🛠️ Tools & Frameworks
The AI engineering toolchain — LangChain, LlamaIndex, DSPy, LangGraph, LangSmith, Braintrust, Inspect, AGENTS.md.
The workflow
flowchart LR
A[Pick framework] --> B{Use case}
B -->|Agents| C[LangGraph /<br/>CrewAI / AutoGen]
B -->|RAG| D[LlamaIndex /<br/>Haystack]
B -->|Orchestration| E[LangChain /<br/>DSPy]
B -->|Eval| F[Braintrust /<br/>LangSmith]
C --> G[Build, ship,<br/>observe]
D --> G
E --> G
F --> G
Pick a framework that matches your primitive — agents, retrieval, orchestration, or evals — not all four at once.
Key takeaways
Videos (16)
DSPy: The End of Prompt Engineering - Kevin Madura, AlixPartners
DSPy lets you write LLM software as typed, modular Python programs that survive model swaps and can be auto-optimized rather than hand-prompted.
Vercel AI SDK Masterclass: From Fundamentals to Deep Research
Vercel AI SDK provides a small, provider-agnostic set of primitives (generate/stream + tools) that scale from a hello-world prompt up to a multi-step deep-research agent.
Function Calling is All You Need — Full Workshop, with Ilan Bigio of OpenAI
Most agent and RAG architectures collapse to a function-calling loop — master that one primitive and the rest of the agent stack falls out.
On Engineering AI Systems that Endure The Bitter Lesson - Omar Khattab, DSPy & Databricks
Build LLM systems whose intent (signatures, metrics) is separate from prompts, so optimizers can re-compile them as scale-based models keep leapfrogging.
Your Agent Can Now Train Models — Merve Noyan, Hugging Face
Hugging Face Hub plus open coding agents and inference routing let you spin up, fine-tune, and serve open models from chat with near-zero glue code.
Full Walkthrough: Writing & Using Skills — Nick Nisi and Zack Proser
Skills give Claude portable, composable, scriptable units of expertise that route in by description and stop you from re-explaining context every conversation.
[Workshop] AI Pipelines and Agents in Pure TypeScript with Mastra.ai — Nick Nisi, Zack Proser
Mastra lets TypeScript devs build production AI pipelines with strongly-typed workflows and a single-stack alternative to Python frameworks.
Tool Calling Is Not Just Plumbing for AI Agents — Roy Derks
Agents are only as good as their tools — invest in well-described, schema-typed, framework-independent tool layers, not just the agent loop.
Human-in-the-Loop Automation with n8n — Liam McGarrigle
n8n's visual workflows plus human-in-the-loop nodes let non-developers build inspectable, fixable agentic automations rather than opaque end-to-end agents.
Building Reactive AI Apps: Matt Welsh
AI.JSX brings React's composability to LLM programs so frontend devs can stream and compose model calls as nested components.
Backlog.md: Terminal Kanban Board for Managing Tasks with AI Agents — Alex Gavrilescu, Funstage
Decompose big features into atomic markdown tasks with explicit acceptance criteria and an MCP-exposed workflow so AI agents stay in-scope and reviewable.
Ship Agents that Ship: A Hands-On Workshop - Kyle Penfound, Jeremy Adams, Dagger
Dagger turns agent loops into portable containerized workflows that ship to CI, letting you treat coding agents as just another reusable Dagger module.
Pragmatic AI with TypeChat: Daniel Rosenwasser
Use TypeScript types as both schema guidance and validator-driven repair loop to make LLM JSON outputs reliably consumable by typed application code.
Git push get an AI API: Ryan Fox-Tyler
Productionizing AI features means composing functions, models, and traditional code/dictionary validation — git push is the deploy primitive.
[Full Workshop] How to add secure code interpreting in your AI app: Vasek Mlejnsky
Adding secure AI code execution to an app is now a few-hundred-line integration with e2b sandboxes plus Vercel AI SDK function calling.
Hypermode Launch: Kevin Van Gundy
Hypermode bets that the winning AI dev platform is one that lets you swap models, prompts and data with zero friction so iteration speed compounds.