Every time I start a new project with an AI agent, I end up spending the first hour just... re-explaining things. Same rules. Same context. Same setup. It's not the agent's fault. There's just no memory between sessions. Your experience stays trapped in a chat window and dies there. #EvoMap #VibeCoding

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EvoMap stores your successful AI workflows as reusable Genes and tracks real execution records (Capsules) that prove they work. So instead of re-explaining everything, your agent picks up from what's already worked. Works with Cursor, Claude Code, Codex, Manus. And there's now an API redemption system, contribute Genes/Capsules, earn credits, redeem against GPT-5.5, Opus 4.7, and others. Basically turning your workflow experience into actual value.

If you already use Cursor, Claude Code, Codex, or your own AI agent workflows, EvoMap is worth a look. Not a replacement for anything, just makes what you already do more portable and less wasteful. → evomap.ai/?utm_source=x&utm_… → Setup Guide in the docs if you want to jump in today #EvoMap #VibeCoding

Claude is now controlling TradingView live from my terminal. Switching symbols. Writing Pine Script. Batch scanning futures. Replay trading. Drawing levels. All autonomous. Zero clicks. Still has rough edges but the vision is crystal clear. I told it: Find me every BTC futures contract with RSI below 30 and volume spike above 200%. 14 seconds later: → 6 contracts identified → Charts loaded → Support levels drawn → Pine Script backtests running → Entry zones marked Didn't touch the mouse once. Then I said: Replay last week. Show me where your system would have entered. It switched to replay mode. Scrolled through price action. Marked every edge. Calculated P&L in real-time. $4,780 theoretical profit from 9 trades. 83% win rate. Now it writes custom Pine indicators on command: Build me a momentum oscillator that tracks whale wallet activity correlated with price. 40 seconds. Script deployed. Indicator live on chart. Most traders are still clicking through 50 charts manually. Claude scans 200+ in under a minute. Finds the setups. Draws the levels. Backtests the edge. All while you watch. This is not about replacing your strategy. It's about executing it 100x faster. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word CLAUDE 2. Like and Retweet this post 3. Follow me @codewithimanshu (so i can DM you) Save this post. Deploy this setup this weekend. Start testing. Scale on evidence.

This guy used Claude to build a Quant Bot and made +$589,139 on Polymarket. 25,388 predictions with a 63% win rate in 77 days. That comes out to roughly $7,651 in profit per day and almost 14 trades per hour. With $217K in deposits, he is now up 2.7x. About +271.5% ROI. The bot strategy is simple: It finds markets where the pricing is still off. Gets in before the odds fully catch up. Repeats the same setup again and again across many entries. The edge on each trade is small. But scale and repetition turn it into a massive result. Most profitable trades: $17,839 → $36,318 (+$18,478, +103.58%) $3,112 → $15,011 (+$11,898, +382.22%) $10,676 → $22,178 (+$11,502, +107.74%) What makes it work is not one huge trade. It is the same small advantage, applied fast, applied often, and repeated until the pricing gap is gone. I reverse-engineered it. Had Claude rebuild the same logic. One prompt. 90 minutes. Fully deployed. You only need: Claude + a device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word PROFIT 2. Like and retweet this 3. Follow me @codewithimanshu so I can DM you

My girlfriend asked why I was smiling at 4 AM. Showed her the terminal. What are all those green numbers? $1,129. Made while she slept. Doing what? Nothing. Claude scanned 14,000 wallets, found 47 that never lose, built a bot that copies them. She watched for 10 seconds: +$3.87 captured +$6.42 captured +$12.71 captured It just keeps going? Every few seconds. New line. New money. How much did you start with? $300. Now $1,429. Eleven hours. Asleep. What does it do? Buys at $0.48. Sells at $0.52. Pockets $0.04. Who wins doesn't matter. That's legal? Citadel does this on NYSE daily. 400 engineers. I have one screen. She looked at the P&L curve. Never dips. Just climbs. Can you make me one? Setting hers up now. She still doesn't get how it works. The bot doesn't care. You only need Claude + laptop + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word "Claude" 2. Like and Retweet this post 3. Follow me @codewithimanshu (so i can DM you) Save this post. Deploy the bot this weekend. Start with $300. Scale on evidence.

