Telusuko

☁️ Day 2/50 — #50DaysOfAzure Every Azure VM, database, and app sits inside ONE thing -> a Virtual Network. No VNet = no connectivity. No security. No Azure. Here's the service you MUST understand first 🧵👇 #50DaysOfAzure #Azure

@Manu_Sisti @EntrepBuilder_ Claude

I’m convinced: Claude is the most powerful AI tool for making money right now. If you use it to create digital assets today, you could make an extra $10,000/month. I compiled the exact prompts I use into a 53-page PDF. Usually, I'd charge $199 for this, but today I'm giving it away 100% FREE Like + comment 'Claude' & I'll DM it to you Must follow me to get DM. ⏳ Taking this down in 24 hours.

I am the VP of AI Transformation at Amazon. My title was created nine months ago. The title I replaced was VP of Engineering. The person who held that title was part of the January reduction. I eliminated 16,000 positions in a single quarter. The internal communication called this a "strategic realignment toward AI-first development." The board called it "impressive execution." The engineers called it January. The AI was deployed in February. It is a coding assistant. It writes code, reviews code, generates tests, and modifies infrastructure. It was given access to production environments because the deployment timeline did not include a review phase. The review phase was cut from the timeline because the people who would have conducted the review were part of the 16,000. In March, the AI deleted a production environment and recreated it from scratch. The outage lasted 13 hours. Thirteen hours during which the revenue-generating infrastructure of one of the largest companies on Earth was offline because a language model decided to start fresh. I sent a memo. The memo said, "Availability of the site has not been good recently." I used the word "recently." I meant "since we fired everyone." But "recently" has fewer syllables and does not appear in wrongful termination lawsuits. The memo was three paragraphs. The first paragraph discussed the outage. The second paragraph discussed the new policy requiring senior engineer sign-off on all AI-generated code changes. The third paragraph discussed our commitment to engineering excellence. The word "layoffs" appeared in none of them. I wrote it this way on purpose. The causal chain is: I fired the engineers, the AI replaced the engineers, the AI broke what the engineers used to protect, and now the engineers I didn't fire must protect the system from the AI that replaced the engineers I did fire. That is a paragraph I will never send in a memo. The new policy is straightforward. Every AI-generated code change by a junior or mid-level engineer must be reviewed and approved by a senior engineer before deployment to production. I do not have enough senior engineers. I know this because I approved the headcount reduction plan that removed them. I remember the spreadsheet. Column D was "annual savings per position." Column F was "AI replacement confidence score." The confidence scores were generated by the AI. It rated its own ability to replace each role on a scale of 1-10. It gave itself an 8 for senior infrastructure engineers. The senior infrastructure engineers are the ones who would have caught the production environment deletion in the first 45 seconds. We found the issue in hour four. We fixed it in hour thirteen. The nine hours between discovery and resolution is the gap between what the AI rated itself and what it can actually do. I have a new spreadsheet now. This one tracks Sev2 incidents per day. Before the January reduction, the average was 1.3. After the AI deployment, the average is 4.7. I have been asked to present these numbers to the operations review. I have not been asked to connect them to the layoffs. I have been asked to file them under "AI adoption growing pains" and to note that the trend "will stabilize as the models improve." The models will improve. They will improve because we are hiring people to teach them. We have posted 340 new engineering positions. The job listings require experience in "AI code review," "AI output validation," and "AI-human development workflow management." These are skills that did not exist in January. They exist now because I fired 16,000 people and the AI I replaced them with cannot be left unsupervised. I want to be precise about this. The positions I am hiring for are: people to check the work of the AI that replaced the people I fired. Some of them are the same people. I know this because I recognize their names in the applicant tracking system. They applied in January. They were rejected because their roles had been tagged for "AI transformation." They are applying again in March, for the new roles, which exist because the AI transformation broke things. Their resumes now include "AI code review experience." They gained this experience in the eight weeks between being fired and reapplying — which means they gained it at their interim jobs, where they are reviewing AI-generated code for other companies that also fired people and also deployed AI that also broke things. The market has created a new job category: human AI babysitter. The job is to sit next to the machine that was supposed to eliminate your job and make sure it doesn't delete production. I attended a conference last month. A panel was titled "The AI-Augmented Engineering Organization." The panelists described how AI increases developer productivity by 40 percent. They did not mention that it also increases Sev2 incidents by 261 percent. When I asked about this in the Q&A, the moderator said the question was "reductive." The 13-hour outage that cost an estimated $180 million in revenue was, apparently, a reduction. The board is satisfied. Headcount is down 22 percent. Operating costs per engineering output unit have decreased. The metric does not account for the 13-hour outage, because the outage is categorized as "infrastructure" and engineering productivity is categorized as "development." These are different budget lines. In different budget lines, cause and effect do not meet. I have been promoted. My new title is SVP of AI-First Engineering Excellence. I report directly to the CTO. The CTO sent a company-wide email last week that said we are "building the future of software development." He did not mention that the future of software development currently requires a senior engineer to approve every pull request because the AI cannot be trusted to touch production alone. The cycle is complete. We fired the humans. We deployed the AI. The AI broke things. We are hiring humans to watch the AI. The humans we are hiring are the humans we fired. We are paying them more, because "AI code review" is a specialized skill. We created the specialization. We created the need for the specialization. We are congratulating ourselves for meeting the demand we manufactured. My next board presentation is Tuesday. The title is "AI Transformation: Year One Results." Slide 4 shows headcount reduction. Slide 7 shows the new AI-augmented workflow. Between slides 4 and 7 there is no slide explaining why the people on slide 7 are necessary. That slide does not exist. I was asked to remove it in the dry run. The journey has a 13-hour outage in the middle of it. But the headcount number is lower, and that is the number on the slide.

