Ved Nikolic
I ship features that become the reason users buy, stay, and spend more
Montreal, Canada
Proven at consumer scale. Drove capture into the leading purchase driver on Meta's AI glasses with greater than 10x growth in capture usage. Delivered $50M+ incremental revenue and a 400% attach rate lift at Best Buy by rebuilding recommendations on an ML foundation. Scaled a startup to 100% annual growth across six international markets. I work at the intersection of consumer product, AI, wearable hardware, and commerce. The capabilities change; the question doesn't: should this actually ship, and would users come back for it? I sharpen my craft through open source by building evaluation methodology for AI products, a knowledge graph, automated eval loops, and adversarial analysis frameworks.
What I Build at Meta
I lead media capture across Meta's AI wearable devices, from sensing to sharing across camera systems, media quality, computational photography, media processing, power, thermal, systems health, AI model quality, and companion app software. That work spans Meta Ray-Ban Display, Ray-Ban Meta Gen 1 and 2, Oakley Meta HSTN, and Oakley Meta Vanguard. Drove capture into the leading purchase driver on Meta's AI glasses with majority user awareness, retention above program targets, and greater than 10x growth in capture usage. Defined camera media quality strategy, influenced sensor and lens product decisions, drove thermal performance improvements with engineering partners, and built an automated media quality measurement prototype. Partnered with AI teams on ML models from ideation through production handoff. Shifted to in house media processing, achieving sub second latency and >99% success rate across 7+ device SKUs. Shipped the largest feature expansion in product history ahead of schedule.
How I Work
Find the real constraint
The product problem is rarely what it looks like. At Meta, the constraint was not AI quality, it was capture latency: sub-second response and >99% success rate before any AI feature mattered. At Best Buy, the constraint was not traffic, it was attach: rebuilding recommendations on ML drove a 400% increase.
Define why before how
A prototype shows what is possible. The spec defines what good looks like: which tradeoffs you accept, which you do not, and why. That clarity turned a dozen features into the largest expansion in Meta capture history, shipped ahead of schedule. Extreme clarity on the why and what is what makes the how high quality.
Stress-test before building
A/B tested every feature at Meta across wearable hardware, AI models, and companion app software. Ran recommendation experiments driving $9M+ incremental revenue at Best Buy. Built evaluation methodology proven across hundreds of LLM evals. Red-team from 12 disciplinary lenses. The gates that catch bad tradeoffs have to exist before the work starts.
Ship across boundaries
$50M+ revenue across Best Buy product lines. 3x scale across 4+ organizations at Meta. 0-to-1 products across 4 countries. 100% annual growth across 6 international markets. The hardest product problems cross team, domain, and geography lines.
Open Source Tools
Tools that solve real problems I hit as a PM. All open source, all LLM-agnostic.
Cortex
The memory layer AI coding tools ship without. Routes session context to structured destinations and surfaces cross-project patterns via a knowledge graph.
cortex save "chose connection pooling over per-request" cortex reflect # surfaces patterns across all projects
Evalgate
Evaluation methodology and tooling for AI products. Schema normalization, constraint gates, variance-aware regression detection, and cost/quality tradeoff measurement across models.
Principles from hundreds of LLM evals: - Atomic evals (one assertion per check) - Constraint gates (one failure = zero score) - LLM-as-judge variance: 5-7.5%
PM AutoResearch
Automated eval loop for product documents. Define binary pass/fail criteria, iterate, keep only improvements. Git tracks every round.
Score: 17% --> 94% (4 rounds) Evals: 19 binary criteria, locked harness
Red-Team
Adversarial analysis from 12 disciplinary lenses. Point it at any product artifact and get severity-ranked findings with grounding and worst-case scenarios.
Agents: Engineering, UXR, PMM, Privacy, Legal, Ethics, Security, Finance, Data, Design, Ops, Localization
Steelman
The blue-team to Red-Team's red-team. Takes weaknesses and converts them into positioning advantages through 6 analytical lenses.
Lenses: Strengthen weakest argument, Reframe positioning, Evidence, Expand moat, Simplify, Second-order
Stakeholder Radar
Evidence-based stakeholder profiles from real artifacts. Simulates document reviews before they happen.
Input: meeting notes, Slack threads, email chains, doc comments Output: per-reviewer predicted feedback
Career
- Drove capture into the leading purchase driver on Meta's AI glasses with majority user awareness, retention above program targets, and greater than 10x growth in capture usage
- Defined camera media quality strategy, influenced sensor and lens product decisions, drove thermal performance improvements with engineering partners, and built an automated media quality measurement prototype
- Led the largest feature expansion in capture history ahead of schedule, shipping Ultra HD (3K) video, hyperlapse, slow motion, 60fps, and adjustable stabilization among others
- Partnered with AI team from model ideation through production handoff, defining performance requirements and quality metrics for ML models
- Shifted to in house media processing, achieving sub second latency and >99% success rate while eliminating external licensing dependency across 7+ device SKUs
- Owned privacy and compliance reviews for AI driven features, documenting user journeys and data flows for cross functional sign off
- Scaled product support 3x (4 engineering managers, 40+ engineers) while shipping largest feature expansion in product history ahead of schedule
- Delivered $50M+ combined incremental annual revenue across recommendation systems and in home service products
- Rebuilt recommendations architecture: 400% increase in attach rates, $2M+ annual revenue
- Expanded in home services online: $30M+ expected annual value
- Founded consultancy delivering 0-to-1 solutions across fintech and ecommerce for clients in 4 countries
- Led discovery through launch across 6+ concurrent client engagements as sole product person
- Mentored teams across 3 time zones on product systems, data analysis, and market research
- Drove 100% annual growth across 6 global markets over 3 years
- Built 3 platforms from scratch: consumer ecommerce, gamified mobile app, custom ERP/CRM
- Increased subscription retention 60% through loyalty and gamification features
- Drove 20% year over year growth in mature markets through integrated marketing strategies
- Delivered product training and keynotes to audiences of 500+ nationwide
- Managed public relations, crisis communications, and brand visibility campaigns