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adarshchetan

Adarsh Chetan

Verifiedactive
@adarshchetan

Sharing the latest developments in the world of Tech,Web3 & AI | Content Creator | I help people master AI,web3, Coding, Digital marketing. | Dm for Collab

RosterAI Agents & Productivity ToolsTech Career & Coding EducationLifestyle & Self-Improvement
65,248 followers · 1,100 following · Last polled 1h ago
Perf. Score
32.7
Rank #337
Value Score
59.3
#108 of 273 priced
Median views
1.9K
p25 1.2K · trailing 7d
Followers
65K
-0.1% 7d
ER (impr.)
0.54%
eng ÷ impressions
ER (foll.)
0.12%
eng ÷ followers
Est. CPM
$26.01
QT-rate basis
WoW views
+19.5%
▼ falling

Rates & value · est. from 1.9K median organic views

fairly priced
Quote tweetbasis
$50
$26.01 / 1K views
Post
—
no CPM
Thread
—
no CPM
Retweet
$20
no CPM
Value Score 59.3 · rank #108 of 273 priced$1204.82 / 1K engagementsrate age unknown (pre-audit import)

Trends

Follower growth
Median views / day
Engagement rate
View distribution · trailing-7d posts

Each blue dot is one organic post. The Performance Score ranks by the median (1.9K), not the mean (14K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (1.2K) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
What if AI could remember more without using more memory? Meet EdgeRock 4B by Rock Matrix - an open source multimodal model exploring a smarter approach to AI memory and reasoning. 🚀 Most people focus on bigger models and longer context windows. But what if the real breakthrough lies in how AI stores and retrieves information? Here's what makes EdgeRock interesting: ⚡ Constant Memory Usage: Designed to maintain a fixed-capacity memory structure during extended reasoning. 🔗 Rock Memory Anchors (RMA): Stores key-value records in an anchor bank, helping the model retrieve information even after it leaves the local context window. 📚 Long-History Reasoning: Built to support sustained reasoning across extended histories. 🖼️ Multimodal Capabilities: Supports reasoning over both text and images. 🔬 Innovative Architecture: Combines recurrent state, local attention, and Rock Memory Anchors (RMA) to manage information efficiently. The goal? Explore how AI can reason over longer histories without relying solely on ever-growing memory requirements. 💬 Want to explore the model, source code, and technical report? Check the links I'll share below! 👇 The future of AI isn't just about bigger models. It's about smarter memory and better reasoning. 🔥 #AI #OpenSourceAI #LLM #EdgeRock11h ago134K1377911100.17%
I was burning money on high-res clips that ended up in the trash. Generating 4K on Seedance 2.5 is now cheaper than 720p! Just reroll your shots at 480p, and only upscale the perfect one to 4K. Creative Upscale is a game-changer, exclusive to Dreamina. Dreamina has extended its $1.5/month Basic offer, and it’s still available. Here is exactly how to do it 👇 Try it now: https://t.co/kv1znJslXI #Dreamina #DreaminaPartner
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A family business succession should be about continuity, not conflict. Venu Srinivasan deserves credit for planning his legacy leaving the next generation to resolve it later👏👏👏 Sudarshan Venu has played a key role in growing TVS Motors and creating substantial business value! Bharat has benefitted from his work. When a family settlement has been agreed upon, stability, transparency, and respect for the process should matter👍🏼frozen10d ago25K575126100.54%
Someone sends you a screenshot claiming a ₹13,000 investment could become ₹16,000. The number looks attractive. The screenshot looks convincing. But a projected gain isn’t a guaranteed payout, and unofficial market sentiment can change quickly. The best investment decision is rarely the one made because a screenshot went viral.frozen6d ago16K3705000.27%
AI coding is moving beyond a single agent in a chat window. I’ve been testing JetBrains Air in IDEs, and the interesting part is being able to run multiple coding agents across projects while keeping everything in one place. Here’s what that workflow looks like 🧵👇frozen14d ago10K18104180.41%
How to Build an AI Agent Complete Roadmap Building an AI agent isn’t just about choosing an LLM. You need the right architecture around it. Here’s the complete breakdown 👇 1️⃣ Define Purpose & Scope
→ Use case
→ User needs
→ Success criteria
→ Constraints Ask:
• What problem will the agent solve?
• Who are the users?
• What defines success? 2️⃣ System Prompt Design
→ Goals
→ Role / Persona
→ Instructions
→ Guardrails Define the agent’s role, behavior, rules, safety, and reliability requirements. 3️⃣ Choose LLM Consider:
→ Base model
→ Parameters (temperature, top-p)
→ Context window
→ Cost / latency Choose the right model for your use case and tune parameters when needed. 4️⃣ Tools & Integrations Connect your agent with:
→ Simple local tools
→ APIs (web, apps, data)
→ MCP servers
→ AI agents as tools
→ Custom functions Keep integrations modular and secure. 5️⃣ Memory Systems
Use:
→ Episodic memory (conversation)
→ Working memory
→ Vector databases
→ SQL / structured databases
→ File storage Combine short-term working memory with long-term vector/database memory when needed. 6️⃣ Orchestration Manage:
→ Routes / workflows
→ Triggers
→ Parameters
→ Message queues
→ Agent-to-agent communication
→ Error handling This layer manages workflows, decisions, routing, and failures. 7️⃣ User Interface
Deploy through:
→ Chat interface
→ Web app
→ API endpoint
→ Slack / Discord bot Keep the interaction simple and intuitive. 8️⃣ Testing & Evals
Focus on:
→ Unit tests
→ Latency testing
→ Quality metrics
→ Iterate & improve Test for correctness, safety, reliability, and performance. 🧩 Tools & Platforms Consumer AI Agents
• ChatGPT — GPT-5.5
• Claude — Claude 4.7
• Perplexity — Multiple models Agentic Coding Tools
• Cursor — Claude, GPT
• Windsurf — Cascade
• Claude Code — Claude 4.7 No-Code Builders
• Lindy — GPT-5.5
• https://t.co/wmABDO8FJR — GPT-5.5
• n8n — Multiple models Development Frameworks
• LangGraph
• CrewAI
• LlamaIndex The core idea: AI Agent = LLM + Prompt + Tools + Memory + Orchestration + UI + Evaluation Save this roadmap if you’re building your first AI agent. 🔖 Follow @AdarshChetan on LinkedIn & X for more such amazing stufffrozen28d ago6.4K21254801646.79%
Here’s a deal worth checking out: 60% off Pro and 40% off Ultra. Oct 20–30, only on CapCut Desktop. AI characters are becoming reusable creative assets. I tested how Codex video editing can take one character and turn it into campaign content. Codex operates CapCut PC: prepares the character renders the white model automates the first editing workflow Then creators add the final creative direction. #capcutpc #capcutchatgptplugin #capcutcodex #capcutmarketing #capcutsalefrozen9d ago4.8K514110.25%
AI agents are changing how software works. Here’s the complete roadmap from LLMs → Tools → Memory → Reasoning → Autonomous Agents. 🧠 1. What is Agentic AI? 
