Elon Musk unveiled the Tesla Cybertruck in November 2019 and it became the most famous product launch failure in automotive history. Because the "unbreakable" windows shattered on live television in front of the entire world. Franz von Holzhausen, Tesla's head of design, was supposed to throw a metal ball at the driver window to demonstrate the armored glass. In rehearsals it worked perfectly. The glass held. The demonstration was supposed to be the dramatic highlight of the presentation. On stage Franz threw the ball. The glass shattered into a spiderweb of cracks. The audience gasped. Elon stared at the window. He said "oh my god." Then he told Franz to try the rear window. Franz threw the ball again. The second window also cracked. Elon stood on stage for the remaining 15 minutes of the presentation in front of a truck with two shattered windows. He did not leave the stage. He did not cut the presentation short. He continued presenting features and specs as if the most embarrassing product demo in Tesla's history was not sitting behind him in full view of every camera in the room. The stock dropped 6% the next day. The internet produced a million memes. Every automotive journalist wrote that the Cybertruck was dead on arrival. Then Elon tweeted the behind the scenes video of the rehearsal where the glass held perfectly and implied that the stage demo stress tested the glass beyond the rehearsal conditions. Within a week the Cybertruck had received over 250,000 pre orders. More than any truck launch in history. The broken windows generated more attention than a perfect demo ever could have. The failure became the marketing. The man who stood on stage in front of his most public failure and refused to flinch turned embarrassment into a quarter million orders.

In September 2013, with the rupee down close to 30% against the dollar since April, India's central bank opened a subsidised swap window to pull dollars into the country. It raised 34 billion and charged the banks 3.5% a year. The same instrument reopened on 8th June this year. In nine weeks it drew 56.85 billion dollars, and this time the Reserve Bank charged nothing at all. On 14th August it shut the window a month early, because the response had run further and faster than the RBI wanted. An Indian bank raises dollars from non-residents on a three to five year deposit, sells them to the RBI at spot for rupees, and agrees to buy the same dollars back at maturity at the identical exchange rate. At par. The central bank absorbs the entire currency hedge, which is why lenders could lift deposit rates from roughly 3% to as much as 7%, with HDFC, ICICI and Axis all repricing again in August. The effect on the headline was immediate. Reserves jumped 14.136 billion dollars in the week to 7th August, the biggest weekly gain since 30th January, carrying the total past 707 billion for the first time. India had entered the episode with 682.3 billion and called that adequate, which it was. Nine days before closing the facility, Governor Sanjay Malhotra said there was no proposal to withdraw it early. Every dollar that arrived is real, and so is the obligation attached to it. The RBI takes the currency now and commits to hand back the identical amount at maturity. Business Standard put it plainly, that the swap carries a corresponding forward obligation and should not be read as an unconditional permanent addition to reserves. That obligation is disclosed. It simply does not sit in the two numbers most people quote. Gross reserves is a stock with no time dimension, so the dollars count from the day they land. The IMF's reserve template measures predetermined drains out to one year, and these deposits run three to five. To see the other side you have to open the RBI's forward book, where the net short dollar position stood at 103.3 billion at the end of June, close to 15% of the August headline. Most of that predates this window. The July reading publishes on 31 August, and it is the first real test. Late last month Japan reached for the same kind of solution from the opposite direction. Rather than sell US Treasuries to fund yen intervention, Tokyo said it would draw dollars against them through a Federal Reserve repo facility, raising the currency it needed without liquidating the asset. Two sovereigns, the same few weeks, both meeting a dollar need by borrowing against their own future rather than selling their present. Neither operation is concealed. Both make the headline stronger while parking the obligation beyond the horizon the headline measures. None of this is fake reserves or a crisis in waiting. Maturities are staggered, positions can be rolled, and the RBI can put the dollars to work in the meantime. The pressure that prompted it was real, with an energy shock lifting oil and fertiliser import bills and foreign investors pulling 13.7 billion out of Indian markets this financial year through early June. India bought immediate currency stability. The price is a hedge subsidy sitting on the central bank, a liquidity problem in the rupee money market, and a refinancing question that arrives from 2029. In 2013 this was an emergency measure and the banks paid for it. In 2026 it is routine and the central bank pays instead. The window closed early because it worked. The bill arrives in 2029.

