By 2026, quantum computing moved from theoretical promise to practical reality. Quantum advantage became real in specific applications that classical systems couldn't solve.

What changed?

The problem with early quantum computing was its fragility. Quantum states collapsed easily. Decoherence destroyed calculations. It was science at best—promise at worst.

By 2026, the technology matured dramatically. Error correction breakthroughs stabilized qubits. Hybrid quantum-classical approaches became standard. Real businesses integrated quantum processors.

The milestone: Quantum Advantage Became Real

Researchers demonstrated quantum computers outperforming classical systems in specific tasks. This isn't theoretical anymore—it's measurable. Companies running quantum advantage applications report:

• 5x speedup in optimization problems
• 10x improvement in material simulations
• Complete breakthroughs in drug discovery timelines

Three breakthrough areas:

1. Materials Science - Quantum simulation models molecules and materials at atomic scale. Drug development timelines compressed from years to months.

2. Logistics & Manufacturing - Quantum optimization solves routing and scheduling problems classical computers couldn't handle. Supply chains optimized. Factories reconfigured.

3. Finance - Quantum algorithms process risk models and pricing calculations faster. Portfolio optimization improved. Algorithmic trading enhanced.

The timeline: Quantum-Safe Cryptography Gains Urgency

Q-Day is near. When quantum computers become powerful enough to break today's encryption. Nearly all digital communications are at risk.

The response is already underway:

• Post-quantum cryptography standards adopted
• Hybrid encryption schemes deployed
• Financial institutions lead migration
• Government agencies accelerate protection

The technology landscape evolved.

Quantinuum's H2 quantum processor combined with Microsoft's software stack created a hybrid quantum-classical approach. Major vendors entered the race globally. American, Chinese, and European tech leaders pushing boundaries.

The business adoption is accelerating.

Enterprises aren't waiting for perfection. They're integrating quantum where it matters most. Pharma companies testing quantum for drug discovery. Banks testing quantum for risk modeling. Logistics firms testing quantum for optimization.

The talent challenge is real.

Organizations lack quantum expertise. Training programs emerging. Certifications gaining credibility. The skills gap mirrors zero trust earlier.

The standards debate matters.

IEEE and other bodies addressing quantum computing standards. Without standards, interoperability suffers. Without frameworks, investment hesitates.

2026 is the turning point.

Quantum advantage is real. Quantum security is urgent. Quantum adoption is accelerating. The lab has moved to the production floor.

The infrastructure is maturing.

Cloud platforms provide quantum access. Software stacks abstract the hardware. Developers write quantum code without quantum physicists.

The future applications expanding.

• AI integration quantum machine learning
• Climate modeling quantum simulation
• Financial fraud quantum detection
• Supply chain quantum optimization

2026 proves quantum computing is no longer experimental. It's the new frontier. Organizations embracing quantum will have a competitive advantage. The question isn't whether quantum works—it's how to use it.

The bottom line: quantum computing achieved real advantage in 2026. The race is on globally. The future belongs to those who adapt.

Quantum advantage is real. The future is quantum.

This isn't about AI writing more code. It's about AI understanding the entire context.

What is repository intelligence?

It's the emerging paradigm where AI understands not just lines of code, but the relationships, dependencies, and historical evolution of codebases. GitHub's repository intelligence leverages millions of repositories to teach models about how code actually works in practice.

Why the shift?

The problem with previous AI tools was they treated code as isolated lines. Repository intelligence treats code as a living system—understanding how a function interacts across modules, how historical commits inform current decisions, and how code patterns evolve over time.

Three key capabilities:

1. Contextual Understanding - AI that knows your codebase's unique patterns, naming conventions, and architectural decisions.

2. Change Prediction - Predicting how modifications to one module affect the entire system.

3. Historical Learning - Learning from past mistakes and successful implementations across your organization's repositories.

GitHub's repository intelligence specifically:

The sheer volume matters. With billions of repositories, the data enables models to learn from real-world code patterns rather than synthetic training data.

The result is AI that doesn't just write code—it writes code that works.

The impact is already visible.

Companies using repository intelligence report:

• 40% reduction in onboarding time for new developers
• 30% improvement in code quality metrics
• 25% faster feature delivery

But the benefits go beyond productivity. The deeper insight comes from understanding the 'why' behind code decisions. Repository intelligence can explain historical decisions, predict future risks, and suggest architectural improvements.

The challenge is data quality.

The model only works if you feed it real, clean data. Organizations need to provide consistent code documentation and maintain well-structured repositories.

The technology is maturing.

In early 2026, Opus 4.5 and Sonnet 4.5 emerged as top performers for coding work. Anthropic's shift toward developer-focused models reflects industry consensus: developers need tools that work.

The future is already here.

Repository intelligence transforms AI from a code generator to a code architect. The question isn't whether AI can write better code—it's whether we can use it to write better systems.

The bottom line: repository intelligence is no longer experimental. It's the new standard. Organizations integrating this technology will have a competitive advantage in software quality, delivery speed, and developer experience.

The old model of security was built on two assumptions that no longer hold: trust the inside, distrust the outside. In 2026, that's dead.

Enter Zero Trust.

What was once theoretical is now practical. Zero Trust isn't just about adding more layers. It's about a complete reversal of assumptions. Never trust, always verify. Every access request is evaluated based on policy regardless of source.

Why the shift? Three realities:

1. Remote work is permanent. Employees access data from anywhere.
2. Cloud environments have expanded beyond IT's control.
3. Supply chains are interconnected. A compromise in a third-party can breach your internal systems.

The architecture requires continuous verification. Identity is the primary factor. Devices must be authenticated. Data access must be limited. The principle of least privilege applies everywhere.

This means breaking down the perimeter concept. There is no internal zone that's safe. Every access point is a potential threat. Every user is a potential threat.

The implementation involves multiple components. Multi-factor authentication becomes ubiquitous. Continuous monitoring tracks behavioral patterns. Micro-segmentation limits lateral movement. Zero Trust Network Access extends to all devices.

But the real story isn't just about technology. It's about culture. Zero Trust requires that every actor understands their role. Every action matters. Every assumption can be wrong.

Organizations that have adopted Zero Trust see measurable improvements. Fewer breaches when security incidents occur. Faster detection and response. Better compliance and audit outcomes. Reduced attack surface from the start.

The challenge is adoption. Legacy systems need modernization. New tools must be integrated. Training is essential. Culture must shift. It's a marathon, not a sprint.

The bottom line: perimeter security has failed. Zero Trust is the new standard. Organizations that adopt it early will have a competitive advantage. Organizations that wait may find themselves defending against attacks that bypass traditional controls.

The technology is ready. The time is now.