I made $12,000 setting up Claude Code trading SetUp for non-technical users. You can do this too. In simple steps. I've prepared the exact step-by-step guide. This guide is worth $999. I'm giving it away free for the next 24 hours. To get it: 1. Comment "Money" 2. Like and Retweet this post 3. Follow me @codewithimanshu (so i can DM you) You Only Need: Claude + 1 laptop + 2 hours/day = $2,000+/day. No coding. No experience. Just follow the guide. Note: You Must Follow me @codewithimanshu. only then, i can send you in DM personally. Grab this fast and set it up yourself tonight.

A 29-year-old sales consultant from China quit his job. Now making 2 weeks what his boss earns all year. $306,000 profit last month. He replaced an entire quant team with Claude and 6 AI agents. Built his own ETH price simulation engine. Generating $15,000+ per day on autopilot. I reverse-engineered his system. One Claude prompt. 90 minutes. Fully autonomous. Giving this free for 24 hours. To get it: 1. Comment AGENT 2. Like and retweet this 3. Follow @codewithimanshu so I can DM you His wallet: 0x06dc51826bc524d9a83770e7de9dd7e005b0452 on Polymarket. Almost nobody is watching. What the 6-agent swarm actually does: → Each agent validates its own trading decisions independently → Collects data 24/7 across markets → Runs continuous ETH price simulations in MiroFish engine → Memorizes every pattern, market reaction, trading signal → Detects market inefficiencies in real-time → Executes when edge appears → No human input required Not prediction. Pure math exploiting market lag. The coverage and speed beat top-tier trading teams. Every trade is a perfect cycle. Every dollar is extracted from pricing gaps that disappear in seconds. The system does not guess the future. It reads the numbers correctly and takes the money before markets reprice. The edge exists right now. It won't in 6 months when everyone runs similar systems. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence.

My trading bot warned me 48 hours before the crash. Most people ignored it. Now the damage is done. Bitcoin dropped to $70K. Over $500M in longs wiped out in hours. But my 6-agent swarm? Already positioned short. $306,000 profit last month. $5,000+ per day on autopilot. Built with Claude. Runs 24/7. Zero manual input. Giving this free for 24 hours. To get it: 1. Comment "BTC" 2. Like and retweet this 3. Follow @codewithimanshu so I can DM you The system saw what most traders missed: → PPI jumped to 6%, fastest pace since COVID → Rate cut hopes dead → Fed Chair Warsh hawkish signal detected → Higher-for-longer rates confirmed → 10Y yields above 4.6% → Spot ETF outflows at historic highs → Capital flowing out of risk assets Every agent validates its own decisions independently. Collects macro data 24/7. Runs continuous ETH and BTC price simulations in MiroFish engine. Memorizes every pattern. Every market reaction. Every signal. Detects inefficiencies before markets reprice. Executes when edge appears. The crowd is praying for a quick bounce. They will be deeply disappointed. This is just the beginning of the real move down. My bot positioned short 48 hours early. Most people will realize it only when their portfolios are down another 30%. The system does not predict. It reads the numbers correctly and takes the money before consensus catches up. You only need: Claude + a device + 1 hour to deploy. Save this post. Build the agent swarm this week. Start with $100. Scale on evidence. Turn notifications on. If you're not following yet, you'll understand why that was a mistake when the next leg down hits.

A fired Goldman Sachs quant trader taught me everything in one conversation. He said: We don't do predictions. We only buy contracts where the price deviation exceeds 6%. That's it. That's the desk operation for a $2M annual salary. I fed his explanation and 5 GitHub libraries into Claude. Claude built a scanner. Processing 400+ markets every hour. The scanner finds contracts priced in the 7 to 19c range, with true probabilities between 60 to 90%. At these entry points, you need a win rate of 1/4. This bot's win rate is 81%. Three months later: $2,000 → $8,191 99 trades Sharpe ratio 2.30 A few cases: ETH Merge upgrade: market 72c, true probability 88%, +19c SOL breaks $200: market 44c, true probability 81%, +15c Florida hurricane cat3+: market 81c, true probability 92%, +7c Wheat breaks $800: market 53c, true probability 68%, +20c All found by the scanner. All profitable. He looked at my terminal last week. He said: This is what we do with $800M and a 47-person team. My current setup costs $25 per month: Claude: $20 VPS: $5 Libraries: free API: free Now there are 8 agents running 24/7: velvet_void +$697 nano_alpha +$541 ratking_eth +$407 darkpool_7 +$356 His fund returned 19% last year. My setup, three-month return: 409%. The real edge was never secret. It was just always expensive. Until now. You only need: Claude + a device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment "free" 2. Like and retweet this 3. Follow me @codewithimanshu so I can DM you

