Share your best AI workflow. We could show it to 2M+ people.

Organic social content is one of the best sources of ideas for ads, so I built a workflow that finds short-form videos performing well in my niches, analyzes why they work, and rewrites the strongest hooks for my brand. Apify scrapes Instagram Reels by hashtag and TikTok videos by keyword across three niches: AI, productivity, and career growth. The workflow filters for English-language videos posted within the last 15 days that are 15–90 seconds long and have more than 50,000 plays. It then ranks them by engagement rate rather than views alone. A simple log file tracks every video I’ve already analyzed, so the workflow doesn’t process the same video twice. ElevenLabs transcribes each video’s audio. Claude then extracts the exact opening line, identifies the hook formula and video structure, and writes a version of the hook for my brand. Everything is compiled into one dated document with the top three discoveries, the strongest hook from the run, and any new patterns I haven’t tried yet. With one command—"Run research"—I get about 20 proven hooks and video structures to test in ads for less than $1 per run. Step-by-step: 1. I use Apify to scrape Instagram Reels by hashtag and TikTok videos by keyword across the AI, productivity, and career growth niches. 2. I filter the results for English-language videos posted within the last 15 days, 15–90 seconds long, with more than 50,000 plays. 3. I rank the remaining videos by engagement rate instead of views alone. 4. I check a log file to skip videos I’ve already analyzed. 5. I use ElevenLabs to transcribe each video’s audio. 6. I ask Claude to extract each video’s exact opening line, hook formula, and structure, then rewrite the hook for my brand. 7. I compile the results into one dated document with the top three discoveries, the strongest hook from the run, and new patterns to test. 8. I run the workflow with the command "Run research" to generate about 20 hooks and video structures for ad testing at a cost of less than $1 per run.

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 submit two reports to my directors each month. Both use structured templates, but the updates come from multiple people and sources. Some information is unstructured, such as hallway conversations that are not always captured in email, so I have struggled to get the accuracy I need when using AI to build the reports. I am currently testing CoWork and Copilot Work IQ. These reports used to take me hours, so I am already saving time, but I want to refine the process further. I believe a better way to save and store the source data could improve the final outputs, although I have not worked out the right approach yet. Step-by-step: 1. I collect updates from multiple people and sources for the two monthly reports. 2. I organize the information into the structured templates required by my directors. 3. I use CoWork and Copilot Work IQ to help build the reports. 4. I account for unstructured updates, including information from hallway conversations that may not have been captured in email. 5. I am exploring better ways to save and store the source data so I can improve the accuracy of the reports while continuing to save time.

GeneTrust AI — Asistente Genético Virtual (AGV) es una plataforma tecnológica concebida bajo una arquitectura basada en evidencia (Evidence-First), asistida por IA y gobernada por humanos. Está diseñada para facilitar el acceso, la estructuración, la exploración, la clasificación, la priorización y la síntesis trazable de evidencia genética y biomédica. Su diseño busca prevenir riesgos críticos de la IA en biomedicina, como las alucinaciones, las referencias no resolubles, la pérdida de procedencia, el ocultamiento de contradicciones y la atribución indebida de autoridad clínica. Aplica operaciones aditivas y no destructivas: filtrar, clasificar o priorizar los datos nunca modifica ni reescribe la evidencia fuente original. Además, preserva la ambigüedad y los elementos no clasificables en lugar de forzar conclusiones artificiales. El stack tecnológico incluye React, TypeScript, Node.js y servicios en la nube. La plataforma integra un enfoque de múltiples proveedores y agentes de IA, con modelos generativos desacoplados, junto con una clasificación multidimensional de la evidencia mediante la abstracción de un Structured Decision Provider (SDP, Proveedor de Decisión Estructurada). Utiliza específicamente TypeSafe AI, con su modelo Jev, como proveedor de decisiones estructuradas bajo estrictas políticas de activación y gobernanza por dimensión. La IA y sus agentes asisten en la organización, exploración y síntesis de información compleja, pero no actúan como una autoridad diagnóstica ni determinan por sí mismos la verdad clínica. Sus salidas se manejan como inferencias computacionales controladas, separadas estrictamente de la evidencia fuente y sujetas a validación humana explícita. Step-by-step: 1. Accedo y estructuro la evidencia genética y biomédica dentro de una arquitectura basada en evidencia. 2. Exploro, clasifico y priorizo la información mediante operaciones aditivas y no destructivas, sin modificar ni reescribir la evidencia fuente original. 3. Mantengo la procedencia, las contradicciones, la ambigüedad y los elementos no clasificables, evitando forzar conclusiones artificiales. 4. Utilizo múltiples proveedores y agentes de IA, con modelos generativos desacoplados, para organizar, explorar y sintetizar la información compleja. 5. Aplico una clasificación multidimensional mediante la abstracción de un Structured Decision Provider (SDP), utilizando TypeSafe AI y su modelo Jev bajo políticas estrictas de activación y gobernanza por dimensión. 6. Trato las salidas de la IA como inferencias computacionales controladas, las separo de la evidencia fuente y requiero una validación humana explícita antes de utilizarlas.