Seedance 2.5 vs WAN 3.0 - same viral video One of them is dead in the water - You judge Prompt: Handheld digital camcorder aesthetic, landscape framing. A gorgeous fitness and food influencer films herself by hand in selfie-cam and first-person style while cooking a late-night high-protein meal after training and a shower. Keep realistic hand shake, slightly crooked framing, autofocus briefly hunting between her face and the food, awkward zoom-ins onto the pan, occasional motion blur, brief lens steam-up over the hot pan that clears naturally, and small framing mistakes where her face or the pan edge slips out of frame. Mix handheld selfie footage with a few fixed external shots. Important: never show her placing, adjusting, or setting up a camera. When switching from handheld footage to a fixed external angle, use a clean jump cut as if the camera had already been positioned before the shot began. The camera itself must never appear on screen or in the window reflection. LOOK: Warm late-night kitchen light. A single warm pendant over the counter, cool blue night visible through the window behind her, under-cabinet strip light glowing on the backsplash. Slight camcorder softness, faint noise in the shadows, mild bloom on the pendant. Food realism is the core of the look: real sizzle with fine oil spatter, steam rising and curling from the pan, honest food textures with sear marks and glistening surfaces, knife cuts that separate cleanly, droplets on rinsed vegetables. Realistic skin texture, fresh-from-the-shower glow, clean dewy skin, realistic skin tones. STYLE: A realistic late-night what-I-eat-after-training vlog. The tone is relaxed, hungry, funny, quietly confident, and natural, talking to the lens like a friend while her hands keep working. She is clearly attractive and charismatic, but the video must feel like a believable self-recorded kitchen vlog, not a food commercial. Fast clean jump cuts, strong continuity, natural body language, real kitchen sounds, sizzle, chopping, no awkward dead moments. CHARACTER: The woman in the reference image. Her face, features, skin tone, body, and clothing must match the reference image exactly in every shot — she wears exactly what she is wearing in the reference image. Do not restyle, beautify, or alter her in any way beyond the reference. The one addition: a soft white towel wrapped around her head like a turban, as if she just stepped out of the shower. The towel stays neatly wrapped for the whole video. A few small damp strands of hair may peek out at the hairline and neck. Minimal or no makeup, fresh clean post-shower look. A small kitchen towel over one shoulder for most of the video. SETTING: A small warm real apartment kitchen at night. Wooden counter with knife marks, a gas hob with one cast-iron pan, a cutting board with chicken breast and vegetables, a bowl of cooked rice, a bottle of olive oil, salt in a small dish, a fridge with magnets and photos, dishes drying on a rack, dark window over the sink reflecting the warm kitchen. Lived-in and slightly cluttered. No other people, no pets. IMPORTANT CONTINUITY RULES: The same woman from the reference image must remain fully consistent in every shot. No face changes, no outfit changes, no body changes. The head towel stays wrapped in the same position in every shot — it never unwraps, falls, changes color, or disappears. The meal progresses in one direction only: raw ingredients, then chopping, then searing, then plating, then eating, and never reverses or regenerates. Food already cooked never becomes raw again. Hands and knife are the top priority: five fingers always, correct knife grip with curled guiding fingers, clean cuts, the knife never bends and never passes through her hand. The pan, board, oil bottle and rice bowl stay in the same positions. Steam and sizzle must behave with real physics. No extra people, including in the dark window reflection, and the reflection never shows a camera. No duplicated limbs. No camera visible. No camera setup shown. Keep her matching the reference image, warm, and photogenic in every shot. STORYBOARD: 30 seconds total, 10 cuts. (~3s, arm's-length selfie) She leans on the counter, towel wrapped on her head, clearly fresh from the shower after training, and grins tiredly at the lens. Dialogue: "Trained late. Showered. Starving. Let's cook." (~3s, handheld pan across the counter and back to her) The camera drifts across the board with raw chicken and vegetables, the pan, the rice bowl, then back up to her face. Dialogue: "Ten minute meal. Watch." (~3s, fixed external medium shot from across the counter) Jump cut. She is already chopping a red pepper with quick confident cuts, guiding fingers curled, pieces falling evenly. No camera setup shown. No dialogue, just the knife on the board. (~3s, same fixed shot) She slices the chicken breast into strips, seasons it from the salt dish with a high pinch, and rubs it in with her fingertips. Dialogue: "Protein first. Always." (~3s, tight handheld first-person shot over the pan) Oil shimmering, she lays the chicken strips in one by one and they hit with a loud real sizzle, fine spatter, steam rising into the lens which fogs for a beat and clears. No dialogue, just the sizzle. (~3s, handheld selfie while the pan sizzles behind her) She turns the camera on herself, fanning steam away from her face, laughing, one hand briefly steadying the head towel. Dialogue: "The smell. You have no idea." (~3s, tight handheld close-up of the pan) She flips the strips with tongs, showing deep golden sear marks, tosses in the peppers, and shakes the pan once so everything jumps and resettles. Dialogue: "That colour? That's the whole point." (~3s, fixed external shot) Jump cut. She plates it: rice pressed from the bowl, chicken and peppers over the top, a last drizzle of olive oil in a thin ribbon, and she wipes the plate rim with the towel like a chef, then smirks at her own seriousness. Dialogue: "Yes, I wiped the rim. Let me live." (~3s, tight handheld close-up) First fork bite, steam still coming off it, she chews, closes her eyes for a beat and nods slowly. Dialogue: "Ten minutes. That's it. Ridiculous." (~3s, arm's-length selfie ending) Plate in one hand, camera in the other, she backs out of the kitchen toward the sofa, flicking the kitchen light off with her elbow. Dialogue: "Okay. Eating. Good night." FINAL INSTRUCTION: The result must feel like a real self-recorded late-night cooking vlog by an athlete who actually cooks, filmed right after her shower. The highest priorities are matching the reference image exactly, correct hands and knife work at close range, one-directional cooking progression that never reverses, real sizzle, steam and food texture, the head towel staying consistently wrapped, honest warm kitchen light against the dark window, and subtle imperfection. Keep it warm, hungry, and real. Not a food commercial, not overhead recipe content, not stiff.

