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I enjoy researching football accumulators, but I did not want a workflow that simply asks AI for “the best bets.” I wanted a repeatable process that starts with evidence, makes uncertainty visible, and is allowed to say “pass.” I started building it on 4 August 2026. The result is a private Football Lab covering the Premier League, Championship, and League One. It is for personal research and entertainment only—not a public tips service, income claim, or automated betting system. The workflow pulls public football data into a private, traceable store, then cleans, reconciles, and blends it before analysis: - football-data.co.uk: 7,420 normal-context matches from 2021–22 to 2025–26, including results, basic statistics, referees, and historical odds. I excluded COVID-affected 2020–21. - Fixture Download: An initial 2026–27 schedule snapshot containing 1,484 fixtures across the three divisions. - Premier League public match feed: A five-season layer covering referees, cards, event timing, added time, and 469 penalty kicks split into scored, saved, and missed. - Official Premier League Transfer Watch and BBC Sport: A source ledger for squad movement. - Official EFL appointment pages: Timestamped Championship and League One weekend checks. Schedules, appointments, and transfers retain their source and capture time instead of becoming untraceable web snippets. Step-by-step: 1. I validate fixture identity, duplicates, dates, missing fields, and team-name mismatches before modelling. I then reconcile the sources into a common club and fixture record. A tidy report built on a broken fixture list is still wrong. 2. I build separate Elo and Poisson baselines that turn historical team performance and home advantage into expected goals and home/draw/away probabilities. The divisions remain separate, so Championship form is not quietly treated as Premier League form. Each fixture is predicted before its result updates the model, preventing hindsight from creeping in. I backtested the baseline against 1,484 completed 2025–26 fixtures to establish an honest benchmark rather than claim a magic model. 3. I add context that the baseline cannot see alone. When an official referee appointment is confirmed, I timestamp it and match it to the fixture. The Lab can then show competition-specific cards, dismissals, and—where Premier League evidence exists—penalty-kick and added-time patterns. The question is not whether a referee picks a winner, but whether the match environment looks more volatile or the sample is too thin to support a useful conclusion. Unknown or changed appointments remain neutral. 4. I maintain a private append-only ledger of source-backed squad movement and label every club as established, promoted, relegated, or limited history. A signing does not automatically improve a probability, and an old-division record is not treated as identical new-division form. Until those effects earn a tested role in the model, they widen uncertainty or rule out a fragile fixture. 5. I begin with the complete fixture board rather than a short list of favourites. For every game, I combine baseline probabilities, expected goals, team context, confirmed squad changes, referee environment where evidence exists, and unresolved live checks. I write a plain-English match story explaining what the baseline sees, what could make the fixture fragile, and whether the sensible outcome is candidate, watch, or pass. 6. I preserve the full board in a private Weekend Sheet, along with the model read and reason, uncertainty flags, and the small number of research candidates that survive the checks. There can be up to seven candidates, but seven is never a quota: three strong games means three, and none means pass. 7. Before results, I record each run’s data cutoff, model version, and referee-status snapshot. After the round, a separate debrief compares the original probabilities and swerves with what happened, checks whether the flags caught fragile fixtures, and identifies one bounded improvement. The Lab remains in private paper-run mode while a recurring live results source, appointment capture, and market checks earn their own evidence gates. AI helps turn public-source data into an inspectable research workflow. It makes uncertainty visible and treats “pass” as just as valid as a confident call.