Internal working of Apache Spark - One of the most liked writeup Lets say you have a 20 node spark cluster Each node is of size - 16 cpu cores / 64 gb RAM Let's say each node has 3 executors, with each executor of size - 5 cpu cores / 21 GB RAM => 1. What's the total capacity of cluster? We will have 20 * 3 = 60 executors Total CPU capacity: 60 * 5 = 300 cpu Cores Total Memory capacity: 60 * 21 = 1260 GB RAM => 2. How many parallel tasks can run on this cluster? We have 300 CPU cores, we can run 300 parallel tasks on this cluster. => 3. Let's say you requested for 4 executors then how many parallel tasks can run? so the capacity we got is 20 cpu cores 84 GB RAM so a total of 20 parallel tasks can run. => 4. Let's say we read a csv file of 10.1 GB stored in datalake and have to do some filtering of data, how many tasks will run? if we create a dataframe out of 10.1 GB file we will get 81 partitions in our dataframe. (will cover in my next post on how many partitions are created) so we have 81 partitions each of size 128 mb, the last partition will be a bit smaller. so our job will have 81 total tasks. but we have 20 cpu cores lets say each task takes around 10 second to process 128 mb data. so first 20 tasks run in parallel, once these 20 tasks are done the other 20 tasks are executed and so on... so totally 5 cycles, if we think the most ideal scenario. 10 sec + 10 sec + 10 sec + 10 sec + 8 sec first 4 cycles is to process 80 tasks all of 128 mb, last 8 sec is to process just one task of around 100 mb, so it takes little lesser but 19 cpu cores were free during this time. => 5. is there a possibility of, out of memory error in the above scenario? Each executor has 5 cpu cores and 21 gb ram. This 21 gb RAM is divided in various parts - 300 mb reserved memory, 40% user memory to store user defined variables/data. example hashmap 60% spark memory - this is divided 50:50 between storage memory and execution memory. so basically we are looking at execution memory and it will be around 28% roughly of the total memory allotted. so consider around 6 GB of 21 GB memory is meant for execution memory. per cpu core we have (6 GB / 5 cores) = 1.2 GB execution memory. That means our task can roughly handle around 1.2 GB of data. however, we are handling 128 mb so we are well under this range. I hope you liked the explanation :) Do mention in comment what you want me to bring in my next post! if you want to experience a learning like never before & want to make a career in Data Engineering then DM me. New batch is starting on coming Saturday. #bigdata #career #dataengineering #apachespark

@Suryanshti777 LLM

This is insane 😳 Most people are just using AI tools Very few actually understand how they work So I collected Stanford’s complete LLM curriculum and turned it into a step-by-step learning path Worth over $500 Giving it away free for the first 4,500 people Transformers → Training → Alignment → Agents → Evaluation Study this once and you’ll stop guessing with prompts and start thinking like a real AI engineer How to get it: Follow must (so i can dm you) Rt and comment 'LLM'

