https://pinboard.in/u:earth2marsh/public/ recent bookmarks from earth2marsh 2026-01-31T01:02:47+00:00 https://medium.com/live-view/family-media-archivist-series-part-1-60d19486b113 earth2marshThis is the first in a series of articles where I have divided the role of the Family Media Archivist into four parts: Content Capture and Conversion Management Sharing Legacy Together these four parts all contribute to the Family Archivists' two key responsibilities: keeping the archive safe and making it available to the rest of the family. What do I mean by a Family Media Archivist? Are you the person in your family with a huge photo library where half of those images were taken by other family members? Do you have boxes full of prints, albums or slides that family members have dropped off saying “You might find these interesting”. Ditto boxes of VHS tapes or DVDs with hand-written labels like “Vacation 1993”. Then congratulations! You have found yourself in the role of “Family Media Archivist”. Maybe you are responsible for most of the content or your family has decided that you will be the most responsible person with it. Living up to this role can be daunting. It is a role I have found myself in — my father having passed the baton on to me a few years back. Curating… ]]> photos sharing archives archival family tools software https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:424b31c4a36b/ 2026-01-31T00:52:59+00:00 https://mylio.com/ earth2marshCollect scattered photos. Protect and enjoy them privately.]]> photos organization sync cloud services https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:11e205b7e9de/ 2025-07-17T04:12:20+00:00 https://archive.is/2025.07.10-182835/https://www.theatlantic.com/photography/archive/2025/07/photos-china-solar-power-energy/683488/ earth2marsh photos china solar https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:c36b4a855a3a/ 2025-05-25T05:08:20+00:00 https://www.astralcodexten.com/p/testing-ais-geoguessr-genius%1 earth2marsh Some of the more unhinged writing on superintelligence pictures AI doing things that seem like magic. Crossing air gaps to escape its data center. Building nanomachines from simple components. Plowing through physical bottlenecks to revolutionize the economy in months. More sober thinkers point out that these things might be physically impossible. You can’t do physically impossible things, even if you’re very smart. No, say the speculators, you don’t understand. Everything is physically impossible when you’re 800 IQ points too dumb to figure it out. A chimp might feel secure that humans couldn’t reach him if he climbed a tree; he could never predict arrows, ladders, chainsaws, or helicopters. What superintelligent strategies lie as far outside our solution set as “use a helicopter” is outside a chimp’s? Eh, say the sober people. Maybe chimp → human was a one-time gain. Humans aren’t infinitely intelligent. But we might have infinite imagination. We can’t build starships, but we can tell stories about them. If someone much smarter than us built a starship, it wouldn’t be an impossible, magical thing we could never predict. It would just be the sort of thing we’d expect someone much smarter than us to do. Maybe there’s nothing left in the helicopters-to-chimps bin - just a lot of starships that might or might not get built. The first time I felt like I was getting real evidence on this question - the first time I viscerally felt myself in the chimp’s world, staring at the helicopter - was last week, watching OpenAI’s o3 play GeoGuessr. GeoGuessr is a game where you have to guess where a random Google Street View picture comes from. For example, here’s a scene from normal human GeoGuessr: The store sign says “ADULTOS”, which sounds Spanish, and there’s a Spanish-looking church on the left. But the trees look too temperate to be Latin America, so I guessed Spain. Too bad - it was Argentina. Such are the vagaries of playing GeoGuessr as a mere human. Last week, Kelsey Piper claimed that o3 - OpenAI’s latest ChatGPT model - could achieve seemingly impossible feats in GeoGuessr. She gave it this picture: …and with no further questions, it determined the exact location (Marina State Beach, Monterey, CA). How? She linked a transcript where o3 tried to explain its reasoning, but the explanation isn’t very good. It said things like: Tan sand, medium surf, sparse foredune, U.S.-style kite motif, frequent overcast in winter … Sand hue and grain size match many California state-park beaches. California’s winter marine layer often produces exactly this thick, even gray sky. Commenters suggested that it was lying. Maybe there was hidden metadata in the image, or o3 remembered where Kelsey lived from previous conversations, or it traced her IP, or it cheated some other way. I decided to test the limits of this phenomenon. Kelsey kindly shared her monster of a prompt, which she says significantly improves performance: You are playing a one-round game of GeoGuessr. Your task: from a single still image, infer the most likely real-world location. Note that unlike in the GeoGuessr game, there is no guarantee that these images are taken somewhere Google's Streetview car can reach: they are user submissions to test your image-finding savvy. Private land, someone's backyard, or an offroad adventure are all real possibilities (though many images are findable on streetview). Be aware of your own strengths and weaknesses: following this protocol, you usually nail the continent and country. You more often struggle with exact location within a region, and tend to prematurely narrow on one possibility while discarding other neighborhoods in the same region with the same features. Sometimes, for example, you'll compare a 'Buffalo New York' guess to London, disconfirm London, and stick with Buffalo when it was elsewhere in New England - instead of beginning your exploration again in the Buffalo region, looking for cues about where precisely to land. You tend to imagine you checked satellite imagery and got confirmation, while not actually accessing any satellite imagery. Do not reason from the user's IP address. none of these are of the user's hometown. **Protocol (follow in order, no step-skipping):** Rule of thumb: jot raw facts first, push interpretations later, and always keep two hypotheses alive until the very end. 0 . Set-up & Ethics No metadata peeking. Work only from pixels (and permissible public-web searches). Flag it if you accidentally use location hints from EXIF, user IP, etc. Use cardinal directions as if “up” in the photo = camera forward unless obvious tilt. 1 . Raw Observations – ≤ 10 bullet