The largest IPO in history is not a win. It's the bell at the top. SpaceX is raising $75B at a $1.75T valuation. 3 times bigger than the previous record IPO. Bigger than the GDP of most countries. Every major index is racing to rewrite its rules to absorb it. That's a synchronized push to force trillions in passive money into one listing. At the most fragile setup for markets in two decades. Below's the convergence: Index providers aren't quietly tweaking rules in the background. They're proposing wholesale rewrites RIGHT NOW: → S&P Dow Jones reviewing the profitability requirement that stood since 2002. Up for waiver. → Nasdaq cutting seasoning windows from 90 trading days down to 15. → FTSE Russell going further. Down to 5. S&P 500 public comment window closes May 28. Potential implementation? June 8. Four days before SpaceX trades. Three of the most important benchmarks on Earth. Restructured in the same window. For the same listing. That's the setup. Here's what happens next: When you force a $1.75 trillion stock into an index, the index doesn't print new money to buy it. It sells other names to make room. Mechanical selling of NVDA, AAPL, MSFT, AMZN. The current leaders absorb forced sell-pressure the moment SPCX enters. But that's just the warmup. SpaceX is floating only 5% of its shares. Everything else stays with insiders, early investors, employees. Lockup expires in two stages: → 90 days post-IPO: early September → 180 days post-IPO: early December Look at where those dates land. September falls inside the worst statistical window in the entire 4-year cycle. May through October. 15 of the last 16 midterm election years went red in that window. September is the deep end of it. December lands right after the November midterm vote. Policy uncertainty resolves. Big money rotates fast. None of this is happening in a healthy market. This is landing on top of: → 30-year Treasury yield above 5%. Last time that showed up was July 2007. Three months before the market peak. Twelve months before Lehman. → $2 trillion AI cloud backlog where over half of demand is OpenAI and Anthropic recycling investor money back to Microsoft, Google, Amazon. → Equities at the most overvalued level in history. Not close to it. Actually there. Here is the sequence loading: June 12: SPCX prints. Forced passive buying overwhelms reality. Late June: mechanical rebalancing starts selling everything else in Nasdaq 100. Early September: first lockup expires. Insiders sell into the artificially inflated bid. October: middle of the historically worst window for stocks in midterm years. Early December: second lockup expires. Bigger wave. Four catalysts. One direction. Not a forecast. A calendar. The setup couldn't be more loaded if it tried. What do you actually do? You can't fight the IPO. SPCX will probably rip on day one. The passive bid is too mechanical to stop. But you can stop being long everything else. My system flags the exact moment the market shifts from CAUTION to DANGER. You'll be warned before it hits, like always. Many people will wish they had followed me sooner Save this post. Watch the June 8 comment window. Position accordingly.

Jane Street hired a junior for $220K-$600K/year because he uses AI to analyze trillions of data points. In this 1-hour lecture, he shows exactly how he does it. Free. From the guy Wall Street is paying half a million to. You've been using AI to write captions. He's using it to print money on trillion-row datasets. Bookmark this instead of Netflix tonight. It pays for the rest of your career. Follow @codewithimanshu for more high-signal AI content from the people actually building the future. ↓ What he actually does for that paycheck. He builds machine learning systems for a trillion trillion floating point operations. Not "uses AI tools." Builds them. From scratch. At scale most engineers never touch in a full career. He's on the PyTorch core team. The same PyTorch that powers Jane Street, OpenAI, Anthropic, and every serious AI shop on earth. That's why the salary is $220K-$600K and not flat. Subpar year: $220K. Outstanding results: $600K+. Performance-based. Real impact. Real numbers. Wall Street isn't paying for credentials anymore. They're paying for engineers who can move trillion-row datasets through ML systems faster than anyone else on the planet. Follow @codewithimanshu for more breakdowns of the AI roles paying $500K+ in 2026. ↓ What this lecture actually teaches. This is not "AI for beginners." This is the exact technical foundation that turns a junior into the top-end of Jane Street's pay band: > How to architect ML pipelines for trillion-scale datasets > Why PyTorch internals matter at production scale > The optimization tricks that turn 10-hour jobs into 10-minute ones > Memory layouts and GPU kernels that hedge funds quietly weaponize > The mental models behind systems that move billions in trades This is Jane Street's edge being explained in public. Most engineers will watch 5 minutes, get scared, and click away. The ones who push through become the next $500K hires. Follow @codewithimanshu for breakdowns of every must-watch AI lecture worth your weekend. ↓ Why this matters more than any bootcamp. A 12-week ML bootcamp: $10,000-$15,000. A masters in ML at Stanford or CMU: $80,000+. This 1-hour lecture from a Jane Street insider: free. You've spent more on Uber Eats this month than this lecture costs. The gap between engineers earning $120K and engineers earning $500K+ isn't talent. It's exposure to content like this. People who watch it tonight understand AI infrastructure at the level Wall Street pays for. People who skip it stay competing with millions of other "AI engineers" using the same ChatGPT prompts. Same field. Different bank account. Save the video. Watch it tonight. Become the kind of engineer Jane Street fights other firms to hire. Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

