Your code tells a betterstory than your resume.
Inspect real repositories, run adaptive AI technical vivas, and share evidence-backed Skill Proof Cards with human oversight.
Static Repo Parse
File Citations
Adaptive Code Viva
Trade-Off Defense
Commit Lineage
Non-Authorship Guard
Test Depth Signal
Edge Assertions
OpenAPI Contracts
Schema Validation
Boilerplate Split
Custom vs Starter
Zero Auto-Reject
Human Oversight
Skill Proof Card
Candidate Controlled
Static Repo Parse
File Citations
Adaptive Code Viva
Trade-Off Defense
Commit Lineage
Non-Authorship Guard
Test Depth Signal
Edge Assertions
OpenAPI Contracts
Schema Validation
Boilerplate Split
Custom vs Starter
Zero Auto-Reject
Human Oversight
Skill Proof Card
Candidate Controlled
From source files directly to code-defense questions.
TalentLens indexes modular boundaries, API error handling, and test coverage—then generates targeted technical viva prompts tied to exact file lines.
Resumes tell you what candidates claim. Not what they can prove.
Keyword-driven screening
Candidates can list technologies without demonstrating practical understanding. Automated filters reward dense buzzword lists while overlooking developers who build clean, maintainable systems without resume-optimized phrasing.
Tutorial projects look alike
Recruiters struggle to distinguish superficial project copies from projects candidates genuinely understand. Two portfolio links with identical README screenshots can mask vastly different levels of architectural comprehension.
Interviews lack consistent evidence
Generic questions and inconsistent assessments make it difficult to compare candidates fairly. Disconnected trivia prompts rarely connect back to the actual software a candidate has designed or debugged.
Technical hiring needs evidence, not just better-looking resumes.
Turn technical claims into inspectable evidence.
TalentLens replaces opaque resume scoring with an auditable verification pipeline—connecting source artifacts, adaptive technical questioning, and explicit rubrics into a structured Skill Proof Card.
Every skill claim should have a reason.
Inspect how TalentLens structures a candidate evaluation—linking skill claims to repository files, code excerpts, technical viva defenses, evaluation timestamps, and explicit observation-vs-inference boundaries.
Alex Morgan
Selected: React · 3 repository file citation(s) + Viva VIVA-Q-02
Click “Explore the Proof Card” or any skill card above to inspect file ranges, code snippets, viva responses, and Verified Observation vs. Inferred Assessment distinctions.
React — Strong evidence
High evidence consistency
Consistent alignment between modular component tree, custom state-synchronization hooks, and accurate trade-off explanation during code defense.
RUB-FE-REACT-04
Candidate explains a selected implementation.
2026-10-18
Sample snapshot · Non-calibrated demo
Repository contains 14 typed React components, 3 custom hooks, and explicit `AbortController` effect cleanup in `useOrderStream.ts`.
Basis: Direct AST & file inspection + recorded viva walkthrough.
Candidate demonstrates strong practical command of React state lifecycles and asynchronous UI synchronization for Full Stack product work.
Basis: Synthesized against rubric RUB-FE-REACT-04 from code + viva consistency.
Feature container & optimistic UI boundary
Separates presentational row rendering from filter state and handles concurrent status transitions cleanly.
Custom subscription & cleanup hook
Uses AbortController teardown, reconnect backoff, and stable ref callbacks to avoid stale closures.
Accessible compound UI component
Implements keyboard Escape handling, aria-labelledby linkage, and controlled/uncontrolled state support.
Inspected hook cleanup and stale-closure prevention referenced during interactive viva:
useEffect(() => {
const controller = new AbortController();
const stream = connectOrderEvents(workspaceId, {
signal: controller.signal,
onEvent: (evt) => latestHandlerRef.current(evt),
});
return () => {
controller.abort();
stream.dispose();
};
}, [workspaceId]);Anchor: src/hooks/useOrderStream.ts:L38–L56
“Why did you store `onEvent` inside `latestHandlerRef` instead of listing it in the `useEffect` dependency array?”
Candidate Response Summary: Explained that inline parent callbacks would retrigger stream teardown and reconnect on every render; storing the latest callback in a ref keeps the WebSocket/SSE subscription stable across renders unless `workspaceId` changes.
Rubric Outcome: Meets rubric criteria for React effect lifecycle mechanics, referential stability, and subscription cleanup.
Illustrative product demonstration using fictional candidate data. Confidence indicators reflect qualitative consistency across mock artifacts and have not been statistically calibrated against live candidate cohorts.
Demonstrate skills beyond resume keywords
Show how you structure real components, APIs, and data flows instead of hoping automated keyword filters parse your PDF.
Defend real engineering decisions
Articulate architectural trade-offs and implementation choices in software you actually built through repository-grounded viva.
Pinpoint exact technical skill gaps
See precisely which role expectations—such as integration testing, schema validation, or concurrency—lack supporting evidence.
