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  • Trading: The UNTHINKABLE is About to Happen to Stocks (but most will invest wrong)

    Watch on YouTube: https://www.youtube.com/watch?v=6pm5KqQw6RI

    Channel: Tom Nash

    Introduction

    The video from Tom Nash argues that most investors lose money not because they lack information, but because they lack a repeatable system that turns data into disciplined action. By treating stocks as underlying businesses and applying a set of predefined rules, the presenter claims investors can remove guesswork, stay invested through volatility, and achieve long‑term compounding.

    What the Video Covers

    Tom Nash outlines a three‑part framework:

    • A mindset shift: focus on earnings, fundamentals, and business quality rather than price noise or media opinion.
    • Practical rules for entering, adding to, and trimming positions (trim rules, double‑down DCA).
    • A quantitative quality screen (the 125‑point test) used to evaluate whether a stock meets the criteria for a long‑term hold.

    He also shares historical examples, personal portfolio results, and invites viewers to a free live masterclass where the full methodology and two undisclosed stock picks will be revealed.

    Core Framework Explained

    Trim Rules

    Instead of letting winners run indefinitely, the presenter suggests taking partial profits at predefined price levels and recycling that cash into a dollar‑cost‑averaging (DCA) pool. The example given is:

    • Sell 10 % of a position when it is up +50 % from the purchase price.
    • Sell an additional 20 % when the position is up +100 %.
    • The proceeds from these sales are directed to a DCA fund that can be deployed when market conditions meet the double‑down criteria.

    Double‑Down DCA

    The approach adjusts the size of regular purchases based on recent price action relative to a yearly high:

    • If a broad index (e.g., S&P 500) is down ≥10 % from its yearly high, double the monthly DCA amount.
    • If an individual stock is down ≥20 % from its yearly high, double the monthly DCA amount for that stock.
    • If a stock rises >20 % within a 30‑day window, reduce the DCA amount to half‑speed (i.e., buy half the usual monthly amount).

    These rules are intended to increase exposure during pullbacks while tempering buying during rapid rallies, thereby averaging into positions without trying to pick exact tops or bottoms.

    125‑Point Quality Test

    The presenter describes a checklist that scores a stock across multiple dimensions. A higher score indicates a stronger fundamental profile and, in his view, a higher likelihood of delivering long‑term price appreciation. The components mentioned include:

    • Cash versus debt levels
    • Revenue growth trends
    • Operating margin
    • Short interest
    • Institutional ownership
    • Scalability of the business model
    • Five‑year track record of beating the S&P 500
    • Presence of a durable moat
    • CEO quality and track record
    • Recession‑proof characteristics
    • Alignment with secular trends
    • Mission‑centric culture
    • Talent depth and elite employee base

    Each factor contributes points toward a total of 125; the presenter uses this score to decide whether to hold, add to, or trim a position.

    Historical Examples and Performance Numbers

    To illustrate the power of staying the course, the video walks through price trajectories for four stocks:

    • Palunteer: $6 → $7 → $10 → $23 → $18 → $6 → $17 → $75 → $177 → $200 → $137 → $209
    • Nvidia: $20 → $150 → $94 → $230
    • Data Dog: $133 → $105 → $188 → $293
    • Bloom Energy: $156 → $119 → $280

    The associated returns cited are:

    • Palunteer ≈ 2600 %
    • Nvidia ≈ 1500 %
    • Data Dog ≈ 120 %
    • Bloom Energy ≈ 80 %

    The presenter’s personal portfolio, weighted as 40 % Palunteer, 40 % S&P 500, and 20 % Tesla, is said to have returned roughly +565 % versus a +75 % benchmark (S&P 500) over the same period, translating to an approximate +1300 % raw gain before weighting.

    Additionally, a Vanguard study is referenced showing that wealthy portfolios typically hold about 90 % in U.S. stocks, keep cash around 13 %, and exhibit low trading activity (≈ 8 % turnover), underscoring the value of long‑term, low‑turnover investing.

