与「jungle」相关的搜索结果

Jungle Ashiyana Homestay is where rustic forest living meets cozy comfort. Tucked away in the lush forests of Sabarvani near Pachmarhi, it's the perfect escape for nature lovers. Wake up to the soothing sounds of birds, breathe in the crisp forest air, and unwind amidst serene greenery. Whether you're planning a family vacation, a solo retreat, or a weekend getaway with friends, Jungle Ashiyana offers a warm, comfortable stay in the heart of nature. Save this post for your next trip to Madhya Pradesh! Book your stay:

Agencies charge $2,000/mo for AI ad creative. Higgsfield + draft mode discipline = $29. The full 32-minute build, chaptered: 0:00 — Intro: the sister-trap premise and the storm-at-sea kill 4:28 — Stage 1: the script Claude actually writes for you 5:24 — Stage 2: the asset factory inside Cinema Studio 10:36 — The naming trick that saves 400 wasted generations 11:25 — Stage 3: the pirate cabin, shot by shot 14:01 — The talking parrot fix that made the scene alive 15:41 — Directing a cannonball through a sea battle 18:01 — The ship blast that transitions into the desert 18:46 — Full pirate scene playback: wet-deck fall into the ocean transition 19:45 — Desert scene: ostrich riders and dust transitions 30:00 — Full jungle sequence playback 31:30 — The twist ending that reframes the whole film The frame this post opens with (agencies at $2,000/mo for AI ad creative, a $29 stack on the other side) is the pitch. What Adil actually shows is one operator building a 32-minute cinematic short across three settings, solo, in Krea Cinema Studio with Claude writing every prompt. The whole build is on screen, timestamp by timestamp. The turn comes at 10:36. He names every asset, tags them the same in Krea Cinema Studio and Claude, and the scene generator auto-matches inputs by those tags. That's why the pirate captain keeps the same face 20 minutes later. Skip this step and you burn 400 generations wondering why the model keeps drifting. At 17:19 he stops typing and draws. He sketches where the cannonball lands and where the crew stands, then feeds the picture to Claude. One drawing beats ten sentences. Every failed batch you avoid is credits back in your pocket. The whole thing runs on draft-first discipline. Batch cheap in low-res, iterate the prompt in Claude, commit to 4K once. That's how one person out-produces the retainer. He did it alone. In Krea. With Claude.

When your dog thinks he’s the king of the jungle… but the goat, cat, or squirrel has other plans 😂🐶💥 From epic stare-downs to zoomie chaos and total fails, dogs vs the animal kingdom never disappoints! Who’s winning in your house — the dog or the ‘prey’? Drop your funniest pet battle stories below! Tag your dog mom/dad crew!

Seedance 2.0 is pretty wild 100% AI What if Jurassic Park was real? PROMPT: Format: Found-footage vacation video, 15 seconds, seven shots with hard cuts — a tourist's camera roll from one full day, morning to dusk. Image reference: Use the attached gate photo as the exact visual reference for Shot 1 — match its architecture, proportions, torch placement, lighting, and signage precisely. Main subject: Young woman, blond hair in a loose low bun shedding strands, oversized faded olive t-shirt, denim shorts, white canvas sneakers, disposable-camera strap on her wrist. Realistic skin texture, minimal makeup, wide-eyed tourist joy. Maintain perfect visual consistency — identity, hair, clothing, proportions — across every shot and location. Location: A vast island dinosaur preserve. Jungle mountains, open savanna paddocks, wooden observation structures, tall electrified perimeter fences, dirt tour roads. Dinosaurs are photorealistic animals — muted natural hides, birdlike motion, visible breathing weight. Filmed like wildlife, never like monsters. Camera style: Mid-range smartphone, handheld by her friend. Heavy shake, autofocus hunting, motion blur on pans, compression grain, exposure pumping. Each cut is abrupt and mid-motion, like real unedited camera-roll clips. 00:00–00:03 — THE GATES (morning, filmed from inside a moving open-top tour jeep). The camera films forward over the seats as the jeep approaches the colossal timber gates from the reference image — flared stone-and-wood towers, flaming torches, jungle pressing in on both sides, the road still wet from morning rain. The doors groan open. She spins around from the front seat into frame, gripping the roll bar, and half-shouts over the engine: "Oh my god, this is wild! I can't believe we're actually here!" — as the jeep passes under the archway into shadow and out again. 00:03–00:05 — SAVANNA (late morning, wide shot from a raised wooden platform). She stands small at the rail against an open plain where a herd of long-necked giants wades through heat haze, one rearing slowly against the treeline. The camera zooms in shakily, overshoots, corrects. 00:05–00:07 — RIVER CROSSING (midday, low angle through the jeep's open side). Past her shoulder as the jeep fords a shallow river: a crested duck-billed dinosaur drinks twenty meters away, lifting its head to watch them pass, water dripping from its jaw. She raises her disposable camera; the phone catches its tiny click. 00:07–00:09 — JUNGLE FENCE (afternoon, handheld walking shot). Dim under the canopy beside a tall humming fence. She peers through the wire — and a small crested predator peers back from the ferns, head tilting in quick birdlike jerks. She backs up a slow half-step, whispering "okay... hi." Autofocus argues between wire and eye. 00:09–00:11 — THE HERD RUNS (late afternoon, chaotic pan from the jeep at speed). A flock of small striped dinosaurs floods across and around the vehicle. The camera whips left-right, blurred bodies streaking past, her whooping off-mic, dust on the lens. 00:11–00:13 — QUIET GIANT (golden hour, static shot at a wooden overlook). The frame finally settles: silhouetted against low sun, she stands at a rail as an immense horned dinosaur grazes just below, close enough to hear grass tearing. She isn't laughing anymore — just watching. 00:13–00:15 — DUSK ROAR (blue hour, half-framed and accidental). Filmed as the phone was being lowered, frame tilted: a colossal, deep bellow rolls in from the darkening jungle far off. Everyone freezes and turns toward it. Cut to black mid-turn. Audio: Ambient only, shifting per location — jeep engine and gate groan, her excited line over the motor, savanna wind, river wading, fence hum, thundering small feet, grass tearing, and the single distant bellow that swallows everything. No music. No narration. Style & quality boosters: Raw consumer camera-roll aesthetic, abrupt mid-motion cuts, heavy handheld instability, natural motion blur, golden-hour lens flare, coherent animal mass and physics — ground tremor, water displacement, dust. No text overlays, no watermarks. Goal: One unforgettable day inside the world's most famous dinosaur preserve, told through seven imperfect tourist clips — opening on the gates everyone recognizes, and closing on the sound that reminds them what lives beyond the fences.

