API Developer Resources Robust data infrastructure demands clear documentation. 🔹 Fetch live market prices and historical OHLCV 🔹 Build custom price widgets and data screeners 🔹 Deploy exact code scripts via Python and Node 7/8

API access A month ago, we launched the public beta of our enterprise API with grok-beta and grok-vision-beta. We’re adding grok-2-1212 and grok-2-vision-1212, offering better accuracy, instruction-following, and multilingual capabilities. Pricing is now $2/1M input tokens and $10/1M output tokens. Sign up with $25 free credits: In the coming weeks, image generation is also coming to our API, enabling developers to generate photorealistic images for their applications.

"Every API team is about to serve an agentic consumer, whether they design for it or not." — A sharp piece from Pinecone on what it actually means to design agent-friendly APIs. Designing Agent-Friendly APIs The post outlines six principles for AX (Agent Experience) design. Here are the three that matter most. 🔴 Highlight 1: Errors must be instructions, not status codes "Invalid request" burns token budget while the agent guesses. The right design includes three elements in every error: what was wrong (specific field and expectation), the fix (concrete next action), and a docs link when necessary. "Response is too large. To reduce the size, try a lower top_k value." gives an agent everything it needs to self-correct on the next call. RFC 9457 Problem Details for machine-readable codes, and separate error codes for missing vs. invalid credentials — these aren't nice-to-haves, they're the difference between autonomous recovery and budget drain. 🔵 Highlight 2: One-endpoint-per-MCP-tool mirrors fail twice Large APIs converted 1:1 into MCP servers can consume "hundreds of thousands of tokens before the first call." Cloudflare exposes `search()` and `execute()` over a typed SDK — covering 2,500 endpoints for about a thousand tokens. The principle: instead of `list_users` + `list_events` + `create_event`, offer `schedule_event`. Curate workflow-shaped tools; treat the agent surface as a product, not a reflection of your internal endpoint structure. 🟡 Highlight 3: TTFSC — the metric that finally makes AX quality legible The author ran Claude Sonnet 5 against Pinecone's API with zero preloaded context. All three trials succeeded unattended. Median: 6 turns to first successful call, ~$0.30/run, ~90 seconds/run. The agent choosing raw REST: 3 turns. The agent choosing the Python SDK: nearly half its turns reading call signatures from TypeErrors. An older model's first action was installing a retired 2024 package — the current-generation went straight to the modern API. Deprecated shapes persist in model priors for years; ignoring them is a design decision with real economic consequences. Agents externalize the costs human developers used to silently absorb — docs hunting, debugging, workarounds — into measurable, billable, churn-inducing expenses. AX is now a required design discipline alongside DX. #APIDesign# #AIAgents#

Claude API struggling again. This is my mental model of the backend for Claude and Codex PS: Notice how Claude for Government has a pristine 100% uptime? We're second class citizens after the drone-targeting software ofc

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