Peak quality!

Chat is this peak quality #fnaf #blender3d

quality fluid animation is insane in Camelot movie y'all... missing out this peakshow more

Sneak peak at my red nylons... better quality coming... soon. P.S: DMs open for losers wanting to serve.show more

During its active period, the Giza pyramid was covered... entirely with high-quality, smooth, and polished white limestone, and a golden pyramid stood at its peak. Was the golden pyramid's purpose to harvest electricity from the ether?show more

𝐒𝐥𝐚𝐦𝐦𝐞𝐫𝐬 𝐒𝐜𝐨𝐮𝐭 𝐃𝐚𝐲 3B/RHP Bode Rohrback (Libertyville, 2026) showed... well at the Slammers Scout Day. Listed at 6-foot-2, 180-pounds, the right-handed hitter took a quality round of BP with an upper-80s peak EV. 👤:show more

OF Ryan Bradford (Seckman, 2025) is a high level... athlete that put together a quality showing at today’s #PBRFG23 workout: ▫️5-foot-10, 180-pounds ▫️6.70 60 ▫️95.4 mph max EV, 353 ft. peak ▫️T87 mph OF 👤:show more

My second experience with the NIO ET9 and I... can tell you this car is wild! Quality feels amazing, suspension is next level (literally), vertically integrated in-house developed electrical architecture, 925V at 100% SoC, 600kW peak charging, steer by wire and the list goes on and on My full tour:show more

Simply put, one of the best rebounds you’ll ever... see. Blew by Embiid, grabbed the ball at its peak, tossed a quick elbow just in case, scrapped 4 on 1, too strong for Lowry and Tobias Harris, to make a quality pass??? One of the most important boards ever grabbed by a Knickshow more

Russian cities of Smolensk and Sochi now also experience... fuel-supply disruptions and queues at petrol stations. The situation is still far from the peak of Russia’s fuel crisis around six weeks ago, but local shortages are beginning to reappear across different regions. This comes despite Moscow’s plans to import up to 400,000 tonnes of petrol per month. Russia has also relaxed fuel-quality requirements, temporarily allowing the production, import and sale of older Euro-2, Euro-3 and Euro-4 standard petrol.show more

There was a time when Saurav Joshi was so... poor that he lived in a rented house and didn't even own a bicycle. The people of India made him rich by watching his low-quality, average content. Now he travels in luxury cars, and he has even started speaking badly about the very country that gave him everything. Just because he had to wait on the road for two minutes due to a political leader's convoy, he started saying, I face so many problems in India. We have to suffer a lot in India. In India, we have to stay silent and tolerate everything. Just because Saurav Joshi had to wait on the road for two minutes, he said so many things against India. When Saurav Joshi was poor, he had no problem with anything. But after becoming rich, he started finding problems with everything. Saurav Joshi is a perfect example of a peak liberal mentality.show more

Ling-3.0-flash is built as a fast, reliable execution engine... for agent workflows. It shines in long-running tasks, tool calling, and high-volume production work where speed and stability matter more than massive reasoning depth. At 124B parameters with only 5.1B active, it keeps costs low while delivering quick responses and strong instruction following I ran a practical task using Ling-3.0-flash capabilities (based on its documented agent strengths in coding and tool use). Real Test Run I tested Ling-3.0-flash on a content creation task that matches your workflow. I asked it to generate a short, viral-style YouTube Shorts script for football highlights, including captions, title suggestions, and thumbnail ideas. Input Prompt: "Create a 30-second YouTube Shorts script for a dramatic Ronaldo goal from the 2026 World Cup qualifiers. Include engaging English narration, 3 multilingual caption versions (English, Spanish, Portuguese), a catchy title, and thumbnail description. Make it feel real and exciting for football fans." Process: The model first outlined the structure: intro hook, key action description, emotional peak, and call to action. It generated the script, then created caption variants, optimized the title for clicks, and suggested a thumbnail layout. It handled iterations well when I asked for adjustments, such as making it more dramatic or adding player stats. Total interaction took under 2 minutes with low token use. Result: - Script: Solid, ready-to-record narration with natural flow. - Captions: High-quality and culturally adapted. - Title & Thumbnail: Click-worthy and on brand. This demonstrates its strength for creators who need fast, high-quality content assets. Official account : Demo:show more

