# patrickaudley.com — LLM context (full corpus) > Complete content as machine-readable Markdown. Per the llmstxt.org spec, this is the long-form companion to [/llms.txt](https://patrickaudley.com/llms.txt). Canonical site: . License: Creative Commons BY-NC-SA CAv2.5. --- ## Home page # Patrick Colm Audley *Hacker · Full-Spectrum Technologist · Polymath* A seasoned technologist with thirty years of international startup experience and a broad palette of scientific, business and computing skills. I work the long boundary between deep technical research and pragmatic commercial execution — whichever side a problem needs. Currently I split my time between [Blackcat Informatics® Inc.](https://blackcatinformatics.ca/) — the consultancy I've run since 1994 — and applied research at the intersection of Topological Data Analysis, manifold learning, Neuro-symbolic artificial intelligence, and large-language-model semantics. The through-line across more than thirty years has been the same: find elegant solutions to complex technical problems, exploit problem isomorphisms, and ship code that does what it says on the tin. Earlier in my career I was instrumental to numerous tech launches, acquisitions, pivots, and exits across telephony, high-performance computing, crowdsourcing, bioinformatics, and security — over twenty-five startup launches across the years. I've moved high-throughput systems to the cloud (a social platform supporting massive concurrent counts in the early 2010s, scientific analysis pipelines for genetics and satellite imagery, multi-continent high-resiliency financial deployments) and learned, every time, that minimum-footprint systems built on open-source primitives outlast the elaborately-architected ones. I'm an active member of the local Vancouver and Alberta startup and development communities and enjoy fostering new talent. Dangle a good problem in front of me, tempt me with a cup of coffee, and I'll yak your ear off on just about any topic. Spouse: Erin Audley. ### Currently - **Helping organisations shape their AI and large-data strategies** through the current technology inflection — not the buzzword version, the version where you have to actually decide what to build, what to wait on, and what to ignore. - **Working at the convergence of energy, compute, and autonomous systems** — applying AI and systems engineering to hard, commercially consequential problems in power generation, cooling, resilient infrastructure, and climate technology. - **Building the mechanics layer for neuro-symbolic AI** — combining higher-order semantic spaces, latent-space dynamics, topological and manifold structure, and explicit logic. [Logit Loom](https://github.com/paudley/logit-loom) makes token generation programmable and inspectable; [GMEOW](https://github.com/Blackcat-Informatics/gmeow-ontology) supplies attributed claims, reasoning, revision, and portable memory. - **Building [PurRDF](https://blackcatinformatics.ca/purrdf/) ** — the RDF 1.2 toolkit, now at 1.0. One from-scratch Rust engine carried byte for byte into Python, WebAssembly/JavaScript, and C: native codecs, canonicalization, SPARQL 1.1/1.2, SHACL, ShEx, and entailment through Web Ontology Language 2 Description Logic. Ranked full-text search, spatial predicates, and vector similarity are answered from SPARQL itself, in-process. The premise: a graph should mean the same thing in every runtime, and it should not need a PostgreSQL instance standing beside it. - **Maintaining a handful of small libraries** — the structured-error library [e](https://github.com/paudley/e) and a fast C++ [LRU cache](https://github.com/paudley/lru_cache) first written in 2004 and still useful. ### Open Source - **[PurRDF](https://github.com/Blackcat-Informatics/purrdf)** (Rust / Python / WebAssembly / C) — Developed as part of our work on neuro-symbolic engines, PurRDF provides the public, open-source foundations for representing, querying and reasoning over structured knowledge. It is designed to be useful independently of that work. PurRDF brings graph queries, logical reasoning, validation and retrieval into one coherent RDF 1.2 toolkit. Text search, spatial relationships and vector similarity can participate in the same query over the same dataset, without assembling a second database stack. Correctness includes knowing what an answer establishes. Reasoning produces reports of its completeness and limits; governed queries distinguish complete answers from partial results. The same Rust engine runs natively and in WebAssembly, with Python, JavaScript and C interfaces. - **[Logit Loom](https://github.com/paudley/logit-loom)** (Rust) — Rust toolkit for observing, transforming, steering, stopping, resuming, and accounting for token generation. Logit Loom provides backend-neutral contracts for ordered logit transforms, exact-byte token observers, bounded generation controls, content-bound plans and receipts, plus a llama.cpp adapter with identity-bound checkpoints and explicitly scoped steering. - **[gmeow](https://github.com/paudley/gmeow)** (Python) — Local Gmail intelligence for agents and automation. Loopback REST + MCP over Gmail, backed by PostgreSQL, semantic search, attachment sidecars, local object storage, and a knowledge graph. Built for trusted single-user local systems rather than hosted mailbox ingestion. - **[gmeow-ontology](https://github.com/Blackcat-Informatics/gmeow-ontology)** (Rust / Python / RDF 1.2) — GMEOW is our pre-release work on a computational foundation for knowledge and reasoning. It brings logic, language and mathematics into one coherent