Learn Data Science & Engineering @DataSciencePY

#Claude #Gemini #ChatGPT

🚨 The Tech Industry is witnessing the birth of a NEW engineering era. First came Software Engineering. Then Data Engineering exploded. Now… Gen AI Engineering is changing everything. We are moving from: ➡ Writing logic → Designing intelligence ➡ Building apps → Building autonomous systems ➡ Dashboards → Decision-making AI ➡ Static pipelines → Self-learning workflows A few years ago: • Data Scientists predicted outcomes • Data Engineers moved data at scale But today? 🔥 Gen AI Engineers are building systems that can: ✔ Understand human language ✔ Generate code ✔ Automate workflows ✔ Analyze massive datasets ✔ Create content ✔ Act like intelligent copilots The shift is MASSIVE. The future tech stack is no longer just: Python + SQL + Spark It’s becoming: LLMs + Vector DBs + Agents + RAG + Real-time Data + AI Orchestration And the biggest opportunity? 💡 Engineers who combine: Data + AI + Systems Thinking will dominate the next decade. Because the real winners won’t be the people who simply use AI… They’ll be the people who BUILD with AI. The next generation of engineers will not just create software. They’ll create digital intelligence. 🤯 We are still early. Very early. And this may become the biggest technology shift of our lifetime. 🚀 #AI #GenAI #DataScience #DataEngineering #MachineLearning #LLM #ArtificialIntelligence #BigData #Tech #Innovation #FutureOfWork x.com/GoogleDeepMind…

Gemini Omni doesn't just build scenes that look real, it reasons about what should happen next. It combines an intuitive understanding of physics with Gemini's knowledge of history, science, and cultural context. Rolling out today starting with video outputs to Google AI Plus, Pro and Ultra subscribers globally through the @Geminiapp + Google Flow, and @YouTube Shorts this week. Incredible #Gemini x.com/sundarpichai/s…

Claude #Cheatsheet

Try it out #cursor #AI

Cursor @cursor_ai

3 weeks ago

Introducing Composer 2.5, our most powerful model yet. It's more intelligent, better at sustained work on long-running tasks, and more reliable at following complex instructions. For the next week, we’re doubling the included usage of the model.

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x.com/tradermorin/st… Build an AI system on whatever you like trading health or finance - that’s the way to learn GenAI #Claude #ChatGPT

Morin @TraderMorin

3 weeks ago

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How to Build Personal AI System with Claude #Claude

Nav Toor @heynavtoor

3 weeks ago

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Best introductory course on Calude by Anthropic #GenAI #Claude #ChatGPT

AI is creating a new kind of engineer. 🤯 Not the one who memorizes syntax. The one who builds systems with AI at the center. The smartest engineers today are using AI to: ⚡ Write code ⚡ Debug faster ⚡ Automate workflows ⚡ Generate architectures ⚡ Learn 10x faster ⚡ Build entire products solo This is the biggest shift in tech since the internet. 🚀 The gap is no longer: “Who can code?” It’s: “Who can leverage AI better than everyone else?” Average developers use AI like autocomplete. Top engineers use AI like a team. 🔥 The future belongs to: 🧠 AI-native builders ⚙️ System thinkers 🚀 Fast executors Adapt fast… or get outpaced. Reference x.com/eng_khairallah…

ChatGPT Claude - master how to use efficiency.

maqxbt @maqxbt

3 weeks ago

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Master AI in 30 Days #AI

AI Edge @aiedge_

3 weeks ago

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#ChatGPT #codex #openai

OpenAI just sent shockwaves through fintech. 💀📉 Today, ChatGPT became a personal finance assistant. Connect your bank accounts via Plaid → and GPT-5.5 can now: 💰 Analyze spending 📊 Track subscriptions 📈 Understand investments 🧠 Remember savings goals 💳 Answer questions using your real transaction data And this is only the beginning. Next: ⚡ Tax estimates ⚡ Credit card recommendations ⚡ Financial planning ⚡ AI-native banking experiences Most fintech startups built dashboards. OpenAI built: 👉 An intelligent financial operating system. That changes everything. The scary part? People don’t want 10 finance apps anymore. They want ONE AI that understands their entire financial life. AI is no longer replacing features. It’s replacing products. 🔥