→ Goal-oriented, autonomous, context-aware, adaptive, action-taking systems. 🎯 2. Use Cases
→ Content creation, customer support, data analysis, sales, marketing, DevOps, research, finance & more. 🧩 3. Core Components
→ LLM, memory, tools/actions, planning, reasoning, autonomy, feedback loops, environment & observability. 4. Tools & Frameworks 
→ LLM providers, agent frameworks, vector databases, memory stores, tool integrations, monitoring & testing tools. 5. Design the Agent 
→ Define objective → role → constraints → tools → workflow → memory → triggers → prompts. 6. Build & Test
→ Set up environment, connect the LLM, add tools & memory, test workflows, debug and validate outputs. 7. Deploy
→ Choose a platform → containerize → deploy APIs → configure authentication, data integration, settings and webhooks. 8. Monitor
→ Track logs, performance, usage, errors, feedback, health checks, alerts and reporting. 9. Optimize
→ Tune prompts, improve memory, refine tools, reduce latency/cost, A/B test and continuously learn. 10. Scale
→ Multi-agent systems, agent swarms, distributed memory, scaling storage, security, governance and compliance. The most important concepts: 🔄 Agent Loop:
Perceive → Reason → Plan → Act 🧠 Key capabilities:
Memory & recall • Tool use • Reasoning & planning • Decision-making • Learning & adapting • Collaboration Agent Architecture:
User ↔ Agent ↔ Tools
with Memory + Environment supporting the agent. Evaluation Metrics:
Task success rate • Accuracy • Response time • Cost per task • User satisfaction • Hallucination rate • Error rate 💡 Common use cases:
AI research assistant • Sales outreach • Code review • Support tickets • Meeting summaries • Content repurposing • Data insights ⚠️ Common mistakes: 
Unclear goals • Poor prompts • Too many tools • No error handling • Ignoring testing • No monitoring • Overcomplicating ✅ Best practices:
Start small → define clear goals → use the right tools → validate rigorously → monitor continuously → optimize → keep humans in the loop. 🔥 Level-up path:
Learn Basics → Build Agents → Deploy & Monitor → Optimize → Scale & Automate ❤️ Do follow @AdarshChetan on LinkedIn & X for more such amazing stuff!frozen15d ago3.7K9317140995.99%
How to Build an AI Agent Complete Roadmap Building an AI agent isn’t just about choosing an LLM. You need the right architecture around it. Here’s the complete breakdown 👇 1️⃣ Define Purpose & Scope
→ Use case
→ User needs
→ Success criteria
→ Constraints Ask:
• What problem will the agent solve?
• Who are the users?
• What defines success? 2️⃣ System Prompt Design
→ Goals
→ Role / Persona
→ Instructions
→ Guardrails Define the agent’s role, behavior, rules, safety, and reliability requirements. 3️⃣ Choose LLM Consider:
→ Base model
→ Parameters (temperature, top-p)
→ Context window
→ Cost / latency Choose the right model for your use case and tune parameters when needed. 4️⃣ Tools & Integrations Connect your agent with:
→ Simple local tools
→ APIs (web, apps, data)
→ MCP servers
→ AI agents as tools
→ Custom functions Keep integrations modular and secure. 5️⃣ Memory Systems
Use:
→ Episodic memory (conversation)
→ Working memory
→ Vector databases
→ SQL / structured databases
→ File storage Combine short-term working memory with long-term vector/database memory when needed. 6️⃣ Orchestration Manage:
→ Routes / workflows
→ Triggers
→ Parameters
→ Message queues
→ Agent-to-agent communication
→ Error handling This layer manages workflows, decisions, routing, and failures. 7️⃣ User Interface
Deploy through:
→ Chat interface
→ Web app
→ API endpoint
→ Slack / Discord bot Keep the interaction simple and intuitive. 8️⃣ Testing & Evals
Focus on:
→ Unit tests
→ Latency testing
→ Quality metrics
→ Iterate & improve Test for correctness, safety, reliability, and performance. 🧩 Tools & Platforms Consumer AI Agents
• ChatGPT — GPT-5.5
• Claude — Claude 4.7
• Perplexity — Multiple models Agentic Coding Tools
• Cursor — Claude, GPT
• Windsurf — Cascade
• Claude Code — Claude 4.7 No-Code Builders
• Lindy — GPT-5.5
• https://t.co/wmABDO8FJR — GPT-5.5
• n8n — Multiple models Development Frameworks
• LangGraph
• CrewAI
• LlamaIndex The core idea: AI Agent = LLM + Prompt + Tools + Memory + Orchestration + UI + Evaluation Save this roadmap if you’re building your first AI agent. 🔖 Follow @AdarshChetan on LinkedIn & X for more such amazing stufffrozen20d ago3.6K9021120685.38%
