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The Rundown Workflow Hub asks members to “share your best AI workflow” and features the community’s top-voted workflows in its daily newsletter. Most posts rightly celebrate workflows that work and produce something useful. This one is about why “no” can also be a successful workflow outcome. A good AI workflow does not turn every idea into a project. It gives an idea the right amount of effort, then produces a clear answer—including a fast, well-documented no. In my first Workflow Hub post, I showed the capture workflow I call ReelForge: turning a useful public Reel, TikTok, or short video into a source-linked research note rather than another forgotten save. This is what happened to one of those notes. A Reel pitched an “AI operating system” for solo consultants: pull together client context, prepare the human before a call, then turn the transcript into follow-up drafts. At first glance, it sounded promising. The mechanism was clear, the problem was real, and the demo had exactly the kind of glossy “one person runs everything” energy that makes it tempting to jump straight to a build. We did not. ReelForge captured the source and separated the useful mechanism from the creator’s bigger claims. From there, a primary routing workflow ran a defined first pass: did the signal merit direct resolution, deeper specialist work, human review, or a reasoned stop? It earned deeper work. Hermes sent the pack into a specialist workflow, where assigned agents collaborated to enrich the evidence, check the market claims, and produce something concrete: a pre-call brief and a post-call follow-up pack. That made the opportunity inspectable rather than another confident paragraph about what an agent *could* do. The enriched pack then went to Jon T for formal review. The review surfaced the problem: Teams and Granola already cover a large part of the obvious transcript, summary, and meeting-preparation wedge. The idea had a workable mechanism, but not a sharp enough reason to become a new product. So the final route was a deliberate no. No unnecessary build. No “let’s just test it” theatre. No orphaned Notion page waiting to become somebody’s future problem. The first visual shows that five-step pass: Step-by-step: 1. Capture the signal. 2. Add evidence and context. 3. Choose the effort. 4. Hand off to human authority when needed. 5. Record the finish. The second visual shows the decision underneath it. A signal can earn direct resolution, a specialist pipeline, or a reasoned stop. Human review is a conditional handoff, not a fourth outcome. That is the rule I care about: the output is not agent text. It is the right next end state. ReelForge was the capture layer in the first post. This is the routing layer that stops captured signals from becoming a very organised pile of work nobody should do. Read my first Workflow Hub post here: https://app.therundown.ai/community/posts/eb787b9d-f7a0-4fe4-8e1d-166bb5c29cb7?ref=db29b880a9904700 Future posts can show the builds that survived this test. This one shows why the test matters first.

I’m learning Chinese online and record all my lessons. I asked Claude Code to transcribe the recordings and walked through the process of using the Whisper API with its guidance. Then I asked Claude Code to analyze the transcripts and identify mistakes I could fix that would make a big difference. It found that my teachers had not corrected several phrases I was repeating. Claude Code taught me which phrases to practice, and I’m now making fewer mistakes. Step-by-step: 1. I recorded all of my online Chinese lessons. 2. I asked Claude Code to guide me through transcribing the recordings with the Whisper API. 3. I asked Claude Code to analyze the transcripts for mistakes that would make a significant difference if corrected. 4. I reviewed the repeated phrases my teachers had not corrected. 5. I practiced the phrases Claude Code identified, which helped me make fewer mistakes.

I recently built a month-end close workflow in Awish for a client at a finance company. The problem was not creating the final report. The real bottleneck was collecting data from different systems, checking what was missing, reconciling totals, chasing exceptions, and getting the report to the right people. I built the entire process in Awish by describing what I wanted. Step-by-step: 1. I opened the Awish chat and wrote: “At every month-end close, collect journal entries, invoices, vendor bills, and financial records from NetSuite together with reporting workbooks from Excel and SharePoint. Check submission completeness, reconcile totals across sources, identify missing data or unusual variances, prepare the management-reporting workbook, send unresolved exceptions to Finance in Microsoft Teams for approval, and once approved export the final report to PDF, store it in SharePoint, and distribute it through Outlook.” 2. Awish understood the request, planned the workflow, and selected NetSuite, Excel, SharePoint, Microsoft Teams, and Outlook for the required steps. 3. I reviewed the plan, connected the client’s accounts, and approved the automation. 4. At month-end, Awish pulls the required financial data and reporting files, checks whether anything is missing, reconciles totals, and flags unusual variances. 5. It updates the management-reporting workbook and sends only the unresolved exceptions to the Finance team in Microsoft Teams. 6. Once Finance approves the exceptions, Awish finalizes the report, exports it to PDF, stores it in SharePoint, and sends it to the authorized recipients through Outlook. The useful part is that Finance no longer has to spend most of the close manually collecting and checking information before making a decision. The repetitive reconciliation work is handled automatically, while the team retains control over unexplained exceptions and the final report. Trigger → Analyze → Approval → Action Month-end close → Reconciliation \u0026 variance checks → Finance approval → Final report \u0026 distribution

