Month / June 2025

by dilanshah

Build and Sell n8n AI Agents — an eight-plus-hour, no-code course

AI Agents and Automation

  1. AI agents have two parts: a brain—that is, a large language model with memory—and instructions in the system prompt. Together they let the agent make decisions and take actions through connected tools.
  2. Reactive prompting beats proactive prompting: begin with no prompt, then add lines only when errors appear. This makes debugging simpler.
  3. Give each user a unique session ID so the agent’s memory stays separate, enabling personal conversations with many users at once.
  4. Use Retrieval-Augmented Generation, or R-A-G. The agent asks a question, looks up an answer in a vector database, then crafts the reply—boosting accuracy.

AI Workflows and Best Practices

  1. AI workflows—straight, deterministic pipelines—are usually cheaper and more reliable than free-roaming agents, and they’re easier to debug.
  2. Wire-frame the whole workflow first. Mapping eighty to eighty-five percent of the flow upfront clarifies what to build.
  3. Combine agents in a multi-agent system: an orchestrator assigns tasks to specialist sub-agents. That raises accuracy and control.
  4. Apply an evaluator–optimizer loop. One component scores the output; another revises it, repeating until quality is high.

AI Integration and Tools

  1. n8n is a powerful no-code platform for AI automations; you can create and even sell more than fifteen working examples.
  2. Open Router picks the best large language model for each request on the fly, balancing cost and performance.
  3. Eleven Labs adds voice input to an email agent. Pair it with Google Sheets for contacts and the Gmail API for sending mail.
  4. Tavly offers a thousand free web searches per month—handy for research inside AI content workflows.

AI Agent Development Strategies

  1. Scale vertically first: perfect one domain—its knowledge base, data sources, and monitoring—before branching out.
  2. Test rigorously, add guard-rails, and monitor performance continuously before you hit production.
  3. Use hard prompting: spell out examples of correct and incorrect behavior right in the system prompt.
  4. Allow unlimited revision loops when refining text, so the workflow can keep improving its answer until it satisfies you.

AI Business Applications

  1. Three-quarters of small businesses already use AI; eighty-six percent of adopters earn over one million dollars in annual AI-driven revenue.
  2. AI-guided marketing lifts ROI by twenty-two percent, while optimized supply chains trim transport costs five to ten percent.
  3. AI customer-service agents cut response times sixty percent and solve eighty percent of issues unaided.
  4. The median small business spends just eighteen-hundred dollars a year on AI—under one-fifty a month.

AI Development Techniques

  1. Structure prompts with five parts: overview, tools, rules, examples, and closing notes.
  2. Debug one change at a time—alter a single line to isolate the issue.
  3. Log usage and cost in Google Sheets to track tokens and efficiency.
  4. Use polling in workflows: check task status at intervals before moving on.

AI Integration with External Services

  1. In Google Cloud, enable the Drive API, set up OAuth, and link n8n for file workflows.
  2. Do the same with the Gmail API to trigger flows and send replies.
  3. Build a Pinecone vector index (for example, with text-embedding-3-small) for fast R-A-G look-ups.
  4. Generate graphics through OpenAI’s image API to save about twenty minutes per post.

Advanced AI Techniques

  1. Use a routing framework to classify inputs and dispatch them to the right specialist agent.
  2. Add parallelization so different facets of the same input are analyzed simultaneously, then merged.
  3. Store text as vectors in a vector database for semantic search—meaning matters more than keywords.
  4. Deploy an M-C-P server as a universal translator between agents and tools, exposing tool lists and schemas.

AI Development Challenges and Considerations

  1. Remember: most online agent demos are proofs of concept—not drop-in, production-ready templates.
  2. Security matters; an M-C-P server could access sensitive resources, so lock it down.
  3. Weigh agents versus workflows; use agents only when you need complex reasoning and flexible decisions.
  4. Supply high-quality context—otherwise you risk hallucinations, tool misuse, or vague answers.

AI Tools and Platforms

  1. Alstio Cloud manages open-source apps like n8n for you—install, configure, and update.
  2. Tools such as Vellum and L-M Arena let you compare language-model performance head-to-head.
  3. Supabase or Firebase cover user auth and data storage in AI-enabled web apps.
  4. In self-hosted n8n, explore community nodes—for instance, Firecrawl or Airbnb—to expand functionality.
by dilanshah

🚨 The AI Cyber‑Warfare Threat: Insights from Geoffrey Hinton on DOAC

In his appearance on The Diary Of A CEO with Steven Bartlett, Geoffrey Hinton—the so-called “Godfather of AI”—issued a compelling warning about AI’s dual-use potential. While AI offers immense benefits, “at least half” of its development is likely directed towards offensive cyber operations. This includes crafting more potent attacks, designing new malware, and automating exploits in real time.

