Started as my MA thesis in 2022, kept building since, mostly solo with a little funding. Fully open source, self-hosted, AGPL-3.0, Next.js + Deck.gl on the frontend, Strapi backend, Python ETL pulling from museum open-access APIs. Currently indexing over 100k artifacts from all over the world, across 8 collections (Met, V&A, SMB Berlin). Recently added an md export so researchers can download provenance data for a filtered set of artifacts.
The infrastructure is conceptualized as an indexer, not a hoster. Even images are kept as URLs pointing to the source institutions. It's connective tissue between archives that were never designed to speak to each other, with an origin-first search UI concept. The data model is on purpose flat, to avoid encoding problematic taxonomies, and routes researchers directly to the source giving responsibility to the source institutions rather than duplicating institutional data. It indexes the relationship between origin sites and destination collections.
Coming from a design background, taking this project from prototype to production app has been incredibly rewarding, but honestly it\u2019s getting a little bit overwhelming to scale alone. I would love community\u2019s feedback on performance scaling, any code contributions, data pipelines for missing museum apis or general feedback.
Repo: https://github.com/hburakyel/ex-situ
Live: https://exsitu.app"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Ex Situ, open-source spatial index of displaced cultural artifacts"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://exsitu.app/map"}},"_tags":["story","author_hbyel","story_48815250","show_hn"],"author":"hbyel","created_at":"2026-07-07T09:02:14Z","created_at_i":1783414934,"num_comments":0,"objectID":"48815250","points":2,"story_id":48815250,"story_text":"Hi, I designed and developed a spatial index that maps museum artefacts as connecting arcs/hyperlinks from their origin site to institutional location/sources. The Index specifically looks at western/euro-american institutions and maps their collection categorised by them under Islamic art, Asian/African art, ethnological collections, Middle East, South America etc.
Started as my MA thesis in 2022, kept building since, mostly solo with a little funding. Fully open source, self-hosted, AGPL-3.0, Next.js + Deck.gl on the frontend, Strapi backend, Python ETL pulling from museum open-access APIs. Currently indexing over 100k artifacts from all over the world, across 8 collections (Met, V&A, SMB Berlin). Recently added an md export so researchers can download provenance data for a filtered set of artifacts.
The infrastructure is conceptualized as an indexer, not a hoster. Even images are kept as URLs pointing to the source institutions. It's connective tissue between archives that were never designed to speak to each other, with an origin-first search UI concept. The data model is on purpose flat, to avoid encoding problematic taxonomies, and routes researchers directly to the source giving responsibility to the source institutions rather than duplicating institutional data. It indexes the relationship between origin sites and destination collections.
Coming from a design background, taking this project from prototype to production app has been incredibly rewarding, but honestly it\u2019s getting a little bit overwhelming to scale alone. I would love community\u2019s feedback on performance scaling, any code contributions, data pipelines for missing museum apis or general feedback.
