Manthan Patel

LLMs are AI models, but not all AI models are LLMs.

Building upon traditional approaches, these eight specialized models advances AI's ability to understand, reason, and generate across different domains and modalities.

Here's architectures of these 8 state-of-the-art models:

1️⃣ LLMs (Large Language Models)
These foundational models process text token-by-token, enabling everything from creative writing to complex reasoning.

2️⃣ LCMs (Large Concept Models)
Meta's newer approach encodes entire sentences as “concepts“ in SONAR embedding space, transcending word-level processing.

3️⃣ VLMs (Vision-Language Models)
These multimodal combine visual and textual understanding to interpret images and generate text about them.

4️⃣ SLMs (Small Language Models)
Compact yet powerful models optimized for edge devices with tight energy and latency constraints.

5️⃣ MoE (Mixture of Experts)
These models activate only relevant expert networks per query, dramatically improving efficiency while maintaining performance.

6️⃣ MLMs (Masked Language Models)
The OG bidirectional models that look at both left and right context to understand meaning in text.

7️⃣ LAMs (Large Action Models)
Emerging models that bridge understanding with action, executing tasks through system-level operations.

8️⃣ SAMs (Segment Anything Models)
Foundation models for universal visual segmentation with pixel-level precision.

Here's how these specialized architectures differ from traditional approaches:

Traditional AI:
- One model architecture applied to many tasks
- Often excels in one area but underperforms in others
- Requires significant compute and data for general capabilities

Specialized Architectures:
- Purpose-built for specific modalities and tasks
- Optimized for particular constraints (speed, size, precision)
- Open up new capabilities like concept-level understanding, visual segmentation, and action execution

Understanding these distinctions is essential for selecting the appropriate model architecture for specific applications, making more effective and contextually appropriate AI interactions.

These specialized models aren't alternative approaches; they're redefining technologies.

✅ Process information in ways that match specific tasks and domains
✅ Optimize for different constraints like size, speed, accuracy, and multimodality
✅ Generate more reliable, contextual, and useful outputs for targeted applications

Matching the right architecture to the right task is essential. It saves time, boosts productivity, and creates a more natural flow in AI-human interactions.

Over to you: What specialized AI architecture do you think would benefit your work the most?

Everyone's building AI agents, but few understand the Agentic frameworks that power them.

These two distinct frameworks are the most used frameworks in 2025, and they aren't competitors but complementary approaches to agent development:

𝗻𝟴𝗻 (𝗩𝗶𝘀𝘂𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻)
- Creates visual connections between AI agents and business tools
- Flow: Trigger → AI Agent → Tools/APIs → Action
- Solves integration complexity and enables rapid deployment
- Think of it as the visual orchestrator connecting AI to your entire tech stack

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 (𝗚𝗿𝗮𝗽𝗵-𝗯𝗮𝘀𝗲𝗱 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) by LangChain
- Enables stateful, cyclical agent workflows with precise control
- Flow: State → Agents → Conditional Logic → State (cycles)
- Solves complex reasoning and multi-step agent coordination
- Think of it as the brain that manages sophisticated agent decision-making

Beyond technicality, each framework has its core strengths.

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗻𝟴𝗻:
- Integrating AI agents with existing business tools
- Building customer support automation
- Creating no-code AI workflows for teams
- Needing quick deployment with 700+ integrations

𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵:
- Building complex multi-agent reasoning systems
- Creating enterprise-grade AI applications
- Developing agents with cyclical workflows
- Needing fine-grained state management

Both frameworks are gaining significant traction:

𝗻𝟴𝗻 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Visual workflow builder for non-developers
- Self-hostable open-source option
- Strong business automation community

𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺:
- Full LangChain ecosystem integration
- LangSmith observability and debugging
- Advanced state persistence capabilities

Top AI solutions integrate both n8n and LangGraph to maximize their potential.
- Use n8n for visual orchestration and business tool integration
- Use LangGraph for complex agent logic and state management
- Think in layers: business automation AND sophisticated reasoning

Over to you: What AI agent use case would you build - one that needs visual simplicity (n8n) or complex orchestration (LangGraph)?

I've compiled 10,000+ Make.com templates that I use for automating my clients' businesses.

I'm giving away all these templates that get clients paying me $5K/month, for FREE.

It has hundreds of automation templates, segmented by 15+ categories:

👉 AI Tools
👉 Sales & CRM
👉 Marketing & Lead Gen
👉 Surveys and Document
👉 IT Systems
👉 Business Operations
👉 Website Building

And honestly, I wouldn't even call this a template pack.

