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NTE Version 1.4 Playable Character Revealed! • Phase I BlackBird — S Class Psyche Element • Phase II AkaneRin — S Class Lakshana Element Are you ready for them?? #NTE

54,002 Aufrufe • vor 12 Tagen •via X (Twitter)

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⚽️ 8v6 Phase of Play | Attacking Wide Areas| Cutbacks + Crosses Created on: TacticalPad® ☑️ Aim: ✔️ Combine in wide areas and create scoring chances using crosses or cutbacks. ☑️ Space and Set Up: ✔️ Half pitch: Funnel pitch toward mini goals. ✔️ 3 Mini Goals in a line centrally 🥅. Approx 5-8 yards from centre circle ⭕️. ✔️ Attacks are alternate ⚽️1️⃣: ⚫️s try to combine and score in large goal. ⚽️2️⃣: 🔴s build up and score in mini goals. ☑️ Play the Game: ✔️🔴s defend the large goal and score in any of the 3 x mini goals 🥅. They line up in a 1-4-3. ✔️ ⚫️s score in the large goal and defend the 3x mini goals 🥅. ✔️ ⚫️s line up in a 3-3 but they have 2xFB operate on the diagonal lines of the pitch. The FBs are behind the attack to recycle the ball ⚽️ for the ⚫️s. ☑️ Notes: ✔️ Encourage ⚫️s to use 2 and 3 player combinations to get behind the 🔴s defence. ✔️ Playing wide is a preference but if the 🔴s offer a clear central route to goal then use it. ✔️ Wall passing, Set passes, Overlaps and Underlaps are great moves to help players create chances. 🔁 Flip the roles of the players to allow them to practice attacking and defending. Temisan Williams @TheCoachesArea Lloyd Owers @FootballTrnng Brett Godwin The Sporting Resource Felix Lehmann @ExchangeCoaches The Coaching Family @RJPcoach TheBeardedCoach 205 Academy Tom Skeath Breakthrough Soccer 𝗙𝗖𝗖 𝗛𝘂𝗯 | 𝗙𝗼𝗼𝘁𝗯𝗮𝗹𝗹 𝗖𝗼𝗮𝗰𝗵 goTeam! Sports 🇬🇧 🇺🇸

JUST COACH

22,868 Aufrufe • vor 2 Jahren

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👑

19,992 Aufrufe • vor 9 Tagen