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Never came across this issue on Model 3 before Tesla Model 3 windshield wiper fluid spray stopped working Had front window replaced Wiper fluid was as good as empty Refilled wiper fluid Spray audibly working, but…. No washy washy

82,400 Aufrufe • vor 9 Monaten •via X (Twitter)

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Breaking news 🗞️ 🚨 Tesla just quietly solved a problem in Australia. The Model X is gone from our market. But the new Model Y L Premium AWD might actually be the closest thing we have to a replacement. And honestly… it makes a lot of sense. ⚡ Tesla Model Y L – Key Specs • 0–100 km/h: ~5.0 sec • Range: ~681 km WLTP • Top speed: 201 km/h • Seating: 6 adults • Supercharging: 250 kW • ~288 km added in 15 min 💰 Australian pricing (before on-road costs) Model Y Long Range AWD → $68,900 Model Y L Premium AWD → ~$74,900 So for roughly $6k more, you get: ✔ 3 rows ✔ 6 seats ✔ Much larger cabin ✔ ~400L extra cargo capacity ✔ Longer wheelbase ✔ Even more range 📊 Quick comparison Model Y Long Range AWD • 5 seats • ~600 km range • 0–100 km/h: 4.8 sec • $68,900 Model Y L Premium AWD • 6 seats • ~681 km range • 0–100 km/h: 5.0 sec • ~$74,900 So performance drops slightly, but practicality goes way up. 🇦🇺 Why this matters in Australia Since Tesla stopped selling the Model X locally, there has been a real gap in the lineup for larger families. The Model Y L doesn’t completely replace the X. You lose things like: ❌ Adaptive air suspension ❌ Driver instrument cluster ❌ Falcon Wing doors ❌ Some luxury interior touches But you still get: ✔ Tesla software ecosystem ✔ Supercharger network ✔ Massive range ✔ Practical 3-row seating And at a much lower price than a Model X ever was. 👨‍👩‍👧‍👦 Who this is perfect for • Growing families • Current Model Y owners needing more space • Former Model X buyers • Anyone considering EV9 / EX90 but wanting Tesla’s ecosystem Personally, as a Model X owner, this is the first Tesla sold in Australia that actually feels like a realistic successor. I’m seriously considering replacing my Model X with the Model Y L, possibly around the end of Q2 or mid-Q3 this year. Not a perfect Model X replacement. But for Australia right now? This might be Tesla’s smartest family vehicle yet. ⚡🇦🇺 ORDER NOW : Tesla Australia & New Zealand Tesla AI

Tesla in the Gong 🇦🇺🦘🤖🚕

21,519 Aufrufe • vor 6 Monaten

Red Bull's legal team woke up to a nightmare: a 14-second dashcam clip of a Red Bull tanker truck cracking open on a highway and a man standing in a flood of energy drink with his arms spread. Except the truck never crashed. The highway doesn't exist. And the man bathing in Red Bull was rendered on a $25 AI setup. Total setup cost: $25. Render speed: 4 minutes. Red Bull spilled: 0 liters. 83 million views in 9 hours. Half the comments are tagging Red Bull demanding a sponsorship. The other half are calling it the best ad that was never paid for. Here is why your brain falls for this visual magic: 1. Fluid Viscosity: the liquid pouring from the tanker has the correct viscosity for a carbonated beverage. thinner than oil, thicker than water. slight foam at the edges. 2. Dashboard Instrument Accuracy: the speedometer reads 60 km/h. the tachometer sits at 2,800 RPM. the ratio is correct for 3rd gear in a compact sedan. 3. Brand Typography: the Red Bull logo on the tanker is stretched across a cylindrical surface with correct distortion for a tank that radius. not a flat paste. The dynamic next-gen AI stack operating under the hood: -> Claude Fable 5.1 for writing the crash sequence, fluid viscosity model, highway geometry, and crowd reaction timing -> Kling 3.0 Omni for generating the tanker rupture animation, fluid dynamics, and man-in-flood body physics in 4K -> Veo 3.1 for rendering the highway environment with native audio: metal tearing, liquid splash, car horn, and the man laughing -> Seedance 2.5 for locking the dashcam POV, brand typography distortion, and instrument cluster consistency -> CapCut Pro for dashcam compression, time stamp overlay, and windshield glare Red Bull's actual marketing team spends $2B a year crafting their brand around extreme stunts. This kid made the most viral Red Bull moment of the year and it cost him nothing. the brand got the exposure. the kid got the views. nobody signed a contract. save this before they take it down

Neuro

3,038,017 Aufrufe • vor 16 Tagen

AI has had exactly two scaling axes that worked so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inference