model, spanning people and organisations, scientific observations, and the beliefs, intentions and interpretations of human and machine agents. The ambition is to reason across those domains while preserving the distinctions that give their information meaning. A measurement, a model output and a human assertion can share a claim structure without becoming interchangeable. Evidence, standpoint, disagreement and revision belong alongside the claim. Reasoning must be equally inspectable: its premises, assumptions and limits belong alongside its conclusions. Grounded agent memory is one application. The broader work asks how knowledge can move between vocabularies, mathematical representations and reasoning systems without silently changing its meaning—and how to establish what each transformation preserves, changes or loses. - **[gmeow-gts](https://github.com/Blackcat-Informatics/gmeow-gts)** (Rust / Python / Go / TypeScript) — GTS packages files with the relationships that give them meaning: where they came from, what was done to them, and which claims or decisions depend on them. The package carries its own history, rather than relying on the system it came from to explain it. New contributions arrive as layers: another analysis, a corrected claim, a higher-resolution original, a signed review. They can be appended without rewriting what came before, combined into larger packages, or delivered progressively as a reader needs them. Hashes and optional signatures let recipients check integrity and attribution. - **[ai-notes](https://github.com/paudley/ai-notes)** (Shell) — Working notebook for local-model experimentation, ML papers worth tracking, and small reusable bits of inference plumbing. Public so the notes serve as their own changelog. - **[nonlinear-semantic-graphs](https://github.com/paudley/nonlinear-semantic-graphs)** (Python) — Discovering emergent knowledge-graph relations in high-order semantic vector spaces — manifold learning + spectral analysis applied to LLM embeddings to surface relations that aren't explicit in the source corpus. - **[lru_cache](https://github.com/paudley/lru_cache)** (C++) — Fast, thread-safe Least-Recently-Used cache as a C++ template, with a comprehensive unit-test suite. First written in 2004 and still useful — the kind of small library that just needs to work. ### Publications - **Emergent Knowledge Graphs from High-Order Semantic Spaces** (2025-10-01). Working paper · Blackcat Informatics Inc. (2025). - **Evaluating Usability and Security with Users of All Ages** (2007-04-30). CHI 2007 — Workshop on Usability and Security. - **OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy** (2003-10-10). BMC Bioinformatics 4:47 (2003). doi:10.1186/1471-2105-4-47. ### Coverage - **[Cogneto: to identity and beyond!](https://www.infoworld.com/article/2199876/cogneto-to-identity-and-beyond.html)** — InfoWorld, 2007-05-25. InfoWorld article on Cogneto's identity technology; used as third-party support for Patrick Audley's Founder and CTO role at Cogneto Development Inc. Supports: Patrick Audley was Founder and CTO of Cogneto Development Inc. during the 2006-2007 adaptive-authentication period. - **["Powered by Your Mind" Academics, Psychology, Technology Drive New Authentication Solution](https://www.cutimes.com/2007/01/16/powered-by-your-mind-academics-psychology-technology-drive-new-authentication-solution/)** — Credit Union Times, 2007-01-16. Credit Union Times article on Cogneto's cognitive-biometric authentication work; used as third-party support for Patrick Audley's Cogneto role and adaptive-authentication work. Supports: Patrick Audley worked on Cogneto's cognitive-biometric and adaptive-authentication technology as Founder and CTO. ### Patents - **Globally Aware Authentication System**. US 2008/0222706 A1, published 2008-09-11. - **System and method for user polling over a network**. US 8,510,385 B2, published 2013-08-13. ### The Arts Outside the technology work, I'm involved in [Odd Numbered Productions](https://oddnumberedproductions.ca), our arts vehicle, where I make work at the intersection of AI, sensor data, and cryptographic art. ### Contact - Email: - Phone: +1 (780) 670-0484 - GitHub: - GitLab: - LinkedIn: - Crunchbase: - ORCID: - Google Scholar: - X: - Twitter: - YouTube: - Stack Overflow: - PyPI: - Facebook: - Reddit: - Mail care of: Blackcat Informatics® Inc., 112 Westbourne Rd., Spruce Grove, AB, Canada T7X 1R6 --- ## Curriculum Vitae # Patrick Colm Audley — Curriculum Vitae *Hacker · Full-Spectrum Technologist · Polymath* Spruce Grove, AB · · +1 (780) 670-0484 · · ORCID [0000-0003-4382-7625](https://orcid.org/0000-0003-4382-7625) · Wikidata [Q139770478](https://www.wikidata.org/entity/Q139770478) · ENS `patrickaudley.com` · Ethereum [0x3FEFC1B6D9b6A851bfC274fEa1929c31c6F23cf2](https://etherscan.io/address/0x3FEFC1B6D9b6A851bfC274fEa1929c31c6F23cf2) ### Summary My career is, in essence, a single sentence: **I translate the impossible to the real.