It's literally every automation I've built over 2 years of running a 6-figure agency.

⚠️ 10,000+ Make Automation- https://tally.so/r/31GQXp

Already 150+ agency owners who scaled to 6-figures are using these exact templates.

2025 is the Year of AI Agents, not just standalone LLMs.
 
Anthropic has been using this new approach called Multi-Component AI Agents with Feedback Loops.
 
AI Agents go beyond basic LLMs with structured parts that work together, letting them solve problems on their own and get better with practice.
 
Here's how AI Agents work:
1️⃣ Perception Layer
Agents take in information through special modules that understand context and track what's happening, helping them see the full picture.
 
2️⃣ Cognitive Core
The thinking and planning parts work together, mixing logical reasoning with goal-setting to make smart choices.
 
3️⃣ Execution Framework
A dedicated action layer picks the best moves and uses outside tools, while checking how well things are working.
 
4️⃣ Learning Loop System
Key feedback paths connect what happened to memory storage, creating a cycle that makes the agent better over time.
 
5️⃣ Multi-Tool Integration
Special outside tools like Web, Code, and API access let an agent do more than what's built in.
 
Whether you're handling complex workflows or tackling multi-step problems, AI Agents deliver better results through their connected design, giving you more reliable performance and flexible responses.
 
Here's how AI Agents differ from traditional LLMs:
 
LLMs:
Work as single units focused mainly on generating text
Process inputs and create outputs without structured decision paths
Don't have clear ways to learn from their results
 
AI Agents:
Function as multi-part systems with specialized modules for different thinking tasks
Include clear feedback paths linking results back to reasoning
Use outside tools through purpose-built connection points
 
Understanding these distinctions helps when building systems that can handle complex tasks with less human input.
 
AI Agents aren't just different; they're more advanced systems:
 
✅ Process information through purpose-built thinking
✅ Learn constantly from their results
✅ Change strategies based on what worked before
 
The feedback loop design matters. It turns one-time interactions into ongoing learning relationships, creating systems that actually get better with time.
 
Over to you: What tasks do you think would benefit the most for AI Agents?

AI Agent Architecture

The diagram below illustrates the core architecture of AI agents.

Step 1: Perception
The agent processes inputs from its environment through multiple channels. It handles language through NLP, visual data through computer vision, and contextual information to build situational awareness. Modern systems incorporate audio processing, sensor data, and state tracking to maintain a complete picture of their surroundings.

Step 2: Reasoning
At its core, the agent uses logical inference systems paired with knowledge bases to understand and interpret information. This combines symbolic reasoning, neural processing, and Bayesian approaches to handle uncertainty. The reasoning engine applies deductive and inductive processes to form conclusions and even supports creative thinking for novel solutions.

Step 3: Planning
Strategic decision-making happens through goal setting, strategy formulation, and path optimization. The agent breaks complex objectives into manageable tasks, creates hierarchical plans, and continuously optimizes to find the most efficient approach. This includes sequential planning, tactical adjustments, and simulations to test potential outcomes.

Step 4: Execution
This layer mold plans into actions through intelligent selection, tool integration, and continuous monitoring. The agent leverages APIs, code execution, web access, and specialized tools to accomplish tasks. Advanced systems support parallel and distributed execution, with implementations extending to cloud infrastructure and edge computing.

Step 5: Learning
The adaptive intelligence component combines short-term memory for immediate tasks with long-term storage for persistent knowledge. This system incorporates feedback mechanisms, using supervised, unsupervised, and reinforcement learning to improve over time. Analytics, model management, and meta-learning capabilities enable continuous enhancement.

Step 6: Interaction
The communication layer handles all external exchanges through interfaces, integration points, and output systems. This spans text, voice, and visual communication channels, with specialized components for human-AI collaboration. The agent selects appropriate formats and delivery methods based on the context.

What makes AI agent different from automation and workflows is the feedback loops between components. When execution results feed into learning systems, which then enhance reasoning capabilities, the agent achieves truly adaptive intelligence that improves with experience.

In your view: Which component has the biggest gap between theory and practice?

Most people will satisfice AI take over in 2026.

A few will be the ones building it.

10 skills that separate the two:

1. Prompt Engineering
Stop getting generic AI outputs. Learn to write prompts that make AI reason, not just respond. This is the foundation everything else builds on.

