CHAPTERS
- 0:00 – 1:19
Why “vibe coding” is becoming a core AI PM interview round
Ankit and Aakash set the context for a fast-emerging interview format that blends product sense with hands-on AI prototyping. They contrast it with traditional PM and coding rounds and explain why feature-building on existing products is the most realistic prompt style.
- •Vibe coding = hybrid of product thinking + rapid prototype creation
- •AI PM interview loops increasingly assess tool fluency via screen share
- •Most real prompts are feature additions to mature products (not greenfield)
- •High compensation and high bar for AI-native execution
- 1:19 – 2:35
The prompt: ship an AI-first LinkedIn feature that helps people keep in touch
Ankit lays out the mock interview: design and prototype a LinkedIn feature that reduces barriers to networking and increases engagement. The constraints are explicit: it must be AI-first, and the candidate must use AI tools live to prototype.
- •Assume PM role at LinkedIn; focus on staying in touch with network
- •AI should reduce the cognitive/awkwardness barriers to outreach
- •Interview structure: ~30 minutes prototyping inside a 45-minute session
- •Outcome expectation: concept + tangible prototype within LinkedIn constraints
- 2:35 – 4:20
Clarifying the business goal: move users from passive consumption to active networking
Aakash probes for the “why now” and success criteria, anchoring to LinkedIn’s mission and platform engagement. Ankit frames the objective as lowering cognitive load to drive retention and active participation in the network.
- •Goal: convert passive content consumers into active network engagers
- •Core lever: reduce cognitive load / barriers to initiating contact
- •Tie to mission: economic opportunity via engagement and retention
- •Early agreement on what success should influence (engagement → retention)
- 4:20 – 9:37
Speed-running product sense: segmenting users and picking a focus
Aakash quickly applies a traditional PM segmentation approach to identify who to build for. He compares job seekers vs. ‘professional profile maintainers,’ using rough sizing to prioritize which cohorts are both large and addressable.
- •Segmentation with estimated sizing to guide prioritization
- •Job seekers: referrals, target-company connections, learning from seniors
- •Professional-profile maintainers: large cohort with overlapping needs
- •Decides to focus on soft job searchers / profile maintainers as core
- 9:37 – 11:06
Shifting from user buckets to the real hurdle: why outreach feels awkward
Ankit redirects from segmentation to the underlying friction that prevents outreach. Aakash articulates the key blockers: forgetting relationship context, lacking a reason to reach out, and difficulty writing a non-awkward message.
- •Primary hurdle: no genuine/non-awkward reason to reach out
- •Weak ties matter most; context is missing or stale
- •Users forget how they know someone and what to say
- •Problem framing: ‘cold outreach is hard’ + ‘notifications aren’t enough’
- 11:06 – 13:02
Live LinkedIn workflow audit: discovering friction in the current product
Aakash opens LinkedIn and audits the existing ‘My Network’ and ‘Catch Up’ surfaces to find concrete issues. He surfaces broken/empty states, confusing notification coupling, and missing relationship context when viewing connections.
- •Catch Up empty state and unclear triggers for reconnecting
- •Job changes/birthdays tied oddly to notification settings
- •Broken navigation and poor discoverability of who to contact
- •Profile view lacks “why/how we’re connected” and recent interaction cues
- 13:02 – 17:13
From audit to solution direction: recommend weak ties + provide context + draft outreach
They converge on a solution direction: proactively surface “warm” signals for weak ties and generate context-aware draft messages. Ankit emphasizes that weak ties are the value and the product must supply genuine reasons plus better contextual writing than today’s AI message helper.
- •Two-part product need: (1) who to contact (2) what context to use
- •AI messaging exists but feels robotic and lacks relationship context
- •Feature should turn weak-tie prompts into authentic outreach moments
- •Focus on warmth + contextuality as core design principles
- 17:13 – 21:02
Lightweight PRD in minutes: goals, non-goals, metrics, and definitions
Aakash sketches a minimal PRD optimized for prototyping speed: define weak connections, outline non-goals to prevent spam, and set leading/lagging metrics. Ankit adds nuance: be explicit about what context is available on LinkedIn vs. external relationship history.
