Building LinkedIn Carousels with MCP Servers, NVIDIA's Groq Acquisition, and the Vibe Coding Wars

Automating LinkedIn Carousels with MCP Servers and Claude Code

TLDR: A developer built a custom MCP server that generates visually consistent LinkedIn carousels in 10 minutes by using sequential image generation with visual context—each slide references the previous one, ensuring design consistency without manual intervention.

This is one of those practical automation stories that reveals how AI tools are actually being used in production workflows. The author faced a common problem: LinkedIn carousels perform exceptionally well for engagement, but creating visually consistent multi-slide decks is tedious and time-consuming. The solution involves Claude Code, a custom MCP server, and Google's Nano Banana Pro image generation model.

The key insight here is deceptively simple: "show, don't tell." Rather than describing desired visual styles in elaborate text prompts, the system passes each previously generated slide as visual context for generating the next one. This image-to-image approach aligns better with how multimodal models actually work—they've been trained on visual examples, so providing visual references produces more consistent results than text descriptions alone.

What makes this technically interesting is the MCP (Model Context Protocol) architecture. MCP servers are programs that AI models can invoke as tools, allowing them to interact with external systems in controlled ways. The carousel generator MCP server handles the tedious orchestration: calling the image generation API, maintaining session state, passing visual context between generations, and managing the iterative refinement process.

The workflow is remarkably streamlined. The author writes post copy, develops the carousel concept collaboratively with Claude Code, generates multiple versions of the first slide to establish the visual direction, then asks the system to generate the remaining slides in that style. The entire process takes 5-10 minutes including revisions.

One particularly clever feature is selective regeneration. If slides 1-4 and 6-7 are perfect but slide 5 needs adjustment, the system can regenerate only slide 5 using slide 4 as the visual reference. This maintains consistency while dramatically speeding up iteration. Without this capability, fixing one slide would require regenerating the entire deck—a friction that would discourage refinement.

For architects and team leads, this represents a pattern worth studying: using AI to handle tedious orchestration tasks while keeping humans focused on creative direction and quality control. The MCP server encapsulates complexity behind natural language interfaces. The author doesn't need to know which tools to call or in what order—Claude Code figures that out based on instructions like "regenerate slides 02 and 04 with slightly smaller font."

The brand guidelines integration is also noteworthy. By pre-defining visual standards (colors, fonts, spacing rules), the system can apply consistent branding automatically. This separation of brand definition from content creation mirrors good software architecture principles: define your constraints once, then let the system enforce them consistently.

Key takeaways:

• Visual context (image-to-image) produces more consistent results than text-only prompts
• MCP servers enable AI models to orchestrate complex multi-step workflows
• Selective regeneration is crucial for practical iteration—full regeneration creates friction
• Pre-defined brand guidelines enable consistent output without per-request specification
• The first slide establishes visual DNA; invest time there and let subsequent slides inherit

Tradeoffs:

• Automated consistency gains uniformity but may sacrifice creative spontaneity
• MCP server complexity requires upfront investment but pays off through repeated use

Link: How I Generate LinkedIn Carousels in 10 Minutes

NVIDIA Acquires Groq for $20 Billion

TLDR: NVIDIA made its largest acquisition ever by licensing Groq's technology in a $20 billion deal, bringing Groq's LPU technology and 90% of staff including CEO Jonathan Ross into NVIDIA's AI factory architecture.

This is a significant consolidation move in the AI hardware space. Groq has been one of the more interesting alternative approaches to AI inference, with their Language Processing Units (LPUs) designed specifically for low-latency inference workloads. Unlike GPUs which were originally designed for graphics and adapted for AI, LPUs were purpose-built for the sequential nature of language model inference.

The deal values Groq at nearly triple its previous $6.9 billion valuation, suggesting NVIDIA sees substantial strategic value beyond just eliminating a competitor. Groq's technology will be integrated into NVIDIA's AI factory architecture, likely addressing use cases where latency matters more than throughput—real-time applications, interactive agents, and scenarios where GPU batch processing introduces unacceptable delays.

