Category / AI

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

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.

by dilanshah

Rise of AI Development Environments

The rise of Cursor, Copilot + VSCode, Replit, and Qwen2.5 among others, have caused me to rethink my ways. Focus will still be key in discerning what to build.


AI development environments change the global technology conversation. They also influence the pace of hiring and team augmentation decisions.

Qwen2.5-Coder Open Source

Alibaba Group has released the Qwen2.5-Coder open-source model. Qwen2.5-Coder-32B-Instruct is currently the best-performing open-source code model (SOTA), matching the coding capabilities of GPT-4o. Qwen2.5-Coder offers six different model sizes: 0.5B, 1.5B, 3B, 7B, 14B, and 32B.

Project: https://qwenlm.github.io/blog/qwen2.5-coder-family/

Each size provides both Base and Instruct models. The Instruct model engages in direct dialogue. The Base model serves as a foundational model for developers to fine-tune.

Github: https://github.com/QwenLM/Qwen2.5-Coder

Huggingface: https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f
Additionally, it provides two scenarios, code assistants and Artifacts, for exploration.

Code Assistants: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-demo
Artifacts: https://huggingface.co/spaces/Qwen/Qwen2.5-Coder-Artifacts

by dilanshah

The Future of Augmented Reality Glasses

Meta

  • Five years ago, plans were announced to create AR glasses to blend the digital and physical worlds.
  • Orion, the latest AR glasses from Meta, aims to enhance presence, connectivity, and empowerment.
  • AR glasses offer unrestricted digital experiences with large holographic displays.
  • Contextual AI integration allows for proactive addressing of user needs.
  • Orion stands out for its lightweight design, indoor/outdoor versatility, and emphasis on interpersonal interactions.
  • Orion represents a significant advancement in AR glasses technology, combining wearability with advanced features.
  • The groundbreaking AR display in Orion offers immersive experiences and a wide field of view.
  • Orion’s unique design maintains a glasses-like appearance, allowing users to see others’ expressions.
  • Orion’s capabilities include smart assistant integration, hands-free communication, and immersive social experiences.
  • While not yet available to consumers, Orion serves as a polished product prototype for future AR glasses development.

OP: Introducing Orion, [Meta’s] First True Augmented Reality Glasses

Snap

Sophia Dominguez, the Director of AR Platform at Snap, discussed Snap Spectacles and the company’s AR initiatives at the Snap Lens Fest original post here.

