DishaBuilds @DishaBuilds

🚀 Introducing GLM-5.3 — Built for Coding. Engineered for Agents. Ready for Cyber Defense. The latest release from Z.ai pushes open-source AI another step forward with major improvements in both software engineering and cybersecurity tasks. What's New in GLM-5.3? 🔹 Top-Tier Coding Performance Post-trained on a powerful 743B parameter base model, GLM-5.3 delivers strong capabilities across code generation, debugging, refactoring, and complex software workflows. 🔹 Advanced Agentic Reasoning Designed to handle multi-step tasks, tool usage, planning, and autonomous workflows more effectively—making it well-suited for modern AI agent systems. 🔹 Breakthrough Cybersecurity Performance GLM-5.3 sets a new benchmark among open models for security-related tasks, including vulnerability analysis, threat investigation, security reasoning, and defensive cyber workflows. Why This Matters The race is no longer just about who has the biggest model. It's about who can build models that excel in real-world execution: → Writing production-ready code → Operating as reliable AI agents → Defending systems against security threats → Integrating into enterprise workflows As AI moves from chatbots to autonomous systems, coding ability and cybersecurity reasoning are becoming two of the most valuable capabilities a model can have. GLM-5.3 is a strong signal that open models continue to close the gap in areas that matter most for production use. The future of AI won't be defined only by intelligence. It will be defined by what that intelligence can reliably do. 📖 Read the technical blog: z.ai/blog/glm-5.3 #AI #LLM #OpenSourceAI #GLM53 #CyberSecurity #AIAgents #AgenticAI #SoftwareEngineering #MachineLearning #GenerativeAI

I’ve noticed something about solopreneurs: They rarely fail because they don’t have enough ideas. They struggle because one person is trying to be the strategist, marketer, salesperson, content writer, analyst, operations manager and sometimes even the developer. That’s where AI becomes interesting. Not as a replacement for the founder. But as the operating system behind the founder. This 9-step Claude workflow is a good example of how you can structure a business from idea → validation → positioning → sales → operations → product → daily execution. And the part I find most useful? Don’t throw everything into one giant AI chat. Create systems. Give each function its own context. Give AI your business rules. Build reusable files. Connect your existing tools. Turn repetitive work into workflows. The real advantage of a solopreneur in the AI era may not be having more resources. It may be knowing how to make one person operate like a small, well-coordinated team. That’s a very different way of thinking about building a business. Save this framework for when you’re building something of your own. Follow @AamirAnsar94694 for more practical AI, tech & business insights. #Solopreneur #Entrepreneurship #AI #Claude #BusinessStrategy #Startups #AITools #FutureOfWork

🚨 Fable 5.5 Leak: > Claude Fable 5.5Expected to become Anthropic's most capable model yet > direct response to GPT-6 > Expected to keep Fable 5 pricing > Deployment has reportedly been slowed by restrictions > Fable 5.5 has already shown extremely advanced cyber capabilities in testing Do you think Fable 5.5 actually gets released this month?

Gemini 3.7 Flash (High) by @GoogleDeepMind just landed in the Code Arena: WebDev and Text Arena! This release is a strong improvement from its previous model Gemini 3.6 Flash (High): #19 -> #8 in the Code Arena: WebDev. By category, it ranks in the 10 top in domains for: - #7 in Data & Analytics - #8 in Simulations, Gaming and Content Creation Tools - #9 in Reference-Based Design Congrats to the @GoogleDeepMind team on this release!

Google DeepMind @GoogleDeepMind

a month ago

Gemini 3.7 Flash is here. It’s stronger for coding, knowledge work, and web development. 🧵

