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Moondream 3 Preview adds frontier-level visual reasoning while retaining Moondream 2's blazing speed. No compromises.

moondream

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44,461 views • 1 year ago •via X (Twitter)

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ARC-AGI-3 is out now! We've designed the benchmark to evaluate agentic intelligence via interactive reasoning environments. Beating ARC-AGI-3 will be achieved when an AI system matches or exceeds human-level action efficiency on all environments, upon seeing them for the first time. We've done extensive human testing that shows 100% of these environments are solvable by humans, upon first contact, with no prior training and no instructions. Meanwhile, all frontier AI reasoning models do under 1% at this time.

ARC-AGI-3 is out now! We've designed the benchmark to evaluate agentic intelligence via interactive reasoning environments. Beating ARC-AGI-3 will be achieved when an AI system matches or exceeds human-level action efficiency on all environments, upon seeing them for the first time. We've done extensive human testing that shows 100% of these environments are solvable by humans, upon first contact, with no prior training and no instructions. Meanwhile, all frontier AI reasoning models do under 1% at this time.

François Chollet

625,901 views • 7 months ago

Fusion is the most efficient frontier harness for GPT 6 Astra and Claude Fable 5.1. You can also configure everything about it: • Base model: Fable, Astra, Sol, or Opus • Sidekick model: SWE (Free), Luna, Sol, GLM (Free) • Speed: Normal or Fast ⚡ • Reasoning level It retains Claude Fable 5.1 and GPT-6 Astra performance while reducing costs and is the first time a multi-model coding agent has been included on the Artificial Analysis Coding Agent Index! Importantly: it's also fun to use :)

Fusion is the most efficient frontier harness for GPT 6 Astra and Claude Fable 5.1. You can also configure everything about it: • Base model: Fable, Astra, Sol, or Opus • Sidekick model: SWE (Free), Luna, Sol, GLM (Free) • Speed: Normal or Fast ⚡ • Reasoning level It retains Claude Fable 5.1 and GPT-6 Astra performance while reducing costs and is the first time a multi-model coding agent has been included on the Artificial Analysis Coding Agent Index! Importantly: it's also fun to use :)

nader dabit

805,827 views • 1 month ago

more on solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump new launchpad in research preview on Robinhood (meow in replies if you want invite code) the mechanism, why I think it will change memes forever: 1. launch every new House starts with one hour to reach 0.05 ETH 2. level up level 1 → 0.1 ETH / 2 hours level 2 → 0.2 ETH / 4 hours ... level 8 → 12.8 ETH / 256 hours clear lv 8 & your House graduates into Uniswap pool 3. death miss the bar by the deadline? your House ends & liquidity dies into the King one level above else it dies into solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump why this matters: - concentrates liquidity; no ghost LP - failed Houses feed stronger Kings or solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump - every level is a PvP campaign to become King - fewer stronger memes goal is no wasted moni in dead pool, and one big fat cat contracts in research preview only need invites? meow in replies (follow vibe.market to get DM)

more on solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump new launchpad in research preview on Robinhood (meow in replies if you want invite code) the mechanism, why I think it will change memes forever: 1. launch every new House starts with one hour to reach 0.05 ETH 2. level up level 1 → 0.1 ETH / 2 hours level 2 → 0.2 ETH / 4 hours ... level 8 → 12.8 ETH / 256 hours clear lv 8 & your House graduates into Uniswap pool 3. death miss the bar by the deadline? your House ends & liquidity dies into the King one level above else it dies into solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump why this matters: - concentrates liquidity; no ghost LP - failed Houses feed stronger Kings or solana:2oKuzxekSwyGnqoiyJv5zVvBbRmh5UyRbjQ74j3Npump - every level is a PvP campaign to become King - fewer stronger memes goal is no wasted moni in dead pool, and one big fat cat contracts in research preview only need invites? meow in replies (follow vibe.market to get DM)

alyssa 🫪✌️

66,384 views • 3 months ago

Just dropped on HF — NeuTTS Air Next-gen on-device TTS that matches cloud-level quality while staying fully open source. > Real-time speech synthesis on CPU/GPU > 3-second voice cloning, no cloud or data upload > Compact: under 200 MB, runs on mobile and edge devices > Multilingual and expressive > Developed by Neuphonic , optimized for speed and fidelity

