Claude Developer Platform: Structured Outputs in Public Beta

This weekend's development on the AI Mock Interview System has been a masterclass in stability and configuration. Debugging real-time audio (Vapi) is tough! Here's the error log: ❌ Errors Encountered no-room / Meeting ended: Caused by inconsistent environment variables (double definition of the workflow ID) and initial confusion between the old and new Vapi call syntax. 400 Bad Request: The most persistent error. This means the Vapi server rejected the data structure we were sending. The Fix i tried✅ i executed a major architectural pivot: abandoned the old, unstable vapi.start(workflowId) syntax and switched to the new declarative workflow system. i then define the entire conversation flow (the generator object) directly in constants/index.ts and pass that structured data into vapi.start(generator, ...). This eliminated instability and adherence to the latest SDK best practices. buh another error start when i try to call.. The Current Battle The 400 Bad Request persists, indicating a final, hidden configuration flaw within the declarative generator object itself. The Vapi server is rejecting the file because the chosen LLM model or voice configuration is likely invalid or requires an API key that hasn't been authorized in the Vapi dashboard. TBH at this point IDK, but I'm almost there! Now, time to simplify that model config and kill the final bug. 💀 i'd like help if u know what's causing the error.... 🙏🏾 #Nextjs #Vapi #Debugging #AIAgent #FrontendDev

🚀 Excited to highlight a major upgrade from Claude Developer Platform: Structured Outputs are now live in public beta for Sonnet 4.5 and Opus 4.1! 🎉 👉 What’s new? With this rollout, you can define a JSON schema or tool definition—and Claude will guarantee that the output exactly matches your specification. No more parsing failures, missing fields, or messy type mismatches. 💡 Why this matters for devs and teams: Reliable production-systems: When downstream code expects data in a certain format, this reduces one big source of failure. Cleaner code: Less error-handling boilerplate, fewer retries, and fewer surprises. Better for agent-based/workflow systems: When multiple components pass structured data around, consistency becomes critical. 🔧 Getting started: Use the beta header: structured-outputs-2025-11-13 when calling the API. Choose your mode: JSON schema mode (for data extraction, reports) Strict tool mode (for agent workflows with tool calls) Define your schema or tool spec ahead of time, integrate with your SDK of choice (Python, TS) and you’re off. If you’re building systems where format, types, schema validity matter—which is basically every serious AI-driven product—this is a meaningful leap forward. 🔗 Check it out here: https://lnkd.in/d6RyyTdY #Claude #ai #developer #OpenRouter

Anthropic unveiled Agent Skills, a new feature that allows Claude to use specialized folders containing scripts, instructions, and resources to perform complex, task-specific actions across Claude apps, Claude Code, and the API. https://hubs.li/Q03SkxRT0

🚀 We just launched one of the biggest upgrades to Jet Admin. You can now generate SQL queries and HTTP API calls simply by typing what you need — no SQL syntax, no API docs, no engineering handoff. In this short demo (👇 the video you’re about to watch), you’ll see how Ask AI can: ⚡ Turn natural language into ready-to-run SQL 🔌 Generate and modify HTTP requests (like Stripe refunds) 🔍 Validate results instantly 🛠️ Fix query errors with one click 🧩 Connect queries directly to tables, charts, and action buttons ➕ Add metadata to an API call without touching JSON This brings us even closer to a world where anyone can build complete internal tools—fast, safe, and without writing code. #nocode #internaltools #JetAdmin #AskAI #SQL #APIs

Funny how often our work turns into the same loop: someone needs a quick SQL fix, someone else needs an API call updated, and suddenly the whole team is blocked waiting for engineering. This update finally breaks that cycle! I’ve seen it generate a query, fix an error and plug everything into an action — all in minutes, not hours. It feels like the workflow we always wished we had!

Understanding Pipes in NestJS: What They Are, Benefits, and How to Use Them 🧩What Are Pipes in NestJS? In NestJS, Pipes are classes that are used to transform or validate incoming data before it reaches your controller’s method. They work like internal middleware, processing and sanitizing data automatically. In short: Validation: Ensures that incoming data meets certain rules. Transformation: Converts data types (e.g., from string to number). Pipes are executed after NestJS resolves the route parameters but before the controller method is invoked. ✅ Centralized validation — keeps validation logic out of your controllers. Let’s create a simple DTO and apply the built-in ValidationPipe to a controller method. Pipes in NestJS are a clean and powerful way to handle data transformation and validation. If you want to write scalable, enterprise-grade applications — mastering pipes is a must! Official Nestjs Documentation: Pipe https://lnkd.in/gzE-ctqb