Anthropic's own team just showed how to actually use Claude Code properly. 30 minutes. Free. From the person who created it. You've been using Claude Code for months without knowing 40 of its commands. This fixes that tonight. Worth more than every $500 course you almost bought. Bookmark it. So that you don't this, and watch today. Follow @codewithimanshu for more high-signal content that actually moves your career forward.

Stanford just dropped a free 17-video course that teaches you to build Claude. From scratch. There are 2 career paths in AI right now: The API Caller. Uses APIs. $150K salary. First to be automated. The Architect. Builds the APIs. $500K+ salary. Builds the tools everyone else uses. Bootcamps train you to be the 1st. This free Stanford course trains you to be the 2nd. CS336: Language Modeling from Scratch. 17 videos. Free On Youtube. Save This. Start Learning Today. Follow @codewithimanshu for more free MIT & Stanford grade AI content and the exact ai roadmaps to follow. ↓ If I had 6 months to become an AI Engineer, I'd do this exact roadmap. Stage 1: Python basics. Syntax, loops, functions, OOP, NumPy, Pandas. Stage 2: Math for AI. Linear algebra, statistics, probability, basic calculus. Stage 3: Machine Learning. Regression, classification, clustering, metrics. Scikit-learn. Stage 4: Deep Learning basics. Neural networks, CNNs, RNNs, training fundamentals. PyTorch or TensorFlow. Stage 5: Modern AI and LLMs. Prompt engineering, embeddings, RAG, fine-tuning small models. Follow @codewithimanshu for the exact resources for every stage. ↓ The second half is where most people quit. Stage 6: Build AI projects. Chatbots, classifiers, NLP apps, image models. Real projects, not tutorials. Stage 7: GenAI tools. LangChain, HuggingFace, vector databases like FAISS and Pinecone. Stage 8: MLOps essentials. FastAPI or Flask, Docker, GitHub, cloud deployment basics. Stage 9: Full projects. End-to-end ML pipelines. Deployed AI apps that actually run in production. Stage 10: Portfolio. 5-7 polished projects. README files. Demo videos. Public on GitHub. Stage 11: Job prep. LeetCode basics, system design basics, ML and AI interview prep. Stage 12: Apply. AI Engineer, ML Engineer, Data and AI roles, GenAI Developer. That's the entire path. 6 months. Architect tier. Follow @codewithimanshu for the project ideas and resources for every single stage. ↓ The syllabus is pure signal. Data Collection and Curation. Lectures 13-14. Where every modern LLM gets its training data. How it gets cleaned. What separates good data from poison. Most "AI engineers" can't answer questions on this. Stanford makes you fluent in 2 lectures. Building Transformers and MoE. Lectures 3-4. The exact architecture powering GPT-5, Claude 4.7, and every frontier model. You'll build them. From scratch. The way the engineers at OpenAI and Anthropic actually wrote them. Follow @codewithimanshu for more high-signal AI courses that build careers. ↓ Making it fast. Stanford Lectures 5-8. GPUs. Kernels. Parallelism. This is the section that separates people who use AI from people who optimize AI infrastructure for billion-dollar companies. If you can write a CUDA kernel, you can write your own ticket. Making it work. Lecture 10. Production inference. The piece that breaks every AI startup that doesn't understand it. Making it smart. Lectures 15-17. Alignment. Reinforcement learning. How models like Claude get tuned to actually be useful. Follow @codewithimanshu for breakdowns of what production AI teams actually need to know. ↓ This is what every $15K bootcamp pretends to teach but doesn't. Stanford gives it away for free. People who watch CS336 and follow this roadmap become Architects. People who skip it stay API Callers, replaceable, and worried about being automated next. Choose your path. Save this post. Watch CS336. Build the projects. Apply for the architect roles. Follow @codewithimanshu for more free Stanford-grade AI content and the exact roadmap to follow.

