Moltworker uses Anthropic's API for AI processing.

Ties the agent's capabilities to Anthropic's model performance and pricing.

Research Anthropic's models and pricing to understand the cost implications and potential limitations of Moltworker.


The agent can be integrated with Telegram, WhatsApp Business, Facebook Messenger, Shopify, and WooCommerce through their APIs.

Cloudflare's New AI Agent - Moltworker - Crushes $1,5000/m Tasks (Crazy)

Enables the agent to interact with customers across various platforms and automate tasks within e-commerce workflows.

Explore the APIs of these platforms to understand the integration possibilities and limitations for building similar AI agents.


The agent is deployed as a separate Cloudflare Worker instance for each client to ensure isolation.

Provides security and data privacy for each client's AI agent.

Implement a similar isolation strategy when deploying AI agents for multiple clients.


The business model focuses on providing AI agent services to local businesses, real estate agents, and e-commerce stores.

Identifies specific target markets for AI agent services.

Research these target markets to understand their specific needs and challenges that AI agents can address.


Pricing strategies include setup fees ranging from $800 to $5,000 and monthly fees ranging from $300 to $600, depending on the complexity and value provided.

Provides a benchmark for pricing AI agent services.

Develop a pricing model that considers the complexity of the AI agent, the value provided to the client, and the competitive landscape.


The strategy involves cloning an existing Moltworker setup and deploying a new worker for each client, then customizing it for their specific needs.

Offers a repeatable process for scaling AI agent deployments.

Create templates and documentation to streamline the deployment and customization process for new clients.


The agent is secured using Cloudflare Access authentication.

Protects the agent from unauthorized access.

Implement robust authentication and authorization mechanisms to secure AI agents.


The agent's permissions are initially limited and gradually increased as its capabilities are proven.

Reduces the risk of unintended consequences and ensures the agent's behavior is predictable.

Adopt a least-privilege approach when granting permissions to AI agents.


The importance of documenting workflows and prompts for replication and improvement.

Enables efficient scaling and continuous improvement of the AI agent.

Establish a system for documenting workflows, prompts, and configurations to facilitate replication and optimization.


The agent can be used for local service business automation (answering FAQs, booking appointments, sending confirmations), lead qualification, and e-commerce customer support.

Identifies specific use cases for AI agents.

Develop targeted marketing materials and sales pitches for these specific use cases.


The video highlights the importance of offering a free demo to potential clients to showcase the agent's capabilities and gather feedback.

Provides a strategy for acquiring new clients.

Develop a compelling demo that showcases the agent's capabilities and addresses the specific needs of potential clients.


The video emphasizes the need to start now and be early in the market before AI agents become mainstream.

Creates a sense of urgency and encourages immediate action.

Prioritize the development and deployment of AI agent solutions to capitalize on the current market opportunity.


OpenAI acquired OpenClaw and Meta acquired Manis AI, both autonomous agent platforms, indicating a trend towards mainstream adoption of AI agents.

OpenAI Bought OpenClaw — Here’s What It Told Me About Making Money in 2026

Highlights the increasing importance of AI agents and the strategic moves of major tech companies in this space.

Monitor acquisitions and developments in the AI agent space by major tech companies like OpenAI, Meta, and potentially Anthropic.


The speaker uses Manis AI to build lead lists of small businesses with specific revenue targets, leveraging the SimilarWeb integration for analysis.

Demonstrates a practical application of AI agents for lead generation and qualification.

Explore using Manis AI or similar tools with SimilarWeb integration for targeted lead generation, focusing on businesses with specific revenue profiles.


The speaker recommends using High Level (a SaaS platform) to create and schedule social media content for small businesses, offering a free trial to potential clients as a way to get their foot in the door.

Provides a concrete method for offering value upfront and securing clients in the AI marketing space.

Evaluate High Level or similar platforms for their AI-powered content creation and scheduling capabilities. Consider offering free trials or sample content to potential clients.


The speaker emphasizes selling 'activity' (consistent social media posting) rather than 'results' (increased sales or engagement) to small businesses, framing it as providing a 'heartbeat' for their online presence.

Offers a specific and effective positioning strategy for AI-powered social media services.

When offering social media services, focus on the value of consistent activity and presence, rather than promising specific ROI metrics.


The speaker targets small businesses with 500-1000 followers that haven't posted in 6 months to a year, as they likely understand the value of social media but have encountered challenges in maintaining it.

Identifies a specific niche market with a high likelihood of conversion for AI-powered social media services.

Focus marketing efforts on small businesses with a dormant social media presence, highlighting the ease and affordability of AI-powered solutions.


The speaker generates revenue by selling AI-powered social media marketing services to small businesses on a subscription basis, collecting $27,000 in a month.

Validates the viability of a SaaS model for AI marketing services.

Develop a subscription-based pricing model for AI marketing services, offering different tiers based on the frequency of posts, number of platforms, and other features.


The speaker mentions using Gemini (Google's AI) and Manis AI to build lead lists.

Highlights the use of multiple AI tools for lead generation.

Experiment with both Gemini and Manis AI for lead generation to determine which tool is more effective for specific needs.


The speaker uses AI to create images and captions for social media posts, which are then scheduled using High Level.

Demonstrates a workflow for automating social media content creation and scheduling.

Implement a workflow that leverages AI for content creation and scheduling, using tools like High Level to automate the process.


The speaker suggests hiring overseas team members to scale the AI marketing agency after acquiring initial clients.

Provides a strategy for scaling the business by leveraging affordable labor.

Develop a plan for hiring and training overseas team members to handle content creation and scheduling tasks as the business grows.


The speaker offers a free four-part AI FastTrack training and a free trial to High Level in exchange for liking, subscribing, and commenting on the video.

Demonstrates a lead generation strategy using valuable free content and software trials.

Create valuable free content (e.g., training, templates) and offer free trials to software tools to attract leads and build an audience.


The speaker mentions that Anthropic might be the next company to acquire AI agent tech.

Provides a prediction about future acquisitions in the AI agent space.

Monitor Anthropic's activities and potential acquisitions in the AI agent space.


The AI system is capable of understanding a user's goal ('get a six pack quickly') and determining if it's clear and achievable.

Demonstrates basic goal understanding and task initiation capabilities.

Evaluate the system's ability to handle more complex or ambiguous goals.


The AI system personalizes workout plans based on user-provided data (age, weight, height).

Shows a basic level of personalization based on user profile information.

Investigate the depth and sophistication of the personalization algorithm. How does it adapt to user progress and feedback?


The AI system remembers previous turns in the conversation (e.g., 'Perfect' after the user agrees to a personalized plan).

Indicates a basic conversational memory or state management.

Assess the system's ability to maintain context over longer and more complex conversations.


The AI system recommends 'aesthetic strength training' based on the user's goal.

Suggests the system has some knowledge of fitness domains and can map goals to relevant training approaches.

Analyze the knowledge base and reasoning process used to make this recommendation. How does it handle conflicting or nuanced goals?


The AI system is used to promote a product (Step Boost Maxx insoles) using a discount code.

Suggests a potential revenue model through product placement or affiliate marketing.

Explore the potential for integrating relevant product recommendations into the AI's responses, ensuring they align with user goals and preferences.


The AI system asks for specific user data (age, weight, height) to personalize the workout plan.

Highlights the importance of data collection for personalization and the need for privacy considerations.

Review the data privacy policies and security measures in place to protect user information.


The AI system uses a conversational tone and confirms user input ('Got it').

Demonstrates basic natural language processing and conversational capabilities.

Evaluate the system's ability to handle different communication styles and adapt to user feedback on its tone.


The AI system is being compared to the user ('What's the difference between me and you?').

Implies the system is positioned as a distinct entity with its own capabilities.

Investigate the system's self-awareness and ability to differentiate itself from human users.


OpenAI released GPTOSS 120B and GPTOSS20B open weight models, allowing local execution and customization.

OpenAI's Open Source Release: Download it Before Your Competition Does

Enables cost-effective AI solutions, reduces reliance on API calls, and provides greater control over data and model behavior.

Download and experiment with GPTOSS models to understand their capabilities and limitations.


OpenAI released safeguard versions of GPTOSS models with built-in safety checks.

Facilitates the development of AI agents that interact with customers safely and responsibly.

Prioritize using safeguard models when building customer-facing AI applications.


OpenAI is releasing open models to compete with Meta's Llama and other open-source AI initiatives.

Increased competition in the open-source AI space benefits developers and businesses by providing more options and driving innovation.

Monitor the competitive landscape and evaluate different open-source models based on performance, features, and licensing terms.


OpenAI launched Open AI for countries to help governments build AI infrastructure using open models.

Expands the OpenAI ecosystem and increases adoption of its technology.

Explore opportunities to partner with governments or organizations involved in AI infrastructure development.


OpenAI is pushing Open Responses to make it easier to build AI agents across different providers.

Increases the portability and interoperability of AI agents, reducing vendor lock-in.

Investigate Open Responses and consider using it to build AI agents that can be deployed across multiple platforms.


The speaker recommends using HuggingFace to download the models and tools like Transformers, O Lama, or VLLM to run them.

Provides a starting point for accessing and utilizing the open-weight models.

Explore HuggingFace for model downloads and research Transformers, O Lama, and VLLM for runtime environments.


The speaker suggests offering custom AI solutions to businesses, specialized fine-tuning, and AI agent development as revenue streams.

Provides actionable business models leveraging the open-weight models.

Develop a business plan focusing on one or more of these revenue streams, targeting specific industries or use cases.


The speaker offers a free four-part AI FastTrack training and access to their AI agency owner community.

