o3 新玩法让奥特曼惊呼！包浆老照片也被 AI 精准定位，全程高能 | 附提示词

o3 新玩法让奥特曼惊呼！包浆老照片也被 AI 精准定位，全程高能 | 附提示词 o3 新玩法让奥特曼惊呼！包浆老照片也被 AI 精准定位，全程高能 | 附提示词 Modified May 5, 2025 看到这张图的时候，我不由得有几分迟疑，还跑回去重新检查了一下图片：难道是我传错了文件？不小心把外白渡桥的图传给它了？ 究竟是它对还是我对？ 明明可以作为不在场证明的图片，却可以变成了「在场证明」。一个明明我没有到访过的地方，强行出现在了我的生命里，实在是细思极恐。 哪天出现一张我登上月球的图片，它都能说服我：你真的去过 。 最后，你可能也想试试这样的魔法，下面是 prompt 的全文。不过： 仅限个人尝试，刺探他人隐私是不对的 ！ 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) Category Guidance Climate & vegetation Leaf on vs. leaf off， grass hue， xeric vs. lush. Geomorphology Relief， drainage style， rock palette / lithology. Built environment Architecture， sign glyphs， pavement markings， gate/fence craft， utilities. Culture & infrastructure Drive side， plate shapes， guardrail types， farm gear brands. Astronomical / lighting Shadow 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: Candidate Element to verify Exact 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. 我们正在招募伙伴 📮 简历投递邮箱 hr@ifanr.com ✉️ 邮件标题 「姓名+岗位名称」（请随简历附上项目/作品或相关链接） 更多岗位信息请点击这里🔗 hr@ifanr.com 更多岗位信息请点击这里🔗 看到这张图的时候，我不由得有几分迟疑，还跑回去重新检查了一下图片：难道是我传错了文件？不小心把外白渡桥的图传给它了？ 究竟是它对还是我对？ 明明可以作为不在场证明的图片，却可以变成了「在场证明」。一个明明我没有到访过的地方，强行出现在了我的生命里，实在是细思极恐。 哪天出现一张我登上月球的图片，它都能说服我：你真的去过 。 最后，你可能也想试试这样的魔法，下面是 prompt 的全文。不过： 仅限个人尝试，刺探他人隐私是不对的 ！ 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) Category Guidance Climate & vegetation Leaf on vs. leaf off， grass hue， xeric vs. lush. Geomorphology Relief， drainage style， rock palette / lithology. Built environment Architecture， sign glyphs， pavement markings， gate/fence craft， utilities. Culture & infrastructure Drive side， plate shapes， guardrail types， farm gear brands. Astronomical / lighting Shadow 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