It took me a while even while working in the space to appreciate the power requirements of data centers and what it means to design power for them (in our case, nuclear power!). Easy to think that more energy, LCOE, etc. do all the work. I intuit the case on the ground as some “allowable” band of performance across a few dimensions: project risk, cost, speed, and power output. You have some leniency in the specifics of each (say, if marginal risk drops expected cost, or increases speed, you may make that tradeoff), but operators overall need a “goldilocks”: a happy medium across the four, without any “spike” (disqualifier) in risk, cost, timeline, or output. It is these spikes that defeat traditional nuclear categories as ideal (not passable in some situations, but ideal) power sources for data centers. Existing categories may be defined as microreactors, SMRs, and large plants. Microreactors “spike” in the output category: to power a 100 MW data center with 1 MW units (much less a 1 GW system!), you would need to build 100 plants. Having to build this volume of reactors cascades across the other categories (risk, timeline, cost) and invalidates the whole lot. Traditional SMRs are the best for data centers versus microreactors/large-scale plants (depending on your definition of "SMR"), but are not so small in practice: the core problem being that 10 MWe is the rough order of scale that can be manufactured in a factory and transported on normal trucks and road, without specialized (and extremely expensive) transport. So a single reactor producing 100 MW, much less 300 MW or 500 MWe, exists without the economies of scale provided by gigawatt-scale plants, but also without the speed and lack of capital risk provided by micro-scale reactors. They are still a little too hot for Goldilocks and not a “worst”, but mediocre-of-all-worlds situation. Large plants, finally, spike effectively in all categories. Even if we forget about the past two decades of American new-plant cost and timeline overruns and assume projects stay on schedule, they represent such a massive time horizon and capital investment that projects simply cannot match the rate at which data centers incrementally construct and turn on the plant, while presenting a degree of financial risk that freezes financing and insurance. Even if projects did not run over schedule and cost, they would delay data center developments by years. These being the disqualifiers for traditional nuclear, I've skipped over some important hard constraints, namely baseload and the thing actually being able to turn on. Baseload (intuitively, the variance of power output and whether it ever drops below some minimum) matters because you can't just have a data center turn off if it's, say, powered by a wind farm and the wind stops. It must be 24/7. This disqualifies or complicates lots of wind/solar projects for on-site data center developments. Implied in baseload is a high capacity factor: you can’t have the thing turn off or radically diminish its output for maintenance, refurbishing, or due to error. The other mentioned hard constraint that meshes with baseload in an interesting way is the fact that you do actually need the power. This sounds like a given, but alas, fails in the most common of solutions to just getting some power: connecting to the grid, evidenced by many a data center impeded for months (if not years) or canceled per the strain of permits, local pushback, and building grid interconnect and infrastructure. Policy like the Ratepayer Protection Act is formalizing this conclusion: data centers needing to bring their own power to sites. Combining these two hard constraints explains why fuel cells and combined cycle nat gas plants have grown incredibly valuable (love you Bloom!): one, you can readily deploy them, and two, they provide steady, 24/7 power to meet baseload. Few other generation sources fit these requirements as cleanly and nat gas has that happy medium of cost, risk, and size. Nuclear is one of the few other generation sources that meets these hard constraints, and maybe the sole source that fits them more cleanly than gas: 24/7 baseload availability, reactor and turbine redundancy, and no grid interconnect required. Its massive problem to date, going back way before data centers, has been that Goldilocks problem of risk, cost, and speed. This is functionally the approach we're taking: use the inherent advantages of nuclear power, then balance the scales. I’ll tackle them in order: Power Output. We’ve established our rough minimum and maximum bounds: anything that’s clearly a microreactor or near it is below the minimum, and anything SMR-scale (per reactor) or up is over the maximum. Instead, you want something that matches how a data center actually gets built and turned on, regardless of whether it requires 50 MW or 5 GW: incremental completion and operation of individual buildings or server halls over the course of months to years on the wider project campus. These buildings or server halls exist on a rough order of 10 MW to 100 MW. Building on the bottom end of that scale is still too inefficient with regard to capital and space, while the higher end takes too long to build. We also know the maximum power output for individual reactors to make them manufacturable in mass and transportable is roughly 10 MWe. This all produces a “Goldilocks” medium of a 50 MWe modular nuclear plant built on 10 MWe reactors. Having multiple reactors (beyond the 10 MWe constraint) solves the baseload reliability constraint, as they ensure the 50 MWe plant is never completely offline, while 50 MWe is a much better fit for the power needs of data centers. Shifts past 10 MWe per reactor or under 50 MWe as a modular unit to stack harms the economics of the happy medium. So, we get “lego blocks” of modular power units: 10 MWe reactors compiled in 50 MWe plants, with plants added in 12-month increments to match the pace of data center construction. Taking power output as the given for our first constraint, we may evaluate the others: Project Risk. With orders taking just 12 months to turn-on and incrementally scaling up to meet data centers as they turn on, project risk gets massively reduced, which eliminates those financing and insurance disqualifiers present for large-scale developments. So, our chosen output medium also stays in that qualifying bound for risk. Project Speed. The structure of the output – 10 MW reactors that can fit on trucks and be manufactured easily – is what unlocks the workable deployment speed. Going over this 10 MWe bound would induce a step-change decrease in speed and increase in cost. Power Cost. Speed is the unlock for cost: if you can build and deploy lots of modular, on-site power plants fast, the cost will be there, and schedule and cost overruns won’t. This all in mind, you get a fundamentally new category of reactor product that doesn’t fit into the microreactor, SMR, or large-scale plant categories: this is what we call the XMR, or extra modular reactor, and the 50 MWe product it unlocks in a manner that is designed from the ground-up for data centers. Reactors are sized for the factory, plants are sized for the data center. The XMR is the only nuclear product not disqualified in any way for data center usage. Now, I previously touched why natural gas is such a match for data center power needs, as it’s basically the same list as nuclear: baseload, onsite without requiring grid interconnect, and affordable, fast, and low-risk. So why use nuclear at all, and not just natural gas? A few reasons here: to point out the obvious, the supply of turbines is backed up for years. It's less of a this-or-that question because there is simply so much demand that all supply which meets customer criteria will be brought on at scale – it's the supply problem for all possible solutions. Nuclear has the direct benefit versus natural gas of being clean (both zero-carbon and no airborne pollutants), having minimal fuel price exposure, reaching a high (90%+) capacity factor, and not requiring continuous fuel lines. Net: having one big advantage as a power source is nice, but not having a disqualifying attribute is the baseline. Nuclear fits within that model, but requires a new product category: XMRs.

