@THE_RENJUN izin dm ia mbess!>___<

MALAM MBESS

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Malem ini tayang mbess janlup

MBESS AKHIRNYA MBES AAAAA

$PPSI This may be one of the most overlooked “AI infrastructure” plays in the market. But not in the way most people think. Pioneer Power Solutions is not building AI chips. REPOST. BOOKMARK. SUBSCRIBE 1$ I’m the lab 24/7 They’re solving the thing nobody talks about enough: HOW TO ACTUALLY POWER AI. The AI buildout has a massive hidden problem: the grid is too slow. Utilities are quoting: • 3–7 year delays • transmission bottlenecks • interconnection backlogs • power shortages Hyperscalers and industrial operators need power NOW. That’s where PRYMUS comes in. PRYMUS is PPSI’s new rapid-deploy mobile power platform. Key specs: • scalable 1MW–10MW systems • natural gas / RNG / LPG / diesel capable • integrated battery storage (mBESS) • advanced controls remote monitoring • trailer-mounted fully mobile • deployable in ~6 months The first commercial deployments are scheduled for H2 2026. Just announced: • $6M PRYMUS award For: • one of the nation’s largest logistics/package delivery companies System details: • two ~1.2MW distributed generation systems • 8 paralleled 400kW natural gas engines • two 480kW battery storage systems • switchgear controls remote monitoring • fully trailer-mounted The CEO stated PRYMUS only launched ~5 months ago. That matters because one PRYMUS deal alone represents roughly: 20–25% of PPSI’s ENTIRE 2025 revenue base. Now imagine multiple deployments. FY2025 revenue: • $27.6M ( 21% YoY) Q1 2026: • revenue softness due to project timing • BUT gross margin improved sharply to 13.6% • operating losses narrowed • backlog increased sequentially Backlog: • $13.9M • up 11% sequentially Cash: • $13.6M • NO bank debt Management also implemented: • ~$1.5M annualized cost reductions Now let’s discuss the real speculation everyone is watching. PPSI has repeatedly referenced unnamed customers that strongly resemble some of the biggest infrastructure operators in the world. The company disclosed: • Fortune 100 e-commerce customer • EV depot/grid gap deployments • expansion plans across U.S. Canada in 2026 Who fits that profile best? Many investors believe Amazon.SpaceX ecosystem. $AMZN $SPCX PPSI disclosed: • mobile microgrid deployment • for a “cutting-edge rocket launching facility” • using natural gas generation battery storage But investors immediately connected the dots toward SpaceX-related infrastructure. That matters because: rocket launches, AI clusters, edge compute, and mission-critical facilities all require ultra-stable rapid-response power systems. And $PPSI specifically stated PRYMUS supports: • modular data centers • AI edge compute • next-generation AI chip testing • high variability AI workloads INCLUDING: next-generation $NVDA AI chipsets. PRYMUS is essentially positioned as: “power infrastructure for the AI deployment bottleneck.” And unlike giant incumbents: CAT, Cummins, Aggreko, Generac, etc. PPSI focuses on: • speed • mobility • modular deployment • rapid energization • edge deployments Traditional utility-scale deployments can take years. PRYMUS targets: “power in months.” Relevant markets include: • distributed generation • microgrids • edge AI infrastructure • modular data centers • backup power • temporary power • EV fleet infrastructure Combined TAM estimates run into: hundreds of billions long term. Meanwhile: global data center electricity demand is projected to nearly double by 2030. AI is forcing an infrastructure arms race. And power availability is becoming the gating factor. PPSI is tiny. But it’s operating directly inside: • AI power scarcity • edge infrastructure growth • distributed generation • mobile microgrids • rapid deployment energy systems Very few sub-$100M companies are this directly exposed to: THE AI POWER BOTTLENECK. $BE $SIVE $PLUG $EOSE $TE $HYLN $MRAM $MSFT $IREN $NBIS $NVTS $VOO $AMD $DGXX $SOFI $MU $TGEN $GOOG $MSFT

Ring e-guith na mbess na chirion.

JAJAN LEOT HEWANNYA MBESS ∧＿∧ 　 (｡･ω･｡)つ━★・*。 ⊂/　 /　 。* 　しーＪ　 ＊ •

Hallo haloo selamat malam mbull!! Ada kabar baik buat kalian.. Untuk saat ini base bull lounching @MIDNIGHTRPW yaitu base en es ef we guyss!! Tetapi, TETAPI!! hanya untuk mencari partner, FWB/FWA... dan mencari teman aja ea!! Base sudah bisa dipakai dan dipergunakan! Rules akan etmin post menyusul, ya pokoknya follow aja dulu mbull.. Kurang apa etmin menyediakan mbess ini, biar apa? Biar enggak salah lapakk!! 😒