The AI hype cycle of 2025 left many in the "trough of disillusionment." Generative AI tools became ubiquitous, yet few saw the transformative spark. Enter 2026, where the real revolution begins: agentic AI.

Beyond Chatbots

The fundamental shift isn't about better text generation—it's about action. While generative AI chatbots could draft emails and summarize documents, AI agents can now execute end-to-end workflows.

At MIT, researchers describe agentic AI as systems that "perceive, plan, act, and learn." Unlike traditional chatbots that wait for explicit prompts, agents autonomously execute multi-step tasks with memory, tool access, and adaptive reasoning.

The 2026 Reality

Microsoft's latest analysis highlights seven trends defining AI's next chapter:

1. Agentic automation replacing manual workflows
2. Interoperable agents breaking vendor lock-in
3. Hardened governance with security-audited releases
4. AI-driven cybersecurity at enterprise scale
5. Autonomous robotics in manufacturing
6. Massive infrastructure investments (billions for data centers)
7. ROI-focused adoption demanding immediate value

Amazon's millionth robot, coordinated by DeepFleet AI, exemplifies this shift—improving warehouse efficiency by 10% through autonomous decision-making.

Why It Matters

The business case is compelling:

• Faster time-to-value: Companies demand immediate ROI, not years of R&D
• Rapid prototyping: Accelerated product development cycles
• Human augmentation: Pair programming with AI agents
• End-to-end automation: From scheduling to execution

The Architecture Shift

According to Stack AI's 2026 guide, workflow architecture now matters more than ever. The key questions:

• How much autonomy should agents have?
• What happens when things go wrong?
• How do we ensure safety at scale?

Oracle's recent AI Agent Studio updates answer these with capabilities for "workflow orchestration, content intelligence, contextual memory, and ROI measurement."

Looking Ahead

The next decade won't just be about AI—it'll be about agency. Systems that think, plan, and act will reshape industries from healthcare to logistics. The organizations that thrive will be those that learn to work with agentic AI, not against it.

The question isn't whether AI will become agent-based—it already is. The question is: are you ready?

In 2023, "prompt engineering" was the buzzword of the decade. We were all obsessed with finding that perfect prompt, crafting our golden questions to coax AI into spitting out exactly what we wanted. It was impressive work, sure, but it was just the warm-up.

By late 2024, we started noticing something strange. The best prompts were becoming harder to find. The AI was getting smarter at figuring out what we needed, even when we weren't being super specific. And then came 2025, and the landscape shifted completely.

Today, in 2026, the conversation has moved beyond "how do I write a better prompt?" to "how do I design systems that work with AI agents autonomously?"

From Single Shots to Multi-Agent Workflows

The old days of single-shot prompting are largely a relic now. Sure, you can still use isolated prompts for quick tasks, but the real value is in orchestration.

The prompt engineering of 2026 isn't about finding the perfect one-shot prompt anymore. It's about:

• Designing clear agent protocols that specify roles, goals, and handoffs
• Creating multi-agent workflows where specialized agents collaborate
• Building feedback loops that allow agents to self-correct
• Establishing context management systems that preserve information across iterations

Think of it less like prompting and more like software architecture. You're not writing a request to a function; you're designing a system of functions that talk to each other.

The New Skill Stack

If you're building your career for 2026, here's what you should be focusing on:

1. Agent Orchestration Design

This is the new frontier. You need to be able to:

• Define clear agent roles and responsibilities
• Design handoff protocols between agents
• Create feedback mechanisms for agent collaboration
• Handle failure modes and recovery strategies

2. Prompt Quality Control

The prompt of 2026 is about consistency and reliability, not just cleverness. You need to:

• Ensure prompts work across different agents and contexts
• Create fallback mechanisms for when prompts fail
• Build in human-in-the-loop validation points
• Measure and track prompt effectiveness

3. Context Engineering

The ability to manage and manipulate context is super important. This includes:

• Summarization for long contexts
• Pruning irrelevant information
• Creating context hierarchies
• Managing agent memory and state

The Practical Takeaway

The "best prompt" in 2026 is the one that fits into a robust, multi-agent system. It's not about the magic bullet. It's about building systems that can:

• Handle ambiguity gracefully
• Self-correct when things go wrong
• Collaborate across specialized functions
• Evolve and adapt over time

What This Means for You

If you're an individual contributor: Focus on learning how to integrate AI agents into your existing workflows. The skill that separates good AI users from great ones is the ability to design robust, multi-stage processes that use multiple AI capabilities.

If you're building a career in AI: Stop thinking about "prompt engineering" as a standalone skill. Start thinking about agent systems design — the ability to architect workflows that leverage multiple AI agents working together.

The field of "prompt engineering" hasn't disappeared; it's evolved into something much more complex and powerful. And if you want to be at the cutting edge in 2026 and beyond, you need to be ready to think about systems, not just prompts.

I recently set out to build a fully local AI agent using OpenClaw and Ollama on my Proxmox server. The idea was simple: run a capable, private AI assistant with GPU acceleration and a clean web interface.

In reality, it turned into a deep dive into agent systems, model limitations, and performance tuning.

Here’s a detailed breakdown of my setup, the challenges I faced, and what actually made things work.

⚙️ My Setup

• Proxmox host
• Ubuntu VM → running Ollama with GPU passthrough (RTX 2080)
• LXC container → running OpenClaw
• Cloudflare Tunnel → exposing the UI externally

This separation allowed me to isolate workloads:

• GPU-heavy inference in the VM
• lightweight orchestration in the container

🚧 Challenges I Faced

1. OpenClaw Service Failing to Start

The systemd service kept crashing with errors related to the working directory:

Changing to the requested working directory failed: No such file or directory

Fix:

• Corrected the WorkingDirectory path in the service file
• Ensured proper permissions for the OpenClaw user

2. Telegram Bot Not Responding

Even after setting up the bot via BotFather, OpenClaw showed:

access not configured

Fix:

• Properly paired the device via OpenClaw
• Approved the chat after initiating a message
• Verified bot token and chat ID mapping

3. Large Model Memory Limitations

I initially tried running large models like:

qwen2.5-coder:32b

But it required ~50GB RAM, which was not feasible locally.

Lesson:

Stick to smaller models unless you have high-end hardware.

4. Requests Timing Out in OpenClaw

This was the biggest issue.

• Direct curl calls to Ollama were fast
• But OpenClaw requests kept timing out

Logs revealed:

• repeated tool call failures
• retries inside the agent loop
• eventual timeouts

5. GPU Confusion

At one point, I thought the GPU wasn’t working because responses were slow.

After checking nvidia-smi:

• VRAM usage was high ✅
• GPU utilization was near 0% ❌

This led to an important realization:

The bottleneck wasn’t inference — it was the agent orchestration.

