Amar

Microsoft Senior AI developer just showed how they build AI agents with Claude at Microsoft. 34-minutes. free. By Microsoft team Opus 4.7 + 1,400+ pre-built MCP tools plug Claude into agent → give it tools → ship to production worth more than any $500 vibe-coding course.

@elonmusk U gotta time machine

First Starship V3 launch later this week!

The people getting the craziest results from Claude aren’t using “better prompts.” They’re copying the workflow of the people who built systems like Claude in the first place. Boris Cherny’s CLAUDE.md anatomy is basically a masterclass in how elite AI operators think: • Plan before execution • Split tasks into focused agents • Track failures like a real engineering system • Verify everything before shipping • Eliminate root causes, not symptoms Read it once and you realize: Most people are talking to AI. A small group is building infrastructure around AI. That’s the real divide right now. And honestly, this changes how you see AI completely. Because the highest leverage isn’t hidden inside some secret prompt. It’s in creating an environment where AI can think clearly, execute systematically, and improve over time. That’s why two people can use the same model and get wildly different outcomes. One gets content. The other gets a scalable execution system. This is probably one of the most important mindset shifts for anyone serious about AI.

@Thebestfigen How does each sliced tile have a smooth mosaic surface?

Unbelievable skills. Master!

@hvgoenka Only I no A #ai #openai

Pure desi jugaad at work. A plastic bottle, some white powder, and suddenly you have a perfect detector for inner tubes!

📂 claude setup ┃ ┣ 📂 models ┃ ┣ 📂 claude haiku 4.5 ┃ ┣ 📂 claude sonnet 4.6 ┃ ┣ 📂 claude opus 4.7 ┃ ┣ 📂 extended thinking ┃ ┗ 📂 adaptive thinking ┃ ┣ 📂 setup ┃ ┣ 📂 user preferences ┃ ┣ 📂 memory ┃ ┣ 📂 projects ┃ ┣ 📂 custom instructions ┃ ┗ 📂 workflow optimization ┃ ┣ 📂 projects ┃ ┣ 📂 persistent workspaces ┃ ┣ 📂 uploaded files ┃ ┣ 📂 spreadsheets ┃ ┣ 📂 product briefs ┃ ┣ 📂 prompts ┃ ┗ 📂 project context ┃ ┣ 📂 memory ┃ ┣ 📂 conversation history ┃ ┣ 📂 writing preferences ┃ ┣ 📂 ongoing projects ┃ ┣ 📂 language style ┃ ┗ 📂 workflow habits ┃ ┣ 📂 connectors ┃ ┣ 📂 google drive ┃ ┣ 📂 github ┃ ┣ 📂 slack ┃ ┣ 📂 notion ┃ ┣ 📂 hubspot ┃ ┗ 📂 jira ┃ ┣ 📂 claude code ┃ ┣ 📂 terminal access ┃ ┣ 📂 codebase analysis ┃ ┣ 📂 debugging ┃ ┣ 📂 refactoring ┃ ┣ 📂 api integrations ┃ ┗ 📂 command execution ┃ ┣ 📂 cowork ┃ ┣ 📂 desktop automation ┃ ┣ 📂 app control ┃ ┣ 📂 workflow execution ┃ ┣ 📂 productivity agent ┃ ┗ 📂 local system access ┃ ┣ 📂 advanced features ┃ ┣ 📂 context window ┃ ┣ 📂 research mode ┃ ┣ 📂 incognito chats ┃ ┣ 📂 web search ┃ ┗ 📂 code execution ┃ ┣ 📂 productivity ┃ ┣ 📂 research workflows ┃ ┣ 📂 content creation ┃ ┣ 📂 coding workflows ┃ ┣ 📂 business operations ┃ ┗ 📂 deep analysis ┃ ┣ 📂 optimization ┃ ┣ 📂 auto memory ┃ ┣ 📂 structured prompts ┃ ┣ 📂 project instructions ┃ ┣ 📂 reusable context ┃ ┗ 📂 system organization ┃ ┣ 📂 collaboration ┃ ┣ 📂 saved chats ┃ ┣ 📂 imported projects ┃ ┣ 📂 shared workflows ┃ ┣ 📂 team productivity ┃ ┗ 📂 knowledge management ┃ ┣ 📂 security ┃ ┣ 📂 privacy controls ┃ ┣ 📂 incognito mode ┃ ┣ 📂 memory controls ┃ ┣ 📂 secure chats ┃ ┗ 📂 permission settings ┃ ┣ 📂 usage ┃ ┣ 📂 writing ┃ ┣ 📂 analysis ┃ ┣ 📂 coding ┃ ┣ 📂 brainstorming ┃ ┗ 📂 automation ┃ ┗ 📂 scaling ┣ 📂 ai workflows ┣ 📂 productivity systems ┣ 📂 personal operating system ┣ 📂 business automation ┗ 📂 ai powered execution

People freaking out over my AI spend. What nobody sees: Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question: How would we build software in the future if tokens don't matter? We constant run ~100 codex in the cloud, reviewing every PR, every issue. If a fix on main lands, @clawsweeper will eventually find that 6 month old issue and close it with an exact reference. We run codex on every commit to review for security issues (as it's far too easy to miss). We run codex to de-duplicate issues and find clusters and send reports for the most pressing issues. We have agents that can recreate complex setups, spin up ephemeral crabbox.sh machines, log into e.g. Telegram, make a video and post before/after fix on the PR. There's codex that watch new issues and - if it fits our documented vision well, automatically create a PR of it. (that then another codex reviews) We have codex running that scans comments for spam and blocks people. We have codex instances running that verify performance benchmarks and report regressions into Discord. We have agents that listen on our meetings and proactively start work, e.g. create PRs when we discuss new features while we discuss them. We build clawpatch.ai to split all our projects into functional units to review and find bugs and regresssions. We do the same split for security with Vercel's deepsec and Codex Security to find regressions and vulnerabilities. All that automation allows us to run this project extremely lean.

