APAC Sales Leader

What to do with daily 2hr standup? It eats my gym time.

Padhle likhe gawar hire kar lete h kya ji jira m likeh status update nai padh paate? Ek hi bakwas dicuss ho rai h roz din. Iske upar se 8 ghnata code bhi chaiye. What do program managers think of themselves ? Ye saare milke humko pagal bana rahe ., itne ga**d phati k ye.. I'm planning to say it outright to Manager, that if standups are going to run for 1.5 hrs then I will be commuting in this time. I dont get time for myself, if I just sit and listen nonsense that i can read.

Veteran Crime Reporter A Selvaraj's Sensational Scoops

- A Selvaraj, a seasoned crime reporter in Tamil Nadu since 1994, has a reputation for breaking sensational stories. - In 1998, he exposed a major cheating racket led by Divya Mathaji in Tiruchi. - His notable stories include the high-profile suicide of 2G scam accused Sadiq Batcha. Source: [The Times Of India](https://timesofindia.indiatimes.com/city/chennai/parai-player-drowns-in-pond-in-chennai/articleshow/115940882.cms), [The Times Of India](https://timesofindia.indiatimes.com/city/chennai/tamil-film-actors-son-among-seven-detained-for-link-with-ganja-peddlers/articleshow/115940496.cms)

Deepseek vs Chatgpt

*Why DeepSeek's AI innovations are blowing people's minds (and possibly threatening Nvidia's $2T market cap) in simple terms...* First, some context: Right now, training top AI models is INSANELY expensive. OpenAI, Anthropic, etc. spend $100M+ just on compute. They need massive data centers with thousands of $40K GPUs. It's like needing a whole power plant to run a factory. *DeepSeek just showed up and said "LOL what if we did this for $5M instead?" And they didn't just talk - they actually DID it.* Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook. How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like "what if we just used 8? It's still accurate enough!" Boom - 75% less memory needed. Then there's their "multi-token" system. Normal AI reads like a first-grader: "The... cat... sat..." DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you're processing billions of words, this MATTERS. But here's the really clever bit: They built an "expert system." Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed. Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It's like having a huge team but only calling in the experts you actually need for each task. The results are mind-blowing: - Training cost: $100M → $5M - GPUs needed: 100,000 → 2,000 - API costs: 95% cheaper - Can run on gaming GPUs instead of data center hardware "But wait," you might say, "there must be a catch!" That's the wild part - it's all open source. Anyone can check their work. The code is public. The technical papers explain everything. It's not magic, just incredibly clever engineering. *Why does this matter? Because it breaks the model of "only huge tech companies can play in AI." You don't need a billion-dollar data center anymore. A few good GPUs might do it.* *For Nvidia, this is scary. Their entire business model is built on selling super expensive GPUs with 90% margins. If everyone can suddenly do AI with regular gaming GPUs... well, you see the problem.* And here's the kicker: DeepSeek did this with a team of <200 people. Meanwhile, Meta has teams where the compensation alone exceeds DeepSeek's entire training budget... and their models aren't as good. *This is a classic disruption story:* Incumbents optimize existing processes, while disruptors rethink the fundamental approach. DeepSeek asked "what if we just did this smarter instead of throwing more hardware at it?" *The implications are huge:* - AI development becomes more accessible - Competition increases dramatically - The "moats" of big tech companies look more like puddles - Hardware requirements (and costs) plummet Of course, giants like OpenAI and Anthropic won't stand still. They're probably already implementing these innovations. But the efficiency genie is out of the bottle - there's no going back to the "just throw more GPUs at it" approach. Final thought: *This feels like one of those moments we'll look back on as an inflection point. Like when PCs made mainframes less relevant, or when cloud computing changed everything.* *AI is about to become a lot more accessible, and a lot less expensive. The question isn't if this will disrupt the current players, but how fast?* One of the reason of market fall across the globe