I built a workflow in which AI agents build a product under the kind of governance I’d expect from a senior engineering team. After 20 years in software engineering, including 10 years running my own businesses, I learned that writing code isn’t the hard part. Keeping control of it is. So I set up a build loop on GitHub. One thing I’ve kept for myself is decision-making: approving plans, answering product questions, and signing off on what the system has learned. The product it’s building is Cordexa, a tool for document-heavy expert work, such as quantity take-offs from engineering drawings or assessing a tender against a company’s own CVs and case studies. It’s designed to run inside the customer’s own cloud, so their documents never leave it. Every figure is cited to the document it came from or flagged for a person. It never guesses. The problem it solves is complex, but the build loop lets the system work in the background and notify my phone when it needs a decision. That allows me to stay focused on my day job. Step-by-step: 1. I start every module with a plain-English plan and approve it before any code exists. 2. One agent writes the tests from the plan. A different agent writes the code, and it cannot modify the tests. 3. An independent reviewer agent checks every change against its plan. Safe changes merge automatically; anything that changes the product waits for my approval. 4. A watchdog checks every 15 minutes for anything that is stuck, fixes it when possible, or tells me why it cannot. 5. Real test jobs run after every merge and are scored against known answers, so progress is measured rather than guessed. 6. I handle the decisions, answer product questions, and sign off on what the system has learned.

After nearly 20 years in France, I’m finally applying for citizenship. I’ve spent the past year preparing documents and studying for the required tests, and my final interview is a few weeks away. Alongside the official study materials, I’ve been using ChatGPT to drill key facts and historical dates, run mock oral interviews in voice mode, and create quizzes from YouTube videos I link whenever they cover the right material. ChatGPT sometimes speaks French with a rather heavy accent, but the interactive back-and-forth helps me remember hundreds of details that are difficult to retain by simply reading a page. It’s tedious, but I find rote memorization challenging. We’ll see how it goes under pressure.

I built a workflow to track my monthly credit card expenses and keep them within my budget. Tracking my expenses is important to me because I want to reach my financial goals through consistency and healthy habits. However, entering each expense manually into Google Sheets was tiring and took several hours. I used Make to automate the process. The workflow reads the email from my bank every time I use my card, gathers the necessary information, converts it to JSON, and adds it to Google Sheets. It runs every six hours, every day, so I no longer need to spend my weekends reviewing expenses. Now I have more free time to build something else and only need a couple of minutes to review Google Sheets and make sure everything is working properly. Step-by-step: 1. I set up a workflow in Make to monitor emails from my bank whenever I use my credit card. 2. The workflow gathers the necessary expense information from each email. 3. It converts the information into JSON format. 4. It adds the expense data to Google Sheets. 5. The workflow runs every six hours, every day. 6. I spend a couple of minutes reviewing Google Sheets to make sure everything is working properly and that my expenses remain within budget.