You don't need a GPU for fast studio grade... voice cloning anymore. Qwen3 TTS (1.7B Q4_K_M) + mainline llama.cpp is officially the fastest way to generate zero shot voice clones using 100% pure CPU execution. Following up on my last post where we ran the Q8 model on a GPU, we just took local C++ voice synthesis a massive step further. The open source community quantized Alibaba's SOTA Qwen3 TTS model down to Q4_K_M GGUF, completely freeing local audio pipelines from dedicated graphics hardware. Here is the real world benchmark and hardware breakdown of running SOTA voice cloning on CPU: # Architecture & Model Setup Using Qwen3-TTS-12Hz-1.7B-Base-Q4_K_M.gguf paired with the 8 bit multimodal projector (mmproj-Q8_0.gguf), llama.cpp executes the entire pipeline in pure C++. No PyTorch, no CUDA dependencies, and no VRAM bottlenecks. # Real-World Memory Footprint - Baseline RAM: 1.6 GB system idle. - Peak Generation RAM: 8 GB RAM during active voice synthesis. - Requirement: Any basic machine with at least 8 GB of system RAM can run this easily. # Real World CPU Benchmarks - Google Colab Free Tier (Throttled 2 Core CPU): Synthesizes a 5 sec studio quality audio clip (~8 words) in 45 seconds. - Modern Consumer CPU (Intel i5/i7 13th/14th Gen or AMD Ryzen 7000/9000): generation should drop to 5 to 20 seconds (nearly 1:1 real-time generation speed!). # Zero Shot Voice Cloning Quality Pass any 5 to 20 second .wav audio sample to the C++ engine using the --tts-speaker-file flag. It yields clean, natural sounding cloned speech with virtually zero quality loss compared to unquantized FP16 weights. To make testing seamless, I built an updated zero config Google Colab notebook. It pulls the official pre built llama.cpp CPU binaries (zero compilation time!) launches a live Gradio web app right in your browser. Record a 5 second clip from your mic (or drop a .mp3, .wav file), type text, and generate cloned audio on CPU. Native C++ audio models are making edge based, offline AI voice agents a reality. Links to the free Q4 CPU Colab notebook and the Q4_K_M GGUF HuggingFace repository are in the replies below! Which models have you been running on your CPUs? What CPU hardware are you using for local inference?show more

🚨 READ THIS POST CAREFULLY BEFORE BUYING STOCKS The... 10-year yield is at 5.35%. The dollar is rising. Inflation is still hot. That combo alone doesn't crash markets. Something underneath does. And right now, the warning signs are everywhere: → Private credit: Seven major platforms (Blue Owl, Apollo, Ares, BlackRock, Blackstone) have capped withdrawals. $4.6 billion is trapped. → Auto loan delinquencies just hit 5.5% at 90+ days. Worse than the 2008 peak. → Office CMBS delinquencies at 12.3%. Higher than 2008. → Corporate bankruptcies at 16-year highs. 372 large filings this year. → S&P 500 is being held up by 5-7 names. Every other sector fell in September. → Six banks have already failed this year. The pattern is always the same: Rates stay high → something built on cheap money breaks → the break spreads → by the time the Fed cuts, the damage is done. You can't time the top. But you can prepare: 1. Raise cash gradually. Not all at once. 2. Park it in short-term Treasuries (SHV, BIL, SGOV) at 5%+. 3. Watch the leaders, not the index. When NVDA, AAPL, MSFT break on volume, that's your signal. 4. Avoid zombie companies. Russell 2000 is a third zombies. 5. Own quality. No debt, strong cash flow, pricing power. 6. Don't short the top. You can stay irrational longer than you can stay solvent. 7. Write down your plan now. For 10%, 20%, 30% drops. The goal is not to predict the crash. It is to survive it with capital to buy the bottom. For the record, I've called every major turn for the last 10 years, including the exact BTC bottom at $16K, the $111K top in October, and the current pump from $60K 10 days before it started. The next call will be even more important. I will guide you through this liquidity squeeze, and when the real bottom finally forms, I’ll call it here publicly. Turn notifications on. If you’re not following yet, you’ll understand why that was a mistake later.show more