  1. Cyber‑Attacks Supercharged by AI
    • From reactive to proactive: AI not only defends networks but also enables automated scouting for vulnerabilities and weaponized code generation.
    • Escalating sophistication: Cyber‑criminals and state actors are already leveraging AI to build advanced phishing campaigns. They are also using it to develop malware. This forces a continuous escalation in cyber warfare.
  2. Biological Risks: AI‐Designed Viruses

Hinton raised the specter of AI-aided bioengineering. This crossover risk—where cyber AI knowledge facilitates biological threats—represents a chilling frontier.:

“There’s people using it to make nasty viruses” .

  1. Election Manipulation Beyond Digital Borders
  • AI’s ability to model and influence human behavior isn’t limited to malware. According to Hinton, AI-driven tools can:
  • Craft hyper-personalized messaging to sway individuals,
  • Potentially manipulate public opinion and democratic processes.
  1. Urgent Call for Safety‑First Governance

Hinton emphasized that the moment to act is now:

  • Governments should mandate major AI firms to allocate a portion of compute resources toward safety testing,
  • This includes rigorous safety evaluations prior to release and independent oversight .
  • Without safeguards, profits and power will continue to outweigh safety—leaving us vulnerable.

📝 What a Responsible Defense Looks Like

If you’re thinking about policy and strategic frameworks, here’s a roadmap inspired by Hinton’s analysis:

Key Focus Area Recommended Action

  • Regulation & Oversight Governments must require safety audits for AI models before deployment.
  • Safety‑first R&D Major AI labs should allocate dedicated compute to adversarial safety research.
  • Global Cooperation Collaboration across countries to counter cross-border misuse including bio‑threats.
  • Public Awareness Inform citizens and organizations about AI-driven threat evolution—phishing, malware, targeted political influence.

Final Thoughts

Hinton’s warnings aren’t speculation; they’re grounded in current tech trajectories. AI isn’t just a tool; it’s fast becoming the weapon of choice in cyber and bio conflict.

But there is hope. With proactive safety commitments, regulations tailored to dual-use risk, and global collaboration, we can choose to channel AI’s power responsibly. The question is: will society act before technology outruns us?

by dilanshah

How Direct File is Quietly Redefining Government Software

In a rare but powerful move, the U.S. government has open-sourced one of its most impactful digital public services: Direct File, a platform that allows taxpayers to file their federal returns electronically—completely free of charge and without third-party intermediaries.

At a glance, Direct File might seem like just another government web form. But beneath the surface lies a thoughtfully engineered system that’s not just about taxes—it’s a case study in modern government software, scalable infrastructure, and user-first design.

Let’s break it down.

🧾 What is Direct File?

Direct File is a web-based, interview-style application that guides users through the federal tax filing process. It works seamlessly across devices—mobile, desktop, tablet—and is available in both English and Spanish.

Built to accommodate taxpayers with a wide range of needs, it translates the complexity of IRS tax code into plain-language questions. On the backend, it connects with the IRS’s Modernized e-File (MeF) system via API to handle real-time tax return submissions.

🧠 The Tech Stack: Government Goes Modern

The project reflects a significant leap forward in how federal systems are built and deployed.

  • Fact Graph: At the heart of Direct File is a “Fact Graph”—an XML-based knowledge graph that smartly handles incomplete or evolving user information during the filing process.
  • Programming Stack:
    • Scala for the logic and backend (running on the JVM)
    • Transpiled to JavaScript for client-side execution
    • React frontend in the df-client directory
    • Containerized for Speed: Docker is used for seamless local deployment.
      • This spins up the backend (port 8080) and Postgres DB (port 5432).
  • Modular Architecture:
    • fact-graph-scala: Core tax logic
    • js-factgraph-scala: Frontend port
    • backend: Auth, session management
    • submit: MeF submission engine
    • status: Monitors submission acknowledgments
    • state-api: Bridges federal and state systems
    • email-service: Handles user notifications

🤝 Built by Public Servants, Not Contractors Alone

Unlike many large-scale federal tech initiatives, Direct File was created in-house at the IRS, in partnership with:

  • U.S. Digital Service (USDS)
  • General Services Administration (GSA)
  • Contractors like TrussWorks, Coforma, and ATI

This hybrid structure ensured agile execution while maintaining strong public stewardship.

