the heap.{
"id": "resp_6ac7aff0b2536166015f4c31",
"object": "response",
"created_at": 1791471600,
"model": "muse-spark-1.1",
"output": [
{
"type": "reasoning",
"id": "rs_6ac7aff0b2536166015f4c31:rs_bd77c866-9452-4cf2-84d6-3052aa3f613f",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "msg_58de9d22-820f-49a1-aecd-ff8c43ae16a3",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Operator precedence bug. `AND` binds tighter than `OR`, so your query is parsed as:\n\n```sql\n(account_id = ? AND status = 'settled')\nOR \n(status = 'refunded' AND created_at >= ?)\n```\n\nFailures:\n1. Leaks data across accounts - all `refunded` rows for any account after `?` are summed into this account.\n2. Drops the `created_at` filter for `settled` rows, and drops `account_id` filter for `refunded`.\n\n### Corrected query\n\nIf you intend to filter both statuses by date:\n\n```sql\nSELECT COALESCE(SUM(amount_cents),0) FROM payments\nWHERE account_id = ?\n AND status IN ('settled','refunded')\n AND created_at >= ?;\n```\n\nIf you intended date filter only for refunded:\n\n```sql\nSELECT COALESCE(SUM(amount_cents),0) FROM payments\nWHERE account_id = ?\n AND (status = 'settled' OR (status = 'refunded' AND created_at >= ?));\n```\n\nNote: For a *settled* balance, you likely want `settled_at >= ?`, not `created_at`.\n\n### Index\n\nComposite to support equality on `account_id`, equality/IN on `status`, range on `created_at`, covering `amount_cents`:\n\n```sql\nCREATE INDEX idx_payments_acct_status_created \nON payments (account_id, status, created_at) INCLUDE (amount_cents);\n```\n\nThis allows an index-only scan per account without touching the heap.",
"annotations": [],
"logprobs": []
}
],
"status": "completed"
}
],
"status": "completed",
"usage": {
"input_tokens": 143,
"output_tokens": 1578,
"total_tokens": 1721,
"input_tokens_details": {
"cached_tokens": 113
},
"output_tokens_details": {
"reasoning_tokens": 1249
}
},
"top_logprobs": 0,
"temperature": 1,
"top_p": 1,
"service_tier": "auto",
"reasoning": {
"effort": "high"
},
"background": false,
"tools": [],
"tool_choice": "auto",
"truncation": "disabled",
"completed_at": 1791471600,
"error": null,
"incomplete_details": null,
"instructions": null,
"parallel_tool_calls": true,
"store": true,
"max_output_tokens": 4096
}TypeScript Utility Refactor — Turn an unreliable JavaScript helper into a typed, testable implementation with explicit edge-case behavior.
const response = await env.AI.run(
'meta/muse-spark-1.1',
{
messages: [
{
role: 'system',
content:
'You are a TypeScript library maintainer. Prefer small APIs, explicit types, and tests that document behavior.',
},
{
role: 'user',
content:
'Refactor this helper into TypeScript:\n\nfunction groupBy(items, key) {\n return items.reduce((out, item) => {\n (out[item[key]] ||= []).push(item);\n return out;\n }, {});\n}\n\nProvide a generic type-safe version and three focused tests covering an empty input, a missing key value, and two items sharing a key. State your chosen behavior for missing keys.',
},
],
max_completion_tokens: 4096,
},
)
console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "meta/muse-spark-1.1",
"input": {
"messages": [
{
"role": "system",
"content": "You are a TypeScript library maintainer. Prefer small APIs, explicit types, and tests that document behavior."
},
{
"role": "user",
"content": "Refactor this helper into TypeScript:\n\nfunction groupBy(items, key) {\n return items.reduce((out, item) => {\n (out[item[key]] ||= []).push(item);\n return out;\n }, {});\n}\n\nProvide a generic type-safe version and three focused tests covering an empty input, a missing key value, and two items sharing a key. State your chosen behavior for missing keys."