** ### Selected Outcomes - **Certified financial filings as CFO** of a Canadian reporting issuer; bridged regulated capital markets with cryptocurrency tokenisation (ABT Capital Markets, 2018–2024). - **Founded a UK security startup; raised £1.2 M** and contributed to a portfolio of 12+ adaptive-authentication patents (Cogneto, 2006–2007). - **Operated a 30 TB PostGIS geospatial pipeline** running near-real-time wildfire detection on heterogeneous satellite feeds; served multiple governments and commercial customers (Tanka Technologies, 2017–2022). - **Lead architect on a national PKI buildout** for a CLEC: 2,000+ systems, 1,000+ networks, 16 cities; led data-forensic investigations as a CSIRT member (GT Group Telecom, 1997–2001). - **Built a 100-processor research cluster** with custom PXE / imaging and Sun Grid Engine for the University of Dundee School of Life Sciences (2002–2004). - **30+ year Vancouver / Alberta startup-community veteran**; 25+ launches as a fractional executive via Blackcat Informatics; deep network across founders, funders, and operators (1994–present). - **Co-author of OXBench** (BMC Bioinformatics 2003), a foundational benchmark for protein multiple-sequence alignment that has guided the field for two decades. ### Experience #### Blackcat Informatics® Inc. · 1994–present **Founder & Principal Management Consultant** · *Management Consulting · Fractional CTO* High-end IT and management consulting for the Vancouver and Alberta startup communities. Domain expertise across high-performance and cloud computing, military-grade security, carrier-grade networking, blockchain, bioinformatics, machine learning, satellite imaging, and cryptology. - CTO-at-large engagements: high-level management, executive oversight, hands-on execution. - Solution architecture for complex technical problems — sparse-resource design and future-proofing. - Cloud navigation: clusters, grids, lambdas, microservices; devops automation across providers. - Startup process and execution from idea through early-stage funding. #### Axes · 2025–present **Lead Researcher** · *AI · data intelligence* #### FydoDx (Biocode AI) · 2023–present **Fractional CTO** · *Veterinary AI · clinical-workflow automation* Fractional CTO for an AI-powered veterinary platform — real-time transcription, automated SOAP notes, dosage verification, and multilingual clinical records across 80+ languages. - Technology roadmap and architecture for AI-driven clinical documentation. - Hiring strategy, developer mentorship, and engineering process foundations. - AI/ML pipeline direction — model selection, fine-tuning strategy, integration patterns. - Security posture, compliance framework, and risk management for health-adjacent data. #### ABT Capital Markets Inc. · 2018–2024 **President, COO & CFO** · *Fintech · regulated capital markets · tokenisation* FinTech firm developing a compliant blockchain platform for tokenisation of Private Equity, Venture Capital, Real Estate, and other alternative assets. Asset Backed Tokens™ enable retail access to traditionally institutional markets. - Operated a Canadian reporting issuer for several years; certified financial filings as CFO. - Bridged regulated capital markets with the wild west of cryptocurrency. - Performed deal due diligence and led legal vetting. - Authored Solidity contracts and worked across multi-blockchain solutions. #### Tanka Technologies Inc. · 2017–2022 **CTO** · *Geospatial intelligence · satellite imagery analysis* Global-scale, high-throughput, real-time geospatial analysis and event-correlation systems — fusing public satellite feeds (GOES-R, MODIS, VIIRS, Himawari) with statistical and ML detection. - Built a worldwide early wildfire-detection pipeline with NRT analysis of heterogeneous satellite feeds. - Delivered data to multiple governments and government agencies plus commercial customers. - Operated a 30 TB PostgreSQL / PostGIS data store and routinely tuned heavy spatial queries. - Collaborated with university research clusters on LiDAR integration and forest-composition analysis. #### Strutta · 2015–2021 **CTO** · *Social marketing · brand engagement platforms* Built social-promotion platforms with a team of social-marketing experts; instrumented and shaped brand engagement at scale. - Ran the dev teams over several product iterations: hiring, team-building, training, direction. - Set the broad technical direction of Strutta and its fleet of related companies. - Operated as senior management inside a public-company structure. #### Mobio Technologies Inc. · 2013–2021 **Managing Director, Technology** · *Cloud platforms · public-market incubator* Owned the technical landscape across the Mobio / LX Ventures portfolio: acquisition due diligence, budget planning, cloud infrastructure, hiring, team management. *Mobio was acquired by LX Ventures, which subsequently rebranded back to Mobio.* - Built a large-scale cloud stack supporting an Alexa top-5000 site (2014). - Led multi-platform deploys across AWS, GCE, and Heroku. - DBA of last resort for the portfolio — fixing the evil problems no one else would. - Executive-team duties for a public-market incubator. #### Cogneto Development Inc. · 2006–2007 **Founder & CTO** · *Cognitive biometrics · adaptive authentication* Founded Cogneto in February 2006; raised £1.2 million; assembled a world-class engineering team and built a tightly-coupled user-centric / high-security software programme. - Contributed to a rich patent portfolio spanning over twelve patents. - Drove the meshing of user-centric design with adversary-aware security engineering. - Touched almost every facet of building a first-rate startup — funding, hiring, IP, GTM. #### University of Dundee · 2002–2004 **High-Performance Computing Manager** · *Computational biology · academic HPC* Specified, provisioned, and operated research-grade computing for the School of Life Sciences. - 20 TB IBM-based SAN with Tivoli Storage Manager — 2 Gbps pure NFS. - New Cisco-based network backbone with NAT and intrusion detection. - 100-processor research cluster on custom PXE / imaging, Sun Grid Engine, and a bespoke deployment package. - Self-discovering monitoring tools that mapped switch