🔒 Security Without Obscurity

Despite being open source, Direct File excludes any code that touches:

  • Personally Identifiable Information (PII)
  • Federal Tax Information (FTI)
  • Sensitive But Unclassified (SBU) data
  • National Security Systems (NSS) code

This reflects a disciplined balance between transparency and trust—one that more government software projects should emulate.

📜 Legal Framework

Direct File is anchored in a suite of progressive digital policies:

  • Source Code Harmonization And Reuse in IT Act of 2024
  • Federal Source Code Policy
  • Digital Government Strategy
  • E-Government Act of 2002
  • Clinger-Cohen Act of 1996

Together, these policies mandate that custom-developed government software should be shared and reused, not siloed.

💡 Why This Matters

Direct File represents a milestone for civic tech, open government, and digital service delivery:

✅ 1. Open Source, Real Impact
It’s not often we see real, working government platforms open to inspection and reuse. This invites contributions from civic technologists and helps other governments learn from U.S. innovation.

🧩 2. Designing for Complexity
Converting complex tax logic into user-friendly language—using a structured knowledge graph—is a pattern applicable well beyond taxes (think healthcare, benefits, or housing).

🛠️ 3. Engineering Innovation
The Fact Graph and modular backend architecture reflect best practices in modern backend design—resilient, flexible, and portable.

🔐 4. Trust and Privacy by Design
The selective code release shows how governments can be open while still securing sensitive systems.

🌐 5. Interoperability with State Systems
The state-api integration is especially forward-thinking. It could pave the way for smoother federal–state collaboration in everything from benefits to compliance.

🚀 Getting Started with the Code

Want to explore it locally? You’ll need:

  • Java
  • Scala
  • Maven
  • SBT
  • Coursier
  • Docker

Then: 1. Clone the repo

2. Run:

docker compose up -d --build

3. Navigate to /direct-file/df-client

4. Run:

npm run start

Frontend runs at http://localhost:3000

Final Thoughts

Direct File shows that government software doesn’t have to be clunky, slow, or hidden. With the right talent and commitment, it can be modern, secure, and open.

This project is not just about taxes—it’s about showing the public sector what’s possible when we build with purpose and publish with pride.

📎 Explore the repo (once public): github.com/irs-directfile

📩 Want to build something similar? Contact me

by dilanshah

AI Market Dynamics: Consolidation, Opportunity, and Innovation

The latest episode of “No Priors” featuring Sarah and Elad delves deeply into the current state of the AI market, revealing intriguing trends and untapped opportunities.

Consolidation and Expansion in AI

The AI market is undergoing notable consolidation in specialized sectors such as Large Language Models (LLMs), healthcare, and coding. These sectors are primarily driven by proprietary data sources, effective distribution channels, and widespread user adoption.

However, new entry points are emerging rapidly through open-source innovations like Microsoft’s Copilot and CodeStroll. These initiatives highlight a significant shift toward democratizing access to advanced AI capabilities. Yet, their ultimate success will heavily depend on their scalability and the consistent quality of outputs.

Meanwhile, markets for sales automation, productivity tools, and financial analytics remain largely fragmented without clear market leaders. This lack of dominance creates substantial room for innovation, competition, and investment, marking these sectors as particularly promising for entrepreneurs and innovators.

Intersection of Biotech and AI: Uncharted Opportunities

AI continues to unlock groundbreaking possibilities in biotechnology. Fertility treatments, stem cell differentiation, and egg maturation stand out as under-explored areas ripe with substantial commercial potential. Despite the enormous promise, many innovations remain undervalued and underfunded.

Conversely, groundbreaking research in muscle rejuvenation, tooth regeneration, and dental gene therapies faces significant hurdles due to their perceived low commercial appeal and associated developmental barriers. These scientific advancements await commercial champions willing to address these challenges and unlock their potential.

Tackling AI Development Challenges

The concept of building an “AI world model” encapsulates numerous open-ended research questions and challenges. Currently, scaling model size and the volume of training data remains fundamental for enhancing knowledge acquisition and pattern recognition in AI systems.

Reinforcement learning, despite its promise, struggles with challenges like adaptability and overfitting. To overcome these limitations, there’s a critical need to develop universal training environments and improved mechanisms for capturing and utilizing trace data effectively.

Novel AI Approaches

Innovative approaches such as evolved systems and self-selecting systems are pushing boundaries in AI development. These methodologies often yield superior outcomes by navigating search spaces through unconventional strategies, as evidenced by recent successes in molecular evolution experiments and advanced protein design.

By continually exploring and embracing such novel methodologies, AI development is set to achieve breakthroughs previously thought unattainable.

For a deeper exploration of these topics, watch the full episode on YouTube.