20 LangGraph Concepts You Need to Know in 2026 Want to build reliable AI agents? Start with these essential concepts 👇 1️⃣ LangGraph Basics — Build stateful AI workflows. 2️⃣ Graphs & Nodes — Define tasks and connect workflow steps. 3️⃣ State Management — Track messages, inputs, and intermediate results. 4️⃣ Edges & Flow Control — Control how execution moves between nodes. 5️⃣ Conditional Routing — Route queries based on intent or conditions. 6️⃣ Tool Calling — Connect agents with APIs, databases, and custom tools. 7️⃣ Memory — Retain conversation context and user preferences. 8️⃣ Multi-Agent Workflows — Coordinate specialized agents. 9️⃣ RAG with LangGraph — Retrieve relevant context before generating answers. 🔟 Human-in-the-Loop — Add human review and approval. 1️⃣1️⃣ Checkpointing & Persistence — Save state and resume after failures. 1️⃣2️⃣ Streaming & Async Execution — Stream responses and run long tasks efficiently. 1️⃣3️⃣ Error Handling & Retries — Recover from failures with retry logic. 1️⃣4️⃣ Observability with LangSmith — Trace, debug, and monitor workflows. 1️⃣5️⃣ Subgraphs — Build reusable, modular workflows. 1️⃣6️⃣ LangChain Integration — Combine LangGraph with LangChain tools, retrievers, and prompts. 1️⃣7️⃣ Custom Nodes & Advanced Patterns — Implement custom logic, parallel execution, and advanced workflows. 1️⃣8️⃣ Evaluation & Testing — Measure accuracy, relevance, and tool performance. 1️⃣9️⃣ Real-World Projects — Combine RAG, tools, memory, multi-agent systems, and human approval. 2️⃣0️⃣ Best Practices — Design modular graphs, manage state, handle errors, and keep workflows scalable. The big picture: LangGraph helps you build AI systems that can reason, use tools, manage state, collaborate, and recover from failures. 🔖 Save this cheat sheet for your AI engineering journey. Follow @AdarshChetan on LinkedIn & X for more developer resources! 🚀frozen12d ago3.2K801870705.53%
How to Build an AI Agent Complete Roadmap Building an AI agent isn’t just about choosing an LLM. You need the right architecture around it. Here’s the complete breakdown 👇 1️⃣ Define Purpose & Scope
→ Use case
→ User needs
→ Success criteria
→ Constraints Ask:
• What problem will the agent solve?
• Who are the users?
• What defines success? 2️⃣ System Prompt Design
→ Goals
→ Role / Persona
→ Instructions
→ Guardrails Define the agent’s role, behavior, rules, safety, and reliability requirements. 3️⃣ Choose LLM Consider:
→ Base model
→ Parameters (temperature, top-p)
→ Context window
→ Cost / latency Choose the right model for your use case and tune parameters when needed. 4️⃣ Tools & Integrations Connect your agent with:
→ Simple local tools
→ APIs (web, apps, data)
→ MCP servers
→ AI agents as tools
→ Custom functions Keep integrations modular and secure. 5️⃣ Memory Systems
Use:
→ Episodic memory (conversation)
→ Working memory
→ Vector databases
→ SQL / structured databases
→ File storage Combine short-term working memory with long-term vector/database memory when needed. 6️⃣ Orchestration Manage:
→ Routes / workflows
→ Triggers
→ Parameters
→ Message queues
→ Agent-to-agent communication
→ Error handling This layer manages workflows, decisions, routing, and failures. 7️⃣ User Interface
Deploy through:
→ Chat interface
→ Web app
→ API endpoint
→ Slack / Discord bot Keep the interaction simple and intuitive. 8️⃣ Testing & Evals
Focus on:
→ Unit tests
→ Latency testing
→ Quality metrics
→ Iterate & improve Test for correctness, safety, reliability, and performance. 🧩 Tools & Platforms Consumer AI Agents
• ChatGPT — GPT-5.5
• Claude — Claude 4.7
• Perplexity — Multiple models Agentic Coding Tools
• Cursor — Claude, GPT
• Windsurf — Cascade
• Claude Code — Claude 4.7 No-Code Builders
• Lindy — GPT-5.5
• https://t.co/wmABDO8FJR — GPT-5.5
• n8n — Multiple models Development Frameworks
• LangGraph
• CrewAI
• LlamaIndex The core idea: AI Agent = LLM + Prompt + Tools + Memory + Orchestration + UI + Evaluation Save this roadmap if you’re building your first AI agent. 🔖 Follow @AdarshChetan on LinkedIn & X for more such amazing stufffrozen10d ago3.1K7719130635.48%
How to Build an AI Agent Complete Roadmap Building an AI agent isn’t just about choosing an LLM. You need the right architecture around it. Here’s the complete breakdown 👇 1️⃣ Define Purpose & Scope
→ Use case
→ User needs
→ Success criteria
→ Constraints Ask:
• What problem will the agent solve?
• Who are the users?
