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Titana Prime is definitely that frame. - Easy Mode Level Cap✅ - Destroy Damage Attenuation Enemies✅ - Speed Runs/Movement✅ - Survivability✅ - There's even some Hidden Tech FULL BUILD: #Warframe #TennoCreate

21,479 views • 2 months ago •via X (Twitter)

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$ICP technical analysis update - the breakout is playing out EXACTLY as we want. remember when I said we were breaking a 4-year descending wedge at $7.64? we did. now we're at $6.20 with a massive 457K volume spike. wait, but why is the price is DOWN? this is EXACTLY what you want to see. we broke out to ~$10. shorts got liquidated. profit-taking hit. weak hands shook out. this pullback to $6.20 is a RETEST of the breakout level. that volume spike is ABSORPTION. smart money is loading this dip. the best breakouts ALWAYS retest before the real move. the pattern is still intact: descending wedge breakout confirmed ✅ proved we can break above $8-9 ✅ now retesting support at $6-7 ✅ textbook. old resistance becomes new support. we consolidate for a bit and then we run. fibonacci targets unchanged: $16 (0.236 fib) - first stop $24 (0.382 fib) - psychological break $38 (0.618 fib) - acceleration zone $59-93 (full retracement) - primary target when it runs, it runs FAST. $6 to $16 happens in days. $16 to $24 happens even faster. why this pullback is bullish: every major breakout retests. $BTC retests every level. $ETH retested $200 before $4,800. $SOL retested $20 before $260. $ZEC also retested many times. $ICP just retested at $6. this is your entry. fundamentals unchanged: Caffeine Web3 mode Q1 2026 ✅ Swiss incorporation complete ✅ Half of ICP locked ✅ All VC's fully vested ✅ AI market → $5-10 TRILLION ✅ Only blockchain that can host full end-to-end apps on chain ✅ nothing changed except you got a better price. risk/reward right now: risk: retest $5 (19% downside) reward: $16-59 (160-850% upside) 8:1 to 45:1 risk/reward. the chart is screaming: 4-year base complete ✅ wedge breakout confirmed ✅ volume absorption NOW ✅ fib levels mapped ✅ everything aligned. fortunes are made when it's -8% and people are doubting. not when it's +300% and trending. you're either buying this retest at $6. or watching it run to $16-59 without you. this is the trade. DONT FK IT UP ~spec

𝙯𝙨𝙥𝙚𝙘

37,902 views • 8 months ago

A team tested Pi0, Pi0 Fast, Gr00t, and ACT on real robot arms in manufacturing tasks. (🔖 Bookmark this for later!) The task was precise: place thin rectangular frames from a messy stack into a holder. The team fine-tuned each model on 100 real trajectories and compared training time, inference speed, motion quality, and success rates. ⬇️ Here’s a breakdown of what they found Pi0 (Original) ✅ Strongest overall performance in precise pick-and-place ✅ High success rate even in edge cases ✅ Longest training time (~11 hours, ~$30 per run) ✅ Inference time of 80 ms causes short pauses between actions Despite delays, it handles complex scenarios well… solid for high-precision tasks, but slow to train. Gr00t ✅ Trains fast (~2 hours, ~$5 per run) ✅ Performs almost as well as Pi0 on large-object tasks ✅ Struggles with fine precision; random movement in some trials ✅ More training didn’t fix jitter or random offsets Best suited for tasks where exact precision isn’t critical. Not ready for manufacturing-grade accuracy without more tuning. Pi0 Fast ✅ Promised faster training, but results were underwhelming ✅ Training at 6 hours still showed low success rates ✅ Inference was slower than expected ✅ Not reliable for generalizing even slightly new tasks Currently too unstable for real-world deployment. Doesn’t live up to the “Fast” name yet. ACT (Baseline) ✅ 200MB model—lightweight, but limited ✅ Struggles with stacked objects or ambiguous scenes ✅ Success rates around 70% in best-case setups ✅ Can’t match newer models on precision or generalization Still a solid baseline, but clearly a generation behind in robustness. 🚨 Extra Notes All newer models share a common issue: •Inference takes longer than a frame (80 ms vs 33 ms), so robots “pause” between chunks. •This results in jittery movements, but not a dealbreaker unless tasks are time-sensitive. Language-conditioned tasks also fell short: after training on two labeled tasks, the model couldn’t generalize to a third unseen combination using only text prompts. ✅ The good news? These models adapt well to new robot arms with quick fine-tuning. ❌ The bad news? There’s still no plug-and-play solution for improving performance after deployment. Reinforcement learning or DAgger-style data collection during real-world operation may be the next big step, something many teams in robotics are actively working on.

Ilir Aliu

21,844 views • 1 year ago