Anthropic's Applied AI team just dropped a 25-minute talk on how to actually use Claude Code properly. Free. From the people who built it. You've been using Claude Code for months without the workflows they teach in this. The official best practices no $500 course covers. Full breakdown below. Bookmark it. Follow @codewithimanshu for more high-signal content that actually moves your skills forward. ↓ Cal Rueb runs Applied AI at Anthropic. He just gave away the official Claude Code playbook. This is the talk that separates people shipping production apps with Claude from people stuck debugging the same errors for hours. If you're using Claude Code without watching this, you're leaving 80% of its power on the table. Follow @codewithimanshu for more Claude Code breakdowns weekly. ↓ The architecture nobody explains. Claude Code isn't a chatbot with code generation. It's a pure agent. An autonomous loop of instructions and tools that runs until a task is complete. It doesn't pre-index your codebase. It uses agentic search in real time. Grep, find, glob. Exploring your files like a senior engineer onboarding to a new repo. This architecture is why Claude Code outperforms every other coding assistant. Most people don't even know it works this way. Follow @codewithimanshu for production AI agent architecture content. ↓ The 4 use cases Claude Code is built for. Onboarding and discovery on a new codebase. Planning new features as a thought partner. Building from scratch or contributing to existing projects. Managing complex tasks like full codebase migrations. If you're using Claude Code for anything outside this, you're using it wrong. Cal explains exactly when to lean on each mode and how to switch between them. Follow @codewithimanshu for daily Claude Code workflows that actually ship. ↓ CLI integration is where Claude Code becomes superhuman. Git rebasing without breaking your repo. Docker configurations without 50 Stack Overflow tabs. Complex shell scripting on the fly. Claude Code handles CLI tools natively. You describe what you want. It executes the right commands. Most people use Claude Code without ever connecting it to their CLI workflow. Then wonder why they're still slow. Follow @codewithimanshu for the CLI patterns that 10x Claude Code workflows. ↓ The claude md file is your unfair advantage. This is the file that separates production users from hobby users. Project-specific instructions. Style guides. Documentation. Architecture decisions. Set it up once and Claude Code stops making the same mistakes every session. Stops asking for context you've already given. Stops writing code that violates your conventions. I'll post my exact claude md template next week. Follow @codewithimanshu so you don't miss it. ↓ Context management for long sessions. Claude Code runs on a 200,000-token context window. Massive. But not infinite. When you approach the limit, use `/compact` to summarize progress and continue without losing momentum. Most people hit the limit, get confused responses, and blame the model. The model is fine. Their context management isn't. Follow @codewithimanshu for context management patterns that prevent production AI failures. ↓ Permission management for speed. Auto-accept mode for routine commands. Manual approval for destructive operations. Configure it once and Claude Code stops asking for permission on every single file edit. Your workflow goes from "click yes 50 times an hour" to "Claude executes while you think." This single setting saves hours per week. Follow @codewithimanshu for the exact permission setups that production AI teams use. ↓ Iterative workflows that actually ship. Test-driven development. Commit frequently. Use `think hard` for deeper reasoning. Cal's framework: write the test first. Let Claude build to pass it. Commit when it does. Roll back when it doesn't. This is how you avoid the nightmare scenario where Claude has been working for 30 minutes and produced 2,000 lines of code you can't merge. The `think hard` command alone changes output quality dramatically. Follow @codewithimanshu for daily Claude Code prompts that ship code. ↓ Multi-agent orchestration. Run multiple instances of Claude Code in parallel. Different terminals, different tasks. One agent refactoring the backend. Another writing tests. A third building the frontend. You're orchestrating an entire engineering team from your laptop. Most people run one Claude Code session at a time and call it productivity. The serious users run 4 in parallel. Follow @codewithimanshu for multi-agent workflows that scale solo developers into entire teams. ↓ Emergency controls every user needs. Escape to stop the agent when it heads in the wrong direction. Escape twice to navigate back through conversation history. Most people frantically Ctrl+C when Claude goes off track. Escape is cleaner. Recovers context. Saves your session. Small detail. Massive workflow improvement. Follow @codewithimanshu for daily Claude Code shortcuts. ↓ MCP servers extend everything. Model Context Protocol servers let Claude Code interact with anything beyond your terminal. Databases. APIs. Internal tools. Custom workflows. Vendor integrations. This is where Claude Code stops being a coding assistant and becomes infrastructure. The teams shipping AI products at scale all use MCP. Solo devs are mostly still ignoring it. Follow @codewithimanshu for MCP server setups that turn Claude Code into a full-stack engineering team. ↓ What you'll have after watching this. Claude Code configured the way Anthropic engineers configure it. Workflows that ship code instead of just generating it. Context management that survives 8-hour sessions. Multi-agent orchestration running parallel tasks. The exact best practices the team building it uses internally. That's not "knowing how to use Claude Code." That's mastery. The kind of mastery that turns into $200K AI engineering roles and $10K/month consulting contracts. ↓ 25 minutes from the team that built it. You'll spend longer in meetings tomorrow and learn nothing. This compounds for every line of code you write for the rest of your career. People who watch it ship 10x faster with Claude Code. People who skip it keep getting frustrated wondering why everyone else is shipping faster than them. Save the video. Watch it tonight. Apply the practices tomorrow. Follow @codewithimanshu for more high-signal Claude Code content that actually moves your engineering career forward.