365 626 5K 2.6M 413

Claude + Obsidian turned 400 dead notes into a brain that answers in 2 seconds Claude reads it, links it, files it. Your second brain now builds itself for $0. Most note apps are graveyards. You dump 400 notes in, you never link them, you never find them again. Notion locks your data in a database you rent. Obsidian gives you the files but leaves the work to you. This fixes the work. You drop a source. Claude reads it, pulls out the people and ideas, cross-links everything, and files it into a clean Obsidian vault of plain Markdown you own. Ask a question 3 months later, it answers from everything it has read and cites the exact pages. Knowledge compounds like interest. It is built on Andrej Karpathy's LLM Wiki pattern. 6,800 stars. MIT licensed. No subscription, no database, no lock-in. Here is the full build. PART 1 — Setup. 2 lines, 2 minutes. Clone the repo and run the setup script. git clone github.com/AgriciDaniel/c… cd claude-obsidian && bash bin/setup-vault.sh That one script wires up your graph view, color scheme, CSS, and the full wiki skeleton. Open the folder in Obsidian: Manage Vaults → Open folder as vault → select claude-obsidian/. Open Claude Code in the same folder. Type 1 word: /wiki Claude checks the setup, asks you 1 question "What is this vault for?" and scaffolds the entire structure. Done. PART 2 — Install it as a plugin instead (cleaner). If you'd rather not clone, install it as a Claude Code plugin. 2 steps. claude plugin marketplace add AgriciDaniel/claude-obsidian claude plugin install claude-obsidian@agricidaniel-claude-obsidian Confirm it landed: claude plugin list Then /wiki in any session. PART 3 — The 6 commands you'll actually use daily. This is the whole loop. Memorize these 6 and you're running. Feed it a source. It creates 8 to 15 linked pages: ingest research-paper.pdf Feed it a pile at once. Parallel agents read all of them, then cross-reference: ingest all of these Ask your brain anything. It reads the cache, scans the index, drills into pages, cites them: what do you know about X? Turn the current conversation into a permanent note: /save Run autonomous web research 3 rounds, search, fetch, synthesize, file: /autoresearch prediction markets 2026 Clean the vault. Finds orphans, dead links, stale claims, missing links: lint the wiki At the end of every session Claude refreshes a hot cache. Next session opens with full recent context. No recap, no re-explaining. PART 4 — Kill the copy-paste with MCP. By default you paste sources in. MCP lets Claude read and write vault notes directly. The filesystem option needs no plugin at all paste this into your terminal: claude mcp add-json obsidian-vault '{ "type": "stdio", "command": "npx", "args": ["-y", "@bitbonsai/mcpvault@latest", "/path/to/your/vault"] }' --scope user Swap /path/to/your/vault for your real path. Now Claude touches your notes itself. PART 5 — The real unlock: one brain, every project. This is the move 95% of people miss. Point every other Claude Code project at this same vault. Drop this into any project's CLAUDE.md: ## Wiki Knowledge Base Path: ~/path/to/vault When you need context not already in this project: 1. Read wiki/hot.md first (recent context cache) 2. If not enough, read wiki/index.md 3. If you need domain details, read the relevant domain sub-index 4. Only then drill into specific wiki pages Do NOT read the wiki for general coding questions. Your coding projects, your content workflow, your research all drinking from 1 knowledge base. Read it once, use it everywhere. PART 6 — Stack kepano's official Obsidian skills on top. The Obsidian creator ships 5 agent skills 35,000 stars that teach any agent the native formats: Markdown, Bases, JSON Canvas, the CLI. The vault above is already aligned with them. Add them in 2 lines: /plugin marketplace add kepano/obsidian-skills /plugin install obsidian@obsidian-skills Now Claude writes proper wikilinks, callouts, and database views instead of dumb text. Add this to any existing vault (no clone). Already have a vault you love? Copy WIKI.md into its root, then paste this exact prompt into Claude: Read WIKI.md in this project. Then: 1. Check if Obsidian is installed. If not, install it. 2. Check if the Local REST API plugin is running on port 27124. 3. Configure the MCP server. 4. Ask me ONE question: "What is this vault for?" Then scaffold the full wiki structure. 5 lifehacks that change the math. Set the vault as a Personal use case with PARA mode projects, areas, resources, archives. Your todo brain and your idea brain stop fighting: bash bin/setup-mode.sh Install the Obsidian Web Clipper browser extension. Every article you read hits .raw/ in 1 click, ready for ingest all of these on Sunday night. Run lint the wiki once a week. It surfaces contradictions as [!contradiction] callouts with both sources. Your brain flags when it disagrees with itself. Turn on Obsidian Git. The vault auto commits every 15 minutes. Your second brain has a full undo history. Use /think before any hard decision. It runs a 10-stage reasoning loop over the problem instead of a snap answer. /think should I niche down my account or post broad? The honest progression. Day 1: 5 seeded pages, a tiny graph. Week 2: 40 sources in, the graph is a web, queries start citing things you forgot you read. Month 2: you stop searching Google for things you already know you ask your own vault first. It answers in 2 seconds with sources. The spoon-feeding stops. The brain starts feeding you. You own every byte. Plain Markdown, on your disk, forever.