topology and surfaced problem areas as the network grew. - Involved in several biological research projects. #### GT Group Telecom · 1997–2001 **Sr. Ecommerce Architect — PKI & Cryptography** · *Competitive Local Telco · founding-team security* Founding-team security and PKI architect for a national CLEC. Designed and supervised construction of two high-security PKI vaults and the surrounding infrastructure. - Coordinated a national audit of GT's networks: 2000+ systems, 1000+ networks, 3 autonomous routing domains, 16 cities. - Key member of GT's Computer Security Incident Response Team; led several data-forensic investigations. - Designed PKI-based applications in Perl, C, C++, and PHP — including the Realtorlink Hybrid Authentication system and Secure Private Internet Messaging (SPIM). - Provided sales-support for PKI and security products. - Responsible for network security across all of Group Telecom's high-security networks. - Responsible for design and implementation of all PKI-centric applications, services, and facilities. - Delivered business assessments for potential partners and clients. ### Earlier Roles - **2026–present** — CTO-Backup & Sr. Developer, Karve Information Technology Ltd. · *Vertical SaaS · field-service platforms* - **2022–2023** — Senior Developer, Ardan Labs · *Go consulting · training & knowledge transfer* - **2022–2022** — Park Board Candidate, TEAM for a Livable Vancouver · *Civic engagement · Vancouver politics* - **2020–2022** — CTO, ESG Analytics · *Impact investing · ESG data* - **2017–2018** — Technical Advisory Board Member, Calyx Bio-Ventures Inc. · *Bio-ventures advisory* - **2015–2015** — Acting CTO (interim), PlaceSpeak Inc. · *Civic tech · location-based consultation* - **2011–2013** — Chief Architect & VP Engineering, Mobio Technologies Inc. · *Lean-startup cloud · payments* - **2011–2011** — Integration Architect, Dominion Enterprises · *Enterprise integration* - **2010–2011** — Sr. Developer, one45 Software · *Medical-education software* - **2008–2010** — VP Technology, Chaordix · *Crowdsourcing platforms* - **2008–2008** — Hacker — Security & Code Uplift, Galaxy Telecom · *VOIP · code-base auditing* - **2004–2006** — Sr. Software Architect, Adzilla New Media · *ICAP · inline web modification* - **1996–1997** — System Administrator / MIS, Axion Internet Inc. · *ISP infrastructure* ### Selected Open-Source Work - **[PurRDF](https://github.com/Blackcat-Informatics/purrdf)** (Rust / Python / WebAssembly / C · MIT OR Apache-2.0) — Developed as part of our work on neuro-symbolic engines, PurRDF provides the public, open-source foundations for representing, querying and reasoning over structured knowledge. It is designed to be useful independently of that work. PurRDF brings graph queries, logical reasoning, validation and retrieval into one coherent RDF 1.2 toolkit. Text search, spatial relationships and vector similarity can participate in the same query over the same dataset, without assembling a second database stack. Correctness includes knowing what an answer establishes. Reasoning produces reports of its completeness and limits; governed queries distinguish complete answers from partial results. The same Rust engine runs natively and in WebAssembly, with Python, JavaScript and C interfaces. - **[Logit Loom](https://github.com/paudley/logit-loom)** (Rust · MIT OR Apache-2.0) — Rust toolkit for observing, transforming, steering, stopping, resuming, and accounting for token generation. Logit Loom provides backend-neutral contracts for ordered logit transforms, exact-byte token observers, bounded generation controls, content-bound plans and receipts, plus a llama.cpp adapter with identity-bound checkpoints and explicitly scoped steering. - **[gmeow](https://github.com/paudley/gmeow)** (Python · AGPL-3.0) — Local Gmail intelligence for agents and automation. Loopback REST + MCP over Gmail, backed by PostgreSQL, semantic search, attachment sidecars, local object storage, and a knowledge graph. Built for trusted single-user local systems rather than hosted mailbox ingestion. - **[gmeow-ontology](https://github.com/Blackcat-Informatics/gmeow-ontology)** (Rust / Python / RDF 1.2 · CC BY 4.0 / AGPL-3.0-only) — GMEOW is our pre-release work on a computational foundation for knowledge and reasoning. It brings logic, language and mathematics into one coherent model, spanning people and organisations, scientific observations, and the beliefs, intentions and interpretations of human and machine agents. The ambition is to reason across those domains while preserving the distinctions that give their information meaning. A measurement, a model output and a human assertion can share a claim structure without becoming interchangeable. Evidence, standpoint, disagreement and revision belong alongside the claim. Reasoning must be equally inspectable: its premises, assumptions and limits belong alongside its conclusions. Grounded agent memory is one application. The broader work asks how knowledge can move between vocabularies, mathematical representations and reasoning systems without silently changing its meaning—and how to establish what each transformation preserves, changes or loses. - **[gmeow-gts](https://github.com/Blackcat-Informatics/gmeow-gts)** (Rust / Python / Go / TypeScript · MIT OR Apache-2.0) — GTS packages files with the relationships that give them meaning: where they came from, what was done to them, and which claims or decisions depend on them. The package carries its own history, rather than relying on the system it came from to explain it. New contributions arrive