Provides resources for learning how to land AI clients and build a business.

Sign up for the AI FastTrack training and join the AI agency owner community.


The speaker emphasizes the importance of identifying a specific problem to solve with the open-weight models and building a solution around it.

Focuses development efforts on creating valuable and marketable solutions.

Brainstorm potential use cases for the open-weight models and prioritize those that address specific, well-defined problems.


The speaker highlights the advantage of being early in adopting and utilizing these open-weight models.

Early adopters can establish themselves in the market and gain a competitive edge.

Prioritize learning and experimenting with the open-weight models to gain a head start.


Pomelli AI is a free website scanner that extracts images and text to create brand data/DNA for marketing campaigns.

Pomelli AI Got a Massive Update — Google’s FREE Website Scanner Does What Some Charge $2,500 For

Allows users to quickly gather brand assets and generate marketing materials.

Explore Pomelli AI for extracting brand data and generating marketing campaigns for clients.


The presenter's AI agency in Hawaii uses AI tools to offer services like website rentals ($100/month), social media posting ($2,000/month), and AI search optimization ($8,000-$10,000/month).

Provides pricing benchmarks and service offerings for an AI-powered marketing agency.

Benchmark pricing and service offerings against the presenter's agency to identify competitive advantages and potential service gaps.


The presenter offers a four-part AI FastTrack training, a 30-day free trial of an AI software tool (GoHighLevel), and a prompt document in exchange for liking, subscribing, sharing, and commenting on the video.

Demonstrates a lead generation strategy using free resources and a freemium model.

Analyze the AI FastTrack training and GoHighLevel to understand the presenter's client acquisition and service delivery methods.


Pomelli AI's new update includes a 'Photoshoot' feature that allows users to create product photoshoots using images from the customer's website.

Expands Pomelli AI's capabilities to include product photography, potentially attracting e-commerce clients.

Test the 'Photoshoot' feature with different product types and industries to assess its effectiveness and identify potential use cases.


The presenter suggests selling content packs generated by Pomelli AI to small businesses for $300-$2,000 per month, or offering wholesale prices to agencies for $300-$600 per month.

Provides pricing strategies for monetizing content generated by Pomelli AI.

Evaluate the pricing tiers and target audience (small businesses vs. agencies) to determine the most profitable revenue model.


The presenter uses GoHighLevel to automate social media posting for clients, offering a 30-day free trial.

Highlights the importance of automation tools for scaling social media management services.

Explore GoHighLevel's social media planning and automation features to streamline content delivery and client management.


The presenter recommends targeting businesses with 500-1000 social media followers that haven't posted in 6 months to a year, offering them two weeks of free social media content as an irresistible offer.

Provides a specific lead generation strategy with a defined target audience and value proposition.

Implement the lead generation strategy by identifying businesses that meet the criteria and crafting a compelling offer of two weeks of free social media content.


The presenter uses Gemini to generate a list of high-ticket local businesses with a weak or inactive social media presence.

Demonstrates the use of AI for lead generation and qualification.

Develop similar prompts for Gemini to identify potential clients in specific niches or geographic areas.


The presenter emphasizes selling social media *posting* rather than social media *marketing*, focusing on taking over the task of posting daily content rather than guaranteeing specific results like sales or leads.

Defines a clear service offering and manages client expectations by focusing on content delivery rather than performance metrics.

Refine service offerings to emphasize content creation and delivery, rather than promising specific marketing outcomes.


The presenter uses a specific email template offering two weeks of free content generated from the prospect's website.

Provides a concrete example of an effective outreach strategy.

Adapt the email template to different industries and service offerings, focusing on personalized messaging and a clear value proposition.


Meta acquired Manus AI for $2 billion and integrated it into their ad platform within a month, demonstrating the rapid pace of AI adoption in advertising.

Connect Manus AI to Meta Ads — Then Flip It Into a Paid Service (Here's How - Step by Step)

Highlights the value Meta places on AI-driven ad optimization and the speed at which these technologies are being integrated.

Monitor Meta's use of Manus AI for insights into future AI-driven ad strategies and potential competitive advantages.


Manus AI is a multitasking autonomous agent capable of handling 50 tasks in parallel, including accessing different browsers and platforms.

Demonstrates the potential of AI agents to automate complex workflows and access multiple data sources.

Investigate Manus AI's architecture and capabilities to understand how it achieves multitasking and autonomous operation.


The speaker prefers Manus AI over Claude because it's browser-based and doesn't require installation, offering compartmentalized access.

Highlights the importance of accessibility and ease of use in AI tool adoption.

Consider browser-based deployment options for AI tools to improve accessibility and reduce friction for users.


Manus AI can generate ad performance review slides and cross-analysis reports within minutes, providing valuable insights for ad optimization.

Demonstrates the potential of AI to automate ad analysis and reporting, saving time and resources.

Explore using Manus AI or similar tools to automate ad performance analysis and generate actionable insights.


The speaker suggests leveraging Manus AI's audit capabilities to offer ad audit services to small businesses, even without deep ad expertise.

Provides a concrete strategy for monetizing AI tools and services by leveraging their capabilities to provide value to clients.

Develop a service offering based on Manus AI's ad audit capabilities, targeting small businesses that need help with their ad campaigns.


The speaker recommends outsourcing ad implementation to specialists on platforms like Upwork after securing a client using Manus AI's audit.

Outlines a scalable agency model that leverages AI for lead generation and outsourcing for service delivery.

Build a network of ad specialists on platforms like Upwork to handle ad implementation for clients acquired through Manus AI-driven lead generation.


The speaker uses Gemini with a specific prompt to identify small businesses running Google Ads, including their contact information and ad evidence.

Provides a valuable lead generation strategy for targeting businesses already investing in online advertising.

Adapt the speaker's Gemini prompt to identify potential clients in specific industries and locations for targeted outreach.


The speaker highlights the importance of creative content in advertising and uses AI avatars to create content and grow reach.

Emphasizes the need for creative solutions in advertising and suggests using AI to automate content creation.

Explore using AI avatars and other AI-powered tools to create engaging content for advertising campaigns.


The speaker offers a four-part AI fast track training, prompt documents, and software trials to viewers who like, subscribe, and comment on the video.

Demonstrates a lead generation strategy using free content and resources to attract potential clients.

Analyze the speaker's AI fast track training and prompt documents to understand their content and value proposition.


NotebookLM allows custom instructions up to 10,000 characters to define AI behavior, tone, audience level, formatting, and focus.

These Secret NotebookLM Settings Make It 10x More Powerful

Enables highly tailored AI responses, reducing the need for repetitive instructions and reformatting.

Experiment with different custom instruction sets to optimize AI output for specific tasks and client needs.


NotebookLM offers different response styles (default, learning guide) and length controls (short, long) to tailor AI output.

Provides flexibility in generating different types of content, from quick answers to detailed explanations.

Utilize response style and length controls to optimize AI output for different use cases, such as quick research or in-depth analysis.


NotebookLM's interactive audio overview feature allows users to engage in live Q&A with AI hosts summarizing uploaded documents.

Enables active learning and deeper understanding of complex information through interactive conversation.

Incorporate interactive audio overviews into workflows for efficient information absorption and knowledge sharing.


NotebookLM offers 'Fast Research' and 'Deep Research' web search options to import external sources directly into notebooks.

Streamlines research workflows by automating web searches and source gathering.

Use 'Deep Research' for comprehensive competitive analysis and 'Fast Research' for quick information gathering.


NotebookLM integrates with Google Drive, allowing users to import documents directly into notebooks without downloading and re-uploading.

Simplifies the process of incorporating existing documents and client files into NotebookLM workflows.

Connect NotebookLM to Google Drive to easily access and integrate existing documents into notebooks.


NotebookLM allows users to convert synthesized notes into sources to overcome the source limit on the free version.

Provides a workaround for managing large projects with limited source slots, albeit with a trade-off in citation accuracy.

Utilize the 'convert notes to source' feature to manage source limits on the free version, but be mindful of the loss of inline citations.


NotebookLM's Studio panel can generate mind maps, flashcards, quizzes, video overviews, slide decks, and infographics from uploaded sources.

Transforms research into multiple content formats, increasing leverage and creating client deliverables.

Explore the Studio panel to generate diverse content formats from research, such as slide decks and infographics, for client presentations and marketing materials.


The speaker offers a free four-part AI FastTrack training that covers AI tools for landing clients and running a business, including a 30-day free trial of AI software for client management and automation.

Provides access to valuable training and software for building an AI-driven business.

Sign up for the free AI FastTrack training to learn about AI tools for client acquisition and management, and take advantage of the 30-day free trial.


Combining deep research, private documents, custom instructions, and the Studio panel creates a powerful workflow for consulting work, allowing for informed client interactions and impressive deliverables.

Enhances the quality and efficiency of consulting services by leveraging AI for research, analysis, and content creation.

Implement the recommended workflow for consulting projects, combining deep research, private documents, custom instructions, and the Studio panel to deliver high-quality results.


The video highlights the competitive advantage gained by users who know and utilize the hidden settings and features of NotebookLM.

Emphasizes the importance of continuous learning and exploration of AI tools to stay ahead of the competition.

Continuously explore and experiment with new features and settings in AI tools to maximize their potential and gain a competitive edge.


Offer AI visibility and marketing services to local businesses.

AI Just Took Over Google Search — Here’s How to Get Paid With AEO + GEO + SEO

Capitalizes on the growing need for AI-driven marketing solutions.

Develop and market AI visibility and marketing packages tailored to local businesses.


Businesses are willing to pay for help showing up in AI search (e.g., ChatGPT).

Validates the market demand for AI SEO services.

