The Start: Harmony with Route-LLM

For everyone who wants to embark on the OpenClaw adventure, there is one piece of advice: start with Route-LLM. It worked perfectly for me for a long time. The beauty of this system (via Abacus AI) is its versatility; all top models are ready for you and the system switches automatically based on your query. It is the ideal "entry-level harmony" for a creative process in which you don't want to constantly tinker with the back end yourself.

The Wall: Context and Costly Tokens

But those who build big run into big limits. My project – orchestrating a complete pipeline for long videos – requires my agent to have an enormous memory. OpenClaw must constantly send along large amounts of documentation and history.

Here I ran into two major problems:

1. The "Forgetfulness": The standard models fell short on context memory for the complex structures I was building.
2. The Token Bill: Because there was no caching, I paid for the full set of instructions and documents again with every single query.

I tried Gemini via Google Studio. Although Gemini can cache fantastically, I noticed that Google's API blocks an agent workflow (like OpenClaw) fairly quickly. Something that never happened with Route-LLM, but there I was missing the much-needed efficiency.

The Revolution: DeepSeek V4 (April 2026)

Around April 25, 2026, something happened that caught the entire AI community off guard: the release of the latest DeepSeek model. A lot is claimed in the media, but in my world the rule is: the proof is in the numbers.

The first results of my tests are spectacular:

• Smart Caching: DeepSeek understands that my "Tool Docs" don't change. Thanks to their Prefix Caching, I now pay up to 90% less for the tokens my documentation takes up. In recent logs I saw a Cache Hit ratio of no less than 85%!
• Agent-friendly: Where other major players put up barriers for agents, DeepSeek gives free rein.
• Better Context Retention: The model flawlessly maintains the thread of the creative process, even with enormous files.

The Next Step: The Intelligent Recovery Loop

Now that the infrastructure is in place, the real adventure can begin. I have developed tools that allow OpenClaw to check the quality of a video through specific mathematical calculations for image, audio and speech. In essence, this toolkit is a smart agent flow that collectively renders a final verdict on the work produced.

Auto-QC: Self-Correcting Capability

When the sensors detect an error, an automatic recovery loop kicks in that makes human intervention unnecessary:

1. Detection: The QC agent marks a scene as "failed" (e.g. due to an incorrect balance in the music).
2. Analysis & Re-prompting: The AI agent analyzes the error and rewrites the instructions with extra emphasis (e.g. "no music, raw ambient only").
3. Reproduction: A new version is generated and immediately re-scanned by the mathematical sensors.
4. Validation: This process repeats itself (up to a maximum of 6 times) until quality is guaranteed.

In short: Auto-QC is a tireless, intelligent orchestration that not only checks videos, but autonomously improves them until they meet the standard. Only when the machine truly cannot resolve it after six attempts is human assistance called in.

The OpenClaw adventure continues, but without the fear of an 'empty' memory or an unaffordable token bill. The machine learns, corrects and creates – in complete harmony.

Het bericht The OpenClaw Adventure: How I Broke Through the “Context Wall” with DeepSeek V4 verscheen eerst op Utilewebsites.

OpenClaw: The Operating System for the 24-Hour Agent

A crucial player in this revolution is OpenClaw. You can best think of OpenClaw as an 'operating system' for AI agents. It enables agents to run continuously, plan actions, and manage tools without a human constantly having to press a button.

Developments are moving at lightning speed: this week NVIDIA announced 'NemaClaw'. This is a massive step forward for the business market. NemaClaw makes it possible to run agents like those from OpenClaw within enterprise solutions. It adds the much-needed security layers and scalability that companies require to give AI agents safe access to their internal data and systems.

Custom AI Agents: RAG and Specific Tools

The beauty of this technology is that you can develop AI agents very specifically for a certain routine. There are two main forms:

1. The Knowledge Assistant (RAG): By using Retrieval-Augmented Generation (RAG), you "train" the agent with your specific knowledge, documents, and company culture. The agent then no longer responds or acts based on general internet data, but specifically from your expertise.
2. The Tool Specialist: You can also equip an agent with various tools to perform tasks.
   • Example: An agent that doesn't just read a customer email, but directly checks the CRM system, generates an invoice in PDF format, and sends it to the accounting department via a linked API.