OpenClaw was spending most of its time preparing and retrying requests rather than actually generating tokens.

6. Model Compatibility with Tools

Not all models behaved the same in an agent setup.

Here’s what I observed:

• DeepSeek models → no tool support
• Phi3 → very fast, but unreliable tool handling
• Mistral → supports tools, but noticeably slower
• LLaMA 3.x → mixed performance

Key takeaway:

Model choice matters more than raw size or speed.

7. Cloudflare Tunnel Latency

Using a Cloudflare tunnel added extra latency and sometimes affected WebSocket behavior.

Accessing OpenClaw locally was consistently faster.

💡 What Actually Fixed It

After a lot of trial and error, these changes made the biggest difference:

1. Choosing the Right Model

Instead of chasing the biggest or fastest model, I focused on balance:

• tool compatibility
• response consistency
• acceptable speed

2. Reducing maxTokens

Limiting output length had an immediate impact:

"maxTokens": 60-100

This reduced generation time and improved responsiveness significantly.

3. Adjusting Context Window Size

Another major improvement came from tuning the context window.

Large context windows:

• increase memory usage
• slow down token processing
• add unnecessary overhead

By keeping the context window smaller and more focused, I was able to:

• reduce latency
• improve overall throughput
• make responses more consistent

4. Disabling Tools (Game Changer for Speed)

When I disabled tools:

• no more retries
• no agent loops
• instant responses

Tradeoff:

• lost memory and automation features

But for general chat and quick responses, this made the system feel dramatically faster.

5. Understanding the Real Bottleneck

The biggest realization from this setup was:

It wasn’t GPU, network, or even the model — it was the agent loop.

OpenClaw introduces:

• structured reasoning
• tool execution cycles
• validation and retries

All of which add latency, even on powerful hardware.

🚀 Final Setup (What I Recommend)

After all the experimentation, here’s the setup that worked best for me:

Fast Mode (Daily Use)

• lightweight model
• tools disabled
• low maxTokens
• optimized context window

Result:

• fast, responsive experience
• ideal for chat and coding

Agent Mode (When Needed)

• tool-capable model
• tools enabled
• controlled token limits
• slightly higher latency

Result:

• more powerful workflows
• automation and memory support

🧠 Key Takeaways

• Local AI setups involve real tradeoffs between speed and capability
• GPU acceleration helps, but orchestration matters more
• Agent frameworks introduce significant overhead
• Model compatibility with tools is critical
• Tuning parameters like maxTokens and context window can drastically improve performance

Final Thoughts

This project gave me a much deeper understanding of how modern AI systems actually work under the hood.

It’s not just about running a model — it’s about how everything around it is orchestrated.

If you’re building a similar setup, my advice would be:

Start simple, measure everything, and optimize step by step.

I was recently playing around to modernize web applications by implementing a micro-frontend (MFE) architecture using Module Federation. This project involved creating a distributed system where multiple independent applications work together to create a seamless user experience. Here are the key lessons learned along the way.

🏗️ Architecture Overview

The application is built around a Shell + MFE architecture with four main components:

• 🏠 Shell Application - The orchestrator that manages the overall flow
• 🔐 Authentication MFE - Handles user authentication and verification
• 💳 Feature MFE - Manages core functionality and processing
• 🛡️ Verification MFE - Handles identity verification and compliance

🎯 Key Lessons Learned

Module Federation Requires Careful React Management

Challenge: Ensuring React is properly available across all MFEs was one of our biggest hurdles.

Solution: Implementing a robust React availability system:

const ensureReactAvailable = () => {
  if (typeof window !== "undefined") {
    if (!(window as any).React) {
      (window as any).React = React;
    }
    if (!(window as any).ReactDOM) {
      import('react-dom').then((ReactDOM) => {
        (window as any).ReactDOM = ReactDOM;
      });
    }
  }
};

Lesson: Module Federation requires explicit React sharing, and you need to handle both client and server-side scenarios carefully.

Environment Isolation is Critical

Challenge: Different teams working on different MFEs needed isolated development environments.

Solution: Implementing a sophisticated cookie isolation.

# Development: Port-specific cookies
app-session-token_port_3001

# Staging: Subdomain-specific cookies  
app-session-token_staging

# Production: Standard cookies
app-session-token

Lesson: Environment isolation prevents conflicts and allows teams to work independently without affecting each other.

Error Handling Must Be Comprehensive

Challenge: Module Federation loading failures can break the entire application.

Solution: Implementing multiple layers of error handling

const loadAuthMFE = async () => {
  try {
    // Primary: Module Federation
    if ((window as any).__FEDERATION__) {
      return await (window as any).__FEDERATION__.instance.loadRemote("auth/AuthMFE");
    }
    
    // Fallback: Direct import
    return await import("auth/AuthMFE");
  } catch (error) {
    // Graceful degradation
    return { 
      default: () => <ErrorComponent message={error.message} /> 
    };
  }
};

Lesson: Always have fallback mechanisms and graceful degradation strategies.

State Management Across MFEs is Complex

Challenge: Sharing state across multiple independent applications.

Solution: Centralized state management in the shell with careful prop passing:

const mfeProps = {
  someState: props.initialData?.someState,
  globalConfig: {
    someFunc: globalConfig.someFunc,
  },
};

Lesson: Design your data flow carefully and ensure all MFEs have access to the data they need.

Build Performance Requires Optimization

Challenge: Building multiple applications with shared dependencies.

Solution: Using Turbo for monorepo builds and implemented careful dependency management:

{
  "build": {
    "dependsOn": ["^build", "check-types"],
    "inputs": ["src/**", "components/**", "*.config.js"],
    "outputs": [".next/**"]
  }
}

Lesson: Monorepo tooling like Turbo is essential for maintaining fast build times.

CSS Architecture Needs Centralization

Challenge: Styling consistency across multiple independent applications.

Solution: Centralized styling in the shell with minimal MFE-specific CSS:

// Shell manages all global styles

import "@acme/ui-core/styles.css";
import "@acme/design-system";

// MFEs have minimal globals.css 

Lesson: Centralize as much styling as possible to maintain consistency.

TypeScript Configuration is Critical

Challenge: Type safety across multiple applications with shared types.

Solution: Shared TypeScript configurations and careful type definitions:

{
  "extends": "@acme/typescript-config/nextjs.json",
  "compilerOptions": {
    "baseUrl": ".",
    "paths": {
      "@/*": ["./*"],
      "@acme/component-library": ["../../packages/component-library/src"]
    }
  }
}

Lesson: Invest in proper TypeScript setup early - it pays dividends in maintainability.

Development Workflow Requires Coordination

Challenge: Multiple teams working on different MFEs simultaneously.