Focus marketing efforts on the value proposition of improved AI search visibility.


Offer a GEO package for $1,000 monthly retainer.

Provides a benchmark for pricing AI SEO services.

Develop tiered pricing models for AI SEO services, starting at $500/month and scaling to $1000+/month.


Use rank tracking software to monitor and improve client online visibility.

Demonstrates the importance of tracking and reporting on AI SEO performance.

Implement rank tracking software to monitor client progress and demonstrate ROI.


Offer a four-part AI fasttrack training for free to generate leads.

Attracts potential clients and builds authority in the AI SEO space.

Create and promote a free AI fasttrack training series covering AI search visibility and client acquisition.


Offer a free trial to a software tool that helps get clients, grow business, and increase search visibility by getting more reviews.

Incentivizes sign-ups and showcases the value of the software.

Partner with a relevant software provider to offer a free trial as part of the lead generation strategy.


Use question-based headings to optimize content for AI search.

Improves content discoverability and relevance for AI-powered search engines.

Incorporate question-based headings into website content and client SEO strategies.


Strengthen entity data (name, address, phone number, services, reviews) to build trust with AI.

Enhances business credibility and visibility in AI search results.

Ensure consistent and accurate business information across all online platforms, including Google Business Profile, Apple Maps, and Yelp.


Use Go High Level to claim and optimize Google Business Profile listings.

Streamlines GBP management and improves local search ranking.

Utilize Go High Level or similar tools to manage and optimize Google Business Profiles for clients.


Send review requests to customers to improve review engagement and sentiment.

Increases positive reviews and enhances business reputation in AI search.

Implement a review request system to solicit feedback from satisfied customers.


Claim business listings on Apple Maps, Yelp, and other directories (citations).

Ensures consistent business information across multiple platforms and improves local search visibility.

Claim and optimize business listings on relevant online directories.


Google's Gemini offers guided learning on AI search visibility and other topics.

Provides a free resource for learning about AI SEO best practices.

Utilize Google's Gemini guided learning feature to stay up-to-date on AI SEO strategies.


AI models analyze the sentiment of reviews.

Highlights the importance of positive reviews for AI search ranking.

Focus on providing excellent customer service to encourage positive reviews.


AI search tools like Perplexity often cite Reddit, local news, and niche blogs.

Indicates the importance of building a strong online presence and generating mentions on relevant platforms.

Develop a content marketing strategy that includes guest blogging, social media engagement, and local media outreach.


Google's AI mode, powered by Gemini 3 Flash, generates AI answers directly in search results, significantly impacting organic click-through rates.

Google AI Mode In Search Is Changing SEO — Here’s How You Profit First

Organic click-through rates dropped by 61% when AI answers are shown.

Shift SEO strategy to focus on being cited within AI-generated answers, not just ranking on page one.


Gemini 3 Flash powers Google AI mode, offering faster responses, better reasoning, and improved handling of complex questions related to buying decisions, comparisons, and local businesses.

AI-generated answers are becoming more sophisticated and influential in user decision-making.

Optimize content to directly address complex questions related to buying decisions, comparisons, and local businesses to improve chances of being cited by Gemini 3 Flash.


Traditional SEO methods (keyword rankings, backlinks) are becoming less effective as Google prioritizes AI-generated answers.

Businesses relying solely on traditional SEO may experience significant traffic decline.

Diversify SEO strategy to include content optimization for AI citation, focusing on answering real questions and building brand authority.


The opportunity lies in helping businesses get cited within Google's AI-generated answers.

New service offering for SEO agencies to focus on AI citation optimization.

Develop a service offering focused on optimizing content and brand presence to be cited in Google's AI answers. Target businesses experiencing traffic decline.


Focus on structuring content around real questions people ask, building brand presence across multiple platforms, and earning authority signals from credible sources.

Increased likelihood of being cited as a trusted source by Google's AI.

Implement a content strategy focused on answering common questions, actively build brand presence on various platforms (local news, directories, review sites), and seek out opportunities for credible sources to reference the business.


AI overviews are expanding into commercial and navigational searches, impacting businesses targeting customers ready to buy.

Significant opportunity to capture high-intent traffic by being cited in AI answers for commercial searches.

Prioritize optimizing content for commercial searches, focusing on providing information relevant to purchasing decisions. Target businesses selling products or services.


The speaker offers a free four-part AI FastTrack training and a 30-day free trial of AI business software.

Opportunity to learn AI tools and strategies for building an AI-powered business and landing clients.

Sign up for the free AI FastTrack training and utilize the 30-day free trial of the AI business software to explore potential applications and benefits.


The speaker mentions a free community called the AI control room for people building AI-powered businesses.

Access to a network of individuals working on AI-related projects and businesses.

Join the AI control room to connect with other AI enthusiasts, share knowledge, and potentially collaborate on projects.


Stitch uses Gemini 2.5 Pro for UI design, making it a powerful design agent.

Website Creation Is Now 10x Faster (Google Stitch + AntiGravity Just Changed Everything)

Leverages advanced AI for high-quality UI design, potentially reducing design time and effort.

Evaluate Gemini 2.5 Pro's capabilities and limitations for UI design tasks.


Anti-gravity is an agentic development platform that builds software, not just provides suggestions.

Automates software development, potentially reducing development time and cost.

Investigate Anti-gravity's agentic development capabilities and its integration with other tools.


Stitch has 'agent skills' that allow Anti-gravity to interact with it, read designs, modify them, and pull entire projects.

Enables automated design modification and project management, streamlining the website creation process.

Explore Stitch's agent skills and their integration with Anti-gravity for automated design workflows.


Anti-gravity can build websites in a loop, creating multiple websites simultaneously with the same style but different content.

Enables rapid website creation for multiple clients or projects, increasing efficiency and scalability.

Test Anti-gravity's website loop functionality to automate the creation of multiple websites.


MCP (Model Context Protocol) is used to connect Anti-gravity to Stitch, allowing Anti-gravity to access Stitch projects.

Provides a bridge between the design and development tools, enabling seamless data transfer and workflow automation.

Implement the MCP setup to connect Anti-gravity and Stitch for accessing design guidelines and projects.


Anti-gravity uses Gemini 3 Pro during preview, scoring 76.2% on swb bench verified, almost matching clawed sonnet 4.5.

Demonstrates Anti-gravity's ability to handle real production code, not just toy projects.

Monitor the performance of Gemini 3 Pro in Anti-gravity and compare it with other models like Claude Sonnet 4.5.


Template marketplace model: Building website templates in Stitch, turning them into full applications with anti-gravity and selling them for $200-$1000.

Offers a potential revenue stream by creating and selling working website applications.

Evaluate the feasibility of creating and selling website templates as working applications using Stitch and Anti-gravity.


Done-for-you service model: Building custom sites for local businesses on a subscription basis, using anti-gravity to maintain and add features.

Provides a recurring revenue stream by offering website design and maintenance services.

Explore the possibility of offering subscription-based website design and maintenance services to local businesses.


White label agency model: Partnering with marketing agencies to provide website development services, splitting the revenue.

Allows leveraging existing marketing agency networks to acquire clients and generate revenue.

Investigate potential partnerships with marketing agencies to offer website development services.


The video promotes a four-part AI FastTrack training that includes client acquisition systems and AI tools.

Offers a comprehensive training program for building an AI services business.

Evaluate the AI FastTrack training program to learn about client acquisition and AI tools.


Stitch was built from Google's acquisition of Galileo AI and rebuilt with Gemini 2.5.

Highlights the evolution of Stitch and its reliance on advanced AI models.

Monitor the development and updates of Stitch and its underlying AI models.


The video mentions the importance of design fundamentals (white space, hierarchy, call to action placement) even when using AI tools.

Emphasizes the need for design expertise to create effective websites, even with AI assistance.

Ensure that design principles are incorporated into website creation workflows, even when using AI tools.


The video suggests building a template in Stitch, exporting it to Anti-Gravity, and then using Anti-Gravity to generate multiple websites with the same guidelines but different content.

Provides a structured workflow for efficient website creation and scaling.

Implement the suggested workflow to create and scale website development projects.


The video suggests offering website design as a subscription, charging $500 per month for 20 clients, resulting in $10,000 per month recurring revenue.

Presents a specific pricing and revenue model for subscription-based website design services.

Evaluate the feasibility of offering website design subscriptions at the suggested pricing and client volume.


RLMs can potentially double the performance of GPT-5 Mini on long context tasks while maintaining comparable or cheaper API costs.

Significant performance boost and cost efficiency for long context AI voice agents.

Prioritize research and development into RLMs using smaller, cost-effective models like GPT-5 Mini.


RLMs can scale effectively with the number of documents in a corpus for multi-hop retrieval tasks.

Addresses the limitations of traditional retrieval methods in handling large knowledge bases for AI voice agents.

Investigate the scalability of RLMs with different knowledge graph sizes and retrieval strategies.


The speaker emphasizes the importance of code environments (like a ripple) for RLMs, suggesting that models trained on code are better at manipulating objects in memory.

Code-based environments may improve the reasoning and action capabilities of AI agents.

Explore using code-based environments for multi-agent orchestration and memory management.


The speaker suggests that RLMs can be optimized for specific scaffolds through RL or SFT, potentially leading to more efficient and generalizable agents.

Offers a path to improve the performance and adaptability of AI agents across various tasks.

Research and experiment with RL and SFT techniques to optimize RLMs for specific agent scaffolds.


The speaker positions RLMs as a bet on the 'bitter lesson,' suggesting that human-engineered scaffolds should be minimized in favor of allowing the language model to manage context and tool calling itself.