2026: The Year of AI Implementation

We are looking ahead to 2026, the year in which AI definitively makes its entry to take over our repetitive, rhetorical tasks. We are moving from "thinking about AI" to "working with AI." Think of:

• Email Management: Agents that filter your inbox, prepare drafts, and schedule appointments based on priority.
• Oversight & Verification: Automatically checking contracts for deviations or validating data in complex spreadsheets.
• Customer Service: Agents that solve complex problems 24/7 by independently searching for solutions within systems.

AI Agents on Wall Street: A Glimpse into the Future

Wall Street is often seen as the 'canary in the coal mine.' Large banks and hedge funds are the first to test these new technologies for efficiency. What works there eventually spreads to every other sector. At this moment, a fundamental shift is occurring: we are moving from AI assistants to autonomous AI agents.

The Shift: From Assistance to Execution

Where AI previously mainly helped with looking up information, AI agents are now taking over the execution. Tasks that previously took teams of analysts weeks are now happening in hours. Think of:

• Research Synthesis: Lightning-fast analysis of market data.
• Compliance: Automating documentation that previously consumed thousands of billable hours.
• Risk Modeling: Quants are being supported by systems that run continuously in real-time.

While humans are still "in the loop," that circle is getting wider and wider. Agents are handling more and more of the workflow independently.

Three Structural Shifts

There are three major changes that will cascade from the financial world into other sectors:

1. Organizational Speed: In traditional workflows, handoffs between people (e.g., from analyst to manager) cause delays. AI agent pipelines eliminate this friction. A research agent directly feeds a decision agent. Humans oversee the entire pipeline instead of managing each individual step.
2. Auditability as a Feature: In regulated sectors, a 'paper trail' is essential. AI agents are better at this than humans: every decision is automatically logged and timestamped. Compliance becomes a built-in part of the system (compliance by design), rather than an afterthought expense.
3. "Swarm Risk": When hundreds of AI systems respond to the same signal at the same time, it can lead to a 'flash crash.' Managing this collective behavior is becoming a new discipline.

Practical Lessons for Your Business

What can you do today to prepare?

• Replace Meeting Chains with Agent Workflows: Identify where handoffs and endless meetings cause delays. Build workflows where AI compresses the chain.
• Implement Traceability from Day One: Don't wait for regulation. Ensure all your AI workflows build an audit trail now.
• Train Leaders to Manage Hybrid Teams: Managing a team consisting of both humans and AI agents requires new skills. You need to know when to trust the output and when to verify.

Conclusion

The era of AI agents is not science fiction; it is currently being rolled out on Wall Street and made accessible to the rest of the world by players like NVIDIA and OpenClaw. Whether you work in healthcare, the legal sector, or business services: the playbook is being written now.

Het bericht The Rise of the AI Agent: From Assistant to Autonomous Powerhouse verscheen eerst op Utilewebsites.

The tactic for AEO:

• Direct questions in headings: Use H2 headings that literally state the questions your ideal customer is asking.
• The "Snippet" rule: Directly under the heading, give a concise and clear answer in 2 to 4 sentences. This is what AI bots scan for their direct answers.
• Schema Markup: Use technical Schema code (such as FAQ and How-to tags) so that AI bots can understand your data directly and index it without having to "read" the entire page.

What is GEO (Generative Engine Optimization)?

GEO is the strategy for the long term and for authority. It is not about "ranking high" in a list, but about becoming the trusted source that is cited and summarized by AI models (such as Gemini and GPT-4) in their answers and narratives.

When an AI generates a complex answer about your industry, you want your data, your research and your vision to be used as a reference.

The tactic for GEO:

• Unique data and original research: AI models are trained on general knowledge. They are hungry for original insights and statistics that cannot be found anywhere else.
• E-E-A-T (Expertise & Authority): Optimize your author bios. The AI must be able to verify why you are an expert in your field.
• Depth of content: Write long-form content that fully explores complex topics. Support your claims with citations from experts and external sources to increase your own credibility.