Just dropped on HF — NeuTTS Air Next-gen on-device TTS that matches cloud-level quality while staying fully open source. > Real-time speech synthesis on CPU/GPU > 3-second voice cloning, no cloud or data upload > Compact: under 200 MB, runs on mobile and edge devices > Multilingual and expressive > Developed by Neuphonic , optimized for speed and fidelity

steven

73,642 views • 1 year ago

( كيف تكتب بالعربي وتحدد اي جزء في الصورة: ) 1- استخدم نموذج يدعم اللغة العربية 2- حدد المكان level 10 floor of this tower 3- حدد التأثير green neon border with grids 4- حدد الكتابة: "شقة رقم 10" 5- شروط التحكم في التوليد علشان ما يخربط: - don't change the view or transition - no morph or adds. only focus on the scene. ___________ البرومت كامل: a dynamic green neon border with grids cover only the level 10 floor of this tower. hud pointer comes out of the floor 10 write in white glow medium size: "شقة رقم 10". don't change the view or transition. no morph or adds. only focus on the scene.

( كيف تكتب بالعربي وتحدد اي جزء في الصورة: ) 1- استخدم نموذج يدعم اللغة العربية 2- حدد المكان level 10 floor of this tower 3- حدد التأثير green neon border with grids 4- حدد الكتابة: "شقة رقم 10" 5- شروط التحكم في التوليد علشان ما يخربط: - don't change the view or transition - no morph or adds. only focus on the scene. ___________ البرومت كامل: a dynamic green neon border with grids cover only the level 10 floor of this tower. hud pointer comes out of the floor 10 write in white glow medium size: "شقة رقم 10". don't change the view or transition. no morph or adds. only focus on the scene.

فيستا360 للتصوير العقاري

19,166 views • 2 months ago

Moonshot AI is casually giving developers free daily access to Kimi K3 😳 no subscription no upfront payment just sign in and start using one of the largest open AI models available what you get for $0: - Kimi K3 with 2.8T parameters - 1M token context window - strong coding and reasoning performance - native vision capabilities - free daily credits that refresh automatically why this is worth checking: > access a frontier model without paying API fees > long context for large codebases and documents > works on web, desktop, mobile, and CLI getting started takes less than 2 minutes: 1. go to 2. create a free account 3. Kimi K3 is available as the default model 4. start chatting or coding with your daily free credits bonus: Moonshot Together lets you invite friends for a chance to earn 3, 7, 15, 30, or even 365 days of Kimi Membership through its rewards program benchmark highlights: > 2.8T parameter MoE model > 1M context window > strong performance across coding, browsing, and reasoning benchmarks important: free credits reset daily, rate limits apply on the free tier, and the open-weight release is expected on July 27 A simple way to try one of the latest frontier AI models without paying for API access

Moonshot AI is casually giving developers free daily access to Kimi K3 😳 no subscription no upfront payment just sign in and start using one of the largest open AI models available what you get for $0: - Kimi K3 with 2.8T parameters - 1M token context window - strong coding and reasoning performance - native vision capabilities - free daily credits that refresh automatically why this is worth checking: > access a frontier model without paying API fees > long context for large codebases and documents > works on web, desktop, mobile, and CLI getting started takes less than 2 minutes: 1. go to 2. create a free account 3. Kimi K3 is available as the default model 4. start chatting or coding with your daily free credits bonus: Moonshot Together lets you invite friends for a chance to earn 3, 7, 15, 30, or even 365 days of Kimi Membership through its rewards program benchmark highlights: > 2.8T parameter MoE model > 1M context window > strong performance across coding, browsing, and reasoning benchmarks important: free credits reset daily, rate limits apply on the free tier, and the open-weight release is expected on July 27 A simple way to try one of the latest frontier AI models without paying for API access