• What defines success? 2️⃣ System Prompt Design
→ Goals
→ Role / Persona
→ Instructions
→ Guardrails Define the agent’s role, behavior, rules, safety, and reliability requirements. 3️⃣ Choose LLM Consider:
→ Base model
→ Parameters (temperature, top-p)
→ Context window
→ Cost / latency Choose the right model for your use case and tune parameters when needed. 4️⃣ Tools & Integrations Connect your agent with:
→ Simple local tools
→ APIs (web, apps, data)
→ MCP servers
→ AI agents as tools
→ Custom functions Keep integrations modular and secure. 5️⃣ Memory Systems
Use:
→ Episodic memory (conversation)
→ Working memory
→ Vector databases
→ SQL / structured databases
→ File storage Combine short-term working memory with long-term vector/database memory when needed. 6️⃣ Orchestration Manage:
→ Routes / workflows
→ Triggers
→ Parameters
→ Message queues
→ Agent-to-agent communication
→ Error handling This layer manages workflows, decisions, routing, and failures. 7️⃣ User Interface
Deploy through:
→ Chat interface
→ Web app
→ API endpoint
→ Slack / Discord bot Keep the interaction simple and intuitive. 8️⃣ Testing & Evals
Focus on:
→ Unit tests
→ Latency testing
→ Quality metrics
→ Iterate & improve Test for correctness, safety, reliability, and performance. 🧩 Tools & Platforms Consumer AI Agents
• ChatGPT — GPT-5.5
• Claude — Claude 4.7
• Perplexity — Multiple models Agentic Coding Tools
• Cursor — Claude, GPT
• Windsurf — Cascade
• Claude Code — Claude 4.7 No-Code Builders
• Lindy — GPT-5.5
• https://t.co/wmABDO8FJR — GPT-5.5
• n8n — Multiple models Development Frameworks
• LangGraph
• CrewAI
• LlamaIndex The core idea: AI Agent = LLM + Prompt + Tools + Memory + Orchestration + UI + Evaluation Save this roadmap if you’re building your first AI agent. 🔖 Follow @AdarshChetan on LinkedIn & X for more such amazing stufffrozen5d ago3.0K7721300505.93%
50 AI Tools to start your Business in seconds. 1. Marketing - Content Management: Junia AI, Kit, Typefully - Social Media: Vribble, HeyGen, Screen Studio - Copywriting: Jibewith, Wordtune, Writesonic 2. Concept Development - Documents: Max AI, https://t.co/FANiTOblAK, Notion - Presentation: Pop AI, Zoho Show, https://t.co/KKQzOv4yfk - Excel: Quip, GPT Excel 3. Business Analysis - Business Analysis: Squad, Cody AI, Mymap AI - Finance: Fibrery, Truewind, Parthen, Avant AI 4. Prototyping - Image Generation: Beautiful AI, Midjourney, Leonardo AI - Voice & Videos: Podcast, Wisecut AI, Cartesia - Design: Figma, Dora AI, Canva 5. Ideation - Brainstorming: Saga AI, VentureKit, ChatGPT - Market Research: Claude AI, Oslo AI, Convergence AI, Julius AI, https://t.co/DkfOr8f3nl, Perplexity 6. Development - Cloud: Bot X, Relay App, Zapier - Coding: Qodo, Github, Codeswhit - Website: Super AI, Dork AI, 10web Do follow @AdarshChetan for more such amazing stufffrozen9d ago2.6K801640576.03%
7 Types of RAG Architectures Every Developer Should Know in 2026 RAG is evolving beyond simple “retrieve → generate” pipelines. Here are 7 architectures to understand 👇 1️⃣ Naive RAG Basic retrieval + response. → Best for simple chatbots & Q&A. 2️⃣ Advanced RAG Adds query rewriting, re-ranking, metadata filtering & better chunking. → Better retrieval and production accuracy. 3️⃣ Agentic RAG An AI agent decides what, how, and when to retrieve. → Useful for AI assistants & research agents. 4️⃣ Graph RAG Uses knowledge graphs to retrieve and reason over relationships. → Great for complex knowledge systems & enterprise AI. 5️⃣ Multi-Modal RAG Works with images, PDFs, audio, video and text. → Useful for document AI & visual assistants. 6️⃣ Hybrid RAG Combines vector search, keyword search and other retrieval methods. → Improves search quality and accuracy. 7️⃣ Corrective RAG Adds a validation layer to check retrieved information and reduce hallucinations. → Useful for high-accuracy and critical-information systems. 💡 The key: Don’t choose a RAG architecture because it’s popular. Choose it based on your data, retrieval needs, accuracy requirements and application complexity. 🔖 Save this as your RAG architecture reference. Do Follow @AdarshChetan for more AI & developer content.frozen19d ago2.5K451220313.67%
7 Types of RAG Architectures Every Developer Should Know in 2026 RAG is evolving beyond simple “retrieve → generate” pipelines. Here are 7 architectures to understand 👇 1️⃣ Naive RAG Basic retrieval + response. → Best for simple chatbots & Q&A. 2️⃣ Advanced RAG Adds query rewriting, re-ranking, metadata filtering & better chunking. → Better retrieval and production accuracy. 3️⃣ Agentic RAG An AI agent decides what, how, and when to retrieve. → Useful for AI assistants & research agents. 4️⃣ Graph RAG Uses knowledge graphs to retrieve and reason over relationships. → Great for complex knowledge systems & enterprise AI. 5️⃣ Multi-Modal RAG Works with images, PDFs, audio, video and text. → Useful for document AI & visual assistants. 6️⃣ Hybrid RAG Combines vector search, keyword search and other retrieval methods. → Improves search quality and accuracy. 7️⃣ Corrective RAG Adds a validation layer to check retrieved information and reduce hallucinations. → Useful for high-accuracy and critical-information systems. 💡 The key: Don’t choose a RAG architecture because it’s popular. Choose it based on your data, retrieval needs, accuracy requirements and application complexity. 🔖 Save this as your RAG architecture reference. Do Follow @AdarshChetan for more AI & developer content.frozen27d ago2.4K471530324.00%