The Power of the Combination

It is not a choice between AEO or GEO; you need both to remain relevant in 2026.

• Use AEO for your product pages, pricing and practical frequently asked questions (high purchase intent).
• Use GEO for your whitepapers, in-depth blogs and trend reports (thought leadership and brand preference).

The Urgency of Now

We are currently in a critical window. By the end of this year, an estimated 40% to 60% of all searches will end without a click to a website. AI models are now actively learning which sources they can trust.

Companies that start building this "AI authority" now are creating a foundation that competitors will find difficult to break through later. Once an AI recognizes you as the specialist, you become the default in every generated answer.

Conclusion: Stop blindly chasing clicks, and start building a platform that AI cannot ignore.

From Public Visibility to Internal Intelligence: RAG & LangChain

What AEO and GEO do for your public visibility (how AI models understand and cite your website), RAG (Retrieval-Augmented Generation) does for your internal business intelligence.
What is RAG?

RAG is a technique where an AI model (such as GPT-4) does not only answer based on its training, but first retrieves relevant information from your own data sources (documents, knowledge bases, CRM, ERP) and then uses that information to generate an accurate, contextual answer.

In practice:

An employee asks a question to an internal AI assistant.
The assistant searches in your own documentation, project history or product catalog.
The answer is not "generic", but based on your unique business data.

The role of LangChain

LangChain is a framework that makes building RAG systems easier. It offers:

Document loaders (PDF, Word, databases, APIs)
Vector stores (to make text searchable for AI)
Chains (workflows that combine retrieval + generation)
Agents (AI that independently decides which data it needs)

With LangChain you can, for example:

Build an internal chatbot that answers based on your support tickets and manuals.
Create a customer portal where AI automatically gives product advice based on previous orders and stock.
Create a knowledge base assistant that helps employees with complex processes without them having to search through dozens of documents.

The same technique that makes your website "AI-ready" (structured data, clear answers, unique insights) is the technique that makes your internal systems smarter.

Het bericht What is AEO (Answer Engine Optimization)? verscheen eerst op Utilewebsites.

The pace at which new AI tools appear is almost impossible to keep up with. The changes they bring are enormous: some people love it, others fear it — it stirs up a lot. At the same time, there’s a lot of hype around AI, and there’s a risk that in the financial world we’re heading toward a “bubble” that could eventually burst. Not because AI lacks potential, but because we might be making it bigger than it currently is.

Can AI take over autonomous tasks?

My answer: no, I would never give an LLM 100% autonomous control.

In everything involving AI, you must remain the conductor — otherwise things go wrong.
As a software engineer, you can accelerate your work in every aspect, but the overall picture — the total concept — must be managed, monitored, and reviewed by you: whether it’s code, text, images, or video.
If you don’t, AI can lose its way. AI can oversee the whole only to a certain extent, and we’re not there yet.

Maybe that will change in the future. But from all the tests and experiments I’ve done (as a programmer, with images, video, and concept writing), it’s clear: AI is certainly a useful tool, but I would never let it operate 100% autonomously.

Research supports this view:

• According to a report by McKinsey & Company, only about 1% of companies worldwide are mature in their AI deployment; most are still experimenting. GlobeNewswire+3McKinsey & Company+3McKinsey & Company+3
• Another study found that generative AI projects often stall: about 30% of gen-AI projects are discontinued after the Proof of Concept phase. Rubrik+1
• A survey by Writer found that companies without a formal AI strategy have only a 37% success rate, compared to 80% for companies with a strategy. writer.com+1

These figures show that AI is already doing a lot, but the autonomy promised by many tech enthusiasts is still a long way off in practice.

Is a new era beginning?

Yes — a lot is happening, and it’s happening fast. The way we work is changing: faster, more efficient, with smaller teams and far more output. Concepts can be set up quickly; small demos or tests can be developed rapidly.