Suggests a shift in agent design towards more autonomous and adaptable systems.

Explore architectures that minimize human-engineered scaffolds and maximize the language model's control over context and tool usage.


The speaker mentions the limitations of existing language models in handling long context and the high cost of training models on longer sequences.

Highlights the need for alternative approaches like RLMs to overcome these limitations.

Prioritize research into methods that circumvent the need for extremely long context windows.


The speaker suggests that any task can be framed as a long context task, implying that RLMs could be beneficial even for tasks that don't initially seem to require long context.

Broadens the potential applications of RLMs beyond traditional long context tasks.

Explore the applicability of RLMs to a wider range of tasks, including those that can be reframed as long context problems.


The speaker mentions the potential to equip RLMs with retrievers, combining the benefits of both approaches.

Offers a hybrid approach that could improve performance and flexibility.

Investigate the integration of retrieval mechanisms with RLMs.


The speaker highlights the limitations of retrieval methods for dense prompts, suggesting that RLMs are more suitable for tasks requiring dense reasoning over large amounts of data.

Identifies a specific niche where RLMs excel compared to traditional retrieval methods.

Focus on tasks involving dense reasoning over large datasets when deploying RLMs.


The speaker mentions that the distinction between a query and a context is not necessary and that all context can be treated as the prompt.

Simplifies the architecture and allows for more flexible handling of information.

Design the system to treat all input as part of the prompt, allowing the RLM to decide what is relevant.


The speaker mentions that the Ulong benchmark is a good test for context rot, as it requires dense reasoning over the entire input.

Provides a benchmark for evaluating the effectiveness of RLMs in mitigating context rot.

Use the Ulong benchmark (when available) to evaluate the performance of RLMs.


The speaker emphasizes the importance of allowing the language model to manage its own context, rather than relying on human-engineered scaffolds.

Leads to more adaptable and robust systems.

Design the system to allow the RLM to manage its own context, rather than relying on human-engineered scaffolds.


The speaker suggests that RLMs can be used to create the illusion of a language model call that can handle near infinite context without degradation.

This is a key selling point for businesses that need to process large amounts of data.

Market RLMs as a solution for handling near infinite context without degradation.


The speaker mentions that RLMs can be used to defer the management of context, calling sub-models, and dealing with tool calling to the language model itself.

This simplifies the development process and allows for more flexible systems.

Design the system to defer the management of context, calling sub-models, and dealing with tool calling to the language model itself.


The speaker mentions that RLMs are not meant to be a replacement for every agent available, but rather a replacement for the base language model function call.

This allows for a more modular and flexible architecture.

Design the system to be modular, allowing for the integration of other agents as needed.


RLMs can double performance on long context tasks while being cost-effective.

Demonstrates a clear advantage in performance and cost for specific long-context tasks, a key selling point.

Prioritize benchmarking RLMs on relevant business use cases to quantify potential ROI.


The speaker sees RLMs as a bet on the bitter lesson, favoring model-driven context management over human-engineered scaffolds.

Suggests a long-term trend towards more autonomous and adaptable AI agents.

Invest in research and development of RLMs that can learn and optimize their own context management strategies.


The speaker believes that any task can be framed as a long context task.

Expands the potential applications of RLMs beyond traditional long-context scenarios.

Explore how RLMs can be applied to improve performance on seemingly short-context tasks by treating reasoning and tool usage as long context.


Ripple environment (code execution) is seen as a useful context container for RLMs.

Provides a concrete implementation strategy for RLMs, allowing them to interact with and manipulate data in a flexible way.

Develop RLMs that can leverage code execution environments to process and reason about information.


RLMs can scale well in multi-hop retrieval tasks.

Demonstrates the potential of RLMs for complex information gathering and analysis.

Develop RLMs that can perform multi-hop retrieval over large datasets.


The speaker emphasizes the importance of treating the context as an object that the language model can interact with.

Highlights a key design principle for RLMs, enabling them to reason about and manipulate information more effectively.

Design RLMs with mechanisms for inspecting, transforming, and partitioning context.


The speaker suggests that RLMs can be optimized with RL or SFT.

Offers a path for improving the performance and efficiency of RLMs.

Explore using RL and SFT to train RLMs to better manage context and make decisions.


The speaker mentions the limitations of single model calls and the need for higher abstraction.

Justifies the need for multi-agent orchestration systems.

Investigate how RLMs can be integrated into multi-agent systems to improve their overall performance.


The speaker mentions the potential for RLMs to replace the base language model function call.

Suggests a potential disruption of the current AI agent architecture.

Explore the possibility of replacing the base language model function call with RLMs in existing AI agent architectures.


The speaker mentions the potential for RLMs to be equipped with retrievers.

Offers a way to improve the performance of RLMs in retrieval tasks.

Investigate how RLMs can be combined with retrievers to create more powerful information retrieval systems.


The speaker highlights the limitations of current language models regarding context length and context rot.

Reinforces the need for alternative approaches to long-context processing.

Focus on developing RLMs that can overcome the limitations of current language models regarding context length and context rot.


The speaker mentions the potential for RLMs to be used in summarization tasks.

Offers a specific application area for RLMs.

Develop RLMs that can be used for summarization tasks.


The speaker mentions the potential for RLMs to be used in code domains.

Develop RLMs that can be used in code domains.


The speaker mentions the potential for RLMs to be used in deep research tasks.

Develop RLMs that can be used in deep research tasks.


The speaker mentions the potential for RLMs to be used in interactive environments.

Develop RLMs that can be used in interactive environments.


Focus on a single, simple offer (website template) and avatar (contractors) to reach $1M ARR before diversifying.

He Makes $178,000/month Selling One Website Template

Avoids spreading resources too thin, allows for mastery and efficient scaling.

Prioritize a single core AI voice agent offering for a specific business niche.


Cold calling is crucial for initial customer acquisition, messaging refinement, and building resilience before investing in paid advertising.

Provides invaluable market insights and sales experience that cannot be replicated through courses or other means.

Mandate a period of cold outreach and direct customer interaction for the AI agency's sales team before launching paid campaigns.


Beginners often overthink optimization and perfection; prioritize action and learning from mistakes.

Prevents analysis paralysis and accelerates the learning process through real-world experience.

Encourage rapid prototyping and iteration of AI voice agent solutions, focusing on core functionality over polished features initially.


The type of revenue (recurring, low-churn) is as important as the amount. Low-ticket, high-volume recurring revenue provides stability and reduces client management overhead.

Creates a more sustainable and scalable business model compared to high-ticket, project-based revenue.

Design AI voice agent packages with a focus on recurring monthly subscriptions rather than one-time project fees.


AI can be used to automate website creation based on client input via onboarding forms.

Significantly reduces the time and cost associated with website development, enabling scalability.

Develop an AI-powered system that generates initial AI voice agent configurations and knowledge graphs from client onboarding data.


Partnerships should complement skill sets, not duplicate them.

Ensures a balanced team with diverse expertise, maximizing efficiency and innovation.

When considering partnerships, prioritize individuals with skills that directly address gaps in the AI agency's capabilities (e.g., backend development, AI research).


Setting realistic expectations with clients is crucial to avoid dissatisfaction and churn.

Reduces the risk of refunds and negative reviews, improving client retention and brand reputation.

Clearly communicate the capabilities and limitations of AI voice agents, emphasizing their role in system improvement rather than guaranteed lead generation.


Focus on mastering one channel, one product, and one avatar before expanding.

Allows for specialization and efficient resource allocation, leading to faster growth and higher profitability.

Concentrate initial marketing and sales efforts on a specific industry or niche for AI voice agents.


Early websites were 'terrible' but the high level of support kept the client.

Highlights the importance of customer service in early stages.

Provide exceptional customer support during the initial rollout of AI voice agents, even if the technology is not perfect.


Email deliverability problems persisted even at $34k/month revenue.

Highlights that technical issues are normal even at scale.

Allocate resources to address core technical issues like email deliverability early on, but don't let them block progress.


Beginners should focus on getting reps and experience, even if it means selling free trials or low-ticket offers.

Builds confidence, skills, and a track record that can be leveraged for higher-ticket sales later.

Offer free trials or discounted introductory packages for AI voice agents to attract initial clients and gain valuable experience.


Switching from Facebook lead form ads to a landing page with a booking funnel improved lead quality.

Highlights the importance of qualifying leads before scheduling calls.

Experiment with different lead generation methods, including landing pages with booking funnels, to identify the most effective approach for attracting qualified leads for AI voice agents.


The speaker used Google Maps and his iPhone to find leads in the early days.

Highlights that complex tools are not necessary to start.

Start with simple lead generation techniques like manual searches on Google Maps or industry directories before investing in expensive lead generation tools.


Users are struggling to provide sufficient context to AI agents like Claude Code, limiting their effectiveness.

How I Use Obsidian + Claude Code to Run My Life

Highlights the need for efficient context management in AI agents.

Develop methods for easily injecting relevant context into AI agents, such as file referencing or knowledge graph retrieval.


Obsidian's ability to create interlinked notes allows for surfacing patterns and insights that are difficult to identify manually.

Demonstrates the power of knowledge graphs for uncovering hidden connections and generating novel ideas.

Invest in building knowledge graph capabilities for AI agents, enabling them to identify patterns and generate insights from large amounts of data.


The presenter uses custom commands within Obsidian to automate tasks and access insights from their personal knowledge base.

Illustrates the potential for personalized AI agents that can be tailored to specific user needs and workflows.

Develop a system for creating and managing custom commands for AI agents, allowing users to automate tasks and access personalized insights.