Claude vs. Claude Code vs. Cowork. Anthropic offers three distinct ways to interact with Claude, and each one targets a fundamentally different workflow. Think of it as: Chat for thinking, Code for building, and Cowork for doing. Here's a quick breakdown: 1️⃣ Claude Chat This is the conversational AI assistant most people already know. You type a prompt, Claude responds, and you iterate together. - Turn rough ideas into structured plans through conversation - Write emails, reports, essays, and long-form content - Research and summarize complex topics in minutes - Analyze documents, PDFs, and images - Build interactive prototypes through Artifacts The key here is that everything happens through conversation. You're thinking with Claude, not delegating work to it. It's available on every device, has a free tier, and supports persistent memory across sessions. The tradeoff is that it has no direct access to your local files (upload only), and it can't generate raster images natively. 2️⃣ Claude Code This is a terminal-native coding agent. You describe what you want in plain English, and Claude reads your codebase, writes code, runs tests, fixes errors, and ships the result. - Build and debug entire features across the full codebase - Write, run, and fix tests automatically - Manage git workflows and create pull requests - Spawn multiple parallel agents working on different parts of a task simultaneously It handles the full development cycle end to end, from planning to execution to testing. With the CLAUDE(.)md configuration file, you can teach it your project's conventions, patterns, and constraints so it writes code the way your team expects. The tradeoff is a steeper learning curve compared to Chat, and token costs can add up during heavy sessions. 3️⃣ Claude Cowork This is the newest addition. Anthropic describes it as Claude Code for the rest of your work. It's an agentic desktop assistant that automates file management and repetitive tasks through a GUI. You describe an outcome, and Claude plans, executes, and delivers finished work: formatted documents, organized file systems, spreadsheets with working formulas, and synthesized research. - Direct local file access and editing (no upload/download cycle) - Schedule recurring tasks automatically - Assign tasks remotely via Dispatch from your phone - Computer Use lets Claude control your screen directly It runs inside a sandboxed virtual machine on your computer, so Claude can only access folders you explicitly grant. You don't need to know how to code to use it. The tradeoff is that your computer must stay awake for tasks to run, and it's still in research preview. Here's how to think about choosing between them: → If you need to think through a problem or get writing/research help, use Chat → If you're building software and want an autonomous coding partner, use Code → If you have a clearly defined deliverable that involves local files and desktop workflows, use Cowork All three are included in the same subscription starting at $20/month, which makes it one of the highest-leverage subscriptions in productivity software right now. I've put together a visual below that maps the workflow of each product side by side. Also, if you want to go deeper into Claude Code specifically, my co-founder wrote a detailed article covering the anatomy of the .claude/ folder, a complete guide to CLAUDE(.)md, custom commands, skills, agents, and permissions, and how to set them all up properly.

This GitHub repo changed how I think about job hunting. 🤯 Career-Ops GitHub Repo Instead of manually applying to 500 jobs…
It turns your entire career search into an AI-powered operating system. 🚀 What it does 👇 ⚡ Scans 45+ company career portals
⚡ Scores jobs with an A→F system
⚡ Generates ATS-optimized resumes automatically
⚡ Tracks applications like a sales pipeline
⚡ Runs batch evaluations using AI agents
⚡ Creates tailored PDFs for every role
⚡ Supports Claude + Gemini workflows Built by an engineer who:
🔥 Evaluated 740+ job offers
🔥 Generated 100+ tailored CVs
🔥 Landed a Head of Applied AI role This isn’t “AI applying blindly.” It’s:
🧠 AI-assisted career strategy
📊 Signal over noise
🎯 Precision over mass applying The biggest lesson? Future engineers won’t just use AI to code. They’ll use AI to:
• Learn
• Build
• Network
• Negotiate
• And optimize their careers end-to-end. Most candidates still apply manually.
Top candidates are building systems. ⚡ #Claude #Copilot #ChatGPT