@elonmusk X is the root cause for current war situations, crown in USA with wrong hands coz X helped them, later they ditched #X too!! Any social media is a trap & hallucination, follow your genuine own pulses 4 any next steps in your own life, influencers dont pay for ur losses

Only 𝕏 is open, the other social media companies manipulate the results behind closed doors

Skills, Subagents, MCP, and Hooks are not four versions of the same thing. They solve four different problems. Mix them up, and your Claude Code setup starts breaking in ways no one can explain. Here’s the decision tree I use: SKILLS → “Load knowledge only when needed” • Markdown file + helper code • Loaded per task, not always on • Best for: specialized knowledge, file formats, repeat workflows Use when the same knowledge appears often but would bloat context. SUBAGENTS → “Give a side task its own workspace” • Separate session with its own memory • Returns clean output, not the mess • Best for: deep research, parallel work, messy exploration Use when the main thread would get cluttered. MCP → “Connect to external systems” • Persistent server exposing tools/data • Handles auth, state, multi-language • Best for: APIs, databases, SaaS, internal tools Use when the agent needs to reach something, not just know it. HOOKS → “Enforce behavior every time” • Triggered on lifecycle events • Runs automatically, no exceptions • Best for: validation, formatting, security, logging Use when you can’t rely on the model to remember. Mental model: • Skills = what the agent knows • Subagents = where it thinks • MCP = what it can reach • Hooks = what it must obey They don’t compete. They stack. Common mistakes: • Building MCP when a Skill was enough • Overloading main context instead of using Subagents • Trusting the model instead of enforcing with Hooks • Treating Skills like docs instead of tools Hot take: Most MCP servers should’ve been Skills. People build connections when they need knowledge—and pay for it with latency, auth issues, and brittle systems. Where do you draw the line between Skill vs MCP?

@PawanKalyan JGF - Janasena Gold foundation to build AP

‘KGF’ get ready, here comes ‘JGF’

@Google Linux shell? All rpm packages support? Citrix workspace? Idea to done - magic cursor ✨ #cursor Ready to switch from #Thea #macbookpro to #googlebook ? #google #android #andriodio2026

Introducing Googlebook, the first laptop designed for Gemini Intelligence. It’s crafted for heavyweight performance, built with Gemini at the core and perfectly synced with your Android phone. Coming this fall. 💻✨ #TheAndroidShow

@Airaasayss Two Binary bits next to each other - High Low

Einstein says it’s easy… is it? 🤔🤔 Solve this without using a calculator

Knowledge of sanatana Dharma 🚩

@anandmahindra Macintosh to apple, iSeries #transformation Tractor to suv/ev #leadership

When I saw this video, my first thought was that the earth, very often, sends us messages. This tree, facing constant winds, is still standing, & has neither fallen nor been uprooted. Instead it’s been shaped by it. And developed its own unique aesthetic & identity. We have to recognize & accept that tough times & tragedies are part of the journey. They don’t define us, they simply give us our own unique personalities & capacities… #MondayMotivation

I've completed Learning Activites across Google's developer ecosystem! #DevBadges developers.google.com/profile/badges…

The creator of Claude Code teaches more about vibe-coding in 30 minutes than most tutorials do in hours. Save this - it'll change how you build forever.

The Roadmap to Mastering Tool Calling in AI Agents machinelearningmastery.com/the-roadmap-to…

@SaiAnvii Corporate on bench?

Government Vs Corporate Employee 💥💥💥

If you're serious about AI engineering (in 2026), then learn these 13 concepts: 1 How Vector Database Works → newsletter.systemdesign.one/p/what-is-a-ve… 2 How RAG Works → newsletter.systemdesign.one/p/how-rag-works 3 Design Personal Chat Assistant → newsletter.systemdesign.one/p/ai-chat-assi… 4 LLM Concepts - A Deep Dive → newsletter.systemdesign.one/p/llm-concepts 5 How to Design an AI Agent → newsletter.systemdesign.one/p/how-do-ai-ag… 6 What is Reinforcement Learning → newsletter.systemdesign.one/p/what-is-rein… 7 LLM Evals 101 → newsletter.systemdesign.one/p/llm-evals 8 Context Engineering 101 → newsletter.systemdesign.one/p/what-is-cont… 9 AI Coding Workflow 101 → newsletter.systemdesign.one/p/ai-coding-wo… 10 Agentic Patterns, Simply Explained → newsletter.systemdesign.one/p/agentic-desi… 11 How AI Agents Work → newsletter.systemdesign.one/p/ai-agents-ex… 12 Multi-Agent Architectures, Clearly Explained → newsletter.systemdesign.one/p/multi-agent-… 13 How MCP Works → newsletter.systemdesign.one/p/how-mcp-works What else should make this list? === 👋 PS - Want my System Design Playbook (for Free)? Join my newsletter with 200K+ software engineers now: → newsletter.systemdesign.one/join === 💾 Save now & repost to help others learn AI engineering. 👤 Follow @systemdesignone + turn on notifications.

@PapalakeyPapa #jailreddy మూర్ఖపు మూక, ఇప్పటికే ఆ నియోజకవర్గాలను త||నా లో కలిపేస్తాం అంటున్నాడు

మంచి ఆలోచన 🫡🙏🏻