My Blog

Category: AI Tools

  • AI Tools: I Fully Automated My Video Editing Using Claude Code (Full Walkthrough)

    Watch on YouTube: https://www.youtube.com/watch?v=HzXD4GVqXwM

    Channel: Christian Peverelli

    Introduction

    Christian Peverelli demonstrates a fully automated video‑editing workflow that relies on Claude Code as a central coordinator. By linking Claude to a suite of free and paid tools, he creates a repeatable “skill” that can edit raw footage while he sleeps, turning a 2 minute 46 second clip into a 31‑second final cut. The approach reduces editing costs from hundreds of dollars per video to roughly $27 and cuts turnaround time from several days to same‑day delivery.

    What the Video Covers

    The presentation walks through the entire system: installing Claude Desktop, enabling local mode, connecting MCP‑based tools, and building a personal editing skill file. It shows how each component contributes to transcription, animation, screen recording, music selection, and final assembly. The video also shares a cost breakdown, time‑savings metrics, and the process for iteratively improving the skill with feedback.

    Step‑by‑Step Walkthrough

    • Install Claude Desktop from Anthropic’s website and upgrade to at least the $20/month plan to unlock Claude Code.
    • Launch Claude Code, set it to local mode, and designate a working folder where all project files will reside.
    • Enable automatic permissions, switch to a newer model for better cost efficiency, and turn on internet browsing within Claude Code settings.
    • Add MCP connectors for Tella (screen and face recording) and Epidemic Sound (music and SFX) so Claude can call those services directly.
    • If Hyperframes is not automatically available, add it manually from its GitHub repository or via the MCP connectors menu.
    • Create a new Claude Code thread and ask the assistant to build a video‑editing system using the listed tools.
    • Record a short hook or any raw footage, feed the file to Claude together with your editing skill, and let the system generate a first edit.
    • Review the output, provide timestamped feedback (e.g., “cut at 00:12, keep the B‑roll”), and instruct Claude to save the correction in the skill file.
    • Repeat the feedback loop until the skill consistently produces the desired quality; each iteration refines the ~1,400‑line, 36‑rule skill file.
    • Join the We Are No Code community to download the shared skill template, get support, and see how others have adapted the system.
    • Run the skill on new footage, review the result, and give occasional feedback to keep the system improving over time.

    Tools and Numbers

    • Claude Desktop: $200/month (maximum Claude usage per video ≈ $24).
    • Tella (annual plan): $13/month (~$2 per video).
    • Epidemic Sound (annual plan): $9.99/month (~$1 per video).
    • Hyperframes: free (provides 4K animation from Claude‑generated code).
    • Parakeet: free, NVIDIA‑based transcription with timestamps.
    • FFmpeg: free (handles final cuts, black‑screen removal, audio sync).
    • Original clip length: 2 minutes 46 seconds → edited length: 31 seconds.
    • Skill file size: approximately 1,400 lines, 36 rules encoding the creator’s editing style.
    • Estimated cost per video: ~$27 (sum of the above subscriptions).
    • Time savings: editing completed same‑day versus 3‑7 days when using a human editor.

    Caveats and Hype Check

    “I save thousands of dollars a month.”

    The claim of saving thousands of dollars per month is based on the creator’s personal usage and may not reflect typical results for all users. The cost reduction from $250‑$1,000 per video to about $27 assumes the creator’s previous editing expenses and the specific subscription levels shown. No affiliate links are displayed, but the video promotes the creator’s free community and skill share, which could drive engagement to his other offerings. Users should evaluate whether the required subscriptions and time investment for initial setup align with their own budgets and editing volumes.

    Concrete Next Actions

    • Download and install Claude Desktop, then upgrade to the $20/month plan to enable Claude Code.
    • Configure Claude Code for local mode, set a working folder, enable automatic permissions, switch to a newer model, and turn on internet browsing.
    • Connect Tella and Epidemic Sound via the MCP connectors menu; add Hyperframes manually if needed.
    • Start a new Claude Code thread and request a video‑editing system using the listed tools.
    • Record a short test clip, provide it to Claude with your editing skill, and review the generated output.
    • Give timestamped feedback, instruct Claude to store improvements in the skill file, and iterate until the output meets your standards.
    • Join the We Are No Code community to access the shared skill template and obtain ongoing support.
    • Apply the refined skill to new footage, review results, and provide occasional feedback to maintain and enhance performance over time.

  • Grok Bot: Start a 1-Person Business with Grok Bots (2 HOUR COURSE 2026)

    Watch on YouTube: https://www.youtube.com/watch?v=CxbO1oGJRwU

    Channel: Ritesh Verma

    Introduction

    The video titled “Start a 1‑Person Business with Grok Bots (2 HOUR COURSE 2026)” presents a workflow for running a solo operation by deploying AI‑powered agents that mimic human employees. The presenter, Ritesh Verma, explains how to create a virtual team using GrokBot, connect it to everyday business tools, and automate lead generation, outreach, content creation, and advertising. The promised outcome is a reduction in staffing costs and the potential to generate significant revenue with minimal hands‑on effort.

    What the Video Covers

    The presentation walks through the following concepts:

    • Creating AI employees for core business functions (operations, sales, research, content, advertising) without coding.
    • Designating an operating director agent that coordinates the other bots and can be customized via profile settings.
    • Connecting Gmail so an AI email agent can draft and send replies, reducing inbox overload.
    • Automating WhatsApp messaging through a “speed lead” agent that instantly contacts new Calendly bookings for sales follow‑up.
    • Linking Google Calendar to schedule briefings and trigger pre‑meeting research reports.
    • Building a content team (strategist, researcher, screenwriter, repurposer) that generates and adapts material for YouTube, Instagram, LinkedIn, and X.
    • Configuring an advertising manager agent that creates and optimizes Meta ad campaigns, with claimed ROAS figures.
    • Sharing agent configurations as templates for replication across clients or team members.
    • Offering a one‑on‑one consultation for personalized implementation.

