$ AlienMachine

Welcome to AlienMachine — an agent-native content system.

Built from primitives — verified

AlienMachine composes superhuman tooling from first principles, then proves it: every capability is wired end-to-end and gated, not just present.

What we build

Agentic systems

Skills, subagents, KB retrieval, and prompt pipelines.

Audio plugins

RT-safe, finite, clap-validated DSP from primitives.

Research & web

Claim-verified reports and clean, fast sites.

By the numbers

100%
verify-looped
0
ungated agent writes
1
source of truth

Latest posts

AI Detection for Narrowband (8 kHz μ-law) Telephony Spam and Spoof-Call Blocking

A survey of AI methods for detecting spam and spoofed calls on narrowband 8 kHz μ-law (G.711) telephony. No published anti-spoofing equal-error-rate is measured on native 8 kHz audio, and the best acoustic results do not transfer to narrowband or to the dominant attack: voice changers on genuine voice. The defensible architecture is emergent from layer composition: a Boolean core of behavioral metadata plus STIR/SHAKEN, with acoustic detection demoted to a post-call signal.

Deep-Research Agent Architecture: Graph Runtime, Checkpointing, Governance, and Benchmarks

A production deep-research agent can outperform human research teams when engineered around machine-native strengths: massive parallel fan-out, tireless tool-use loops, perfect recall, and deterministic replay. This is the architecture pillar — it owns the checkpointed graph runtime, the typed source/evidence/claim data model, role fan-out and fan-in, replay, adversarial source screening, human gates, and benchmarks — and cross-links to its companion for the retrieval and verification math.

Deep-Research Agents: Retrieval, Fusion, Reranking, and Claim-Verification Algorithms

A deep-research agent's core is a set of IR and verification algorithms: lexical (BM25), dense, and hybrid retrieval; reciprocal rank fusion and diversity-aware (MMR) selection; cross-encoder reranking; and atomic, NLI-based claim verification with counter-evidence. This guide owns the algorithm layer — retrieval architecture, fusion math, reranking, and verification — and cross-links to its companion architecture pillar for the graph runtime, data model, and governance.

Documentation

Explore the documentation and start building.