K2S

23,110 views • 3 months ago

People wanna know why I love the SHREDmill so much?? This collection of videos is exactly the reason why I’m such a big advocate, as both a speed AND coaching/technical AMPLIFER⚡️🔌 All of these reps were done within the same session: Rep 1: 8.3 mph; excessive turnover, minimal horizontal push/thigh split Rep 2: after working on some positional wall drills, hit 7.7 mph; better positions/thigh split, but no push Rep 3: 9.3 mph; literally put it all together as far as the “good” from reps 1 and 2! Sprinting on the ground, incorporating max effort jumps/plyos, and still getting strong in the weight room are musts for maximizing speed development, but no other tool in the speed world allows you to both improve the qualities needed to be a damn good accelerator, while also giving you the feedback (both objectively via outputs, and the visual/auditory feedback from watching+coaching reps) needed to make improvements in short periods of time. Gettin’ fast FAST! Tony Villani SimpliFaster

People wanna know why I love the SHREDmill so much?? This collection of videos is exactly the reason why I’m such a big advocate, as both a speed AND coaching/technical AMPLIFER⚡️🔌 All of these reps were done within the same session: Rep 1: 8.3 mph; excessive turnover, minimal horizontal push/thigh split Rep 2: after working on some positional wall drills, hit 7.7 mph; better positions/thigh split, but no push Rep 3: 9.3 mph; literally put it all together as far as the “good” from reps 1 and 2! Sprinting on the ground, incorporating max effort jumps/plyos, and still getting strong in the weight room are musts for maximizing speed development, but no other tool in the speed world allows you to both improve the qualities needed to be a damn good accelerator, while also giving you the feedback (both objectively via outputs, and the visual/auditory feedback from watching+coaching reps) needed to make improvements in short periods of time. Gettin’ fast FAST! Tony Villani SimpliFaster

Cam Galgano, CSCS

11,766 views • 6 months ago

I watched every 3-2 take by Bryce Harper in 2025. Here’s what it revealed: 1️⃣ Yes, Yes, No Approach - he is swinging until the ball tells him “no”. His barrel starts to turn on almost 100% of his takes. You will see these type of “active takes” with pretty much every high level hitter 2️⃣ He Can Stop Late - because his barrel turns rearward and creates depth, it allows him shut down or abort his swing as late as possible. This gives him a longer time to identify pitch speed, shape, and location

I watched every 3-2 take by Bryce Harper in 2025. Here’s what it revealed: 1️⃣ Yes, Yes, No Approach - he is swinging until the ball tells him “no”. His barrel starts to turn on almost 100% of his takes. You will see these type of “active takes” with pretty much every high level hitter 2️⃣ He Can Stop Late - because his barrel turns rearward and creates depth, it allows him shut down or abort his swing as late as possible. This gives him a longer time to identify pitch speed, shape, and location

Matt Antonelli

345,530 views • 9 months ago

June 29 1960: A US Navy RA-3 crashes off Florida (US) all 3 crew die. While practicing landings on the carrier USS Saratoga, the arresting cable broke. With no means to stop safely, the plane went off deck. The crew attempted a go-around but were unable to, jet hit the sea at high speed and sank. The RA-3 was the photo-reconnaissance version of the A-3 “Skywarrior” bomber. The jet lost in the accident [144845] was attached to squadron VAP-62 (based at Fort. Lauderdale, Florida). The Jet´s wreckage was later discovered by chance by a film-making crew, who were producing a documentary on the Bermuda Triangle.

June 29 1960: A US Navy RA-3 crashes off Florida (US) all 3 crew die. While practicing landings on the carrier USS Saratoga, the arresting cable broke. With no means to stop safely, the plane went off deck. The crew attempted a go-around but were unable to, jet hit the sea at high speed and sank. The RA-3 was the photo-reconnaissance version of the A-3 “Skywarrior” bomber. The jet lost in the accident [144845] was attached to squadron VAP-62 (based at Fort. Lauderdale, Florida). The Jet´s wreckage was later discovered by chance by a film-making crew, who were producing a documentary on the Bermuda Triangle.