2. AI Agents
Automate entire workflows end-to-end. Not just single tasks — full processes that run while you sleep.

3. Workflow Automation
Connect your apps. Kill repetitive work. One automation can save 10+ hours a week.

4. AI Coding Assistants
Ship code without being a developer. Cursor, Codex, Claude Code — pick one and start building.

5. AI App Builders
Launch MVPs in hours, not months. Tools like Emergent, Lovable, and Replit let you go from idea to product in a single afternoon.

6. RAG (Retrieval-Augmented Generation)
Make AI accurate with your own data. No more hallucinations. No more generic answers.

7. AEO/GEO
Show up when AI searches for answers. SEO is evolving. If you're not optimizing for AI search, you're invisible.

8. AI Tool Stacking
Stop using tools in isolation. Layer them into one system that multiplies your output.

9. AI Content Generation
Scale content without scaling headcount. One person can now do what used to take a team of five.

10. LLM Ops
Track cost, accuracy, and actual ROI. If you can't measure it, you can't improve it.

The barrier to building just disappeared.

You don't need a CS degree.
You don't need to raise funding.
You don't need permission.

You just need to start.

2026 rewards builders. Not watchers.

Over to you: Which skill are you learning first?

Pari Tomar and I mapped out the entire AI Engineering roadmap to learn AI in 2026.

It's not just about prompting ChatGPT.

It's 13 layers from math foundations to production systems:

1️⃣ Mathematical Foundations
Linear algebra, calculus, probability, and statistics form the bedrock. You can't understand how neural networks learn without knowing gradient descent or matrix multiplication.

2️⃣ Programming Foundations
Python is non-negotiable. Add NumPy, Pandas, and software engineering practices like clean code and testing. This is where most people skip ahead too quickly.

3️⃣ Data Engineering
Data sources, collection, storage, cleaning, and feature engineering. Bad data in, bad AI out. Most AI projects fail here before they even start.

4️⃣ Classical Machine Learning
Regression, classification, clustering, dimensionality reduction. Yes, you still need to know Random Forests and XGBoost in 2026.

5️⃣ Deep Learning
Neural network basics, CNNs, RNNs, and Transformers. This is where the architecture choices start to matter.

6️⃣ Natural Language Processing
Text processing, embeddings, NLP tasks, and the LLM concepts like pretraining, fine-tuning, and RLHF.

7️⃣ Generative AI
Autoregressive models, GANs, diffusion models, and multimodal systems. The stuff powering ChatGPT, Midjourney, and Sora.

8️⃣ Reinforcement Learning
Agents, environments, Q-learning, and policy gradients. This is how AI learns to make decisions through trial and error.

9️⃣ LLM Systems Engineering
Prompt engineering, RAG, tool use, and memory systems. Where theory meets practical LLM applications.

🔟 Agentic AI
Autonomous agents, planning, task decomposition, and multi-agent orchestration. The hottest topic in AI right now.

1️⃣1️⃣ MLOps
Experiment tracking, deployment, infrastructure, and monitoring. Because a model that only runs in Jupyter notebooks isn't production-ready.

1️⃣2️⃣ AI in Production
Latency, cost optimization, scaling, reliability, and observability. The unsexy work that separates demos from real products.

1️⃣3️⃣ AI Product Engineering
AI UX, human-AI interaction, feedback loops, and evaluation in the real world. Building AI that people actually want to use.

This is what separates hobbyists from AI Engineers.

AI Engineers understand the stack. They know when to use classical ML vs deep learning, when RAG is overkill, and why their model latency is 10x higher than expected.

Each layer builds on the previous one. Skipping layers creates blind spots that show up later in production.

Over to you: Which layer are you focusing on right now?

Comment "AI" and I'll send you the full roadmap.

Thanks to Pari Tomar for putting this together. Follow her for more amazing content.

Agentic Architectures are the hottest thing under the sun right now.

And you're still confused which Agentic Architecture to choose?

Simply put, Agentic Architectures are a 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝘄𝗮𝘆 𝗳𝗼𝗿 𝗟𝗟𝗠𝘀 𝘁𝗼 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 via a 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵.

What this means for 𝘺𝘰𝘶:
You can now build a 𝗳𝘂𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺 using multiple agents organized in patterns that fit your specific needs.

Agentic Architectures are organizational frameworks, allowing AI systems to efficiently distribute workloads, share knowledge, and combine specialized capabilities. No more one-size-fits-all approaches!