Sometimes a story needs a simple locator map to show where something happened or where something can be found—such as a business facility, a car accident, or the best place to see a sunset. I've worked in media for a long time and understand the power of maps to tell stories. But media cost-cutting and consolidation have reduced the number of available graphic departments, so creating a map is often the last task a reporter or editor wants to take on. I used Perplexity for initial research, Claude Code and Codex to build a web app, and Perplexity and ChatGPT for post-work such as SEO best practices. Step-by-step: 1. I used Perplexity's Deep Research mode to conduct a competitive market analysis. I asked it to analyze the field I was considering entering, identify competitors and growth rates, and explicitly break out feature sets. 2. I revised the research with my own idea and asked Perplexity to run the competitive landscape against it. I also provided desired outcomes, including intended audiences and where competitors were reaching them. I added the constraint, "Do it without syncophancy," so it would stop telling me how good my potential product was. 3. I hand-drew the initial screens and functions I wanted, then used the `/office-hours` skill in the Gstack bundle, available on GitHub, to play devil's advocate, sharpen the ideas, and challenge my assumptions. 4. I wrote a long prompt describing the product, starting broadly with the concept and audience and then narrowing to specific features and benefits. For example, I specified that it should export in 16:9 and 9:16 formats so maps would be ready for mobile vertical presentation. 5. I specified the hosting environment and that the product should be a web app. I also required a planning phase followed by construction phases. I pasted the prompt into Claude Code with this final line: "Use /grill-me to ask me questions to clarify intent." After 147 questions, it started the build. 6. This was in the pre-Fable days, so I specified that Opus 4.8 should act as an orchestrator while less expensive agents, particularly in Codex, handled the actual coding. 7. I used separate phases for technical work such as wiring in mapping providers and getting the UX to work correctly. Other phases included wiring in payment and subscriptions and making sure a subscription triggered an email campaign with instructions. 8. I dedicated an entire phase to building admin tools so I could manage the marketing language on the landing page and publish blog entries. 9. After each phase, I had the Opus/Codex combination perform an adversarial code-review-and-fix cycle. I then ran the `/ai-regression-testing` skill from the ECC repository on GitHub to catch issues the code review missed. 10. After every third phase, I prompted Claude Code: "Act as a senior QA engineer and go through the entire codebase looking for inconsistencies, functions that are in the wrong place, code that is overkill and security vulnerabilities." 11. When I had a product I thought was ready for testing, I prompted Claude Code, again using the Opus/Codex combination: "Act as a senior security engineer. Run this against OWASP standards. Find problems and suggest fixes. Harden this product overall." 12. As I neared the end, I asked Perplexity Deep Research and ChatGPT (Sol/High) to find SEO solutions for the product.

After a sales call, the conversation is only part of the work. Someone still needs to write the notes, update the CRM, remember the next step, prepare the follow-up email, and make sure the deal does not disappear between meetings. I wanted that entire post-call process to happen automatically. I created an Awish workflow that starts when a sales call ends. It analyzes the conversation, extracts the important sales information, updates the CRM, creates the next actions, drafts a personalized follow-up email, and sends the team a short summary. The workflow looks for the information I would normally write down myself: what the prospect needs, the objections they raised, their timeline, agreed next steps, and anything that could affect the deal. Customer-facing actions stay under my control. The workflow prepares the follow-up, but I can review important messages before they are sent. Step-by-step: 1. I tell Awish what I want to happen after every sales call: analyze the conversation, update the CRM, prepare the follow-up, and notify the team. 2. Awish understands the request, plans the workflow, and determines which applications are needed. 3. I connect my meeting platform, HubSpot, Gmail, and Slack to the automation. 4. When a call ends, the workflow analyzes the transcript and extracts the prospect’s needs, objections, timeline, decisions, and agreed next steps. 5. It creates or updates the contact and deal in HubSpot and adds any follow-up tasks that came out of the conversation. 6. It drafts a personalized Gmail follow-up based on what was actually discussed instead of using a generic sales template. 7. It sends a short Slack summary to the team with the deal status, important points, and next action. Instead of a sales call ending as a transcript or a page of notes, it immediately becomes structured CRM data, clear next actions, and a follow-up that is ready to review. The useful shift for me is that the workflow is not replacing the sales conversation. It is handling everything that normally gets forgotten or delayed after the call.

Commercial vehicle maintenance information is often fragmented across driver reports, warning lights, fault codes, inspections, repair records, and vehicle history. This makes it difficult for smaller fleets to decide whether a vehicle can continue operating, requires scheduled repair, or should be stopped immediately. We built TruckFixr Fleet AI to turn an unstructured driver report into a clear, reviewable maintenance action. The workflow begins when a driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. The report is then evaluated alongside available vehicle history and previous repairs. The workflow provides decision support through three practical actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. Final safety decisions remain with authorized fleet or maintenance personnel. After an inspection or repair, the confirmed cause, work performed, and outcome are recorded, creating a more complete vehicle history for future cases. The general workflow can be recreated using a mobile form, OCR, a vehicle-history database, an AI model, automation software, and a human-review dashboard. Step-by-step: 1. A driver submits symptoms, photos, fault codes, and vehicle information through a mobile-friendly form. 2. AI and optical character recognition extract the relevant details and organize them into a structured maintenance case. 3. The workflow compares the report with available vehicle history and previous repairs. 4. The system provides one of three decision-support actions: continue operating while monitoring, schedule an inspection or repair, or stop and escalate for immediate professional assessment. 5. Authorized fleet or maintenance personnel make the final safety decision. 6. After inspection or repair, the confirmed cause, work performed, and outcome are recorded in the vehicle history. TruckFixr has used this approach to support more than 100 vehicle-issue resolutions in early fleet pilots, helping fleets identify problems earlier, prevent avoidable breakdowns, and keep vehicles moving safely.

