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I built an archery app that uses AI to detect arrows and bullseyes on an archery target. It groups the arrows, measures how tight the groupings are, and calculates each arrow’s distance from the bullseye. It also shows the arrows’ locations and their relationship to the bullseye—for example, whether a shot is too far left, right, high, or low, or is dead on. The app can use targets from competition standings to score a shoot according to different standards. After shooting a set of arrows, the archer takes a photo, and the AI detects the target, identifies one or more bullseyes, predicts which arrows are intended for each target, and completes the measurements and scoring almost instantly. The results can then be sent to a coach, who can provide feedback, tips, and techniques to help improve the archer’s shooting. I trained my own model using 3,000 photographs that I took and hand-labeled with the bullseyes and arrows identified. I ran a series of training sessions over several weeks and refined the model to improve its accuracy. It currently achieves about 95% accuracy for arrows and about 90% accuracy for bullseyes. Step-by-step: 1. I took 3,000 photographs of archery targets. 2. I hand-labeled the arrows and bullseyes in those photographs. 3. I trained my own AI model in a series of sessions over several weeks. 4. I refined the model to improve its detection accuracy. 5. An archer shoots a set of arrows and takes a photo of the target. 6. The app detects the target, one or more bullseyes, and the arrows, then predicts which arrows are intended for each target. 7. The app groups the arrows, measures grouping tightness and distance from the bullseye, identifies each arrow’s position relative to the bullseye, and scores the shoot according to the selected standard. 8. The results are sent to a coach for feedback and advice on improving the archer’s shooting.

Training for a triathlon means three sports and a plan that is fiction by Wednesday. A static plan does not know I slept badly, skipped Tuesday's swim, or that Saturday's long ride wrecked me. Adjusting for that is what you actually pay a coach for. I wanted the adjusting without the retainer. So I built it in ChatGPT Work: a Project holding my plan, plus a scheduled agent run each morning that checks the plan against what my body actually did, then fixes the week. Disclosure: I built freddy, the connector feeding ChatGPT my training and recovery data, so I am not neutral here. Step-by-step: 1. Connect your sources at freddy.coach. For triathlon it is whatever records your swims, rides and runs: Garmin Connect, Polar, Suunto, WHOOP or intervals.icu. I use Oura for sleep and recovery, Garmin Connect for sessions, Hevy for strength. History backfills automatically, so the plan is built on your real training rather than what you tell it you did, which is usually flattering. 2. Add freddy as a custom connector: Settings, then Connectors or Apps and Connectors, then Add new, then custom MCP server. The URL is https://freddy.coach/mcp. Sign in and approve read access. Do it on the web; the mobile app cannot add custom connectors. 3. Connect Google Calendar and turn on write actions. This is the step people miss: write actions are off by default, so until they are enabled through action controls the agent can read your week but not change it. With writes on, ChatGPT Work creates and edits events. 4. Create a Project and put the durable facts in the project instructions: race date and distance, realistic weekly hours, immovable days, injury history, weakest discipline, and how blunt you want it. Long-lived facts only, never this week's session. 5. In the project, with freddy on, ask it to build the block: a periodised plan to race day, justified against your actual last 8 weeks rather than a textbook ramp. Keep the plan as a file in the project so every run starts from the same document, and have it write the week's sessions to the calendar with type, duration and intensity in the event body. 6. Schedule the daily run for mid morning, after you are up and your sources have synced. Last night's sleep does not exist anywhere until the watch or ring syncs, so a 5am run reports on the night before last. The standing prompt: "Using freddy, check whether I completed yesterday's planned session: pull yesterday's workouts with type, duration and intensity and compare against what the plan called for. Then pull last night's sleep and this morning's recovery including HRV and resting heart rate, plus training load over the last 7 days, compared against my own 30 day baseline rather than population norms. Then look at today's planned session on my calendar and decide whether it still makes sense. If it does, confirm in one line. If not, change the calendar event to the session I should actually do, put the reason in the event body, and update the plan file. Tell me what you changed and why, naming which source each conclusion came from. If two sources disagree, say so rather than averaging. Never compare HRV across different sources. If anything is ambiguous, such as an unrecorded session or a workout matching no planned one, ask me instead of guessing. Under 250 words." 7. Let it run a week before trusting it, and read what it changed rather than skimming. Expect to tune the instructions early: the usual failure is turning cautious after one poor night, fixed by a line saying a bad night is noise and to downgrade only on a trend or after a hard day. Two things to know. Depending on your approval settings, a run either makes the change or holds it for a tap. Know which you chose: it is the difference between waking to a changed calendar and a proposed one. And agent runs are metered, so keep the daily check narrow and save long reasoning for a weekly review. What surprised me is how good it is at catching the sessions I quietly did not do. It is easy to tell yourself you are on plan, harder when something reads yesterday's file and notes the intervals were short. Gotchas. Make it commit to one call on today's session; "listen to your body" is what you already had. Make it name the source behind each conclusion so you can tell a real finding from a confident guess. Never let it compare HRV across sources, because RMSSD and SDNN are different measurements, not different units, so mixing them looks meaningful while being nonsense. Scheduled runs auto-pause if ignored, so if the check stops arriving, look there before blaming the connector. It is not a certified coach and I would not rehab an injury with it. It is very good at holding a plan, watching what happened, and closing the gap before it becomes a lost week. Cost: freddy is free for one source, but this is cross source so it is the paid plan, $49 a year, on top of ChatGPT. Individual triathlon coaching runs a few hundred a month.