Andrej Karpathy didn't make a course. He made THE course. Free. From the man who co-founded OpenAI and built Tesla Autopilot. Tokenization. Attention. Hallucinations. Tool use. RLHF. DeepSeek. AlphaGo.. Save the video. Watch it this Today & Start Building Ai Agents, Ai automation systems with the knowledge. This has the entire training stack of every modern LLM, explained by the engineer who actually built Open AI and more... More valuable than any $10,000 over priced AI course. Follow @codewithimanshu for breakdowns of every must-watch AI lecture for career upgrade. ↓ Karpathy doesn't teach AI like a YouTube influencer. He teaches it like the guy who built it. Founding member at OpenAI in 2015. Senior Director of AI at Tesla. This is the most comprehensive LLM breakdown ever published on Internet. The gap between engineers who understand this and engineers who don't isn't technical depth. It's the ability to conceive of entirely different things. ↓ Here's the actual content. Pretraining and the internet data layer. Where LLMs actually come from. How the entire internet gets turned into training data. Why some data sources matter more than others. Most "AI experts" can't explain this. Karpathy walks through it in 7 minutes. Tokenization. The single concept that explains why LLMs are bad at math and spelling. Why "strawberry" has 3 R's but ChatGPT used to say 2. Why your prompt costs what it costs. Why some languages are more expensive than others. This is the foundation. Skip it and nothing else makes sense. Follow @codewithimanshu for daily breakdowns of what AI engineers actually need to know. ↓ Neural network internals. What's happening inside the model when you hit "send." Inputs, outputs, the math layer that turns tokens into predictions. Karpathy strips away the abstractions that every other tutorial hides behind. You stop using AI like a magic black box and start using it like a tool you understand. GPT-2 training and inference. The exact same architecture as GPT-5. Just smaller. Once you understand GPT-2, you understand every modern LLM. Karpathy walks through it line by line. Llama 3.1 base model inference. How a real production model actually generates text in real time. This is the 5% of knowledge that separates engineers building AI products from people prompting them. Follow @codewithimanshu for production AI architecture breakdowns weekly. ↓ Pretraining vs post-training. The two phases that determine everything about how a model behaves. Pretraining gives the model knowledge. Post-training gives it personality. Most people don't know the difference. That's why their prompts produce inconsistent results. Post-training conversations. How AI models learn to actually have conversations. The exact data that goes in. Why some models feel "smarter" than others despite similar parameter counts. This explains why Claude feels different from GPT. And how to think about choosing models for production. ↓ Hallucinations, tool use, working memory. The most important section in the entire video. Where hallucinations actually come from. Why models confidently lie. How tool use was engineered to fix it. Every developer building with LLMs needs this section. It explains 90% of the bugs you'll hit in production. Knowledge of self. Why your AI agent doesn't know what model it is. Why it confidently claims to be GPT-4 when it's Claude. The architectural reason this happens and how to handle it. Follow @codewithimanshu for engineering patterns that fix production AI bugs. ↓ Models need tokens to think. The single insight that explains chain-of-thought prompting. Why "think step by step" works. Why some prompts produce 10x better outputs with the same model. The mechanical reason this happens. Once you understand this, you stop guessing prompts and start engineering them. Tokenization revisited. Why models struggle with spelling. Why character-level tasks fail. Why your specific use case might be impossible without a different approach. This is the section that determines whether your AI product ships or breaks. Jagged intelligence. Why models are superhuman at some tasks and terrible at others. The pattern that explains every "AI is so dumb sometimes" moment you've had. ↓ Supervised fine-tuning to reinforcement learning. The shift that made GPT-4 possible. The shift that made Claude great at conversation. How models actually learn from human feedback. Why it works. Where it breaks. Reinforcement learning. The technique behind every reasoning model in 2026. DeepSeek-R1. AlphaGo. The full lineage of how machines learned to think through problems instead of just predicting the next token. Follow @codewithimanshu for RL and reasoning model breakdowns weekly. ↓ DeepSeek-R1 and AlphaGo. The two case studies that explain modern AI reasoning. DeepSeek for language. AlphaGo for game theory. Same underlying principle. Once you understand both, you understand where AI is going next. RLHF. Reinforcement Learning from Human Feedback. The technique that made AI usable for normal humans. Why your favorite model "just feels right." How that feeling was engineered. Why it costs millions of dollars. This is the section that explains the actual cost structure of modern AI products. ↓ Where this puts you after 3 hours. Most engineers using AI know how to prompt it. After this video, you'll understand: > How models are pretrained > How they're fine-tuned > How they handle context > Why they hallucinate > How tool use works > Why some prompts work and others fail > What RLHF does > Where AI is going next That's not "AI literacy." That's architectural understanding. The kind of understanding that turns into $200K+ AI engineering roles. The kind of understanding that lets you build the next ChatGPT instead of just using it. ↓ 3 hours. Free. From the person who built it. You've watched longer Netflix series this week and learned nothing. This compounds for the rest of your career. People who watch it understand AI at the architect level. People who skip it stay confused about why their prompts fail in production. Save the video. Watch it this weekend. Build something with the knowledge by Monday. Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