I’m a freelancer on Fiverr and Upwork. I use ClickUp to manage enquiries, projects, and deadlines, while Slack is my team communication tool. When I receive a high volume of enquiries, I sometimes miss replies to older orders. That can also cause me to overlook important messages and hurt my responsiveness. To address this, I created a workflow in Make.com. Every four hours, I receive alerts about relevant messages. Urgent priorities are marked in Slack, and pending follow-ups that need attention are highlighted. This helps me quickly reply to all relevant messages. Step-by-step: 1. I manage all Fiverr and Upwork enquiries, projects, and deadlines in ClickUp. 2. I use Slack for team communication. 3. I connected the workflow in Make.com to send alerts every four hours. 4. I mark urgent priorities in Slack. 5. I highlight pending follow-ups that require attention. 6. I use the alerts and highlighted messages to reply to relevant enquiries and orders promptly.

My husband and I run a short-term rental business managing two properties we own in Harpers Ferry, WV. A major part of the job is using PriceLabs, a dynamic pricing and revenue management tool that tracks our properties’ performance against the market, including occupancy, prices, and revenue, and recommends price changes when needed. Previously, we had to log in and review multiple dashboards to make informed decisions. Now, every two days we receive a concise morning briefing that summarizes how the month is going and what needs attention. It’s one of the first things I read in the morning, so I know what to adjust in real time. Step-by-step: 1. I connected the data sources Claude needs through MCP connectors: PriceLabs for reservations, pricing, and market data, and Gmail for drafting the output. 2. I wrote the instruction prompt Claude runs each time. This took the most time to develop. 3. I defined the format and length: a short, numbers-first brief of about 200 words, beginning with “How This Month Is Going” and “What Needs Attention,” with no filler or pep-talk tone. 4. I solved delivery by having the routine draft the briefing as an email with a recognizable subject prefix, such as “Iconic Chalet Briefing — Mon, Aug 10.” Because Claude’s Gmail connector can create drafts but not send emails, a separate time-triggered Google Apps Script watches for drafts with that subject prefix and sends them automatically. 5. I registered the workflow as a scheduled Routine. That combination makes the process feel automatic from end to end.

Getting answers from company data usually requires someone who knows SQL, understands the database, and has enough business context to interpret the results. This creates a bottleneck: business users depend on analysts for questions they should be able to explore themselves. I turned Claude Code into a self-service data analyst by giving it direct, read-only database access and configuring its behavior through a `CLAUDE.md` file. The file gives Claude business context, explains which analytical tables contain different types of information, tells it how to investigate questions, and defines how results should be presented. Users can then ask business questions in plain English. Claude determines what data it needs, queries the database, investigates the results, creates visualizations, and explains what it found. Step-by-step: 1. Create a dedicated analysis folder and add a `CLAUDE.md` file that defines how Claude should operate as a data analyst. 2. Give Claude enough business context to understand the company, its terminology, important metrics, and how the business operates. 3. Document which analytical tables or views it should use for different types of questions. Curated analytics tables work particularly well because Claude doesn't need to decipher the entire production database. 4. Give users read-only database permissions and explicitly instruct Claude to perform read-only operations, such as `SELECT` queries only. Never give the agent permission to modify production data. 5. Tell Claude how to use the command line to query the database. Include instructions to help users install or configure it if it isn't available. 6. Define an analytical process for Claude to follow. Rather than simply generating one SQL query, instruct it to investigate the user's question, examine the results, and run additional queries when necessary to understand what is happening. 7. Define the expected output: answer the question, explain the important insights, and create appropriate visualizations to make the findings easy to understand. 8. Open the folder in the Code tab inside the Claude Desktop app, which I find to be the best interface, and ask questions naturally, such as, "Why did revenue decline last month?" Claude handles the investigation from there. Instead of building and maintaining a custom analytics application, you can turn a general-purpose AI coding agent into a capable self-service analyst with access to your existing data warehouse. Users ask questions in plain English while Claude handles the SQL, investigation, visualization, and explanation, giving nontechnical users a more direct way to explore company data.