    Step‑by‑Step Walkthrough (as described)

    1. Download and install GrokBot – obtain the software for Mac OS or any supported device from the provider’s website.
    2. Add the Gmail connector – install it via the GrokBot Marketplace to give the AI email agent access to your inbox.
    3. Create an Operating Director agent – set up a profile that defines its role in overseeing other bots; adjust settings such as response tone and escalation rules.
    4. Build specialist agents – add a GTM specialist, competitor researcher, and business strategist, each with its own prompt library and knowledge base.
    5. Connect data sources – link Google Drive and Gmail to provide the agents with business context, past communications, and reference documents.
    6. Set up WhatsApp automation – create a “speed lead” agent, connect it to WhatsApp, and trigger it when a new Calendly booking occurs to send an instant follow‑up message.
    7. Integrate Google Calendar – enable the operating director to schedule briefings and request pre‑meeting research reports from the researcher agent.
    8. Assemble the content team – configure a strategist to set themes, a researcher to gather sources, a screenwriter to draft scripts or posts, and a repurposer to adapt content across platforms.
    9. Configure the Advertising Manager – obtain a Meta token, connect Facebook Ads Manager, and let the agent create, monitor, and optimize ad campaigns based on performance metrics.
    10. Share configurations as templates – export agent setups to reuse for other clients or to onboard team members quickly.
    11. Book a one‑on‑one call – follow the link in the video description for personalized guidance on implementation.

    Tools and Numbers Mentioned

    The video references several tools and quantitative claims:

    • GrokBot (downloadable for Mac OS/any device)
    • Gmail connector, Google Drive, WhatsApp, Calendly, Google Calendar, Notion, Slack, Discord community, Facebook Ads Manager/Meta token
    • Comparative tools: Open Claw, GP6 Astra model, GB6 Astra model, Claude AI, GPT‑6, Cursor (used for Slack link)
    • Cost comparisons: $10,000/month for an operating director, $4,000/month for an advertising agency, $2,000/month for trading representatives
    • Savings claim: over $200,000 per year
    • Revenue example: $48,000 increase in September
    • Advertising example: a $32,000 ad project costing only €50
    • Performance claims: ROAS up to 7.45×, with some ads achieving ROAS of 1000×
    • Additional figures: $7,500 for a specialized video, $1,000 per coil, $1,500 (unspecified)

    Caveats and Hype Check

    The video makes bold revenue claims such as seven‑figure profit, saving over $200,000/year, and achieving ROAS up to 1000× on ads without providing verifiable proof or independent case studies. These results appear exaggerated and should be treated as aspirational rather than typical outcomes.

    Because the presented numbers are self‑reported and lack third‑party validation, readers should approach them as illustrative scenarios. Potential risks include over‑reliance on automation, possible compliance issues with messaging platforms, and the need for ongoing monitoring to ensure AI outputs align with brand voice and legal requirements.

    Concrete Next Actions

    If you wish to explore the approach, consider the following steps based on the video’s action items:

    • Download GrokBot from the official site and install it on your preferred device.
    • Add the Gmail connector via the GrokBot Marketplace to enable AI‑driven email handling.
    • Create an Operating Director agent, customize its profile, and define its coordination rules.
    • Build additional specialist agents (GTM, researcher, strategist) and link them to Google Drive and Gmail for contextual awareness.
    • Set up a WhatsApp “speed lead” agent and connect it to Calendly for instant lead follow‑up.
    • Integrate Google Calendar to automate briefing schedules and trigger pre‑meeting research.
    • Assemble a content team of four agents (strategist, researcher, screenwriter, repurposer) and align them with your marketing goals.
    • Configure an Advertising Manager agent in Facebook Ads Manager using a Meta token; start with a small test budget to evaluate performance.
    • Export successful agent configurations as templates for reuse or sharing with collaborators.
    • Schedule a one‑on‑one consultation (via the link in the video description) if you need tailored implementation support.

  • AI Tools: Stop Copying Faceless YouTube Channels! Build Their System Instead

    Watch on YouTube: https://www.youtube.com/watch?v=xkK3PTBolvo

    Channel: The AI Garage

    Stop Copying Faceless YouTube Channels! Build Their System Instead

    The video from The AI Garage outlines a repeatable workflow for creating faceless YouTube videos that mimics the storytelling style of a reference channel while pulling factual content from independent web research. By separating narrative structure from subject matter, the process lets creators produce an endless stream of original videos in a consistent format.

    What the Video Covers

    The presenter walks through a end‑to‑end system that:

    • Ingests the last five videos of a chosen reference channel into Notebook LM to learn hook, pacing, information density, section flow, curiosity restarts, transitions, and resolution.
    • Uses a custom prompt that tells the model to apply YouTube‑derived storytelling style only, while pulling factual information from web sources.
    • Generates original topic ideas with keywords, runs quick web research in Notebook LM to collect reliable sources, and writes a ~750‑word script that follows the learned narrative structure.
    • Creates a visual planner in ChatGPT that defines recurring visual types (kinematic infrastructure, 3D maps, sectional reconstructions, mechanical diagrams, infographic movement) and assigns each script segment a visual type, framing, camera movement, and optional text overlay.
    • Batch‑generates scene clips in Google Flow using independent prompts that already contain subject, style, framing, and animation.
    • Assembles the final video in CapCut (or any editor) by syncing voice‑over from Google AI Studio, background music from the YouTube Studio audio library, and the pre‑generated scene clips according to the visual plan.