"When freedom is outlawed, only outlaws will be free."... 🖐️ Bundle of hand-rubbed Nepalese Charas — the living gold of the Himalayas. 🍫 Charas is the oldest known cannabis concentrate on Earth, born thousands of years ago in the humid high valleys of Nepal and India where dry-sifting simply doesn’t work. The ancient method: skilled hands gently rub the flowering tops of living plants at peak resin ripeness — no drying, no machines — collecting pure trichomes straight from the living herb. Slow, deliberate strokes yield the finest quality (just a few grams of premium per day), scraped into these iconic finger bundles or temple balls. The result? A sticky, fragrant, full-spectrum resin that captures the plant’s complete terpene orchestra — earthy, spicy, floral, with that unmistakable incense-like depth you only get from live, hand-rubbed resin. Insider secret: The slower the rub, the purer the charas. First passes give the cleanest, most potent layer; haste adds vegetal debris and dulls the spirit. Age it properly and the flavors deepen into something profound — a true “alive” experience that modern extracts can only dream of. Tied to Lord Shiva since Vedic times, charas has been the sacred herb of sadhus and ascetics for millennia — smoked in chillums to deepen meditation, dissolve ego, and commune with the divine. As Eastern wisdom teaches through the lens of plant intelligence: the cannabis plant is a teacher, a bridge. “We must cultivate our own garden,” wrote Voltaire — and nowhere is that truer than in the mindful art of coaxing resin from living sacred plants. Controversial truth: These open traditions and thriving markets were alive and respected along the old Hippie Trail until international prohibition pressure shuttered them in the 1970s–80s. What was once cultural heritage and medicine became criminalized — yet the landraces, the knowledge, and the practice endure in remote Himalayan pockets. This is living proof that sacred plants still unite us across borders, cultures, and eras. Honored to share this piece of preserved history from our travels. It reminds us: the plant remembers. We are merely its humble stewards. How do you connect with charas — chillum ritual, meditation, or pure appreciation of the craft? Drop your stories or rituals below 👇 Let’s keep these ancient genetics and traditions alive. #LandraceBureau #NepaleseCharas #HandRubbed #HimalayanHash #SacredPlants #CharasHistory #ShivaHerb #PlantIntelligence #PreservationNotProhibitionshow more

llama.cpp isn't just for text LLMs anymore. Pure C++... zero shot voice cloning just officially landed in mainline. Text generation was only step one. If you’re building autonomous local AI agents, real time voice assistants, or edge workflows, instant low latency audio is the missing piece. Thanks to PR #26254, Alibaba’s state of the art Qwen3 TTS model family is now natively supported directly inside the llama.cpp repository under the multimodal (mtmd) framework. No Python bloat. No massive PyTorch CUDA overhead. Just raw, hyper optimized C++ running GGUF voice weights. Here is why this native update is a massive deal for the open source local AI stack: # Multimodal Architecture (.gguf + mmproj) Qwen3-TTS splits the workload between the base language model backbone and a multimodal projection adapter. llama.cpp handles this using the llama-tts binary, mapping the text model alongside its --mmproj projector to process audio tokens seamlessly. # Zero Shot Voice Cloning in Seconds You don't need fine tuning or massive dataset training. Feed the C++ engine a single 5 to 10 second .wav audio sample using the --tts-speaker-file flag, and it accurately clones the exact timbre, tone, and accent on the fly. # Real World T4 GPU Benchmark & Resource FootprintRunning the 1.7B Base model in 8-bit quantization (Q8_0): - VRAM Footprint: ~7 GB peak VRAM during active zero-shot cloning. - Audio Quality: Studio grade, natural-sounding voice output in seconds. • - Execution: Direct execution via native compiled binaries or sub process calls. # Coming Next to llama-server (PR #26603) Beyond CLI execution, a native POST /tts HTTP endpoint is currently being added to llama-server, which will soon allow you to trigger voice generation directly via standard REST API requests! # quick note on Colab compilation: Because this code was merged into mainline very recently, pre-built third-party binaries haven't fully caught up yet. Compiling llama-tts directly from source on Google Colab's free CPU instance can take about 1 hour (or ~1-2 minutes if targeting single GPU arch like -DCMAKE_CUDA_ARCHITECTURES=75). Be patient during the build step, or compile it locally on your own rig for instant execution! To test this out yourself, I built a zero config Google Colab notebook that compiles llama.cpp, downloads the Q8_0 GGUF files from HuggingFace, and spins up an interactive Gradio Studio UI so you can record/upload 3 second clips and clone voices in real time. Stop sleeping on native C++ audio. The era of bulky Python audio pipelines is officially over. Links to the free Google Colab notebook and the official ggml org GGUF HuggingFace model repository are in the replies below! available in q4 and q8 both variants, 1 GB and 1.85 GBs respectively (requires additional ~500MB mmproj gguf) Are you building local voice agents yet? What does your current audio stack look like? Drop your setups below!show more