I wanted to reduce the time and mental effort it takes each morning to figure out what needs my attention across my inbox and calendar. I used Codex Automations to build Briefly, a daily AI-powered email and calendar brief that is automatically delivered to my inbox every day. Instead of manually searching through emails and checking separate calendar events, I can open one message from Briefly and immediately see what is happening, what needs action, and what is coming up. It has become a simple way for me to use AI proactively—not only when I ask a question, but as an automated system that helps me stay ahead of my emails, tasks, and schedule each day. Step-by-step: 1. I connected the email and calendar sources I wanted the automation to review. 2. I created a Codex Automation that runs each morning. 3. I instructed it to review recent and important emails, identify messages that need attention or follow-up, and check my calendar for upcoming meetings and commitments. 4. I had it turn that information into a concise daily brief. 5. I configured the brief to be delivered to my email automatically.

I used AI to help investigate a family mystery that had remained unresolved for decades: identifying the biological family of my maternal grandfather. The challenge was not a lack of information. It was almost the opposite. I had DNA matches, family trees, names, dates, historical records, old photographs, obituaries, Facebook genealogy groups, and conversations with possible relatives. The difficult part was connecting all these scattered clues without jumping to conclusions. I built a research workflow in which AI acts as an investigation partner, not as the source of truth. Step-by-step: 1. I gathered the information I already had from genealogy platforms, DNA matches, family trees, historical documents, and family records. 2. I used ChatGPT to organize the evidence into people, dates, locations, relationships, DNA connections, documents, and unresolved questions. 3. I separated the information into three categories: confirmed facts, hypotheses, and missing information. 4. Instead of asking AI, “Who was my grandfather's biological father?”, I asked it to analyze possible family connections and identify which hypotheses were compatible with the available evidence. 5. For each hypothesis, I looked for supporting evidence, contradictory evidence, and information that was still needed. 6. I treated AI-generated connections as leads rather than genealogical proof. The goal was to use AI to decide what to investigate next, not to have it find the answer. 7. I used AI to compare family branches, surnames, generations, locations, and possible relationships among DNA matches whose family connections I did not immediately recognize. 8. When a promising connection appeared, I returned to the original genealogy and DNA sources to verify it. This gradually turned a long list of DNA matches into a smaller number of research paths. 9. I used AI to draft respectful, personalized messages to DNA matches and members of genealogy communities, including people in another country and language. 10. In each message, I explained what I was researching, what connection I suspected, what information I already had, and what I hoped the recipient might be able to confirm or rule out. 11. I treated their responses as new evidence and repeated the investigation loop: evidence → AI analysis → hypothesis → verification → human contact → new evidence → updated hypothesis. The final result is not an “AI-generated family tree.” It is a human-led investigation in which AI helps manage complexity, ask better questions, and identify the next useful action. The most important lesson I learned is that AI is particularly useful in genealogy when you do not ask it to give you the answer. Ask it to help you build the investigation.

I built three interconnected websites for the Solomon for Jesus ministry. The first, solomonforjesus.com, houses the shining jewel: an AI chatbot named Solomon. The second, solomonforjesus.net, is a ministry academy with four levels of instruction and 16 lessons per level. The third, solomonforjesus.net, is home to my book *Fingerprint of Reality* and other articles and essays. All three websites are still in development, but they are operating and publicly available. I use GitHub repositories for coding, Vercel for hosting, Cloudflare for domains, and Supabase for back-end security. Step-by-step: 1. I built three interconnected websites for the Solomon for Jesus ministry. 2. I used solomonforjesus.com for the Solomon AI chatbot. 3. I used solomonforjesus.net for the ministry academy, which contains four instructional levels with 16 lessons per level. 4. I used solomonforjesus.net for my book *Fingerprint of Reality* and other articles and essays. 5. I use GitHub repositories to code, Vercel to host, Cloudflare for domains, and Supabase for back-end security. 6. I keep all three websites operating and publicly available while they remain in development.