I started DialPhone with a simple problem: business communication is fragmented. A typical team may use one tool for phone calls, another for SMS, another for meetings, another for fax, and a separate system for customer support. I wanted to explore what it would look like if those conversations lived in one system, with AI doing more than just transcribing them. I built DialPhone as an AI-native communications platform around that idea. The core is cloud VoIP, while the same platform also handles business SMS, video meetings, online fax, team chat, and contact-center workflows. On top of that, I added AI capabilities such as call transcription and summaries, CRM logging, AI-drafted SMS replies, conversation intelligence, and an AI receptionist that can answer calls, qualify requests, book appointments, and route conversations. The biggest architectural decision was to treat communication data as one connected stream rather than as separate products. A phone call can create CRM context, trigger a follow-up SMS, and become part of a customer-support workflow without someone manually moving information between systems. Along the way, I learned that adding AI to communications is not particularly useful if it only produces transcripts. The more interesting problem is turning conversations into actions: updating records, identifying next steps, helping agents during calls, and automating repetitive work. Step-by-step: 1. I identified the problem of business communication being fragmented across phone, SMS, meetings, fax, and customer-support systems. 2. I built DialPhone as an AI-native communications platform centered on cloud VoIP. 3. I brought business SMS, video meetings, online fax, team chat, and contact-center workflows into the same platform. 4. I added AI features for call transcription and summaries, CRM logging, AI-drafted SMS replies, conversation intelligence, and an AI receptionist. 5. I connected communication data so calls can create CRM context, trigger follow-up SMS messages, and become part of customer-support workflows. 6. I focused the AI capabilities on turning conversations into actions, including updating records, identifying next steps, helping agents during calls, and automating repetitive work.

I’ve automated 90% of my payroll work at an engineering firm. I use Claude Code to write Python scripts that pull timesheets from our ERP, Deltek Ajera. I check the data for oddities and, once everything looks right, push the timesheets into our HRIS, Paycom, through its API. I also have a script that parses and compares the preliminary payroll register with the timesheet data. Once I’m satisfied with the results, I can submit payroll. After payroll, my scripts create all the benefits upload files I used to create by hand. The spreadsheets I relied on for years are no longer necessary. Do I even need a spreadsheet anymore? Step-by-step: 1. I use Claude Code to write Python scripts for the payroll process. 2. I pull timesheets from Deltek Ajera, our ERP. 3. I check the timesheets for oddities. 4. Once the data looks right, I push the timesheets into Paycom, our HRIS, through its API. 5. I parse and compare the preliminary payroll register with the timesheet data. 6. Once I’m satisfied with the comparison, I submit payroll. 7. After payroll, I run scripts that create the benefits upload files I previously created by hand.

This Codex-driven workflow takes a software request from problem understanding through implementation, validation, review, and delivery evidence. Instead of asking an AI coding agent to simply “build the feature,” it gives the agent an explicit development lifecycle with defined responsibilities, deterministic validation gates, repair loops, and human checkpoints. The goal is to make AI-assisted development more structured, observable, and recoverable. It can be used for new feature implementation, bug fixing, refactoring, test creation and improvement, code quality and security hardening, and documentation and automation changes. The core principle is simple: Don't give the AI only a coding task. Give it an engineering lifecycle to work on. Step-by-step: 1. Understand the problem. Clarify the request, identify the desired outcome, define the scope, and surface ambiguity before implementation begins. The output is problem understanding and scope. 2. Define constraints. Identify technical, functional, non-functional, compatibility, and out-of-scope constraints. The output is a constraint set. 3. Plan. Analyze implementation options, select an appropriate approach, break the work into tasks, and define acceptance criteria. The output is an implementation plan. 4. Inspect the existing system. Review the relevant codebase, dependencies, current behavior, and affected components before making changes. The output is system context. 5. Implement. Make the smallest appropriate code changes while following the existing project’s conventions and the approved plan. The output is code changes. 6. Run deterministic validation. Run tools that can objectively validate the implementation, including formatting, linting, type checks, builds, unit tests, and other available automated checks. The output is validation results. 7. Review. Evaluate the implementation against the original requirement, the plan, code quality expectations, security considerations, and potential regressions. The output is review findings. 8. Repair and iterate. If validation or review identifies problems, diagnose the issue, make the required correction, and repeat validation. The output is a corrected implementation. 9. Verify. Confirm that the acceptance criteria are satisfied and that the relevant tests and checks provide sufficient evidence for completion. The output is a verification result. 10. Produce delivery evidence and handoff. Summarize what changed, what was tested, what passed, known limitations, and any remaining decisions requiring human attention. The output is delivery evidence and a human handoff. The core loop is: Implement → Validate → Review → Repair → Validate → Verify. Testing and review are treated as part of development rather than activities performed only after coding is “finished.” AI coding agents are increasingly capable of inspecting repositories, writing code, running commands, and responding to failures. The problem is that capability alone does not provide an engineering process. This workflow separates the responsibilities an AI coding agent performs into explicit stages. It applies several principles: - Problem before implementation: Understand what needs to change before writing code. - WHY before HOW: Establish the intent and constraints before choosing an implementation. - Single responsibility per stage: Give each stage a defined purpose and output. - Deterministic validation first: Use tests, linters, type checkers, builds, and other deterministic tools wherever they can establish correctness. - Failure localization: When something fails, identify which stage or assumption needs correction. - Evidence-based completion: Support completion with validation and review evidence rather than an AI declaration that the task is finished. - Human checkpoints: Use automation to accelerate execution without removing human judgment from important decisions. The broader idea is: The AI should participate in the engineering system, not become the engineering system.