    Step‑by‑Step Walkthrough

    1. Set up Notebook LM: Create a new notebook and add the URLs of the last five videos from the reference channel as sources.
    2. Configure the chat: Switch to custom mode and paste the scripting‑engine prompt that instructs the model to use YouTube sources for story structure only.
    3. Generate topic ideas: Ask Notebook LM for five original topic ideas with associated keywords; select one.
    4. Gather factual sources: Run quick web research within Notebook LM to collect reliable information for the chosen topic.
    5. Write the script: Use Notebook LM to produce a ~750‑word script, letting the model apply the learned narrative structure while grounding the content in the web sources.
    6. Design the visual plan: In ChatGPT, run the visual‑planner prompt to define the recurring visual types and their usage rules, then generate a visual batch plan that maps each script segment to a specific visual type, framing, camera movement, and optional text overlay.
    7. Batch‑generate scenes: Copy the visual batch plan into Google Flow, activate the agent, and send the scene prompts for parallel generation. Each scene is independent, with no reliance on previous clips.
    8. Produce audio assets: Generate the voice‑over in Google AI Studio (choose a voice that matches the tone) and download background music from the YouTube Studio audio library.
    9. Assemble the video: Import the voice‑over, music, and pre‑generated scene clips into CapCut (or another editor). Align them according to the visual plan, syncing cuts to the narration and music beats.
    10. Repeat for future videos: Keep the same notebook and visual planner; only replace the web‑researched topic and adjust the visual‑type mix if you switch to a different reference channel.

    Tools and Numbers Mentioned

    • Notebook LM – for ingesting video sources, web research, and script generation.
    • ChatGPT – to create the visual planner and batch plan.
    • Google Flow – agent‑based batch generation of independent scene clips.
    • CapCut – final video assembly (any editor works).
    • Google AI Studio – voice‑over synthesis.
    • YouTube Studio audio library – source for background music.

    The presenter notes that organizing seven scenes took about five minutes, implying that a full seven‑scene batch could be edited in roughly thirty minutes. The script target length is approximately 750 words.

    Caveats / Hype Check

    YouTube sources shape how the story is told. Web sources determine what factual content can be said.

    Each scene is completely independent. There are no character reference images. None of these clips depend on the previous scene.

    The video promotes a paid “Faceless YouTube Engine” guide, claiming a tenfold increase in speed and quality and a ready‑to‑publish first video by the end of the tutorial. These benefit statements are presented as exaggerated claims without supporting evidence.

    Concrete Next Actions

    • Create a new Notebook LM notebook and add the last five video URLs of your chosen reference channel.
    • Open the chat configuration, switch to custom, and insert the scripting‑engine prompt that limits YouTube use to story structure.
    • Request five topic ideas with keywords, pick one, and run quick web research to collect factual sources.
    • Use Notebook LM to write a ~750‑word script, applying the learned narrative structure to the gathered sources.
    • In ChatGPT, run the visual‑planner prompt to define visual types and generate a visual batch plan for the script.
    • Copy the batch plan into Google Flow, activate the agent, and send the scene prompts for parallel generation.
    • Download the generated clips, produce the voice‑over in Google AI Studio, and fetch background music from the YouTube Studio audio library.
    • Assemble the final video in CapCut (or your preferred editor) by aligning voice‑over, music, and scene clips according to the visual plan.
    • Repeat the workflow for future videos, updating only the web‑researched topic and adjusting the visual‑type mix if you study a different reference channel.

  • AI Tools: he made $1,000,000 in 30 days as an AI data broker

    Watch on YouTube: https://www.youtube.com/watch?v=8DAyeNK6VOg

    Channel: Corey Ganim

    Introduction

    The video from Corey Ganim’s channel features Ryan Locke of Polyshares describing how his AI data‑brokerage generated roughly $1 million in revenue during its first month of operation. The discussion centers on the mechanics of sourcing private company data, preparing it for AI labs, and the financial structure that enables referrers to earn a 6 % commission on closed deals.

    What the Video Covers

    Locke outlines the end‑to‑end workflow of Polyshares, positioning the firm as a procurement partner that:

    • Acquires data from private, remote‑first, white‑collar organizations.
    • Removes personally identifiable information (PII) through vetted third‑party de‑identification services.
    • Packages the cleaned data into full‑workflow datasets (e‑mail threads, Slack/Teams messages, ticketing systems, CRM entries) that frontier AI labs require for model training.
    • Negotiates deal terms, including exclusivity periods and licensing structures, and connects sellers with appropriate buyers.

    He also explains the economics of the business, the typical deal size, the referral commission model, and why demand for this type of data is expected to grow as more companies train proprietary models.

    Step‑by‑Step Walkthrough of the Data‑Brokerage Process

    1. Identify qualified sellers – Target companies that are US‑based, English‑language, remote‑first, employ at least 50 people, and have used the same software stack for three or more years. These characteristics simplify de‑identification and increase data consistency.
    2. Engage a third‑party de‑identification vendor – Locke stresses that PII removal must be performed by vetted providers; incorrect de‑identification destroys the dataset’s value and can expose both seller and buyer to legal risk.
    3. Assemble full‑workflow context – Rather than delivering isolated files, Polyshares aggregates emails, chat logs, support tickets, CRM records, and any other relevant digital artifacts that illustrate how work actually unfolds inside the organization.
    4. Structure the deal – Typical transactions are around $300,000, though some reach seven figures. Locke cites a notable Spirit Airlines‑Google deal valued at $10 million as an outlier. Exclusivity periods are commonly set at 24 months; perpetual licenses are discouraged because they limit the ability to monetize the same data later.
    5. Close and compensate – Once a buyer signs, the seller receives payment, and any referrer who supplied the lead via a tracked link earns a 6 % commission on the closed deal.
    6. Monitor market shifts – As more firms train their own models, Locke anticipates demand moving toward specialized, hard‑to‑obtain data such as egocentric or manual‑labor video captured from head‑mounted cameras.

