Evidence-Based Profiles: Proving What You Can Do

The biggest lie in professional networking is the skills section. Everyone lists the same buzzwords — 'machine learning', 'leadership', 'strategic thinking' — and none of it is verifiable. Recruiters have learned to ignore it entirely. We decided to fix this by building profiles grounded in evidence rather than self-promotion.

GitHub Contribution Heatmaps

When you connect your GitHub account to TandamConnect, we pull your contribution data and render it as a heatmap directly on your profile. This isn't just a vanity metric — it gives recruiters and collaborators an immediate, visual sense of your consistency, your active periods, and the repositories you contribute to. A dense heatmap across multiple repos tells a very different story than sporadic commits to a single fork.

Endorsements That Mean Something

LinkedIn endorsements are a joke. Your college roommate endorses you for 'Kubernetes' and suddenly it's on your profile. On TandamConnect, endorsements come from verified collaborators — people who have actually worked with you on the same projects, in the same organizations. We track collaboration signals and only allow endorsements from people with a real working relationship.

1. Endorser must have shared project or organization history with the endorsee
2. Endorsements are weighted by the endorser's own skill level in that domain
3. Stale endorsements (older than 12 months with no renewed collaboration) are flagged
4. Endorsement counts are secondary to endorser quality — five endorsements from senior engineers outweigh fifty from strangers

The Skill Verification Pipeline

Beyond endorsements and heatmaps, we're building a skill verification pipeline that cross-references multiple signals. If you claim expertise in TypeScript, we look at your commit history, the complexity of your contributions, your code review activity, and whether your endorsed skills align with your actual output. This multi-signal approach makes it dramatically harder to fake competence.

// Example: skill confidence scoring
interface SkillSignal {
  source: "github" | "endorsement" | "project";
  weight: number;
  recency: Date;
}

function computeConfidence(signals: SkillSignal[]): number {
  return signals.reduce((score, s) => {
    const age = Date.now() - s.recency.getTime();
    const decay = Math.exp(-age / (365 * 24 * 60 * 60 * 1000));
    return score + s.weight * decay;
  }, 0);
}

What's Next

We're working on integrating more data sources — GitLab, Bitbucket, Linear, and Jira — so your profile reflects your work wherever it happens. The goal is simple: make it impossible to fake and easy to prove. If you're someone who ships real work, TandamConnect will make that obvious to anyone who looks at your profile.