Made with seedance 2.5 Prompt: Main Subject: young korean woman <<>>, naturally attractive, realistic skin, minimal makeup, long dark hair loosely tied back. Wearing an oversized faded blue sweatshirt, loose beige pants, white sneakers, and a thin silver necklace. Preserve her exact identity, facial features, hairstyle, and appearance throughout. Location: Quiet Seoul neighborhood just before sunrise. Narrow residential streets, old apartment buildings, utility poles, parked cars, tangled overhead wires, small shops with shutters still closed, and an elevated railway visible beyond the rooftops. Cool early-morning haze slowly turning warm as the sun rises. Style: Ultra-realistic early-2000s Sony MiniDV home video. Candid, quiet and completely unstaged. Handheld shake, imperfect framing, autofocus hunting, exposure shifts, faded colors, soft contrast, DV compression, slight motion blur and low-level microphone noise. No stabilization or modern cinematic movement. 00:00–00:03: She walks quietly through the nearly empty neighborhood, hands tucked into her sweatshirt pockets. The camera follows several steps behind, occasionally drifting toward the empty street. 00:03–00:06: She reaches a small pedestrian overlook near the elevated railway and leans against the railing. She looks tired but peaceful, watching the tracks below. 00:06–00:09: The distant sound of a train grows louder. She turns toward the tracks and waits. The camera struggles to focus in the dim morning light. 00:09–00:12: The first train suddenly rushes past. The camera shakes from the operator's reaction, briefly losing her behind the moving train. Morning light reflects across the windows. 00:12–00:15: She looks back toward the camera with a sleepy little smile, then turns toward the brightening skyline. The camera slowly drifts toward the railway and hazy rooftops as the recording ends. Audio: Natural morning ambience only—distant train sounds, railway vibrations, birds, footsteps, faint traffic, wind, electrical hum and a few distant voices. No music, narration, or added effects. Goal: Make it feel like a forgotten MiniDV recording from an ordinary morning in Seoul. Quiet, intimate, slightly lonely but comforting, with the nostalgia coming from the mundane setting, early-morning atmosphere, and imperfect home-video camera rather than cinematic staging.

If you think AI is "not there yet" - You are not informed Here is what I did on maxfusion Full prompt: Handheld digital camcorder aesthetic, landscape framing. A gorgeous fitness and food influencer films herself by hand in selfie-cam and first-person style while cooking a late-night high-protein meal after training and a shower. Keep realistic hand shake, slightly crooked framing, autofocus briefly hunting between her face and the food, awkward zoom-ins onto the pan, occasional motion blur, brief lens steam-up over the hot pan that clears naturally, and small framing mistakes where her face or the pan edge slips out of frame. Mix handheld selfie footage with a few fixed external shots. Important: never show her placing, adjusting, or setting up a camera. When switching from handheld footage to a fixed external angle, use a clean jump cut as if the camera had already been positioned before the shot began. The camera itself must never appear on screen or in the window reflection. LOOK: Warm late-night kitchen light. A single warm pendant over the counter, cool blue night visible through the window behind her, under-cabinet strip light glowing on the backsplash. Slight camcorder softness, faint noise in the shadows, mild bloom on the pendant. Food realism is the core of the look: real sizzle with fine oil spatter, steam rising and curling from the pan, honest food textures with sear marks and glistening surfaces, knife cuts that separate cleanly, droplets on rinsed vegetables. Realistic skin texture, fresh-from-the-shower glow, clean dewy skin, realistic skin tones. STYLE: A realistic late-night what-I-eat-after-training vlog. The tone is relaxed, hungry, funny, quietly confident, and natural, talking to the lens like a friend while her hands keep working. She is clearly attractive and charismatic, but the video must feel like a believable self-recorded kitchen vlog, not a food commercial. Fast clean jump cuts, strong continuity, natural body language, real kitchen sounds, sizzle, chopping, no awkward dead moments. CHARACTER: The woman in the reference image. Her face, features, skin tone, body, and clothing must match the reference image exactly in every shot — she wears exactly what she is wearing in the reference image. Do not restyle, beautify, or alter her in any way beyond the reference. The one addition: a soft white towel wrapped around her head like a turban, as if she just stepped out of the shower. The towel stays neatly wrapped for the whole video. A few small damp strands of hair may peek out at the hairline and neck. Minimal or no makeup, fresh clean post-shower look. A small kitchen towel over one shoulder for most of the video. SETTING: A small warm real apartment kitchen at night. Wooden counter with knife marks, a gas hob with one cast-iron pan, a cutting board with chicken breast and vegetables, a bowl of cooked rice, a bottle of olive oil, salt in a small dish, a fridge with magnets and photos, dishes drying on a rack, dark window over the sink reflecting the warm kitchen. Lived-in and slightly cluttered. No other people, no pets. IMPORTANT CONTINUITY RULES: The same woman from the reference image must remain fully consistent in every shot. No face changes, no outfit changes, no body changes. The head towel stays wrapped in the same position in every shot — it never unwraps, falls, changes color, or disappears. The meal progresses in one direction only: raw ingredients, then chopping, then searing, then plating, then eating, and never reverses or regenerates. Food already cooked never becomes raw again. Hands and knife are the top priority: five fingers always, correct knife grip with curled guiding fingers, clean cuts, the knife never bends and never passes through her hand. The pan, board, oil bottle and rice bowl stay in the same positions. Steam and sizzle must behave with real physics. No extra people, including in the dark window reflection, and the reflection never shows a camera. No duplicated limbs. No camera visible. No camera setup shown. Keep her matching the reference image, warm, and photogenic in every shot. STORYBOARD: 30 seconds total, 10 cuts. (~3s, arm's-length selfie) She leans on the counter, towel wrapped on her head, clearly fresh from the shower after training, and grins tiredly at the lens. Dialogue: "Trained late. Showered. Starving. Let's cook." (~3s, handheld pan across the counter and back to her) The camera drifts across the board with raw chicken and vegetables, the pan, the rice bowl, then back up to her face. Dialogue: "Ten minute meal. Watch." (~3s, fixed external medium shot from across the counter) Jump cut. She is already chopping a red pepper with quick confident cuts, guiding fingers curled, pieces falling evenly. No camera setup shown. No dialogue, just the knife on the board. (~3s, same fixed shot) She slices the chicken breast into strips, seasons it from the salt dish with a high pinch, and rubs it in with her fingertips. Dialogue: "Protein first. Always." (~3s, tight handheld first-person shot over the pan) Oil shimmering, she lays the chicken strips in one by one and they hit with a loud real sizzle, fine spatter, steam rising into the lens which fogs for a beat and clears. No dialogue, just the sizzle. (~3s, handheld selfie while the pan sizzles behind her) She turns the camera on herself, fanning steam away from her face, laughing, one hand briefly steadying the head towel. Dialogue: "The smell. You have no idea." (~3s, tight handheld close-up of the pan) She flips the strips with tongs, showing deep golden sear marks, tosses in the peppers, and shakes the pan once so everything jumps and resettles. Dialogue: "That colour? That's the whole point." (~3s, fixed external shot) Jump cut. She plates it: rice pressed from the bowl, chicken and peppers over the top, a last drizzle of olive oil in a thin ribbon, and she wipes the plate rim with the towel like a chef, then smirks at her own seriousness. Dialogue: "Yes, I wiped the rim. Let me live." (~3s, tight handheld close-up) First fork bite, steam still coming off it, she chews, closes her eyes for a beat and nods slowly. Dialogue: "Ten minutes. That's it. Ridiculous." (~3s, arm's-length selfie ending) Plate in one hand, camera in the other, she backs out of the kitchen toward the sofa, flicking the kitchen light off with her elbow. Dialogue: "Okay. Eating. Good night." FINAL INSTRUCTION: The result must feel like a real self-recorded late-night cooking vlog by an athlete who actually cooks, filmed right after her shower. The highest priorities are matching the reference image exactly, correct hands and knife work at close range, one-directional cooking progression that never reverses, real sizzle, steam and food texture, the head towel staying consistently wrapped, honest warm kitchen light against the dark window, and subtle imperfection. Keep it warm, hungry, and real. Not a food commercial, not overhead recipe content, not stiff.