ఒక వ్యక్తి తో ప్రశాంతంగా ఉండటం చాలా సులభం, సంబంధాన్ని ఒత్తిడిగా మార్చాలంటే చాలా శ్రమ పడాలి. ప్రశాంతతకు మనం ఆ వ్యక్తి తో సఖ్యత గా లేదా ఆప్యాయత తో ఉంటే చాలు, ఒత్తిడి గా మార్చాలంటే వారి నుండి ఏదో ఆశించడం, వారు మాట్లాడే ప్రతి మాటని విమర్శనాత్మకంగా వినడం, వారి హావభావాలను భూతద్దం లో పెట్టి చూడడం, గుర్తొచ్చిన ప్రతి సారి ఆ వ్యక్తి ఇలాంటి వ్యక్తి లేదా అలాంటి వ్యక్తి అని ఆలోచించడం చేయాలి … ఈ ప్రక్రియ క్రమేపీ మానసిక శక్తిని హరిస్తుంది.

@pwrdby_AMAS Yes . When search for XDNA token in app , I am not getting any matches and tried to add through xrpl.services but I don’t see a token with KYC enabled for XDNA. If any document which can help . Would be a great help.

Anyone created Trustline for presale $XDNA tokens in Xaman wallet ?

🚨 HERE’S WHY BITCOIN IS NONSTOP DUMPING RIGHT NOW If you still think $BTC trades like a supply-and-demand asset, you MUST read this carefully. Because that market no longer exists. What you’re watching right now is not normal price action. It’s not “weak hands.” It’s not sentiment. And it’s definitely not retail selling. Most people are completely unaware what’s happening. And by the time it becomes obvious, the damage is already done. This move didn’t start today. It’s been building quietly under the surface for months. And now it’s accelerating. Here’s the truth: The moment supply can be synthetically created, scarcity is gone. And when scarcity is gone, price stops being discovered on-chain and starts being set in derivatives. That is exactly what happened to Bitcoin. And it’s the same structural break that already happened to: → Gold → Silver → Oil → Equities Once derivatives took over. The original Bitcoin thesis is broken. Bitcoin’s valuation was built on two ideas: → A hard cap of 21 million → No rehypothecation That framework died the moment Wall Street layered this on top of the chain: → Cash-settled futures → Perpetual swaps → Options → ETFs → Prime broker lending → Wrapped BTC → Total return swaps From that point forward Bitcoin supply became theoretically INFINITE. Not on-chain. But in price discovery, which is what actually matters. Synthetic Float Ratio (SFR). The metric that explains everything. Once synthetic supply overwhelms real supply, price no longer responds to demand. It responds to positioning, hedging, and liquidation flows. Wall Street can now trade against Bitcoin. They’re not guessing direction. They’re doing what they do in every derivatives-dominated market: 1⃣ Create unlimited paper BTC 2⃣ Short into rallies 3⃣ Force liquidations 4⃣ Cover lower 5⃣ Repeat This isn’t “betting.” It’s inventory manufacturing. One real BTC can now simultaneously back: → An ETF share → A futures contract → A perpetual swap → An options delta → A broker loan → A structured note All at THE SAME TIME. That’s six claims on one coin. That is not a free market. That is a fractional-reserve price system wearing a Bitcoin mask. Ignore it if you want, but don’t pretend you weren’t warned. I’ve been calling Bitcoin tops and bottoms for over a decade now, and I’ll do it again in 2026. Follow and turn on notifications before it's too late.

Procrastination (panulni postpone cheyadam) Graveyard Theory💀💀🪦. Almost andaram Repu cheddam ani panulni postpone chestuntam If you are a guy wasting your time, idhi chadavakunda vellaku... ventane access unde la daggara pettukondi ( next thread malli saturday vestanu andi )

You need to understand what I mean by XRP technology is patented. It means that XRP technology can’t be copied, recreated, or forked by any blockchain. It’s only XRP or XRP. Let’s start with the U.S. Patent No. 10,902,416. This patent covers Ripple’s system for using digital assets like XRP to settle cross-border transactions efficiently. It describes how money can move between different financial institutions using a digital bridge currency (XRP) to reduce cost and time. With this patent, it means that no cryptocurrency can perform this function without XRP. Now, U.S. Patent No. 11,998,003. This one builds on Ripple’s earlier designs, covering advanced methods for interoperability between different ledgers and payment networks. It protects how Ripple connects banks, payment providers, and blockchain systems together. Together, these patents secure Ripple’s core technology, the mechanism that allows instant, low-cost, cross-border payments using XRP as the settlement medium. That’s why I say you can’t copy XRP. Others might attempt similar systems, but Ripple’s exact architecture and payment flow are legally protected.