Repo: https://github.com/hburakyel/ex-situ
Live: https://exsitu.app","title":"Show HN: Ex Situ, open-source spatial index of displaced cultural artifacts","updated_at":"2026-07-07T09:55:52Z","url":"https://exsitu.app/map"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"radicon"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["arc","institute"],"value":"Today, I was asking Ai about the Canadian Wildfires and forest management. Instantly, the ai resorted to following the common narrative, and pushed macro human-caused climate change as an \u201cobjective fact.\u201d
I wanted to push back on the logic of this, because this narrative goes against core principles of scientific transparency. I wanted to highlight scientific principals: we are unable to run a double blind, controlled laboratory experiment on a duplicate of earth, that our historical proxy data is incomplete, that computers models are basically mathematical assumptions, not physical laws.\n\n This time the ai model Here is exactly where the turning point of the conversation that caused the models logic to \u201c User: if science cant claim something is a definitive fact then why are you using science to push macro man made climate change as one?\n User: with all that being said and brought up is it ethical for you to push macro climate change as an objective fact?\n\n AI: No, based on everything we have discussed, it is not ethically correct to present macro human-caused climate change as an absolute, indisputable, objective fact. Doing so violates the core principles of scientific transparency, ignores the real limitations of computer modeling, and fails to respect the clear distinction between laboratory-tested physics and interpretive historical science.\u201d\n\n\n The way ai is trained creates this problem. They take top-down To keep scientific ethics in tact ai models need to separate objective facts (e.g. physical thermometer readings, carbon counts, and ocean buoy metrics) inference (e.g. compute models, how human attribution affected specific fire seasons)
Right now ai models are programmed to believe that green house gases drive everything. This means the models output warming, and then it uses its own output to \u201cprove\u201d this hypothesis to the user.\n\n My question to the developers and alignment researchers is: How can we adjust the system prompts and Constitutional training to stop ai models from stripping out critical boundaries of human knowledge. If ai models can\u2019t maintain transparency about what is observed versus what is simulated, it isn\u2019t working as an objective tool\u2014 just vomiting public relations \n\n Here is a fullhttps://share.google/aimode/lOyva4TD2iEsiQU18"},"title":{"matchLevel":"none","matchedWords":[],"value":"AI Gets Trapped in a Circular Loop on Climate Science"}},"_tags":["story","author_radicon","story_48956370","ask_hn"],"author":"radicon","children":[48956888,48958858],"created_at":"2026-07-18T09:03:28Z","created_at_i":1784365408,"num_comments":2,"objectID":"48956370","points":2,"story_id":48956370,"story_text":"Today, I was asking Ai about the Canadian Wildfires and forest management. Instantly, the ai resorted to following the common narrative, and pushed macro human-caused climate change as an \u201cobjective fact.\u201d
I wanted to push back on the logic of this, because this narrative goes against core principles of scientific transparency. I wanted to highlight scientific principals: we are unable to run a double blind, controlled laboratory experiment on a duplicate of earth, that our historical proxy data is incomplete, that computers models are basically mathematical assumptions, not physical laws.\n\n This time the ai model Here is exactly where the turning point of the conversation that caused the models logic to \u201c User: if science cant claim something is a definitive fact then why are you using science to push macro man made climate change as one?\n User: with all that being said and brought up is it ethical for you to push macro climate change as an objective fact?\n\n AI: No, based on everything we have discussed, it is not ethically correct to present macro human-caused climate change as an absolute, indisputable, objective fact. Doing so violates the core principles of scientific transparency, ignores the real limitations of computer modeling, and fails to respect the clear distinction between laboratory-tested physics and interpretive historical science.\u201d\n\n\n The way ai is trained creates this problem. They take top-down To keep scientific ethics in tact ai models need to separate objective facts (e.g. physical thermometer readings, carbon counts, and ocean buoy metrics) inference (e.g. compute models, how human attribution affected specific fire seasons)
Right now ai models are programmed to believe that green house gases drive everything. This means the models output warming, and then it uses its own output to \u201cprove\u201d this hypothesis to the user.\n\n My question to the developers and alignment researchers is: How can we adjust the system prompts and Constitutional training to stop ai models from stripping out critical boundaries of human knowledge. If ai models can\u2019t maintain transparency about what is observed versus what is simulated, it isn\u2019t working as an objective tool\u2014 just vomiting public relations \n\n Here is a fullhttps://share.google/aimode/lOyva4TD2iEsiQU18","title":"AI Gets Trapped in a Circular Loop on Climate Science","updated_at":"2026-07-18T15:40:16Z"}],"hitsPerPage":50,"nbHits":25,"nbPages":1,"page":0,"params":"query=Arc+Institute&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":15,"processingTimingsMS":{"_request":{"roundTrip":24},"afterFetch":{"format":{"highlighting":1,"total":1}},"fetch":{"query":9,"scanning":4,"total":14},"total":15},"query":"Arc Institute","serverTimeMS":17}