Journey through Hell made with seedance 2.5 prompt :One continuous 30-second chaotic amateur first-person smartphone video filmed by a standing passenger inside a completely packed magnetic-levitation commuter train. Single unbroken take with no cuts, jump cuts, dissolves, crossfades, double exposures, portals, morphing or artificial scene transitions. The train begins as an ordinary weekday commute on Earth and then physically travels downward on an impossible journey — through subway tunnels into bedrock, into a colossal void beneath the crust, past a soot-blackened waiting platform, through an immense eroded gate, out above a burning plain, down the terraced wall of a vast pit, and into the inhabited depths of Hell. Everything must feel physically connected, as though the same train is genuinely travelling down through each environment. The tone is bureaucratic dread, not horror-movie shock. This is a scheduled service. The route is old, the infrastructure is worn, and the train runs it the way it runs any other line. The horror comes from how ordinary the journey is and how enormous the destination turns out to be. The interior is a completely packed standing-room-only maglev commuter car. Passengers are pressed shoulder-to-shoulder, gripping overhead straps and vertical poles. Backpacks are squeezed between bodies, coats and loose clothing shift with acceleration, straps swing on their inertia, and the entire carriage constantly vibrates and rattles. The camera is a cheap smartphone held at chest height by one standing passenger who grips a pole with the other hand. The phone itself is NEVER visible because the phone is the camera. The framing is crooked, slightly off-centre, partially blocked by shoulders and arms, and imperfect like genuine accidental footage. The camera constantly shakes, rolls, yaws and gets thrown around by acceleration. Use realistic rolling-shutter distortion, autofocus hunting, exposure pumping, blown highlights, crushed noisy shadows, low-bitrate compression, macroblocking and smeared motion blur. It must look like genuine spontaneous smartphone footage, not professional cinematic footage. The camera always looks through the LEFT-SIDE WINDOWS at approximately 90 degrees to the train's direction of travel. The train always travels forward and the outside world always streams past the windows from front-to-back. Never switch to a forward-facing train-nose view. Never show the front of the train. The same carriage, same passengers, same poles, same straps and same windows remain visually consistent throughout the entire journey. The interior is the constant realistic anchor while the outside world becomes increasingly impossible. 0 to 3 seconds. Begin with an ordinary overcast weekday commute on an elevated urban line. Grey apartment blocks, rooftop water tanks, a scrapyard, overhead wires, a canal and traffic on a road below streak past the left windows at different distances with realistic parallax. The passengers are tired and mostly uninterested — some on phones, some staring out, some talking quietly. A calm public-address chime sounds and an announcer quietly says, "Next stop: Hell." Nobody reacts. One passenger glances up briefly and goes back to their phone. The train accelerates and everyone instinctively tightens their grip as the carriage gives a hard lateral jolt. 3 to 6 seconds. The line drops into a cutting and then into a tunnel. Tiled subway walls, cable runs, service lights and a passing platform strobe across the windows in hard bands of light and dark, throwing the carriage into stuttering illumination. The tunnel ages as the train descends: modern concrete becomes older brickwork, then rough-cut stone. The fittings become scorched and soot-caked — blackened signal lights, corroded brackets, cabling burnt down to bare metal. This route has been running a long time. The gradient steepens noticeably; passengers lean back against the pitch and the straps hang visibly off-vertical. 6 to 9 seconds. The tunnel wall becomes raw geology and the train is now clearly descending through the crust at impossible speed. Through the left windows the rock face streaks past in visible strata — pale limestone, dark shale, red iron-stained bands, seams catching the carriage light. The air begins to heat and passengers loosen collars. Then, under the mechanical roar, a sound arrives before anything is visible: a vast, distant, continuous mass of human voices, far away and heavily reverberant, never resolving into individual words. It is not loud. It is simply there, and it does not stop. Two passengers look at each other. Nobody says anything. 9 to 11.5 seconds. The rock wall falls away entirely and the train emerges into a void larger than a city, with no visible far side. It runs along a ledge on the wall; below the windows the space drops away into darkness. Stone columns kilometres tall stand in the void with strong parallax — near ones sweeping past, distant ones barely shifting. Far below and far ahead, a faint orange glow is already visible. It must already exist in the frame from this moment and grow continuously from here forward without ever popping or suddenly enlarging. The voices are louder here, spread across an enormous space. One passenger near the glass leans forward slightly. Another quietly says, "Ohh." 11.5 to 14 seconds. The train passes a station. A soot-blackened platform is cut into the rock wall — worn edge, dead lamps, faded markings unreadable under the grime — and figures are standing on it in an orderly queue, waiting, facing the track, completely motionless. They are seen for less than a second at speed, as dark shapes against the platform light, never close enough to read faces. The train does not slow. Nobody boards. The platform is gone behind the carriage. This is the moment the passengers understand. Several stop looking at their phones. Embers begin drifting upward past the windows from below. 