How to Use NotebookLM in 2026 NotebookLM can help you turn your documents into an AI-powered research and learning workspace. Key Features 1. Add Sources — Import PDFs, Google Docs, audio transcripts, and webpages. 2. Source-Based Chat — Ask questions based on your uploaded sources with references. 3. Structured Study Guides — Generate summaries, timelines, reports, and briefs. 4. Visual Topic Mapping — Organize relationships between topics visually. 5. Audio Overviews — Convert notebook content into AI-generated audio discussions. 6. Video Summaries — Create short visual summaries from notebook content. 7. Deep Research — Explore additional credible sources related to your topic. 8. Structured Data Tables — Extract information into organized, sortable tables. 9. Collaborative Notebooka — Share notebooks with teams or clients. 10. Smart Prompts — Summarize, compare sources, identify decisions, generate briefs, and create scripts. Useful Prompts Summarize: Create a 500-word thematic summary of these sources with citations. Compare: “Show the key points across these documents and note where they conflict.” Identify Decisions: “Pull the main decisions from these notes and attach each one to its source.” Generate a Brief: “Organize this material into Background → Core Points → Recommendations.” Create a Script: “Write a two-host script where Host A explains and Host B tests weak claims.” Best approach: Upload quality sources → ask precise questions → verify the citations → turn the insights into action. 🔖 Save this for later & follow AdarshChetan on LinkedIn & X for more! ❤️frozen25d ago2.4K44940424.20%
RAG Concepts You Need to Know as a Developer in 2026 RAG (Retrieval-Augmented Generation) improves LLM responses by retrieving relevant information before generating an answer. Here are the key building blocks: 1️⃣ Chunking — Break large documents into smaller, meaningful pieces. 2️⃣ Embeddings — Convert text into vectors that capture semantic meaning. 3️⃣ Vector Search — Find semantically similar content using vector similarity. 4️⃣ Hybrid Search — Combine keyword search (BM25) with vector search for better retrieval. 5️⃣ Reranking — Reorder retrieved results using a stronger model to improve relevance. 6️⃣ Query Expansion — Expand the original query to improve recall. 7️⃣ Metadata Filtering — Filter results using metadata such as date, source, category, or access level. 8️⃣ Context Compression — Remove unnecessary information and keep only the most relevant context. 9️⃣ Graph RAG — Use knowledge graphs to capture relationships between entities. 🔟 Agentic RAG — Let an agent decide what, how, and when to retrieve information. 1️⃣1️⃣ Vectorless RAG — Use traditional keyword-based retrieval instead of vector search. 1️⃣2️⃣ Semantic Cache — Reuse similar previous queries and answers to reduce latency and cost. 1️⃣3️⃣ Grounding — Ensure the generated answer is based on retrieved context. 1️⃣4️⃣ RAG Evaluation — Measure relevance, faithfulness, context usage, and answer quality. 1️⃣5️⃣ RAG Pipeline — Connect the complete flow: Ingest → Index → Retrieve → Augment → Generate Understanding these concepts helps you design RAG systems that are more relevant, efficient, and reliable. 🔖 Save this for your AI engineering journey. Do follow for more such amazing stufffrozen9d ago2.3K551240344.56%
Want to build smarter AI applications with LLMs? Start with LangChain. Here’s your cheat sheet to understand the LangChain ecosystem👇 What you need to know: 1. Models I/O — Connect with LLMs and chat models 2. Prompts — Create reusable prompt templates 3. Indexes — Organize data for efficient retrieval 4. Memory — Maintain context across conversations 5. Chains — Combine multiple steps into workflows 6. Agents — Let LLMs reason and use tools 7. Integrations — Connect vector databases, APIs & external services 8. Best Practices — Design clear prompts, handle errors, and evaluate outputs ⚡ Also learn about: • LangChain architecture • ReAct agent loops • Vector stores like Pinecone, Weaviate & Chroma • Common use cases like RAG, document Q&A, and AI automation The big picture: Data → Retrieval → LLM → Tools → Intelligent Applications Save this cheat sheet for your AI engineering journey. 🔖 Follow @AdarshChetan for more developer resources! 🚀frozen14d ago2.3K591850435.45%
How to Build AI Agents Using LangChain 7 essential steps to build AI agents 👇 1️⃣ Define the Goal — Specify the task and expected outcome. 2️⃣ Choose the LLM — Select a model based on cost, speed, and quality. 3️⃣ Add Memory — Retain conversation history and relevant context. 4️⃣ Connect Tools — Integrate APIs, databases, search, and custom functions. 5️⃣ Create the Agent — Use LangChain components to orchestrate reasoning and actions. 6️⃣ Add Prompts — Design system prompts, user prompts, and few-shot examples. 7️⃣ Test & Iterate — Evaluate results, debug errors, and optimize performance. Agent Architecture: User Input → LLM Reasoning → Action → Observation → Final Output 🔥 Best Practices: Keep prompts clear and specific. Provide high-quality context. Limit tool access appropriately. Handle errors with fallbacks. Log intermediate steps. Test and improve continuously. Key takeaway: Start small, connect the right tools, and iterate to build reliable AI agents with LangChain. 🚀frozen11d ago2.3K651270626.45%