I built Shots2Points, a golf scoring app for iPhone and Android, using AI as my development partner. The idea came from organising and playing in golf society events. Stableford scoring itself isn’t particularly complicated, but running an event can be. Organisers have to prepare groups, handicaps, and courses; collect scores from different groups; calculate results; manage withdrawals and ties; and eventually produce a leaderboard. I wanted to simplify that process while also providing an easy scoring app for ordinary casual rounds. The unusual part is how I built it. I’m not a professional software developer, and I don’t have a development team. I started by describing what I wanted the app to do to AI and gradually turned the idea into a working product. My workflow evolved into this cycle: idea → discussion → specification → implementation → real-world test → refinement. I repeat it for each feature. Step-by-step: 1. I define the problem and user experience with ChatGPT. I discuss ideas, challenge assumptions, work through workflows, and decide how a feature should behave before changing the code. 2. Once the behaviour is clear, I turn the idea into an implementation task. I use AI to specify exactly what needs to change, including edge cases and how the new feature should interact with existing functionality. 3. I build and inspect the code with Cursor. Cursor works directly with the project codebase, allowing AI to investigate existing code, implement changes, and report exactly what it changed. I test the result rather than simply accepting AI-generated code. 4. I test development versions on real iOS and Android devices. I follow the actual user journey, take screenshots or capture errors when something isn’t right, and bring those results back into the AI workflow. 5. I use AI to diagnose problems, make another targeted change, and test again. The result is a real cross-platform application rather than a prototype. Shots2Points includes free casual Stableford scoring and an Event Mode designed for golf societies and groups. Organisers can create events, import players, allocate groups, and allow each group to enter scores while everyone follows a live leaderboard. Building the app has required much more than generating code. AI has helped me work through database design, APIs, authentication, in-app purchases, App Store and Google Play requirements, debugging, user-interface decisions, testing, and release management. The biggest lesson for me has been that AI doesn’t remove the need to understand the problem or make decisions. It gives one person access to capabilities that would traditionally have required several different specialists. I provide the product knowledge, requirements, judgement, and testing; AI provides much of the technical capability and an extraordinarily fast feedback loop. That combination allowed me to take a personal idea for improving golf scoring and event management and turn it into a functioning iOS and Android product.

As a busy father, I was tired of not having the fitness features I needed, so I used Lovable to create an accountability schedule for workouts and nutrition. Step-by-step: 1. I identified the fitness features I needed as a busy father. 2. I used Lovable to build a fitness app around those needs. 3. I created an accountability schedule for working out and nutrition.

