my last post, I talked about why charging for "time" is a trap. You need to charge for value. But how do you calculate that value? Usually, AI projects fall into two specific buckets: 1. The "Efficiency" Play (Saving Money) This is the comfortable zone. It’s easy to scope and easy to sell. • "We automate data entry." • "We reduce support tickets by 30%." The math is linear: Hours saved x Hourly rate = Value It’s safe. But it’s finite. You can’t save more than 100% of a cost. There is a mathematical ceiling on the value you provide. 2. The "Growth" Play (Making Money) This is where the real leverage lives. • "We use AI to personalize cold outreach and double conversion." • "We build a predictive model to reduce churn." • "We create a new AI-driven revenue stream." The math here isn't linear; it’s exponential. If you increase conversion rates on a scalable product, the upside has no limit. The Trap: Most businesses (and consultants) get stuck in Bucket #1. They obsess over cutting costs. But you can’t shrink your way to greatness. If you want to sell "expensive, well-justified results," don't just look for where the client is bleeding cash. Look for where they are leaving money on the table. Efficiency pays the bills. Growth builds the empire.

Hey all! I want to share something different I’m building. It’s a bit outside what I normally do—more of a practical application—but I had a lot of fun building it, and I think I’m on track to create something consistent and useful (maybe even revenue-generating). What it isI’m building an insider-trading intelligence platform that alerts me to significant insider trades—think CEOs buying large amounts of their own company’s stock, or politicians loading up on positions that suggest they may know something we don’t. As a thank-you to this community, I’ll give anyone who’s interested exclusive free access once it’s ready. Stay tuned. What the app does today 🧠 Advanced AI AnalysisUses GPT-5 to analyze insider trading data and generate high-confidence trading signals (70–95% accuracy scores) with reasoning, entry points, stop losses, and profit targets. 🏛️ Congressional Trading IntelligenceAutomatically detects when politicians trade and analyzes their moves for potential informational edge. Think examples like high-profile NVDA options or senators trading ahead of policy announcements. 📊 Real-Time MonitoringTracks 30+ insider transactions daily from OpenInsider.com, applies an 8-filter validation system, and generates comprehensive reports in seconds. 🎯 Signal UpgradesWhen politicians and corporate insiders trade the same names, the system upgrades signal confidence and flags possible policy-driven opportunities. 📈 Performance TrackingMaintains leaderboards of top-performing political traders, tracks win rates, and compares party-level performance. Why this matters Politicians can legally trade while having access to non-public context via committees, policy work, and regulatory discussions. This platform levels the playing field by tracking their moves in real time and analyzing the intelligence behind them. Real exampleThe system flagged a ~$3M NVDA options buy by a House Financial Services Committee member just before AI-regulation hearings—the kind of edge institutions pay up for.

I don't have any question here but want to share, because it feels like a huge win for me but nobody in my life understands - the other day, I made two huge milestones. 1. After researching and learning about RAG methodologies and strategies for weeks, I finally got a RAG ingestion workflow working that seems like it's going to work for my use-case and yield really good, accurate results. I have a lot to add to it yet to further automate, but determining how to chunk it out and what tools to use, and actually understanding a bit of the "why" behind all of these choices, was a milestone for me. I haven't officially starting my testing yet but already it's accuracy is looking SO much better. (For the curious - I'm manually breaking up the chunks semantically, I'm using an LLM to enrich each chunk with metadata/tags/chunk summaries/example questions, and I switched to openai embeddings which apparently are better with semantic search). I also setup the metadata in a way that will make it easier to translate into a postgresql table if I decide I need hybrid search in the future to further increase accuracy, which learning about postgres in and of itself was a large endeavor. 2. I got one of my agent workflows setup with hybrid text/image RAG! I had to tag my RAG docs to look for the images and make the metadata flag it, then I had to set up a firebase storage, and I'm just so damn pleased that it all actually worked. When my chat test came back with the images along with the answer, I straight up giggled. For some people here, these might seem like pedestrian or super easy, afternoon type projects. For other people here, this might seem almost like another language still. I hope by sharing that I inspire someone else to keep going! I love reading posts like these myself (thanks for always posting @Didac Fernandez !) so I thought I'd try and share too.

I recently found the Lindy.ai agent build platform. The company claims SOCII and HIPAA compliance. Has anyone here explored Lindy? I’m wondering whether or not I should add another tool to my already overflowing toolkit! 😂

The Virtual Company Project: Finance Department The first block of the Virtual Company project will be the Finance Department. I've broken it down into four distinct functions to start, but there will be more—such as Financial Analyst and Payroll Specialist—in the future. Currently, I don't have enough data to simulate these additional roles. The four roles: - Victor (Controller):This workflow will receive bank transactions in the form of bank statements and transfer this information into the accounting books for future reconciliation with Accounts Payable (AP) and Accounts Receivable (AR). - Donna (Accountant #1):This workflow will process invoices dropped into a specific Google Drive folder as PDFs. The flow will be triggered, run OCR to extract all data into a structured JSON format, and then add the information into a simulated ERP using Google Sheets. - Mark (Accountant #2):Similar to the invoice process, but focused on expenses. I also aim to implement a more dynamic method for inputting expenses, such as taking pictures and sending them via email, WhatsApp, or Telegram to the workflow, which will process them and add them to the books. - Andrew (CFO):This AI agent will manage and extract all information related to financial data. It will be capable of pulling data from all books and responding to financial data requests made by the AI Orchestrator agent (CEO). This agent will interact with human users indirectly via the orchestrator. The workflows are currently at version 1. There are many improvements and additions that can be incorporated into the entire Virtual Company project for financial operations, but for now, I'm satisfied with the progress of this first block. This is just a concept, but with a few adjustments tailored to the specific software and frameworks each company uses, it can be easily adapted for real-world application—which is the ultimate goal of this project. Stay tuned for next chapter!