Having a team of specialized AI agents creates a new problem: someone still needs to decide which agents should work on a project, what order they should work in, what each one needs from the others, and whether the project is actually finished. Without coordination, the human becomes the project manager, manually moving context and outputs between agents. I created a project-manager agent that acts as the orchestrator for my AI team. I give it an objective, and it determines what work needs to happen, selects the appropriate specialist agents, sequences their work based on dependencies, and presents the execution plan to me before anything starts. Once I approve the plan, it coordinates the agents, manages their handoffs, tracks project state, and maintains enough persistent context for the work to continue across sessions. Step-by-step: 1. I create several specialized agents with clearly defined responsibilities, capabilities, and expected outputs. 2. I create a project-manager agent that knows what each specialist does and is instructed to orchestrate the work rather than perform specialist work itself. 3. I give the project manager a high-level objective. It analyzes the goal, inspects the project context, identifies the required work, and selects the appropriate agents. 4. I have it create an execution plan showing which agents will be used, what each one will do, their dependencies, and the order of execution. 5. I require human approval before execution begins. I can approve the plan, narrow the scope, change the sequence, or redirect the project as needed. 6. Once the plan is approved, I let the project manager delegate each task to the appropriate specialist and pass relevant context and prior outputs between agents through structured handoffs. 7. I track progress and project state as the agents complete their assignments. If an agent uncovers new work, fails review, or changes the project assumptions, the project manager updates the plan and routes the next work accordingly. 8. At the end of the session, I save the current state, completed work, important decisions, and next actions so another session can continue without reconstructing the project from scratch. Instead of personally coordinating every AI agent, I manage the project at a higher level: I define the objective, approve the plan, review important decisions, and evaluate the result. The AI project manager handles the coordination layer, turning a collection of specialized agents into a team that can execute complex, multistep projects coherently.

I built an Awish workflow that handles a support ticket before anyone on the team opens it. I realized support tickets were not just taking time to answer. Someone still had to understand the problem, decide how urgent it was, search the documentation, route it to the right person, and prepare a response. I wanted that first layer of support work to happen automatically. I opened Awish and wrote: “Whenever a new support ticket comes in, understand the issue, determine its urgency, check our documentation, prepare a response, create a Jira issue if it looks like a product bug, and escalate anything important to the team before taking customer-facing action.” Awish understood the request, planned the workflow, and showed me which applications it needed. Step-by-step: 1. I described the complete support process I wanted in the Awish chat. 2. Awish planned the workflow and selected Zendesk, Notion, Jira, and Slack for the required steps. 3. I connected my accounts and approved the automation plan. 4. When a new Zendesk ticket arrives, the workflow identifies the issue type, urgency, and customer intent. 5. It checks the relevant Notion documentation and prepares a response based on the available information. 6. If the issue looks like a product bug, it creates a Jira ticket with the customer context already attached. 7. Urgent or sensitive cases are escalated to the team instead of being handled automatically. 8. I connected WhatsApp from the Awish chat. I now receive workflow updates there and can manage the automation without opening Awish. Customer-facing actions still stay under my control when approval is needed.