我的跨境电商自动化运营日记（二） 最近两个月，我一直在尝试做一个开品分析 Agent。很多时候一个产品做不起来，不是广告没打好，而是产品从一开始就选错了。 而且，我发现大部分卖家的选品流程其实非常低效：打开 Amazon、看 Best Seller、 Helium10、 Jungle Scout、 TikTok，然后凭经验判断能不能做。虽然看起来专业，但本质上仍然是人工分析。 于是我开始想，能不能把整个开品流程拆解成一条数据流水线？让 AI 自动完成。 最近基于 Hermes 做出了第一版系统。 整体流程如下： Amazon Listing → Review Mining → Pain Point Analysis → Demand Validation → 1688 Supplier Scan → Profit Simulation → Go / No-Go 整个流程最重要的一步是评论挖掘。因为销量告诉你什么产品卖得好，而评论告诉你为什么卖得好以及为什么卖得不够好。这是机会所在。 第一层：评论挖掘 我抓取 Amazon 前50个 Listing，累计约 3 万条 Review，然后通过 Hermes 调用分析链路： 先进行 Embedding，再进行聚类，最后提取高频抱怨。 这里有个有意思的发现，很多卖家喜欢分析五星好评，实际上最有价值的是三星评论。 因为一星评论往往是情绪发泄，五星评论往往过于笼统，三星评论反而最容易暴露产品真实缺陷。 比如说我看到有个 Portable Blender 类目。Agent 最终发现：电池续航不足、清洗困难、无法处理冰块。这三个问题占全部负面反馈的 42%。 这时候其实就不用再去分析了，而是要去验证市场上是否已经有人解决这些问题。 第二层：需求验证 很多选品工具喜欢展示搜索量，但搜索量经常会骗人。 真正有价值的是：需求增长是否来自真实购买意图。 所以我让 Hermes 同时分析：Amazon Search Trend、TikTok 视频增长、Reddit 讨论量 如果只有 TikTok 数据上涨。 系统会判定：可能是短期流量驱动。 如果 Amazon 搜索量同步上涨，则说明真实需求正在增长。 这一步帮我过滤掉大量伪需求产品。 第三层：供应链匹配 这是很多 AI 选品项目做得最差的一步。 发现需求很容易，找到能赚钱的供应链很难。 我的做法是：直接抓取 1688 和 Alibaba。 分析工厂数量、MOQ、成本区间、发货周期，然后让 Hermes 自动生成 BOM 风险评估。 比如说：如果一个产品只有两三家工厂能生产。即使需求很好，系统也会降低评分，因为供应链风险太高。 第四层：利润模拟 这一步不是预测销量，而是预测失败。 我给 Hermes 建立了一个利润模型。 默认模拟三种场景： 1.广告成本上涨20% 2.客单价下降10% 3.物流成本上涨15% 如果产品在三种情况下仍然盈利，才会进入推荐列表。 这目前是整个系统最重要的规则，不知道能不能跑通，后面再随机应变 基本思路，先保住本金，再往外延伸利润 因为我发现往往毁了一个卖家的是低估了风险.... 暂时先写这么多

After a long day of driving we’ve finally had the time to explore a bit and it was WORTH it. This is somewhere in the Watkins Glen State Park in New York. For a moment I felt like I was deep in the jungle searching for an ancient lost city.