Everyone is talking about running 1,000 AI agents overnight from a phone. Almost nobody is talking about the architecture that makes it possible. The secret isn't more intelligence. It's better systems. Here's the framework. Layer 1: Mobile Control Your phone becomes the command center. Not a notification app. Not a dashboard. A real control interface. You can: → Launch new tasks → Check progress → Redirect work → Review results while the actual execution happens elsewhere. The future isn't working from your laptop. It's managing work from your laptop. Layer 2: Parallel Execution One AI agent is useful. One hundred agents is a force multiplier. One thousand agents is an operating system. The trick is isolation. Each agent gets: • Its own context • Its own workspace • Its own objective • Its own memory No collisions. No context pollution. No agents overwriting each other's work. This is why modern AI workflows feel fundamentally different from a single chatbot conversation. Layer 3: Verification This is where most people fail. They let the same model: Do the work Verify the work That's like letting students grade their own exams. Production systems separate those roles. Builder Agent → Creates Judge Agent → Verifies Auditor Agent → Challenges Only verified outputs move forward. Not confident outputs. Verified outputs. Layer 4: Cost & Safety Controls The difference between an AI workflow and an AI disaster is usually one missing rule. Every autonomous system needs: ⏱ Runtime limits 💰 Spending limits 🔒 Permission boundaries If an agent can't stop itself, it shouldn't run unattended. If an agent can spend unlimited money, it shouldn't run unattended. If an agent can take irreversible actions, it definitely shouldn't run unattended. Layer 5: Agent Swarms This is where things get interesting. A manager agent receives a goal: "Research 500 startups." Instead of doing the work itself, it creates: → 50 Research Agents → 50 Validation Agents → 50 Ranking Agents → 10 Summary Agents The work gets distributed. Results get merged. The manager delivers the final answer. One task. Hundreds of workers. Layer 6: Persistent State Most AI users still think the conversation is the system. It isn't. The conversation is just the interface. The real system is: Memory. Projects. Databases. Knowledge graphs. Files. Workspaces. When state survives beyond a chat session, agents stop acting like assistants and start acting like infrastructure. Layer 7: The Real Bottleneck The bottleneck isn't model intelligence anymore. GPT-6. Gemini. Claude. DeepSeek. Grok. All of them are becoming incredibly capable. The bottleneck is orchestration. Who can coordinate the most agents? Who can verify results? Who can manage memory? Who can control costs? Who can scale safely? Those are the questions that matter now. The biggest misconception about AI is that the future belongs to better prompts. It doesn't. The future belongs to people who know how to build systems of agents that can think, coordinate, verify, and execute without constant supervision. That's how you go from one chat window... to an AI workforce.