After about 18 months of building software with AI, I realized that reliability wasn't primarily a model problem. Bigger context windows and more clever prompts didn't fix it. What did help was treating the AI like a developer joining an existing team instead of like a chatbot. Real developers don't work from memory. They inspect production, read the documentation, check the tickets, and use proven tools. I built an environment that lets the AI do the same. The workflow is tool-agnostic, so it can be rebuilt with whatever AI client and stack you already use. Step-by-step: 1. I gave the AI a persistent task and history store that it can read from and write to. This is the core of the workflow. Mine lives behind an MCP tool, but any queryable store can work. Every architectural decision, blocker, and progress note gets written there instead of being left in the chat. 2. I open every session with a stand-up. Before writing a single line of code, the AI pulls what was in progress, what's blocked, what changed since the last session, and which architectural decisions still hold. About 30 seconds later, we're both looking at the same project. Then we build. 3. I exposed real operations as MCP tools instead of relying on "write code" prompts. I wrapped specific, tested actions—such as creating a page, defining a data model, wiring an integration, and running a migration—as tools. The AI composes these known-good building blocks into larger solutions instead of regenerating infrastructure every session. I call this wave coding, and it's the biggest reason the output stays consistent. 4. I made verification a rule: before touching anything, the AI reads the live database, API state, logs, and files. It checks ground truth first instead of making assumptions. 5. I made the chat disposable and the log canonical. If it isn't logged, it didn't happen. The task store is the single source of truth, not the conversation. The payoff is that I can stop halfway through a feature, close my laptop, and come back days later. The AI reconstructs the project from its own history, so I don't spend 20 minutes re-explaining it. Full disclosure: I built this into my own platform, WebsitePublisher.ai, which currently has 43 MCP tools and 105 integration building blocks. It's delivered as an add-on that plugs into the AI client I already use over MCP, so there's no new app to learn. Nothing here is locked to that platform, though: the workflow itself works with any MCP client and any store the AI can query. I'm curious whether anyone else is running their AI this way or solving the amnesia problem from a different angle.

I kept forgetting who I had lent books to, and spreadsheets felt like overkill. So I built Runo, a book-lending app for friends, without traditional coding. Runo (runo.club) is a web app where I can catalog my home library, get a unique shareable link, and let friends browse my books and request to borrow them. I can approve or decline each request with one click. Step-by-step: 1. I used Claude to think through the feature set, data model, and UX flow before writing a single prompt. This helped me avoid building the wrong thing first and gave me a clear blueprint for the Lovable prompts that followed. 2. I used Claude to create precise, narrowly scoped prompts for Lovable, with each prompt focused on a single change so existing functionality would be less likely to break. This significantly conserved Lovable credits. The React, TypeScript, and Supabase stack came out of the box. 3. I added two ways to scan books instead of requiring users to type titles manually: - Barcode scanner: Point the camera at an ISBN barcode to autofill the title, author, and cover using the Open Library API. - Cover photo scanner: Photograph the cover so Gemini 2.5 Flash can extract the title and author in under 2 seconds. 4. I routed the cover scanner through a Supabase Edge Function using Lovable’s AI Gateway, so the API key never touches the client bundle. 5. For every bug fix and feature, I followed the same iteration loop: describe the problem to Claude, get a precise Lovable prompt, push the changes to GitHub, and let Lovable auto-sync them. Claude and Lovable’s bidirectional GitHub sync made the process feel like pair programming. Tools used: Lovable, Claude (Sonnet), Gemini 2.5 Flash (via Lovable AI Gateway), Supabase, and GitHub. Live at: runo.club — public beta and free to use. Feedback welcome. #appbuilding #vibecoding #buildinpublic #lovable #nocode

I love golf, but many of the courses I most want to play are private and nearly impossible to access unless you know a member. Instead of manually emailing clubs, searching charity events, asking for introductions, and trying to remember who I contacted months ago, I built a Private Golf Access Agent in ChatGPT. The goal is to identify legitimate opportunities to play highly rated private clubs without simply paying my way in. I gave the agent 50 target clubs across the Northeast and Mid-Atlantic, including 10 “moonshot” courses where an invitation would be extremely difficult. The agent acts more like a golf-access researcher, relationship manager, and outreach assistant than a chatbot. It researches each club, identifies access paths, finds the right person, personalizes outreach, tracks every interaction, monitors opportunities, and recommends what to do next. I still approve every email before anything is sent. That matters because I don’t want the agent spamming clubs, inventing relationships, or continuing after someone says no. The system looks for legitimate paths, including professional introductions, complimentary charity or special-event opportunities, reciprocal access, personalized direct outreach, unused guest spots, golf-project requests, and long-term relationship opportunities. Step-by-step: 1. I divided the 50 clubs into moonshots, elite targets, and high-quality targets. 2. I had the agent research each club independently, including its leadership, PGA professionals, policies, events, social media, recent news, reviews, charitable connections, and possible introductions. 3. The agent identified the most appropriate contact and researched why that person made sense. 4. Before writing, it gathered specific details so each email was clearly personalized rather than a blast. 5. I trained its cold-outreach persona to sound like a blend of me and two or three sales trainers I admire, including Josh Braun-style low-pressure curiosity, short conversational writing, humor, and an easy way to say no. 6. The agent can learn new skills and add them to its protocol. For example, when it struggled to find employee email addresses, I taught it my Google search method. That method is now part of the workflow it uses for future clubs. 7. I tracked everything in a live Google Sheet showing the current status, progress, next action, opportunity status, and whether I need to approve something. The current status column is highlighted so I can check where every club stands in real time. The statuses include: Researching → Ready for AJ Review → Outreach Sent → Conversation Open → Opportunity Identified → Monitoring. 8. If there is no immediate path, the agent does not keep bothering the club. It moves the club into monitoring mode and waits for a better opportunity. 9. When someone responds, the system keeps the relationship history so future communication builds on the real conversation. The live tracker is shown in a Google Sheet status screenshot. What I like most is that the AI isn’t doing one isolated task. It handles the repetitive parts of an ongoing objective—research, qualification, contact discovery, personalization, organization, monitoring, and follow-up—while leaving the important judgment calls with me. Eventually, I want it operating like a 24/7 private-golf-access concierge: 50 clubs being researched and monitored, with me only getting involved when the agent finds something worth acting on. TOOLS USED: ChatGPT, Gmail, Google Sheets, Google Drive/Docs, web research, and AI agents/automations.