  • Snap Spectacles can be connected to a battery pack for extended use beyond the standard 45 minutes, with a focus on B2B interactions that directly engage consumers.
  • There is a push for Snap to collaborate with various businesses, such as those in location-based entertainment or museums, to expand the Snap Spectacles ecosystem.
  • Sophia Dominguez has been involved in AR for over a decade, starting with Google Glass, and now oversees developers and partners creating lenses on Snapchat.
  • Snap’s approach to AR emphasizes personal self-expression as a catalyst for AR lenses, transitioning to world-facing AR lenses like those in Snap Spectacles.
  • Snap’s long-term vision is to make AR ubiquitous and profitable for developers, aiming to integrate digital objects seamlessly into the real world.
  • Snap’s focus on consumer-level AR use cases includes self-expression as a core feature, offering a variety of options for users to engage with AR content.
  • Snap’s AR platform also caters to enterprise and B2B applications, collaborating with stadiums, museums, and other businesses for unique AR experiences beyond consumer-facing lenses.
  • Snap’s technology, like Snapchat cam, is designed for venues to integrate into large screens or jumbotrons, focusing on consumer desires for virality and joy rather than just enterprise solutions.
  • The company aims to increase ubiquity by making lenses fun and approachable, partnering with entities like the Louvre to explore augmented reality possibilities in a consumer-friendly manner.
    HTC Vive has delved into location-based entertainment more than Meta, and Snap is prioritizing connected experiences, ensuring fast connectivity and optimizing for various use cases like museum activations.
  • Snap collaborates closely with developers, offering grants and support without strings attached to foster innovation in the augmented reality space, aiming to be the most developer-friendly platform globally.
  • Snap’s Spectacles have evolved over the years, from simple camera glasses to AR display developer kits, with the latest fifth generation focusing on wearability, developer excitement, and paving the way for consumer adoption.
  • The company has revamped Lens Studio to encompass mobile and Spectacles lenses, emphasizing ease of use and spatial experiences, aiming to create a seamless ecosystem for developers across different platforms.
  • Snap values feedback and collaboration with developers, striving to provide pathways for monetization and support for creators building on both mobile and Spectacles platforms.
  • Snap’s Spectacles offer a unique immersive experience, leveraging standalone capabilities and spatial interactions, aiming to enable emergent social dynamics and experiences not possible on other devices.
  • Developers are considering the length of time users spend on devices like Zoom calls or workouts, with a focus on creating a seamless experience for users on the go.
  • The new SnapOS manages a dual processing architecture for Spectacles, with Lens Studio being the primary pipeline for developers to create content for the device.
  • Snap is actively listening to developer feedback and working on enabling WebXR on Spectacles to support a variety of use cases and experiences.
  • The operating system for Spectacles includes features like connected lenses, hands and voice-based UI, and social elements out of the box to facilitate easier development.
  • The ultimate potential of spatial computing is envisioned as a way to break free from the limitations of screens, allowing for more natural interactions and connections in the real world.
  • Snap aims to empower developers to explore the possibilities of augmented reality and spatial computing, emphasizing ease of use and continuous improvement based on user feedback.

by dilanshah

Useful Resources for AI

Newsletters/blogs:
– TLDR AI (https://tldr.tech/ai) – Andrew Tan
– Ben’s Bites (https://lnkd.in/gNY8Dmme)
– The Information *paid subscription required (https://lnkd.in/gbkaFbvf)
– Last week in AI (https://lastweekin.ai/)
– Eric Newcomer (https://www.newcomer.co/)

Podcasts:
– No priors with Sarah Guo + Elad Gil (https://lnkd.in/g7Wmr6XT)
– All-in podcast – not AI specific but they talk a lot about it (https://lnkd.in/gH35UeUy)
– Lex Fridman (https://lnkd.in/gjw7zsWX)

Online courses:
– DeepLearning.ai by Andrew Ng – https://lnkd.in/gWcn5UTK

Institutional VC writing: 
– Sequoia (https://lnkd.in/g-cKpn8Y)
– A16z (https://lnkd.in/g6JxqwZA)
– Lightspeed (https://lnkd.in/gczzdEcd)
– Bessemer (https://www.bvp.com/ai)
– Radical Ventures (https://lnkd.in/guCe5Mnt); Rob Toews (https://lnkd.in/ggH8HfT8) and Ryan Shannon (https://lnkd.in/gRrBzePx)
– Madrona (https://lnkd.in/gy5D8yNG)

Industry Conferences:
– Databricks Data + AI Summit (https://lnkd.in/gF5QyXYv)
– Snowflake (https://lnkd.in/gavqzw65)
– Salesforce Dreamforce (https://lnkd.in/gJk4r58N)

Academic Conferences:
– NeurIPS (https://neurips.cc/)
– CVPR (https://cvpr.thecvf.com/)
– ICML (https://icml.cc/)
– ICLR (https://iclr.cc/)

Books:
– Genius makers, by Cade Metz (https://lnkd.in/gr_78MB9)
– A Brief History of Intelligence, by Max Bennett (https://lnkd.in/g2uCrPzS)
– The worlds I see, by Fei-Fei Li (https://lnkd.in/gY8Qsvis)
– Chip Wars, by Chris Miller (https://lnkd.in/g6ZAZSCG)

The original author of this post was Kelvin Mu on Linkedin.