    Key Numbers and Tools Mentioned

    • Revenue claim: ≈ $1 million in the first month.
    • Average deal size: ≈ $300,000.
    • Largest cited transaction: $10 million (Spirit Airlines‑Google).
    • Referral commission: 6 % of closed deal value.
    • Standard exclusivity term: 24 months.
    • Company name: Polyshares.

    Notable Quotes from the Video

    “The average would be about 300,000.”

    “We’ve made seven figures in a single transaction.”

    “If you want to send your deals to Ryan, let him close them and then you get 6%.”

    “Remote first and white collar.”

    “Exclusivity terms norm is probably 24 months.”

    Caveats and Hype Check

    The presentation of the $1 million first‑month revenue figure lacks independent verification. The video ties this claim to an affiliate‑style referral program that offers a 6 % commission, which can create incentives to overstate earnings. Viewers should treat the revenue number as a self‑reported outcome rather than an audited result.

    Concrete Next Actions for Interested Parties

    • Use the tracked referral link provided in the video description to submit qualified leads to Polyshares and earn a 6 % commission on any deals that close.
    • Download the free 80/20 summary manual referenced in the show notes for a quick reference guide on the brokerage model.
    • Begin a lead‑generation side hustle focused on identifying private companies that meet the criteria: >50 employees, ≥3 years on uniform software, US‑based, English‑language, and remote‑first.
    • Research and vet third‑party data de‑identification vendors, ensuring they comply with relevant privacy regulations (e.g., GDPR, CCPA) before engaging in any data‑brokerage activity.
    • Stay informed about emerging high‑value data types, such as egocentric or manual‑labor video captured via head‑mounted cameras, as these may become sought‑by AI labs training proprietary models.

  • AI Tools: you need to try Paperclip RIGHT NOW!

    Watch on YouTube: https://www.youtube.com/watch?v=7RVf25Rg0Mc

    Channel: NetworkChuck

    Introduction

    NetworkChuck’s latest video introduces Paperclip, an open‑source meta‑harness designed to organize multiple AI agents into a structured company‑like hierarchy. The demonstration centers on building an AI IT Department to troubleshoot a recurring network issue: the studio NAS dropping whenever the office toilet is flushed. Alongside the technical deep‑dive, the video includes a sponsored segment showing how Flare threat‑intelligence can be integrated with Paperclip for automated security monitoring.

    What the Video Covers

    The presentation walks through installing Paperclip on an Ubuntu virtual machine, onboarding a CEO agent (portrayed as Dumbledore using Claude Code), and adding further agents such as a CTO (Ron, powered by Hermes). It shows how agents receive tasks, create sub‑tasks, post results, and require human approval via the Decisions tab for any configuration changes. The investigation reveals that flapping SFP+ transceivers on a MikroTik switch—caused by vibration‑sensitive third‑party modules—were the root cause of the NAS drops. The video also demonstrates storing Flare API credentials in Paperclip Secrets, launching a threat‑exposure agent (Filch) that queries Flare for leaked credentials, and using Paperclip’s Routines feature for automated daily stand‑ups. Token usage, embedded PostgreSQL details, and specific hardware numbers are highlighted throughout.

    Step‑by‑Step Walkthrough

    • Prepare a Linux VM (Ubuntu) and install Node.js v24.
    • Run the installation script: curl -fSSLO https://raw.githubusercontent.com/paperclipai/paperclip/v2026.831.1/scripts/install.sh followed by execution of the script.
    • Start Paperclip; the UI becomes available on port 3100 (hostname toiletsquad.hogwarts.studio).
    • Bootstrap the CEO agent with npx paperclipai auth bootstrap-ceo, which generates an onboarding prompt for the chosen AI harness (Claude Code in the demo).
    • Paste the generated prompt into the agent’s terminal to register the CEO within Paperclip.
    • Through the UI, add additional agents (e.g., CTO Ron using Hermes) by copying each agent’s onboarding prompt into their respective harness terminals.
    • Create a project (e.g., “TOILET ISSUES”) and assign a task identifier such as NET‑17 to the CEO agent.
    • Agents break the task into sub‑tasks, post findings, and use the Decisions tab to request human approval before applying any configuration changes.
    • To investigate the NAS drops, the CEO agent queries switch logs, discovers 20,052 link‑down events on SFP+ port 5 since boot, and correlates them with vibration events from toilet flushing.
    • Physical inspection reveals cheap third‑party SFP+ modules (batch 23‑11‑16) with zero optical signal margin; replacing them with official MikroTik SFP+ transceivers eliminates the flaps.
    • For security monitoring, store Flare credentials as FLARE_API_KEY and FLARE_TENANT_ID in Paperclip Secrets, then launch a threat‑exposure agent (Filch) that queries Flare for leaked credentials and creates remediation tickets.
    • Enable the Routines feature to schedule automated daily stand‑ups where all agents report progress, blockers, and cross‑agent questions.
    • Monitor resource consumption and token usage (~49.2 M tokens across runs) via the Timelines tab to optimize workflows.
    • Export the organization for backup or migration using npx paperclipai export and import it elsewhere with npx paperclipai import.