The 'Ghost' command, which answers questions in the user's voice, demonstrates the potential for AI agents to mimic individual communication styles.

Highlights the importance of personalization and voice profiling in AI agents.

Explore techniques for building voice profiles and enabling AI agents to communicate in a user's unique style.


The 'Challenge' command pressure tests the user's beliefs, highlighting the potential for AI agents to promote critical thinking and personal growth.

Demonstrates the value of AI agents as thinking partners that can challenge assumptions and promote intellectual development.

Incorporate features into AI agents that encourage critical thinking and challenge users' perspectives.


The 'Emerge' command surfaces implied ideas, showcasing the ability of AI agents to identify unspoken patterns and generate novel insights.

Highlights the potential for AI agents to uncover hidden connections and generate creative ideas.

Develop algorithms that can identify implied ideas and surface them to users, promoting creativity and innovation.


The 'Drift' command compares stated intentions against actual behavior, revealing areas where the user is avoiding certain tasks or ideas.

Demonstrates the potential for AI agents to provide personalized feedback and promote self-awareness.

Explore methods for tracking user behavior and providing personalized feedback on areas where they may be avoiding certain tasks or ideas.


The 'Trace' command tracks the evolution of an idea over time, demonstrating the ability of AI agents to provide historical context and insights.

Highlights the value of AI agents for understanding the development of ideas and projects.

Develop features that allow users to track the evolution of ideas and projects over time, providing historical context and insights.


The presenter uses Obsidian and Claude Code to manage their personal life, demonstrating the potential for AI agents to improve productivity and well-being.

Highlights the broad applicability of AI agents for personal and professional use.

Target marketing efforts towards individuals and businesses seeking to improve productivity and well-being.


The presenter expresses concerns about privacy when sharing personal information with AI agents, highlighting the importance of data security and user control.

Emphasizes the need for robust privacy measures in AI agent development.

Prioritize data security and user control in AI agent development, ensuring that users have full control over their data and privacy settings.


The 'Connect' command links different domains, showcasing the potential for AI agents to generate cross-disciplinary insights.

Demonstrates the value of AI agents for connecting disparate ideas and generating novel solutions.

Develop algorithms that can identify connections between different domains and generate cross-disciplinary insights.


The presenter delegates tasks to AI agents by providing them with access to their Obsidian vault, highlighting the potential for AI agents to automate complex workflows.

Demonstrates the efficiency gains that can be achieved through AI-powered automation.

Develop tools and APIs that allow users to easily delegate tasks to AI agents, automating complex workflows and improving productivity.


The presenter uses daily notes and reflections to generate ideas and delegate tasks, emphasizing the importance of continuous learning and self-improvement.

Highlights the potential for AI agents to support personal growth and development.

Incorporate features into AI agents that encourage continuous learning and self-improvement, such as personalized recommendations and feedback.


The presenter uses Cloud Code to schedule meetings, demonstrating the potential for AI agents to automate calendar management and improve time management.

Highlights the value of AI agents for streamlining daily tasks and improving productivity.

Develop features that allow AI agents to automate calendar management and improve time management, such as intelligent scheduling and meeting reminders.


The presenter speculates that managing the Obsidian vault becomes the primary focus, rather than managing the agent directly, suggesting a shift towards data-centric AI management.

Indicates a potential future trend in AI interaction where data quality and organization are paramount.

Develop tools and strategies for efficient knowledge base management, focusing on data quality, organization, and accessibility for AI agents.


Focus on promoting the AI agency's own brand and thought leadership through content creation (podcasts, articles, social media) to drive traffic and build a 'holding pattern' of potential clients.

Become a Master Marketer Using Claude Code

Essential for attracting clients and establishing credibility in the AI space.

Allocate significant time and resources to content marketing and thought leadership initiatives.


Implement a 'holding pattern' strategy (email newsletter, podcast, valuable content) to nurture leads before direct sales pitches.

Increases conversion rates by warming up potential clients and building trust.

Develop a content calendar and automated email sequences to engage and educate leads.


Utilize 'selling events' (webinars, live demos, targeted email campaigns) to convert leads from the 'holding pattern' into paying clients.

Provides a structured approach to converting leads into revenue.

Create a series of webinars and email campaigns showcasing the agency's expertise and services.


Leverage AI tools like Claude for marketing support, including content creation, research, and campaign optimization.

Increases efficiency and effectiveness of marketing efforts.

Integrate AI tools into the agency's marketing workflow and train staff on their use.


Use AI to analyze past marketing performance (e.g., podcast views, comments) to identify what resonates with the target audience and optimize future content.

Data-driven insights for improving marketing ROI.

Develop AI-powered analytics dashboards to track marketing performance and identify areas for improvement.


Consider using WhisperFlow or similar tools for voice-based interaction with AI models like Claude to streamline prompt creation and iteration.

Can significantly speed up the process of working with AI models.

Evaluate and potentially adopt voice-based AI interaction tools.


The speaker emphasizes the importance of sales and marketing over purely technical skills for CEOs, even in AI-focused businesses.

Highlights the need to prioritize revenue generation and client acquisition.

Ensure the agency leadership team has strong sales and marketing expertise, or hire experienced professionals.


Retargeting website visitors and email subscribers with paid campaigns on platforms like Facebook can be an effective way to drive conversions.

Increases the likelihood of converting warm leads into paying customers.

Implement retargeting campaigns on relevant platforms, targeting users who have interacted with the agency's website or content.


Directly reaching out to potential enterprise clients identified through email sign-ups can lead to high-value deals.

Personalized outreach can be highly effective for securing enterprise clients.

Implement a system for identifying and contacting potential enterprise clients who sign up for the agency's email list.


The speaker highlights the importance of understanding the basics of sales and marketing before diving into AI tools.

Ensures that AI tools are used effectively to support business goals, rather than as a distraction.

Provide sales and marketing training to technical staff to ensure they understand the business context of their work.


The speaker uses Claude Code to create visuals and scannable documents for presentations, demonstrating a practical application of AI for content creation.

Showcases the potential of AI for creating engaging and informative content.

Explore using AI tools like Claude Code for creating marketing materials and presentations.


The speaker emphasizes the importance of being a promoter and actively seeking traffic to the business.

Highlights the need for proactive marketing efforts to drive growth.

Develop a comprehensive marketing plan that includes both organic and paid traffic generation strategies.


The speaker uses a blueprint to illustrate the sales process, emphasizing the importance of traffic generation, holding patterns, and selling events.

Provides a clear framework for understanding and optimizing the sales process.

Adapt the speaker's blueprint to the agency's specific business model and use it to guide marketing and sales efforts.


Avoid loading default MCP configurations to save significant token space.

Elite Context Engineering with Claude Code

Reduces wasted tokens, allowing for more effective use of the context window.

Delete the default `mcp.json` and load MCP servers on demand using `claude --mcp-config <specific_config>`.


Replace bloated `claude.md` with context priming using dedicated `/prime` commands for specific tasks.

Significantly reduces the always-loaded context, improving agent performance and freeing up tokens.

Audit and trim `CLAUDE.md` to absolute universals, then create `/prime` commands for common tasks like `/prime-voice`, `/prime-widget`, etc.


Utilize sub-agents to delegate tasks that would bloat the primary agent's context window, leveraging their separate context windows.

Keeps the primary agent's context clean and focused, improving performance and allowing for more complex tasks.

Delegate tasks like web scraping to sub-agents, ensuring each sub-agent has a focused, single-purpose prompt.


Delegate tasks to entirely new top-level primary Claude Code instances for complete control over context, model, and tools.

Provides a fresh 200K context window for delegated tasks, enabling more complex and independent operations.

Use a `/background` command to launch independent Claude Code instances with focused prompts and a report file convention for completion signals.


Adopt a report file rename convention for background agents to signal task completion.

Provides a reliable and standardized method for monitoring the progress of background agents.

Implement a system where background agents rename their report file upon completion of a task.


Ensure each sub-agent and background agent has a focused, single-purpose prompt for optimal performance.

Improves the efficiency and effectiveness of individual agents by minimizing context clutter.

Audit existing agent prompts to ensure they are concise and focused on a single task.


Track the 'Core Four' (Context, Model, Prompt, Tools) for every sub-agent spawned to maintain control and optimize performance.

Ensures consistent and effective management of all agents in a multi-agent system.

Implement a system for tracking and managing the 'Core Four' for each sub-agent.


Explore the 7 context engineering techniques not covered in the video by enrolling in IndyDevDan's TAC (Tactical Agentic Coding) course.

Potentially unlocks additional strategies for optimizing context management and agent performance.

Investigate the TAC course and evaluate its potential value for the agency.


Implement `--strict-mcp-config` flag to override global MCP configs for focused agent sessions.

Ensures that agents only use the necessary MCP tools, preventing unnecessary token consumption.

Integrate the `--strict-mcp-config` flag into the agent spawning process.


The progression from in-loop to out-of-loop to ZTE (Zero Touch Engineering) should be a guiding principle for automation.

Provides a roadmap for increasing automation and reducing manual intervention in agent workflows.

Develop a strategy for transitioning agent workflows towards ZTE.


Offer context engineering audits and optimization services to businesses using AI voice agents.

Creates a new revenue stream by leveraging expertise in context management.

Develop a service offering focused on auditing and optimizing context usage for AI voice agents.


Use the insight 'What's better than a prompt? A prompt chain. What's better than a prompt chain? An agent. What's better than an agent? Many focused, specialized agents.' to guide multi-agent system design.

Reinforces the importance of focused, specialized agents in multi-agent systems for optimal performance.

Prioritize the development of focused, specialized agents over complex, multi-step prompts.