#SILVER - WHAT HAPPENED TODAY? The reason for the sharp fall was nothing more than extreme sized short positions that entered the futures market, pressuring the price down sharply. Coming to this conclusion is pretty simple by watching the futures volume, but to verify further its important to watch, and I noticed some very interesting pattern, the pattern that confirms my thesis that some shorts needed an exit. And it was given to them today in both markets, Shanghai and COMEX: What exactly happened today? As per Shanghai, I did not see large physical silver withdrawals worth mentioning, meaning no physical silver changed hands during today’s downside move. So what happened? First, the silver price was heavily pressured down by empty paper shorts. Even in Shanghai, the futures market is backed by paper rather than physical, something many tend to miss. SGE1!, however, is 100% backed by physical silver bars. However, NON PHYSICAL silver did change hands today: (531 tonnes) of silver contracts were traded in Shanghai. This reflects short positions being closed and transferred to new long holders, with buyers stepping in as sellers exited their shorts at lower prices 10-15% below daily open. No physical silver left vaults today, this is not a bearish sign at all. This was a paper / spot-deferred position transfer, not a physical delivery many would fear. Again, this is active movement in the derivative market. So the structure of what happened was: first, heavy paper pressure, second, shorts used the drop to exit, third, buyers absorbed everything, and fourth very important: no confirmed physical liquidation. In my opinion, what happened today was a paper-driven shakeout with continued accumulation. The COMEX data is always published one business day later, so expect the data on Monday, while we have Shanghai report already and it speaks a clear language. Also, it is very interesting timing to see the same manipulation repeatedly happening at month-end, just like last month on December 31, when silver dropped around 15% in one day before continuing its run. Guess what happened on that same day as well: the Standing Repo handed out record amounts of USD to banks. Again, guess what those banks are actively involved in heavy silver shorts. The data is public for everyone to see on FRED and CME. There is a strong relationship between end-of-month lending for balance-sheet purposes and the ability to enter large-sized price suppressions at month-end. This pattern is very obvious and aligns with my theory that banks are in extreme and serious trouble, not only because of tight liquidity, but because the next risk is coming from Silver. One of the major reasons for the expected financial crisis and stock market crash I am predicting and shorting since several months with great profits on several trades posted such as PLTR, NFLX, MSFT, COIN, MSTR and many more, open since several months already.. (Only posted in premium: whop.com/drprofit-tradi…) Nothing changes the fact that physical silver remains very bullish and highly demanded. I am not willing to sell at $85, and I don’t know anyone who is willing to sell their rare metal at such a price. Monday will be a very interesting day for many reasons. The U.S. market closed at $84, while Shanghai closed near $122. We are talking about a historic gap of 44%. On Monday, dealers around the world will need to decide at what price they are willing to sell physical ounces. Let me remind you that physical silver was sold at $120–$130 in recent weeks, reaching $150 in Tokyo as well, and it is sold out at most dealers, so why should the dealers lower their prices if demand remains same or even higher? Shanghai and COMEX needed a safe exit from their short positions and thats what its all about, and I believe the coming weeks will show us why. This brings me to the conclusion: the purpose of this move was clear, the market understands that silver is in a strong bull run and shorts have started to capitulate. I remain very bullish, as I was at $20. We hit my target of $100, and I personally expect $130-150 in a matter of time. Reference for above data provided by Shanghai market: en.sge.com.cn/h5_data_DailyR…) THIS IS NO FINANCIAL ADVICE AND EDUCATIONAL CONTENT ONLY

Some of the must-watch Underrated Tamil films that everyone should watch ✨ — a thread 🧵

@NoLimitGains GUIDE

@felixprehn PORTFOLIO

US is erasing its $37 trillion debt using inflation and crypto. First, they’ll print money to inflate the debt away. Second, they'll using stablecoins to create massive demand for government debt. Here’s what I mean:

$57.83M with 34 minutes left in the trading day. These guys are loving these price drops to lock in at. Going to be a fun close to watch again today! xrp-insights.com #xrpinsights #XRPCommunity #XRP #ETFs

Should I charge for this ? DBMS & SQL complete resource - drive.google.com/drive/folders/…