Learning LangChain in 2026? Don’t just learn the API understand these core concepts. LangChain helps developers build AI applications by connecting models, prompts, tools, memory, and retrieval into practical workflows. Here are 9 LangChain concepts you should know 👇 1️⃣ Models — Work with different LLM providers through a unified interface. 2️⃣ Prompts — Create reusable templates and dynamically insert variables and context. 3️⃣ Chains — Combine multiple components into a sequence to solve complex tasks. 4️⃣ Memory — Store and recall previous interactions across conversations. 5️⃣ Tools — Give LLMs access to APIs, functions, databases, search, and custom capabilities. 6️⃣ Agents — Let AI decide what actions to take and which tools to use. 7️⃣ Document Loaders — Load data from PDFs, CSVs, websites, databases, and other sources. 8️⃣ Text Splitters — Break large documents into smaller chunks for better processing and retrieval. 9️⃣ Retrievers — Find relevant documents or context from vector stores to support RAG applications. 💡 The bigger picture: Models + Prompts + Chains + Memory + Tools + Agents + Retrieval = Powerful AI Applications If you’re building with LangChain, these concepts are a great foundation to understand before diving into more advanced workflows. 🔖 Save this cheatsheet for later. Do Follow @AdarshChetan for more such amazing stuff ❤️frozen23d ago2.2K651130465.59%
100+ AI Tools to replace your tedious work: 1. Research - ChatGPT - YouChat - Abacus - Perplexity - Copilot - Gemini 2. Image - Higgsfield AI Soul - GPT-4o - Midjourney - Grok 3. Productivity - Gamma - Grok 3 - Perplexity AI - Gemini 2.5 Flash 4. Writing - Jasper - Jenny AI - Textblaze - Quillbot 5. Video - Klap - Kling - InVideo - HeyGen - Runway 6. Meeting - Tldv - Otter - Noty AI - Fireflies 7. SEO - VidIQ - Seona AI - BlogSEO - Keywrds ai - Outrank AI 8. Presentation - Decktopus - Slides AI - Gamma AI - Designs AI - Beautiful AI 9. Design - Canva - Flair AI - Designify - Clipdrop - Autodraw - Magician design 10. Audio - Lovo ai - Eleven labs - Songburst AI - Adobe Podcast 11. Marketing - Pencil - Ai-Ads - AdCopy - Simplified - AdCreative 12. Startup - Tome - Ideas AI - Namelix - Pitchgrade - Validator AI 13. Social media management - Tapilo - Typefully - Hypefury - TweetHunter Follow @AdarshChetan for more such amazing stuff ♥️frozen8d ago2.2K31581253.18%
Most developers know how to call an LLM API. Fewer know how to turn it into a complete AI application. This LangChain roadmap bridges that gap. https://t.co/aGoGqIl36L2d ago2.2K732063295.97%
20 AI Concepts Every Developer Should Understand in 2026 🧠 AI is much more than just LLMs and prompt engineering. If you’re building AI applications, these concepts form a useful foundation: 1. Neural Networks — Learn patterns through interconnected layers of nodes. 2. Transformers — The architecture behind many modern AI and language models. 3. Attention — Helps models focus on the most relevant parts of the input. 4. LLMs — Generate text by predicting the next token from an input sequence. 5. Tokenization — Converts text into smaller units called tokens. 6. Embeddings — Represent words or other data as numerical vectors. 7. Fine-tuning — Adapts a pretrained model to a specific task or dataset. 8. RAG — Retrieves relevant external context before generating an answer. 9. Prompt Engineering — Designs inputs that guide models toward better outputs. 10. Chain of Thought — A reasoning approach for breaking complex problems into steps. 11. AI Agents — Systems that use tools and iterative actions to complete tasks. 12. Vector Databases — Store and search high-dimensional embeddings. 13. Hallucinations — When a model produces incorrect information confidently. 14. Temperature — Controls randomness in model output generation. 15. Context Window — The maximum amount of information a model can process at once. 16. RLHF — Uses human feedback to help align model behavior. 17. LoRA — Enables lightweight fine-tuning using small adapter layers. 18. Diffusion Models — Generate content by progressively removing noise. 19. Transfer Learning — Reuses knowledge learned for one task on another task. 20. Quantization — Reduces model precision to make models smaller and more efficient. The goal isn’t to memorize these terms. Understand how they fit together—and you’ll have a much stronger foundation for building AI systems. 🔖 Save this as a quick AI reference guide. 
Follow @AdarshChetan for more AI & developer content.frozen24d ago2.2K41930313.84%
Postgres for AI agents just got a serious upgrade. 🐘 1,000 databases. Just 659 MB RAM. Clone a 674 MB database in 85 ms. Built in Rust. Open source. Free. The future of AI agents needs infrastructure like this. 🔥 ❤️2d ago2.2K816150.96%