as layers: another analysis, a corrected claim, a higher-resolution original, a signed review. They can be appended without rewriting what came before, combined into larger packages, or delivered progressively as a reader needs them. Hashes and optional signatures let recipients check integrity and attribution. - **[ai-notes](https://github.com/paudley/ai-notes)** (Shell · MIT) — Working notebook for local-model experimentation, ML papers worth tracking, and small reusable bits of inference plumbing. Public so the notes serve as their own changelog. - **[nonlinear-semantic-graphs](https://github.com/paudley/nonlinear-semantic-graphs)** (Python · CC-BY-NC-SA-4.0) — Discovering emergent knowledge-graph relations in high-order semantic vector spaces — manifold learning + spectral analysis applied to LLM embeddings to surface relations that aren't explicit in the source corpus. - **[lru_cache](https://github.com/paudley/lru_cache)** (C++ · GPL-2.0) — Fast, thread-safe Least-Recently-Used cache as a C++ template, with a comprehensive unit-test suite. First written in 2004 and still useful — the kind of small library that just needs to work. ### Publications - [Emergent Knowledge Graphs from High-Order Semantic Spaces](https://github.com/paudley/nonlinear-semantic-graphs) · *Working paper · Blackcat Informatics Inc. (2025)* · 2025-10-01 - **Evaluating Usability and Security with Users of All Ages** · *CHI 2007 — Workshop on Usability and Security* · 2007-04-30 - [OXBench: A benchmark for evaluation of protein multiple sequence alignment accuracy](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-4-47) · *BMC Bioinformatics 4:47 (2003)* · 2003-10-10 · doi:[10.1186/1471-2105-4-47](https://doi.org/10.1186/1471-2105-4-47) ### Patents - [Globally Aware Authentication System](https://patents.google.com/patent/US20080222706A1) · US 2008/0222706 A1 · published application; legal status abandoned · 2008-09-11 - [System and method for user polling over a network](https://patents.google.com/patent/US8510385B2) · US 8,510,385 B2 · granted · 2013-08-13 ### Education - **School of Hard Knocks (Internet Edition)** — Young Hacker · 1993–1995 ### Languages English (Native) · French (Professional working proficiency) · Mandarin (Learning) ### Skills & Technologies Software Architecture · Data Science · Latent Space Dynamics · Neuro-Symbolism · Spatial Analysis · Machine Learning Security Operations (MLSecOps) · Cloud Computing · Agile Methodologies · Software Development · Databases · Start-ups · Integration · Web Applications · Unix · Linux · E-commerce · Security · Mobile Applications · MySQL · SaaS · Software Design · Subversion · Mobile Devices · Architecture · Amazon Web Services (AWS) · Web Development · Scalability · Apache · Software Engineering · OOP · Product Development · Entrepreneurship · Network Security · Web Services · AJAX · Scrum · Cryptography · LAMP · Android · Managed Services · Data Center · PKI · REST · Program Management · STL Metaprogramming · Security Management · Open Source · Cryptocurrency · Go · Python · C++ · Ruby · JavaScript · Perl · PHP · jQuery · XML --- ## Notes *Working notes, position pieces, and short essays. Mostly things I would otherwise post elsewhere — cross-posted here so they outlive any one platform.* Author: [Patrick Colm Audley](https://patrickaudley.com/) · License: Creative Commons BY-NC-SA CAv2.5 · Canonical homepage: --- ### PurRDF 1.0: one RDF engine, one behavior, every language **Date:** 2026-09-02 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7501321819077898240/) · **Tags:** `rdf`, `rdf-1.2`, `sparql`, `shacl`, `shex`, `semantic-web`, `knowledge-graphs`, `linked-data`, `rust`, `python`, `webassembly`, `open-source` > PurRDF 1.0 is out: an RDF 1.2 toolkit written once in Rust and carried byte for byte into Python, WebAssembly/JavaScript, and C. Ranked full-text search, spatial predicates, and vector similarity are answered from SPARQL, in-process, over the same dataset — the three jobs for which an RDF project has usually run PostgreSQL beside its triple store. **PurRDF 1.0.0 is out.** It is an RDF 1.2 toolkit written once as a from-scratch Rust core and carried verbatim into Python, WebAssembly/JavaScript, and C. The problem it exists for is that RDF tooling fragments along two axes. Across languages, every ecosystem has its own parser, with its own bugs and its own subset of the spec, so moving a graph from a Rust service to a Python pipeline to a browser silently changes what the data means three times. Across time, RDF 1.2 — triple terms, reifiers, base-direction literals — is the current revision of the standard, and almost no incumbent library carries it. So the engine is implemented once. Every published crate builds for `wasm32-unknown-unknown`, and there are deliberately no Cargo feature flags anywhere in the workspace: a data carrier must not have optional behavior. The part I find most useful in practice is what it removes from an architecture. The three jobs that usually keep a PostgreSQL instance running beside a triple store — ranked full-text search, spatial predicates, and vector similarity — are answered inside PurRDF: in-process, over the same dataset, from the same SPARQL query, with no second database and no sync job. Full-text ranking is exact fixed-point BM25 with no floating point in the crate. GeoSPARQL 1.1 reads geometry as exact rationals and decides every topological relation over an exact DE-9IM, with no GEOS and no PROJ. Nearest-neighbour search is exact, not approximate. All three are deterministic, which means the same rows in the