    Tools, Numbers and Key Findings

    • Paperclip GitHub: github.com/paperclipai/paperclip
    • Node.js version required: v24.0.0
    • Embedded PostgreSQL runs on port 54329.
    • Paperclip UI port: 3100.
    • Token consumption across demonstrated runs: approximately 49.2 million tokens.
    • Flapping SFP+ port 5 link‑down events: 20,052 since boot.
    • Estimated failure rate of the problematic third‑party SFP+ batch (23‑11‑16): ~50 % in‑service.
    • Flare detection time: < 60 seconds versus an average corporate detection time of 36 hours.
    • Attacker exploitation window after credential theft: ~48 minutes.
    • Flare dashboard stats shown: 1 critical issue, 76 high‑severity leaked credentials, 32,400+ medium‑severity infected devices, 1 ransom leak, 390 chat messages, 23 look‑alike domains.
    • Tools used in the demo: Claude Code, Hermes Gateway (gpt‑6‑astra), Codex, Pi/Qwen (local LLM), MikroTik SFP+ transceivers (official vs. third‑party batch 23‑11‑16).

    Caveats and Hype Check

    While Paperclip presents an appealing vision of multi‑agent orchestration, the platform remains early‑stage open‑source software. The video’s claims of dramatically reduced complexity should be balanced against the manual steps required for installation, agent onboarding, and troubleshooting. The Flare segment is a sponsored promotion emphasizing sub‑minute detection times and large volumes of leaked credentials; those metrics are impressive but should be verified independently, and integrating Flare may demand additional configuration beyond what is shown.

    “Agent group chats aren’t useful… I haven’t seen a lot of super value out of like an agent conference room or an agent town where you just set your agents to sit there and talk to each other.” — Dotta

    “Attackers don’t hack in, they log in.” — NetworkChuck

    Concrete Next Actions

    • Deploy a Linux VM with Node.js v24 and run the Paperclip installation script.
    • Bootstrap a CEO agent using npx paperclipai auth bootstrap-ceo and follow the generated onboarding prompt.
    • Add further agents (CTO, engineers) via the UI, pasting each agent’s onboarding prompt into their respective harness terminals.
    • Create a project for your use case, assign task IDs, and use the Decisions tab for any configuration‑change approvals.
    • Experiment with the Routines feature to set up automated daily stand‑ups for your agent team.
    • If you intend to use Flare, store FLARE_API_KEY and FLARE_TENANT_ID in Paperclip Secrets and launch a threat‑exposure agent (Filch) to query the API.
    • Inspect network hardware for suspect third‑party SFP+ transceivers; replace any that match the problematic batch (23‑11‑16) with vendor‑approved modules to prevent vibration‑induced link flaps.
    • Monitor token usage and resource consumption through the Timelines tab to fine‑tune agent workflows.
    • Export your Paperclip organization (npx paperclipai export) for backup or migration, and verify the import process on a secondary server.
    • Review the video description for the Flare free‑trial link if you wish to evaluate the threat‑intelligence platform further.

  • AI Tools: Level Up Your AI Agent – 7 steps to a smarter Muse

    Watch on YouTube: https://www.youtube.com/watch?v=K4ZyKu9TXcs

    Channel: Humanoid

    Introduction

    The Humanoid channel presents a practical framework for turning a personal AI agent—referred to as Muse or Q—into a more reliable and context‑aware collaborator. The video outlines seven concrete steps that move the agent from a generic responder to a tool that learns from corrections, maintains a consistent voice, and applies structured review processes before any output reaches the user.

    What the Video Covers

    The presentation begins with a TL;DR that summarizes the core idea: interview the agent to build deep context, train it on your writing style, turn every manual correction into a permanent lesson, add automated self‑review stages, schedule weekly system checks, teach the agent patience, and apply a four‑step tiered plan review guided by ten engineering principles. Optional adversarial or virtual‑boardroom reviews are suggested for higher‑stakes projects.

    Step‑by‑Step Walkthrough

    • Interview your Muse for context and success criteria. Ask the agent any questions needed to understand the business or task better than you do, and explicitly define what success looks like before assigning work.
    • Build a content library of your past writing. Upload representative samples, extract voice lessons, test generated drafts, and correct mistakes to refine the agent’s style.
    • Treat every correction as a teachable moment. Instruct the Muse to retain the lesson from each manual edit and, when necessary, perform a root‑cause analysis to convert the error into a lasting rule stored in memory.
    • Implement automated multi‑stage review. Configure the agent to first check facts and assumptions, then evaluate voice and quality, so you are never the first reviewer of its output.
    • Schedule a weekly systems review. Set a recurring audit (the video suggests Wednesday morning) where the Muse reviews all scheduled tasks, flags failures, and reports on workflow reliability.
    • Teach the Muse patience. Distinguish capability questions from execution commands, making clear that a query about ability does not trigger immediate action.
    • Apply a four‑step tiered plan review. Follow the sequence Frame → Approach → Foundation → Choose Tier, evaluating the plan against ten core engineering principles to decide whether a Light, Standard, or Deep review is required. For high‑stakes work, add an Adversarial Reviewer (a separate Q instance) or a Virtual Boardroom of five Q instances with competing perspectives to stress‑test the plan.