    Tools, Thresholds and Rules Summary

    • Trim rules: sell 10 % at +50 %, sell an additional 20 % at +100 %; recycle proceeds into DCA.
    • Double‑down DCA triggers:
      • Index ↓≥10 % yearly high → 2× monthly buy.
      • Stock ↓≥20 % yearly high → 2× monthly buy.
      • Stock ↑>20 % in 30 days → ½× monthly buy.
    • Quality test: 125‑point scale covering cash/debt, revenue growth, operating margin, short interest, institutional holding, scalability, 5‑yr S&P beat, moat, CEO, recession‑proof, secular trend, mission‑centric culture, talent.

    Caveats and Hype Check

    The video presents impressive personal returns (e.g., a weighted +565 % versus a +75 % benchmark) and claims of 500 %+ outperformance. These results are shown without independent verification or audited documentation, so they should be viewed as illustrative rather than guaranteed outcomes for any investor.

    The promotion of a free masterclass creates a sense of urgency (“Oct 17, 2 p.m. ET”) that may encourage sign‑ups before the viewer has fully evaluated whether the system aligns with their own risk tolerance, investment horizon, and financial situation.

    As with any strategy, past performance does not guarantee future results, and the effectiveness of the trim rules, double‑down DCA, and quality test will depend on market conditions, individual stock selection, and disciplined execution.

    Concrete Next Actions

    If you wish to experiment with the framework presented, consider the following steps:

    • Reserve a spot for the free masterclass on October 17 at 2 p.m. ET via the video description or pinned comment.
    • Create a one‑page checklist for each stock you are evaluating, assigning points for each of the 125‑quality factors mentioned.
    • Determine a baseline monthly DCA amount that fits your budget and set up automatic transfers to a dedicated investment account.
    • Define your personal trim rules (e.g., take 10 % profit at +50 %, 20 % at +100 %) and decide how you will recycle the proceeds into your DCA pool.
    • Implement the double‑down logic: monitor your chosen index and each holding’s yearly high; increase purchases when the thresholds are met and reduce to half‑speed when a stock spikes >20 % in 30 days.
    • Avoid making buy or sell decisions based solely on news headlines or social media chatter; rely on your pre‑defined rules and quality scores.
    • Track your portfolio’s performance against a benchmark such as the S&P 500 on a quarterly basis to assess whether the process is adding value.
    • Review each holding’s quality score every quarter; use the results to decide whether to hold, add, or trim the position.
    • Maintain a simple journal of emotions and decisions to detect moments when you deviate from the system and to reinforce disciplined behavior.
    • After the masterclass, integrate the two undisclosed stock picks into your watchlist and apply the same quality‑scoring and DCA framework before allocating capital.

  • AI Tools: this is boring but you’ll outwork everyone

    Watch on YouTube: https://www.youtube.com/watch?v=E8Oagqi5CBM

    Channel: Daniel Barada

    Introduction

    The video titled “this is boring but you’ll outwork everyone” from Daniel Barada’s channel introduces a practical framework for measuring and improving personal performance. Rather than treating skill as a single static level, the presenter explains Jared Tendler’s “inchworm theory,” which visualizes ability as a range between a peak (A‑game) and a trough (C‑game). The core idea is that genuine progress occurs when both ends of that range shift forward together, making the average performance higher and more stable.

    What the Video Covers

    The presentation breaks down the inchworm model into several digestible concepts:

    • Performance is a fluctuating range, not a fixed point.
    • Most daily activity lives in the middle of that range; variation is normal and driven by factors like stress, sleep, and emotion.
    • Expanding the front means pushing the A‑game higher through deliberate practice, learning, and taking on slightly harder challenges.
    • Raising the back involves reducing the frequency, intensity, or recovery time of the C‑game—those worst‑case behaviors that cost time, money, or emotional energy.
    • Real improvement is visible when the old C‑game starts producing outcomes that the former self would have respected.
    • When both ends move forward, the range compresses, raising the average and lowering volatility.
    • Development cycles consist of picking one front‑end target and one back‑end weakness, working on both, then reassessing the range.
    • Effective tracking relies on observable behaviors rather than vague traits like “disciplined” or “confident.”
    • Early warning signs and pre‑planned minimum responses help intercept C‑game patterns before they escalate.
    • A new cycle begins when former A‑game behaviors appear in ordinary performance and former C‑game behaviors fade.