Solution: Clear development workflow with team-specific environments:

# Team A uses devA
export NODE_ENV=devA pnpm dev
# Team B uses devB  
export NODE_ENV=devB pnpm dev

Lesson: Establish clear development workflows and environment isolation early.

🛠️ Technical Implementation Insights

Module Federation Configuration

Each MFE exposes its main component:

// apps/auth/next.config.js
new NextFederationPlugin({
  name: "auth",
  filename: "static/chunks/remoteEntry.js",
  exposes: {
    "./AuthMFE": "./components/AuthMFE.tsx",
  },
  shared: {
    react: { singleton: true, eager: true },
    "react-dom": { singleton: true, eager: true },
  },
})

Error Boundary Implementation

Implementing comprehensive error boundaries for each MFE:

<ErrorBoundary
  onError={(error, errorInfo) => {
    console.error("💥 Auth MFE Render Error:", error, errorInfo);
  }}
  fallback={
    <ErrorScreen
      title="Authentication Component Error"
      message="The authentication component encountered an error."
      showIcon={false}
    />
  }
>
  <AuthComponent {...mfeProps} />
</ErrorBoundary>

📈 Performance Optimizations

Bundle Splitting

Implementing careful bundle splitting to optimize loading:

splitChunks: {
  chunks: 'all',
  cacheGroups: {
    vendor: {
      test: /[\\/]node_modules[\\/]/,
      name: 'vendors',
      chunks: 'all',
      priority: 10,
    },
    componentLibrary: {
      test: /[\\/]node_modules[\\/]@acme[\\/]component-library[\\/]/,
      name: 'component-library',
      chunks: 'all',
      priority: 20,
    },
  },
}

Lazy Loading

MFEs are loaded on-demand to improve initial page load:

const AuthMFE = lazy(() => import("auth/AuthMFE"));
const FeatureMFE = lazy(() => import("feature/FeatureMFE"));

🚨 Common Pitfalls and Solutions

1. React Singleton Issues

Problem: Multiple React instances causing hydration errors.

Solution: Ensure React is shared as a singleton across all MFEs.

2. CSS Conflicts

Problem: Styling conflicts between MFEs.

Solution: Centralize global styles and use CSS modules for component-specific styles.

3. Build Performance

Problem: Slow builds with multiple applications.

Solution: Use Turbo for parallel builds and implement proper caching.

4. Development Complexity

Problem: Complex local development setup.

Solution: Create comprehensive scripts and documentation for team onboarding.

🎯 Best Practices Established

1. Always have fallback mechanisms for Module Federation loading
2. Implement comprehensive error boundaries for each MFE
3. Use TypeScript strictly across all applications
4. Centralize shared dependencies in packages
5. Implement proper environment isolation for team development
6. Document everything - especially the integration points
7. Test cross-MFE functionality thoroughly
8. Monitor performance and bundle sizes regularly

🔮 Future Considerations

As we continue to evolve this architecture, we're considering:

• Runtime Module Federation for even more flexibility
• Advanced caching strategies for better performance
• Automated testing for cross-MFE integration
• Performance monitoring and alerting
• Advanced error tracking and recovery mechanisms

Conclusion

Implementing Module Federation for a Nextjs application was a challenging but rewarding journey. The key to success was:

• Careful planning of the architecture
• Comprehensive error handling at every level
• Proper tooling for development and build processes
• Clear communication and documentation
• Iterative improvement based on real-world usage

The result is a modern, scalable application architecture that allows teams to work independently while maintaining a cohesive user experience. The lessons learned here can be applied to any micro-frontend architecture implementation.

Two of the most innovative AI tools for developers in 2025 are Claude Code by Anthropic and Gemini CLI by Google. Both deliver AI-powered coding and terminal interaction—but cater to slightly different developer needs. Let’s break down the latest features, strengths, weaknesses, and which is best for what.

Pros & Cons:

Claude Code (Anthropic)

Pros:

• Deep codebase awareness: Excellent at understanding large and complex projects.
• IDE + Terminal integration: Works with VS Code, JetBrains, and terminal natively.
• Smart Git workflows: Can generate commits, refactor across files, and understand diffs.
• High-quality completions: Powered by Claude Opus 4, with long-context reasoning and code integrity.
• Memory, undo, and logging built-in.
• Robust SDKs for TypeScript and Python.

Cons:

• Closed source: Not open for contributions or customization.
• Limited to Claude’s models: No third-party plugin ecosystem (yet).
• CLI-only is more code-centric: Less general-purpose compared to Gemini.

Gemini CLI (Google)

Pros:

• Open-source & extensible: Under Apache 2.0 license, community-driven.
• Reason-and-act agent loop: Ideal for chaining tasks and intelligent automation.
• Massive context window (1M tokens): Great for long files, conversations, or context-rich tasks.
• Multimodal support: Ties into Veo, Imagen, and Google Search.
• Built-in terminal UX: Friendly for both developers and technical creators.
• Free-tier (preview): Generous rate limits: 60 RPM, 1,000 req/day.

Cons:

• Preview-stage: May be less stable or polished than Claude Code.
• No deep codebase integration (yet): Best for task-oriented or isolated coding help.
• Limited IDE integration (for now): Most interactions are CLI-only.

Comparison Table:

Verdict: Which One is Best for What?

Final Thoughts:

• Pick Claude Code if you want a serious AI coding companion that integrates deeply with your codebase, understands your Git workflow, and supports structured development inside IDEs.
• Pick Gemini CLI if you want an AI-powered shell companion for general tasks, fast scaffolding, open extensibility, and multimedia integration — and you're okay being part of its growing preview stage.

Both tools push the boundaries of what an AI in your terminal can do — but they serve different developer mindsets. Choose based on whether you need precision and polish (Claude) or freedom and flexibility (Gemini).

How structured context is transforming AI from stateless tools to intelligent, memory-driven systems

Introduction

Language models have become shockingly capable — they can summarize books, write code, and carry on conversations that feel intelligent. But under the hood, they’re still just... guessing the next token.

What gives these models real usefulness isn't just their raw capability — it's context.

And that’s where Model Context Protocol (MCP) comes in.

MCP is emerging as a fundamental layer in AI architecture. It's how developers give language models memory, identity, tools, and goal-awareness. It turns dumb-but-powerful token predictors into stateful, smart assistants.

In this post, we’ll break down why Model Context matters, what MCP is, and how it’s powering the next generation of AI applications.

The Problem with Stateless AI

Large language models (LLMs) like GPT-4 are stateless by design. Each prompt is treated in isolation. The model has no idea who you are, what you want, or what happened five minutes ago — unless you tell it again.