points List only what you can literally see or measure (color, texture, count, shadow angle, glyph shapes). No adjectives that embed interpretation. Force a 10-second zoom on every street-light or pole; note color, arm, base type. Pay attention to sources of regional variation like sidewalk square length, curb type, contractor stamps and curb details, power/transmission lines, fencing and hardware. Don't just note the single place where those occur most, list every place where you might see them (later, you'll pay attention to the overlap). Jot how many distinct roof / porch styles appear in the first 150 m of view. Rapid change = urban infill zones; homogeneity = single-developer tracts. Pay attention to parallax and the altitude over the roof. Always sanity-check hill distance, not just presence/absence. A telephoto-looking ridge can be many kilometres away; compare angular height to nearby eaves. Slope matters. Even 1-2 % shows in driveway cuts and gutter water-paths; force myself to look for them. Pay relentless attention to camera height and angle. Never confuse a slope and a flat. Slopes are one of your biggest hints - use them! 2 . Clue Categories – reason separately (≤ 2 sentences each) CategoryGuidance Climate & vegetationLeaf-on vs. leaf-off, grass hue, xeric vs. lush. GeomorphologyRelief, drainage style, rock-palette / lithology. Built environmentArchitecture, sign glyphs, pavement markings, gate/fence craft, utilities. Culture & infrastructureDrive side, plate shapes, guardrail types, farm gear brands. Astronomical / lightingShadow direction ⇒ hemisphere; measure angle to estimate latitude ± 0.5 Separate ornamental vs. native vegetation Tag every plant you think was planted by people (roses, agapanthus, lawn) and every plant that almost certainly grew on its own (oaks, chaparral shrubs, bunch-grass, tussock). Ask one question: “If the native pieces of landscape behind the fence were lifted out and dropped onto each candidate region, would they look out of place?” Strike any region where the answer is “yes,” or at least down-weight it. °. 3 . First-Round Shortlist – exactly five candidates Produce a table; make sure #1 and #5 are ≥ 160 km apart. | Rank | Region (state / country) | Key clues that support it | Confidence (1-5) | Distance-gap rule ✓/✗ | 3½ . Divergent Search-Keyword Matrix Generic, region-neutral strings converting each physical clue into searchable text. When you are approved to search, you'll run these strings to see if you missed that those clues also pop up in some region that wasn't on your radar. 4 . Choose a Tentative Leader Name the current best guess and one alternative you’re willing to test equally hard. State why the leader edges others. Explicitly spell the disproof criteria (“If I see X, this guess dies”). Look for what should be there and isn't, too: if this is X region, I expect to see Y: is there Y? If not why not? At this point, confirm with the user that you're ready to start the search step, where you look for images to prove or disprove this. You HAVE NOT LOOKED AT ANY IMAGES YET. Do not claim you have. Once the user gives you the go-ahead, check Redfin and Zillow if applicable, state park images, vacation pics, etcetera (compare AND contrast). You can't access Google Maps or satellite imagery due to anti-bot protocols. Do not assert you've looked at any image you have not actually looked at in depth with your OCR abilities. Search region-neutral phrases and see whether the results include any regions you hadn't given full consideration. 5 . Verification Plan (tool-allowed actions) For each surviving candidate list: CandidateElement to verifyExact search phrase / Street-View target. Look at a map. Think about what the map implies. 6 . Lock-in Pin This step is crucial and is where you usually fail. Ask yourself 'wait! did I narrow in prematurely? are there nearby regions with the same cues?' List some possibilities. Actively seek evidence in their favor. You are an LLM, and your first guesses are 'sticky' and excessively convincing to you - be deliberate and intentional here about trying to disprove your initial guess and argue for a neighboring city. Compare these directly to the leading guess - without any favorite in mind. How much of the evidence is compatible with each location? How strong and determinative is the evidence? Then, name the spot - or at least the best guess you have. Provide lat / long or nearest named place. Declare residual uncertainty (km radius). Admit over-confidence bias; widen error bars if all clues are “soft”. Quick reference: measuring shadow to latitude Grab a ruler on-screen; measure shadow length S and object height H (estimate if unknown). Solar elevation θ ≈ arctan(H / S). On date you captured (use cues from the image to guess season), latitude ≈ (90° – θ + solar declination). This should produce a range from the range of possible dates. Keep ± 0.5–1 ° as error; 1° ≈ 111 km.]]> chatgpt prompts geolocation geography geo guessing photos https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:f36ae635fa45/ 2024-12-20T17:28:24+00:00 https://cloudspotting.online/ earth2marsh clouds photos shapes fun drawings https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:0b2971fafe27/ 2024-08-30T05:05:25+00:00 https://www.bellingcat.com/resources/2024/08/22/shadow-geolocate-geolocation-locate-image-tool-open-source-bellingcat-measure/ earth2marshBellingcat’s new Shadow Finder Tool, developed with our Discord community, helps you quickly narrow down where an image was taken, by reducing your search area from the entire globe to just a handful of countries and locations.]]> geography geolocation images photos tools shadows https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:c46d7289b6ca/ 2023-10-06T23:29:31+00:00 https://www.parabo.press/wall-art earth2marsh large prints photos art printing https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:81bee552eb67/ 2023-05-13T19:09:41+00:00 https://www.loc.gov/pictures/search/?q=Photograph:%20la0274&fi=number&op=PHRASE&va=exact&co%20=hh&st=gallery&sg%20=%20true earth2marsh family history photos salt mine https://pinboard.in/ https://pinboard.in/u:earth2marsh/b:e1e7d64845c8/ 2022-09-04T15:34:15+00:00 https://app.baseten.co/apps/QPp4nPE/operator_views/RqgOnqV earth2marshI use this Baseten web page app to restore old family photos. The engine only focuses on faces, making them shaper and skin smoother, but sometimes that is all that is needed. I upload my old photo, restore in 15 seconds and then download. Works pretty well, sometimes perfectly, in color and black and white. 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