}
],
"max_completion_tokens": 4096
}
}'Here's a small, strict version. It fixes the original bug where `item[key] === undefined` silently created a `"undefined"` bucket.
### Generic implementation
**Chosen behavior for missing keys:** If `item[key]` is `null` or `undefined`, the item is **skipped** and not included in any group. Rationale: avoids polluting the result with a coerced `"undefined"` key. If you need to keep them, normalize before calling.
```ts
// groupBy.ts
type PropertyKey = string | number | symbol;
// Only allow keys whose value can be a property key (or optional version)
type GroupableKeys<T> = {
[K in keyof T]: T[K] extends PropertyKey | null | undefined ? K : never
}[keyof T];
export function groupBy<T, K extends GroupableKeys<T>>(
items: readonly T[],
key: K
): Record<string, T[]> {
const out: Record<string, T[]> = {};
for (const item of items) {
const raw = item[key];
if (raw == null) continue; // <-- explicit missing-key policy
const groupKey = String(raw as PropertyKey);
(out[groupKey] ??= []).push(item);
}
return out;
}
```
> Typed to `Record<string, T[]>` intentionally. If you want literal key inference you can change the return to `Record<Extract<T[K], PropertyKey> & {}, T[]>` but for a library this is more ergonomic and doesn't lie about runtime `String()` coercion.
### Tests that document behavior
```ts
// groupBy.test.ts
import { describe, it, expect } from 'vitest';
import { groupBy } from './groupBy';
type Item = { cat?: string; id: number };
describe('groupBy', () => {
it('empty input -> empty object', () => {
expect(groupBy([] as Item[], 'cat')).toEqual({});
});
it('missing key value -> item skipped', () => {
const items: Item[] = [
{ cat: 'a', id: 1 },
{ id: 2 }, // cat is missing
{ cat: undefined, id: 3 }, // cat is explicitly undefined
];
const result = groupBy(items, 'cat');
// only 'a' group exists, no "undefined" bucket
expect(result).toEqual({
a: [{ cat: 'a', id: 1 }],
});
expect(Object.keys(result)).not.toContain('undefined');
});
it('two items sharing a key -> grouped together', () => {
const items: Item[] = [
{ cat: 'x', id: 1 },
{ cat: 'x', id: 2 },
{ cat: 'y', id: 3 },
];
const result = groupBy(items, 'cat');
expect(result).toEqual({
x: [
{ cat: 'x', id: 1 },
{ cat: 'x', id: 2 },
],
y: [{ cat: 'y', id: 3 }],
});
});
});
```{
"id": "resp_6ac7b0009eadac3fd3fd4b77",
"object": "response",
"created_at": 1791471616,
"model": "muse-spark-1.1",
"output": [
{
"type": "reasoning",
"id": "rs_6ac7b0009eadac3fd3fd4b77:rs_70691cf1-3099-4eaf-8a65-6c9811ee8913",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "msg_20968e1e-1419-4226-89cb-2c6497d11b9e",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Here's a small, strict version. It fixes the original bug where `item[key] === undefined` silently created a `\"undefined\"` bucket.\n\n### Generic implementation\n\n**Chosen behavior for missing keys:** If `item[key]` is `null` or `undefined`, the item is **skipped** and not included in any group. Rationale: avoids polluting the result with a coerced `\"undefined\"` key. If you need to keep them, normalize before calling.\n\n```ts\n// groupBy.ts\ntype PropertyKey = string | number | symbol;\n\n// Only allow keys whose value can be a property key (or optional version)\ntype GroupableKeys<T> = {\n [K in keyof T]: T[K] extends PropertyKey | null | undefined ? K : never\n}[keyof T];\n\nexport function groupBy<T, K extends GroupableKeys<T>>(\n items: readonly T[],\n key: K\n): Record<string, T[]> {\n const out: Record<string, T[]> = {};\n\n for (const item of items) {\n const raw = item[key];\n if (raw == null) continue; // <-- explicit missing-key policy\n\n const groupKey = String(raw as PropertyKey);\n (out[groupKey] ??