How to Build an AI Second Brain With Claude + Obsidian That Gets Smarter Every Day Your best ideas are scattered everywhere. A few are in Notes. Some are buried in browser tabs. Others are locked inside old Claude conversations you'll never open again. Every time you sit down to work, you waste energy rebuilding context from memory. A second brain solves that. Instead of starting from scratch every day, you create a system that remembers everything for you. Your notes, projects, goals, ideas, research, decisions, and lessons all live in one place. Claude sits on top of that system, helping organize, connect, retrieve, and expand your knowledge over time. The result? You stop asking AI the same questions. You stop losing great ideas. And your knowledge compounds instead of disappearing. This approach is inspired by Andrej Karpathy's LLM Wiki concept—a personal knowledge system that grows more valuable every day you use it. Here's a complete step-by-step guide. What You're Actually Building Think of it as two layers: Obsidian = Memory Obsidian stores everything you know as simple markdown files on your computer. Because everything is plain text: You own the files You aren't locked into one platform Any AI can use them Your knowledge remains portable forever Notes can link to other notes, creating a network of connected ideas. Over time, that network becomes your personal knowledge graph. Claude = Intelligence Claude becomes the operating layer. It can: Read your vault Connect ideas Organize information Answer questions across your notes Help maintain the system Obsidian stores the knowledge. Claude helps you use it. Together, they become your second brain. Step 1: Install Claude Desktop Download Claude Desktop: claude.com/download Install it and sign in. A paid Claude plan is recommended because you'll need access to Claude Code and MCP integrations. After opening Claude Desktop: Sign in Open the Code tab Confirm Claude Code is available This is the version that can interact with files and external tools. Step 2: Install Obsidian Download Obsidian: obsidian.md Install and launch it. Create a new vault: Create New Vault → Name it "Brain" (or anything you prefer) Choose a location on your computer. This folder becomes your second brain. Create a few notes and experiment with links: [[Goals]] [[Business]] [[Ideas]] Linked notes are the foundation of the system. Step 3: Enable Obsidian's Local API Claude needs a way to access your vault. Inside Obsidian: Open Settings Community Plugins Enable Community Plugins Browse Plugins Search: Local REST API Install and enable it. Open the plugin settings. You'll see an API key. Copy it. You'll need it in the next step. Keep Obsidian running. Step 4: Connect Claude to Obsidian Now connect Claude using MCP. Inside Claude Code, configure the Obsidian MCP connection using your API key. Once connected, test it with: List every file in my Obsidian vault. If Claude successfully returns your notes, the integration is working. If not: Ensure Obsidian is open Verify the plugin is enabled Double-check your API key Step 5: Create Your Personal Context File A second brain isn't useful if it doesn't know who you are. Instead of manually writing everything, let Claude interview you. Use a prompt like: Interview me one question at a time to build my personal profile. Ask about my goals, projects, strengths, weaknesses, communication preferences, work style, and priorities. When finished, create a structured CLAUDE.md file. Answer honestly. Treat it like onboarding a co-founder. When complete, Claude generates: CLAUDE.md This becomes the permanent context layer for your entire system. Future conversations start with context already loaded. No more repeating yourself. Step 6: Create Project-Based Workspaces Separate your life into projects. For example: Content Business Learning Investments Health Each project gets its own folder. A recommended structure: Project/ ├── Inputs ├── Process ├── Outputs ├── Feedback ├── CLAUDE.md Inputs Ideas, research, resources Process Work-in-progress Outputs Final deliverables Feedback Results, lessons, analytics CLAUDE.md Project-specific instructions This keeps Claude focused on one objective at a time. Step 7: Work Inside Individual Projects One mistake people make is opening the entire vault for every task. Don't. Open the specific project you're working on. This gives Claude: Less noise Better focus Faster responses Higher quality outputs Your master vault manages life. Project vaults execute work. Step 8: Turn Repeated Tasks Into Skills Every repetitive task should become a reusable workflow. Examples: Writing LinkedIn posts Researching competitors Summarizing podcasts Preparing sales calls Generating content ideas Teach Claude your process once. Then save it as a reusable skill. Instead of explaining repeatedly, you'll simply say: Run my LinkedIn Content Skill. Or: Run my Weekly Research Skill. This creates enormous leverage over time. Step 9: Connect Live Data Sources Your notes are valuable. Live information makes them powerful. Connect: Google Calendar Gmail Slack Notion Drive GitHub Now Claude can understand both: Static Context What you've learned Dynamic Context What's happening now Examples: Review today's meetings and create action items. Summarize new emails and add tasks to my project system. Find unfinished commitments from last week's notes. Your second brain becomes proactive instead of reactive. Step 10: Put Maintenance on Autopilot The best systems maintain themselves. Create recurring tasks that: Organize notes Link related ideas Archive old files Surface unfinished projects Generate daily summaries A simple daily workflow: Every Morning Process new notes Organize inboxes Link related concepts Create summary Every Week Review goals Surface stalled projects Highlight patterns Every Month Archive completed work Extract lessons learned Update priorities The system becomes smarter automatically. One Rule You Should Never Break Never rely on prompts for security. Use permissions. Bad: "Don't delete anything." Good: Read-only access Scoped API keys Restricted folders Limited permissions Assume that if an AI system technically can perform an action, it eventually might. Protect your data through access controls, not instructions. Bonus: Use Existing Open-Source Templates You don't have to build everything from scratch. Several open-source projects already implement this concept. Popular options include: Claude-Obsidian A self-organizing knowledge system based on the LLM Wiki concept. Obsidian Second Brain Includes dozens of pre-built commands and workflows. Second Brain Starter Guides you through creating a proactive AI assistant using Obsidian and Claude. These projects provide a strong starting point while remaining fully customizable. The Bigger Idea Most people use AI as a chatbot. The highest-leverage users use AI as an operating system. A second brain changes the relationship entirely. Instead of asking AI isolated questions, you give it context, memory, and continuity. Your knowledge compounds. Your work compounds. And every day the system becomes a little smarter than it was the day before. That's when AI stops being a tool and starts becoming infrastructure.