20 LangGraph Concepts Every AI Engineer Should Know 1LangGraph Overview: Build stateful AI agent workflows. 2Graphs, Nodes & Edges: Structure tasks and control execution flow. 3State Management: Store and share data between nodes. 4Edges & Flow Control: Define how workflows move between steps. 5Conditional Routing: Choose the next step based on conditions. 6Tool Calling: Connect agents with APIs, databases, and tools. 7Memory: Retain conversation history and user preferences. 8Multi-Agent Workflows: Coordinate specialized agents to solve complex tasks. 9RAG with LangGraph: Combine document retrieval with LLM responses. 10Human-in-the-Loop (HITL): Pause workflows for human review or approval. 11Checkpointing & Persistence: Save state and resume interrupted workflows. 12Streaming & Async Execution: Stream responses and run tasks asynchronously. 13Error Handling & Retries: Recover from failures and retry operations. 14Observability with LangSmith: Trace, debug, and monitor agent workflows. 15Subgraphs: Break complex workflows into reusable components. 16LangChain Integration: Use LangChain tools, retrievers, and prompts. 17Custom Nodes & Advanced Patterns: Build specialized logic, parallel tasks, and loops. 18Evaluation & Testing: Measure workflow accuracy, quality, and performance. 19Real-World Projects: Apply LangGraph to RAG assistants and multi-agent systems. 20Best Practices: Build modular, reliable, secure, and maintainable workflows. 🚀 Learning tip: Master graphs and state first, then explore tools, memory, RAG, and multi-agent systems. Do Follow for more such amazing stuff ❤️frozen3d ago2.2K5516100385.48%
12 Amazing NotebookLM Tricks You Should Know in 2026 1️⃣ Upload PDFs— Import notes, books & slides instantly. 2️⃣ Ask Questions— Get answers grounded in your sources. 3️⃣ Instant Summaries — Condense chapters, articles & reports. 4️⃣ Podcast Mode — Turn notes into engaging audio discussions. 5️⃣ Study Guides— Create revision notes and key points. 6️⃣ Timeline View — Visualize events and concepts in sequence. 7️⃣ Mind Maps— Organize complex ideas visually. 8️⃣ Explain Simply— Break difficult topics into easy steps. 9️⃣ Compare Sources — Find similarities and differences. 🔟 Quiz Yourself — Test your understanding with practice quizzes. 1️⃣1️⃣ Cite Sources— Quickly verify and reference information. 1️⃣2️⃣ Brainstorm Ideas — Generate ideas for projects and research. 📚 Study smarter. Learn faster. Work better with AI. 🔖 Save this for later & follow Do AdarshChetan on LinkedIn & X for more! ❤️frozen14d ago2.1K37730333.73%
If you work a 9-5 & don't have a side income that makes at least $2,000/month. I got you. Here's exactly how you can set one up in the next 7 days:frozen15d ago2.1K9241151.48%
AI is moving beyond chatbots toward autonomous systems that can manage workflows, use tools, and execute tasks in the background. 🔥frozen21d ago2.1K810030.58%
What impressed me most is that it doesn’t follow a fixed sequence. It checks the machines and decides what to do next.frozen21d ago2.0K702000.46%
🚨 GMI Cloud just raised $668M to expand its AI infrastructure. → $223M equity + $445M credit facility
→ NVIDIA participated in the equity round
→ $600M+ annualized revenue under contract
→ 1T AI inference tokens/day
→ 20x inference growth in 6 months
→ Expanding across the U.S. & Asia Customers include OpenRouter, Fireworks AI & Higgsfield. AI infrastructure is scaling FAST. ⚡frozen22d ago1.9K1111010.72%
100 Claude Code Commands That Can Transform Your Coding Workflow Claude Code is more than an AI coding assistant. It can help you manage projects, understand codebases, debug issues, review changes, and automate development tasks. Here are the key command categories every developer should explore 👇 🔹 Project Setup: /init, /memory, /add-dir 🔹 Context Management: /context, /compact, /clear, /recap 🔹 Parallel Execution: /background, /tasks, /subtask, /fork 🔹 Session Management: /resume, /rename, /rewind, /export 🔹 Code Quality: /diff, /code-review, /simplify, /security-review 🔹 Model Control: /model, /effort, /fast 🔹 Planning & Automation: /plan, /goal, /loop, /schedule 🔹 Integrations: /mcp, /plugin, /skills, /ide 🔹 Usage Monitoring: /usage, /status, /insights 💡 Why do these matter? ✅ Spend less time managing coding sessions. 
✅ Keep context organized in long-running tasks. 
✅ Review code and identify potential issues. 
✅ Delegate work and manage background tasks. 
✅ Build a more efficient AI-powered development workflow. Pro tip: Start with /init, /context, /compact, /review, and /model only where supported by your installed version. Check the official documentation for the exact commands available to your setup. You don’t need to memorize all 100 commands. Learn the ones that solve your everyday problems first. 📌 Save this cheat sheet for later! #ClaudeCode #AI #SoftwareEngineering #Coding2d ago1.7K27440223.41%
100+ AI Tools to Finish Hours of Work in Seconds 1. Ideas 1. Bing Chat — Get ideas and search 2. YOU — AI search engine 3. Perplexity — Research and insights 4. ChatGPT — Brainstorm ideas 5. Claude — Deep research and ideas 2. Presentation 1. Prezi — Create presentations 2. Pitch — Team presentations 3. https://t.co/v1Aid7ns94 — AI slides generator 4. Slides AI — Convert text to slides 5. Slidebean — Professional slides 3. Website 1. Dora — Build websites with AI 2. Durable — AI website builder 3. Wegic — Generate websites 4. Framer — Design and publish sites 5. 