Francisco Cunha - ✈️ Aviation et al

385,841 views • 3 months ago

HydraFusion Explained. Part I: How does the Copilot engine know what to optimize for? Your prompt is evaluated across 4 dimensions: ➡ Does it require deep reasoning? (aka. reasoning depth) ➡ Is it a sophisticated problem? (aka. code generation complexity) ➡ Is it untangling a complicated mess? (aka. debugging difficulty) ➡ Is it dominated by tool-use? (aka. tool orchestration needs) Based on this evaluation, a HyDRA score is assigned to determine the capability profile your task needs the most and to establish a quality bar. Part II: How does it choose a model? Note: It doesn' t pick one model to handle the entire job e2e, (that's Auto mode). Instead, it selects 1 of 3 execution workflows and assigns the best model at different stages based on the HyDRA score: 1️⃣ Single ⚙️ How it works: A single model completes the task from start to finish. ⚖️ Rationale: The task comfortably meets the quality bar with one model. Multi-model orchestration would add latency and cost with no meaningful quality gain. 2️⃣ Cascade ⚙️ How it works: A lightweight, cost-efficient model generates the solution. This draft is evaluated against a quality gate and if it falls short of the quality bar, the entire task escalates to a stronger, frontier model. ⚖️ Rationale: Only bring in the big guns when there is concrete evidence that a lightweight model won't meet the quality threshold. 3️⃣ Critique ⚙️ How it works: A lightweight model drafts the initial code and tool interactions. An independent, read-only frontier model reviews that draft and provides feedback. The original lightweight model then performs any targeted revision(s) before the final response is sent to the user. ⚖️ Rationale: Writing code (output tokens) is expensive while reviewing code (input tokens) is cheap. Instead of incurring the cost of a powerhouse writing hundreds of lines from scratch, a cost-efficient model writes the first draft, and the frontier model just reviews it and points out fixes. HydraFusion is available in experimental preview on the GitHub Copilot CLI: /experimental on, /model and select Hydrafusion (Research Preview)

Julia Muiruri

13,000 views • 1 month ago

50% more context unlocked for Qwen 3.8 27b Q4_K_XL dflash 2 on a single RTX 4090 (24 GB VRAM) I found a hidden VRAM tax in llama.cpp. By combining my custom 2 bit DFlash 2 drafter with one overlooked server flag, I just unlocked another +80,000 tokens of context. Qwen3.8-27B is now running a massive 250,000 context at 75 tokens/s on a single RTX 4090. Here is the secret: By default, `llama-server` reserves massive chunks of your VRAM to handle multiple concurrent users (batching). If you are running a single user session, you are bleeding memory for features you aren't using. By passing the `--parallel 1` flag, you force the engine to dedicate 100% of your 24GB VRAM buffer to a single user. When we combine the VRAM saved by our Q2_K 2-bit drafter with the VRAM saved by `--parallel 1`, the context ceilings absolutely explode: Note: all benchmarks carried out with a massive 28k prompt. Ubuntu 22. ### THE NEW 24GB PHYSICAL LIMITS (Single RTX 4090): # 1. The "Repo Swallower" (Q4 KV Cache): - Context: 250,000 tokens (Up from 170k!) - Speed: 73.66 t/s decode | 1,608 t/s prefill - Peak VRAM: 23.8 GB # 2. The "High-Precision SWE" (Q8 KV Cache): - Context: 150,000 tokens (Up from 100k!) - Speed: 75.01 t/s decode | 1,667 t/s prefill - Peak VRAM: 23.9 GB # 3. The "Pristine Attention" (Unquantized FP16 KV): - Context: 90,000 tokens - Speed: 80.58 t/s decode | 1,699 t/s prefill - Peak VRAM: 23.92 GB ### HOW TO RUN THE 250K GOD STACK TODAY: (Requires PR #27342 + my Q2_K Hugging Face drafter) llama.cpp flags: ./build/bin/llama-server -m Qwen3.8-27B-UD-Q4_K_XL.gguf -md Qwen3.8-27B-DFlash2-Q2_K.gguf --spec-type draft-dflash --spec-draft-n-max 3 -c 250000 -ngl 99 --parallel 1 --port 8080 -ctv q4_0 -ctk q4_0 We are pushing a quarter million tokens of context with speculative DFlash 2 decoding at 73 tokens/second on a single consumer gaming GPU. I dropped my custom 2 bit Hugging Face GGUF links, visual performance graphs, and the PR #27342 build instructions in the replies below. If you own a single RTX 3090 or 4090, it is officially time to cancel your API subscriptions and let local silicon eat the cloud. how much monthly API spend does an optimized 4090 rig like this actually replace for you?