Automate context bundle creation and loading for AI voice agents to improve session handoff and reduce cold starts.

Enhances user experience by providing seamless transitions between agents and reducing latency.

Implement context bundles for AI voice agents and integrate the `/loadbundle` command.


Leverage context priming to tailor the initial context of AI voice agents to specific user intents and tasks.

Improves the accuracy and relevance of AI voice agent responses by providing targeted context.

Create `/prime` commands for common user intents and tasks in AI voice agent applications.


Giving agents their own dedicated compute environments (sandboxes) is a powerful paradigm shift, enabling more complex and autonomous workflows.

I Gave Gemini 3 Pro Its Own Computer (IndyDevDan)

This allows agents to perform full-stack development and complex tasks without impacting the user's local machine or requiring constant supervision.

Prioritize development of robust agent sandboxing capabilities, potentially leveraging existing solutions like E2B.


The focus is shifting from raw model performance to the overall agentic experience and the ecosystem built around agents.

Raw benchmark scores are becoming less important than how well agents perform in specific use cases.

Emphasize building a seamless and intuitive agentic experience, focusing on usability and practical applications.


Reprogramming agents with custom skills and syntax (e.g., using backslash commands) allows for extending their functionality and tailoring them to specific tasks.

This enables creating specialized agents with unique capabilities and workflows.

Develop a system for easily adding and managing custom skills and syntax for agents, potentially using memory files or knowledge graphs.


The best-of-n approach, where multiple agents are deployed to solve the same problem and the best result is selected, can improve the quality and reliability of agentic workflows.

This allows for mitigating the limitations of individual agents and increasing the chances of finding a successful solution.

Implement a mechanism for running multiple agents in parallel and selecting the best output based on predefined criteria.


Full-stack application development is a valuable benchmark for evaluating agentic capabilities, as it requires combining multiple services and technologies.

This demonstrates the ability of agents to perform complex engineering tasks and create real-world applications.

Use full-stack application development as a key performance indicator (KPI) for evaluating and improving agentic systems.


The presenter uses E2B to host and run agent sandboxes.

Provides a concrete tool to investigate and potentially integrate with for agent sandboxing.

Evaluate E2B as a potential platform for hosting agent sandboxes and managing compute resources.


The presenter emphasizes the importance of agents being able to access and utilize environment variables within their sandboxes.

This allows agents to securely access API keys and other sensitive information without exposing them directly in the code.

Implement a secure mechanism for providing agents with access to environment variables within their sandboxes.


The presenter highlights the ability to copy files from the agent sandbox to a local machine.

This allows for easy access to the output of agentic workflows and further processing or integration with other systems.

Provide a mechanism for easily copying files and data between agent sandboxes and local machines.


The presenter mentions the potential for agent pipelines to overcome the limitations of handing a ton of work to individual agents.

Agent pipelines can break down complex tasks into smaller, more manageable steps, improving efficiency and reliability.

Investigate and develop agent pipeline capabilities, allowing for the orchestration of multiple agents to solve complex problems.


The presenter's audience is highly technical and interested in deep engineering use cases and agentic engineering.

Provides insight into the target audience for AI agency's services.

Tailor marketing and service offerings to appeal to a highly technical audience with a strong interest in agentic engineering.


The presenter mentions the importance of long durations of agentic work as a key benchmark.

This indicates that the ability of agents to perform sustained and complex tasks is highly valued.

Focus on developing agents that can handle long-running and complex tasks, and use this as a key selling point.


The presenter uses backslash commands to trigger specific agent skills.

This demonstrates a simple and effective way to extend agent functionality and tailor them to specific tasks.

Explore the use of custom commands or keywords to trigger specific agent skills and workflows.


The presenter emphasizes the importance of working prototypes over error-ridden code.

This highlights the need for agents to produce functional and reliable output.

Prioritize the development of agents that can generate working prototypes with minimal errors.


The presenter highlights the use of memory files to store and reuse prompts.

This allows for efficient and consistent agent behavior.

Implement a system for storing and managing prompts in memory files or a knowledge graph, allowing for easy reuse and modification.


The presenter uses third-party benchmarks to evaluate model performance.

Highlights the importance of independent validation of model capabilities.

Incorporate third-party benchmarks into the model evaluation process to ensure objective and reliable performance assessments.


Context engineering is shifting from retrieval (RAG) to navigation, empowering LLMs to manage their own context using tools and environments.

From Retrieval to Navigation: The New RAG Paradigm

This represents a fundamental shift in how we build AI agents, moving from pre-defined knowledge retrieval to dynamic exploration and discovery.

Investigate and prototype navigation-based context management systems, focusing on providing LLMs with the right tools (e.g., file system access, code execution) to explore and understand information.


File system search, where LLMs are placed in a sandboxed shell with file navigation commands, is a promising technique for context delivery, especially for coding agents.

This approach allows LLMs to explore and understand large codebases and knowledge bases without relying solely on vector databases.

Develop tools and environments that allow LLMs to interact with file systems and other knowledge sources using shell-based commands.


Context rot, the decline in performance as context window size increases, is a significant problem. Models struggle to effectively use context beyond a certain point (around 100k tokens).

This limits the effectiveness of large context windows and highlights the need for better context management techniques.

Research and implement techniques to mitigate context rot, such as context summarization, filtering, and re-ranking.


Positional biases (lost in the middle) affect how LLMs interpret context, with information at the beginning and end of the context window being better utilized than information in the middle.

This can lead to inaccurate or incomplete answers if relevant information is located in the middle of the context window.

Experiment with different context ordering strategies and attention mechanisms to mitigate positional biases.


Recursive Language Models (RLMs) offer a compelling solution for context handling by combining summarization, context folding, and sub-agents, leveraging LLMs' code understanding.

RLMs allow LLMs to scale their understanding of content far exceeding their context window (10M+ tokens).

Explore and implement RLM architectures, focusing on code execution environments and the ability to call other LLMs in fresh context windows.


RLMs can be improved through supervised fine-tuning and reinforcement learning by training models on examples of effective context management using the RLM environment.

This can further enhance the performance of RLMs and instill a deeper understanding of context management in the model weights.

Develop training datasets and RL environments for RLM agents, focusing on verifiable tasks of long context question answering.


The RLM library provides tools for inspecting the actions taken by the model, which is valuable for debugging and understanding how the model is managing context.

This allows for better control and optimization of the RLM agent's behavior.

Integrate similar inspection tools into our multi-agent orchestration systems to monitor and debug agent interactions.


The video highlights the use of GPT 5.2 as an RLM, suggesting a potential benchmark model for long-context tasks in 2026.

Provides a target performance level to aim for when developing our own long-context AI agents.

Track the capabilities of GPT 5.2 (or its successors) and compare its performance to our own models on long-context tasks.


The example of extracting marriage proposals from Pride and Prejudice demonstrates a practical application of RLMs for knowledge extraction from large text corpora.

This showcases the potential of RLMs for automating complex information retrieval tasks.

Develop similar use cases for our AI voice agents, such as extracting key information from customer support transcripts or sales call recordings.


The video mentions the importance of hardware and memory (VRAM and GPUs) in processing and running calculations for large context windows.

This highlights the need for efficient model architectures and hardware optimization to handle long-context tasks.

Invest in hardware infrastructure and explore model compression techniques to optimize performance and reduce memory footprint.


Context engineering is shifting from retrieval (RAG) to navigation (file system-based agents).

Navigation-based agents offer more flexibility and control over context management for AI agents.

Investigate and prioritize development of file system-based agent harnesses for AI voice agents.


Context rot and positional bias (lost in the middle) degrade performance in large context windows.

Simply increasing context window size isn't enough; effective context management is crucial.

Research and implement techniques to mitigate context rot and positional bias in our systems.


Recursive Language Models (RLMs) allow LLMs to manage their own context by using a code execution environment (REPL) and calling sub-agents.

RLMs can scale understanding of content far exceeding the context window.

Explore and prototype RLM-based architectures for multi-agent orchestration.


RLMs can be improved through supervised fine-tuning and reinforcement learning to instill context management skills.

Training can significantly improve the performance of RLMs.

Develop training datasets and RL environments for improving context management in our AI agents.


The Chroma technical report highlights the impact of distractors on LLM performance within context windows.

Noisy or unstructured data can significantly degrade LLM performance.

Prioritize data cleaning and structuring techniques to reduce noise in knowledge bases.


File system search techniques are commonly used in coding agents, demonstrating their effectiveness in navigating large codebases.

Proven method for navigating complex information spaces.

Adapt file system search techniques for navigating business data and knowledge graphs in our AI voice agents.


The video mentions GPT 5.2 being used in an RLM example.

Indicates the state of LLM technology in 2026.

Monitor advancements in LLM technology and adapt our solutions accordingly.


The presenter uses Pride and Prejudice as an example for RLM, showing the ability to extract information from large texts.

Demonstrates the ability to process and understand complex narratives.

Use similar examples to test and showcase the capabilities of our AI agents.


The video highlights the importance of minimizing token usage while maximizing context relevance.

Efficient context management is crucial for cost-effective AI agent operation.

Implement token optimization strategies in our AI agent designs.


The presenter mentions the RLM library from the authors of the paper.

Provides a starting point for implementing RLM.

Evaluate and potentially integrate the RLM library into our multi-agent orchestration systems.


The video mentions the limitations of hardware and memory (VRAM and GPUs) in processing large contexts.

Hardware constraints can limit the scalability of AI agents.

Optimize our AI agent designs for efficient resource utilization.


The shift from RAG to navigation implies a need for more sophisticated agent tooling and environments.

Simple retrieval is insufficient for complex tasks.