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.

“A good engineer knows when not to use AI.” I built a semi-automated workflow to summarize my live streams. With it, I can record and edit a stream in just one day of work. Not everyone has time to watch an entire stream, so creating a summary of the main moments is important for people who want to watch it later. Many tools create Shorts from videos by analyzing transcripts, but they are expensive and do not work well for visually heavy content such as gameplay. Gemini can analyze both video and audio and has a long context window, so I decided to use Google AI Studio to identify important moments. I also created scripts to automate parts of the video-editing process. Some of the scripts can be executed by an agent, and the prompts can be turned into Skills when using the API. Step-by-step: 1. I record the video with separate tracks for the microphone and background audio. I keep the microphone on the first track so the LLM does not identify only the background audio. 2. I compress and cut the video with `ffmpeg`, then extract the audio tracks. Google AI Studio has implicit video requirements: files must be under 400MB and under one hour long. The AI analyzes only one frame per second, so I can also lower the FPS to save space. I use the audio tracks later in the workflow. 3. I upload the processed videos to Google Drive, which makes them easier to use in Google AI Studio. 4. I use Gemini with temperature 1 and a high thinking level to select the important moments. I add the system prompt and specify which part of the video I am uploading: "The video is part X of the stream. Please make a structured script according to the system instructions." This helps Gemini understand what types of moments may appear in the video. The system prompt was created for gameplay presentations but can be adapted for other content. 5. I use Gemini Pro with temperature 1 and a high thinking level to find the timestamps for the selected moments. I keep timestamp generation separate from moment selection so Gemini has more thinking time for each task. Gemini Pro works better than Flash when handling time. I add the system prompt along with a copy of the response from the previous step. - As an extra check, after execution I continue the conversation with: "Check if the analysis was cut off too early (context truncation), ignoring that the video continued and generating false positives for timestamps. Check the last events especially." 6. I send the data to Blender. I create a JSON file containing the AI’s response and use a script to add the original video, the microphone track, and the background audio track. The script cuts and marks the important moments based on the JSON. Because it is not possible to send multiple audio tracks from a single video, I send the tracks separately. I also make sure to use the correct FPS, either 30 or 60. 7. I edit the video manually. The AI’s timestamps are not perfect, so I may add or remove sections, or correct a position that the AI identified incorrectly. I then render the video. 8. As an extra, I can automate standardized edits. For example, I use three different camera positions, so I add clips to three different tracks depending on the position I want. I use a script to change the position and scale of the clips on those tracks. 9. As another extra, I transcribe the audio. I mute the background audio and save only the microphone audio as an MP3, then use Whisper to transcribe it. I can use the transcript as YouTube subtitles or embed it directly into the video. I use Whisper-WebUI with Log probability Threshold -0.5, No Speech Threshold 0.5, Patience 2, and Hotwords `\u003cmy name and terms in other languages I usually use\u003e`. I use an LLM to translate the transcript into other languages. 10. Finally, I create tags and titles. I upload the transcript to Google Drive and use it in Google AI Studio with a system prompt to generate tags and titles for the video. I also use the assistant in Google AI Studio to analyze and summarize my channel for use in LLMs. This helps me choose better titles and tags.

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.

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.

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.

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.