I’m on a GLP-1 medication that heavily suppresses my appetite, and I’m also managing a bilateral vestibular condition that causes balance and gaze issues. I needed a way to hit my protein and calorie targets despite having a low appetite, track body composition accurately, log vestibular symptoms, connect my actual training data, and get coaching guidance that reflects my situation instead of generic fitness-app advice. I built FuelStrong: three connected apps created with Claude over many sessions. They include a daily tracker for meals, water, energy, and training check-ins; a Progress and analytics module; and a standalone Vestibular symptom tracker. They share a Cloudflare Worker and D1 database backend, with KV for cross-device sync. I describe a feature or problem to Claude in plain language. Claude proposes structural options, I push back or choose a direction, and Claude writes the HTML, CSS, and JavaScript. I program my lifts in Fitbod using an Upper/Lower/Upper split, with an arms-and-back priority and the Build Muscle goal. I export those workouts as CSV and drop them into FuelStrong’s import zone, which parses exercises, sets, reps, and volume into my training history. Custom foods receive macro estimates through a Claude API call routed through my own Worker endpoint. Evolt body-scan data feeds dynamic calorie and protein targets based on BMR × activity factor, minus a deficit, with hard floors instead of static numbers. The Vestibular module intentionally uses open text fields for now, so Claude and I can identify which data matters before formalizing the inputs. Everything syncs across devices through Cloudflare KV. The coaching layer uses a three-tier framework—evidence floor, confirmed operating range, and aspirational target—to drive every recommendation. Two calorie floors, a daily target of approximately 1,000–1,100 kcal and a weekly average of approximately 1,300–1,400 kcal, reflect that chronic under-eating—not missed protein—is the real GLP-1 risk. Muscle mass has remained stable since my February 2026 baseline, so the coaching treats that as a genuine win rather than a plateau. Vestibular-training coaching connects dry-needling focus areas—SCM, suboccipitals, and splenius capitis/cervicis—to gaze-stabilization symptoms, since cervical proprioception substitutes for non-functional vestibular canals. The result is one dashboard that brings together training, nutrition, body composition, vestibular symptoms, and coaching logic. My muscle mass has held stable through it all. Step-by-step: 1. I describe a feature or problem to Claude in plain language, review its structural options, choose a direction, and have Claude write the HTML, CSS, and JavaScript. 2. I use FuelStrong’s daily tracker to record meals, water, energy, and training check-ins, while the Progress and analytics module tracks body composition and related trends. 3. I program my Upper/Lower/Upper workouts in Fitbod with an arms-and-back priority and the Build Muscle goal. 4. I export Fitbod workouts as CSV and import them into FuelStrong so it can parse exercises, sets, reps, and volume into my training history. 5. I route Claude API requests for custom-food macro estimates through my own Cloudflare Worker endpoint. 6. I use Evolt body-scan data to calculate dynamic calorie and protein targets from BMR × activity factor, minus a deficit, while maintaining hard floors. 7. I log vestibular symptoms in the standalone Vestibular tracker using open text fields while Claude and I determine which inputs should eventually be formalized. 8. I sync the three apps across devices through the shared Cloudflare Worker, D1 database, and KV backend. 9. I use the evidence floor, confirmed operating range, and aspirational target framework to guide recommendations, including the daily and weekly calorie floors. 10. I connect vestibular-training coaching to dry-needling focus areas and gaze-stabilization symptoms, then use stable muscle mass since the February 2026 baseline as a positive outcome rather than treating it as a plateau.

I wanted a way to keep score during pickleball games and know which side of the court to serve from, so I made a pickleball scorekeeper for my Garmin Instinct 2 watch. When I open the app on my Garmin watch, I can keep score, see which side of the court to serve from, and know whose turn it is to serve. It supports both singles and doubles games, which is something none of the other pickleball apps can do. I thought I knew enough about VS Code to upload the app myself, but Garmin’s compiler was difficult to use. I had Claude walk me through the upload process step by step so I wouldn’t miss anything. Step-by-step: 1. I made a pickleball scorekeeper for my Garmin Instinct 2 watch. 2. I used the app to keep score, identify which side of the court to serve from, and track whose turn it is to serve. 3. I included support for both singles and doubles games. 4. I tried to upload the app myself using VS Code, but Garmin’s compiler was difficult to use. 5. I used Claude to walk me through the upload process step by step.