Let's understand first Single Agent System and Multi-Agent System. Each approach has distinct advantages:

Single-Agent System:
- 𝗦𝗶𝗺𝗽𝗹𝗲𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 with one AI agent connecting directly to tools & memory
- 𝗟𝗼𝘄𝗲𝗿 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 without inter-agent communication overhead
- 𝗘𝗮𝘀𝗶𝗲𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 with fewer components to integrate
- 𝗜𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 focused, domain-specific tasks with clear boundaries

Multi-Agent System:
- 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 across specialized AI agents
- 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 that can grow with complexity
- 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 for improved performance on complex tasks
- 𝗜𝗱𝗲𝗮𝗹 𝗳𝗼𝗿 cross-domain problems requiring multiple types of expertise

Multi-agent systems are more implementable as they allow for custom architectures, distributed workloads, tailored precisely to your problem's complexity.

Multi-Agent System Patterns
1️⃣ Parallel: Multiple agents process simultaneously for maximum speed and throughput.

2️⃣ Sequential: Agents work in sequence, each refining previous outputs for complex tasks.

3️⃣ Loop: Circular flow enables iterative improvement until desired quality is reached.

4️⃣ Router: One agent directs inputs to specialized paths based on content analysis.

5️⃣ Aggregator: Consolidates multiple inputs into comprehensive unified outputs.

6️⃣ Network: Interconnected agents share knowledge bidirectionally for complex reasoning.

7️⃣ Hierarchical: Manager-worker structure handles complexity through delegated subtasks.

Multi-agent systems win because you can mix-and-match patterns to solve exactly your problem.

Agentic Architecture examples:
1️⃣ Hierarchical: Parent-child agent delegation with clear authority flows

2️⃣ Human-in-the-loop: AI systems with human oversight at critical points

3️⃣ Shared tools: Multiple agents accessing common resources efficiently

4️⃣ Sequential: Agents working in chain order, each building on previous outputs

5️⃣ Database with tools: Centralized knowledge with specialized access methods

6️⃣ Memory transformation using tool: Raw data conversion into structured AI memory

Over to you: Which agentic architecture pattern you like?

Everyone's using Claude, but few understand how to actually structure prompts for it.

Most people write one-line prompts and wonder why the output feels off.

Anthropic actually published a 10-component framework for writing prompts that get consistent, high-quality responses.

Here's the exact structure they recommend:

1️⃣ Task Context Define WHO Claude is and WHAT it needs to do. Give it a role or persona upfront.

2️⃣ Tone Context Set the communication style. Friendly? Formal? Technical? This shapes every word in the response.

3️⃣ Context Data, Documents, and Images Feed Claude everything it needs to know. Background info, reference docs, relevant files.

4️⃣ Detailed Task Description and Rules Lay out specific rules for the interaction. What should it do? What should it avoid?

5️⃣ Examples Show Claude what good output looks like. 3-5 examples usually does the trick.

6️⃣ Conversation History Include prior interactions if they exist. Helps maintain continuity.

7️⃣ Immediate Task Description The actual request you want fulfilled right now.

8️⃣ Think Step by Step Explicitly tell Claude to reason through the problem before responding.

9️⃣ Output Format Specify exactly how you want the response structured. JSON? Bullet points? Prose?

🔟 Prefilled Response Start Claude's answer for it. Prevents the chatty preamble most people complain about.

Here's what most people get wrong:

❌ They dump everything into one paragraph
❌ They skip the examples
❌ They don't specify output format
❌ They forget to give Claude permission to say "I don't know"

And here's what actually works:

✅ Use XML tags to separate sections (Claude is trained to recognize them)
✅ Put your key question at the END for long prompts
✅ Match your prompt style to your desired output style
✅ Break complex tasks into smaller prompts (prompt chaining)

This framework works whether you're building AI agents, automating workflows, or just trying to get better responses from Claude in your daily work.

The difference between a mediocre prompt and a great one is structure, not cleverness.

Over to you: Which component do you usually skip when prompting Claude?

Your AI model is using its entire brain to answer "what's the weather today." That's the problem with dense LLMs.

⇒ Mixture of Experts fixes this by routing each query to only the relevant specialized sub-networks.
⇒ DeepSeek-V3 activates 5.5% of its parameters per token and still matches GPT-4 level performance.

Here are two key breakthroughs explaining this 👇

📌 Concept 1: "Dense LLMs Have a Scaling Problem"
Dense LLMs like GPT-3 and LLaMA are built so every parameter fires on every input. That's it.