🚨 Someone moved fast. Claude can now reportedly embed invisible watermarks into generated text—designed to survive copy-pasting and even light edits. So what happened next? A developer built Claude Watermark Remover. Paste Claude-generated text in. Remove the statistical watermark. Keep the meaning. Free. No signup required. Built and shipped within hours of the watermark announcement. The AI arms race isn't just model vs model anymore. It's watermarking vs watermark removal. And it's happening in real time.

Most people think they know Claude. They've barely scratched the surface. What most users learn first is just Level 1. The real leverage starts after that. Level 1: Install the Desktop App Even if you spend 90% of your time in the browser. One day you'll need Claude to interact with local files, codebases, or folders. That's not the moment you want to be downloading software, logging in, and granting permissions. Set it up before you need it. Future you will thank you. Level 2: Escalate on Failure Most people start with the biggest model available. Wrong move. Start with Sonnet. If it struggles, that's evidence the task is actually difficult. Then upgrade. You'll discover that most tasks never needed the most expensive model in the first place. And when you switch models, stay in the same conversation. Claude already has the context. Starting over means paying the context tax twice. Level 3: Match Thinking to the Task Not every task deserves deep reasoning. "Convert this table to CSV." One correct answer. Low thinking. "Should we charge $40 or $60?" Trade-offs. High thinking. The biggest mistake is turning up reasoning on tasks with a single correct outcome. You often get a beautifully explained wrong answer. Level 4: Know When It's Not a Chat If a task will take more than three back-and-forth messages, it's probably not a chat problem. It's a system problem. Use Projects. Use Cowork. Use persistent context. If you've explained the same background twice in different chats, you've already waited too long. That work deserves a home. Level 5: Define the Output First Most people do this backwards. They ask a question. Get 1,000 words. Then ask for a document. Then ask for a spreadsheet. Then ask for a presentation. Three prompts wasted. Instead: "Create a one-page Word document." "Build an Excel model." "Generate a presentation." Start with the destination. Claude works backwards from there. Level 6: Turn Corrections Into Systems This is where power users separate themselves from everyone else. The second time you correct Claude for the same thing... Stop correcting it. Document it. Examples: → Don't use em dashes → Give the answer before the explanation → Never start with a summary → Keep responses under 200 words A repeated correction isn't feedback anymore. It's a rule. And rules belong in Skills. Correct twice. Write once. Never repeat yourself again. Your First 20 Minutes With Claude Install the desktop app Leave most settings alone Connect one useful tool Create a Project for recurring work Turn one recurring correction into a Skill Most people stop at step four. The real compounding starts at step five. Because the future of AI isn't better prompting. It's building systems that remember, improve, and scale with you. Prompts get results. Systems get leverage.