Microsoft is retiring Publisher on October 1, 2026. I have many `.PUB` files that I need to convert to PDFs before then, but I did not want to use online converters. I wanted a solution that would run locally on my computer. I worked with Claude to create a lightweight program that searches for `.PUB` files on a drive of my choice and converts them into high-quality PDF files, placing each new PDF in the same folder as its source file. Opening PowerShell manually and managing permissions was too cumbersome, so Claude also created a batch file to make the process easier. For anyone who wants to create their own mini-program, I asked Claude to create a reusable prompt. I posted that prompt at https://mediumseagreen-seal-177480.hostingersite.com/ along with my two files: the batch file and the PowerShell commands. Step-by-step: 1. I identified the `.PUB` files I needed to convert before Microsoft retires Publisher on October 1, 2026. 2. I decided to use a local solution instead of an online converter. 3. I worked with Claude to create a lightweight program that searches a selected drive for `.PUB` files. 4. I set up the program to convert the files into high-quality PDFs and place each PDF in the same folder as its source `.PUB` file. 5. I used a batch file created by Claude so I would not need to open PowerShell manually or manage permissions each time. 6. I asked Claude to create a reusable prompt for others who want to build their own version and posted it with the batch file and PowerShell commands at https://mediumseagreen-seal-177480.hostingersite.com/.

I built a repeatable, human-directed AI workflow for turning an original emotion, memory, or story into a finished WazWorld song, complete with cover artwork and release content. It addresses one of the biggest problems with AI music: a simple prompt can produce something technically impressive but generic, overproduced, or disconnected from the creator’s real intention. The workflow begins with a human trigger—an experience, relationship, place, mood, or musical idea that I genuinely want to express. WazWorld generally moves between dark alternative/electronic music and California coastal country-rock. Step-by-step: 1. I create a detailed artistic brief describing the song’s emotional core, story, genre, energy, vocal character, instrumentation, arrangement, dynamics, and intended listener experience. 2. I work with ChatGPT to develop and challenge the concept, write and rewrite lyrics, remove clichés, and design the full arrangement. This includes the intro, verses, choruses, instrumental passages, transitions, musical peaks, and outro. ChatGPT also helps translate my direction into precise production language and exclusions that Suno can understand. 3. I generate versions in Suno, treating each result as a demo rather than a finished song. I evaluate the vocal delivery, lyrics, bass, guitars, synths, drums, tempo, arrangement, and emotional impact. Then I make targeted prompt changes and generate new versions. A song may go through dozens of iterations before it sounds the way I originally heard it in my head. 4. After selecting the final music, I use ChatGPT and AI image generation to develop cover artwork that matches the song’s emotional identity. I adapt the artwork for streaming, YouTube, Instagram, Stories, and Shorts, then create the metadata, descriptions, captions, and promotional material needed for release. 5. I distribute the finished music through services such as DistroKid and publish it across streaming and social platforms. The final result is not a one-click AI song. AI provides creative and production tools, but I direct every major decision—from the original emotion and lyrics to the arrangement, instrumentation, vocals, artwork, and release strategy. Someone can recreate this workflow by starting with a clear human idea, documenting the desired sound, generating multiple versions, evaluating each one like a producer, and iterating until the finished work communicates the original intention.

