Build a Coding Agent Workshop

Join this online San Francisco AI Meetup on Feb 5, 2026, 9-11 AM PT for four talks on AI, ML and computer vision, with practical demos and live Q&A. This virtual gathering is designed for data scientists, ML engineers, and researchers who want to explore cutting-edge topics in AI and visual intelligence while connecting with a global community. What to expect: - Four expert-led talks spanning visual anomaly detection, data-centric pretraining for speech-language models, synthetic data pipelines, and TensorRT optimization for CV workloads. - Real-world applications and actionable takeaways you can apply to manufacturing, healthcare imaging, quality control, or any ML/CV project. - A live Q&A session with speakers, plus opportunities to network with fellow practitioners after the talks. Talk lineup (as described on the event page): - Unlocking Visual Anomaly Detection: Navigating Challenges and Pioneering with Vision-Language Models. Focused on VAD fundamentals, challenges like data scarcity and domain shifts, and how vision-language models can enable explainable, threshold-free detection. This talk targets researchers and practitioners working on AI-driven inspection and multimodal reasoning. - Data-Centric Lessons To Improve Speech-Language Pretraining. Examines how data processing, curation, and synthetic data influence SpeechLM performance. The presenters outline practical data strategies to improve pretraining results for spoken language models. - A Practical Pipeline for Synthetic Data with Nano Banana Pro + FiftyOne. A hands-on walkthrough for generating targeted synthetic data, enriching it with metadata, and managing it in FiftyOne to close real vision gaps and boost model robustness. - Making Computer Vision Models Faster: An Introduction to TensorRT Optimization. An practical guide to accelerating CV inference, covering layer fusion, precision calibration, memory management, and when to apply TensorRT for production-ready performance. About the speakers: - Hossein Kashiani, PhD student at Clemson, focuses on generalizable and trustworthy AI with applications across anomaly detection and multimodal vision. - Vishaal Udandarao, PhD student (ELLIS) researching foundation models and their generalisation in vision-language contexts. - Adonai Vera, ML Engineer & DevRel at Voxel51, with extensive CV experience and a passion for open, production-ready AI. - Tushar Gadhiya, Technical Lead in deep learning and CV, with a track record in academia and indu

SFPL presents a beginner-friendly, four-session workshop designed to demystify AI and robotics through no-code, hands-on learning. This program offers a practical, engaging introduction to how modern technologies in AI and robotics are shaping our world, with remote access to real robots and graphical, code-free interaction. The course runs as a 4-session series across February 2026, giving newcomers a solid foundation in digital literacy and the basics of AI robotics. What you’ll get - A practical, no-code approach to understanding AI and robotics, suitable for absolute beginners with no programming experience - Teleoperating a remote robot arm and doing simple tasks using graphical interfaces - An introduction to how computers perceive the world, including basic computer vision concepts and image analysis - Hands-on activities that connect concepts like APIs, connectivity, and path planning to real-world robot behavior - Remote live instruction provided by Scaleup Robotics AB, with remote robot access for session activities Session schedule (all dates in 2026): - Session 1 — February 7: Introduction to Robotics, core AI technologies, remote robot teleoperation basics, and practice making a robot follow patterns - Session 2 — February 14: Connectivity, what a Web API is, how robots connect via API, coordinate systems, and basic graphical programming (Blockly) with a dance and patrol challenges - Session 3 — February 21: How robots see the world, image pixels, background removal, contours and center-point detection, and a pick-and-place challenge - Session 4 — February 28: Object detection in camera images, AI robotics and computer vision experiments, and reflections on how robotics and AI may shape the world Format and accessibility - The program is hosted by the San Francisco Public Library (SFPL) with remote live instruction via video link and remote robot access - In-person participation is available at SFPL locations; all SFPL locations are wheelchair accessible - All programs are drop-in unless otherwise noted, though reservations are requested for the full series. Call 415-557-4388 for series reservations or accessibility accommodations (availability may vary) - The program is conducted in English unless otherwise noted - The session may be filmed or photographed for archival and promotional purposes; participants can request not to be photographed by informing staff or the photographer About the providers and how to participate - This course is

Explore the intersection of no-code platforms and AI automation in San Francisco. Demos, track sessions, and networking with builders and founders — hands-on shows of visual builders, automations and agent use-cases for makers, founders and product teams.