Invest in developing robust agent environments with tools for navigation, exploration, and reasoning.


The video highlights the importance of verifiable tasks and rewards in training AI agents for context management.

Provides a framework for evaluating and improving agent performance.

Incorporate verifiable tasks and reward mechanisms into our training pipelines.


Bundle the agentic layer AROUND applications, not inside, for cross-repo visibility.

The Codebase Singularity: "My agents run my codebase better than I can"

Enables agents to understand and operate across multiple applications within a codebase, maximizing their effectiveness.

Design agentic layer architecture to wrap around existing applications, not be embedded within them.


Engineers often over-engineer tools, leading to token waste and reduced scalability. Tool design is critical.

Poorly designed tools can negate the benefits of AI agents, increasing costs and hindering performance.

Prioritize tool minimalism and efficiency in agentic layer design. Audit existing tools for token usage and redundancy.


Skills and MCP servers can be replaced with well-crafted prompts leveraging the Core Four (Context, Model, Prompt, Tools).

Reduces complexity and token usage by optimizing prompts instead of relying on external tools.

Invest in prompt engineering expertise and prioritize prompt optimization over tool integration where possible.


Implement closed-loop feedback prompts (Request -> Validate -> Resolve) for self-correcting agents.

Increases the reliability and accuracy of agent outputs by enabling self-correction.

Incorporate the Request -> Validate -> Resolve pattern into all agent workflows.


Adding more compute (e.g., feedback loops) increases confidence in agent results. Don't skip review steps to save tokens.

Prioritizing thoroughness over cost-cutting leads to more reliable and trustworthy AI agents.

Allocate sufficient resources for validation and review steps in agent workflows.


Progressive specialization of prompts is key: start generic (plan, build), then specialize (test-backend, test-frontend, review-code).

Allows for more targeted and effective agent behavior as the codebase grows and becomes more complex.

Design prompts with increasing specificity as the codebase evolves.


Orchestrator agents can kick off end-to-end AI developer workflows (plan-build-review-fix) from a single prompt.

Streamlines development processes and reduces manual intervention.

Develop orchestrator agents capable of managing complex workflows from a single entry point.


Track metrics to determine when the 'Codebase Singularity' is reached, indicating agents outperform humans.

Provides a tangible measure of agentic layer effectiveness and justifies further investment.

Define and track key performance indicators (KPIs) to assess agent performance relative to human developers.


Offer different 'grades' of agentic layer implementation as service tiers, reflecting increasing capabilities and complexity.

Creates a clear value proposition for clients, allowing them to choose the level of agentic layer support that meets their needs and budget.

Package agentic layer services into tiered offerings based on the Class/Grade framework.


The Agentic Layer classification framework (Class 1-3, Grade 1-5) provides a structured way to assess and improve agentic layer power.

Offers a standardized approach to evaluating and enhancing agentic layer capabilities.

Adopt the Agentic Layer classification framework as a core methodology for assessing and improving agentic layer implementations.


Create a dedicated directory for agent review outputs to maintain a persistent record of agent performance.

Facilitates auditing, analysis, and continuous improvement of agent performance.

Implement a standardized directory structure for storing agent review outputs.


Develop specialized prompts tailored to specific projects or sub-projects within a larger codebase.

Improves the accuracy and effectiveness of agents by providing them with context-specific instructions.

Create project-specific prompts for tasks such as testing, building, and reviewing code.


The concept of the 'Agentic Horizon' can be used in marketing to convey the future potential of AI-driven codebases.

Attracts forward-thinking clients who are interested in the long-term benefits of AI automation.

Incorporate the 'Agentic Horizon' concept into marketing materials and presentations.


The video highlights the importance of understanding the 'Core Four' (Context, Model, Prompt, Tools) for effective agentic coding.

Emphasizes the need for a holistic approach to agentic layer design, considering all four dimensions.

Train AI agency staff on the 'Core Four' principles.


The video implicitly positions the creator as a thought leader in agentic layer architecture, providing a competitive advantage.

Leveraging the creator's expertise can enhance the agency's credibility and attract clients.

Highlight the agency's expertise in agentic layer architecture and its alignment with the creator's vision.


Package AI voice agent as a Claude Plugin.

Claude Plugins Give You Superpowers, Here's How to Unlock Them

Direct revenue opportunity, leveraging existing Genesis skills and agent structure.

Prioritize development of a Claude Plugin version of Sunaiva/ReceptionistAI.


Productize AI services as Claude Plugins.

Scalable revenue model with zero marginal cost after plugin creation.

Shift sales focus from 'AI receptionist setup' to selling a pre-configured Claude Plugin.


Offer custom Claude Plugin creation services.

Immediate revenue stream by leveraging expertise in AI agent configuration.

Develop a service offering for creating custom Claude Plugins, priced between $500-2,000.


Leverage existing skills registry as a plugin system.

Rapidly expand plugin offerings by exporting existing skills as Claude Plugins.

Develop tooling to export Genesis skills as Claude Plugins for distribution via GitHub marketplace.


Utilize MCP Bloodstream as a 'super-connector' plugin.

Provide broader tool connectivity than individual plugins, enhancing value proposition.

Develop a Claude Plugin that leverages the MCP Bloodstream architecture for connecting to various tools.


Position AI Memory as 'Plugin memory on steroids'.

Differentiate AI Memory by highlighting its superior depth and capabilities compared to basic plugin memory.

Refine marketing messaging to emphasize the advanced memory capabilities of AI Memory compared to Claude Plugins.


Emphasize the 'virtual employee' positioning.

Capitalize on market fear and demand for AI-powered automation in business roles.

Reinforce the 'virtual employee' narrative in marketing materials and sales pitches.


Implement the content flywheel pattern for marketing.

Improve content generation efficiency and relevance by leveraging customer feedback and past content.

Integrate the content flywheel pattern (intake, analysis, style guide, memory) into the Genesis marketing engine.


Target B2B sales with cross-team plugin sharing in mind.

Increase sales potential by enabling plugin sharing across entire organizations.

Focus sales efforts on larger organizations where cross-team plugin sharing can maximize value.


Analyze the plugin file structure for best practices.

Understand the organization and components of a plugin to build better plugins.

Study the inferred plugin file structure to understand the organization and components of a plugin.


Study the customization flow for plugin design.

Design plugins that can be easily customized by users.

Analyze the customization flow to design plugins that can be easily customized by users.


Monitor Recall (getrecall.ai) as a competitor to AI Memory.

Understand the competitive landscape and identify opportunities for differentiation.

Track Recall's features and marketing to inform AI Memory's development and positioning.


Integrate with tools mentioned in the video.

Increase the value of the AI agent by connecting to the tools that businesses are already using.

Integrate with Slack, Canva, Figma, HubSpot, Amplitude, Notion, Ahrefs, SimilarWeb, Klaviyo, Google Sheets, Monday, and ClickUp.


Understand the competitive landscape and identify opportunities for collaboration.

Monitor the GitHub plugin marketplace for new plugins and opportunities for collaboration.


Capitalize on market fear and demand for AI-powered automation in legal roles.

Develop a legal plugin that automates contract review, NDA triage, compliance workflows, legal briefings, and templated responses.


draw.io XML is more token-efficient than Excalidraw JSON for generating diagrams with Claude, leading to cost savings.

How I Use AI to Map & Design Business Systems (Claude Code)

Reduces API costs and allows for more complex diagrams within token limits.

Prioritize draw.io integration over Excalidraw for diagram generation in our AI agent workflows.


The 'Business X-Ray' agent skill concept, which interviews business owners to map processes, is directly applicable to Sunaiva onboarding.

Provides a clear path to automate and improve customer onboarding by auto-generating AI agent configurations.

Develop a Sunaiva onboarding flow that interviews customers and generates initial AI agent setups based on their business processes.


Visualizing business processes as swim lanes helps identify areas where AI agents can be implemented to reduce owner involvement.

Provides a framework for identifying high-impact automation opportunities for clients.

Incorporate swim lane diagrams into our client engagement process to highlight potential AI agent applications.


AI-generated business maps can be offered as a premium deliverable to clients, showcasing the value of AI integration.

Creates a tangible deliverable that differentiates our services and justifies premium pricing.

Package AI-generated business maps as a value-added service for Talking Widget/ReceptionistAI clients.


Claude Code allows for interactive editing of diagrams directly within VS Code, improving iteration speed and workflow.

Enables faster development and refinement of business process diagrams and AI agent workflows.

Adopt Claude Code and VS Code extensions for internal diagramming and client collaboration.


Rashid's 'Chief Leverage Officer' framework provides a mental model for business optimization using AI agents and digital assets.

Offers a structured approach to identify and implement AI solutions that maximize business leverage.

Incorporate the 'Eliminate, Delegate, Simplify, Automate' filters into our client consultation process.


The video highlights the importance of mapping business processes visually to identify automation opportunities.

Reinforces the value proposition of our AI agency in helping businesses understand and optimize their workflows.

Emphasize the importance of business process visualization in our marketing materials and client presentations.


Claude can be taught specific skills for generating diagrams, such as arrow binding and layout, using skill files.

Improves the quality and consistency of AI-generated diagrams.

Develop a library of Claude skills for generating different types of business diagrams with specific formatting and layout requirements.


draw.io supports multi-page diagrams, allowing for detailed business maps with drill-down process flows.

Enables the creation of comprehensive business maps that capture all levels of detail.

Utilize draw.io's multi-page functionality to create detailed business maps for clients.


The video demonstrates using Claude to generate swim lane diagrams showing the flow of tasks between customer, owner, AI agents, and digital assets.