Controlled, verifiable evidence sharing
Share verified proof with hiring teams via controlled links without exposing private repositories indiscriminately.
Let your work speak for itself.
Whether you are self-taught, graduating from a lesser-known college, or building serious side projects, TalentLens turns genuine engineering effort into structured proof recruiters can inspect.
1. Connect Repo or Alternative Project
Public GitHub optional · Consent-scoped intake
2. Defend Real Implementation Choices
Explain your architecture + uncover exact skill gaps
3. Share Controlled Proof Links
Attach inspectable proof alongside any application
Evaluate evidence. Make better-informed decisions.
Move past surface-level resume claims and uncalibrated screening calls. Inspect verifiable code evidence, comprehension outcomes, and explicit gaps before committing engineering interview hours.
Explainable Rubric Match
Every matched skill cites exact repo files & viva responses
Template Similarity Context
Separates starter boilerplate from custom modules with uncertainty bounds
Accountable Hiring Control
Zero automated candidate rejections; built to focus engineering interviews
Inspect evidence, not just claims
Review structured repository findings and code-defense summaries without manually cloning and auditing dozens of repositories from scratch.
Uniform, rubric-anchored evaluations
Evaluate applicants for the same role using uniform technical criteria rather than ad-hoc reviewer impressions.
Tailored repository interview kits
Equip hiring managers with tailored follow-up prompts grounded in the candidate’s actual codebase and architectural decisions.
Human oversight of all hiring decisions
Structured assessments operate strictly as transparent decision support—keeping shortlisting and hiring judgment firmly in human hands.
Evidence should be inspectable. Decisions should be accountable.
Automated technical assessment carries real responsibility. TalentLens is architected around inspectable provenance, strict consent boundaries, bias-aware design, and human decision ownership.
Explainability
Show the evidence and reasoning behind assessments.
- Every skill assessment links back to specific files, code ranges, or viva responses.
- Explicitly separates Verified Observations from rubric-based Inferred Assessments.
Privacy
Require consent for repository analysis and protect sensitive candidate information.
- Opt-in candidate authorization required before indexing any repository.
- Private source code is never exposed to third parties without explicit permission.
Fairness
Test for unintended disparities and exclude prohibited features from ranking models.
- Excludes college tier, gender, age, and location from candidate ranking logic.
- Uses controlled evaluation scenarios to audit scoring consistency across cohorts.
Human Oversight
Recruiters remain responsible for hiring decisions.
- Assessments operate strictly as structured decision support, never auto-rejection.
- Template similarity reports include uncertainty and require human interpretation.
Methodological note: TalentLens does not claim guaranteed accuracy, zero bias, or automated legal compliance. Confidence indicators represent qualitative evidence consistency and are meaningful only when supported by human review.
Designed for real technical evaluation workflows.
From early-stage engineering teams to campus placement cohorts, TalentLens adapts to wherever practical capability needs to be demonstrated and inspected.
Early-Stage Engineering Teams
Startups hiring their first engineers
Inspect how applicants actually structure code, handle edge cases, and reason about trade-offs before committing engineering interview hours.
High-Volume Graduate Hiring
Companies screening fresher cohorts
Look beyond institution tier and identical resume templates. Differentiate entry-level applicants through verifiable project substance and code comprehension.
Technical Recruiters & Sourcers
Recruiters evaluating project claims
Bridge the gap between recruiter screening and engineering review with clear evidence summaries, template-similarity context, and tailored interview questions.
Software Engineering Candidates
Candidates preparing for technical interviews
Practice defending your own repositories against adaptive technical questions, uncover blind spots in your codebase, and build stronger evidence before interviewing.
Deliberate architecture from verification to hiring.
TalentLens is structured in progressive phases. Phase 1 establishes the foundational verification engine; Phases 2–4 represent planned capabilities.
Core Skill Verification
End-to-end evidence extraction from repository code, technical viva questioning, and inspectable Skill Proof Cards.
- Consent-scoped GitHub intake & AST mapping
- Repository-grounded AI technical viva
- Inspectable Skill Proof Cards with provenance
Recruiter Intelligence
Connecting verified candidate evidence to role criteria for objective, side-by-side technical evaluation.
- Job description skill & rubric extraction
- Side-by-side candidate comparison matrix
- Tailored engineering interview kits
Advanced Verification
Alternative evidence pathways for proprietary code and deeper template similarity auditing.
- Starter template similarity detection
- Controlled fairness evaluation suites
- Alternative assessment paths for NDA work
Ecosystem Expansion
Integrating structured skill proof into university placement cohorts and ATS partner ecosystems.
- College placement cohort dashboards
- ATS & HRIS webhook integrations
- Longitudinal developer progress tracking
Clear answers on methodology and trust.
How TalentLens evaluates technical evidence, protects candidate code, and keeps hiring decisions accountable to people.
Stop competing on claims. Start proving capability.
Build a clearer picture of what you can do—and give recruiters the evidence to see it.
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