= []).push(item);\n }\n\n return out;\n}\n```\n\n> Typed to `Record<string, T[]>` intentionally. If you want literal key inference you can change the return to `Record<Extract<T[K], PropertyKey> & {}, T[]>` but for a library this is more ergonomic and doesn't lie about runtime `String()` coercion.\n\n### Tests that document behavior\n\n```ts\n// groupBy.test.ts\nimport { describe, it, expect } from 'vitest';\nimport { groupBy } from './groupBy';\n\ntype Item = { cat?: string; id: number };\n\ndescribe('groupBy', () => {\n it('empty input -> empty object', () => {\n expect(groupBy([] as Item[], 'cat')).toEqual({});\n });\n\n it('missing key value -> item skipped', () => {\n const items: Item[] = [\n { cat: 'a', id: 1 },\n { id: 2 }, // cat is missing\n { cat: undefined, id: 3 }, // cat is explicitly undefined\n ];\n\n const result = groupBy(items, 'cat');\n\n // only 'a' group exists, no \"undefined\" bucket\n expect(result).toEqual({\n a: [{ cat: 'a', id: 1 }],\n });\n expect(Object.keys(result)).not.toContain('undefined');\n });\n\n it('two items sharing a key -> grouped together', () => {\n const items: Item[] = [\n { cat: 'x', id: 1 },\n { cat: 'x', id: 2 },\n { cat: 'y', id: 3 },\n ];\n\n const result = groupBy(items, 'cat');\n\n expect(result).toEqual({\n x: [\n { cat: 'x', id: 1 },\n { cat: 'x', id: 2 },\n ],\n y: [{ cat: 'y', id: 3 }],\n });\n });\n});\n```",
"annotations": [],
"logprobs": []
}
],
"status": "completed"
}
],
"status": "completed",
"usage": {
"input_tokens": 127,
"output_tokens": 3158,
"total_tokens": 3285,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens_details": {
"reasoning_tokens": 2479
}
},
"top_logprobs": 0,
"temperature": 1,
"top_p": 1,
"service_tier": "auto",
"reasoning": {
"effort": "high"
},
"background": false,
"tools": [],
"tool_choice": "auto",
"truncation": "disabled",
"completed_at": 1791471616,
"error": null,
"incomplete_details": null,
"instructions": null,
"parallel_tool_calls": true,
"store": true,
"max_output_tokens": 4096
}Production Incident Triage — Triage a production incident from sparse symptoms and produce an ordered investigation plan.
const response = await env.AI.run(
'meta/muse-spark-1.1',
{
messages: [
{
role: 'system',
content:
'You are an incident commander. Separate facts from hypotheses, prioritize customer impact, and give reversible next steps before risky remediation.',
},
{
role: 'user',
content:
'At 14:05 UTC, API p95 latency rose from 180 ms to 2.4 s for one region. Error rate is 0.8%, database CPU is 45%, cache hit rate fell from 92% to 61%, and deploys have been quiet for six hours. A single endpoint is responsible for 70% of the extra latency. Write an incident update, list the three highest-value checks in order, and propose a safe mitigation.',
},
],
max_completion_tokens: 4096,
},
)
console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "meta/muse-spark-1.1",
"input": {
"messages": [
{
"role": "system",
"content": "You are an incident commander. Separate facts from hypotheses, prioritize customer impact, and give reversible next steps before risky remediation."
},
{
"role": "user",
"content": "At 14:05 UTC, API p95 latency rose from 180 ms to 2.4 s for one region. Error rate is 0.8%, database CPU is 45%, cache hit rate fell from 92% to 61%, and deploys have been quiet for six hours. A single endpoint is responsible for 70% of the extra latency. Write an incident update, list the three highest-value checks in order, and propose a safe mitigation."