    Step‑by‑Step Walkthrough

    To apply the inchworm theory, the video suggests a concrete process:

    1. Select a life domain (health, work, relationships, inner life).
    2. Identify your current A‑game and C‑game using specific, observable behaviors from the past month.
    3. Choose a front‑end target: a new capability just beyond your normal that you can practice repeatedly.
    4. Choose a back‑end target: the recurring C‑game behavior that costs you the most time, money, or emotional energy.
    5. Define an early warning signal for the back‑end target and a simple minimum response to deploy when that signal appears.
    6. Track both targets over several weeks, noting frequency, intensity, and recovery time for each.
    7. When the old A‑game shows up as routine behavior and the old C‑game diminishes, remap the range and start the next cycle.

    This loop creates a measurable feedback system: you see whether the floor is rising, the ceiling is lifting, and whether the distribution as a whole is shifting forward.

    Tools & Numbers

    The video does not name any specific software, apps, or quantitative tools beyond referencing the YouTube source itself. No financial figures, performance metrics, or paid products are cited. The emphasis is on observable behavior tracking, which can be done with a simple journal, spreadsheet, or habit‑tracking method of your choice.

    Caveats & Hype Check

    The presenter avoids exaggerated revenue promises or sensational claims. The call‑to‑action is modest: a free community, a newsletter, and optional one‑on‑one coaching sessions are mentioned, but there is no pressure to purchase anything. This keeps the focus on the conceptual framework rather than on selling a product.

    “The video does not make exaggerated revenue promises or push paid products; it promotes a free community, newsletter, and optional one‑on‑one calls, which is a modest call‑to‑action without hype.”

    Because the model relies on self‑observation, its effectiveness depends on honest tracking and consistent application. If the chosen behaviors are too vague or the tracking is inconsistent, the perceived shift in range may be illusory.

    Concrete Next Actions

    To begin using the inchworm approach right away, consider the following steps:

    • Pick one area of life you want to improve (e.g., workout routine, project delivery, communication with a partner).
    • Write down your current A‑game and C‑game for that area, using concrete actions you have actually performed in the last four weeks (e.g., “ran 5 km in under 25 minutes” for A‑game; “skipped workout and ate fast food three times” for C‑game).
    • Select a front‑end target that is just beyond your usual performance but achievable with repeated practice (e.g., “add interval sprints to twice‑weekly runs”).
    • Select a back‑end target that represents a costly pattern you want to reduce (e.g., “checking phone during work blocks leading to 20‑minute loss of focus”).
    • Identify an early warning sign for the back‑end target (e.g., “feeling the urge to check notifications after 10 minutes of focused work”) and define a minimum response (e.g., “close the browser tab and take three deep breaths”).
    • Set up a simple tracking method—a notebook, a Google Sheet, or a habit‑app—to log each occurrence of the front‑end practice and each back‑end event, noting intensity and recovery time.
    • Review the logs every two weeks. When the former A‑game appears as regular behavior and the former C‑game noticeably declines, redraw your A‑game/C‑game range and start the next cycle.

    Following this process gives you a tangible way to see whether you are truly expanding your capabilities while also strengthening your baseline—turning the abstract idea of “getting better” into a measurable, repeatable routine.

  • AI Tools: 10 New Github Repos to Make Money in 19 mins

    Watch on YouTube: https://www.youtube.com/watch?v=zwQmodmp1To

    Channel: Jack Roberts

    Introduction

    Jack Roberts presents a rapid survey of ten newly popular GitHub repositories that enable AI agents to perform creative, browsing, reverse‑engineering, workflow orchestration, and security‑audit tasks. The video frames these open‑source projects as alternatives to costly SaaS subscriptions and as building blocks for agent‑driven productivity that could help users reduce expenses and increase output.

    What the video covers

    The presentation walks through each repository, highlighting its core functionality, recent traction metrics (stars, views, installations), and a brief demonstration of how an AI agent might interact with the tool. Roberts also ties the tools to his own Claude Code masterclass and Agentic OS setup, suggesting that combining these repos with his training could unlock money‑making opportunities.