If I had 6 months to become an ML Engineer…
I wouldn’t waste time collecting certificates. ❌ I’d build systems. 🚀 Month 1 → Learn Python + Data Engineering foundations
Month 2 → Master ML + Statistics
Month 3 → Deep Learning with PyTorch
Month 4 → Pipelines, Feature Stores & MLOps
Month 5 → Deploy models using APIs + Docker + Cloud
Month 6 → Scale, monitor, optimize, and ship real-world ML systems publicly. Skills I’d focus on 👇 ⚡ Data pipelines
⚡ Feature engineering
⚡ Experiment tracking
⚡ Model deployment
⚡ Drift detection
⚡ GPU scaling
⚡ CI/CD for ML
⚡ Reliability engineering Because companies don’t hire people who only train models. They hire engineers who can take ML systems from:
🧠 Idea → 📦 Production → 📈 Scale Most people stay stuck:
Watching tutorials.
Saving posts.
Buying courses. Builders get hired. 🔥 What would YOU add to this roadmap?

#Anthropic #Claude Nice workshop !

Mayank Agarwal 💡 @TheAIWorld22

4 weeks ago

Anthropic just showed a 24-minute workshop on how to actually prompt Claude. Taught by the people who built it. Free. No signup. No paywall. I've watched $300 courses that don't cover what they teach in the first 8 minutes.

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🧵 FAANG-Level Data Engineering Interview Q&A (Deep + Real) 1/ Q: Design a real-time analytics system like Uber’s ETA pipeline. A: Ingest streams (Kafka), process with Apache Flink / Apache Spark, use event-time windows + state stores, serve via low-latency DB (Cassandra/Redis). Key: latency vs accuracy tradeoff. ⸻ 2/ Q: How would you design a data warehouse for billions of events/day? A: Columnar storage, partitioning (time/user), clustering, compression, and query optimization. Balance storage cost vs query speed. ⸻ 3/ Q: Explain how you’d handle backfills at scale without breaking production. A: Isolate compute, use versioned datasets, throttle workloads, and ensure idempotency. Never mix live + backfill pipelines blindly. ⸻ 4/ Q: Design a system for near real-time fraud detection. A: Streaming ingestion, feature computation, stateful processing, ML inference layer, and alerting. Critical: low latency + high accuracy. ⸻ 5/ Q: Your pipeline processes 500M records but suddenly slows down. Why? A: Likely causes: data skew, bad join strategy, increased shuffle, small files problem, or inefficient partitioning. ⸻ 6/ Q: How do you ensure data quality at scale? A: Validation layers, schema enforcement, anomaly detection, SLAs, and automated monitoring pipelines. ⸻ 7/ Q: Design a multi-tenant data platform. A: Data isolation, access control, resource quotas, metadata layer, and cost attribution per tenant. ⸻ 8/ Q: What’s your approach to schema design for evolving products? A: Use flexible schemas, versioning, backward compatibility, and avoid tight coupling between producers and consumers. ⸻ 9/ Q: How would you debug a silent data corruption issue? A: Compare historical vs current outputs, trace lineage, validate upstream sources, and add checksums/data audits. ⸻ 10/ Q: Design a system that guarantees exactly-once delivery across failures. A: Combine checkpointing, idempotent writes, transactional sinks, and replayable logs (Kafka). Accept tradeoffs in latency/complexity. ⸻ 11/ Q: Tradeoff: Latency vs Cost vs Accuracy — how do you decide? A: Based on business use-case. Real-time → low latency, higher cost. Reporting → cheaper, batch acceptable. Always align with business value. ⸻ 12/ Q: What separates FAANG-level engineers? A: Not tools. Not syntax. 👉 Thinking in trade-offs, scale, and failure scenarios. ⸻ 🔥 Final Insight: At FAANG level, they don’t test if you can use Python… They test if your system still works when everything breaks. #dataengineering #dataengineeringcommunity