Jupyter

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Jun 28, 2026 · 14:57

Vaidas Razgaitis, Senior Research Engineer at Higharc, presents three tactical focus areas to accelerate the handoff of frontier ML research into production features. First, he advocates for a Research Prototype Taxonomy Document—a technical design document tailored for ML that maps domain context, business goals, type safety, persistence, and system architecture. Second, he details Higharc's monorepo of cleanly isolated microservices with a layered API/business logic/data structure, enabling researchers to own individual services. Third, he explains using Graphite for stack diffs to decompose monolithic prototypes into small, reviewable PRs that tap subject matter experts asynchronously. The talk offers diagnostic questions to assess team velocity across these three levers.

Intro to GraphRAG — Zach Blumenfeld

Jun 30, 2025 · 1:18:35

Zach Blumenfeld introduces GraphRAG using Neo4j, showing how to build knowledge graphs from employee and skill data, combine structured and unstructured data, and use graph traversal with vector search for retrieval. The workshop covers Cypher query patterns for multi-hop similarity, entity extraction from resumes via LLMs, and creating semantic similarity relationships. It demonstrates Leiden community detection for skill clustering and builds a LangGraph agent with four custom tools that balance exact skill matches with vector similarity. Blumenfeld argues that knowledge graphs provide controlled, explainable retrieval logic for agentic workflows, enabling developers to decompose data into graph models that expose domain logic for more accurate retrieval than pure vector search.

Graph Intelligence: Enhance Reasoning and Retrieval Using Graph Analytics - Alison & Andreas, Neo4j

Jun 27, 2025 · 1:41:16

Alison Cossette and Andreas Kollegger from Neo4j demonstrate how graph data science algorithms enhance GraphRAG systems by connecting, clustering, and curating unstructured data. They walk through running KNN similarity (K=25) on document embeddings, then applying Louvain community detection to identify clusters of near-identical chunks—one community had 49 documents with 0.98 average similarity—which can be collapsed via APOC to improve retrieval efficiency while preserving lineage. Using page rank and betweenness centrality, they show how to surface influential documents and track conversation paths across communities. The session uses a preloaded Neo4j AuraDB database with 17K nodes and 774K relationships from the Agent Neo project, and includes practical cipher queries for projecting graphs and running algorithms. They emphasize using graph analytics to diversify retrieval, manage content hygiene, and build accountable agent systems.

Building Agentic Applications w/ Heroku Managed Inference and Agents — Julián Duque & Anush Dsouza

Jun 27, 2025 · 52:35

In this workshop, Heroku Principal Developer Advocate Julián Duque and Product Manager Anush Dsouza introduce Heroku Managed Inference and Agents, a platform designed to make every software engineer an AI engineer by simplifying the attachment of agents and AI to applications. They demonstrate an opinionated, curated set of models (e.g., Claude 4) and an agentic control loop running on Heroku's trusted compute (Dynos) that provides first-party tools like code execution (Python, Node, Go, Ruby), Postgres schema inspection and querying, and document conversion, all streaming responses in real time. The workshop walks through provisioning inference via a single CLI command or add-on, using a Jupyter notebook to chain multiple tools (e.g., HTML-to-markdown then Python execution), and attaching custom MCP servers (like Brave Search) that spin up one-off Dynos and scale to zero. They also show how to expose MCPs remotely via a server-sent events endpoint for use with Cursor or other clients, emphasizing security (read-only database followers, bearer tokens) and future OAuth support. The episode concludes with a call to try the platform via a free trial team valid through the weekend.

Stateful Agents — Full Workshop with Charles Packer of Letta and MemGPT

Apr 19, 2025 · 1:19:34

Charles Packer, lead author of the MemGPT paper and co-founder of Letta, argues that statefulness (memory) is the most important problem to solve for building useful AI agents, since LLMs are inherently stateless transformers. He presents MemGPT's LMOS (Language Model Operating System) approach, which treats memory management as a context compilation problem solved by the LLM itself using tool calling to read and write structured memory blocks. The workshop demonstrates Letta's open-source stack (FastAPI, Postgres, Python) where agents persist state on a server, enabling long-running, learning interactions without context overflow—e.g., an agent can update its core memory (e.g., correcting a user's name) or search archival memory (e.g., recalling user preferences after a reset). Packer also shows multi-agent communication via async message passing between agents running as independent services, and highlights that tools are sandboxed by default with support for Composio integrations. The session includes a live notebook exercise and a low-code UI (ADE) showing context window management and memory editing, emphasizing that true stateful agents improve continuously over time rather…

[Full Workshop] How to add secure code interpreting in your AI app: Vasek Mlejnsky

Feb 6, 2025 · 1:48:16

Vasek Mlejnsky, CEO of E2B, demonstrates how to add secure AI code execution to any app using E2B’s Code Interpreter SDK alongside Anthropic's Sonnet 3.5 and Vercel’s AI SDK. The workshop builds an open-source version of Claude’s artifacts UI, running Python code in isolated Firecracker VMs that start in ~900ms and support custom environments via Dockerfiles. Vasek explains how E2B’s Jupyter-server-based sandbox returns stdout, stderr, runtime errors, and rich outputs like PNG charts, which are streamed to the front end using Vercel’s stream data. He covers security architecture—full root access in a VM that self-destructs on escape—and plans for AWS/GCP self-hosting and snapshot-based agent branching. The episode also addresses customizing sandboxes with private packages, mounting cloud storage (S3, GCS), and interpreting table previews as HTML or images.

Hiring & Building an AI Engineering Team: Dr. Bryan Bischof

Dec 31, 2024 · 29:07

Dr. Bryan Bischof, Head of AI at Hex, argues that building an AI engineering team requires hiring based on product stage—starting with full-stack engineers and data profiles, then adding designers and later MLEs—while avoiding the 'mythical man month' trap in early AI products. He advocates for data intuition over LeetCode in interviews, using a take-home data exercise to assess candidates' ability to extract meaning from data and give feedback. Key attributes he looks for are curiosity, urgency, and product-mindedness, noting that enthusiasm alone is insufficient. Bischof also recommends working directly with domain experts to model AI behavior and suggests a centralized AI platform team to support multiple product teams, rather than having each team build AI infrastructure independently.

Build an AI Research Agent: Apoorva Joshi

Oct 25, 2024 · 27:33

In this workshop, Apoorva Joshi, an AI Developer Advocate at MongoDB, teaches how to build an AI research agent using MongoDB as the memory provider and knowledge store, open-source LLMs from Fireworks AI (Fire Function V1) as the agent’s brain, and LangChain to orchestrate the workflow. The agent searches for research papers, summarizes them, and answers questions based on past research, using tools like ArXivLoader and a MongoDB vector store. Joshi explains key agent concepts—planning with chain of thought and react patterns, short-term and long-term memory, and tool creation—then guides attendees through hands-on sections to build the agent step by step. Attendees learn to create agent tools, implement reasoning with react, and add short-term memory persisted to MongoDB. The workshop emphasizes that agents enable complex, multi-step tasks through iterative reasoning, tool use, and memory, trading higher cost and latency for improved accuracy.