This creates obvious limitations:

• Repetition: You have to reintroduce yourself and your goals.
• Short-term memory: Models can “forget” earlier parts of a conversation.
• No personalization: Every session starts from zero.

This is like using a web app that doesn’t remember your login, settings, or history — frustrating and inefficient.

What is Model Context Protocol (MCP)?

Model Context Protocol (MCP) is a structured way of passing additional context into language models. It defines what the model should know at runtime — beyond just the user’s prompt.

It’s not a formal standard (yet), but many advanced AI systems — including OpenAI’s GPTs, LangChain agents, and enterprise AI stacks — are already implementing MCP-like architectures.

MCP typically includes:

From Prompts to Protocols: Why the Shift?

Before MCP, developers tried to cram everything into a giant prompt. That had downsides:

• Token bloat (you’d hit context limits fast)
• Hard to debug or update
• No separation of concerns

MCP emerged as a clean separation between context and conversation. It lets you modularize state:

{
  "mcp": {
    "system_instruction": "You are an AI Linux assistant.",
    "memory": {
      "user_os": "Ubuntu 22.04",
      "prefers_logs": "journalctl"
    },
    "tools": [
      { "name": "get_logs", "description": "Fetch logs from a server..." }
    ],
    "session_id": "abc-123"
  },
  "prompt": "Why is Gunicorn failing to write a PID file?"
}

Now your AI system becomes:

• Easier to reason about
• Easier to scale and debug
• Capable of evolving with the user

What MCP Unlocks

Here’s what becomes possible when you adopt MCP in your AI system:

Personalized Interactions

Store memory like:

{ "user_name": "Alex", "favorite_framework": "FastAPI" }

Now the model can tailor recommendations without re-asking every time.

Long-Term Memory

Remember past sessions, project status, or decisions made by the user.

Tool-Oriented Reasoning

Expose model-callable functions like:

• run_sql(query)
• fetch_logs(service, range)
• deploy_service(env)

Let the model plan and then act via tools.

Multi-Agent Collaboration

Pass structured context between agents (planner → executor, QA bot → code generator) using MCP as a shared state.

MCP in the Real World

OpenAI GPTs (Custom GPTs)

Every custom GPT uses an internal MCP layer:

• Instructions = system role
• Memory = persistent per-user facts
• Tools = enabled functions
• Files & APIs = augment context and capabilities

LangChain & Semantic Kernel

These frameworks implement “chains,” “agents,” and “memories” — all forms of MCP abstraction:

• ConversationBufferMemory, VectorStoreRetrieverMemory, etc.
• Agent inputs = system + tools + intermediate steps

AutoGen / CrewAI

Multi-agent orchestration relies on MCP-style handoffs — letting one agent know what another just did.

How to Design Your Own MCP Layer

You don’t need a whole framework to use MCP. Here’s how to start:

1. Define your system role (system_instruction)
2. Store persistent user memory in a DB or Redis
3. Build a context assembler that combines:
   • memory
   • system prompt
   • recent history
   • available tools
4. Inject that structure into your model call

Simple example (Python pseudo-code):

context = assemble_mcp(user_id, session_id)
response = openai.chat_completion(
  model="gpt-4",
  messages=build_prompt(context, user_input)
)

Best Practices

• Separate long-term vs session memory
• Use schemas or type systems for tools/functions
• Respect token budgets (truncate old turns or summarize)
• Secure tool execution — don’t let the model call anything directly without validation
• Audit context — log what memory and tools were passed per session

The Future of MCP

MCP isn’t just a temporary workaround — it’s a new programming model for intelligent systems.

As models gain long-term memory, better planning, and richer tool ecosystems, MCP will evolve into:

• Standardized schemas (e.g., OpenMCP)
• Shared memory across apps/agents
• Context negotiation between services
• User-controlled memory UIs ("What do you know about me?")

TL;DR

• AI isn’t smart without context
• MCP provides a structured way to inject memory, role, tools, and session data
• It’s already powering advanced agents, GPTs, and orchestration frameworks
• Start thinking not just about prompts, but about protocols

Next up?

Let me know if you'd like:

• A follow-up on "How to Build Your Own MCP Layer" with real code
• An explainer on tool integration and security
• A diagram-based summary of how MCP flows into model APIs

Microsoft has embarked on a significant transformation of its TypeScript programming language by developing a native compiler and toolset, aiming to enhance performance and developer productivity. This initiative involves porting the existing TypeScript compiler from JavaScript/TypeScript to Go, promising substantial improvements in compilation speed and resource efficiency.

The Need for a Native Compiler:

As TypeScript has grown in popularity, developers working on large-scale projects have encountered performance bottlenecks, including slow build times and high memory consumption. These challenges can hinder development workflows and affect productivity. Recognizing these issues, Microsoft has undertaken the task of creating a native implementation of the TypeScript compiler to address these performance concerns.

Impressive Performance Gains:

The native compiler has demonstrated remarkable performance enhancements across various codebases:

These results indicate that the native compiler can reduce build times by approximately 10 times, significantly enhancing the efficiency of development processes.

Impact on Developer Experience:

Beyond faster build times, the native compiler offers additional benefits:

• Improved Editor Performance: Developers can expect quicker editor startup times and more responsive code navigation features, such as renaming variables and finding references. For instance, loading the entire VS Code project in an editor has been reduced from 9.6 seconds to 1.2 seconds using the native language service.
• Reduced Memory Usage: Preliminary observations suggest that the native implementation consumes roughly half the memory compared to the current JavaScript-based compiler, contributing to a smoother development experience.

Timeline and Future Developments:

Microsoft plans to release a preview of the native compiler capable of command-line type-checking by mid-2025, with a feature-complete solution for project builds and language services expected by the end of the year. This native implementation is slated to be part of TypeScript 7.0 upon reaching parity with the existing compiler. In the interim, Microsoft will continue to maintain the JavaScript-based compiler through the 6.x releases to ensure stability and support for ongoing projects.

Community Engagement and Feedback:

Microsoft is actively seeking feedback from the developer community to refine the native compiler. Developers are encouraged to build and run the Go code from the new repository, which is available under the same license as the existing TypeScript codebase. Regular updates will be provided as new functionality becomes available for testing.

The development of a native TypeScript compiler represents a significant advancement in addressing performance challenges associated with large-scale TypeScript projects. By leveraging a native implementation, Microsoft aims to provide developers with faster build times, improved editor responsiveness, and reduced memory usage, thereby enhancing the overall development experience. This initiative underscores Microsoft's commitment to evolving TypeScript in alignment with the growing demands of modern software development.

For a more in-depth discussion on this topic, you might find the following resources insightful:

A 10x Faster TypeScript - TypeScript

Embarking on a native port of the existing TypeScript compiler and toolset to achieve a 10x performance speed-up.

