Stop Doing It Manually: A Four-Step Framework for Building Your Own Solution

A beginner-friendly guide to turning repetitive workflows into practical tools with AI.Notice and Explore: Find the real problem.Teams sidebar entering client call:Me: “Yo Neil, don't forget to get the ai summary before you leave the teams call.”Neil: “Bet, I got it. Exits call…without my notes…So,…

A beginner-friendly guide to turning repetitive workflows into practical tools with AI.Notice and Explore: Find the real problem.Teams sidebar entering client call:Me: “Yo Neil, don't forget to get the ai summary before you leave the teams call.”Neil: “Bet, I got it. Exits call…without my notes…So, uh, I forgot...”Me: “...pack your things and get out.”Okay, so the last line is hyperbole. But that scenario happens to me a lot. I can't blame Neil; I never remember either. Here's the beauty of the AI world: your ability to solve workflow problems is limited by your imagination, not your skillset. I'm sick of clicking even one button to get call notes, so I went and solved it.Noticing is a muscle; you work around the same thing until you stop seeing it. Identifying the driver of a new problem and solving for it takes a conversation, and I run mine as an adversarial Q&A. I ask the agent for the best path, then make it argue against the path it just handed me.I'm hunting the spot where it inferred something I never said, so I make it show me what it assumed about my machine, my calendar, my language, and the way I work. Every answer hardens the path, exposes alternatives, or kills it.That conversation is how I ended up with whispr1, a small Windows program that watches for a Teams call, records both sides, transcribes on my own machine, and writes a transcript with the Outlook meeting details already in it.The audio is deleted once the transcript lands. There's no dashboard, no account, no web app. A tray icon says it's running, and a folder of text files is the whole product.The Q&A helped me determine the high-level path and what tools existed to support it. I wasn't building a product. We got a “yes, this is possible,” a framework for the build, and the guardrails that had to hold no matter what the design turned into. Once we settled that, it was time to write the plan.Plan and Premortem: Write the plan down, then attack it.Before any code existed, the agent read the real constraints and wrote down what it intended to do, and then I argued with it. A plan worth trusting doesn't guess quietly; instead, it surfaces the open decisions and waits for an answer. One of mine was whether the tool should hand my transcripts to an AI service to summarize them for me.One entry, from the project's UNCERTAINTIES.md:- [x] (summarize — RESOLVED by going local-only) The Anthropic API summarycall sent transcript text off-device. REMOVED: summary.enabled: false,anthropic uninstalled, summarize.py rewritten with no network client.I wanted that feature. Turning it on meant my transcripts leaving my machine, so the feature came out instead. An agent doesn't need you to specify the whole system; it needs the next step that can be checked. Explore — Plan is straight from Anthropic's handbook.I built this in Claude Code. The order runs the same in any of the frontier coding agents.Then I send adversarial agents at the plan with a mandate to poke holes in it. The agent in the same session tends to preserve its own blind spots, so I fan out subagents instead, independent of that session and of each other. This is evaluator-optimizer: one process generates, separate ones judge. Run it as a premortem, before you build rather than after it breaks.Each subagent got a different mandate and read the same written plan, before a line of installer code existed.The one hunting assumptions that only held on my machine came back with a faster-whisper model cache that might not survive a plain file copy, because some machines store that cache as links rather than as real files.The one auditing hardcoded values found the mic_name_match field, where an empty string would silently substring-match every device on the box. The picker now refuses to write an empty value.The one going after the delivery mechanism told me to scrap the hand-passed zip for a repo plus a Release asset, which is what shipped.Execute and Test: Let it build, then run the thing yourself.I'd settled the open decisions before the agent started writing files. I do the imagining and the arguing; it does the typing.The prototype came together in one sitting, and it did the whole job, from watching for a call to writing out a text file. It records two tracks, my microphone and everything coming out of my speakers, which the computer feeds back as another input [a loopback]. Nobody else on the call sees any sign of this.When the call ends, both tracks go through a speech-to-text model already sitting on the disk, and they come back as one timestamped conversation. Nothing leaves the machine. OpenAI released that model, Whisper, under an MIT license, and I run it through the faster-whisper reimplementation so it stays quick on an ordinary processor. The name of my tool is a nod to it.Making it safe to hand to somebody else took several more live tests. A separate audit read every script in the finished repo line by line, traced the network calls, and checked the release for supply-chain characteristics. It came back with nine issues, and I fixed all of them.It confirmed no privilege escalation, no registry