How cinematic can pure motorcycle speed actually become on-screen?... GPT Image 2 + Seedance 2.0 on Fish Creative prompt Create an explosive, high-speed motorcycle action sequence in 16:9, 24fps, exactly 16 distinct cuts, total runtime ≈ 16–17 seconds. A lone rider in a light green helmet and dark riding gear pilots a babypink sportbike along a narrow, winding paved mountain road that cuts through rolling green hills under a brilliant sunny sky with scattered white clouds and distant blue mountain ranges and water visible on the horizon. Preserve the exact bike color, rider silhouette, road markings, green landscape, fencing and lighting throughout — never redesign them. This film is 90 % pure kinetic camera motion and 10 % environmental beauty: aggressive low-to-high tracking shots, continuous forward motion with heavy speed blur, whip pans, snap zooms, sun flares and impact shakes. No text, no graphics, no logos — pure cinematic speed. ### CUT LIST CUT 01 | 0.00–1.00s Dynamic low-side tracking shot: red motorcycle accelerates away from a small parking area onto the winding asphalt road; lush green grass and fence posts streak past with heavy motion blur; bright sun flares at the top of the frame. **CUT 02 | 1.00–2.00s** Camera rises and locks behind the rider as the bike climbs the first gentle incline; green hills roll on both sides, a lone tree appears ahead, distant mountains and water visible on the right. **CUT 03 | 2.00–3.00s** Tight rear tracking; rider leans into a smooth left curve; the road surface and white dashed lines blur heavily; sun remains high and intense. **CUT 04 | 3.00–4.00s** Bike powers up a steeper section of road; camera stays low and centered, grass and fence posts racing past on both sides; distant valley opens on the right. **CUT 05 | 4.00–5.00s** High-speed straight section; camera drops even lower, almost road-level, capturing the red bike centered against the climbing green hillside and bright blue sky. **CUT 06 | 5.00–6.00s** Rider continues the climb; camera rises slightly and banks with the road; lush green slopes fill the left and right frame edges with strong motion blur. **CUT 07 | 6.00–7.00s** Bike approaches a crest; camera pulls back a fraction to reveal more of the endless green hills and the road stretching ahead under the bright sun. **CUT 08 | 7.00–8.00s** Rider leans into the next curve; camera swings wide then snaps back, capturing the red bike against the steep green embankment and metal guardrail. **CUT 09 | 8.00–9.00s** Tight following shot as the motorcycle accelerates past the guardrail; heavy speed blur on the grass and road surface; sun flare intensifies. **CUT 10 | 9.00–10.00s** Camera tilts upward dramatically, almost looking straight up the road; the red bike becomes smaller against the vast blue sky and white clouds while still racing forward. **CUT 11 | 10.00–11.00s** Sweeping aerial-style arc as the bike enters a wide left-hand hairpin; camera orbits slightly, revealing the full curve of the road, green slopes and distant mountains. **CUT 12 | 11.00–12.00s** High-angle tracking shot looking down on the red motorcycle as it carves the long sweeping curve; the road and guardrail form a perfect arc against the green hillside. **CUT 13 | 12.00–13.00s** Camera pulls higher still, showing the bike as a small red shape on the winding road that snakes across the vast green landscape toward distant valleys and peaks. **CUT 14 | 13.00–14.00s** Rider continues through the open hillside section; camera descends slightly and tracks from a higher three-quarter rear angle, capturing the bike, road and rolling green terrain. **CUT 15 | 14.00–15.00s** Wide high-angle shot of the motorcycle navigating the next gentle S-curve; a second distant vehicle appears briefly on the road ahead; lush green fields and scattered trees fill the frame. **CUT 16 | 15.00–16.65s Final high-speed tracking shot as the red bike continues along the open mountain road; camera gradually rises and the frame fills with bright green hills, blue sky and soft sun flare; the sequence ends on a sense of endless forward motion. ### Editing & Sound Extremely aggressive rhythm — continuous forward tracking with occasional whip pans, snap zooms and speed ramps, heavy motion blur on every surface, strong sun flares and subtle impact shakes on every crest or curve. No text, no graphics, no slow-motion. BGM / Audio: electronic / indie-pop beat with driving drums, bright synths and rising energy that matches the speed of the bike. Engine roar, wind whooshes and tire sound on asphalt are locked to the picture as rhythmic elements. Peak energy in the middle curves and end with a bright, open sonic release. Premium AAA quality, photorealistic cinematic rendering, pure speed and freedom aesthetic, consistent red sportbike and rider design, exactly 16 cuts.show more