Just wrapped up a quick proof of concept (POC) involving two interconnected microservices. The first microservice is a scheduled notebook that automates data refreshes—daily, weekly, monthly, or via a custom cron job. It aggregates social media posts from multiple sources, unifies them into a single payload, and sends them to a webhook. The second microservice—the webhook—leverages open-source embedding models from Hugging Face. It converts incoming text into vector embeddings, classifies content as positive or negative using zero-shot models, and determines the most relevant category. The processed results are then stored in a vector database with metadata and embedding representations for efficient retrieval. On top of that, I built a real-time analytics dashboard powered by JavaScript, which reads directly from a Pinecone index to visualize and monitor post insights dynamically. #dataanalytics

TOON vs JSON for LLM Prompts: What Matters for Agent Builders What is it? TOON (Token-Oriented Object Notation) is a compact, human-readable serialization designed for LLM I/O. It preserves JSON’s structure while cutting syntactic overhead, targeting lower token counts and faster prompts. Why teams are testing it Early reports show 30–60% token reductions versus equivalent JSON, which translates to lower cost and higher throughput in agent pipelines that pass large payloads or many tool calls. Treat this as workload-dependent, not universal. Replace JSON—or complement it? Position TOON as a prompt-side format, not a systems-of-record change. Keep JSON for APIs, storage, and contracts; use TOON when talking to models to reduce token tax. Several practitioners frame it as complement, not replacement. Programming relevance for agent creators Throughput & cost: Fewer tokens per tool call improves fan-out patterns and multi-step planners. Useful in agentic RAG, debate, and evaluator loops. Ergonomics: Less punctuation noise can improve model adherence on tabular or key-value payloads; still validate on your stack. Interoperability: Keep a clean boundary—TOON for LLM prompts; JSON for typed interfaces, schema checks, observability, and governance. Use structured-output/tool-calling where available to lock outputs to JSON Schema. When to prefer TOON High-volume agent calls where token cost or context window is the bottleneck. Large, flat payloads (lists, rows, metrics) that suffer from JSON verbosity. When to stick with JSON Public APIs, storage, contracts, and anything audited via schemas and CI/CD gates. Deeply nested objects and strict compatibility with existing SDKs and validators. Practical adoption pattern Keep JSON as the system contract. Add a prompt adapter that converts JSON↔TOON at the model boundary and measure token deltas, latency, and error rate before broad rollout.

Why most developers are still fighting API complexity when a single GraphQL strategy could solve everything? Here's what I learned building projects. GraphQL isn't just another query language. It's a complete mindset shift that changes how you think about APIs. Most developers are stuck in the REST world: • Multiple endpoints for simple data • Over-fetching that kills performance • Under-fetching that requires extra calls • Complex state management on the frontend But GraphQL with Hasura flips this completely. I implemented this approach on our latest project: Step 1: Set up Hasura with PostgreSQL ↳ Instant GraphQL API generation ↳ No boilerplate code needed Step 2: Use declarative data retrieval ↳ Ask for exactly what you need ↳ Nothing more, nothing less Step 3: Implement role-based access control ↳ Security built into your schema ↳ No more manual permission checks Step 4: Add WebSocket integration ↳ Real-time data synchronization ↳ Users see changes instantly The results were incredible. Our API response time dropped by 40 percent. Frontend complexity went down. User experience improved dramatically. The biggest lesson I learned was this. Stop fighting complexity. Embrace tools that eliminate it from the start. GraphQL isn't perfect for everything. But for most modern applications it's a game changer. What's your biggest API challenge right now? Let's solve it together.

Most of us meet 𝗢𝗽𝗲𝗻𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 through auto-instrumentation -- add a dependency and dashboards magically light up. But have you ever wondered what’s actually being collected underneath? What is an 𝗢𝗽𝗲𝗻𝗧𝗲𝗹𝗲𝗺𝗲𝘁𝗿𝘆 𝘀𝗶𝗴𝗻𝗮𝗹 — really? In 𝗣𝗮𝗿𝘁 𝟭 of my series OpenTelemetry Foundations, I go back to the basics: manually creating spans, metrics, and log records with the OTel SDK, and seeing how these raw signals form the foundation of everything that comes later. It’s easy to think of observability as dashboards and magic integrations, but under the hood it all starts here -- with structured signals like 𝘁𝗿𝗮𝗰𝗲𝘀, 𝗺𝗲𝘁𝗿𝗶𝗰𝘀, and 𝗹𝗼𝗴𝘀, each carrying attributes and context that describe what your code is really doing. Once you see them in their raw form, the bigger picture finally starts to make sense. 🔗 Read Part 1 → https://lnkd.in/dnE5mMKk