First 160 characters summarize the event for meta purposes: Learn to build reliable AI agents with LangChain and Oracle AI Database in a hands-on SF workshop by Oracle and AICamp. Overview Oracle and AICamp present a focused, interactive workshop in San Francisco designed for developers who want to elevate AI agent reliability beyond what a single-turn interaction can offer. The session targets memory-centric agent design, showing how to design, implement, and evaluate memory-enabled agents that maintain continuity, reduce drift, and stay grounded over multiple turns. This is a practical, end-to-end lab: from memory concepts to a working reference implementation you can reuse in your own projects. What you’ll learn and do - Understand Agent Memory and why it matters beyond ordinary chat history - Build practical memory abstractions: short-term working memory, semantic memory, episodic memory, and lightweight procedural memory - Implement ingestion, storage, retrieval, summarization, and managed forgetting patterns to keep memory fresh and useful - Integrate real-time internet lookups via Tavily and use Oracle AI Database as the agent’s Memory Core for unified retrieval and persistence - Apply context engineering, prompt engineering, and memory engineering to reduce drift, improve grounding, and maintain consistency across turns - End with a working reference implementation and reusable patterns for scalable agent scaffolding and evaluation harnesses focused on reliability and believability Who should attend This hands-on workshop is ideal for AI developers, AI engineers, AI practitioners, and Python developers who want to design more robust AI agents and explore memory-centric architectures. Format and logistics - Date and time: Thu, Feb 19, 2026, 5:30 PM (local time, Pacific) - Location: San Francisco, CA (in-person) - Registration is required on an external site for admission - Organisers: Oracle and AICamp in collaboration with the SF Bay AI community; the meetup page notes a large, active community (SF Bay Area and global reach) - Registration/Topic call for speakers: topics welcome via the event page Why this stands out If you’ve built AI agents that perform well in a single-turn chat but falter across sessions, this workshop provides a concrete framework for turning stateless loops into memory-augmented agents. Expect deep dives into memory architectures, practical design patterns, and a reproducible setup you can adapt to your own projects. Ex

Charter AI SF Summit 2026 is a hybrid, in-person and virtual event in San Francisco designed for leaders and practitioners eager to move AI from pilot projects to real transformation. On February 24, 2026, the CharterWorks team invites attendees to hear from workplace leaders who are redesigning how work gets done with AI, with practical frameworks and real-world case studies you can apply in your own teams. The day runs 9:00 AM–3:00 PM PST and features a mix of keynote-style remarks, practitioner-led sessions, and interactive discussions that bridge strategy and execution. Who should attend: senior leaders who shape talent and technology decisions, including CHROs, CPOs, CTOs, CIOs, COOs, SVPs, VPs, department heads, and AI transformation leads. You’ll gain clarity on what works today, learn concrete steps to drive adoption and impact, and walk away with leadership approaches to guide teams through AI-driven change. Agenda highlights (selected): - Opening remarks by Charter leadership (Kevin Delaney, Co-founder and editor-in-chief, Charter) - How Walmart is helping millions of workers move to the AI-enabled future (Donna Morris, Chief People Officer, Walmart) - From pilots to impact: translating AI experiments into measurable ROI (Brandon Sammut, Zapier; Hannah Pritchett, Anthropic) - Rethinking the org chart in the age of AI (Aneesh Raman, LinkedIn; Iain Roberts, Airbnb) - Co-leading AI transformation: HR and tech joint leadership (Rani Johnson, Workday; Jessica Swank, Box) - AI beyond basics: advanced use cases and practical techniques (Helen Kupp, Women Defining AI) - Working with AI agents: the future of collaboration (Amjad Masad, Replit) - What journalists are hearing about AI’s impact (Jessica Lessin, The Information; Casey Newton, Platformer) - Measuring AI’s real impact on work (Nicholas Bloom, Stanford) - Redesigning entry-level roles for the AI era (Matt Beane, SkillBench; Fiona Tan, Wayfair) - Beyond reskilling: addressing AI displacement (Sal Khan, Khan Academy) Topics include automation, AI agents, and startups, with sessions aimed at converting AI investments into tangible business outcomes. The event is presented by CharterWorks, a media and research company focused on the future of work, and includes multiple sessions featuring industry leaders and practitioners who’ll share actionable insights and frameworks. Key details: - Date: February 24, 2026 - Time: 9:00 AM–3:00 PM PST - Format: In-person in San Francisco or virtua

n8n's Angel Menendez and ML engineer Mary Newhauser show how to build Retrieval-Augmented Generation (RAG) workflows in n8n using Weaviate for vector search. Learn when to use RAG and practical tips to improve performance and scalability.