same order on a server and in a browser tab — a guarantee the Postgres stack does not make across machines. Also in the box: native codecs for nine syntaxes, RDFC-1.0 canonicalization, SPARQL 1.1/1.2 with full Update and governed execution that returns certified partial answers rather than a wrong one, SHACL Core plus SHACL-SPARQL and SHACL-AF, ShEx 2.1, entailment regimes from RDFS through OWL 2 RL to an OWL 2 Direct-Semantics tableau — each closure returned with a reasoning report, so an incomplete answer can never be delivered as a complete one — and the GTS single-file graph transport. Two honest notes. First, 1.0.0 is not a feature release: it is the same tree as 0.13.0, republished across all 21 crates through Trusted Publishing. What changed is the commitment. From 1.0.0 a breaking change bumps the major version, a minor bump is additive, and a patch bump is bugfix-only. Second, the C ABI is versioned separately and stays 0.x; the workspace's 1.0.0 makes no promise about it. GTS reaches Rust, the CLI, Python and C, and is not exposed by the WebAssembly package. Every engine is gated by its official test suite, vendored and frozen in-repo: 1,105 of 1,105 shexTest validation tests, 129 of 129 W3C SHACL, 862 passing SPARQL evaluation tests, 264 of 264 syntax round-trips, and frozen cross-language GTS vectors. There is a browser playground if you want to try RDF 1.2 without installing anything, and a book if you want the guided tour. The repo is open: [PurRDF](https://github.com/Blackcat-Informatics/purrdf). Discussion is open on [X](https://x.com/paudley/status/2095558962779459703) and on [r/semanticweb](https://www.reddit.com/r/semanticweb/comments/1w6dpk3/purrdf_100_a_fromscratch_rdf_12_sparql_shacl_shex/). *Standalone: · Canonical anchor: * --- ### Logit Loom: turn the token boundary into a programmable surface **Date:** 2026-07-23 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7486090856559677452/) · **Tags:** `rust`, `ai-engineering`, `open-source`, `llama-cpp`, `token-generation`, `model-steering`, `reproducible-inference`, `neuro-symbolic-ai` > Logit Loom turns the token boundary into a programmable Rust surface: compose ordered logit transforms, observe exact token bytes, stop at defined boundaries, checkpoint llama.cpp sessions, and retain serializable receipts of the mechanics that ran. LLM's generate one token at a time, but we usually consume them as turns - that's missing most of the fun! **Logit Loom** turns the token boundary into a programmable surface. Play with the stream, mess with logits, observe exactly what gets admitted, and get right inside the generation loop. I’ve open-sourced a Rust toolkit for building precise, inspectable generation pipelines around `llama.cpp`: compose ordered logit transforms, observe exact token bytes, stop at defined boundaries, checkpoint sessions, and retain serializable receipts of what ran. It’s a foundation for custom sampling and steering, constrained generation, agent runtimes, reproducible inference experiments, and the mechanics layer of neuro-symbolic systems. It's not going to write the fun bits for you, but it will take the tedium OUT of writing those bits. Be your own Dr. Frankenstein and play with the nascent minds of your agentic friends! The repo is open: [Logit Loom](https://github.com/paudley/logit-loom). *Standalone: · Canonical anchor: * --- ### GMEOW: a reasoning-centric super-vocabulary for digital existence **Date:** 2026-06-04 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:share:7468346496736772097/) · **Tags:** `ontology-engineering`, `semantic-web`, `owl`, `rdf`, `linked-data`, `knowledge-graphs`, `knowledge-representation`, `ontology-alignment`, `local-first`, `ai-agents` > A new ontology project: GMEOW, the Global Metadata and Entity Ontology for the Web — a reasoning-centric, OWL 2 DL, gUFO-grounded super-vocabulary for a person's or organization's digital existence. It mints canonical terms and aligns outward to FOAF, REL, DOAP, GEDCOM, PROV-O, ORG, schema.org, vCard and Wikidata, treating provenance, confidence, temporal validity, and coreference as first-class concerns. I've started a new ontology project: **GMEOW** — the Global Metadata and Entity Ontology for the Web. The design goal is a reasoning-centric, OWL 2 DL, gUFO-grounded super-vocabulary for modelling a person's or organization's digital existence. This came out of a practical problem: once you start trying to build local-first agents over real personal or organizational memory, you quickly run into vocabulary fragmentation. Contacts, email, documents, projects, notes, legal agreements, genealogy, publications, accounts, calendars, and social presence all have their own mature-but-isolated ways of describing the world. FOAF, REL, DOAP, GEDCOM, PROV-O, ORG, schema.org, vCard, Wikidata, and others all carry useful structure — but none of them gives you the whole shape. GMEOW's approach is to mint canonical terms and align outward. So rather than rewriting source data, it creates a coherent upper layer where surface vocabularies can map into a common model. The ontology is grounded in gUFO, checked against OWL 2 DL constraints, and built around the idea that provenance, confidence, temporal validity, and coreference are first-class modelling concerns. The project grows by slices. The first slice is entities + contacts. Each slice adds canonical terms, SSSOM alignment tables, fixtures, and coverage reporting so progress is measurable against real data rather than wishful thinking. The toolchain is also part of the point: validate, reason, mappings, Wikidata