Vipul Kumar Kewat

@Saifullahkqalf Thanks, Saif! Really appreciate it. Hope this helps you get even more out of Claude.

@VipulKewatTech Great

Claude isn't just a chatbot anymore. 👇 It's becoming a complete AI workspace for coding, Projects, automation, and collaboration. The sooner you learn the Claude ecosystem, the more you'll get out of AI. 🔖 Bookmark this post. Follow @VipulKewatTech for more AI insights.

@AmberShaheryar Thankyou, Amber! Claude keeps getting more powerful, especially with Projects, Skills, and MCPs.

@VipulKewatTech Claude

@Raxon_AI Thanks a lot, Raxon! Glad you found it valuable. More practical Claude resources are on the way

@VipulKewatTech Amazing share

@shamimRezaai Thanks, Shamim! Appreciate the support. Hope you find the resources useful!

@VipulKewatTech Done

@eng_khairallah1 The biggest shift isn't just having smarter AI models it is learning how to coordinate multiple specialized agents toward a single goal. Orchestration is quickly becoming a core skill for anyone building with AI.

Jacob Bank, former Google product lead: "IF YOU DON'T LEARN HOW TO ORCHESTRATE AGENTS NOW, YOU'LL SPEND 2027 CATCHING UP TO PEOPLE WHO STARTED TODAY." In a 15-minute talk, he shows what one person with the right setup now runs alone. Forty agents. One human. His AI bill is $500 a month, against the $50,000 a human team would cost. That's the math quietly minting the first solo fortunes of the AI era. Watch the talk, then read the article to build your first AI team.

@Orion_Vers7x Thankyou, Maxwell! Really appreciate the support. More practical AI resources and workflows coming soon!

@VipulKewatTech Amazing

🚀 Master Claude in just one week. Learn Projects, Templates, Connectors, and automation with this practical roadmap. Build better workflows—not just better prompts. 🔖 Save this guide. 💬 What's your favorite Claude feature? Follow @VipulKewatTech for more AI tips

@rakib_md007 Thanks so much, Rakib! Glad you found it valuable. Hope it helps you discover some useful Claude resources.

@VipulKewatTech Excellent share

@AmberShaheryar Thanks, Amber! It really is. The biggest shift is not just the model itself, but the workflows and systems you can build around Claude.

@VipulKewatTech Claude is a big game changer Tool

@sairahul1 The progression from prompts to graphs really puts things into perspective. As models become more capable, competitive advantage will come from workflow design, memory, orchestration, and evaluation not just choosing the last model.

Prompt, context, harness, loop & graph engineering, clearly explained! The best AI engineers don't just write prompts anymore. They engineer the entire system around the model. You can think of an AI application as five layers: * Prompt engineering * Context engineering * Harness engineering * Loop engineering * Graph engineering Each layer zooms one level further out. The model sits in the middle. The prompt controls one call. The context controls what that call knows. The harness controls how the model acts. The loop controls how the work continues. The graph controls how an entire organization of agents operates. > Prompt engineering Prompt engineering defines what the model sees during a single call. A typical prompt includes: * a role * instructions * examples * constraints * an output format The techniques here change how the model approaches the task based on the wording it sees. For example: * Chain-of-thought encourages it to work through the problem in steps * Few-shot examples define the expected pattern and edge cases * JSON Schema or XML tags make the output easier for software to parse * Self-consistency samples multiple reasoning paths and selects the strongest result Prompt engineering is about getting one model call right. --- > Context engineering The prompt is only one part of what the model sees. The complete context can include: * the user's query * retrieved documents * conversation history * long-term memory * previous tool outputs * uploaded files * code * API responses The context window is finite and fills up quickly. So the engineering work is deciding what deserves to enter it, what should remain, and what needs to be removed. You do that by: * retrieving only the chunks relevant to the current task * reranking retrieved information before inserting it * summarizing older turns * removing stale and duplicated outputs * keeping important instructions away from the middle of very long contexts * pushing large artifacts into files and loading them only when needed Context engineering determines what the model knows before it starts working. --- > Harness engineering The harness is the code surrounding the model. It defines what the model is allowed to do and what happens when something goes wrong. A harness can manage: * tool calling * retries and error recovery * structured output parsing * schema validation * permissions * model routing * sub-agents * human approval * observability * evaluation One agent might retrieve information. Another might write the code. Another might review it. Another might run the tests. The harness coordinates all of this. Prompt and context engineering focus on improving a model call. Harness engineering deals with everything required to make that call useful inside a real system. --- > Loop engineering In a normal AI session, the human controls the outer loop. You write a prompt. The agent responds. You inspect the result. You decide what it should do next. Then you write another prompt. Both the next action and the quality check depend on you. That becomes the ceiling on how much work the agent can complete without supervision. Loop engineering moves those decisions into the system. The loop can start from: * a user message * an event * a schedule * an incoming email * a webhook * another agent It then keeps thinking, acting, observing and correcting until the goal is complete or a stopping condition is reached. The difficult part isn't making the loop run. It's making it stop correctly. An agent can claim that it's finished while the tests still fail. So completion shouldn't depend only on the agent's word. A production loop needs real signals such as: * a maximum turn or token budget * a no-progress detector for repeated actions * passing tests * successful schema validation * an evaluation score above a threshold * approval from an independent checker The maker produces the work. The checker grades it. The checker's findings become the maker's next instruction. The cycle continues until nothing meaningful remains to fix. By this layer, your job is no longer writing every prompt. Your job is setting the goal, the guardrails and the exit conditions before the run begins. --- > Graph engineering A loop makes one agent's behavior programmable. A graph makes an entire agent organization programmable. This is the layer where you stop designing one autonomous worker and start designing how many workers coordinate. A graph can contain: * planners * researchers * builders * reviewers * distribution agents * shared memory * approval gates * parallel branches * conditional routes * feedback channels * external tools * human decision-makers Every node has a responsibility. Every edge determines how information and work move between those nodes. A loop asks: "What should this agent do next?" A graph asks: "Which agent, tool or workflow should run next, and what state should it receive?" For example, a research agent might gather evidence and write it into shared state. A builder reads that evidence and creates the output. A reviewer checks the result. Different publishing agents then adapt it for X, LinkedIn and a newsletter. Audience feedback flows back into the shared state and changes what the organization does on the next run. The graph can also restructure itself while the work is happening. A difficult task might create a review board. A low-confidence result might add another researcher. A small task might collapse the entire workflow into one agent. A budget problem might reduce the number of branches or route the task to a cheaper model. That is a dynamic agent organization: the graph rewrites itself based on the work, the failures, the available budget and the progress being made. The loop makes an agent autonomous. The graph makes a collection of autonomous agents operate like an organization. --- Every layer builds on the previous one. Prompt engineering controls what the model is told. Context engineering controls what the model knows. Harness engineering controls what the model can do. Loop engineering controls how the work continues without you. Graph engineering controls how entire teams of agents coordinate, adapt and reorganize. The progression looks like this: Prompt engineering → Context engineering → Harness engineering → Loop engineering → Graph engineering The model is becoming a commodity. The system around it is where the real engineering lives now. I wrote a full breakdown of the shift from prompts to autonomous systems Read it below

