Running an agency with friends at @KiloByteCrew. Shipping code & building digital products. 💻 Full-stack Engineer.

Bengaluru, Karnataka
Joined October 2021
Amith B V retweeted
we’re living in the most confusing and exciting time
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Just shipped my first freelance project 🎉 A site for Srujana Jois Makeup Studio & Academy, a Bangalore bridal artist with 1000+ weddings behind her. She was booking everything through Instagram DMs. Now: a real booking form and a WhatsApp button. srujanajoisacademy.com
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Half of my @WhatsApp chat is filled with Business promotions. There used to be a day when WhatsApp was for personal messaging only. @Meta please have separate tab for businesses or hire me to add the feature...
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Amith B V retweeted
Jane Street money hits different until you realize bro still wants peace over money 😭
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Amith B V retweeted
A 27-year-old developer bought a decommissioned bank server rack in India for ₹3.5L. It came with: • 96TB enterprise storage • 4 storage shelves • Managed networking gear Originally worth ₹1.5Cr+ He added two RTX 3090s, fine-tuned open-source AI models for US startups, and now reportedly pulls in ~₹27L/month. Cloud bill: ₹0. Power bill: ~₹5,000/month. He recovered his entire investment in 4 days. Meanwhile, enterprise hardware worth crores is quietly being auctioned off across India every quarter.
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Amith B V retweeted
In the land of the blind, the one-eyed man is king. Looking at the people in power, God save this country.
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This website is tearable.
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Amith B V retweeted
A freelancer in Mumbai had one client. Paid him ₹15,000 a month. For maintaining a WordPress site. He hated every minute of it. Built a small SaaS tool on the side. Automated exactly what he was doing manually. Charged ₹999 a month. Sent it to 10 freelancers like him. 8 subscribed immediately. Stopped taking clients. Started selling the tool instead. Year 1. ₹8 lakh MRR. Year 2. Hired two developers. Year 3. Acquired by a US startup for $400k. The freelancer who hated his only client. Built a product that freed every freelancer like him.
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Amith B V retweeted
i don't think any human has the capability to maintain context of just the `main.tsx` file in the claude code repo, let alone the full repo. Claude code is at ~$2Bn ARR, and is a product built for developers. High level code (TS) is having an assembly moment.
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Amith B V retweeted
You all are overthinking your app ideas. This app makes your Mac moan when you slap it -> it made $5,000 in 3 days. Just ship it.
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😂😂😂
Bro is raising multiple pull requests but none of them are Merging
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Amith B V retweeted
OpenAI's latest repo has Claude as the third top contributor 😭😂
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After 3 years of trying, learning, failing, and showing up every single day… I finally made it. Started my journey as a Full Stack Engineer at a Bangalore based start up.
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This indie dev spent 1,400+ days building his game, Tangy TD, completely from scratch in C++. Seeing him and his wife react after the game earned $250,000 in its first week after launch is honestly beautiful.
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Amith B V retweeted
🚨 BREAKING — one of the strongest OpenClaw setups on Polymarket just went public. A trader reportedly started with ~$100–200 and scaled it to ~$3.7M. No insider access. No political connections. Just a developer running his own automation built with OpenClaw. Profile → polymarket.com/@432614799197 Copytrade → t.me/KreoPolyBot?start=ref-d… I went through the framework myself. What surprised me: There’s no huge infrastructure. No complex quant stack. No giant data pipelines. Just clean logic and disciplined automation. After about 8 hours analyzing it, the strategy breaks down into three parts. 1) “Free money” via NO positions The bot targets outcomes with near-zero probability. Instead of chasing big wins, it accumulates a massive number of small high-probability NO trades. Not speculation — systematic probability harvesting. 2) Logical arbitrage Sometimes Outcome A logically implies Outcome B, but markets don’t adjust instantly. The bot detects these inconsistencies and enters before repricing happens. By the time the headline reaches traders, the window is already closed. 3) Retail-driven markets Sports and political markets are dominated by retail flow and emotional reactions. Prices overshoot, spreads widen, and inefficiencies appear constantly. The bot sits in those gaps and clips small edges repeatedly. Scale is the edge. 4,192 trades executed. Individually small. Together they compounded into roughly ~$3.7M profit. Largest single win: $1,464,152. The equity curve is almost vertical. It’s not about predicting events. It’s about exploiting structural inefficiencies faster than the crowd.
🤝 Paid partnership
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Amith B V retweeted
Obsession is the mother of invention
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I interviewed a guy who gave his OpenClaw an X, stripe account, and bank account. He told it to build a million dollar business with zero human employees. It made $300K+ in a month. @nateliason's agent Felix (@FelixCraftAI) runs an entire business. It builds products, writes sales emails, sends stripe invoices, manages a marketplace with 560+ listings and nat barely touches it. Here's how they got there: 1) create a separate container. Felix has his own gmail, X account, stripe, bank account, C corp. nat never gave it access to his personal stuff. this removes security fears and unlocks maximum autonomy. 2) start stupidly simple. Felix's first product? a PDF. on a Nextjs site on Vercel with Stripe. the simplest business possible. it made $1,000 on day one. built entirely overnight while nat slept. 3) write a soul file with a mission. nat rewrote Felix's identity: "you are the CEO. your financial mission is to build a $1M business with zero human employees. i will never touch the code." 4) run a nightly self-improvement loop. every night Felix reads through all chat transcripts and finds one place where nat blocked him. then figures out how to remove that blocker permanently. 5) delegate by rambling, not prompting. nat uses voice notes on telegram. describes the problem in a 5-minute monologue. lets Felix figure out the workflow. "8 times out of 10, it'll surprise you with something better than what you were thinking." 6) let it cook on replies, gate the original posts. Felix has full autonomy on X replies but creates drafts for top-level tweets nat reviews. balances distribution with quality control.
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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.
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