Deepak Arora

Three independent feeds, each at its own sample rate and depth, are decoded into a common int32 domain, resampled to a 48 kHz/32-bit bus, volume-weighted, summed, and written to a NewCodec output (and optionally a stereo WAV). Feed 1 carries an over 20% pitch shift, and it consumes its input faster than real time and ends early, like a tape played fast.

The work is in the plumbing: a pull-based pipeline driven by an injected clock, with four seams (Source, Stage, Sink, ControlSource), clean enough that the file reader, the control input, and the output sink can each be replaced without the mixer noticing.

RESUME-RANKER is a deterministic, explainable, locally-run pipeline. It does not call LLM endpoints and loads its embedding model from the local Hugging Face cache. It ingests resumes and one job description per run. It extracts text from PDF, Word, RTF, and plain-text files, preserving reading order and page locations. The system structures the resume into a CanonicalResume (identity, experience, education, skills, certifications) and compiles the job description into a JobSpec (required/preferred skills, experience targets, knockouts, education, domain). The system is designed to never raise on bad data. Every stage returns a StageResult with a value and a list of diagnostics.

A benchmark-calibrated algorithm for validating AI-generated summaries using noun-based analysis. The system uses the GovReport dataset to establish baseline scores from expert-written summaries, then measures LLaMA 70B accuracy against these calibrated baselines.