TypeScriptAnders Hejlsberg

With the increasing demand for privacy, security, and control over AI models, hosting your own large language models (LLMs) locally has become a viable option. Ollama is a powerful tool that simplifies this process, allowing users to run and interact with open-source LLMs on their local machines efficiently. This guide will walk you through the installation, configuration, and usage of Ollama to host your own DeepSeek LLM.

What is Ollama?

Ollama is a lightweight framework that enables users to run large language models locally with minimal setup. It supports various open-source models such as Llama, Mistral, and more. Ollama is designed to be user-friendly, making it an excellent choice for developers, researchers, and AI enthusiasts who want to leverage LLMs without relying on cloud-based services.

Prerequisites

Before installing Ollama, ensure that your system meets the following requirements:

• Operating System: macOS or Linux (Windows support via WSL2)
• Hardware: A modern CPU with AVX2 support (GPU acceleration recommended but not mandatory)
• Memory: At least 16GB RAM for optimal performance

Installation :

macOS

1. Open a terminal window.

Verify the installation:

ollama --version

Run the following command to install Ollama:

brew install ollama

Linux

Verify the installation:

ollama --version

Download and install Ollama using the package manager:

curl -fsSL https://ollama.ai/install.sh | sh

Windows (via WSL2)

1. Install Windows Subsystem for Linux 2 (WSL2).
2. Follow the Linux installation steps inside WSL2.
3. Use ollama commands within your WSL2 terminal.

Downloading and Running Models

Once installed, you can start using Ollama to download and run models.

Listing Available Models

To see the available models, run:

ollama list

Downloading a Model

To download a model, use:

ollama pull deepseek-r1:1.5b

You can replace deepseek-r1:1.5b  with any supported model, such as llama2.

Running a Model Interactively

To start an interactive chat session with the model:

ollama run deepseek-r1:1.5b

This will allow you to enter prompts and receive responses from the model in real-time.

Hosting the Model as an API

Ollama provides a simple way to expose the model as an API for integration into applications.

Starting the API Server

Run the following command to start a local API server:

ollama serve

This will expose an endpoint (typically on port 11434) that applications can use to send requests to the model.

Making API Requests

You can interact with the API using curl or any HTTP client:

curl -X POST http://localhost:11434/api/generate -d '{"model": "llama2", "prompt": "Hello, world!"}'

The response will contain the model's generated output.

Customizing Models

You can also fine-tune models by creating a custom Modelfile:

FROM deepseek-r1:1.5b
PARAMETER temperature 0.7

Save this as Modelfile and build it using:

ollama create my-custom-model .

Now, you can run your customized model with:

ollama run my-custom-model

Conclusion

Ollama makes it easy to host and run LLMs locally, providing privacy, control, and reduced latency compared to cloud-based solutions. Whether you're a developer building AI-powered applications or a researcher exploring LLM capabilities, Ollama is a powerful tool that streamlines the process.

By following this guide, you can quickly set up and deploy your own DeepSeek LLMs, ensuring you have full control over your AI experience.

Want to see an example ? "Some-GPT" on TalkingTech.io is now self hosted and using DeepSeek-R1:7b model.

OpenAI has unveiled a game-changer: GPT-4o mini! This powerful yet budget-friendly AI model is poised to revolutionize the world of AI applications by making it much more accessible. Not only is GPT-4o mini remarkably affordable at 15 cents per million input tokens and 60 cents per million output tokens, but it also outperforms previous models in key areas like chat conversations. Benchmarked at an impressive 82% on the MMLU test, GPT-4o mini surpasses even GPT-4 in chat preferences. This marks a significant leap forward in affordability and functionality, making GPT-4o mini over 60% cheaper than GPT-3.5 Turbo.

Here are some highlights from the announcement.

• Intelligence: GPT-4o mini outperforms GPT-3.5 Turbo in textual intelligence (scoring 82% on MMLU compared to 69.8%) and multimodal reasoning.
• Price: GPT-4o mini is more than 60% cheaper than GPT-3.5 Turbo, priced at $0.15 per 1M input tokens and $0.60 per 1M output tokens (roughly the equivalent of 2500 pages in a standard book).
• Modalities: GPT-4o mini currently supports text and vision capabilities, and we plan to add support for audio and video inputs and outputs in the future.
• Languages: GPT-4o mini has improved multilingual understanding over GPT-3.5 Turbo across a wide range of non-English languages.

Because GPT-4o mini is both affordable and speedy, it's perfect for several situations:

• Handling large amounts of data: Need to analyze a whole codebase or a long chat history? No problem!
• Working on a tight budget: Tasks like summarizing lengthy documents become much more cost-effective.
• Delivering quick responses: Think real-time customer service chatbots that need to answer fast.

Just like its bigger brother, GPT-4o, the mini version can remember information from the past (up to October 2023) and process conversations of up to 128,000 words. Plus, it can generate responses of up to 16,000 words at a time. And to make it even better, Open AI is offering ways to fine-tune GPT-4o mini in just a few days!

Want to learn more about GPT-4o mini ? visit official announcement here

Open AI recently announced their new large language model gpt-4o(omni) .

Capabilities:

GPT-4o is a major upgrade over previous models, bringing GPT-4 level intelligence in a faster package. It's also what they're calling a "multimodal" AI, meaning it can understand and respond to text, images, and even video. For example, you could show it a picture of a dish from a foreign menu and it could translate the text, tell you about the food, and even suggest similar dishes.

Accessibility:

OpenAI is rolling out GPT-4o access in phases. It's already available to some paid users of their ChatGPT service, with free users getting a limited version. They plan on making it more widely available soon.

Safety:

Safety is a big focus for OpenAI with GPT-4o. They've built safety features into the model itself and have additional safeguards in place to prevent misuse.

You can view demo videos and learn more about GPT-4o here:

https://openai.com/index/hello-gpt-4o/

I was recently playing around with creating web apps using python, where I came across Flask - A micro framework for python to build web applications. Flask is renowned for its straightforward design, which gives developers the freedom to select the elements they desire and customize their apps to meet their needs.

While developing web apps using Flask is pretty much straightforward, being new to Python and Flask I struggled a little while trying to deploy the apps for production on Ubuntu.

Though there are many articles already written and available on the internet to help the deployment process, none of them are having a concrete information describing each step necessary.

In this article I will try to describe all the steps necessary to deploy a Flask application on Ubuntu with Gunicorn and Nginx.

This article assumes that you already have created your web application using Flask locally and have setup following things setup before you start the deployment process.

1. A server with Ubuntu 22 or higher installed and a non-root user with sudo privileges.
2. You have Nginx installed on the server.
3. You have a domain name configured to point to your server.
4. You have familiarity with the WSGI specification, which the Gunicorn server will use to communicate with your Flask application.