Ask a dense 70B model to translate a sentence into French and it'll:
- Activate all 70 billion parameters
- Use the same compute as if you asked it to write complex code
- Burn through GPU memory regardless of task difficulty

It treats every question like it needs the full brain. No matter how simple the task, the entire model lights up.

The Dense Model Bottleneck
For real-world deployment at scale, this brute-force approach breaks down fast.

- Cost: Training LLaMA 3.1 405B took 30.8 million GPU hours
- Speed: Every token generation requires loading all parameters from memory
- Efficiency: You're paying for 100% of the model even when you only need 5%

But wait...

"If dense models waste so much compute, what's the alternative?"
This is where Mixture of Experts completely flips the architecture.
"Intelligence isn't about using everything you know. It's about knowing exactly which part to use."

🔗 https://lnkd.in/ezPYDJV9

📌 Concept 2: "Mixture of Experts (MoE)"
DeepSeek introduced their V3 model with 671 billion total parameters.
It's not built to activate everything. It's built to route intelligently.
It acts as a "Selective Specialist."

It receives your input and a router network decides: "Which 2 out of 256 experts should handle this specific token?" Then only those experts fire, while the rest stay dormant. The full model never activates at once.

Under the hood, it combines two things dense LLMs simply don't have:
- Sparse Activation: Instead of using all 671B parameters, DeepSeek-V3 activates only 37B per token. That's roughly 5.5% of the total model. So instead of brute-forcing every query, it routes each token to the most relevant specialized sub-networks.
- Gating Network (Router): A small neural network sits at each layer and scores every expert. It picks the top-k experts (usually 2) for each token, combines their outputs, and moves on. Different tokens can hit different experts at different layers, creating dynamic processing paths.

TLDR:
Scaling AI by making every model bigger and denser is hitting a wall. Dense LLMs use everything. MoE models use only what matters. The next generation of AI won't be the biggest, it'll be the most efficient.

🔗 https://lnkd.in/e7YhSv_s

AI agents without proper memory are just expensive chatbots repeating the same mistakes.

After building 50+ production agents, I discovered most developers only implement 1 out of 5 critical memory types.

Here's the complete memory architecture powering agents at Google, Microsoft, and top AI startups:

𝗦𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗠𝗲𝗺𝗼𝗿𝘆)
→ Maintains conversation context (last 5-10 turns)
→ Enables coherent multi-turn dialogues
→ Clears after session ends
→ Implementation: Rolling buffer/context window

𝗟𝗼𝗻𝗴-𝘁𝗲𝗿𝗺 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗦𝘁𝗼𝗿𝗮𝗴𝗲)
Unlike short-term memory, long-term memory persists across sessions and contains three specialized subsystems:

𝟭. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲)
→ Domain expertise and factual knowledge
→ Company policies, product catalogs
→ Doesn't change per user interaction
→ Implementation: Vector DB (Pinecone/Qdrant) + RAG

𝟮. 𝗘𝗽𝗶𝘀𝗼𝗱𝗶𝗰 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗟𝗼𝗴𝘀)
→ Specific past interactions and outcomes
→ "Last time user tried X, Y happened"
→ Enables learning from past actions
→ Implementation: Few-shot prompting + event logs

𝟯. 𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗮𝗹 𝗠𝗲𝗺𝗼𝗿𝘆 (𝗦𝗸𝗶𝗹𝗹 𝗦𝗲𝘁𝘀)
→ How to execute specific workflows
→ Learned task sequences and patterns
→ Improves with repetition
→ Implementation: Function definitions + prompt templates

When processing user input, intelligent agents don't query memories in isolation:
1️⃣ Short-term provides immediate context
2️⃣ Semantic supplies relevant domain knowledge
3️⃣ Episodic recalls similar past scenarios
4️⃣ Procedural suggests proven action sequences

This orchestrated approach enables agents to:
- Handle complex multi-step tasks autonomously
- Learn from failures without retraining
- Provide contextually aware responses
- Build relationships over time

LangChain, LangGraph, and AutoGen all provide memory abstractions, but most developers only scratch the surface.

The difference between a demo and production? Memory that actually remembers.

Over to you: Which memory type is your agent missing?

I built a web agent that tracks my competitors on Instagram automatically.
 
No code. No APIs. Just words.
 
I used Airtop AI to automate this - you describe what you want, and it builds the web agent for you.
 
