Man Jailed Ten Years over Farming Scam in Kaduna

हिमालय पर्वत की एक बड़ी पवित्र गुफा थी।उस गुफा के निकट ही गंगा जी बहती थी।एक बार देवर्षि नारद विचरण करते हुए वहां आ पहुंचे।वह परम पवित्र गुफा नारद जी को अत्यंत सुहावनी लगी।वहां का मनोरम प्राकृतिक दृश्य,पर्वत,नदी और वन देख उनके हृदय में श्रीहरि विष्णु की भक्ति अत्यंत बलवती हो उठी।

और देवर्षि नारद वहीं बैठकर तपस्या में लीन हो गए।इन्द्र नारद की तपस्या से घबरा गए।उन्हें हमेशा की तरह अपना सिंहासन व स्वर्ग खोने का डर सताने लगा।इसलिए इन्द्र ने नारद की तपस्या भंग करने के लिए कामदेव को उनके पास भेज दिया।वहां पहुंच कामदेव ने अपनी माया से वसंतऋतु को उत्पन्न कर दिया।

पेड़ और पौधों पर रंग बिरंगे फूल खिल गए और कोयलें कूकने लगी,पक्षी चहकने लगे।शीतल,मंद,सुगंधित और सुहावनी हवा चलने लगी।रंभा आदि अप्सराएं नाचने लगीं ।किन्तु कामदेव की किसी भी माया का नारद पे कोई प्रभाव नहीं पड़ा।तब कामदेव को डर सताने लगा कि कहीं नारद क्रोध में आकर मुझे श्राप न देदें।

जैसे ही नारद ने अपनी आंखें खोली, उसी क्षण कामदेव ने उनसे क्षमा मांगी।नारद मुनि को तनिक भी क्रोध नहीं आया और उन्होने शीघ्र ही कामदेव को क्षमा कर दिया।कामदेव प्रसन्न होकर वहां से चले गए।कामदेव के चले जाने पर देवर्षि के मन में अहंकार आ गया कि मैने कामदेव को हरा दिया।

नारद फिर कैलाश जा पहुंचे और शिवजी को अपनी विजयगाथा सुनाई।शिव समझ गए कि नारद अहंकारी हो गए हैं और अगर ये बात विष्णु जी जान गए तो नारद के लिए अच्छा नहीं होगा।ये सोचकर शिवजी ने नारद को भगवन विष्णु को ये बात बताने के लीए मना किया। परंतु नारद जी को ये बात उचित नहीं लगी।

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.

one of the things i like about Batman is that it's fundamentally about being orphaned, and being an orphan — how this guy stays tethered to the world in his own weird, abstract, extrajudicial way when he feels that his link to it has been violently severed

Batman does this by permanently recapitulating his own trauma: solving crimes for other people, protecting them. But that's actually just the beginning; he also recapitulates his own experience of orphanness by essentially raising several orphans

Dick is an orphan in the traditional sense: dead parents. Batman sees it happen. Jason, less so: missing dad, known mom who eventually dies. Tim, even less so: parents are around a while, and involved, before both dying. Carrie, even less than that: negligent parents.

Steph: evil father, who openly works against her. And then there's Damian, whose parents are both alive and yet both have identities that totally obliterate their role as his parents. He doesn't relate to them as a mother and father; he can't, and neither can they.

In all these different cases, Batman tries all these different ways of making orphanness for them not what it was for him: Either by trying to teach them the (arguably poor) coping skills early that he learned over a very long time, or by literally legally adopting them, etc.