checks, metadata, content negotiation, docs, build artifacts, and publishing support all need to run cleanly. Ontologies should have CI discipline too. I'm particularly interested in feedback from people working with OWL/RDF, upper ontologies, ontology alignment, semantic-web publishing, personal data stores, provenance models, local-first AI, and agent memory. Where have you seen personal or organizational knowledge graphs break because the vocabulary layer was too thin? The repo is open: [gmeow-ontology](https://github.com/Blackcat-Informatics/gmeow-ontology). *Standalone: · Canonical anchor: * --- ### Gmeow: your mailbox is agent memory, keep it local **Date:** 2026-05-24 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7464480186072285184/) · **Tags:** `open-source`, `ai-agents`, `gmail`, `mcp`, `local-first`, `semantic-search`, `knowledge-graphs`, `archives` > The recurring problem with useful personal agents is that the context they need already exists, but not in the tidy place a demo would like it to be. It is in mail. gmeow makes that mailbox substrate locally useful through loopback REST, MCP, PostgreSQL, semantic search, attachment sidecars, object storage, and a local knowledge graph. The recurring problem with useful personal agents is that the context they need already exists, but not in the tidy place a demo would like it to be. It is in mail. Not just messages, either. It is in labels, half-forgotten attachments, old project threads, invoices, flight changes, account notices, side-channel decisions, introductions, one-off commitments, and all the little “do you remember when...” fragments that make a mailbox closer to institutional memory than correspondence. For a working agent, Gmail is not an inbox. It is a substrate. `gmeow` is my attempt to make that substrate locally useful without turning it into yet another cloud import pipeline. The design is deliberately boring in the places that should be boring: authenticate to Gmail, hydrate the mailbox, cache the useful structure locally, expose a loopback API, and give agents an MCP endpoint they can query without being handed the keys to the entire account. Under the hood it keeps a PostgreSQL catalogue of labels, threads, headers, MIME structure, categories, graph triples, jobs, sync state, and embedding chunks; stores payloads in a BLAKE3 content-addressed object store; builds attachment sidecars; and layers lexical search, semantic search, and graph traversal over the result. The important boundary is this: `gmeow` is not a hosted mail intelligence service. It is for trusted single-user local systems. By default it binds to `127.0.0.1`. It does not pretend to be an Internet-facing application with a half-baked auth layer stapled on afterwards. If you expose it to an untrusted network, you are outside the threat model. That constraint is not an inconvenience; it is the point. The agent-facing surface is the part I care about most. A local coding or research agent should be able to ask questions like: “Find the thread where we decided on the migration plan.” “Show me attachments from this vendor that mention renewal.” “Which people, projects, and topics are connected to this message?” “Summarise the recent unread messages in this category.” “Archive this after recording the relevant metadata.” That is a very different shape from building a mail client. A mail client is an interface for a human sitting at the glass. `gmeow` is plumbing for agents operating beside the human: search, reads, labels, contacts, categories, attachment metadata, archive state, semantic lookup, and graph exploration through REST and MCP. The graph piece is where this starts to get interesting. Mail is full of latent structure: people, organisations, projects, documents, obligations, recurring operational patterns. Some of that structure is explicit in headers and labels. Some of it only shows up once you extract text, classify categories, embed chunks, and let the relationships accumulate. I do not want an agent merely doing keyword search over my mailbox. I want it to build a usable local map. There is also an archival motive here. I have never liked systems where the only complete copy of a working record lives behind someone else's product boundary. `gmeow` keeps the raw RFC822 path in mind, exposes a read-only IMAP service over cached archive objects, and treats attachments as first-class local objects rather than opaque blobs dangling off an API response. If a mailbox is part of your long-term memory, it should be possible to preserve it with some dignity. This is the first member of a pattern I expect to reuse across the Google surface area. Gmail is the obvious starting point because mail is dense with context, but the same local-first shape should work for Drive, Calendar, and the other services agents keep needing to reason about. Each service gets its own small daemon, local cache, object model, MCP tools, and deliberately narrow trust boundary. The cat may end up carrying a different object each time. Current status: early, opinionated, and useful enough to start dogfooding. Python, PostgreSQL, FastAPI-shaped edges, pgvector for embedding chunks, local object storage, optional SOPS-backed secrets, service-account delegation or user OAuth, and compact TOON responses by default for MCP clients that do not need JSON-shaped ceremony. If you are building local agents that need to reason over real mail instead of toy corpora, kick the tires. The repo is open, rough edges included: [gmeow](https://github.com/paudley/gmeow). *Standalone: · Canonical anchor: * --- ### Agentic coding needs human sign-off tied to physical reality **Date:** 2026-05-21 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7463256842350022656/) · **Tags:** `ai-governance`, `software-security`, `ai-coding`, `appsec`, `zero-trust`, `devsecops` > As AI coding agents transition from passive autocomplete tools to autonomous contributors executing entire feature branches, we are racing toward a massive security blind spot: how do we prove a real human actually reviewed and verified agent-generated code before it hits production? As AI coding agents transition from passive autocomplete tools to autonomous contributors executing entire feature branches, we are racing toward a massive security blind spot: How do we prove a real human actually reviewed and verified agent-generated code before it hits production? This is not a *new* problem, but it is definitely a more *urgent* one now. My work focuses heavily on building policy-as-code guardrails for AI agents — using Common Expression Language policies, Git hooks, sandboxing, and Model Context Protocol servers to ensure autonomous agents cannot ship code that violates your team's standards, even if you are not in the loop. But even the most robust automated gates are only half the battle. The ultimate layer of defence-in-depth requires real eyes reviewing critical code. In a fully agentic workflow, traditional SSH or GPG commit signing is no longer sufficient and is often automated. If an agent process or local environment is compromised, or shifted via a sophisticated prompt injection, those stored credentials can be misdirected. Or people can just be lazy. We need a zero-trust developer confirmation model that is cryptographically tied to physical reality: **Biometrically verified:** fast, low-friction validation, such as Face ID or Touch ID, proving a living, authorized developer is actively at the glass. **Temporally verified:** ensuring human approval occurs precisely during the commit window, eliminating replay attacks. **Geophysically verified:** confirming that the developer's physical location aligns with expected telemetry and trusted boundaries. When an autonomous agent proposes a critical architectural change, the final gate should not just be a green checkmark from a CI pipeline. It needs to be an un-spoofable human assertion. I am currently designing this exact defence layer for agent development workflows, and I want to open up the floor to the network: How is your engineering team drawing the line between automated policy enforcement and hard human sign-off? As agents handle larger chunks of the codebase, how do we prevent reviewer fatigue from turning human verification into an automatic rubber stamp? Let's discuss. I am actively looking to take this specific verification framework from a design pattern into a live platform integration. If you are building a biometric fast-ID product or running an enterprise software supply-chain security platform and want to explore a trial integration with this verification framework, [let's connect](#contact). *Standalone: · Canonical anchor: * --- ### Standard Graph Neural Networks need curved semantic manifolds **Date:** 2026-04-22 · **Cross-posted from:** [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7458177770238681088/) · **Tags:** `topological-data-analysis`, `graph-neural-networks`, `manifold-learning`, `knowledge-representation` > Standard GNNs are structurally constrained when mapping complex text attribution; linear aggregation in flat Euclidean space inevitably forces semantic drift. Looking to connect with researchers in TDA, geometric deep learning, and spectral graph theory. Standard Graph Neural Networks are structurally constrained when mapping complex text attribution: linear aggregation in flat Euclidean space inevitably forces semantic drift. To map high-dimensional knowledge faithfully you have to transition to curved semantic manifolds, where the geometry itself carries the relational structure. Across thirty years of building scientific analysis pipelines — genetics, satellite imagery, multi-continent high-resiliency financial applications — the through-line has been the same: representations must remain mathematically faithful to their underlying geometry, or they stop being interpretable the moment the data leaves your dev set. I've recently open-sourced a framework that discovers emergent knowledge-graph relations in high-order semantic vector spaces through manifold learning and spectral analysis. Initial proofs, a small teaser, and the Python codebase live at [paudley/nonlinear-semantic-graphs](https://github.com/paudley/nonlinear-semantic-graphs); the working paper that motivates the design is in [Publications](#emergent-knowledge-graphs). I'm looking to connect with researchers and applied scientists specialising in **Topological Data Analysis**, **Geometric Deep Learning**, and **Knowledge Representation** — especially anyone working on geodesic aggregation or spectral graph theory — to push these ideas into robust enterprise deployments. [Comment on the LinkedIn original](https://www.linkedin.com/feed/update/urn:li:activity:7458177770238681088/) or [drop me a line directly](#contact). *Standalone: · Canonical anchor: * --- ## Build - Generated 2026-10-10T14:33:49-06:00 - Deploy hash: 124c377270567de64a91aec955faa3d65ea588838010beb684855f30e92f6877 - Generator: BespokeIdentityHub_v2.5 Copyright © 2003–2026 Patrick Colm Audley. Original written content licensed [Creative Commons BY-NC-SA CAv2.5](https://creativecommons.org/licenses/by-nc-sa/2.5/ca/).