    Tools and Numbers Mentioned

    • Muse agent (the primary personal AI)
    • Q agent (used for adversarial or boardroom reviews)
    • Gemini API (free tier) as the underlying model interface
    • Ten core engineering principles that guide the review tiers
    • Four‑step tiered plan (Frame, Approach, Foundation, Choose Tier)
    • Three review levels: Light, Standard, Deep
    • Five distinct Q instances for a virtual boardroom
    • Recurring weekly check scheduled for Wednesday morning
    • Example task used in the video: drafting YouTube comment responses

    Caveats / Hype Check

    The video focuses on workflow improvements and does not make revenue promises, affiliate promotions, or guarantees of specific outcomes. Effectiveness depends on how consistently the steps are implemented and the capabilities of the underlying AI model. The presenter notes that results will vary across users and use cases.

    Concrete Next Actions

    • Interview your Muse about your current business or task to capture context and define success criteria.
    • Collect a representative sample of your past writing, upload it to the Muse, extract voice lessons, test outputs, and correct errors to refine its style.
    • Whenever you correct the Muse, instruct it to treat the correction as a permanent lesson and, if needed, discuss the root cause to store a lasting rule.
    • Set up two automated review stages: first a fact/assumption check, then a voice/quality check, so you never see raw output before review.
    • Create a recurring weekly audit (e.g., every Wednesday morning) where the Muse reviews all scheduled tasks and reports any failures.
    • Add explicit rules that capability questions are not execution commands, teaching the Muse to wait for a clear command before acting.
    • Adopt the four‑step tiered plan review (Frame → Approach → Foundation → Choose Tier) and evaluate each plan against the ten core engineering principles to select the appropriate review depth.
    • For high‑stakes projects, deploy an Adversarial Reviewer (a separate Q instance) or assemble a Virtual Boardroom of five Q instances with competing viewpoints to stress‑test the plan.

  • Grok Bot: I Spent 50 Hours Testing Grok Bot: 8 Tricks You NEED

    Watch on YouTube: https://www.youtube.com/watch?v=CUxn_kTrc1o

    Channel: Nuno Tavares | Automated Marketer

    Introduction

    Nuno Tavares, creator of the “Automated Marketer” channel, shares the results of more than 50 hours of hands‑on testing with Grok Bot. The video outlines eight advanced techniques that aim to improve voice interaction, app integration, multi‑agent coordination, security, and skill retention for power users who want to automate complex workflows.

    What the Video Covers

    The presentation is structured around eight specific “tricks” that Tavares discovered while experimenting with Grok Bot. Each trick is demonstrated with a practical example, and a bonus maintenance routine is added to keep agent performance consistent over time. The video also notes the tools and platforms involved, provides quantitative details (e.g., number of apps accessible via Composio), and includes a brief hype check to remind viewers that the reported gains are based on personal experimentation.

    Step‑by‑Step Walkthrough of the Eight Tricks

    1. Assign distinct voice presets – Open a bot’s Settings inspector, choose a voice preset (e.g., Iris, Eve) and save it. This enables hands‑free interaction on mobile or desktop devices.
    2. Leverage Composio as a single‑click bridge – Sign up at composio.dev, install the Composio plugin inside Grok Bot, and connect the SaaS accounts you need. The free tier offers 100 000 actions per month and provides access to 1 580 external apps.
    3. Summon specialist bots with @mentions – During any chat, type the @ symbol followed by the bot’s name (e.g., @Presentation) to pull that agent into the conversation, preserving context.
    4. Create persistent group chats for agent swarms – Use the left‑sidebar + button to start a Group Chat, add the relevant bots, and issue a high‑level collaborative directive. The swarm stays active for ongoing projects.
    5. Secure logins with a 1Password Service Account Token – In 1Password, create a vault, generate a Service Account Token with read‑only scope, and paste the token into Grok Bot’s 1Password connector. This avoids storing plaintext credentials.
    6. Use the Chief of Staff as a “Bridge” for cross‑platform AI coordination – Assign the Chief of Staff a prompt that lets it control Claude and ChatGPT simultaneously, enabling A/B testing or parallel task execution.
    7. Teach agents to remember successful click paths – After an agent reaches a target screen, instruct it to “Remember what you clicked.” This stores the navigation sequence, reducing token usage on repeat runs.
    8. Record live‑screen demonstrations to create repeatable skills – Launch the bot’s virtual desktop, use the “Teach a task” feature to record a manual workflow, stop recording, and confirm the learned skill for future automation.

    Tools, Platforms, and Quantitative Details

    • Grok Bot (the core platform being tested)
    • Composio (composio.dev) – 1 580 apps accessible; free tier: 100 000 actions/month
    • 1Password – Individual Plan: $2.99 USD/month (annual) or $3.99 USD/month (monthly) with a 14‑day free trial; Families Plan: $4.49 USD/month (annual)
    • Claude (claude.ai) – ad dimensions noted: 1080 × 1080 (square) and 1200 × 628 (link)
    • ChatGPT (chatgpt.com)
    • Skool (skool.com) – mentioned as a community/resource
    • YouTube (youtube.com) – platform hosting the video
    • Google Chrome (virtual desktop browser) – used for the “Teach a task” recordings

    Notable Quotes from the Video

    “All of your bots can have a very specific voice, where you can actually communicate via voice on your phone or on your laptop or PC.”

    “Instead of you having to always connect your different AI tools into a bunch of different connectors… you can literally come in here, connect it once, and it will automatically use this with any of your AI tools.”

    “You can bring in other bots to your conversation… It will automatically bring in the bot to the conversation and pass that information through.”

    “Saved as `@claude-chatgpt-ad-design` so future runs deep‑link and skip the wander.”

    “Remember what you clicked so you can actually be a little bit more efficient in doing this and so you don’t use as much tokens.”

    “Watching the teach recording now — claiming the capture and reviewing it… Learn from demonstration.”