But as of 2025, the following still applies:

• You guard the overall concept, the context, and the quality.
• AI assists with execution, suggestions, and efficiency.
• Without human review, AI loses direction, nuance, and consistency.

That means: AI is an accelerator, not a replacement.
Many companies also realize that the technical challenge is often only half of it; the biggest hurdles are strategic, organizational, and cultural.

AI is not a magical box that thinks on its own. It’s an instrument. An incredibly powerful instrument, but it needs a conductor. And that conductor is you.

The value of our work is shifting. It’s no longer about performing the task (AI can do that faster). It’s about the skills AI doesn’t have:

• Critical thinking: Is what AI produces accurate? Logical? Free from bias?
• Strategic questioning: What problem are we actually trying to solve?
• Ethical judgment: Should we do this, just because we can?
• Empathy: How do we present this to a client or colleague?

In conclusion

The promise of AI is great — faster work, more output, new possibilities. But as you rightly point out: without a human director, things go wrong. AI can be a powerful instrument, but humans remain the conductor.

If we handle this well, a new era will indeed begin — one in which we can work smarter, faster, and more creatively. But let’s not forget that we are already working in a phase where AI is supportive — autonomous it is far from being.

The more powerful the orchestra, the more indispensable the conductor.

November 2025 : Waar staan we met AI-tools in de praktijk

Het bericht November 2025: Where Are We with AI Tools in Practice? verscheen eerst op Utilewebsites.

Not everything that moves fast is a hype—sometimes it’s just exponential.

You don’t need to think faster than machines; you need to work with them more intelligently.

We’ve turned this article into a dynamic video presentation — created with Google’s LLM — to bring the key insights to life.
Watch the video below for a concise, engaging overview of The Right Mindset in a World of Exponential Growth and Singularity, or scroll down to read the full in-depth article.

1) Why we underestimate exponential growth

Humans have a persistent exponential growth bias: we read fractions and doublings as if they were straight lines. Recent research shows this directly distorts our estimation of AI progress: participants systematically underestimated how quickly AI capabilities increase.

Our brain loves straight lines; technology does not.

Doublings feel small—until they overtake you.

2) Technostress: the shock of lightning-fast change

Speed itself is a stressor. Recent reviews and studies link the pace of change, uncertainty, and complexity to higher stress and declining performance or well-being. This is not classic technophobia, but an understandable friction between the human pace of adaptation and technological reality.

It’s okay to get tired of ‘faster’—what’s not okay is standing still.

Stress signals: your skills are falling behind your tools.

3) AI and learning: convenience vs. growth

AI can accelerate you—but also unteach you how to think if you outsource everything. In a recent (not yet peer-reviewed) preprint, this is called the comfort-growth paradox: convenience can slow growth. The proposed solution is Enhanced Cognitive Scaffolding: AI as scaffolding that is gradually removed so you take over the work. Experimental educational research with GenAI scaffolds also shows that thoughtful guidance can improve learning outcomes; conversely, over-reliance can create “cognitive debt.” Treat this as preliminary but promising.

Use AI as a springboard, not as a crutch.

Let the assistant help—and then let go.

4) Mindset skills that keep you agile

• Intellectual humility: acknowledging what you (still) don’t know is associated with better judgment and more constructive behavior in conflicts.
• Growth mindset: useful as an attitude, but effects on performance are on average small and context-dependent—don’t oversell it.
• Bias awareness (in humans and AI): large language models show human-like biases (e.g., overconfidence) in a range of tests; keep this in mind.

Humility accelerates learning.

Optimism is good; overestimation is not.

5a) Where people stand now (without artificial boxes)

Instead of hard “three groups” with percentages, recent datasets show this:

• Globally, the feeling that AI offers more benefits than drawbacks is growing, but country differences are large: e.g., China ~83% ‘more benefits’, Netherlands ~36%.
• Adoption and trust are on average higher in emerging economies. At work, 72% in emerging economies use AI at least semi-regularly, vs. 49% in developed economies; organizational adoption is also higher there.

Practically, you could roughly see the public as: early adopters (high adoption/trust), cautious experimenters, and wait-and-see/worried—but the ratio varies by sector and country.