For architects planning AI infrastructure, this acquisition signals that the inference hardware landscape continues consolidating. NVIDIA's dominance in training hardware is well established, but inference has been more contested with various specialized approaches. This deal extends NVIDIA's reach into specialized inference optimization.

Key takeaways:

• NVIDIA continues consolidating AI hardware market through strategic acquisitions
• Groq's LPU technology designed for low-latency inference fills gaps in GPU capabilities
• 90% of Groq staff moving to NVIDIA indicates full technology absorption rather than arm's-length licensing
• Inference hardware specialization remains strategically valuable despite GPU dominance

Link: How I Generate LinkedIn Carousels in 10 Minutes

The Vibe Coding Wars Heat Up

TLDR: AI-powered development tools are seeing massive valuations: Lovable at $6.6B, Cursor at $29.3B, Vercel at $9.3B, and Replit at $3B, signaling intense competition in the AI-assisted coding space.

The fundraising numbers in the AI coding tool space have become genuinely staggering. Swedish startup Lovable raised $330 million at a $6.6 billion valuation just two weeks ago. Cursor—the AI-enhanced code editor—hit a $29.3 billion valuation in November. Vercel sits at $9.3 billion, Replit at $3 billion. These valuations exceed the GDP of some countries.

What's driving these numbers? The hypothesis seems to be that AI-assisted coding will fundamentally change how software gets built, and whichever tools become the default environment will capture enormous value. It's a bet on developer workflow lock-in similar to how IDEs and version control systems became essential infrastructure.

The competition is creating rapid innovation in the space, with each player differentiating through different approaches: Cursor focuses on editor integration, Lovable on app building, Replit on cloud development environments, Vercel on deployment and edge infrastructure. Whether these valuations prove justified depends on whether AI coding tools become essential infrastructure or commodity features that get absorbed into existing tools.

Key takeaways:

• AI coding tool valuations now rival major tech companies
• Multiple differentiated approaches competing: editors, builders, environments, deployment
• Market betting on fundamental transformation of software development workflows
• Competition driving rapid feature development across the space

Link: How I Generate LinkedIn Carousels in 10 Minutes

World Models: The Next Frontier Beyond LLMs

TLDR: Yann LeCun's AMI Labs targets a €3 billion valuation pre-launch, focusing on world models that can understand physics, maintain memory, and plan complex actions—capabilities current LLMs lack.

Looking ahead to 2026, one emerging trend deserves attention: world models. These are AI systems designed to understand and reason about the physical world in ways that current large language models fundamentally cannot. While LLMs excel at pattern matching on text, they struggle with physics simulation, persistent memory across long interactions, and planning multi-step actions in complex environments.

Yann LeCun's AMI Labs is the highest-profile entrant in this space, targeting a €3 billion valuation before even launching. The premise is that next-token prediction—the core mechanism behind current LLMs—is insufficient for AI systems that need to navigate and interact with the physical world. A robot that can predict the next word in a sentence isn't necessarily capable of predicting what happens when you push a stack of boxes.

For architects thinking about long-term AI integration, world models represent a potentially important capability expansion. Current LLMs work well for text processing, code generation, and information synthesis. But applications requiring physical reasoning, persistent state management, or complex multi-step planning may need fundamentally different architectures. Whether world models deliver on this promise remains to be seen, but the investment signals serious bets on this direction.

Key takeaways:

• World models aim to address LLM limitations in physics understanding and persistent memory
• Major investment flowing into approaches beyond next-token prediction
• Target applications include robotics, complex planning, and physical world interaction
• Represents potential paradigm shift from pure language models to embodied AI

Link: How I Generate LinkedIn Carousels in 10 Minutes

This summary was generated from newsletter content. Some articles may have been shortened or consolidated for readability.