50% more context unlocked for Qwen 3.8 27b Q4_K_XL dflash 2 on a single RTX 4090 (24 GB VRAM) I found a hidden VRAM tax in llama.cpp. By combining my custom 2 bit DFlash 2 drafter with one overlooked server flag, I just unlocked another +80,000 tokens of context. Qwen3.8-27B is now running a massive 250,000 context at 75 tokens/s on a single RTX 4090. Here is the secret: By default, `llama-server` reserves massive chunks of your VRAM to handle multiple concurrent users (batching). If you are running a single user session, you are bleeding memory for features you aren't using. By passing the `--parallel 1` flag, you force the engine to dedicate 100% of your 24GB VRAM buffer to a single user. When we combine the VRAM saved by our Q2_K 2-bit drafter with the VRAM saved by `--parallel 1`, the context ceilings absolutely explode: Note: all benchmarks carried out with a massive 28k prompt. Ubuntu 22. ### THE NEW 24GB PHYSICAL LIMITS (Single RTX 4090): # 1. The "Repo Swallower" (Q4 KV Cache): - Context: 250,000 tokens (Up from 170k!) - Speed: 73.66 t/s decode | 1,608 t/s prefill - Peak VRAM: 23.8 GB # 2. The "High-Precision SWE" (Q8 KV Cache): - Context: 150,000 tokens (Up from 100k!) - Speed: 75.01 t/s decode | 1,667 t/s prefill - Peak VRAM: 23.9 GB # 3. The "Pristine Attention" (Unquantized FP16 KV): - Context: 90,000 tokens - Speed: 80.58 t/s decode | 1,699 t/s prefill - Peak VRAM: 23.92 GB ### HOW TO RUN THE 250K GOD STACK TODAY: (Requires PR #27342 + my Q2_K Hugging Face drafter) llama.cpp flags: ./build/bin/llama-server -m Qwen3.8-27B-UD-Q4_K_XL.gguf -md Qwen3.8-27B-DFlash2-Q2_K.gguf --spec-type draft-dflash --spec-draft-n-max 3 -c 250000 -ngl 99 --parallel 1 --port 8080 -ctv q4_0 -ctk q4_0 We are pushing a quarter million tokens of context with speculative DFlash 2 decoding at 73 tokens/second on a single consumer gaming GPU. I dropped my custom 2 bit Hugging Face GGUF links, visual performance graphs, and the PR #27342 build instructions in the replies below. If you own a single RTX 3090 or 4090, it is officially time to cancel your API subscriptions and let local silicon eat the cloud. how much monthly API spend does an optimized 4090 rig like this actually replace for you?

Alok

41,735 views • 2 months ago

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.

Qwen3.8-Flash-Next is starting to feel like the local model Opus fans have been waiting for. Someone ran the NVFP4 176B-class Flash-Next on 2× DGX Sparks, and the results are wild. Real measured scaling → C1: 44.2 tok/s → C2: 64.6 tok/s → C4: 86.8 tok/s aggregate The per-stream speed drops with concurrency, but total throughput keeps climbing. Long-context behavior was even more impressive: → 5K: needle retrieved → 21K: needle retrieved → 84K: needle retrieved → 167K: needle retrieved → 262K: prefill succeeded, but the window was saturated That 167K retrieval test is the one I care about. Long agent runs are where models usually start losing the plot. Flash-Next didn’t. It also held up surprisingly well on physics-heavy reasoning, artifact generation, research workflows, evidence checking, and long-horizon planning. The personality is interesting too. DeepSeek V4 Flash feels like the dependable workhorse. GLM-5.2 feels like the problem-solving machine. Qwen3.8-Flash-Next feels more insightful. It has that rare ability to understand what you’re actually asking rather than just following the surface pattern. The main weakness I’ve noticed is instruction following. It can occasionally drift between prose turns where DeepSeek and GLM stay tighter. And this is why the 256GB M5 Ultra conversation gets interesting. If Apple can pair that huge unified-memory pool with enough bandwidth, this model class becomes genuinely practical for long-running local agents. We’re talking about frontier-class reasoning on hardware sitting on a desk.