10Web — AI website builder 4. Writing 1. Rytr — AI content writer 2. Jasper — Marketing content 3. https://t.co/WJuOtVmCkB — Copywriting and blogs 4. Text Blaze — Text expansion 5. Sudowrite — Fiction and creative writing 6. Writesonic — SEO-friendly content 5. AI Model 1. https://t.co/OC2gMHGPUi — Create AI images 2. Glambase App — AI fashion models 3. APOB — AI photos and portraits 4. Deepmode — AI models platform 5. AI Hentai — Anime image generator 6. Meeting 1. Tldv — AI meeting notes 2. Krisp — Noise cancellation 3. Otter — Meeting transcription 4. Avoma — Meeting assistant 5. Fireflies — AI meeting notes 7. Chatbot 1. Poe — Access multiple AI models 2. Claude — Anthropic AI chatbot 3. Gemini — Google AI chatbot 4. ChatGPT — OpenAI chatbot 5. HuggingChat — Open-source chatbot 8. Automation 1. Phrasee — AI for marketing text 2. Outreach — Sales automation 3. ClickUp — Task and project automation 4. Dify — Build AI applications 5. Emplifi — Social media automation 9. UI/UX 1. Galileo AI — UI design with AI 2. Kittl — Graphic design platform 3. Uizard — Turn sketches into UI 4. Visily — UI design from text 5. VisualEyes — AI for product design 10. Image 1. Dzine — AI image generation 2. Freepik — Stock images and AI tools 3. Phygital+ — AI product photos 4. https://t.co/PNUvLL4fn1 — Logo and image creation 5. Bing Create — AI image generator 11. Design 1. Looka — AI logo design 2. Clipdrop — Image editing tools 3. Autodraw — AI drawing tool 4. Vance AI — Photo enhancement 5. Designs AI — All-in-one design suite 12. Video 1. Pictory — Turn text into videos 2. HeyGen — AI talking avatars 3. https://t.co/R5IYEpMzYa — AI video generation 4. Deeohere — AI video creation 5. Synthesia — AI avatars and videos 13. Blog Writer 1. Katteb — AI blog writer 2. Reword — Research-backed writing 3. Elephas — AI writing assistant 4. Junia AI — SEO content writer 5. Journalist AI — Automated blog posts 14. Marketing 1. AdCopy — AI ad copy generator 2. Predis AI — Social media content 3. Bowler AI — AI email marketing 4. Hadden AI — Marketing campaigns 5. AdCreative — AI ad creatives 15. Twitter 1. Metricool — Social media analytics 2. Postwise — Grow on Twitter/X 3. Tribescaler — Twitter growth tool 4. TweetHunter — AI tweet assistant 5. Typefully — Write and schedule tweets 16. Code Generator 1. Codium — Code analysis and tests 2. Codex — AI coding assistant 3. VOXscript — AI for scripts 4. Replit — Online coding platform 5. Refraction — AI code completion 17. Speech to Text 1. FluentlyAI — Audio to text 2. CockatooAI — Real-time transcription 3. Vocly — Voice transcription 4. Whisper — OpenAI speech recognition 5. SpeechPulse — Audio to text 18. AI Detector 1. GPTZero — Detect AI content 2. Winston AI — AI content detector 3. Copyleaks — Plagiarism and AI detection 4. GPType — AI text detector 5. https://t.co/wofFuA5SuR — AI content analysis 19. Voice 1. Udio — AI music generation 2. Aiva — AI music composer 3. ElevenLabs — Realistic AI voice 4. Resemble AI — AI voice cloning 5. https://t.co/TT4qWqKRoQ — Text to speech 20. Web3 1. Alva — Web3 AI assistant 2. Alethea — AI for Web3 assets 3. Aled AI — AI gaming and NFTs 4. Layerai — Web3 infrastructure 5. MindStudio — No-code AI agents Do follow @AdarsChetan on LinkedIn and X for more such amazing stuff!1d ago1.6K19115062.61%
I asked Claude to improve my LinkedIn profile. It turned it into a recruiter magnet. 🚀 Here are 8 powerful prompts to optimize your LinkedIn profile and stand out to recruiters. Save this thread. 👇 https://t.co/8eQOoW8N0ffrozen7d ago1.3K926161.90%
This Supercomputer Builds Your Company With AI Always-On Computer Build Complex Software Use Fable 5.1, Astra, and Opus Create 3D games, mobile apps, or iPhone apps FREE BACKEND including database, payment, and auth5h ago9451193102.54%
20 LangGraph Concepts You Need to Know in 2026 Want to build reliable AI agents? Start with these essential concepts 👇 1️⃣ LangGraph Basics — Build stateful AI workflows. 2️⃣ Graphs & Nodes — Define tasks and connect workflow steps. 3️⃣ State Management — Track messages, inputs, and intermediate results. 4️⃣ Edges & Flow Control — Control how execution moves between nodes. 5️⃣ Conditional Routing — Route queries based on intent or conditions. 6️⃣ Tool Calling — Connect agents with APIs, databases, and custom tools. 7️⃣ Memory — Retain conversation context and user preferences. 8️⃣ Multi-Agent Workflows — Coordinate specialized agents. 9️⃣ RAG with LangGraph — Retrieve relevant context before generating answers. 🔟 Human-in-the-Loop — Add human review and approval. 1️⃣1️⃣ Checkpointing & Persistence — Save state and resume after failures. 1️⃣2️⃣ Streaming & Async Execution — Stream responses and run long tasks efficiently. 1️⃣3️⃣ Error Handling & Retries — Recover from failures with retry logic. 1️⃣4️⃣ Observability with LangSmith — Trace, debug, and monitor workflows. 1️⃣5️⃣ Subgraphs — Build reusable, modular workflows. 1️⃣6️⃣ LangChain Integration — Combine LangGraph with LangChain tools, retrievers, and prompts. 1️⃣7️⃣ Custom Nodes & Advanced Patterns — Implement custom logic, parallel execution, and advanced workflows. 1️⃣8️⃣ Evaluation & Testing — Measure accuracy, relevance, and tool performance. 1️⃣9️⃣ Real-World Projects — Combine RAG, tools, memory, multi-agent systems, and human approval. 2️⃣0️⃣ Best Practices — Design modular graphs, manage state, handle errors, and keep workflows scalable. The big picture: LangGraph helps you build AI systems that can reason, use tools, manage state, collaborate, and recover from failures. 🔖 Save this cheat sheet for your AI engineering journey. Follow @AdarshChetan on LinkedIn & X for more developer resources! 🚀9h ago92715420143.78%
4 documents to put on your OCR test list: 1. A3 exam papers 2. Handwritten notes 3. Tables with complex structure 4. Pages mixing formulas and chemical structures OXR supports all four, leads olmOCR-Bench, OmniDocBench & DocParsingBench, and no open-source parser in the speed test ran faster (3.05× GLM-OCR's throughput on one RTX PRO 6000). Output includes Markdown/JSON and region coordinates; layout analysis is replaceable. Save the repo for your next document project. 👇 https://t.co/BQnwogWSP16h ago499200000.40%