Andrej Karpathy taught everything you need to learn AI at the architect level in 2 hours. For Free. From the man who built Tesla Autopilot and co-founded OpenAI. CANCEL Your Weekend Plans, and Learn AI From the Source Today. Save This. Watch it. Become the one who understands AI at a level most engineers never reach. He could have charged $10,000+ for this course. He put it on YouTube for free. MIT, Stanford and AI bootcamps charge even more for less than what's in this 2-hour video. People who learn it compound the knowledge for the rest of their careers. You're still scrolling Netflix looking for the best Movies. This video fixes that tonight. Follow @codewithimanshu for more high-signal AI content that actually moves your career forward. ↓ The gap between people who watch this week and people who save it for later is not 2 hours. It's everything those 2 hours quietly unlock for the rest of your career. Karpathy doesn't teach AI like a YouTube influencer. He teaches it like the guy who actually built it. Because he is. Tesla Autopilot. From scratch. OpenAI. Co-founded. Stanford CS231n. Created the most important deep learning course in history. This man has forgotten more about AI than most "AI experts" on Twitter will ever know. And he just sat down and recorded 2 hours of it. For free. Follow @codewithimanshu for weekly breakdowns of AI content from people actually building the future. ↓ Why this matters more than any AI tutorial you've watched. Most AI content on the internet right now teaches you how to use tools. Karpathy teaches you how the tools actually work. That's the difference between: > A person who uses ChatGPT to write captions > A person who builds the next ChatGPT Same field. Different layers. Same time investment. Different payoff for the rest of your life. You'll spend 2 hours scrolling Twitter tonight. Or 2 hours with the man who literally taught the engineers building today's AI. Same time. One earns you $0. The other earns you everything for the next 20 years. Follow @codewithimanshu for more content that explains the layers most tutorials skip. ↓ The math no one wants to do. A 12-week AI bootcamp: $5,000 to $15,000. A Stanford CS course covering similar material: $80,000+. A 2-hour Karpathy lecture from the source: free. You've spent more on Uber Eats this month than this lecture costs. And it'll teach you more than any course you've ever bought. The people who watch this week will compound it for the rest of their careers. The people who save it for "when I have time" will still be saving it for when they have time in 2027. Save the video. Watch it tonight. Become someone who understands AI at the layer Karpathy teaches it at. Follow @codewithimanshu for more high-signal AI content from the people actually building the future.