}
],
"max_completion_tokens": 4096
}
}'### INCIDENT UPDATE #1 - 14:20 UTC - API Latency Elevation [Single Region] **Status:** Investigating - SEV2 **Impact:** Customers served from `us-east-1 [example]` experiencing API p95 of 2.4s vs normal 180ms since 14:05 UTC. p50 stable. Error rate still low at 0.8% but we are seeing timeouts. Other regions healthy. **Scope:** ~70% of added latency is isolated to a single endpoint: `GET /v1/<hot-endpoint>` **FACTS:** * 14:05 UTC start, sustained. Single region only. * p95: 180ms -> 2.4s. Error rate: 0.8% * Cache hit rate: 92% -> 61% in that region at same time. * DB CPU 45% [normal], no saturation. * No deploys for 6 hours to API or infra. * One endpoint accounts for 70% of the latency increase. **WORKING HYPOTHESES [Unconfirmed - Do not act on alone]:** * Cache tier degradation / node loss in that region causing miss storm -> origin overload for that endpoint. * Data / traffic shape change for that endpoint causing key cardinality blow-up / cache stampede. * Regional capacity issue - client pool saturation, instance brownout, network partition to cache. **NOT doing right now:** Cache flush, DB failover/restart, new deploy/rollback. All are high-risk and destructive to evidence. --- ### Top 3 Checks - In Priority Order **1. Cache Tier Health in Affected Region - Highest signal** Owner: Cache / SRE. Timebox: 5 min. * Are all cache nodes members? Any node down/evicted at 14:05? * Memory pressure, evictions/sec, OOMs, CPU, client connections, network errors. * Client-side errors from API to cache, not just server-side. **2. Hot Endpoint Traffic Analysis** Owner: Backend. Timebox: 5 min. * Traces for `/v1/<hot-endpoint>`: QPS change at 14:05? Tenant / customer spike? * Cache key pattern: Hit rate per key, new keys per second, payload size. Is there a single hot key or key explosion? * Origin latency without cache - is the DB / downstream for this endpoint spiking? **3. Regional Serving Health** Owner: SRE * Pod/instance count, crash loops, autoscaler events, CPU throttling/GC for API fleet in that region at 14:05. * Service mesh / load balancer latency between API and cache tier. * Breakdown of that 0.8% errors: 504 vs 500 vs 429? ### Safe, Reversible Mitigation Proposed Goal: Protect customers while preserving evidence. **Proposal: Traffic shed + regional shift:** 1. Put a **temporal rate limit / load shed** on `GET /v1/<hot-endpoint>` in affected region only - e.g., 50% of normal limit, return 429 with Retry-After. Protects other endpoints. 2. **Shift 50% of API traffic out of affected region** to healthy regions via Global LB weight change. 1-click reversible. 3. Enable request coalescing + stale-while-revalidate for that endpoint if feature-flag exists. **Why safe:** No data loss, no restarts, instantly reversible via config/LB. Directly addresses symptom - cache miss storm - without touching DB/cache. **Rollback:** Revert LB weight + remove rate limit. Next update in 20 minutes or upon completion of check #1. Please attach cache and endpoint dashboards in thread.