Effect size measures the strength of a relationship between variables or the difference between groups, providing deeper insights than p-values alone. It helps assess the practical significance of results, making it valuable in fields like psychology, medicine, and social sciences. ✔️ Provides Practical Insight: Quantifies the importance of results beyond statistical significance. ✔️ Enables Comparisons: Allows results to be compared across studies with different scales or sample sizes. ✔️ Supports Meta-Analysis: Useful for combining results from multiple studies. ✔️ Offers Flexibility: Includes measures like Cohen’s d, Hedges’ g, Pearson’s r, and odds ratios for different data types. ❌ Sample Size Sensitivity: Small data sets can produce unstable estimates. ❌ Over-Reliance Risk: Interpreting effect size without confidence intervals can be misleading. ❌ Assumption Sensitivity: Some measures assume normality and equal variance. The image below shows plots of Gaussian densities illustrating various values of Cohen's d, highlighting how larger values indicate greater differences between groups. Image credit to Wikipedia: en.wikipedia.org/wiki/Effect… 🔹 In R: The effectsize package calculates Cohen’s d, Hedges’ g, and other measures, while the MBESS package provides confidence intervals. 🔹 In Python: The statsmodels library offers tools for effect size calculations, and the pingouin library provides user-friendly functions for a range of measures. Looking to learn more about Statistics, Data Science, R, and Python? Subscribe to my email newsletter! See this link for additional information: statisticsglobe.com/newslett… #Python #Statistical #datastructure #R #RStats

The New York Power Authority (NYPA) is collaborating with Cornell University and the Electric Power Research Institute to demonstrate use of a quieter and no-emission mobile battery to power campus events. The mobile battery energy storage system, known as MBESS, is a potential replacement for use of portable diesel generators. #PublicPower ow.ly/Ur9v50XGp6g

You for no tweet na bastad !mbess..

[] ueueueueue kenang"an panit damselnyaaa dah sampee!!! Lusyuu poll apalagi id card sama cardnya 😍😍😍 LOPYUU MBESS MAACII BANYAAK @sifuthut @SOFTIVIES

Effect size measures the strength of a relationship between variables or the difference between groups, providing deeper insights than p-values alone. It helps assess the practical significance of results, making it valuable in fields like psychology, medicine, and social sciences. ✔️ Provides Practical Insight: Quantifies the importance of results beyond statistical significance. ✔️ Enables Comparisons: Allows results to be compared across studies with different scales or sample sizes. ✔️ Supports Meta-Analysis: Useful for combining results from multiple studies. ✔️ Offers Flexibility: Includes measures like Cohen’s d, Hedges’ g, Pearson’s r, and odds ratios for different data types. ❌ Sample Size Sensitivity: Small data sets can produce unstable estimates. ❌ Over-Reliance Risk: Interpreting effect size without confidence intervals can be misleading. ❌ Assumption Sensitivity: Some measures assume normality and equal variance. The image below shows plots of Gaussian densities illustrating various values of Cohen's d, highlighting how larger values indicate greater differences between groups. Image credit to Wikipedia: en.wikipedia.org/wiki/Effect… 🔹 In R: The effectsize package calculates Cohen’s d, Hedges’ g, and other measures, while the MBESS package provides confidence intervals. 🔹 In Python: The statsmodels library offers tools for effect size calculations, and the pingouin library provides user-friendly functions for a range of measures. Looking to learn more about Statistics, Data Science, R, and Python? Subscribe to my email newsletter! For more information, visit this link: eepurl.com/gH6myT #Statistics #DataViz #Python #datavis #Rpackage #Data #RStats

@/Lee Jeno, bapakmu dijupuk Eric loh mbess wkwk

Made me lazy af. I just had to. Ata sauti ilikua imeanza kukua kama ya kindiki sikua na mbess😭

Omg, i managed to dance it at 70% speed😳🤯 rehearsed the parts i usually mbess up, it's still not clean but WAY better. If this continues like this, i may record it today or tomorrow 👀 Oooh, i am excited! 😍

MBESS🤤

Effect size measures the strength of a relationship between variables or the difference between groups, providing deeper insights than p-values alone. It helps assess the practical significance of results, making it valuable in fields like psychology, medicine, and social sciences. ✔️ Provides Practical Insight: Quantifies the importance of results beyond statistical significance. ✔️ Enables Comparisons: Allows results to be compared across studies with different scales or sample sizes. ✔️ Supports Meta-Analysis: Useful for combining results from multiple studies. ✔️ Offers Flexibility: Includes measures like Cohen’s d, Hedges’ g, Pearson’s r, and odds ratios for different data types. ❌ Sample Size Sensitivity: Small data sets can produce unstable estimates. ❌ Over-Reliance Risk: Interpreting effect size without confidence intervals can be misleading. ❌ Assumption Sensitivity: Some measures assume normality and equal variance. The image below shows plots of Gaussian densities illustrating various values of Cohen's d, highlighting how larger values indicate greater differences between groups. Image credit to Wikipedia: en.wikipedia.org/wiki/Effect… 🔹 In R: The effectsize package calculates Cohen’s d, Hedges’ g, and other measures, while the MBESS package provides confidence intervals. 🔹 In Python: The statsmodels library offers tools for effect size calculations, and the pingouin library provides user-friendly functions for a range of measures. Looking to learn more about Statistics, Data Science, R, and Python? Subscribe to my email newsletter! Learn more by visiting this link: eepurl.com/gH6myT #RStats #pythoncode #Data #Rpackage #datascienceenthusiast #Python #VisualAnalytics #programming #datavis

Mimi sina mbess mimi, mimi sina mbess, mimi ni noma si noma mimi ni fire si fire