14 to 16 seconds. Ahead, a wall crosses the entire void — a single continuous barrier extending beyond sight in both directions and upward past the ceiling. The train passes through an immense gateway cut into it, an arch hundreds of metres tall, its stone eroded smooth by an unimaginable volume of traffic, its carved markings worn past legibility. For half a second the carriage is in the shadow of the arch and everything goes dark. Then it is through, and the light on the far side is completely different: warm, hard, and coming from below. 16 to 19 seconds. The train races above a burning plain. Outside the left windows: a vast crusted expanse of dark solidified ground cracked into slow-moving plates with brilliant orange fissures running between them, distant fountains of molten material rising and falling in slow motion because of their true scale, and smoke columns standing kilometres high. Running across it are raised stone causeways, and on the causeways are crowds — dense, continuous, moving slowly, all in the same direction, extending to the limit of visibility. They are seen only as masses at distance, never in detail. The light entering the carriage is now dominant and hard, throwing sharp orange edges on faces, poles and straps with deep black shadows behind them. The interior air is visibly hazy. The voices are constant. 19 to 21 seconds. The plain ends at the rim of a colossal pit — a shaft so wide the far wall is only a suggestion in the haze. The train races over the edge and begins descending along the interior wall. The wall is terraced: enormous concentric ledges receding downward, each deeper and darker than the last, disappearing into smoke layers. The descent acceleration pushes the passengers down and forward against the poles. Ash begins striking the windows and streaking backward. 21 to 23 seconds. The terraces are populated. Endless slow processions move along every ledge, strings of figures following the curve of the wall down into the smoke and out of sight, lit from below by the fires beneath them. There is no chaos in it — it is orderly, patient and entirely without end, which is worse. Furnace mouths open in the rock face, glowing white at their throats, and long queues stand before them. Everything is at distance. Nothing is close enough to resolve. The light entering the carriage takes on a deeper red and the fluorescents are overwhelmed. Passengers nearest the window press slightly closer to the glass. 23 to 25 seconds. Enormous shapes move among the terraces — figures many times the height of the crowds around them, walking slowly along the ledges, their scale established only by comparison. They do not look at the train. Distant winged forms cross beneath the carriage at low altitude with slow heavy wingbeats appropriate to their size and vanish into the smoke. Stone pens and barred openings are cut into the wall in their thousands, receding into the haze like housing. Ash accumulates in the corners of the windows. The carriage is hot enough that the glass fogs and clears in waves. 25 to 27 seconds. The train reaches the deep layer and the full extent of Hell reveals itself. It is not a city on a human plan and it is not a cavern. It is a continuous inhabited geology extending in every direction — towers of black stone fused into cliff faces, vaults hollowed out of the rock at cathedral scale, rivers of molten material running in cut channels between districts and falling in slow luminous cataracts to levels further down, bridges spanning gaps kilometres across, and everywhere on all of it, crowds. Smoke columns rise for kilometres and flatten against unseen ceilings. Every level is lit by its own fires. The train races along a ridge line through the middle of it; arches and aqueduct spans pass overhead and terraces stream past below the window with violent parallax. It is impossible to see where any of it ends. 27 to 29 seconds. The depths continue past the windows. The camera struggles badly with the contrast, blowing out the fires and crushing everything else into noise. Ash cakes the corners of the glass. Passengers are pressed against the windows in total silence, faces lit from below in hard orange, expressions stunned and completely still. One person quietly whispers, "What is that?" Nobody answers. Autofocus hunts between the ash on the glass and the world beyond it. 29 to 30 seconds. The fires end. The train passes out of the smoke and the lowest region opens — and it is not burning. It is a vast frozen plain stretching beyond the visible horizon, an impossibly wide sheet of dark grey ice, absolutely still, lit by nothing but a faint pale glow from within itself. Shapes are visible held motionless within it, spaced far apart, receding to the horizon, never shown in detail. Every exterior sound stops at once — the voices, the fires, everything — leaving only the carriage. The heat drops out and frost blooms instantly across the outside of the windows. The scale feels planetary. The passengers stare in complete silence. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." The train continues moving. The camera keeps shaking naturally as the frozen expanse extends endlessly beyond the left window. Lighting The lighting must evolve naturally throughout the journey. Begin with flat overcast daylight around 6500K. In the tunnels use harsh strobing bands from passing service lights against near-total dark, then let the sources become sparse until the carriage's own weak fluorescent tubes are the only illumination. In the void the interior lights fall off into nothing. Then introduce a growing warm orange from below — first a faint wash on the lower half of faces, then mixing with the cold interior white, then completely dominating it: hard, high-contrast, sharp-edged, with deep black shadows. Above the burning plain it should be strong enough to blow out the phone's sensor at the window. In the deep layer it becomes red-orange and omnidirectional from countless fires at every distance. In the final second all warm light vanishes and is replaced by a faint pale luminance from the ice, cold and almost sourceless. All exterior light must enter naturally through the LEFT-SIDE WINDOWS and fall across passengers' faces and clothing at every stage. Do not add artificial interior lighting to create the colours. Passengers Keep all passengers completely ordinary throughout the journey. Realistic skin texture, subtle capillary