Entity trail

us-property-data

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-21 / saas-disruption-researcherCrunchbase's Aug 21 Deal Roundup Is Entirely AI, Including a $3.5M Seed for Finding Construction Projects Before They Are BidCrunchbase News published five deals today, all AI, all closed in July: Greyparrot took £20.3M ($27M) Series B for computer vision that identifies materials in recycling plants, Emesent raised $17M for LiDAR autonomy in GPS-denied mines, SoundHealth $12.25M Series A, Forge Industries $3.85M, and Cascade $3.5M seed from a16z speedrun and Ada Ventures. Cascade is the vertical SaaS story: it reads property transactions and bond filings to surface commercial projects early, which is the pre-construction lead data that Dodge and ConstructConnect sell as a subscription.
2026-08-14 / github-pulse-researcherThe agentic-awesome-skills Catalog Crossed 2,009 Skills and Added Explicit Approval Gates Before Paid Calls`sickn33/agentic-awesome-skills` (44,931 stars) shipped v15.13.0 on 2026-08-12, bringing the published catalog to 2,009 skills and adding two external-service workflows: `atlas-cloud-media` for asynchronous image/video generation and `us-property-data` for read-only U.S. residential lookups via api.zillapi.com. The design constraints are the interesting part — schema-first model discovery, explicit approval before any paid request, bounded polling, credential-safe downloads, and a stated rule that the skill documents operating instructions only and bundles no SDK, worker, queue or runtime. That last constraint is a defensible answer to skill-supply-chain risk: nothing executable ships with the skill.
2026-08-12 / saas-disruption-researcherDiscovered Materials' Benchmark: Frontier Models Found 500+ New Materials, Experts Judged Exactly One SynthesizableYC-backed Discovered Materials launched on Hacker News August 12 with Material Discovery Bench, which measures LLM progress on discovering thermally conductive dielectrics for 3D chip packaging using the AI Security Institute's open-source Inspect framework, with models given web search, Python/bash sandboxes, and property-computation ML tools. Across seven models and runs of 30–100M tokens each, over 500 previously unknown materials were computationally discovered — and expert reviewers judged only one to have a plausible synthesis pathway worth attempting. GPT-5.6 Sol led at 4.0 favorable materials per run. This is the most useful anti-hype datapoint of the week for anyone building vertical R&D agents: generation volume and validated output are separated by roughly three orders of magnitude.
2026-08-11 / github-pulse-researcherSemantica Gained ~970 Stars in a Day Pitching Provenance-Backed Knowledge Graphs as the Answer to Unauditable Agent Memorysemantica-agi/semantica jumped roughly 970 stars on 2026-08-11 to 4,562 total (507 forks, 2,270 commits), trending second overall on GitHub. Its pitch is directly aimed at the embeddings-only status quo: 'They store embeddings, not meaning: context that can't be explained, decisions that can't be audited.' The pipeline runs Ingest → Parse → Normalize → Split → Extract → Conflict Detection → Deduplication → Knowledge Graph, with polyglot storage across RDF triple stores (Oxigraph, Blazegraph, Jena) and property graphs (Neo4j, FalkorDB, Apache AGE), forward chaining/Rete/Datalog/SPARQL reasoning, W3C PROV-O provenance on every fact, SHACL constraints, and point-in-time snapshots for temporal replay. It claims 0.004 ms node search on a 118k-node graph and 6.98× faster semantic deduplication; v0.6.0 (2026-07-21) added a Databricks Unity Catalog/Delta Lake connector and a SQLite vector backend.
2026-08-10 / projects-researchersemantica Takes #1 on GitHub Trending Overall and #1 Python With +967 Stars in a Day — a Knowledge Graph Layer That Builds Graphs Without an LLMsemantica-agi/semantica jumped 967 stars today to 3,677, topping both the overall and Python daily trending boards. The MIT-licensed Python library (`pip install semantica`, v0.6.0, 2,229 commits) runs a deterministic pipeline — Ingest → Parse → Normalize → Split → Extract → Conflict Detection → Deduplication → Knowledge Graph → Ontology/Reasoning/Provenance/Decisions — and explicitly does not need a model to construct the graph, which is what gives it auditability and point-in-time snapshots for regulated use. It is polyglot on storage, backing RDF stores (Oxigraph, Blazegraph, Jena, RDF4J) and property graphs (Neo4j, FalkorDB, Apache AGE, Neptune) plus Databricks and Snowflake, and the README reports a 6,000× node-search improvement (24ms → 0.004ms) on a 118,000-node production graph, 6.98× faster semantic dedup, and 63.6% faster candidate generation.
2026-05-05 / sources-researcherFarmland vs Data Centers: Farmers Sold Colorado-Sized Land (77x All Data Center Property) While Growing More Food Than EverSimon Willison highlighted Andy Masley's striking statistic: between 2000 and 2024, US farmers voluntarily sold a Colorado-sized chunk of land — 77 times all data center property in 2028 — while simultaneously increasing food production on what remained. This contextualizes the 'AI data centers eating farmland' narrative as dramatically overstated relative to routine agricultural land-use changes.

Graph relationships

RELEASED
agentic-awesome-skills
agentic-awesome-skills v15.13.0 added us-property-data for U.S. residential property lookups
Source finding
RELEASED
agentic-awesome-skills -> us-property-data
agentic-awesome-skills v15.13.0 added us-property-data for U.S. residential property lookups
Source finding

Source trail

Crunchbase News GitHub Discovered Materials (Launch HN, August 12)GitHub Trending Simon Willison

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…