Course Trailer
Student understands the course promise and the networking operating loop.
0 browser capture targets · 2 worksheet/resource files
Self-serve course · $99 standalone · $129 with kit
AI Networking Sprint is a practical course for college seniors and recent grads who know applications alone are not enough. In one week, build a lane, a people map, low-pressure asks, application-contact pairs, conversation prep, a follow-up queue, and a review loop.
What you get
The course teaches the weekly networking loop. The bundle adds the job-search files that hold the map, tracker, prompts, and review rhythm.
Course outcome
The sprint gives saved applications a people map, low-pressure messages, conversation prep, follow-up dates, and a Friday review.
Student workflow
Reduce a scattered search to one useful role family, organization type, problem space, or application cluster.
Use LinkedIn-style browsing and structured research to build a first list of reachable, relevant people.
Draft short messages that create a conversation around learning, context, and next steps.
Treat applications as one part of the system, not a replacement for people, context, and follow-up.
Use replies, silence, conversations, and next actions to choose the next bottleneck.
Preview lesson
Lesson 00 is available as a static preview. It shows the course promise and the weekly operating loop while browser walkthroughs, narration, captions, and final exports move toward the complete media package.
Lesson map
Each lesson points to one concrete action: choose the lane, find people, write the ask, prepare the conversation, follow up, and review.
Student understands the course promise and the networking operating loop.
0 browser capture targets · 2 worksheet/resource files
Student creates one weekly sprint lane and understands the loop.
2 browser capture targets · 2 worksheet/resource files
Student turns broad interests into 2-4 practical role-family hypotheses.
1 browser capture target · 1 worksheet/resource file
Student builds a first 10-15 person list around a target lane.
2 browser capture targets · 2 worksheet/resource files
Student chooses first contacts by relevance, reachability, and question quality.
1 browser capture target · 2 worksheet/resource files
Student drafts and sends/queues three learning-oriented messages.
1 browser capture target · 2 worksheet/resource files
Student prepares questions and a next-step ask for a target conversation.
1 browser capture target · 1 worksheet/resource file
Student pairs meaningful applications with a contact strategy.
1 browser capture target · 2 worksheet/resource files
Student converts conversation learning into ethical next actions.
1 browser capture target · 2 worksheet/resource files
Student creates a follow-up queue and next-step messages.
1 browser capture target · 2 worksheet/resource files
Student reviews the sprint and chooses the next bottleneck to address.
1 browser capture target · 2 worksheet/resource files
Synthetic sprint preview
This example is public-safe and synthetic. It previews the student behavior the course is designed to produce: one lane, mapped people, low-pressure asks, application-contact pairing, prep, follow-up, and review.
Day 1
Choose one target lane and three real targets.
Day 2
Build a people map with near-peers, alumni, team-adjacent operators, and hiring-team signals.
Day 3
Write three low-pressure asks that request context instead of favors.
Day 4
Pair one application with a contact strategy.
Day 5
Prepare five questions for one target conversation.
Day 6
Create a follow-up queue.
Day 7
Review replies, silence, applications, and artifact gaps.
Browser walkthrough
The paid lesson will show how to move from a target lane to a small list of reachable people, then into tracker rows and low-pressure asks. Public previews use staged examples and avoid private student data.
Search
Start from one lane phrase, then search for near-peers, alumni, team-adjacent operators, and role-adjacent profiles.
Qualify
Save people only when there is a specific connection angle: school, project, role path, employer, market, or application target.
Extract
Turn each profile into one tracker row with source, why this person matters, what to ask, and the next action date.
Draft
Use AI to create a short learning ask, then remove generic praise, pressure, and premature referral language.
Launch offer
The course stays self-serve. The strongest first sale pairs it with the Job Search OS Kit so students have the tracker, people map, prompts, and weekly review assets beside the lessons.
Source discipline
The source work stays behind the scenes. The buyer-facing promise is the weekly system: lane, people, messages, application-contact map, conversation prep, follow-up, and review.
380
source-safe rows reviewed
231
pattern groups distilled
374
networking signals checked
11
lessons in the sprint
Voice-agent bridge
The networking intake schema produces a diagnosed stage, missing context, reviewed tasks, and recommended lesson IDs. This keeps the course, async coaching inbox, and reviewed client notes aligned.
Need the full job-search system?