Provides a clear visual representation of how AI agents fit into the overall business process.

Create swim lane diagram templates that can be customized for different business types and AI agent applications.


Codex CLI lacks reusable prompts, making it a non-viable agentic coding tool.

5 Agent PATTERNS to SIMPLIFY Your Agentic Coding (Claude Code, Gemini CLI, MCP)

Highlights a competitive disadvantage for tools lacking reusable prompt functionality.

Prioritize reusable prompt support in our AI voice agent platform and multi-agent orchestration systems.


Reusable prompts are the 80/20 of agent interaction patterns.

Focusing on reusable prompts provides the most value for the least effort.

Prioritize the development and refinement of reusable prompt templates and a user-friendly interface for managing them.


Sub-agents are valuable for specialization AND parallelization, but introduce Claude Code lock-in and debugging challenges.

Highlights the trade-offs between specialized agents and platform dependency.

Develop sub-agent capabilities with a focus on portability and robust debugging tools. Explore alternative agentic frameworks beyond Claude Code.


Information flow between primary and sub-agents is critical for successful multi-agent orchestration.

Poor information flow leads to sub-optimal agent performance and system failures.

Implement robust mechanisms for managing and monitoring information flow between agents, including clear prompt engineering and result validation.


Prompt-to-Sub-Agent pattern enables workflow reusability and orchestration of specialized agents.

This pattern is crucial for complex, multi-step AI workflows.

Focus on developing and refining our prompt-to-sub-agent orchestration capabilities, including tools for workflow definition, parameterization, and debugging.


Wrapper MCP servers provide a single integration point for all agents and services, enabling deterministic code paths alongside agent flexibility.

Centralized control and integration are essential for scalable and maintainable AI systems.

Continue investing in our MCP server architecture and expand its capabilities to integrate with a wider range of services and APIs.


MCP prompts (slash commands) are an underrated feature for workflow reusability within MCP servers.

Simplifies agent interaction and promotes efficient workflow execution.

Prioritize the development and promotion of MCP prompts (slash commands) within our MCP server offerings.


The decision framework emphasizes starting simple and scaling complexity only when needed.

Avoids premature optimization and over-engineering, leading to faster development cycles and more efficient resource allocation.

Incorporate the decision framework into our development process, ensuring that new features are built using the simplest agent pattern that meets the requirements.


The 'Rule of Three' suggests automating tasks after they've been performed three times.

Identifies opportunities for automation and efficiency gains.

Implement a system for tracking repeated tasks and automatically suggesting automation opportunities.


Gemini CLI supports reusable prompts via TOML files.

Provides an alternative to Claude Code for reusable prompts.

Develop a library of TOML reusable prompts for common Gemini tasks.


Full application pattern offers full control and extensibility but is overkill for most problems.

Highlights the trade-offs between control and complexity.

Reserve the full application pattern for long-term vision products and avoid it for simpler use cases.


Dan is building a ton of reusable prompts and fewer sub-agents than before.

Indicates a shift towards simpler agent patterns in practice.

Focus on building a strong foundation of reusable prompts and only use sub-agents when necessary.


Building before validating is an anti-pattern.

Avoids wasted effort on features that don't meet user needs.

Prioritize user validation and testing throughout the development process.


Complexity should be earned, not assumed.

Reinforces the importance of starting simple and scaling complexity only when necessary.

Instill a culture of simplicity and incremental development within the team.


Claude Code has model lock-in and feature lock-in.

Highlights the risk of relying on a single platform.

Design our systems to be model-agnostic and avoid relying on platform-specific features.


Engineers often lack awareness of prompt hooks for pre-tool use command validation.

Claude Code is Amazing... Until It DELETES Production

Prompt hooks offer a last-ditch effort to catch dangerous commands missed by deterministic hooks, preventing catastrophic actions.

Incorporate prompt hooks into AI voice agent and multi-agent systems as a standard security measure.


Interactive installation processes (e.g., using `/install`) can streamline setup and configuration for complex agentic systems.

Simplifies onboarding and reduces friction for users adopting AI agent technologies.

Develop interactive installation scripts for AI voice agent and multi-agent orchestration systems.


Skills can be used to package and reuse collections of resources, such as security measures, across different codebases.

Promotes code reuse and standardization, reducing development time and ensuring consistent security practices.

Package AI voice agent and multi-agent orchestration functionalities as reusable skills.


Global hooks can provide an additional layer of security by applying to all agent operations on a device.

Ensures consistent security across all projects and environments, mitigating risks associated with local configurations.

Implement global hook support in AI voice agent and multi-agent systems.


A patterns file (e.g., YAML) can be used to define blocked commands, read-only paths, and zero-access paths for agents.

Provides a flexible and configurable way to control agent behavior and prevent unauthorized actions.

Develop a patterns file system for AI voice agent and multi-agent systems to manage access control.


The video highlights the risk of agents deleting production assets due to hallucinations or bad prompts.

This is a critical risk that can lead to significant financial and reputational damage.

Prioritize the development and implementation of robust security measures to prevent accidental or malicious data deletion.


The video emphasizes the importance of building trust with agents, either through deterministic controls or by deferring trust to sandboxed environments.

Trust is essential for the safe and reliable operation of AI agents.

Develop a comprehensive trust framework for AI agents, incorporating both deterministic controls and sandboxing techniques.


The presenter almost ran a catastrophic command in a production application, highlighting the ever-present risk of human error.

Even experienced engineers can make mistakes, emphasizing the need for automated security measures.

Promote the use of automated security measures as a way to mitigate human error and prevent catastrophic events.


Offering a skill that prevents data deletion can be a valuable selling point for AI agent services.

Addresses a key concern for businesses considering adopting AI agents.

Market the data deletion prevention skill as a key differentiator for AI voice agent and multi-agent services.


The video demonstrates a workflow for installing and configuring security hooks using an agent.

This workflow can be adapted to other tasks, such as deploying and managing AI agents.

Develop agentic workflows for common tasks, such as deploying, configuring, and managing AI agents.


The video mentions the use of pre-tool use matchers to catch commands before they run.

This is a powerful technique for preventing unauthorized actions.

Incorporate pre-tool use matchers into AI voice agent and multi-agent systems to control agent behavior.


The video highlights the importance of communicating to the user what's happened and what needs to be done next.

Clear communication is essential for building trust and ensuring that users understand how the system works.

Develop clear and informative messaging for AI voice agent and multi-agent systems.


The video mentions that the commands inside the settings run in parallel.

Understanding the execution order of commands is important for ensuring that security measures are effective.

Document the execution order of commands in AI voice agent and multi-agent systems.


Looped LMs show significant performance gains in knowledge manipulation tasks compared to knowledge storage/extraction.

LLMs Don't Need More Parameters. They Need Loops.

Focus development of AI voice agents on tasks requiring reasoning and manipulation of information rather than simple fact retrieval.

Prioritize use cases involving complex reasoning, decision-making, and problem-solving for AI voice agents.


Looped LMs can achieve performance on par with much larger models, suggesting increased parameter efficiency.

Develop smaller, more efficient AI voice agents that can run on edge devices or with lower cloud compute costs.

Investigate and implement looped LM architectures for AI voice agents to reduce model size and computational requirements.


The optimal number of loops for a given task varies, and overlooping can lead to performance degradation.

Implement a dynamic loop adjustment mechanism in multi-agent systems to optimize performance based on task complexity.

Research and develop methods for dynamically adjusting the number of loops in multi-agent systems based on task characteristics and performance feedback.


Early exit mechanisms in looped LMs require careful training to avoid reward hacking and ensure uniform distribution of exit probabilities across loops.

Implement robust training techniques, including entropy regularization, to prevent reward hacking and ensure effective loop utilization in AI voice agents and multi-agent systems.

Incorporate entropy regularization and other techniques to promote uniform exit probability distributions during training of looped LM-based AI voice agents.


Looped LMs offer flexibility in KV cache management, but the optimal strategy depends on the specific application and training methodology.

Experiment with different KV cache management strategies in AI voice agents to optimize performance and memory usage.

Evaluate and compare different KV cache management techniques, such as using the KV cache from the exit loop or averaging across loops, to identify the most efficient approach for AI voice agents.


The speaker highlights the potential of looped LMs for small language models, which can fit on mobile devices.

Explore opportunities to deploy AI voice agents on edge devices using looped LMs to reduce latency and improve user experience.

Investigate the feasibility of deploying looped LM-based AI voice agents on mobile devices and other edge platforms.


The speaker mentions comparing their models against QN3 and Gemma 3, providing a competitive benchmark.

Understand the performance characteristics of competing models to identify areas where looped LMs offer a competitive advantage.

Benchmark AI voice agents and multi-agent systems based on looped LMs against QN3, Gemma 3, and other relevant models to assess their performance and identify areas for improvement.


The looped architecture allows for injecting multi-step thinking into the pre-training pipeline.

This can improve the base model's reasoning capabilities, leading to better performance in downstream tasks.

Design pre-training strategies that incorporate multi-step thinking or looping to enhance the reasoning abilities of AI voice agents.


The speaker mentions the difficulty of training looped models, requiring significant engineering effort and monitoring.

Building and deploying looped LM-based systems requires a strong engineering team and robust monitoring infrastructure.

Allocate sufficient resources and expertise to address the engineering challenges associated with training and deploying looped LM-based AI voice agents.


The speaker's PhD student experienced significant stress during the training process, highlighting the challenges of working with this technology.

Acknowledge and address the potential for stress and burnout among engineers working on looped LM projects.

Implement strategies to support the well-being of engineers working on looped LM projects, such as providing adequate resources, training, and mentorship.