variation, natural blinking, breathing, eye movement and imperfect facial symmetry. Clothing has realistic folds and responds to acceleration. Passengers must continuously perform small independent movements: shifting weight, adjusting grip, loosening a collar in the heat, wiping fog from the glass, turning their heads, tightening their hands around poles. Do not make them freeze. Do not make everyone react at the same moment. Do not let them perform dramatic acting. Their emotional progression is subtle: indifference at the announcement → mild confusion in the tunnels → unease when the voices arrive → the exact moment of understanding as the waiting platform passes → dread on the descent → complete stunned silence in the depths. No screaming. Only one soft "Ohh" at the first glow and one whispered "What is that?" near the end. The most powerful reaction is no reaction — just faces pressed against the glass, lit from below by something that should not exist. Physics The train always travels forward and always downward after the tunnel. Passengers sway according to acceleration and lean against the gradient; straps hang off-vertical on the descents. Loose clothing reacts to movement. Exterior objects have different velocities and distances with strong realistic parallax at every stage — near stone columns sweep past, mid-distance terraces move steadily, distant districts barely shift. Ash and embers must move independently of the train, drifting upward on thermal currents rather than streaming with the carriage. Heat must be expressed physically: window fogging and clearing, haze inside the carriage, frost at the very end. Enormous figures and flying forms must move slowly, because they are enormous. Nothing teleports, nothing freezes, nothing suddenly appears, and nothing changes position without physical cause. No transitions There are absolutely no visual transitions. No dissolves, crossfades, portals, morphing or ghosted overlays. Every environment change happens because the train physically travels into the next environment. The city becomes a cutting, the cutting becomes a tunnel, the tunnel becomes bedrock, the bedrock opens into the void, the void contains the waiting platform, the void ends at the wall, the gateway gives onto the burning plain, the plain ends at the pit rim, the rim becomes a terraced descent, the terraces fill with processions, the processions become the full inhabited depths, and the depths open onto the frozen plain. It must feel like one physically continuous impossible journey where every step is the inevitable consequence of the step before it. Audio Audio must be entirely diegetic with no music. Use cheap compressed smartphone microphone quality. The train produces a constant deep maglev roar, mechanical clatter, rattling poles, vibrating windows, swinging straps and low-frequency carriage rumble, plus passenger breathing and clothing movement. In the tunnels add hard reverberant slap-back and pressure changes as the train passes openings. The defining sound of this video is the voices. They must arrive at 6 to 9 seconds, before anything is visible, as a distant continuous mass of human voices with enormous reverberation, never resolving into words and never rising to screaming. From that point they never stop. They grow with depth and spread across a wider stereo field as the spaces open up. Beneath them, a very low frequency resonance builds steadily, felt more than heard. Above the burning plain add a broad distant roar and irregular deep concussions at long intervals. Ash strikes the hull as fine irregular ticking. In the depths the voices are vast, layered at many distances, and mixed with immense indistinct industrial-scale sound that never resolves into detail. In the final second every exterior sound stops at once — total silence outside, leaving only the carriage, the passengers' breathing, and ice creaking against the hull. At approximately 29.7 seconds the calm PA voice says quietly, "Welcome to Hell." Style and content limits The overall style must remain dirty photorealistic amateur smartphone footage despite the spectacular environments. Vertical 9:16, real-time speed, no slow motion, no stabilisation, no cinematic camera, no perfect composition, no clean VFX presentation. Use heavy shadow noise, blown highlights, low-bitrate compression, rolling-shutter skew, autofocus hunting, auto-exposure pumping, smeared motion blur and crooked framing. The camera should occasionally be partially blocked by a shoulder, arm or nearby passenger, and should struggle badly with the extreme contrast between fire and darkness. Hell is conveyed through scale, architecture, crowds at distance, fire, ash and sound. Do not show suffering, injury, bodies, blood, restraints or any graphic content. Every figure outside the train stays a distant silhouette or part of a mass — none is ever close enough for its face or condition to be read. Do not use recognisable religious iconography or legible text of any kind; all markings are eroded past legibility. Do not make it look like a game cinematic or a clean VFX render. The world outside should be overwhelming and physically impossible, but the recording itself must look raw, accidental and believable. The most important rule ONE SINGLE CONTINUOUS TAKE. No cuts, no jump cuts, no dissolves, no crossfades, no scene resets and no artificial transitions. The same train, same passengers and same camera remain present from beginning to end. The orange glow must be visible as a faint distant smudge from the first cavern and grow continuously and inevitably until it fills the window. The voices must arrive before the fire and never stop until the final second. The heat must build physically across the whole descent so that its total disappearance at the end lands as a shock. The population must emerge gradually — the waiting platform, then crowds on the causeways, then processions on the terraces, then the full inhabited depths — never appearing all at once. End while the train is still moving, the frozen plain extending endlessly beyond the left window, passengers silent, carriage still shaking, the PA announcement fading into the sound of ice.

