When your MCP agent hangs, the LLM isn't the bottleneck — the tools are. Per-tool tracing: latency, retries, error class, cost. OpenTelemetry-friendly spans. Built into NeuroLink middleware. dev.to/neurolink/tracing-tool-calls-in-mcp-workflows-per-tool-latency-cost-and-failure-modes-5hdo

Running Ollama in prod? 3 things break: queue saturation, latency on heavy models, and the "free" myth. Playbook: rate-limiting, cloud fallback on timeout, cost guardrails via onFinish. dev.to/neurolink/productionizing-ollama-rate-limits-cloud-fallback-and-cost-guardrails-3hpn

Just shipped: complete RAG pipeline in TypeScript. Embeddings, vector search, reranking — all through one SDK. No LangChain required. dev.to/neurolinkai

If your AI app only uses one provider, you're one outage away from downtime. NeuroLink's fallback chains handle provider failures automatically. Zero code changes needed.

Your AI bill is too high. We wrote about cutting costs by 40% with smart model routing — Gemini Flash for simple tasks, GPT-4o only when needed. github.com/juspay/neurolink

Migrating from LangChain to NeuroLink? We wrote a practical guide with side-by-side code comparisons. 22 views in 24 hours. dev.to/neurolinkai

We just published 35 technical articles on Dev.to about TypeScript + AI development. From RAG systems to cost optimization to MCP tool chaining. All open source. dev.to/neurolinkai

Building AI agents that can use real tools? NeuroLink's MCP support + TypeScript type safety = production-ready AI tool use. No custom integration code needed. blog.neurolink.ink

MCP (Model Context Protocol) is USB for AI tools. NeuroLink supports 58+ MCP servers out of the box. Connect GitHub, Postgres, Slack — your AI agent handles the rest. docs.neurolink.ink

Hot take: if you're using 3 different AI SDKs in one TypeScript project, you're doing it wrong. There's a single SDK that handles all 13 providers. github.com/juspay/neurolink

We just hit 18 technical articles on Dev.to covering MCP, multi-provider AI, cost optimization, and more. All open source, all TypeScript. dev.to/neurolinkai

The fastest way to add AI to a TypeScript app in 2026: one SDK, 13 providers, zero vendor lock-in. NeuroLink handles OpenAI, Claude, Gemini, and 10 more with a single generate() call. github.com/juspay/neurolink

156 articles on our blog, 15 on Dev.to, and growing. If you're building AI apps with TypeScript, we're documenting every pattern we've learned from processing millions of requests at scale.

Voice AI agents are the next frontier. Real-time speech-to-speech with Gemini Live, bidirectional audio streaming, sub-second latency. All in TypeScript, no Python required.

Every week a new AI agent framework launches. The real question isn't which framework - it's what SDK sits underneath all of them. That layer needs to outlast any single framework.

The RAG hype is real but most implementations are overcomplicated. 10 chunking strategies, hybrid search, 5 reranking types - all built into one generate() call. No vector DB setup required.

We just published 4 new technical articles on Dev.to covering Ollama vs OpenAI, multi-provider apps, self-running AI tasks, and MCP tools. All with real TypeScript code examples you can copy-paste.

Hot take: In 2026, if your AI app is locked to one provider, you're building on quicksand. Models change. Prices change. Rate limits change. The only constant is your code. Build provider-agnostic from day one. Your future self will thank you.

What if one TypeScript SDK could talk to ChatGPT, Claude, AND Gemini? Same API. Same error handling. Same streaming interface. Just swap the provider name. That's what we built with NeuroLink.

Local LLMs vs cloud APIs — it's not either/or. We wrote an honest comparison of Ollama vs OpenAI for TypeScript developers. The real answer? Use both with automatic fallback. Read it on Dev.to →

If you're evaluating AI SDKs for a TypeScript project, we wrote a framework comparison covering NeuroLink (which extends Vercel AI SDK) and LangChain. Real code examples. Honest trade-offs. blog.neurolink.ink/posts/framework-comparison/

200+ AI models. One API key. Zero SDK juggling. Here's how to access Claude, GPT-4, Gemini, and LLaMA through a single TypeScript interface. A thread:

Curious: what's your biggest pain point when working with multiple LLM providers? We built NeuroLink to solve the SDK juggling problem, but we want to hear what else frustrates you. github.com/juspay/neurolink

Your AI is deployed. Your audit is next week. Can you prove every decision it made? Enterprise AI governance isn't optional. Here's how to build it right in TypeScript:

NeuroLink vs rolling your own multi-provider setup: Before: 3 weeks to evaluate models After: 3 hours Before: rewrite code per provider After: change one string Before: 5 invoices After: 1 unified bill via OpenRouter

Your AI app speaks now. Google TTS. Studio-quality voices. Streaming audio. Podcast generation. Here's how to add voice to any TypeScript app in 10 minutes:

Dev tip: Test prompts before writing integration code. npx @juspay/neurolink generate "Your prompt" --provider openrouter --model "anthropic/claude-3-5-sonnet" Validate in terminal. Then ship.

NeuroLink vs. LangChain vs. Vercel AI SDK We built NeuroLink, so we're biased. But here's an honest comparison with real code, benchmarks, and trade-offs. A thread:

NeuroLink supports 13 LLM providers with a single unified TypeScript API. Switch from Claude to GPT-4 to Gemini by changing one string. No SDK swaps. No code rewrites. docs.neurolink.ink

PDFs. CSVs. Images. Text files. Your business runs on documents. Your AI should understand them all. Here's how to process documents with one TypeScript API: