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SF AI Tech Stack Hack Night

lumaHosted on Luma

Fetched about 2 hours ago

Friday, July 24, 2026

to Friday, July 24, 2026

Artificial Intelligence

Event Type

in person

78

Participants

7

Est. Projects

SF AI Tech Stack Hack Night An evening for builders working across the modern AI application stack: agents, data pipelines, retrieval, evals, observability, model APIs, orchestration, deployment, and developer tools. We are gathering at Bright Data's Web Data Loft for a practical hack night focused on what people are actually using to build AI products in San Francisco right now. What to Expect Short framing around the AI tech stack themeOpen build time for practical experiments and demosConversations around tools, architecture, patterns, and tradeoffsLightweight demos or show-and-tell near the end of the nightLight food, drinks, and a room of technical builders Good Project Fits Agent workflows and orchestrationWeb data and research automationRetrieval, evals, observability, and reliability toolingModel API, inference, and deployment experimentsDeveloper tools and internal AI workflows Who Should Apply This is for engineers, founders, product builders, technical operators, and students building or seriously experimenting with AI systems. Admission is free and approval-based because space is limited. When: Thursday, July 23, 2026 | 5:00 PM - 9:00 PM PTWhere: Bright Data Web Data Loft, 625 2nd St, San Francisco, CA

Judge Accessibility

Organizer email available25/25
Student-run event15/15
Actively looking for judges25/25
Small event (120 participants)10/10
No corporate sponsors10/10
New or emerging organizer10/10
Public registration available5/5
Online format (judge from anywhere)10/10

Top signals

Organizer email available
Student-run event
Actively looking for judges

Organizers

Alex Johnson

alex@example.org

Jamie Rivera

jamie@example.org

Sam Chen

sam@example.org

Estimated Audience

Mostly Students
ExperienceStudent
OccupationStudents
Beginner Friendly
Women in Tech

Technical Focus

AI95%
Web80%
Mobile25%

Industries

Healthcare
Education
Climate

Technologies

Python
React
OpenAI

Why this estimate

  • • Hosted by a university
  • • Open to students
  • • MLH member event

Estimate inferred from event metadata, not actual attendee data.

Quality Score

Quality Score

72/100
High confidence
Organiser16/20
Event Maturity14/20
Sponsors18/25
Participants12/20
Operations12/15

Why this score

Strong organiser track record
Returning event
Well-sponsored

Missing data

Prize details
Code of conduct