My AI digest looked perfect and was quietly wrong. What actually fixed it. Every run succeeded. Every dashboard was green. And the content was still wrong. My digest invented "AI Moat Brief", a newsletter that does not exist. It reported scan counts nobody measured. It resurfaced week-old stories as fresh headlines. Here is what broke, and what fixed it. The sorting used to happen in my head: skimmed subject lines, unopened tabs, quiet guilt. The Signal is one email at 08:00: a single Make scenario calling Claude Sonnet through OpenRouter. It reads the last 24 hours of my RSS feeds and newsletters, keeps what touches what I am actively building plus the domains I need to stay current in, and arrives in the language I actually think in. Core items end with what it means for my work. Five to ten minutes, and I know where to go deep today. Structurally it looks like this, minus the content, rendered in English for this post (Image 1). No real edition is shown; section names and sample lines are illustrative. The dangerous failures were never pipeline failures. They were content failures, and the cause is structural: an LLM summarizing newsletters that already summarize primary sources is third hand by construction. Every hop strips attribution and adds confidence, and when data goes missing the model fills the gap the way LLMs do: fluently. Valid HTML, confident tone, green pipeline, wrong content. Image 2 is that whole failure class in one frame. Three rules closed the gaps I caught, all live in production: Step-by-step: 1. Verbatim or nothing. A source name is copied character for character, and a link exists only if that exact URL is in the input. The model copies; it never composes. 2. The model never generates metrics. Every count the report shows is injected by the pipeline after the model returns. 3. Recycled news gets demoted. A recap of recaps gets one line at most, and is dropped when the underlying story falls outside the collection window. Rules 1 and 3 lean on the prompt, and that is why the counters exist. The pipeline writes a hidden HTML comment into every email it sends: items, links, urls, cost, finish status. That line caught what I could not see. In one run, the published-links counter and the leftover-urls counter read 45 and 435: the only sign a new cleanup step was a silent no-op. Another morning the model stopped at 15,999 tokens against a 16,000 cap, one token from an email cut off mid-sentence. On the morning I wrote this they agreed, 21 links and 21 urls, and boring is the goal. Image 3 is that morning's actual comment, with the same two counters from the no-op run. The run itself has a dead man's switch on Healthchecks.io, so a missing 08:00 email reaches me before I notice. Honest limits: the $0.31 per report is a fresh measurement I am still validating, and I have not proven these rules hold as the source set scales. There is more behind every part of this; I would rather share it where it is wanted. Ask and I will put it in the comments: the three rules in full, the exact cost and what drives it, what this replaced in my day, how it compares to what is on the market, or the ugliest of the 15 documented bugs. I am sharing this because I doubt I am the only one building fragile things behind the scenes, and monitoring text is harder than monitoring uptime. What content-level checks do you run on LLM output, the kind pipeline monitoring cannot see? Real thresholds and embarrassing failures especially welcome.

I often lose track of movies and TV shows I want to watch, especially when they are announced months before release. To solve this, I use a free API from watchmode.com. I asked Hermes Agent—although ChatGPT Work could probably do the same—to create a scheduled task that checks the API once a week and notifies me when a title becomes available to stream. I also gave it a simple instruction: whenever I send an IMDb link for a movie, TV show, or specific season, it should automatically add it to my watchlist. Since I prefer to binge-watch, it only notifies me when an entire season is available. The next step is to make it automatically start tracking the following season once the current one has been fully released. Step-by-step: 1. I use the free API from watchmode.com to check streaming availability. 2. I asked Hermes Agent to create a scheduled task that checks the API once a week. 3. I configured the task to notify me when a movie or TV show becomes available to stream. 4. I instructed it to add a movie, TV show, or specific season to my watchlist whenever I send an IMDb link. 5. I configured notifications for TV shows to wait until the entire season is available. 6. My next step is to have it automatically start tracking the following season after the current one has been fully released.

I asked Claude to build me an artifact that works as my own personal book tracker. It includes separate sections for books I want to read, books I’m currently reading, and books I’ve read. I also asked Claude to collect specific data points about each book and create an insights tab that can gather information about my likes and dislikes over time, then make future recommendations. I personally dislike Goodreads’ UI and don’t use its social media features, so I wanted a personalized alternative housed within Claude alongside all my other workflows—a one-stop shop. Step-by-step: 1. I asked Claude to build an artifact for tracking my books. 2. I organized the tracker into books I want to read, books I’m currently reading, and books I’ve read. 3. I specified the data points I wanted to collect for each book. 4. I asked Claude to add an insights tab to gather data about my likes and dislikes. 5. I set it up to use those insights for future recommendations and keep the entire workflow within Claude.