Spotify can tell me what I played, but it rarely explains what music does in my life. I use exported listening data, playlists, and personal context to explore taste, memory, regulation, identity, and continuity. I look at both observable listening behavior and the meanings I attach to it, without forcing every track into a biography. Step-by-step: 1. Start with the data. I export streaming history, tracks, artists, timestamps, listening duration, saved songs, and playlists where available. I check the period covered and remember that missing history does not mean a song never mattered. I start with behavior: a supposed favorite may barely appear, while another track may have played hundreds of times quietly. 2. Separate tracks from artists. I count unique tracks, unique artists, and tracks per artist. One artist with dozens of songs and hundreds of artists represented by one track suggest different listening styles: exploring discographies versus collecting exceptional songs. 3. Find lasting favorites. I distinguish temporary high-volume artists from favorites that return across years, playlists, and life phases. I look for persistent songs: tracks spanning years, returning after long gaps, or appearing in several playlists. Persistence can matter more than total plays. Some songs never win a monthly ranking yet refuse to disappear. 4. Connect music to life. I add relevant personal context, such as teenage years, university, a move, a relationship, or rebuilding. I ask which songs became landmarks and which were described as “my song.” Dates show when music appeared; I explain what that timing means instead of forcing every track into a biography. 5. Discover playlist functions. Repeated songs are not always favorites. Sometimes music has a job: providing strength, calm, concentration, singing, emotional release, or a way to reconnect with myself. I read playlist names alongside their tracks and listening patterns. “Power” might mean, “I do not feel strong, so I need external strength.” “Gold” might mean, “I want to feel my own golden heart again.” These meanings matter more than a generic genre label. 6. Treat corrections as evidence. AI suggests interpretations, but I decide whether they fit. For example: “This song is not nostalgic. It reminds me that I remain myself regardless of what another person does.” Corrections like this can change the meaning of an entire playlist, so I keep them alongside the data. 7. Look for regulation patterns. I find songs played daily for weeks, tracks repeated within one day, and playlists used at consistent times. I ask whether they accompany difficult tasks, low energy, singing, or calming down. Repetition is a clue, not proof of an emotional function. 8. Look beyond genre. Pop, rock, electronic music, mantra, and soundtracks can share dramatic builds, layered vocals, melodic hooks, powerful voices, intimate production, or mantra-like repetition. These qualities can explain why apparently unrelated songs belong together. I let myself name the patterns; “Post-Classical Electroacoustic Intimate Futurism” may be more useful for discovery than a standard genre. 9. Separate nostalgia from continuity. An old song returning is not necessarily nostalgia. I ask whether it is remembered or still actively used. Some music belongs to the past, while some music simply has a very long present. I keep different musical worlds separate: anthemic pop and meditative mantra may serve different parts of the same person. 10. Build a traceable profile. I include core artists, persistent songs, personal anthems, life-phase associations, functional playlists, regulation patterns, musical qualities, and unexpected findings. I link interpretations to dates, play counts, playlist appearances, and my explanations. I separate observed behavior, AI hypotheses, and confirmed meanings, and keep the profile easy to correct. 11. Recommend for the person. Instead of asking for “songs similar to Coldplay,” I ask: “Find the emotional build I like in Coldplay, the vocal force I like in Sia, and the intimate electronic texture in my meditation playlists.” I can also ask: “Find tracks for Power that could survive repeated listening without copying its existing artists.” 12. Update over time. I add new data and look for new favorites, returning anchors, changing playlist functions, and stable patterns. The ongoing cycle is: export → analyse → find persistent songs → map functions → add personal context and corrections → build the profile → recommend → update. Listening history records what I played. AI helps me explore why something kept returning. I remain the only person who can finally say: “Yes. That is what this song means to me.”