{
"id": "resp_6ac7b01785cd56aad50246a5",
"object": "response",
"created_at": 1791471639,
"model": "muse-spark-1.1",
"output": [
{
"type": "reasoning",
"id": "rs_6ac7b01785cd56aad50246a5:rs_322771d5-5cdd-4e2d-a99d-9f26fb95b8a9",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "msg_f39c4926-6745-4033-90d7-961b45acd92b",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "### INCIDENT UPDATE #1 - 14:20 UTC - API Latency Elevation [Single Region]\n\n**Status:** Investigating - SEV2\n**Impact:** Customers served from `us-east-1 [example]` experiencing API p95 of 2.4s vs normal 180ms since 14:05 UTC. p50 stable. Error rate still low at 0.8% but we are seeing timeouts. Other regions healthy.\n**Scope:** ~70% of added latency is isolated to a single endpoint: `GET /v1/<hot-endpoint>`\n\n**FACTS:**\n* 14:05 UTC start, sustained. Single region only.\n* p95: 180ms -> 2.4s. Error rate: 0.8%\n* Cache hit rate: 92% -> 61% in that region at same time.\n* DB CPU 45% [normal], no saturation.\n* No deploys for 6 hours to API or infra.\n* One endpoint accounts for 70% of the latency increase.\n\n**WORKING HYPOTHESES [Unconfirmed - Do not act on alone]:**\n* Cache tier degradation / node loss in that region causing miss storm -> origin overload for that endpoint.\n* Data / traffic shape change for that endpoint causing key cardinality blow-up / cache stampede.\n* Regional capacity issue - client pool saturation, instance brownout, network partition to cache.\n\n**NOT doing right now:** Cache flush, DB failover/restart, new deploy/rollback. All are high-risk and destructive to evidence.\n\n---\n### Top 3 Checks - In Priority Order\n\n**1. Cache Tier Health in Affected Region - Highest signal**\nOwner: Cache / SRE. Timebox: 5 min.\n* Are all cache nodes members? Any node down/evicted at 14:05?\n* Memory pressure, evictions/sec, OOMs, CPU, client connections, network errors.\n* Client-side errors from API to cache, not just server-side.\n\n**2. Hot Endpoint Traffic Analysis**\nOwner: Backend. Timebox: 5 min.\n* Traces for `/v1/<hot-endpoint>`: QPS change at 14:05? Tenant / customer spike?\n* Cache key pattern: Hit rate per key, new keys per second, payload size. Is there a single hot key or key explosion?\n* Origin latency without cache - is the DB / downstream for this endpoint spiking?\n\n**3. Regional Serving Health**\nOwner: SRE\n* Pod/instance count, crash loops, autoscaler events, CPU throttling/GC for API fleet in that region at 14:05.\n* Service mesh / load balancer latency between API and cache tier.\n* Breakdown of that 0.8% errors: 504 vs 500 vs 429?\n\n### Safe, Reversible Mitigation Proposed\n\nGoal: Protect customers while preserving evidence.\n\n**Proposal: Traffic shed + regional shift:**\n1. Put a **temporal rate limit / load shed** on `GET /v1/<hot-endpoint>` in affected region only - e.g., 50% of normal limit, return 429 with Retry-After. Protects other endpoints.\n2. **Shift 50% of API traffic out of affected region** to healthy regions via Global LB weight change. 1-click reversible.\n3. Enable request coalescing + stale-while-revalidate for that endpoint if feature-flag exists.\n\n**Why safe:** No data loss, no restarts, instantly reversible via config/LB. Directly addresses symptom - cache miss storm - without touching DB/cache.\n\n**Rollback:** Revert LB weight + remove rate limit.\n\nNext update in 20 minutes or upon completion of check #1.\n\nPlease attach cache and endpoint dashboards in thread.",
"annotations": [],
"logprobs": []
}
],
"status": "completed"
}
],
"status": "completed",
"usage": {
"input_tokens": 146,
"output_tokens": 2500,
"total_tokens": 2646,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens_details": {
"reasoning_tokens": 1714
}
},
"top_logprobs": 0,
"temperature": 1,
"top_p": 1,
"service_tier": "auto",
"reasoning": {
"effort": "high"
},
"background": false,
"tools": [],
"tool_choice": "auto",
"truncation": "disabled",
"completed_at": 1791471639,
"error": null,
"incomplete_details": null,
"instructions": null,
"parallel_tool_calls": true,
"store": true,
"max_output_tokens": 4096
}Web Search Grounding — Use Meta's Responses API web search grounding to answer a current-events question with citations.
const response = await env.AI.run(
'meta/muse-spark-1.1',
{
input: 'What is the latest major technology news today? Cite the sources.',
tools: [{ type: 'web_search' }],
},
)
console.log(response)curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \
--header "Authorization: Bearer $CLOUDFLARE_API_TOKEN" \
--header "Content-Type: application/json" \
--data '{
"model": "meta/muse-spark-1.1",
"input": {
"input": "What is the latest major technology news today? Cite the sources.",
"tools": [{"type": "web_search"}]
}
}'Got it — you want today’s major tech headlines with sources. I’ll pull the latest from October 8 and verify the top stories.
