@markpm39
Joined June 2025
Situational awareness app on Lodge's primitives
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The Raiders won 3 games last year and are 2-0 now, but they play like the 16th-best team in football. Real or fake for the Raiders, Bengals, Eagles and Seahawks. Every Week 3 call was locked before kickoff, and we grade them all next week in public. Built and run live in Lodge. Data: nflverse (CC-BY).
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We built a digital twin of a Wisconsin paper mill in Lodge, on real power-price, weather and gas data. It forecasts price spikes, plans around them, and cut the bill 28.8% in a 30-day replay (illustrative mill, real data).
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Built a world monitor over the weekend with 17 live layers, chokepoint exposure, futures, time slider inspired by github.com/koala73/worldmoni…. Click Hormuz: 60% of transits are tankers, Brent and LNG update live, scrub back three days and watch it move. None of it is bespoke, it's all Lodge primitives. All feeds, geometry, scoring, replay are part of the normal product. Point them at a supply chain instead and you get a different product.
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Lineage is one of those things that is not glamorous at all but makes data engineering 10x easier and more trustworthy. Those files on the left are literally screenshots that I uploaded of the spec for what I wanted built. So I can not only can I go back to the individual cell each value was derived from, but even the screenshots the idea for the whole build came from in the first place.
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Just made large materializations repairable instead of rebuild-only. Materializations are where Lodge turns raw tables/sources into durable, governed entities (i.e. the thing the rest of the system actually works with). If I find one property out of thousands was mapped incorrectly, I can patch that property instead of rerunning the whole mapping or fiddling with random scripts in standard ETL
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I resolved team, coach, league, conference, and player data across 3 source, 15 tables for this cbb ontology. For example, each player is resolved across all tables so that you can see every game they played in, their aggregated stats, all teams they were on, conferences, coaches they played under, etc. Lodge of course keeps granular lineage so that editing the mappings and purging outputs is very easy and every value can always be traced back to the source.
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some education data visuals
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opennotes is randomly trending on this site that explains it
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how would one feasibly implement i18n in a large codebase without a massive team before ai
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recently built in lodge: mapping + scraping + deduplicating 110 urls + custom app to see it all + built in agent to ask about anything. agent did everything btw, everything's in a clean ontology
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every women's rowing athlete over all available years on public urls: 110 college urls w/ different formatting families.
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since when is there an island in the middle of the Atlantic
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Side by side geographic and relational map of Midwest food facilities, operators, inspections, recalls, weather events, contracts, and their relationships.
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😂
my ai found a message with 88 % reply rate? "hi {first_name}, you came up in my research" it dmed this to 25 c-suite execs ig vague texting works
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I used Lodge to build a graph over the Midwest food supply chain. It uses 51 public datasets across 11 source families, covering nearly 300,000 source records. You can search for entities, filter by entity type and relationship type, and you can also view neighborhoods. I'm still improving it, so please let me know if there's anything else you think would be interesting! I wanted to try to answer questions like - "When a serious safety incident occurs at one food facility, can we trace the graph to find other facilities with similar safety risks? - "When a food product is recalled, can we trace the recalling company to its facilities, related products, inspections, and other connected organizations and see what else may warrant review?" - "When a weather event happens, what facilities does it effect, and what are the downstream effects?" I think making it live could be really cool for things like tracking food recalls live to show and limit exposure. Sources: - U.S. Food and Drug Administration recall, food, import-alert, and import-refusal records. - U.S. Department of Agriculture food, organic-operation, meat-establishment, and related facility records. - Occupational Safety and Health Administration inspection, citation, injury, and establishment records. - U.S. Environmental Protection Agency Facility Registry Service records. - National Weather Service alerts and geographic references. - USAspending federal contract award records. - -Wisconsin and other Upper Midwest public facility, licensing, dairy, workforce-notice, and regulatory records. - Public organization and product information used to connect named entities.
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