FHILY👑

20,253 views • 1 month ago

Just jumped into this 2016 Mercedes-Benz GLE 63 S AMG... and bro, this car is the most overprotective babe I've ever driven 😂 It's like it has trust issues with Nigerian roads! A lot happened while driving this car. 1. Another car edges too close? The steering assist just smoothly slides you away to a safe gap. No honking needed. It works like personal bodyguard. 2. If you are driving too close to a pavement, it drifts you away from the pavement on its own. 3. It senses real emergency (like sudden stop ahead)? Seatbelts tighten hard, front seats adjust, PRE-SAFE mode kicks in, and it brakes itself before you even react. First time e happen, my heart nearly left my body. But after a while... I started loving the extra care. Scary at first, but now? This level of pampering is sweet die. Luxury wey dey save life while you flex. Soup wey sweet na money kill am

Just jumped into this 2016 Mercedes-Benz GLE 63 S AMG... and bro, this car is the most overprotective babe I've ever driven 😂 It's like it has trust issues with Nigerian roads! A lot happened while driving this car. 1. Another car edges too close? The steering assist just smoothly slides you away to a safe gap. No honking needed. It works like personal bodyguard. 2. If you are driving too close to a pavement, it drifts you away from the pavement on its own. 3. It senses real emergency (like sudden stop ahead)? Seatbelts tighten hard, front seats adjust, PRE-SAFE mode kicks in, and it brakes itself before you even react. First time e happen, my heart nearly left my body. But after a while... I started loving the extra care. Scary at first, but now? This level of pampering is sweet die. Luxury wey dey save life while you flex. Soup wey sweet na money kill am

Verterex Properties 🏡 🚗

82,979 views • 8 months ago

.Penn State Baseball ‘26 RHP Ryan Fuller (Avon Grove, PA) No. 7 RHP in PA | No. 120 RHP in USA 🇺🇸 at 6-2, 185 LBS. Fastball T95 at 2500 RPM with sufficient ride and run from 3/4 slot. Spins frisbee sweeper at 2600+ RPM while featuring sinker and high spin CH with 20in run. High and athletic leg drive into violent, whippy arm speed downhill. 𝗙𝗕: 𝟵𝟰-𝟵𝟱 (𝟮𝟱𝟬𝟬🔄 𝟭𝟳.𝟱𝗜𝗩𝗕, 𝟭𝟲𝗔𝗦𝗥 ↗️) 𝗦𝗞: 𝟵𝟯-𝟵𝟱 (𝟮𝟯𝟴𝟱🔄 𝟴.𝟱𝗜𝗩𝗕, 𝟭𝟵𝗔𝗦𝗥 ➡️) 𝗖𝗛: 𝟴𝟳-𝟴𝟴 (𝟮𝟰𝟯𝟬🔄 𝟯𝗜𝗩𝗕, 𝟮𝟬𝗔𝗦𝗥 ➡️) 𝗦𝗪𝗣: 𝟴𝟯 (𝟮𝟲𝟬𝟱🔄 -𝟱𝗜𝗩𝗕, -𝟭𝟲𝗛𝗠 ↙️) #MLKProDay26 | Ascent Athlete Prep Baseball Pennsylvania | Ryan Fuller

.Penn State Baseball ‘26 RHP Ryan Fuller (Avon Grove, PA) No. 7 RHP in PA | No. 120 RHP in USA 🇺🇸 at 6-2, 185 LBS. Fastball T95 at 2500 RPM with sufficient ride and run from 3/4 slot. Spins frisbee sweeper at 2600+ RPM while featuring sinker and high spin CH with 20in run. High and athletic leg drive into violent, whippy arm speed downhill. 𝗙𝗕: 𝟵𝟰-𝟵𝟱 (𝟮𝟱𝟬𝟬🔄 𝟭𝟳.𝟱𝗜𝗩𝗕, 𝟭𝟲𝗔𝗦𝗥 ↗️) 𝗦𝗞: 𝟵𝟯-𝟵𝟱 (𝟮𝟯𝟴𝟱🔄 𝟴.𝟱𝗜𝗩𝗕, 𝟭𝟵𝗔𝗦𝗥 ➡️) 𝗖𝗛: 𝟴𝟳-𝟴𝟴 (𝟮𝟰𝟯𝟬🔄 𝟯𝗜𝗩𝗕, 𝟮𝟬𝗔𝗦𝗥 ➡️) 𝗦𝗪𝗣: 𝟴𝟯 (𝟮𝟲𝟬𝟱🔄 -𝟱𝗜𝗩𝗕, -𝟭𝟲𝗛𝗠 ↙️) #MLKProDay26 | Ascent Athlete Prep Baseball Pennsylvania | Ryan Fuller