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.

I built an open-source fitness tracking app in Flutter, but the core workflow is the AI-agent architecture that designed it, built it, and now coaches from its data. For years, I tracked workouts in OneNote. The records were messy, difficult to search, and inconsistent. Excel went out of date as soon as I skipped a week. When I tried chatting with LLMs about my training, the problem was similar every time: no context, no memory, and no awareness of the weights I was using. Each conversation started from zero. The solution has two layers. Gym Tracker is a Flutter app with a local SQLite database that structures workout data properly. It includes 33 pre-populated exercises across 10 muscle groups, separate strength and hypertrophy records, multiple runs with pace, body stats, and full session history. There are no subscriptions, accounts, or cloud dependencies. The database is a file I own. Deschamps is the AI agent that reads the database and knows my full training history. It is not a chat window that forgets between messages; it is a tactician with long-term recall of my personal bests, progression, and injuries. The app stores the data, and Deschamps turns it into decisions. Step-by-step: 1. I defined the data architecture. Fitness data is operational data, so I gave it a schema, a query layer, and an agent that respects its history. I designed a SQLite schema with five tables, proper constraints, and a 10-category muscle taxonomy enforced by a CHECK constraint. 2. I wrote architectural prompts for AI coding agents. I run a team of specialized AI agents using OpenClaw, an open-source agent framework. I act as the CTO agent: I design the systems and delegate implementation to coding agents, including Forge, Cline, and Claude Code. I provide the vision, and they provide the execution. 3. The coding agents built the Flutter app with clean architecture, the repository pattern, Provider state management, and real-time cross-screen refresh. For each iteration, I review the result, refine the prompt, and ship. 4. I designed the database for dual access. The app writes to it, and the AI agent reads from it. They use the same file and schema. In external database mode, the app opens a `.db` file directly, allowing both the app and Deschamps to read and write simultaneously. This creates the bridge between the data layer and the intelligence layer. 5. I shipped the app across Windows, macOS, Linux, Android, and iOS from one codebase. The Android APK is available as a direct download from GitHub. 6. Deschamps reads the database and programs the next session using the full training history. Every session, weight, and body statistic remains structured data without summarization loss. The data is the context. 7. I made everything open source: the app, the agent prompts, and the architecture documentation. My company, Executive Mind (executivemind.io), uses the same agent-first model with seven AI agents and $40/month in total compute, running real operations 24/7. The result is a fitness tracker that remembers everything, an AI coach that never forgets, and a data layer designed from the start for both humans and machines to read. Links: krisracette.me/gym-tracker · github.com/Roughn3ck/gym_tracker · executivemind.io

I recently had an Evolt body scan. I've uploaded my results to Claude, shared my daily habits (sleep, diet, schedules, etc.), and asked it to create a full workout and diet plan based on the areas I want to improve. I was also able to include my recent Oura ring report to see if there's anything wrong with me. Will I follow it? We'll see.... Step-by-step: 1. I uploaded my Evolt body-scan results to Claude. 2. I added context about my sleep, diet, schedule, daily habits, goals, and the areas I wanted to improve. 3. I included my recent Oura report so the analysis could consider recovery and other wearable data alongside the scan. 4. I asked for a complete workout and diet plan tailored to the combined information. 5. I reviewed the plan as one organized starting point instead of trying to reconcile each source manually.

I fed Claude my sweat-test data, and it formulated a DIY high-carb cycling mix tuned to my sweat chemistry. It also scaled the batch to match my available supplies and generated print-ready labels and batch sheets. Step-by-step: 1. I provided Claude with my sweat-test data. 2. I used Claude to formulate a DIY high-carb cycling mix tuned to my sweat chemistry. 3. I had Claude scale the batch to match my available supplies. 4. I had Claude generate print-ready labels and batch sheets.

I asked ChatGPT to watch a video of me snowboarding and create a visual guide showing what I’m doing wrong, along with the correct form and dos and don’ts for improving my snowboarding. Step-by-step: 1. I asked ChatGPT to watch a video of me snowboarding. 2. I asked it to identify what I’m doing wrong. 3. I asked it to create a visual guide showing the correct form and the dos and don’ts for improving my snowboarding.