The landscape is not a 3-slice pie: it’s a map with regions.

Where you work and live colors your AI attitude.

5b) Three groups in society (indicative classification, Aug 2025)

Suppose we wanted to roughly divide the world’s population by AI attitude into three groups, we would arrive at the following indicative ranges. This is a thinking framework based on synthesis of recent datasets and trends; these are not official measured shares.

Note: this classification is hypothetical and intended as a pragmatic framework for policy, communication, and training. Regional/sectoral differences may deviate substantially.

6) Three concrete takeaways

1. Train your eye for exponential
   Practice with doubling times, S-curves, and order-of-magnitude thinking. This helps reduce the exponential growth bias and improve your planning.
2. Use AI as a learning partner (scaffold)
   Ask for hints, checklists, counterpoints, and a fade-out of help. Keep the bar high enough so you remain cognitively engaged.
3. Be critical and humble
   Calibrate for bias (in human & model), do spot-checks, and maintain your own grip on sources.

Ask for explanation, not just for outcome.

Doubt is not a brake: it’s your steering wheel.

Conclusion

The world is accelerating. Those who combine an open, learning attitude with conscious scaffolding and bias vigilance will not only keep up—but help steer.

Mindset is not a shield against change; it’s your surfboard.

Sources (selection, recent & relevant)

• AI perceptions worldwide — Stanford HAI: AI Index 2025 · Public Opinion (PDF).
• Adoption & trust per economy — University of Melbourne & KPMG: Global Study 2025 (landing) (PDF).
• Exponential growth bias & AI: Meikle (2024), Technology, Mind, and Behavior + KU summary.
• Technostress (speed/complexity): Comprehensive review (2024); AI & technostress (2025).
• AI bias (human-like distortions): Chen et al. (2025), M&SOM.
• Scaffolding with GenAI (education): Computers & Education RCT (2025); Meta-analysis HSS Comm. (2025).
• Comfort-growth paradox (preliminary): Riva (2025) · Enhanced Cognitive Scaffolding (preprint).
• Growth mindset — effect sizes: Sisk et al. (2018) meta-analysis; Macnamara & Burgoyne (2022) meta-analysis.

Het bericht The Right Mindset in a World of Exponential Growth and Singularity verscheen eerst op Utilewebsites.

With AI, the next evolutionary step is at your doorstep: you no longer need to program all logic and rules yourself but can leverage (pre-trained) models that generate text, classify images, or make predictions based on data. The crucial question for you as a developer shifts from “How do I code this?” to “Which AI model do I choose, and how do I integrate it into my system?”

From Model Selection to Privacy

Until recently, AI often ran exclusively on large cloud infrastructures. Nowadays, local LLMs (Large Language Models) are becoming increasingly smaller and more efficient. This allows you to run them on-premises or even on a relatively modest server. This offers significant advantages regarding privacy and security: you keep your business-sensitive data in-house and do not need to send it to external servers.

• Own Model: You can fine-tune an existing AI model with company data without that data being stored elsewhere.
• Infrastructure: You decide whether to run on GPU, CPU, or specialized AI chips, depending on the desired performance.

The Developer as an Orchestrator

Whether you are building a web app in Vue, maintaining a backend in Laravel, or developing a mobile app in Java, your role is fundamentally changing. AI as an abstraction layer means you spend less time on boilerplate code and more on orchestrating different AI components. Consider:

1. Model Selection
   Which AI fits the task? You evaluate performance, reliability, scalability, and cost.
2. Integration
   How do you connect AI to your existing software architecture? This can range from setting up API calls to building microservices that interact with models.
3. Ethics and Privacy
   AI generates output based on vast amounts of data. You are responsible for ensuring safety, compliance, and ethical use.
4. Scalability and Performance
   What hardware is needed? And how do you optimize your model (e.g., through quantization) for faster response times?