    Step‑by‑step walkthrough of the ten repositories

    • OpenCraft Suite – A collection of AI‑controllable creative apps (PhotoCraft, FilmCraft, VectorCraft, etc.) that aims to replicate Adobe Creative Cloud. The suite garnered 39 k+ GitHub stars in ten days and 1.6 million announcement views.
    • Compositor – A 12 MB macOS‑native image editor designed for lightweight AI‑controllable editing. It has attracted ~2.3 million views and roughly 1.7 k stars.
    • Ego Lite – An isolated browser sandbox for AI agents accessible at lite.ego.app, preventing cursor hijacking and preserving user sessions. The project holds 17 k GitHub stars.
    • REA (Letter Run) – A reverse‑engineering tool that extracts UI, logic, and data layers from applications. It rose to 60 k stars in seven days (65 k total) and generated 772 k views on X.
    • 21st.dev MCP integration – Provides production‑ready UI components via the Model Context Protocol, used to build Glaido’s onboarding interface.
    • Skills.sh (Vercel Labs) – A meta‑skill repository that enables agents to discover and install tools, logging over 3.3 million installations.
    • Orca – An orchestrator for parallel agent swarms, holding 89 k stars with about 75 % gained in the last 30 days, and offering a mobile companion app.
    • Zapier MCP Server – Grants agents granular access to thousands of SaaS APIs (Google Workspace, Slack, Notion, etc.) with permission toggles.
    • The Arena Skill – Runs a tournament of 100 Claude instances to identify the best solution, accumulating 4.3 million social‑reel views and 406 GitHub stars.
    • Universal Modder – Automates game modding by reverse‑engineering local game assets and generating playable mods.
    • Cloudflare security‑audit skill – Performs a six‑phase autonomous vulnerability assessment, earning 27 k stars since mid‑September.
    • SlopMonster – Strips AI‑generated “slop” from prose, converting vague copy into concrete, rhythmic marketing language.

    Key tools and metrics

    • OpenCraft Suite: 39 k+ stars (10 days), 1.6 M views.
    • Compositor: 12 MB binary, 2.3 M views, ~1.7 k stars.
    • Ego Lite: 17 k stars, sandbox at lite.ego.app.
    • REA (Letter Run): 65 k total stars, 60 k stars in 7 days, 772 k X views.
    • 21st.dev: MCP integration for UI components.
    • Skills.sh: 3.3 M tool installations.
    • Orca: 89 k stars, ~75 % growth in past 30 days.
    • Zapier MCP Server: API access for SaaS platforms.
    • The Arena Skill: 4.3 M social views, 406 stars.
    • Universal Modder: Game‑asset reverse engineering.
    • Cloudflare security‑audit skill: 27 k stars.
    • SlopMonster: AI‑copy cleanup tool.

    Caveats and hype check

    While the video showcases rapid growth and impressive star counts, it also acknowledges that the revenue potential remains speculative. Roberts promotes his own Claude Code masterclass and Agentic OS setup, implying significant earnings from these tools, but no concrete earnings proof or detailed business case is presented. Claims of making money should therefore be treated as aspirational rather than guaranteed.

    > “Never allow an AI agent to install third‑party community skills without auditing”

    > “The result replaces vague corporate abstractions with concrete, rhythmic, and factual language.”

    Concrete next actions

    • Install PhotoCraft via Claude Code using the OpenCraft Suite repository link.
    • Try Compositor with Homebrew: brew install compositor.
    • Set up the Ego Lite browser sandbox at lite.ego.app for isolated AI browsing.
    • Add 21st.dev to your Claude Code MCP configuration to fetch UI components.
    • Run skills.sh to discover and install needed agent skills, but first audit any unknown skill with an LLM.
    • Deploy Orca orchestrator via Homebrew: brew install orca to run parallel agent tasks.
    • Configure the Zapier MCP Server to grant agents limited SaaS access (e.g., read‑only Gmail).
    • Run the Cloudflare security‑audit skill before pushing any application to production.
    • Use SlopMonster to clean AI‑generated marketing copy and improve conversion.

  • AI Tools: 喺香港用 Claude 唔使 VPN：雲端電腦 Zo Computer 教學

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

    Channel: 阿石OMP

    Overview

    The video from 阿石OMP explains how users in Hong Kong can continue to use Claude Code without triggering Anthropic’s account‑ban mechanisms. The core issue identified is that commercial VPN services often route traffic through IP ranges that Anthropic flags as proxy or VPN endpoints, and they can also leak local system fingerprints. By moving the workload to a US‑based cloud workstation, the user’s local network and device characteristics are isolated from the Claude service, reducing the risk of automated abuse‑prevention triggers.