Once you have everything ready, you can start the deployment process.

Installing Python and other necessary components

First step is to install Python and other required libraries and components from the Ubuntu repositories on your Ubuntu server.

We will need Python Package Manager pip to manage Python packages / libraries. You also need to install the Python development files necessary to build some of the Gunicorn components.

Run the following command on the terminal to update the local package index and install the packages.

sudo apt update
sudo apt install python3-pip python3-dev build-essential libssl-dev libffi-dev python3-setuptools

Setting up the Virtual Environment

In this step you’ll set up a virtual environment in order to isolate the Flask application from the other Python files on your system.

Run following command on terminal to install Python Virtual Environment Package.

sudo apt install python3-venv

Once installed move to your application directory and setup the environment.

cd YOUR_PROJECT
python3 -m venv YOUR_PROJECT_VENV

Here YOUR_PROJECT is the directory name for your Flask application (Assuming you have already created a Flask web application) and YOUR_PROJECT_ENV is name given to the Virtual Environment for your application. You can replace these with your choice of names.

Now that Virtual Environment is created your need to activate / run it. Run following command on terminal to do so.

source YOUR_PROJECT_VENV/bin/activate

Once you do this your command prompt will show you that you are in virtual environment now by displaying (YOUR_PROJECT_VENV) in the terminal.

Installing Flask and Gunicorn

Now that we have setup the virtual environment, we need to install the Framework and server to run our Flask application.

Run the following command in your virtual environment to install Flask and Gunicorn

(YOUR_PROJECT_VENV) $ pip install gunicorn flask

Once we have installed the required libraries let's run our application on localhost to make sure everything is working as expected.

But before doing that we need to open the port 5000 (default port for Flask Application) on the firewall, so our Flask application can be served from that port. Run following command to do so.

sudo ufw allow 5000

Ok we are ready to serve our app locally. Let's do it.

  (YOUR_PROJECT_VENV) $ python YOUR_PROJECT_MAIN_FILE.py

If successful terminal will show you that your application is running on SOME_IP on port 5000

Running on http://0.0.0.0:5000/

You can navigate to this address (replace the IP address with your server IP address) in your browser and see your application running.

Ok. first milestone achieved and we are able to run the Flask application locally on Ubuntu server. But this is not the end as we need to setup the production environment for our app. Let's dive into it in next step.

Setup WSGI

You might be wondering what is WSGI and why do we need it to run a Flask web application. Well! WSGI is the Web Server Gateway Interface. It is a specification that describes how a web server communicates with web applications. Why do we need it ? because A traditional web server (Nginx in this case) does not understand or have any way to run Python applications. I am not going into details for WSGI, you can learn more about it by searching on Google 😄 Let's just setup WSGI.

Let's create an entry point for our application, which will tell the Gunicorn server where to start and how to interact with our application.

nano ~/YOUR_PROJECT/wsgi.py

Above command will create a file named wsgi.py on root of our application and open it for editing in nano editor. Just copy paste following into editor and adjust it to your setup.

from YOUR_PROJECT import app

if __name__ == "__main__":
    app.run()

Save and close the editor once you are done.

Configuring Gunicorn

Once we have setup the WSGI entry point for our application, its time to setup the Gunicorn web server.

Gunicorn stands for 'Green Unicorn' and it's a Python WSGI HTTP Server for UNIX.

I think this definition of Gunicorn is enough for the scope of this article. You can learn more about it if you want here.

Before we move on we need to check that Gunicorn can serve the application correctly.

We can do this by simply passing it the name of our entry point. This is constructed as the name of the module (minus the .py extension), plus the name of the callable within the application. In our case, this is wsgi:app. Also specify the interface and port to bind to so that the application will be started on a publicly available interface. Run the following command to test it out.

(YOUR_PROJECT_VENV) $ cd ~/YOUR_PROJECT
(YOUR_PROJECT_VENV) $ gunicorn --bind 0.0.0.0:5000 wsgi:app

If you don't see any error on the console and see something like following in your terminal, that means Gunicorn is able to serve your application 😄

Listening at: http://0.0.0.0:5000 

You can check if application is running in browser by replacing the IP address with your server's IP address.

Ok, Now we are in good shape as we know that our application is ready to be served to public. We don't need the virtual environment anymore, so you can deactivate it by running following command.

(YOUR_PROJECT_VENV) $ deactivate

From here on we will not be using virtual environment through the terminal but we will use system’s commands. We will need to do a little setup for that.

We need to create the systemd service unit file. Creating a systemd unit file will allow Ubuntu’s init system to automatically start Gunicorn and serve the Flask application whenever the server boots.

In case you don't know much about Systemd. It is a tool that controls several systems in Linux. Want to learn more about systemd ? Here is a comprehensive resource for that.

Let's create a system unit file with .service extension in /etc/systemd/system directory.

sudo nano /etc/systemd/system/YOUR_PROJECT.service

Above command will create the service file and open it in nano editor, where we have to define the service.

Let's start with Unit section.

[Unit]
Description=Description for gunicorn instance serving YOUR_PROJECT
After=network.target

In the Service section we define following things:

1. The User under which process will run. This can be regular user account since it is the owner all of the relevant files.
2. The Group ownership to the www-data group so that Nginx can communicate easily with the Gunicorn processes.
3. The WorkingDirectory is the root path of our web application.
4. The Path environmental variable to point to our app's virtual environment.
5. And the last thing is command to start the service. ExecStart will be doing this.

Overall Service section of the file should look something like this.

[Service]
User=YOUR_USER
Group=www-data
WorkingDirectory=/PATH_TO_YOUR_APP_DIRECTORY
Environment="PATH=/PATH_TO_YOUR_PROJECT_VENV/bin"
ExecStart=/PATH_TO_YOUR_PROJECT_VENV/bin/gunicorn --workers 3 --preload --bind unix:/var/run/YOUR_APPLICATION.sock -m 007 wsgi:app

Remember to replace the values with your own.

Here is breakdown of the Start command:

1. Start 3 worker processes (This can be adjusted to your project needs by changing --workers parameter)
2. Will load application code before the worker processes are forked. This way you can save some RAM resources as well as speed up server boot times. This we have defined in --preload parameter.
3. Create and bind to a Unix socket file, YOUR_APPLICATION.sock, within our /var/run directory. (this is important, because I was trying to put the socket file in my application directory and system was not able to access it. You need to keep it in /var/run directory)
4. And lastly we are specifying the WSGI entry point wsgi:app

Last section to add into service file is Install section.

[Install]
WantedBy=multi-user.target

This defines how / when the service unit will be started at boot. We want this service to start when the regular multi-user system is up and running.