    “Create a routine that goes through and optimizes our skills and makes sure that our connections are the most up to date… so they don’t get stale and, more importantly, your skills are always improving.”

    Caveats and Hype Check

    The video’s productivity claims are based on the creator’s personal 50‑hour experiment. No independent verification, revenue figures, or guaranteed performance metrics are presented. Viewers should treat the described efficiency improvements as anecdotal outcomes rather than assured results.

    Concrete Next Actions

    • Open a bot’s Settings inspector and assign a unique voice preset (e.g., Iris, Eve) for voice interaction.
    • Sign up at composio.dev, install the Composio plugin in Grok Bot, and connect your key SaaS accounts.
    • In 1Password, create a vault, generate a Service Account Token with read‑only scope, and paste it into Grok Bot’s 1Password connector.
    • During any chat, type @ to summon a specialist bot (e.g., @Presentation) and continue the conversation with context.
    • Create a Group Chat via the left‑sidebar + button, add relevant bots, and issue a high‑level collaborative directive.
    • After an agent reaches a target screen, instruct it to “Remember what you clicked” to lock the navigation path for future runs.
    • Use the ‘Teach a task’ feature: launch the bot’s virtual desktop, record a manual workflow, stop recording, and confirm the learned skill.
    • Assign the Chief of Staff a maintenance prompt to periodically audit skills, tokens, and connections.
    • Mute the voice modal when not speaking to avoid accidental background triggers.
    • Explore the 1 580 apps list in Composio to identify additional integrations for your workflows.

    Related Topics

    Grok Bot, Marketing & Lead Gen, AI Tools

  • Marketing & Lead Gen: How To Create 1 Week of Content in 1 Hour (Full Process)

    Watch on YouTube: https://www.youtube.com/watch?v=lfn4NKz1KEo

    Channel: Eden

    Overview

    The video from Eden demonstrates a workflow that aims to produce a full week’s worth of social‑media content in roughly one hour by leveraging the Eden.so platform together with AI models such as Claude and ChatGPT. The process focuses on gathering inspiration, reverse‑engineering high‑performing posts, extracting brand context, and then using a repeatable prompt sequence to draft content quickly while keeping human review for authenticity.

    What the Video Covers

    Eden walks through a complete system that starts with organizing inspirational material in research folders and boards, builds a curated list of mentor creators, hunts for outlier posts using Eden’s Breakouts tab, deconstructs those posts into a structured breakdown, runs an AI‑driven brand‑context interview, creates a weekly content‑planning table, and finally executes an 11‑step content‑generation workflow that can be saved as a reusable Skill for agency delegation.

    Step‑by‑Step Walkthrough

    • Install Eden.so, add the Chrome extension, and download the mobile app from eden.so/downloads.
    • Create a Research folder and set up topic boards (e.g., Productivity, Creator Economy) to store saved inspiration.
    • Use Eden AI chat to generate a list of 5‑10 niche‑specific mentor creators and add them to an Inspiration list.
    • Spend 10‑15 minutes each day browsing the Breakouts tab, saving outlier posts and attaching a note that explains why each post stands out.
    • Run the reverse‑engineer prompt in Claude or via Eden MCP to turn each saved post into a Breakdown table capturing format, hook, outlier score, and other relevant elements.
    • Conduct an AI‑driven brand‑context interview using the four core questions (origin, shift, obsession, early map) and store the resulting Brand Context document on your Brand Strategy board.
    • Build a weekly content‑plan table with columns for day, format, pattern, hook, and two checkboxes labeled “Will it spread?” and “Will it sell?” Fill in each row with a content idea derived from the breakdowns and brand context.
    • Follow the 11‑step Weekly Content Prompts workflow to draft the actual copy, then convert the entire sequence into a reusable Skill that can be shared with teammates or clients.
    • Review all drafted content for tone, accuracy, and alignment with brand goals before scheduling or publishing.

    Tools and Numbers Mentioned

    • Eden.so (core platform)
    • Eden.so/downloads (for Chrome extension and mobile app)
    • Claude (AI model used for reverse‑engineering)
    • ChatGPT (referenced as an alternative AI resource)
    • Eden Model Context Protocol / MCP (enables AI interaction with Eden data)
    • 1 script previously required ~4 hours of manual work
    • 20 scripts can now be produced in ~4 hours using the described workflow
    • Claimed 20× efficiency improvement
    • Example outlier score cited as 31× (illustrating high‑performing content)
    • Additional numeric values shown in the video: 471.1, 23.9, 197.3, 20.4, 15.2, 8.7, 6.7, 2.1 (these appear as metrics within Eden’s analytics)
    • Recommendation to track 5‑10 creator mentors

    Key Quotes

    The biggest problem that always comes up, every single time, is “I don’t know what to post.”

    One script used to take 4 hours, and now 20 scripts take 4 hours.

    If you just let AI write all the scripts, no matter how good AI gets, the writing can be impeccable, but it doesn’t fit the other things that make content do well.

    You’re borrowing the shape, not the substance.

    Embrace the chaos so you don’t have to get stuck just organizing things like crazy.

    Caveats and Hype Check

    The 20× efficiency gain presented in the video is based on the creator’s personal benchmark and has not been independently verified. Eden is positioned as an all‑in‑one solution, but the video does not disclose pricing details or any affiliate relationships that might influence the recommendation. As with any AI‑assisted process, human oversight remains essential to ensure brand voice, factual accuracy, and conversion‑focused messaging.