    The tutorial focuses on Zo Computer, a browser‑based Linux environment hosted in the United States. It shows how to launch Claude Code inside that environment, complete the OAuth login flow without exposing a local VPN, and optionally integrate Claude Max API credits. The creator also discloses a $10 referral incentive for Zo Computer but stresses that the video is intended to help colleagues who have been locked out of their accounts.

    What the video covers

    • Why commercial VPNs lead to Claude account bans (flagged IP ranges, fingerprint leakage).
    • How Zo Computer provides an isolated US‑based Linux workstation accessed through a browser.
    • The process of launching Claude Code directly in Zo Computer’s terminal.
    • The safe OAuth login method: copying the authorization URL and pasting it into Zo Computer’s internal browser, never clicking the “Open” button on the local machine.
    • How to add Claude Max API credits via the “Bring Your Own Key” option.
    • Pricing details for Zo Computer’s Pro and Basic plans, including bundled AI credits.
    • A disclaimer about the creator’s affiliate relationship with Zo Computer.

    Step‑by‑step walkthrough

    • Sign up for Zo Computer at zo.computer and select either the Pro or Basic plan.
    • Once the virtual workstation is ready, open the built‑in browser from the Zo Computer dashboard.
    • Navigate to Settings → AI → Providers and locate the Claude Code entry.
    • Click the “Connect” button next to Claude Code; a modal appears with an OAuth authorization URL.
    • Use the copy icon to copy the URL. Do not click the Open button in the modal.
    • Paste the copied URL into Zo Computer’s internal browser, log in to your Claude account, and copy the verification code presented after authentication.
    • Return to the Connect Claude Code modal in Zo Computer, paste the verification code, and submit to finish linking the account.
    • (Optional) To use Claude Max API credits, go to Settings → AI → Providers → Bring Your Own Key and paste your Claude Max API key.
    • Launch Claude Code from the terminal inside Zo Computer or start a New Chat to begin using Opus, Sonnet, Haiku, or Fable models.

    Pricing and specifications

    • Pro plan: $64 USD per month – 16 CPU cores, 128 GB RAM, 10 active background services, 5 custom domains, $40 USD of bundled AI credits per month.
    • Basic plan: $18 USD per month – lighter resource allocation (exact cores/RAM not specified in the notes), $10 USD of bundled AI credits per month.
    • No permanent free tier is mentioned; both plans are paid subscriptions.
    • The video also references a $200 USD per month figure and $200 USD worth of Anthropic API credits, which appear to be example amounts for high‑usage scenarios rather than Zo Computer pricing.
    • Referral incentive: $10 USD per paying customer referred to Zo Computer (disclosed but no link provided).

    Considerations and limitations

    I strongly advise you NEVER to click this button to open the link directly.

    The video emphasizes that clicking the OAuth “Open” button on a local machine would expose the user’s IP and potentially trigger Anthropic’s abuse‑prevention systems. By keeping the authentication flow inside the Zo Computer browser, the user’s local network remains separate from the Claude service.

    The creator notes that the method is based on personal experience and does not guarantee that accounts will never be banned. Anthropic’s automated systems may evolve, and the effectiveness of isolating the workload could change over time.

    There is no claim of guaranteed revenue or profit from using Claude Code; the tone remains instructional rather than promotional.

    The affiliate disclosure is present, but no referral link is shared, which aligns with a statement that the tutorial aims to assist the community rather than drive marketing revenue.

    Immediate actions

    • Visit zo.computer and create an account.
    • Choose the Pro plan if you need the full 16‑core/128 GB configuration and the $40 USD monthly AI credit bundle, or select the Basic plan for a lower‑cost entry point.
    • After the workstation provisions, open Settings → AI → Providers.
    • Locate Claude Code, click Connect, and copy the OAuth URL (do not use the Open button).
    • Paste that URL into Zo Computer’s built‑in browser, log in to Claude, and copy the verification code.
    • Return to the modal, paste the code, and submit to complete authentication.
    • If you possess a Claude Max API key, add it via Bring Your Own Key under the same Providers menu.
    • Open the terminal inside Zo Computer and run claude-code (or start a New Chat) to begin using the desired models.

  • Marketing & Lead Gen: META lead forms are cooked. Do this instead.