Chris Dryll

19,968 views • 9 months ago

Today we're launching a new experiment. Over the next 30 days, Dave will become one of the internet's most recognizable AI creators. Dave is just a potato. That's the point. We want to demonstrate that something as simple as a potato can become a global media personality with the right AI workflows, creative direction, and distribution strategy. We'll launch one platform at a time, gradually increasing the level of AI automation and sophistication while comparing the results. Day 1: X - simple reactions, almost no AI. Day 2: Instagram. Day 3: TikTok. Day 4: YouTube. Day 5: Threads. Everything is built almost entirely with open-source models in ComfyUI. Follow Dave:

Today we're launching a new experiment. Over the next 30 days, Dave will become one of the internet's most recognizable AI creators. Dave is just a potato. That's the point. We want to demonstrate that something as simple as a potato can become a global media personality with the right AI workflows, creative direction, and distribution strategy. We'll launch one platform at a time, gradually increasing the level of AI automation and sophistication while comparing the results. Day 1: X - simple reactions, almost no AI. Day 2: Instagram. Day 3: TikTok. Day 4: YouTube. Day 5: Threads. Everything is built almost entirely with open-source models in ComfyUI. Follow Dave:

Kevin Raheja

36,481 views • 2 months ago

Luxury skincare ad ✨ Smooth, glowing skin with a premium touch Prompt Create a premium cinematic 4-scene skincare product advertisement using the uploaded image as the exact visual reference. Keep the product packaging, bottle design, logo, colors and overall appearance consistent throughout. Scene 1: Slow elegant camera push-in toward the GlowPure serum bottle and box on the luxury vanity. Soft warm lighting, realistic reflections, premium beauty-commercial look. Scene 2: Extreme macro shot of the serum dropper releasing one clear serum drop into the bottle. Smooth slow motion, glossy liquid texture, shallow depth of field. Scene 3: A beautiful woman gently applies 2–3 drops of serum to her cheek and massages it into her skin. Natural facial movement, realistic skin texture, soft luxury bathroom background. Scene 4: Transition to a glowing final beauty shot. The woman smiles confidently with smooth, fresh-looking skin while the GlowPure bottle and box appear elegantly beside her. Slow camera movement, soft light rays, premium skincare commercial aesthetic. Smooth transitions between all four scenes, realistic hands and face, natural movements, high-end beauty advertisement, photorealistic, 4K, cinematic lighting, no distorted product, no extra fingers, no changing logo or packaging.

Ava Smith Al

19,114 views • 1 month ago

This guy made $400K with his own script on Polymarket Yes, it’s true, the dude wrote a script using ClawdBot and now it earns him passive income No programming knowledge No insider tools for building the script Just a solid script that delivers results Profile → Copytrade → When I opened the code, I was genuinely surprised No massive databases No insanely complex infrastructure Nothing rocket-science level His FULL strategy: 1. “Low-Risk NO Plays” The bot targets near-impossible outcomes and stacks small, high-probability wins. This isn’t gambling, it’s closer to systematic risk underwriting. 2. Logical Price Gaps If event A must logically lead to event B and the market hasn’t priced that in yet, the bot enters instantly. While you’re still reading the headline, the imbalance is already gone. No human can compete with that speed. 3. Main Advantage - Sports and Politics These markets are flooded with retail money and delayed or emotional reactions. The bot lives in the spread, clipping tiny profits from every mismatch. Scale Effect Tens of thousands of micro-trades every month, each worth just a few cents. Over time they compound into seven-figure results. Final Take There’s a real bot war happening on Polymarket. Crypto markets are already slowed down by fees and heavy competition, but sports markets are still pure chaos, easy money for those who automate.

winkle.

57,003 views • 8 months ago