WhatA hiring platform that replaces rigid signal-based interview loops with async, AI-evaluated work samples and structured team-fit assessments that actually predict on-the-job success.
SignalEngineers describe Meta's 6-7 round interview process as deeply inefficient theater — interviewers hunt for predetermined signals while candidates perform, and actual job performance is a coin flip regardless of interview outcome.
Why NowCompanies simultaneously laying off thousands and needing to rehire in 6-18 months creates urgent demand for faster, cheaper, higher-signal hiring — and LLMs can now credibly evaluate technical work samples.
MarketEnterprise technical recruiting; companies spend $4K-$20K per engineering hire through recruiters and lost interviewer time. Incumbents like HackerRank and Karat only address the coding screen slice.
MoatProprietary dataset linking interview performance to actual job outcomes, which no incumbent currently tracks end-to-end, creating a compounding accuracy advantage.
Resource-Based Cloud with Pay-Per-Capacity PricingP5/10A cloud platform where you buy a pool of compute resources (CPU, RAM, disk, IOPS) and spin up as many VMs or containers as fit within that pool, rather than paying per-VM with inflated defaults.
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Managed Self-Hosted Infrastructure Toolkit for Small TeamsC5/10An opinionated, pre-configured toolkit that sets up HA Postgres, autoscaling, backups, and monitoring on cheap VPS providers like Hetzner — giving teams 90% of AWS managed services at 10% of the cost.
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Universal MCP Bridge for Desktop AI AppsC6/10A lightweight local daemon that provides native MCP (Model Context Protocol) support to any AI desktop application, handling local filesystem access, tool routing, and authentication without requiring ngrok or manual tunneling.