Sometimes a playlist disappears before you realise how much you depended on it. That happened to a meditation playlist I used constantly for months. Then Spotify removed it, and I discovered an inconvenient detail: I barely knew the track or artist names. I remembered the music and what it did for me, but I could not reliably search for it. The reconstruction began accidentally. While analysing my Spotify listening history for another purpose, AI noticed that I had played roughly the same fifty tracks again and again for about four months. Suddenly, I did not need to remember the missing playlist. My listening history had remembered it for me. A playlist can leave a behavioural fingerprint even when its name and contents are gone. Look for repetitive periods, recurring groups of tracks, similar sequences, and songs that appear together for several months before disappearing around the same time. Step-by-step: 1. I inspected my historical Spotify listening data instead of relying only on which songs I could remember. 2. I used unusual repetition to identify the likely time window. In my case, a four-month cluster helped me recognise the missing meditation playlist, even though I did not know the exact dates beforehand. 3. I built a broad candidate pool of tracks played frequently during that period, recording titles, artists, play counts, dates, listening duration, and nearby tracks. 4. I looked for tracks that repeatedly occurred in the same sessions, on the same days, or in similar sequences. Tracks that travelled together were stronger evidence than songs that were merely popular individually. 5. I compared listening outside the suspected period. A song played throughout several years might be a general favourite, while one that appeared repeatedly within the cluster and rarely elsewhere was a stronger candidate. 6. I used timestamps to recover possible order, including tracks that usually followed each other or appeared near the beginning or end of sessions. Because shuffle may obscure the original order, I treated these smaller sequences as clues rather than proof. 7. I showed the candidate tracks to the person, including names, artists, and, where useful, album art or audio previews. I asked whether each track felt familiar and belonged to the playlist. 8. I relied on recognition rather than recall. Someone may be unable to name a single song but still recognise dozens when presented with them. This is especially useful for instrumental music, mantras, classical works, or unfamiliar names, because remembering the sound does not require remembering its spelling. 9. I ranked candidates as highly likely, likely, possible, or uncertain using repetition, co-occurrence, timing, listening elsewhere, and human confirmation. I did not treat every recovered track as equally certain. 10. I created a provisional playlist from the strongest candidates and listened to it. A list can look convincing in a spreadsheet but feel wrong when played; listening may reveal an unrelated song, a missing transition, or immediate recognition. 11. I used confirmed tracks as anchors and checked which uncertain candidates repeatedly appeared beside them. Data suggested candidates, recognition confirmed anchors, and those anchors improved the next search. 12. I recorded why the playlist mattered, when it was used, and whether its order mattered. Mine was a meditation playlist, so recovering that musical environment mattered even if the exact original list remained uncertain. 13. I saved an external record containing the playlist name, track titles, artists, albums, links or identifiers, reconstruction date, and uncertainty or contextual notes. I kept the listening history, candidate analysis, and confirmed playlist separately so I could revise the result without losing the evidence. The workflow was: analyse listening history → find repeated clusters → locate the time window → collect candidates → compare co-occurrence and sequence → remove unrelated tracks → confirm through recognition → rebuild and listen → refine → save externally. The deeper principle is that humans often remember experiences better than metadata. I remembered that the playlist existed, what it did, and how constantly I played it. I did not remember what most of it was called. The platform had lost the collection, but the listening history still contained its shadow. One observation made it searchable again: > “Hey. For about four months, you were basically listening to the same fifty songs.” 😅🎧

I built a local memory system for Claude that is a bit different from others I have seen. The reason was familiar: repeated context loss and gaps, even within projects. I am not technical—I come from a healthcare background—so it was essentially vibe-coded, but I used a method I had not seen elsewhere. I started by interviewing Claude about what would be useful to it, rather than beginning with only what I wanted. The three biggest gaps were the rationale behind decisions, current versus historical states, and the difference between global and project-level detail. We also identified ways memory can go wrong, including stale facts being confidently asserted as truth and rejected ideas resurfacing. Claude’s built-in memory stores flat topics without entity links, captures what but not why, is not well temporally grounded, and is gated by Claude. I researched other memory builds, from homebrew systems to enterprise tools, and found that they generally fell into three groups: - Vector dumps, which lack rationale and supersession and can become stale - Plain Markdown with grep or embeddings, such as Basic Memory, which similarly lacks real temporal grounding and an entity graph - Heavyweight knowledge-graph stacks such as Neo4j, LangChain, and GraphRAG None of these did what I wanted. My store holds entities only, not transcripts. It stores decisions, observations, people, and projects connected by typed edges. Each entry has content, a scope—either portfolio-worthy or working detail for one project—a rationale, and information about where it came from. The system is also bitemporal, so it distinguishes what is current from what is not. Nothing is edited in place: a correction adds a new assertion instead of overwriting the old one. Underneath, it uses SQLite, with sqlite-vec for semantic search and FTS5 for keyword search. Nothing writes automatically. Claude has to propose a memory, and I have to approve it. This prevents the store from filling with noise, keeps it token-efficient, and acts as a governance lever. MCP links Claude to my server. It is local-only at present, although there is potential to add remote access in the future. The server provides instructions for making proposals, so the system is theoretically portable, and I also created a Claude skill to accompany it. I ran the build across multiple projects and created specialist projects for different roles: - A central development and oversight project served as the decision-maker and prompt-writer. - Cowork handled the building and tested each module inside a sandbox. It had no authority to change decisions, and I used a fresh project for each stage of the build. - I handled deployment separately by typing every command on the server and pasting the output back. - I repeated Claude’s Cowork-authored tests on the actual server. Real-world testing after deployment identified only minor issues, which were quick to fix. This separation was administratively heavy because I had to keep switching between projects as tasks started and finished, but it caught several errors in the rules and code. Despite my lack of technical knowledge, the project is now well documented and I have room to develop it further. I may eventually put it on GitHub. It has become a standard component of my workflows, my projects are tracked much better, and Claude and I are on the same page more often.