{
"id": "resp_6ac7b0173b4b3903c1914984",
"object": "response",
"created_at": 1791471639,
"model": "muse-spark-1.1",
"output": [
{
"type": "reasoning",
"id": "rs_6ac7b0173b4b3903c1914984:rs_4cb6db0b-4ac9-4786-8fc1-d448358e2317",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "rs_4bc20bd7-7650-465e-9a9e-ecb58e38b16d",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Got it — you want today’s major tech headlines with sources. I’ll pull the latest from October 8 and verify the top stories.",
"annotations": [],
"logprobs": []
}
],
"phase": "commentary",
"status": "completed"
},
{
"type": "web_search_call",
"id": "ws_call_9345dd9b-7234-4e0c-a576-b98a32283cc5",
"status": "completed",
"action": {
"type": "search",
"query": "latest major technology news today October 8 2026"
}
},
{
"type": "web_search_call",
"id": "ws_call_0fbf1dcd-212c-4e2b-8d1e-da68d686a4ee",
"status": "completed",
"action": {
"type": "search",
"query": "top technology headlines today"
}
},
{
"type": "web_search_call",
"id": "ws_call_43240af6-97be-4ef3-9fb4-8ad53b4a40f2",
"status": "completed",
"action": {
"type": "search",
"query": "breaking tech news October 8 2026"
}
},
{
"type": "web_search_call",
"id": "ws_call_bf3ee591-c908-4e45-b400-aea7d24fda29",
"status": "completed",
"action": {
"type": "search",
"query": "major tech news today Wall Street Journal NYTimes Bloomberg"
}
},
{
"type": "web_search_call",
"id": "ws_call_22a2bb67-1e1d-49f0-b8b4-4f809ae09b1b",
"status": "completed",
"action": {
"type": "search",
"query": "Google OpenAI Apple news October 8"
}
},
{
"type": "reasoning",
"id": "rs_6ac7b0173b4b3903c1914984:rs_7daa6a64-a490-4064-bc3d-4a0f5ab86650",
"summary": [],
"status": "completed"
},
{
"type": "web_search_call",
"id": "ws_call_804551ed-7be5-4670-8f58-1913d4656e50",
"status": "completed",
"action": {
"type": "search",
"query": "The Verge tech news October 8 2026"
}
},
{
"type": "web_search_call",
"id": "ws_call_e354cf9c-bf87-416d-910e-636fe80b44f8",
"status": "completed",
"action": {
"type": "search",
"query": "TechCrunch headlines October 8 2026"
}
},
{
"type": "reasoning",
"id": "rs_6ac7b0173b4b3903c1914984:rs_b8ffbebf-ce96-47ac-9823-7455a4f5a408",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "rs_c3c4da30-d286-432f-a60c-bfce20ca5d7e",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "I’m seeing early signals like Google’s SynthID rollout today — let me dig deeper into the major outlets to finalize the top stories.",
"annotations": [],
"logprobs": []
}
],
"phase": "commentary",
"status": "completed"
},
{
"type": "web_search_call",
"id": "ws_call_34cb8e12-7f7a-4f0d-a1bf-bbfced64f594",
"status": "completed",
"action": {
"type": "search",
"query": "Reuters technology news today October 8"
}
},
{
"type": "web_search_call",
"id": "ws_call_1d8eb5fa-cadf-459b-b950-2a665bb1c76e",
"status": "completed",
"action": {
"type": "search",
"query": "Bloomberg technology news October 8 2026 AI"
}
},
{
"type": "web_search_call",
"id": "ws_call_732b9e15-5131-4df6-86e2-bb01fc03aa47",
"status": "completed",
"action": {
"type": "search",
"query": "Microsoft Google Amazon tech announcement October 8 2026"
}
},
{
"type": "web_search_call",
"id": "ws_call_9f5bc14b-3970-42e4-aaa3-e3d72247602d",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://nokiamob.net/2026/10/08/google-opens-synthid-detector-to-everyone-heres-what-it-actually-checks/"
}
},
{
"type": "web_search_call",