persistence, and no secrets sitting in the repo, and it re-verified the zero-network-calls claim by grepping the code rather than taking my word for it. What it wouldn't do was run the installer, on a policy against executing untrusted downloaded code.So I cloned it fresh into a folder with a space and parentheses in the name, on a machine that had never seen it. I ran the installer end to end, twice, and two bugs surfaced within ten seconds. The bundled Python couldn't find its own package. The install was also quietly using an unrelated Python already on that machine, instead of the one it shipped.Neither bug appeared in the plan. Neither appeared in the premortem. Both appeared in the running.Deploy and Maintain: Hand it to a machine that has never seen it.A tool isn't done when it works. It's done when it survives a machine that doesn't share a single one of your assumptions.Handing Whisper to somebody else is what forced mine into the open:The new Teams client rather than the classic one.Classic Outlook, for telling a meeting apart from a call.English words in the window titles, and American date order in the calendar lookup.On an ARM laptop it doesn't run at all.That list is published in the project's install instructions.The installer pulls a roughly 550MB offline bundle attached to a GitHub Release, and it checks your processor type and your Teams version before that download starts instead of after it finishes.It fetches an embeddable Python and get-pip.py, and both get checked against a recorded fingerprint on every build [SHA256-pinned]. The bundle deletes itself after extraction instead of sitting there doubling what the install costs you.Then give it a way to notice the world moved. Re-running the installer after a new release used to fetch nothing at all. A version marker file now sits beside the install, and when the two numbers disagree, the installer re-stages itself.That habit has a name in software: continuous integration and delivery, shortened to CI/CD. In plain terms, the machine checks your work and ships it for you every time you change something, instead of you remembering to. Whisper carries the smallest possible version of it and nothing past that. If you want the real thing, start with GitHub Actions.The objection that landsYou did not avoid a subscription. You hired yourself, and now you own the pager forever. A subscription isn't renting code; it's renting somebody's on-call rotation, their patches, and their handling of edge cases you haven't thought of. When a Teams update moves a window title, the person who fixes that is me.Build versus buy is a capacity question before it's a price question. Weigh the effort to build against the capacity it hands back, and count the workarounds you absorb when you buy instead. An evening against a button I forgot on exactly the calls I most wanted a record of; that one closed fast. A week of hardening for something I'd use twice would not have.Most of what you build this way you'll use twice and delete. Whisper starts at login and catches every call without me touching it. It's still installed. I've written before about knowing whether you've built a tool or a business; whispr cleared that test.None of that removes the maintenance question. It moves it to after you have the answer.Take yours through itWhisper is just the example. The order works the same on things that look nothing like it.Those same four steps gave me a lint script that catches my own writing drifting into machine prose, a wiki an agent maintains instead of me, and a task queue that moves work from claimed to finished without me tracking any of it. There are products I can purchase (or rent) for all of these solutions, but a little discovery and planning saved me those fees and gave me absolute control.So what's the one you keep absorbing?Notice and explore. Catch the thing you work around, then make the agent show you what it assumed.Plan and premortem. Get it on the page, then send adversarial agents to poke holes in it.Execute and test. Let the agent type, then run the thing yourself, because reading it won't tell you what running it will.Deploy and maintain. Get it running somewhere that isn't where you built it, and give it a way to stay current.The code is public at github.com/bryanthood-wph/whispr. An agent extends a working example better than it invents one from a description, so clone that, hand it to your own agent, and tell it what's different about your machine and your habits.On recording other people. Nobody else on the call sees anything. No banner, no icon. Muting your mic doesn't stop it, because capture happens below the mute button at the hardware level. Legality varies by state, country, and employer, and that check is yours. A mundane reason is not a defense. Neither is a good one. Read the policy and ask compliance before anything runs. Not legal advice.This story is published on Generative AI. Connect with us on LinkedIn and follow Zeniteq to stay in the loop with the latest AI stories.Subscribe to our newsletter and YouTube channel to stay updated with the latest news and updates on generative AI. Let’s shape the future of AI together!Stop Doing It Manually: A Four-Step Framework for Building Your Own Solution was originally published in Generative AI on Medium, where people are continuing the conversation by highlighting and responding to this story.

Source: Generative AI Pub — Published August 10, 2026 at 08:37 UTC — Category: Image AI