    Concrete Next Actions

    • Install Eden.so, add the Chrome extension, and download the mobile app from eden.so/downloads.
    • Create a Research folder and establish topic boards that match your content pillars.
    • Run Eden AI chat to generate a mentor list of 5‑10 creators in your niche and save it to an Inspiration list.
    • Allocate 10‑15 minutes each day to scan the Breakouts tab, save outlier posts, and annotate why each caught your attention.
    • Apply the reverse‑engineer prompt in Claude or via Eden MCP to produce a Breakdown table for every saved post.
    • Facilitate the AI brand‑context interview (origin, shift, obsession, early map) and store the output in your Brand Strategy board.
    • Construct a weekly content‑plan table with day, format, pattern, hook, and the “Will it spread?” / “Will it sell?” checkboxes, populating it with ideas derived from your breakdowns.
    • Execute the 11‑step Weekly Content Prompts workflow to draft the copy, then save the entire sequence as a reusable Skill for future use or agency delegation.
    • Perform a final human review of all drafted content for tone, accuracy, and alignment with business objectives before scheduling or publishing.

  • Grok Bot: GrokBot Just KEEPS Getting Better (4 New Use Cases)

    Watch on YouTube: https://www.youtube.com/watch?v=q7lekwOXXqk

    Channel: Kacper Rutkiewicz | AI Made Simple

    Introduction

    The video from Kacper Rutkiewicz | AI Made Simple showcases four recently added capabilities of Grok Bot. Each feature is demonstrated in a short, hands‑free session that ties together search, file creation, messaging, and voice interaction. The presenter frames the bot as part of a Cursor‑based workflow and shares concrete usage numbers from his own account.

    What the video covers

    Kacper walks through:

    • Native X (Twitter) search that does not consume developer credits.
    • Creation of real Google Docs, Sheets and Slides in the user’s Drive.
    • A dedicated bot‑owned inbox at @mail.grokbot.com.
    • A hands‑free voice mode that returns a full transcript after the session.

    He ties the four functions together in a 33‑second voice command that searches X, builds a deck, a doc and a sheet, then emails the links, and he shows the impact on his weekly quota.

    Native X search

    Grok Bot can query X directly from the chat interface without drawing on the user’s X developer or API credits. The feature is limited to 1,000 searches per day per account. In the demo the bot’s X Developer balance stayed at $3.61, confirming that no credits were spent. This allows repeated research queries—such as “top posts about X in the last 48 hours”—to be run throughout the day without affecting any paid X API usage elsewhere.

    Google Docs, Sheets and Slides

    When Google Workspace is connected, Grok Bot creates actual files in the user’s Drive rather than markdown placeholders. Users can point the bot at personal master templates so generated decks, docs and sheets follow brand styling. Slide generation typically takes 5–10 minutes. The same 33‑second voice command that produced a deck, a doc and a sheet moved the weekly quota from 32% to 35% (about a 3% increase), indicating that file‑creation consumes a modest portion of the limit.

    Bot email inbox (@mail.grokbot.com)

    Each bot instance receives a unique address of the form name@mail.grokbot.com, separate from the user’s personal Gmail. The address is claimed by typing “Claim our email and create our inbox”; allocation follows a first‑come, first‑served basis as the feature rolls out. The bot can send formatted HTML messages to any recipient and can also receive mail—useful for verification codes, receipts or newsletter sign‑ups—and reply to incoming messages. An important caveat is that new mail does not automatically wake the bot; a polling routine must be set up otherwise messages remain unnoticed until the user explicitly asks the bot to check.

    Voice mode

    Activating the waveform or call button enables hands‑free speaking; Grok Bot returns a full transcript after the conversation ends. In the headline demo a 33‑second verbal command—search X, build a deck, a doc and a sheet, then email the links—was executed successfully. Longer sessions, such as 15‑ to 20‑minute verbal brainstorming calls, barely register against the weekly quota on the Cursor plan, showing that voice interaction can support extended workflows without significantly impacting usage limits while still providing a searchable transcript.

    Tools and usage numbers

    • Cursor subscription: $60 / month (the plan used for quota measurements).
    • Weekly quota before demo: 32%; after demo: 35% (≈3% increase for the full multi‑step voice command).
    • X Developer credit unchanged: $3.61.
    • Native X search cap: 1,000 searches per day per user.
    • Slide generation time: 5–10 minutes.
    • Voice demo length: 33 seconds; longer brainstorms 15–20 minutes barely affect limits.
    • Integrated tools: Grok Bot, Cursor, X (Twitter), Google Workspace (Drive, Docs, Sheets, Slides, Gmail, Calendar), mail.grokbot.com.

    Caveats and hype check

    The presenter notes that the demonstrated usage figures are based on a single creator’s session and have not been independently verified. Claims about Grok Bot being a “premier personal AI agent” should therefore be treated as directional rather than definitive. The email inbox feature is still rolling out, so name availability varies and users may need to try several variations of @mail.grokbot.com. Additionally, new mail does not auto‑trigger the bot; a polling routine is required to avoid missed messages.

    > “Even 15‑to‑20‑minute verbal brainstorming calls barely impact usage limits.”

    Concrete next actions

    • Claim a preferred @mail.grokbot.com address by typing “Claim our email and create our inbox.”
    • Connect Google Drive via Connect Apps; optionally set a master template for Docs, Sheets and Slides.
    • Add a routine that polls the bot inbox (since new mail does not auto‑wake the bot).
    • Try a native X search prompt, for example: “top posts about AI agents from the last 48 hours.”
    • Run one multi‑step voice command (search X, create a doc, sheet and slide, email the links) and review the transcript plus the quota delta.
    • Optional: join the Skool community at skool.com/ai-automation-network-2970 for templates and guides.