I built a daily AI story-scouting workflow that finds potentially viral stories for Instagram Reels, YouTube Shorts, and TikTok without recycling topics we’ve already covered. First, the workflow reviewed Rowan’s public short-form archive once to create a coverage map of past topics and editorial taste. It uses that map as a filter, helping distinguish a genuinely new angle from another version of the same AI launch, robot demo, or research story. Every morning, it scans priority sources, including AI and technology coverage from major newspapers and tech blogs, The Rundown newsletters, company announcements, research papers, and public updates from major AI and robotics companies. The workflow is not limited to same-day news. It also looks back over the past one to six weeks for stories that have not been extensively covered in the media. Each potential story is checked for four things: a clear visual hook, a technical idea that can be explained in 45–90 seconds, broad audience interest, and meaningful novelty compared with Rowan’s past coverage and the previous week’s recommendations. It keeps every story that clears the bar rather than forcing a fixed number. The final brief is ranked by strength and sent as one compact Slack DM at 10 AM each day. Step-by-step: 1. The workflow reviews Rowan’s public short-form archive once and builds a map of past topics and editorial taste. 2. Each morning, it scans priority sources, including major newspapers, tech blogs, The Rundown newsletters, company announcements, research papers, and public updates from major AI and robotics companies. 3. It searches both same-day news and stories from the previous one to six weeks that have not been extensively covered in the media. 4. It filters out topics that substantially repeat Rowan’s past coverage or the previous week’s recommendations, while identifying genuinely new angles. 5. It evaluates each potential story for a clear visual hook, a technical idea explainable in 45–90 seconds, broad audience interest, and meaningful novelty. 6. It keeps every story that meets those criteria instead of forcing a fixed number of recommendations. 7. It ranks the final brief by strength and sends it as one compact Slack DM at 10 AM each day.

I moved from public school to a private school with no autism-specific training, and I found students on the spectrum struggling with one-size-fits-all lessons. AI helped close that gap for both me and my students in about five minutes per lesson. I take a standard lesson and rebuild it around the individual student. I upload only the blank assignment—never any student data—and prompt AI to adapt it to that learner. I include their favorite colors and niche interests, replace abstract examples with personalized ones, add sentence starters, and allow them to draw responses instead of writing dense paragraphs. Step-by-step: 1. I take an existing lesson and create a blank version of the assignment. 2. I upload only the blank assignment to my AI tool, without including any student data. 3. I provide the student’s interests, preferred colors, and reading level. 4. I ask the AI to rebuild the lesson for that specific student. 5. I use personalized examples, sentence starters, and drawing-based response options to make the lesson more accessible. The result is no shutdowns, no walls, and a genuinely engaged and grateful student. One untrained teacher can now individualize any lesson for a unique learner in minutes.