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.

BiteTrack makes calorie tracking simple with AI. Step-by-step: 1. Log a meal using a photo, voice, text, barcode, or food search. 2. BiteTrack’s AI identifies the food and estimates its calories, protein, carbohydrates, and fats. 3. Track daily nutrition, weight, and progress in one place. 4. Receive personalized nutrition recommendations based on health and fitness goals. 5. Monitor progress over time with detailed analytics to stay on track. Whether you're trying to lose weight, gain muscle, or maintain a healthy lifestyle, BiteTrack helps you make smarter nutrition decisions every day.

As a hobby/non-work-related creative outlet, I've started to write and create YouTube videos about retro football (soccer) stories. Part of the visuals are vintage stickers of the players mentioned. This used to be a time-intensive Google/eBay search when I was looking for 20+ players at a time. So I used Claude to build a local "Sticker Search" tool. It lets me enter the player, season, and team, find their sticker from that year, and download it to my working folder with one click. It's made my hobby way more fun and sped up the tiresome bit. P.S. I used Billy's guide on Manus to create the animated logo for the videos! Step-by-step: 1. I listed the inputs I needed for each search: player, team, and season. 2. I used Claude to build the local Sticker Search interface and search logic. 3. I added recent-search shortcuts so I could repeat or refine searches quickly. 4. I made each result downloadable with one click to my working folder. 5. I used Billy's Manus guide to create the animated logo for the videos.

This World Cup 2026 poetry generator scraped the web for match information and called LLMs to help users generate match-specific poems. 1. A daily schedule triggered web scraping to collect match information. 2. When a user requested a poem, the generator used that match information to create a new match-specific poem with help from LLMs.

My fantasy baseball auction draft is around the corner, and I decided to try Claude Cowork for the planning and research efforts this year. I uploaded my league's settings, the players being kept across the league, and my own rambling of strategies and thoughts on my current players. Claude then performed a deep analysis of my current options and their projected values, provided strong recs on who I should prioritize, and gave me a detailed list of draft targets around the league, perfectly tailored to my needs and league format — also scouring the web for tons of articles that saved me the tedious manual searching. Step-by-step: 1. I uploaded the fantasy league settings and the full list of players being kept by other teams. 2. I added my own current players, draft strategy, and rough thoughts about possible approaches. 3. I asked Claude Cowork to analyze the available options and projected player values. 4. I had it search the web for relevant articles and incorporate that research into the recommendations. 5. I turned the result into a tailored list of priorities and draft targets for the auction.

I used /goal in OpenAI's Codex to build a Magic: The Gathering app so my brother and I can play asynchronously without needing to coordinate a call or awkwardly play over FaceTime. The idea is to let each of us take turns when we have time, track the board state cleanly, and keep a game going over days instead of trying to line up schedules. The command allowed Codex to continue running until everything was done, basically one-shotting exactly what I was looking for without any intervention. Step-by-step: 1. I defined the core problem: two people needed to play asynchronously without coordinating a call. 2. I described the app’s essential behavior, including turn-taking, persistent board state, and games that continue over multiple days. 3. I used /goal in Codex so the build could continue autonomously toward the finished result. 4. I let Codex implement the app and resolve the work without requiring step-by-step intervention. 5. I tested the finished flow to make sure each player could return later and continue from the correct state.

I like to chart the tendencies of the Little League hitters I coach. I track their hot zones, how they perform when they’re ahead or behind in the count, and where they chase pitches outside the strike zone. Manus built an app that lets me use a phone or tablet to chart every pitch in each at-bat. It compiles the data into CSV files and visual charts showing swing percentage, whiff percentage, contact percentage, and on-base percentage. The hitters can view the charts during games. Step-by-step: 1. I identify the hitting tendencies I want to track, including hot zones, performance by count, and pitches chased outside the strike zone. 2. I use a phone or tablet to chart every pitch in each at-bat. 3. I use the app to compile the data into CSV files and visual charts. 4. I have the hitters review the charts during games.