@sairahul1 Great resource. Building experiment, and breaking real projects is the fastest path to understanding LLMs. A repository like this gives developers practical patterns they can adapt instead of starting from scratch every time.

this repo is an absolute goldmine if you're building with LLMs. instead of another "AI agents 101" tutorial... you get 100+ production-ready examples you can actually clone, modify and learn from. inside you'll find: * multi-agent systems * MCP apps * RAG architectures * memory * voice agents * browser agents * generative UI * coding agents * always-on agents * agent skills * fine-tuning examples the fastest way to learn AI isn't watching tutorials. it's reading real code, breaking it, and building your own. this repo gives you the blueprint. highly recommend bookmarking it. 100% open-source. link to the repo: github.com/Shubhamsaboo/a… that said, I wrote a deep dive into the next evolution of AI engineering: moving from prompts to loops, harnesses, and graph-based execution. the article is quoted below.

@humzaakhalid The biggest productivity boost isn't a smarter model—it's eliminating repetitive setup. A system that remembers workflows and connects directly to your tools is far more valuable than rewriting the same instructions every day.

SOMEONE ON REDDIT JUST TURNED CLAUDE INTO AN ENTIRE COMPANY OPERATING SYSTEM WITH ONE REPO (Bookmark this before your competitors notice) Anthropic quietly open-sourced a repo of role plugins for Claude. here is what you can do with it: > every plugin = one specialist with its role > skills fire automatically when they're relevant > one slash command runs an entire workflow > connectors wire Claude into your CRM and data stack The amnesia way: paste your CRM notes, explain your product, explain your process, hope for the best. The team way: type /sales:call-prep. The plugin already knows the workflow and pulls data from your CRM. Hire the team once. Stop briefing a freelancer forever. The full guide is in the article below:

@NainsiDwiv50980 This is a solid collection. Most people focus only on the model, but the real productivity gains come from combining Claude with MCPs, skills, and automation. Definitely worth bookmarking.

If you're using Claude and haven't starred these GitHub repos yet, you're already behind. I put together 8 GitHub repositories that every serious Claude power user should know. ⚡ Claude Code github.com/anthropics/cla… 🧠 Awesome Claude Code github.com/hesreallyhim/a… 🧩 Claude Plugins Official github.com/anthropics/cla… 📚 Awesome Claude Skills github.com/ComposioHQ/awe… 🚀 Claude Code Action github.com/anthropics/cla… 🐍 Claude Agent SDK (Python) github.com/anthropics/cla… 🔌 MCP Servers github.com/modelcontextpr… ⚡ Superpowers github.com/obra/superpowe… These aren't random repositories. They're the tools, plugins, MCP servers, skills, and workflows that separate casual Claude users from builders shipping production-ready AI. Bookmark this. You'll thank yourself later.

@SneakerAssist Yeah

@VipulKewatTech Awesome

@Saifullahkqalf Thanks

@VipulKewatTech Great

@Aiwithkami Thanks a lot, Kami! Happy you found it useful. Looking forward to hearing your thoughts after you try it.

@VipulKewatTech Amazing

@AiwithNomi Thankyou, Nomi! Really appreciate it. Excited to see how others put these ideas into practice as well.

@VipulKewatTech Amazing

@RajJohanpkid Thanks, Johan! Glad you enjoyed it. Hope you get a chance to try some of these workflows yourself.

@VipulKewatTech Amazing post

I'm starting with Projects first—they've made organizing my Claude workflows much easier. What's the first feature you're planning to explore? 👇