Changing Workflows: From Coder to Orchestrator

Traditionally, you wrote all business logic and use cases entirely in code. With AI as an abstraction layer, that focus shifts. You no longer need to program every detail of a functionality (such as text generation or image recognition) yourself. Instead, you choose an AI model that largely performs this task for you.

1. Model Selection Instead of Implementation Details

• Previously: “Which algorithms and data structures do I use?”
• Now: “Which (pre-trained) model best fits my task and data?”

With model selection, your focus shifts towards performance, reliability, and integration, rather than developing the entire logic yourself.

2. Prompt Engineering & Fine-Tuning

• Instead of (exclusively) writing functions and classes, you design the right prompts for the AI model.
• You can then fine-tune the model with company data to better align the AI output with your organization's and users' needs.

Conclusion: A New Skill Set

AI is becoming a powerful abstraction layer on top of existing languages and frameworks. You need to code less ‘mechanically’ but should still understand fundamental principles—from hardware to data processing. The future calls for creative problem-solving and a strategic view on model selection, privacy, performance, and integration.

In summary, AI takes over some routine tasks, but your role becomes broader and more valuable. You are the linchpin in combining model selection, privacy considerations, performance optimization, and system integration. This opens the door for your organization to develop faster, more secure, and more innovative software. With AI as an abstraction layer, you can focus on what truly matters: building impactful solutions.

Het bericht AI as an Abstraction Layer: How Your Role as a Developer Changes verscheen eerst op Utilewebsites.

The Nasdaq Quake

Monday, January 28, 2025, was a day investors will never forget. The tech-heavy Nasdaq (.IXIC) dropped by a staggering 3.1%, partly due to the unexpected rise of DeepSeek. Nvidia, one of the biggest players in the AI hardware market, was the day's biggest loser. Its stock plummeted nearly 17%, marking a record single-day loss in market capitalization for a Wall Street stock, according to LSEG data.

The reason? DeepSeek proves that high-quality AI doesn’t necessarily rely on expensive, closed systems and exclusive hardware. With an open-source model developed at a fraction of the cost, the traditional AI market is being challenged to adapt to a new reality.

Impressive Performance

Despite its modest budget, DeepSeek’s R1 model delivered benchmark test results in January 2025 that are comparable to leading models like OpenAI’s o1. The model excels at tasks such as planning personalized travel itineraries and creating meal plans based on a grocery list – all without noticeable errors.

What makes DeepSeek particularly interesting is its focus on functionalities that add direct value to everyday life. Although the model (still) lacks advanced features like voice mode or image generation – two features ChatGPT does offer – it demonstrates that simplicity and effectiveness often have more impact than an overload of features.

Open-Source: The Future of AI?

The open-source nature of DeepSeek might be its most revolutionary feature. By making the code freely available, the company not only enables developers to adapt and improve the model but also to use it offline. This opens the door to a new generation of AI applications that aren’t dependent on cloud services or expensive subscriptions.

This transparency and cost efficiency have sparked intense discussions about the current economic models in the AI industry. If a high-quality model can be developed for just $6 million, what does that mean for the future of AI development? And how will established companies like OpenAI and Google respond to this disruptive force?

Get Started with DeepSeek

Curious about DeepSeek? You can try the model yourself! Go to chat.deepseek.com to chat with the AI directly. Or, if you have the right hardware, you can download and use the model offline via GitHub.

Do you want to run AI models like DeepSeek locally? With LM Studio, you can easily run LLMs on your own computer. This works on Ubuntu 24.10, as well as Mac and Windows. For installation on Ubuntu 24.10, follow the instructions via our knowledge base (Local LLM on Ubuntu 24.10). If you have the right hardware, you can set this up within an hour. For developers, this also offers API capabilities.

Conclusion

DeepSeek is more than just a new player in the AI market – it’s a wake-up call for the entire industry. With its low cost, open-source nature, and impressive performance, it challenges the status quo and raises questions about how AI is developed and commercialized. Whether you’re an investor, developer, or end-user, one thing is clear: DeepSeek has the potential to change the future of AI forever.

Het bericht DeepSeek: The Game-Changer in the AI Industry verscheen eerst op Utilewebsites.