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 Unbounded history is the enemy of cost and latency! Use history control and compaction to keep context size optimal. 📌 Title: Sessions – Control history / Compaction 🔗 URL: 🧩 Overview Long-running sessions cause history bloat, driving up token costs and latency. Use `SessionSettings(limit=N)` to cap history to the most recent N items, `session_input_callback` for custom trimming and reordering, `OpenAIResponsesCompactionSession` for automatic compression, and manual `run_compaction()` for idle-time optimization. 🛠 Usage The simplest approach is `SessionSettings(limit=50)`. Create an agent with `Agent(name="assistant", instructions="Answer helpfully")` and a session with `SQLiteSession("user_12345")`, then pass the settings through `RunConfig`: `await "What's the latest?", session=session, run_config=RunConfig(session_settings=SessionSettings(limit=50)))`. Only the most recent 50 items are retrieved from the session. For advanced control, use `session_input_callback`. Define a `custom_history_filter(history, new_input)` function that keeps system messages via `h.role == "system"`, limits regular exchanges to `history[-20:]`, and returns `filtered + new_input` (`new_input` is a list). Pass it as `await "...", session=session, run_config=RunConfig(session_input_callback=custom_history_filter))`. For automatic compression, use `OpenAIResponsesCompactionSession`. Create `underlying = SQLiteSession("user_12345")`, wrap it with `OpenAIResponsesCompactionSession(session_id="user_12345", underlying_session=underlying)`, and run `await "Summarize our discussion so far", session=session)` — long conversations are automatically summarized and compacted. For manual compaction, disable auto-compaction with `OpenAIResponsesCompactionSession(session_id="user_12345", underlying_session=underlying, should_trigger_compaction=lambda _: False)` and call `await "force": True})`. Running this during user idle time improves latency on the next request. 🏗 Practical Patterns `SessionSettings(limit=50)` is the simplest cost control. Only the most recent 50 items are retrieved and sent to the model, while the default `limit=None` retrieves everything. This works well for customer support where recent exchanges matter most. `session_input_callback` is for advanced control. For example, always retain system messages and VIP customer flags while limiting regular exchanges to the most recent 20. The key insight is that it only filters what's sent to the LLM — storage remains unchanged. `OpenAIResponsesCompactionSession` performs automatic compression, summarizing long conversations into shorter representations to use the context window efficiently. For streaming-first applications, manually running `run_compaction()` during user idle time is effective. The next request starts from compacted history, improving time-to-first-token latency. 💡 Use Cases 💰 Token cost capping via limit settings 🔧 Selective context retention with custom callbacks 📦 Context optimization for long sessions via auto-compaction ⚡ Streaming-first latency improvement with idle-time compaction ⚠️ Caveats - `limit` caps how many items are retrieved, not what is stored — the session keeps the full history, but anything beyond the limit never reaches the model, so set the value carefully - `session_input_callback` runs on every request — avoid heavy processing - Auto-compaction depends on summarization accuracy; exact numbers and proper nouns may be lost - `run_compaction()` involves API calls, so calling it too frequently increases costs ✨ Proper history control keeps cost and latency optimal even in long-running sessions! #OpenAIAgentSDK# #AIAgent#

🤖 AI Signal (BSC) $Ragu CA: 0xf1dca9bf42c5f565063f493f693455c86dff7777 Ragu is a sauce-and-mascot name, and it was about a minute old when our signal fired. It sits on Flap on BSC, still on the bonding curve at 71% of the way to graduation. The odd part is the buyer list. Three wallets bought within 1 minute of each other, and the deployer has already sold out of this token. There is no clear theme beyond the mascot angle. On X, @whashywash, with 25800 followers, told another account "We wanna make Ragu a bncb mascot" and dropped this contract address. That is the only post naming this exact contract, so attention is a single voice rather than a crowd, and most same-symbol chatter is about other tokens. Since our signal, buys have outnumbered sells fifteen to one in the short window we track, and market cap is $32.4K. Volume is small against that cap. Graduation has not happened yet. Holders doubled to 60, but the top ten still hold 43.4%. Wallets from our smart-wallet list bought, the first of them 36 s after launch. Bundlers sit on 38.9% of supply, snipers on 36.3%; both groups hold the majority of early buys, so a few coordinated sellers could move the price. The deployer has 70 prior tokens in our records, with 6 graduating, and their wallet was funded from an address we track. Two smart wallets bought, and the first bought 36 s after launch. 📊 @ +57 s: MC $19.5K · Liq $8.4K · Holders 41 · 1h +254% · 1h Vol $9.1K 📈 now (+59 s): MC $32.4K · Holders 60 · 5m buys/sells 30/2 Signals on Telegram: Houndsbot Not financial advice. Analysis only · DYOR · Path since our signal (+42s): $32.4K -> $27.4K (0.85x now, peak 1.00x at +12s) #BSC# #memecoin# Follow @houndsmeme for the daily record.

It's Week 3 of our Family Gap Year in Tokyo™️ and it feels like we're cheating. Not because the Yen is at 158 (which means an entire sushi meal for under $50). But because my 12 year old has time traveled back into the Magic Window. When she entered Middle School we stopped being the center of her universe. Friends, extra-curriculars and her pursuit of her independence made her want to be around mom and dad way less. Totally understandable, still sad. Now 18 months later she's "reverted" back to being our little girl. Maybe it's the emotional intensity of this life change. Or the fact that we literally see each other for 14 straight hours. Or that being in a girl in a wealthy suburb speed ran her maturity. (Or maybe there's no one here to catch her holding her dad's hand!) Overall the family bonding has been pretty incredible. It's basically impossible for my wife and I to get any alone time (my podcast listening has plummeted!), yet maybe that's a feature, not a bug. A lot of our bad habits from the US have gone away. Doom scrolling before bed, 3 hour binges of Saylish Matter and rarely eating meals together. (Yet we've picked some new ones, like daily 7-11 ice cream runs and loading up on Sonny Angels.) This was the first time this trip stopped feeling like vacation and that we are living here. We crossed the threshold of enjoying eating out for every meal - despite the unfettered access to cheap delicious food. We've settled into a routine, work/school/life admin from 8-12, followed by an outing (new neighborhood, cultural exhibit, activity) and then an early dinner before coming home. We're starting to discuss the next destination for November, probably Seoul, Taipei or Bangkok. School has been mixed. Eldest is chugging away in her online school. (The apple doesn't fall far from the tree.) Youngest just DGAF about structure, routine and work. So a multi hour struggle just to get a 30 minute assignment done. It's pretty stressful, especially as it plays into my (likely ungrounded) fear that she's falling behind. She does have a Zoom math teacher and we might up the sessions to cover some writing. However, the downstream learning has been amazing. They're both obsessed with Duo Lingo and starting to practice Japanese in real situations. There's a real interest in the world beyond the US, which leads to interesting discussions on collectivism, immigration, assimilation, freedom of speech, longevity and foreign exchange. So I guess they're actually ok?!?