Most Developers Will Never Find These GitHub Repositories There are over 500 million repositories on GitHub. Most are abandoned. Many are duplicates. And a tiny percentage can completely change how you learn, build, and grow as a developer. The problem isn't a lack of resources. It's finding the right ones. Most developers spend years searching for knowledge that already exists inside a few incredible open-source repositories. Here are 19 GitHub repositories worth bookmarking in 2026. 1. Build Your Own X The fastest way to understand technology is to build it yourself. This repository teaches you how to recreate products and systems such as databases, search engines, web servers, neural networks, BitTorrent clients, Docker, and much more. You stop being a user of technology and start understanding how it works underneath. 2. Developer Roadmap One of the biggest problems in tech is not knowing what to learn next. Developer Roadmap solves that. Whether you want to become a frontend developer, backend engineer, DevOps specialist, AI engineer, cybersecurity expert, or product manager, it gives you a clear learning path. No guesswork. Just a roadmap. 3. Free Programming Books Thousands of programming books. Completely free. Covering almost every programming language, framework, and computer science topic imaginable. If you're serious about learning, this repository alone can keep you busy for years. 4. System Design Primer Every developer eventually reaches a point where coding is no longer enough. You need to understand architecture. Scalability. Distributed systems. System Design Primer has become one of the most recommended repositories for learning exactly that. It's used by engineers preparing for interviews at companies like Google, Meta, Amazon, and Microsoft. 5. Coding Interview University Originally created by a self-taught developer preparing for Google interviews. Today it's one of the most comprehensive interview preparation repositories available. Algorithms. Data structures. System design. Interview strategies. Everything organized into a structured learning plan. 6. Project-Based Learning Tutorials are useful. Projects are transformative. This repository contains hundreds of real-world projects that help you learn by building rather than consuming content. Because nobody learns software engineering by watching videos alone. 7. JavaScript Algorithms Algorithms explained with diagrams, code examples, and visualizations. One of the best repositories for understanding how algorithms actually work rather than memorizing them. 8. You Don't Know JS A deep dive into JavaScript. Not the surface-level tutorials. The concepts most developers skip. Closures. Scopes. Prototypes. Execution contexts. If JavaScript feels confusing, this repository explains why. 9. Tech Interview Handbook A complete guide to technical interviews. From resume preparation to coding interviews and system design rounds. A practical companion to Coding Interview University. 10. The Art of Command Line Most developers use only a fraction of what the terminal can do. This repository teaches powerful command-line techniques that can save hours every week. Small improvements. Massive productivity gains. 11. The Book of Secret Knowledge A collection of tools, resources, cheat sheets, scripts, and references gathered from across the developer ecosystem. Think of it as a treasure chest for engineers. 12. Awesome Self-Hosted Want alternatives to paid SaaS products? This repository lists thousands of self-hosted tools for productivity, collaboration, AI, databases, monitoring, security, and much more. Your own infrastructure. Your own control. 13. 30 Seconds of Code Quick, practical snippets that solve everyday programming problems. Perfect when you need solutions fast. 14. GitHub Gitignore Templates Simple repository. Massive impact. Provides ready-made .gitignore files for nearly every programming language and framework. One of the most-used repositories on GitHub. 15. Public APIs A massive collection of free APIs. Need weather data? Finance data? AI tools? Images? Maps? You'll probably find an API here. 16. Ollama One of the most important repositories in the AI era. Allows developers to run powerful LLMs locally. No cloud dependency. No API costs. Just AI running on your own machine. 17. LangChain One of the foundational frameworks for building AI applications and agents. Used by startups and enterprises worldwide. If you're learning AI engineering, you'll eventually encounter LangChain. 18. n8n Open-source automation for the AI era. Connect tools. Build workflows. Create AI agents. Automate repetitive work. All without being locked into expensive SaaS platforms. 19. iFixAi A growing repository focused on AI-powered debugging and software problem-solving. Useful for developers who want faster troubleshooting and code assistance workflows. The Bigger Lesson Most developers think growth comes from finding the next course. In reality, growth often comes from finding the right repository. The best repositories don't just teach concepts. They teach how real engineers think. Bookmark these. Explore one every week. A year from now, you'll have learned more than most developers learn in several years. Which GitHub repository has had the biggest impact on your career? This structure follows the same long-form storytelling style as the AI agent article you shared, but adapted for GitHub repositories.

