V
VEBLEN

QUANT RECRUITING

Deploy agents for signal, humans for conviction alignment.

Deploying autonomous agent swarms to uncover high-signal engineering talent hidden within implementation details.

SCANNING GITHUB REPOSITORIES
ANALYZING COMMIT ARCHITECTURE
CALCULATING CAPABILITY SCORES
01 //

CORE LOGIC

MANUAL SCANNING FAILED

Human bandwidth is insufficient for repository-scale analysis. Signal is distributed across sparse data points: commit diffs, PR comments, and architectural patterns.

- method: keyword search
- result: noise amplification
+ method: semantic analysis
+ result: signal extraction

AGENT SWARM TOPOLOGY

Small, highly specialized agent networks outperform monolithic LLMs. Our architecture mimics elite small teams: 5-7 specialized agents with distinct evaluation heuristics.

02 //

INVERSE FUNNEL

SPECIFICITY → ABUNDANCE

Conventional filtering is destructive. We utilize constructive search: start with a single high-fidelity seed profile and expand via vector similarity in the capability space.

  • Input: 1x Deeply Understood Engineer
  • Process: Vector Pattern Matching
  • Output: 50x High-Confidence Matches
VISUALIZATION
03 //

INTELLIGENT AGENTS

DISTRIBUTED INTELLIGENCE

We have built intelligent, high-precision agents to work together rather than relying on a single monolithic agent or keyword searches posing as AI.

These specialized agents encode the specific sourcing heuristics of our expert team—capturing human intelligence to identify signal where others only see noise.

  • Monolithic AgentLOW FIDELITY
  • Keyword SearchHIGH NOISE
  • Veblen SwarmHIGH PRECISION
Swarm Topology
TARGET
AI: SCANNER
HUMAN: EXPERT
AI: METRICS
HUMAN: INTUITION
AI: SYNTAX
HUMAN: CONTEXT
FIG 3.1: HUMAN-AGENT SYMBIOSIS

Rigorous analysis over volume.