That's mostly it with the service unit file. Let's save the file and close the editor.

At this point we can start Gunicorn service we created and enable it so that it starts at boot. Run following commands to do so.

sudo systemctl start YOUR_PROJECT
sudo systemctl enable YOUR_PROJECT

If you don't see any error on the terminal it means service has successfully started. It will show you in terminal all the instance that were started.

Phew! we are finally done with setting up the Systemd service to start serving our app using Gunicorn at server startup.

Now we will move on the last section of this article which is setting up the Nginx Server to point to our application running on our server.

Setup Nginx

We need to configure the Nginx server to pass all the requests to our application and this will be done using the socket file.

Let's create the Nginx configuration file. Run following command.

sudo nano /etc/nginx/sites-available/YOUR_PROJECT

In editor add following section to instruct Nginx to start listening on port 80 and define the domain name for your web application. In the location section we instruct the Nginx to pass all the requests to our socket file.

server {
    listen 80;
    server_name YOUR_DOMAINE_NAME;

     location / {
        include proxy_params;
        proxy_pass http://unix:/home/YOUR_USER/YOUR_PROJECT/YOUR_PROJECT.sock;
    }
}

Let's save and close. Our application is now available to Nginx to serve it on port 80. But one last thing is remaining and that is to enable the application on Nginx. This is easy. You can just run the following command to create a symlink for the configuration file as configuration is same for sites_enabled.

sudo ln -s /etc/nginx/sites-available/YOUR_PROJECT /etc/nginx/sites-enabled

At this point you can test if your Nginx configuration is correct without any syntax issues by running the following command.

sudo nginx -t

If no error is reported, it means we are good to start the Nginx serving our application. Let's do so by restarting the Nginx so it takes in account our modified configuration. Fingers crossed 🤞

sudo systemctl restart nginx

Hurray! We have concluded our deployment of Flask application on Ubuntu server. One last thing to do is to close port 5000 on firewall and allow full access to the Nginx server

sudo ufw delete allow 5000
sudo ufw allow 'Nginx Full'

You should be able to see your application being served from your domain at this point. All the best.

AI chatbots are becoming very popular these days. If you are running a business and have an online presence like a website or a mobile app, you can create an AI chatbot to engage the visitors. These AI chatbots are not like traditional scripted chatbots. They can be trained on your own private data and can provide near to accurate information to your website visitors and potentially convert them into clients. They are not just limited to providing the information to visitors but are capable of engaging them and can also collect some very useful information, that can be used by businesses to generate leads.

In November 2023 OpenAI announced OpenAI Assistants API along-with new GPT-4 Turbo model having enhanced capabilities then their existing models.

Open AI Assistants API

OpenAI's Assistants API let's developers build agent-like experiences within their own applications. An assistant is a purpose-built AI that has specific instructions. They can leverage extra knowledge, and can call different models and tools to perform tasks. I am not gonna dive deep into Assistants API today instead we will try to create an AI Chatbot using Assistants API and Flowise. You can read more about Assistant's API here

Flowise AI

If you are a developer you can create your own very customized AI apps using Assistants API. They have a very easy to understand documentation available. But it takes time and what if you don't know how to code. Well you are in luck. Flowise is an open source tool, which helps you build AI apps with zero to little coding knowledge.

As per their documentation "Flowise is a low-code/no-code drag & drop tool with the aim to make it easy for people to visualize and build LLM apps." It doesn't only provide extensive integration with OpenAI but has a lot other cool features as well. Click here to learn more about Flowise.

Creating A Chatbot

Let's dive into creating a chatbot using Assistants API and Flowise.

First thing we need to do is to create an API Key at OpenAI platform. So if you haven't signed up at OpenAI, signup now and then navigate to https://platform.openai.com/api-keys to generate an API key.

Here we can create an Assistant too as you can see Assistants menu item in the screenshot above, but same can be done via FlowsAI interface after setting up the OpenAI API key. We are aiming to create Assistant from FlowsAI interface.

Click on "Create new secret key" and copy the key and keep it safe somewhere for now.

Now let's move on to Flowise website to create the chatbot. Click here to navigate to documentation for Flowise.

Remember Flowise is not limited to chatbots only, you can build a customized LLM ochestration flow, a chatbot, an agent with all the integrations available in Flowise.

You can get a running instance of Flowise on any of the cloud providers mentioned here in matter of minutes, but for the purpose of this guide I will setup a local instance only via NPM.

Make sure you have latest NodeJS installed on your machine.

Run following command in terminal

npm install -g flowise

Once installed you can start Flowise server

npx flowise start

That's it. A local instance of Flowise is ready to play with 😃

Navigate to http://localhost:3000/ and you will see following interface.

Note: In order to use Flowise in your production web apps, you will need a hosted instance.

First thing we need to do is to set the credentials. In our case we have OpenAI API key that we generated in previous step. Click on "Credentials" to go to credentials screen, then click "Add Credential" button. A popup screen will appear with a list of providers. Search for OpenAI using search box and click on it.

A popup screen will appear in order to enter the API key. give it a name for identification purpose.

Great! Credentials are stored. Now let's create the Assistant. Click on "Assistants" from the menu to go to Assistants screen and click "Add" to add a new Assistant.

Fill in the information as shown in the screen.

1. Name: You can give it any name you want.
2. Description: Add some description about what your chat will do.
3. Model: Select the model you want to use. (Note OpenAI has different prices for different models.)
4. Credentials: Select the credentials that we created in previous step
5. Assistant Instructions: This is very important field. You have to enter the instructions for the Assistant. Tell the AI assistant what it will be doing, How it will communicate with users. You can give it a name also.
6. File Upload (optional) : You can upload a file containing any information that you want your chatbot to use during the conversation with users.

You can learn more about all these fields in OpenAI documentation as well as Flowise documentation. Once filled click "Add" button to create the Assistant.

Once Assistant is created. Next step is to create a chatflow. From the left menu click on Chatflows and click on "Add New" button.

This will open the canvas screen. In order to add a flow to canvas click on "+" sign on left side of the screen which will pop open a dialog like below.

Search for "OpenAI" in search box and click and drag "OpenAI Assistant" on the canvas.

Select the Assistant we created in previous step and click on "Save" icon from top-right corner.

Once saved you can click on the chat icon to interact and test your chatbot. Try asking some questions.

Well! our chatbot is almost ready to use now. As a last step we need to integrate it in our website. You can click on "</>" icon on top-right corner to see the options on how to integrate it.

You can copy the embed code and paste it in your website, and the chatbot will start appearing on your website.

Note that this embed code also gives you some customization options in order to change the look and feel of your chatbot. You can see the list of all the options by clicking here