How To Build Production-Ready AI Agents in 2026

Most people still give AI agents vague prompts and then wonder why the results are disappointing. The reality is simple: AI agents perform best when they're given clear instructions, defined responsibilities, and a structured workflow. Here's a framework that works: 1. Define the Role Give the agent a specific role and clearly state its expertise and responsibilities. 2. Set a Clear Goal Avoid vague objectives. Define one goal that is specific, measurable, actionable, and outcome-focused. 3. Provide the Right Inputs Tell the agent exactly what information it can use, such as documents, URLs, databases, emails, PDFs, spreadsheets, or messages. 4. Specify Available Tools Define which tools the agent can access—search, email, calendars, APIs, CRMs, databases, or other systems. 5. Create a Workflow Give the agent a process to follow. A simple framework is: Analyze → Research → Plan → Execute → Verify → Deliver 6. Add Decision Rules Tell the agent what to do when information is unclear. Instruct it to ask questions instead of making assumptions. 7. Define Memory Specify what information should be retained for future tasks, such as preferences, previous actions, conversations, and historical outputs. 8. Choose the Output Format Decide how results should be delivered before the task begins—summary, report, recommendations, action plan, next steps, etc. 9. Require Verification Ask the agent to review its work before delivery and verify: • Accuracy • Completeness • Calculations • Duplicates • Goal achievement Common Mistakes to Avoid ❌ Vague goals ❌ Undefined tools ❌ Missing workflows ❌ Unlimited autonomy ❌ No guardrails The most effective AI agent prompt follows this formula: Role + Goal + Inputs + Tools + Workflow + Rules + Memory + Output + Verification Get these nine elements right, and you'll see a dramatic difference in the quality of your agent's performance. Check the infographic for the complete breakdown. This version is cleaner, more professional, and reads naturally as a LinkedIn/X educational post.

@TheAutomationn thank you for sharing i will try it

1. How AI Agents Actually Fail in Production Building an AI agent is easy. Keeping it reliable is hard. Most agents don't fail because of the model. They fail because of the system around the model. An AI agent breaks when: → It has no memory → It can't recover from errors → It trusts every input → It has no approval boundaries → Nobody monitors its decisions The best AI teams don't spend all day testing prompts. They spend their time designing guardrails. The Rule Models generate. Systems validate. Humans approve. That's how production AI works. Most AI failures aren't intelligence problems. They're engineering problems. 2. Everyone Wants AI Agents. Few Need Them. AI agents are becoming the new buzzword. But here's the truth: Most businesses don't need an agent. They need automation. Before building an agent, ask: Can this be solved with: → A workflow? → An API call? → A simple automation? → A rule-based system? If yes, don't use an agent. Agents make sense when: ✅ Multiple decisions exist ✅ Information changes constantly ✅ Tool usage is required ✅ Human reasoning is needed The smartest AI builders know when not to use an agent. Complexity is expensive. Simplicity scales. 3. The AI Builder Stack Nobody Talks About Most people obsess over models. GPT vs Claude vs Gemini. Wrong focus. The best AI products are built on four layers: Layer 1: Models GPT, Claude, Gemini. The brain. Layer 2: Memory Supabase. Vector databases. Knowledge bases. The memory. Layer 3: Workflows n8n. Zapier. Make. The operations. Layer 4: Distribution Websites. Apps. Slack. WhatsApp. The customer experience. Most builders spend 90% of their time thinking about Layer 1. Most successful companies make money from Layers 2, 3, and 4. The model gets attention. The system creates value. These types of posts perform well because they: Start with a strong contrarian statement Challenge common assumptions Teach a framework End with a memorable takeaway.

@TheAutomationn these tools are really time saving thank you for sharing

@TheAutomationn they make automation really easy

Most people are optimizing prompts. The best AI builders are optimizing context. One interesting finding from Claude Code usage: If you leave a coding agent idle for too long, rebuilding context can become significantly more expensive than starting a fresh session with a well-written summary. My takeaway: • Save a summary before stepping away • Document decisions and next steps • Treat context as a valuable asset As AI agents become more powerful, context management may become a competitive advantage. Prompting is a skill. Context management is a system. #AIAgents #ClaudeCode #AIBuilders #DeveloperTools #ArtificialIntelligence

@TheAutomationn great

Elon Musk warns that time is running out: At most, there are 3 years left in which you can still make money selling your work. After that, AI will automate most tasks, and paying a person for their time will no longer make sense. The model of the last 200 years - "I give you your salary in exchange for your time" - is coming to an end. This will trigger the greatest transfer of wealth in modern history. Those who are already making the shift understand it. Those waiting for it to hit the news will arrive too late. In the end, what matters is not how much you work, but what you own. The smartest asset you can build right now isn't Bitcoin or land. It's an income stream that runs on AI and compounds while you sleep. One skill. One niche. $10K/month. Here's the exact playbook ↓

Rahul @sairahul1

2 months ago

x.com/i/article/2080…

12 28 259 2.1M 1K