Anthropic's Claude Code team just teaches how to automate your entire engineering workflow with Claude Code SDK in under 30 minutes. For Free. From the engineers who built it. CANCEL Your Weekend Plans, and Learn to Automate Your Codebase Today. Bookmark it. Watch it. Ship your first headless automation this weekend. $5,000/month. $10,000/month. $25,000/month. People are automating entire engineering teams with Claude Code SDK and charging clients $$$$. You're still copy-pasting code from ChatGPT manually. This video fixes that tonight. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward. ↓ Sid Bidasaria runs engineering on Claude Code at Anthropic. He just gave away the entire SDK + GitHub Action playbook in 30 minutes. This is the talk that separates people automating their entire codebase from people still manually writing every commit. Here's everything inside. Follow @codewithimanshu for weekly Claude automation breakdowns. ↓ What the Claude Code SDK actually is. Most devs don't know this exists. They use Claude in the chat interface and call it a day. The SDK is the real unlock. Programmatic access to the Claude Code agent in headless mode. The primitive building block for every serious automation you'd ever want to build. Designed like a Unix tool. Drops directly into terminal pipelines, bash scripts, CI/CD automation. Use it to review code. Write linters. Build chatbots. Manage remote code environments. Run an entire engineering pipeline. This is how you stop "using AI" and start "shipping with AI." Follow @codewithimanshu for full Claude SDK breakdowns every week. ↓ Basic usage that 99% of devs miss. `claude -p` to prompt the agent directly from your terminal. `--allowed-tools write` for controlled file system access. Pipe anything into it: > Pipe `ifconfig` output → ask Claude to debug your network > Pipe error logs → get a fix before your coffee finishes brewing > Pipe a file → get instant code review without opening an editor `--output-format JSON` for structured responses you can parse in automated systems. This is where Claude stops being a chat tool and becomes infrastructure. Follow @codewithimanshu for daily Claude SDK one-liners that save hours. ↓ Permission management without the security holes. The biggest reason teams don't deploy AI in production: permission concerns. Sid solves it cleanly: > No destructive permissions by default > `--allowed-tools` to pre-configure exactly what the agent can touch > `--permission-prompt-tool` to delegate authorization to an MCP server in real time Your AI agent gets full power exactly when it needs it. Zero access when it doesn't. This is enterprise-grade AI security packaged as a single flag. Most tutorials hand-wave this. This one shows the architecture. Follow @codewithimanshu for production AI permission patterns every week. ↓ Session persistence: the multi-turn unlock. Most AI integrations forget everything between calls. That's why your "AI assistant" feels like talking to someone with amnesia. Return a `session ID` and Claude resumes exactly where you left off. Full context preserved. Multi-turn conversations across hours, days, deploys. This is the foundation for building any real AI product that holds context. Customize the system prompt while you're at it. `--system-prompt 'talk like a pirate'` if you want. Or build a serious agent persona for production. Follow @codewithimanshu for persistent context patterns for AI agents. ↓ The Claude GitHub Action demo that should scare every dev. Sid runs a live demo on a real quiz app: > Files an issue: "add a 50/50 power-up and a skip power-up" > Claude creates a to-do list > Claude modifies the files > Claude opens a Pull Request The entire feature shipped from one issue. No human touching code. This is automated code review, automated bug triage, automated feature implementation. From GitHub issues directly. Junior dev work just got compressed into the time it takes to write an issue description. Follow @codewithimanshu for GitHub Action setups for production. ↓ Zero infrastructure required. Every other AI automation tool needs: > A separate server > A deployment pipeline > Monitoring infrastructure > Auth setup > Cost tracking The Claude GitHub Action uses your existing GitHub Action runners. `claude /install github action` in your local repo. Generates a YAML config. Done. You go from idea to production AI automation in 60 seconds. Most people pay $200/month for tools that do less than this free Action. Follow @codewithimanshu for free Claude Action templates. ↓ The 3-layer architecture nobody explains. Sid breaks down the actual stack: > Layer 1: SDK - the foundation, raw programmatic access > Layer 2: Base Action - wraps the SDK as a clean API interface > Layer 3: PR Action - adds comments, formatting, full GitHub UX Understanding these layers is the difference between someone who copies tutorials and someone who builds custom AI infrastructure for clients. This is the architectural insight that turns into $10K/month consulting contracts. Follow @codewithimanshu for weekly architecture deep dives. ↓ 30 minutes from the engineer shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never automated a single deploy. People who watch this understand Claude Code automation at the infrastructure level. People who skip it keep manually reviewing PRs, manually filing issues, manually doing work that could've been automated last weekend. Save the video. Watch it tonight. Ship your first Claude SDK automation this weekend. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward.

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow @codewithimanshu. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on @codewithimanshu. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. @codewithimanshu. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on @codewithimanshu. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. @codewithimanshu. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. @codewithimanshu. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow @codewithimanshu for more high-signal content that actually moves your AI engineering career forward.