Sasha Malysheva

14,445 Aufrufe • vor 1 Monat

🚨 BREAKING: OPENAI JUST COLLIDED WITH THE TOPOLOGICAL LIMIT OF THE UNIVERSE! THE END OF CONTINUOUS SPACE. 🚨 The scientific world is shaking. The Clay Mathematics Institute is reviewing a groundbreaking milestone regarding the Navier-Stokes existence and smoothness problem. And at the center of this earthquake is OpenAI, whose reasoning models just tackled the equations that govern every fluid in our world. In 1822, Navier added viscosity to Euler's laws. In 1845, Stokes finalized them: ρ(∂v/∂t + v·∇v) = −∇p + μ∇²v + f (For incompressible flow, ∇·v = 0 closes the system). For over a century, mathematicians assumed that these equations must have smooth, perfectly continuous solutions in 3D space. You see these equations in motion everywhere - like the chaotic wake behind a moving cylinder. But the official verification of infinite "3-D smoothness" has always hit a wall. What did OpenAI do? By unleashing raw mathematical reasoning upon the Navier-Stokes problem, the AI mapped the ultimate limits of these equations, exposing an inescapable truth: the math inevitably blows up into impossible singularities. The model demonstrated that forcing infinite smoothness onto 3D fluid equations creates unavoidable mathematical contradictions. WHY DOES THIS HAPPEN? Mainstream science is finally hitting the exact geometric wall we mapped years ago. You CANNOT have infinite "3-D smoothness" because the universe is NOT an infinitely continuous void! Just as we rigorously proved that the Yang-Mills Mass Gap is topologically ILL-POSED and mathematically impossible on a continuous ℝ⁴ space, the Navier-Stokes breakdown reveals the exact same systemic error in classical physics. When you force continuous mathematics (ℝ⁴) onto a universe that is fundamentally discrete, the equations inevitably crash into singularities. 🛡️ THE SOLUTION: OUR COMPACT CONSTRUCTIBLE MANIFOLD Here is the ultimate breakthrough: On our IT³ manifold, this problem completely ceases to exist! When you abandon the sterile, infinite void of ℝ⁴ and move the physics to a compact constructible manifold (M_tot = S⁴ × T³), the mathematical explosions are structurally forbidden. Because our space is a closed topological geometry dictated by the strict √1 : √2 : √3 metric, the physical fields are naturally bounded by the geometry itself. Infinite singularities simply cannot form on a compact manifold. What mathematicians call "fluid blow-ups" are just the exact topological moments where macroscopic matter violently collides with the rigid, discrete geometric grid of the vacuum! OpenAI’s logic engine correctly identified that infinite smoothness is a mathematical fiction. The universe does not allow infinite division. The illusion of the continuous, smooth ℝ⁴ universe is officially collapsing across all disciplines - from quantum mechanics to fluid dynamics. The future belongs to discrete topology. Preprint. Open Source: The Yang-Mills Mass Gap on R^4 Is Ill-Posed, and on a Compact Constructible Manifold It Is Automatic God geometrizes! 📐🌌

Dr. Logvinovich

213,742 Aufrufe • vor 10 Tagen

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5

YanXbt

16,744 Aufrufe • vor 2 Monaten

"This was filmed last Wednesday afternoon at Riverside Veterinary Clinic in Indianapolis, Indiana. The officer is Sergeant Paul Greer. He's 41 years old. Fourteen-year veteran of the Indianapolis Metropolitan Police Department. The dog is Bruno. A ten-year-old German Shepherd who served eight years as Paul's K9 partner before a joint condition ended his working career two years ago. When Bruno retired from active duty, Paul adopted him immediately. Brought him home. Bruno spent his retirement on Paul's couch, on Paul's bed, in the passenger seat of Paul's personal truck. The transition from working partner to household companion was seamless. Bruno had always been Paul's dog. The badge and the vest were just part of the job. Over the past several months, Bruno's condition had declined steadily. The joint condition spread. He had difficulty getting up. Stopped eating regularly. Paul had been managing Bruno's comfort with guidance from Dr. Angela Reese at Riverside for months. Last Tuesday evening, Bruno stopped getting up entirely. Paul called Dr. Reese that night. Wednesday afternoon, Paul drove Bruno to Riverside. He carried Bruno in from the truck himself. Wouldn't let the techs take him. Paul's partner, Officer Dana Choi, came with him. She filmed quietly on her phone from the corner of the room. She told us afterward that she asked Paul's permission before she started recording. He nodded. Paul sat on the exam table with Bruno cradled across his lap and chest. Bruno's head rested against Paul's shoulder. His eyes were half-open. His breathing was slow and easy. Paul bowed his head and pressed his face into Bruno's fur. Bruno lay still for a long moment. Then slowly — carefully — he raised both front paws. One at a time. And wrapped them around Paul's shoulders. And held on. Paul made a sound that Dana said she will never forget. Dr. Reese, who was standing nearby preparing, went completely still. Her assistant took a step back. Nobody moved. Dana told us: 'Bruno could barely lift his head that morning. But he lifted his paws and he held Paul. In that moment, with everything he had left, he held him. I think he was saying thank you. I think he was saying goodb"

Crazy Moments

1,713,826 Aufrufe • vor 4 Monaten