I wanted to learn how to build websites and sell them, but I didn’t know where to start. I used AI—specifically DeepSeek—to help me plan a roadmap. Because I already had experience prompting large language models to get the results I wanted, I asked DeepSeek which areas of knowledge I would need for this path. I also asked it to prioritize each area using statistics and facts. I reviewed the areas I didn’t know and prioritized them, then told the AI that I needed free resources only. I asked it to rank the topics based on what I didn’t know or understood the least. Finally, I asked it to create a Markdown file with all the resources formatted as checklists and imported the file into Notion. Now I have a plan I’m following instead of a “someday I’ll do this, hopefully” idea. Step-by-step: 1. I explained to DeepSeek that I wanted to learn how to build and sell websites but didn’t know where to begin. 2. I asked it to identify the areas of knowledge I would need for that path. 3. I asked it to prioritize those areas using statistics and facts. 4. I reviewed the topics I knew the least about and used that information to prioritize them. 5. I specified that I wanted free learning resources only. 6. I asked DeepSeek to create a Markdown file listing the resources as checklists. 7. I imported the Markdown file into Notion and started following the resulting plan.

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.

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.

I built an automation workflow with n8n and the OpenAI API to summarize AI news. I learned that prompt templates matter a lot, and chunking documents improved my results. My question for the community is about handling OpenAI API rate limits in n8n workflows. Is anyone else building AI news digest automations with n8n and ChatGPT prompts? 1. I built an AI news summarization workflow with n8n and the OpenAI API. 2. I used prompt templates and found that they had a significant impact on the results. 3. I added document chunking, which improved the summaries. 4. I’m looking for advice from others who have handled OpenAI API rate limits in n8n workflows.

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.

The week before leaving for a multi-day hiking trip, I used to spend hours tracking weather sites, trail conditions, and flood and closure alerts—even though I knew everything could change the next day or by the time I left. Now I have Claude handle the research. First, I give it the basics: trail details, entry and exit points, dates, group size, and permit information. These details do not change, so they provide context for everything else. Then I ask it to search for current weather, fire, flood, and closure alerts; permit status; and recent trip reports covering water and trail conditions. I ask it to cite the source for each item. I do not let it answer from memory because conditions are too time-sensitive. I also ask it to build me a Plan B. After years of living in the mountains, I know how quickly conditions can change. Most people skip this, but I recommend always having a backup plan. I ask Claude to turn the terrain into specific decision rules, such as "Turn back if snow above 8,000ft" or "skip the crossing if water's above knee height". Finally, I set it up to give me a daily report—usually five days before the trip, two days before, and on the morning of departure—and to flag only what changed. Step-by-step: 1. I provide Claude with the trail details, entry and exit points, dates, group size, and permit information. 2. I ask it to search for current weather, fire, flood, and closure alerts, permit status, and recent trip reports about water and trail conditions. 3. I require a source citation for each item and do not let it rely on memory because the conditions are time-sensitive. 4. I ask it to create a Plan B and turn the terrain and conditions into clear decision rules, such as "Turn back if snow above 8,000ft" or "skip the crossing if water's above knee height". 5. I schedule reports for five days before the trip, two days before, and the morning of departure, with updates limited to what changed.

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/.

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 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

Post-production for each of my podcast episodes used to be a significant task, even when an episode was audio-only. I’ve now built a workflow with Claude and Descript to automate the production process. I clean up the original recording in Descript, export the transcript to Claude, and run a single command to generate the remaining assets. This includes show notes for Hello Audio, opening and closing scripts written in my voice using anti-AI files to avoid a robotic tone, audio clips and audiograms for social platforms through a custom Claude skill, teaser posts for each audiogram on LinkedIn, and launch-day posts for LinkedIn and Substack, where my audience is. I’m sure there’s a way to streamline the workflow further to include distribution of the posts, audiograms, and episodes. It’s a work in progress 😀 Step-by-step: 1. I clean up the original podcast recording in Descript. 2. I export the transcript from Descript to Claude. 3. I run a single command in Claude to produce show notes for Hello Audio. 4. I generate opening and closing scripts for each episode using my voice and anti-AI files to avoid a robotic tone. 5. I use a custom Claude skill to automate the production of audio clips and audiograms for social platforms. 6. I create teaser posts for each audiogram on LinkedIn. 7. I prepare the main LinkedIn and Substack posts for each episode’s launch day. 8. I’m continuing to look for a way to streamline distribution of the posts, audiograms, and episodes.

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.