    Watch on YouTube: https://www.youtube.com/watch?v=jn-0nhO5iFg

    Channel: Samuel Darby

    Introduction

    Samuel Darby argues that traditional Meta lead forms lose prospects because of delayed follow‑up. He proposes an AI‑powered booking funnel that lets leads self‑qualify, schedule, and instantly chat via WhatsApp or SMS while their intent is still hot.

    What the video covers

    The video explains why lead decay happens after a static form submission, cites a Harvard Business Review study on response time, outlines a four‑step funnel, shows how to build the frontend with Claude and host it on Vercel, and details the direct integration with GoHighLevel using Private Integration Tokens. Darby also promotes his Local Ads Lab membership and a high‑ticket accelerator cohort.

    Step‑by‑step funnel walkthrough

    1. Low‑friction landing page / micro‑quiz – The first question is a simple binary choice (e.g., “Do you own your home? Yes/No”) to trigger a micro‑commitment before asking for contact info.
    2. Opt‑in form – After the quiz, the visitor submits their contact details.
    3. Native calendar self‑scheduling – The lead picks a time slot directly on the page.
    4. Confirmation with instant chat – On the confirmation screen the lead can add the event to Google Calendar and start a two‑way WhatsApp or SMS conversation that is pre‑populated with context.

    All quiz answers are concatenated into a single custom text field called Survey Answers in GoHighLevel to avoid field proliferation. The frontend code is generated with Claude, deployed to Vercel, and communicates directly with GoHighLevel using the Location ID and a Private Integration Token (Contacts, Calendars, Conversations scopes). This bypasses Zapier/Make, reduces latency, and eliminates automation failure points.

    Tools and numbers mentioned

    • $99 per month – price for Local Ads Lab (early/low price, set to rise)
    • 6 participants per cohort – limit for the High‑Ticket Accelerator
    • 2,241 companies – referenced as part of the Lab’s reach
    • 7× more likely – chance of conversion when responding within 1 hour (Harvard Business Review)
    • 60× more likely – reduction in conversion when delayed 24 hours (Harvard Business Review)
    • 1 hour – ideal response window
    • 24 hours – delayed response window
    • Claude – AI used to generate funnel code
    • Vercel – hosting platform for the frontend
    • GoHighLevel (GHL) – CRM/automation platform
    • Private Integration Token – authentication method for direct API access
    • Location ID – identifier for the GHL sub‑account
    • CloseBot, Appointo, KwikFrames, Skool, app.leadflo.co – mentioned as related tools or platforms
    • WhatsApp Business API, SMS – channels for instant post‑booking chat
    • Google Calendar – option for leads to add the booked event

    Caveats / hype check

    The video cites strong conversion multipliers (7×, 60×) from a Harvard Business Review study. These figures are genuine but presented as guaranteed results for the funnel; actual outcomes depend on execution, offer quality, and audience. The $99/mo price for Local Ads Lab is labeled an “early/low price” with an imminent increase, a common urgency tactic. No explicit revenue promises are made, but the emphasis on rapid results and exclusive coaching may overstate typical outcomes.

    Concrete next actions

    • Audit your current Meta lead forms and replace any dead‑end thank‑you page with a self‑scheduling calendar step.
    • Create a micro‑quiz where the first question is a simple binary choice (e.g., “Do you own your home? Yes/No”).
    • In GoHighLevel, add a single custom text field called Survey Answers and map all quiz responses into it via concatenation.
    • Generate the funnel frontend code by prompting Claude with the desired quiz flow, calendar integration, and confirmation screen.
    • Deploy the code to Vercel, connect it to your GHL sub‑account using the Location ID and a Private Integration Token with Contacts, Calendars, and Conversations scopes.
    • Add a one‑click “Add to Google Calendar” button and a WhatsApp/SMS button that opens a pre‑populated chat on the confirmation page.
    • Test the end‑to‑end flow: quiz → opt‑in → calendar pick → confirmation → instant chat, ensuring data appears in GHL.
    • Consider joining Samuel Darby’s Local Ads Lab ($99/mo) for ready‑made funnel templates and Claude prompts if you prefer a shortcut.
    • If you want hands‑on help, apply for the High‑Ticket Accelerator cohort (limited to 6 spots).

  • 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: 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.

  • 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: 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.