1. 𝗜𝗻𝗽𝘂𝘁 → Give it an Instagram username, Google Sheet, and Slack channel
2. 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗲 → Agent opens a browser, logs into Instagram, goes to the profile
3. 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 → Pulls bio, follower count, posts, profile pic, external links
4. 𝗦𝗮𝘃𝗲 → Stores everything in Google Sheets
5. 𝗡𝗼𝘁𝗶𝗳𝘆 → Sends summary to Slack
 
The entire process runs hands-free.
 
𝗣𝗿𝗼 𝘁𝗶𝗽: Getting blocked by CAPTCHAs? Airtop has built-in residential proxies. Enable it, and it bypasses bot detection instantly. I couldn't log in until I turned this on - then it worked without solving a single CAPTCHA.
 
Use this for competitor analysis, influencer vetting, lead generation, or market research.
 
You can even scale it: "Find top 20 creators in [niche] and repeat for each profile." The agent finds them, scrapes them, and stores everything automatically.

Try it here: https://lnkd.in/dGqcc9Bd
 
Comment "AIRTOP" and I'll send you one month free access + template link 👽

Everyone's launching MCP servers for analytics. But they're not all built the same.
 
MCP (Model Context Protocol) is becoming the standard for connecting AI agents to business tools. BI platforms are racing to ship their own.
 
I compared 4 major vendors so you don't have to.
 
1️⃣ Microsoft Power BI MCP Server
 
26 tools for model management, bulk operations, and DAX queries.
 
Limitation: Primarily for Power BI modeling, not end-to-end analytics.
 
2️⃣ ThoughtSpot Agentic MCP Server
 
Strong natural language capabilities. Auto-generates dashboards and suggests next steps.
 
Limitation: Tightly coupled to ThoughtSpot. Less flexibility for embedded use cases.
 
3️⃣ Google Looker + Conversational Analytics API
 
Supports MCP through their API. Good for custom agents within Google Cloud.
 
Limitation: Requires significant dev effort. Not plug-and-play.
 
4️⃣ GoodData MCP Server
 
30+ tools covering the full analytics lifecycle. Alerts, metrics, visualizations, datasource scanning, LDM generation.
 
What sets it apart:
 
→ AI grounded in governed metrics, not raw data guesses
→ Multi-tenant ready with workspace isolation
→ Works with any MCP client (Claude, ChatGPT, Cursor)
→ White-label friendly for embedded analytics
→ Role-based permissions and audit logs built in

GoodData is leading with AI-first analytics. They're exposing governed analytics through MCP, meaning AI agents can create visualizations, set alerts, and query data using natural language. All while respecting existing permissions and security.

Most MCP servers connect AI to data. GoodData connects AI to business logic.
 
Your AI agent doesn't just see "rev_total_q4." It understands that's "Total Revenue for Q4, calculated as gross sales minus refunds, excluding internal transfers."
 
That's the difference between AI that hallucinates and AI you can trust.
 
Try it here: https://lnkd.in/d-2PFSS5
 
Over to you: What's the biggest challenge you've faced connecting AI to your analytics stack?

I build AI agents for a living.
 
And I was still typing every single email by hand.
 
Think about that for a second.
 
I've automated lead gen pipelines, outreach sequences, CRM workflows, client onboarding... but the moment I needed to reply to an email or research a prospect before a call, I was back to typing like it's 2015.
 
The average knowledge worker spends 3 hours a day just typing. And switches between 1,100 tabs.
 
I was that person.
 
Then I found Lemon AI.
 
It's a voice AI agent for Mac. You press the Fn key, talk, and it executes whatever you said. Right where you are. No new tabs. No copy-paste.
 
Here's what changed in my daily workflow:
 
1️⃣ Emails that took 5 min now take 8 seconds I open Gmail, press Fn, and say "reply to this, tell them I'm interested but need pricing by Friday." Full email drafted. Tone matched. Done.
 
2️⃣ Prospect research before sales calls without opening Google I press Fn and say "research this company, find key decision makers and current news." Full brief appears on my screen. Zero tab switches.
 
3️⃣ Calendar, reminders, and file search with just my voice "Set a reminder to collect mails at 1 PM." Done. No clicking through 4 menus.
 
I've tested hundreds of AI tools over the past 2 years.
 
Most of them add steps to your workflow. This one actually removes them.
 
We're all so busy building AI systems for our businesses that we forget the biggest time sink is right in front of us: we're still manually typing and tabbing through everything ourselves.
 
It’s time to reclaim your time and focus.

Try it here: https://heylemon.ai/
 
Over to you: What's the one task you still do manually that AI should've replaced by now?