- •Define weak connection (e.g., no interaction in last ~3 months)
- •Non-goals: spam, out-of-context outreach, messaging strangers
- •Leading metrics: opens/reads, responses; lagging: retention/engagement
- •Context sources: shared employers/schools/locations + engagement signals
- 21:02 – 22:35
Curveball: building a prototyping ‘harness’—design system, strategy, prior features
Ankit challenges Aakash to explain what he’d pre-load to prototype faster. Aakash proposes a reusable harness: LinkedIn design system, strategy/feature context, and an AI prototyping skill pack, then starts assembling it in Claude Code using screenshots as grounding.
- •Top accelerants: design system, product strategy, prior feature context
- •Create a context library and reusable prompt scaffolding
- •Use screenshots to anchor UI fidelity and reduce hallucination
- •Treat AI as ‘middle 60%’: humans do upfront thinking + final refinement
- 22:35 – 26:30
Parallel tool workflow: Claude Chat → Magic Patterns/Lovable/Claude Code
Aakash demonstrates a multi-tool pipeline: use Claude Chat to generate a strong prototyping prompt, then run it across multiple prototyping tools in parallel. He explains why he separates ideation (more trusted model) from UI generation (faster prototyping tools).
- •One prompt, many tools: compare outputs quickly (front-end speed)
- •Magic Patterns for fast front-end; Lovable for build + GitHub flow
- •Claude Code for harness + code-level control with design system
- •Parallelization offsets latency and increases iteration speed
- 26:30 – 30:28
What makes a strong prototyping prompt (and why design system comes first)
Ankit asks for a quick rubric on prompt quality. Aakash outlines four ingredients—clear steps, rich context, explicit output format, and explicit exclusions—and reinforces the value of feeding design-system guidance early, especially for tools without a harness.
- •Prompt quality rubric: steps, context, outputs, and don’ts
- •Iterate prompts in-flight; stop and refine when fidelity is lacking
- •Define/learn the design system before requesting full UI generation
- •Use stronger models for concept generation; tools for rendering
- 30:28 – 34:18
Evaluating five UI concepts and selecting the most promising surfaces
They generate multiple UI concepts (home feed surfaces, network inbox-style tab, constellation map, profile context layers, etc.). Aakash triages them using innovation vs. impact vs. surface area, deciding the best approach combines multiple surfaces rather than a single standalone view.
- •Concept set includes: warm-signal feed, reconnect queue, constellation map
- •Evaluation lens: innovation, success impact, and available surface volume
- •Conclusion: combine concepts (surfaces + context layer) for best outcome
- •Prioritize pragmatic high-volume surfaces over flashy but niche UI
- 34:18 – 37:11
Prototype review: Warm Threads, Reconnect Queue, Drafts-first, and Constellation
Aakash reviews the prototypes produced in Claude Code, assessing fidelity to LinkedIn’s design language and strength of contextual outreach. He unexpectedly finds the practical surfaces (Warm Threads / Reconnect Queue) outperform the ‘killer’ constellation idea once rendered.
- •Warm Threads: strong design-system adherence and high usability
- •Reconnect Queue: clear weekly prioritization with context for outreach
- •Drafts-first: leverages context to reduce message-writing friction
- •Constellation map: intriguing but less compelling in real UI form
- 37:11 – 47:19
Scoring and feedback: bucket-by-bucket evaluation + timing advice
Ankit scores Aakash across the interview framework (user, problem, solution, PRD, prompt, tools, backend) and explains what mattered most: fast-but-deep thinking, real product auditing, and tool fluency. Aakash closes with a meta framework (UPS PPPB) and guidance to practice timing, workflows, and crisp communication.
- •Strongest signals: live audit, harness setup, multi-tool prototyping workflow
- •Improvement: compress early product-sense time to allocate more prototyping time
- •Prompting: clear structure and ability to explain why it’s good
- •Final advice: practice timing, tool confidence, and crisp explanation