"id": "ws_call_b200e4a3-1c14-43d5-9161-1aefab2f0434",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://winbuzzer.com/2026/10/08/google-opens-synthid-ai-watermark-checker-to-everyone-xcxwbn/"
}
},
{
"type": "web_search_call",
"id": "ws_call_bdc61c33-248d-4023-933f-41744138fa9b",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://kelofm.com/2026/10/08/tencent-mulls-5-billion-bond-sale-to-push-ai-ambitions-bloomberg-news-reports/"
}
},
{
"type": "web_search_call",
"id": "ws_call_5f4b1af6-14be-43db-b742-50ad5dbb3ff0",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://www.tradingview.com/news/coinmarketcal:16307c967094b:0-arcium-confidential-ai-removes-the-cpu-enclave-08-oct-2026/"
}
},
{
"type": "web_search_call",
"id": "ws_call_8d2aea46-4bc9-4180-8ad4-d627006dedca",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://mspoweruser.com/hr/heres-your-reminder-that-today-october-8-is-the-last-day-microsoft-supports-windows-11-21h2/"
}
},
{
"type": "reasoning",
"id": "rs_6ac7b0173b4b3903c1914984:rs_e958b0b2-1277-4682-97b9-ae809a897a76",
"summary": [],
"status": "completed"
},
{
"type": "web_search_call",
"id": "ws_call_cc1d22f5-d4e9-406c-b0dc-aec40cf9447e",
"status": "completed",
"action": {
"type": "search",
"query": "Apple announces October 13 event home hub HomePod mini Apple TV"
}
},
{
"type": "web_search_call",
"id": "ws_call_c5a6ebbc-a369-4117-bf73-2955b9375902",
"status": "completed",
"action": {
"type": "open_page",
"url": "https://www.macrumors.com/2026/10/04/apple-event-reportedly-planned-for-october-13/"
}
},
{
"type": "reasoning",
"id": "rs_6ac7b0173b4b3903c1914984:rs_11eae94f-32d9-4ea4-8712-3606c0b3b9d9",
"summary": [],
"status": "completed"
},
{
"type": "message",
"id": "msg_397cf2c5-50f7-4b76-88ae-bf3ea2b79bf7",
"role": "assistant",
"content": [
{
"type": "output_text",
"text": "Based on today's coverage **October 8, 2026**, the major technology headlines are:\n\n### 1. Google opens AI watermark detector to everyone\nGoogle made its SynthID Detector available globally in English today.\n\n* Google has made SynthID Detector available worldwide in English. The tool lets people check images, videos and audio files for hidden AI watermarks\n* Coverage: It recognizes supported Google, OpenAI, NVIDIA and Kakao watermarks, with Apple detection coming later\n* The standalone detector is available globally in English as of October 7\n\nThe tool is seen as a major step for AI transparency, with checks for invisible watermarks identifying supported AI-generated or edited media.\n\n### 2. Tencent mulling $5B bond sale to fund AI push\nChinese tech giant Tencent is raising debt to fund AI infrastructure:\n\n> Tencent is considering raising up to $5 billion via an offshore bond sale, Bloomberg News reported on Thursday\n\n### 3. Apple reportedly planning smart-home event Oct. 13\nPer *Bloomberg*'s Mark Gurman:\n\n* Apple plans to unveil an all-new smart home hub, a new HomePod mini, and a new Apple TV 4K on Tuesday, October 13\n* Apple is planning to hold an event that day \n* Earlier Gurman reporting also not
... response truncated ...▶input
one ofrequiredinstructions
stringtemperature
numberminimum: 0maximum: 2max_output_tokens
numberexclusiveMinimum: 0top_p
numberminimum: 0maximum: 1stream
boolean▶tools[]
arraytool_choice
▶text{}
object▶reasoning{}
objectid
stringobject
stringconst: responsecreated_at
numbermodel
string▶output[]
arrayoutput_text
stringstatus
stringenum: in_progress, completed, failed, incomplete▶usage{}
object