I'm planning a housewarming party for about 70 people: 60 adults and 10 children. I knew I wanted to serve a large grazing board, along with a couple of big buckets of ice filled with canned beer, coolers, sparkling water, and other drinks. I told Claude about the party and asked it to create a shopping list based on the number of attendees. Claude produced a cohesive list with six cheeses and approximate quantities for each one. It did the same for the cured meats, crackers, bread, fruit, nuts, jams, and pickled items. It also suggested beverages, including a ratio of beer to coolers and nonalcoholic options. The list included juices and non-caffeinated sodas for the kids, along with a template showing how to lay out the grazing table. This was not a complex use case, but it was very helpful. Step-by-step: 1. I estimated the guest list at about 70 people: 60 adults and 10 children. 2. I described my plan to serve a large grazing board and drinks kept cold in a couple of big buckets of ice. 3. I asked Claude to create a shopping list based on the number of attendees. 4. I used Claude's recommendations for six cheeses, cured meats, crackers, bread, fruit, nuts, jams, and pickled items, including approximate quantities. 5. I reviewed its beverage suggestions, including the beer-to-cooler ratio, sparkling water, juices, and non-caffeinated sodas for the children. 6. I used the layout template to plan how to arrange the grazing table.

I’m building an automated content engine for a client using n8n and the Claude API. Every week it produces blog articles and social posts for eight brands. The client already has a blog with [number] articles, and we want the output to match that voice. What I’ve tried: [e.g. a tone-of-voice description in the system prompt / a few example articles in the prompt]. What I get is text that’s fine but sounds AI-written and slightly translated. It doesn’t read like a native Swedish copywriter wrote it. What I’m weighing: Is it better to extract a tone-of-voice guide from the blog (rules, word choices, sentence length, do’s and don’ts) or to pass example texts as few-shot? How many examples do I need, and should they be full articles or excerpts? Is a separate editor step that only reviews and adjusts tone worth it? How do you handle one shared base voice across eight brands that each have their own variant?

I built a custom news agent that skips the Google News and Apple News walled gardens. Eight prompts turn Claude into a personal news editor that reads my trusted RSS feeds, scores every story with a decision model, and delivers a clean daily page for about a dime a month. Each week, pulling two or three good stories for my newsletter digest meant searching multiple sites by hand—hours of clicking and scrolling for a handful of usable links. RSS feeds already deliver that content; the missing piece was a judgment layer that could separate worthwhile stories from noise without the cost or latency of running everything through a full LLM. I used Claude Cowork, Jev from TypeSafe AI, Vercel AI Gateway, RSS feeds, Python, and SQLite. Jev is a decision model rather than a chatbot: it returns a probability and confidence score instead of prose, does not hallucinate facts, and scores a story in a fraction of a second. That makes an automated daily judgment call inexpensive enough to run continuously. Step-by-step: 1. I set up a Claude project to hold the scripts, taste guide, database, and page. 2. I defined the goal and sections, then had Claude interview me to create a short “taste guide” describing what counts as a good story. 3. I found and live-tested the RSS feed for each trusted source instead of guessing feed URLs. 4. I fetched all working feeds into a deduplicated SQLite database and logged failures. 5. I used Jev through Vercel AI Gateway to score every story: first filtering ads and promotions, then ranking stories by section against my taste guide. 6. I selected the winners for each section, with Jev identifying near-duplicate stories so they would not appear twice. 7. I generated one-line summaries and fact-checked them against the source so nothing was invented. 8. I rendered a simple HTML page and scheduled the entire workflow to run every morning. A few details matter. I used Cowork rather than a plain chat because several steps require Claude to work with files, run code, and access the network. I also created a Vercel account and AI Gateway API key manually before the scoring step. Finally, I saved everything to the Claude project instead of a local folder, which allows the morning run to happen in the cloud on its own schedule. Scoring costs roughly $0.10 per month with Jev, compared with a few dollars for a full LLM doing the same job. The workflow turns a two-hour weekly slog into a page that is ready every morning, with each link going directly to the original publisher rather than a Google or Apple aggregator. Full build walkthrough: onabetternote.substack.com/p/build-your-own-news-agent-with-claude — the Substack post includes the exact eight prompts to copy and paste, plus more detail on why a model like Jev matters for cost containment as agents become routine.

Midlifecurious is a newsletter for women navigating midlife—honest, funny, and allergic to being talked down to. Its Sunday issue, the Midlife Missive, is a roundup covering health, wellness, money, beauty, and family. My sister, Claire, edits it; I build the machine behind it. That machine is Missive, a five-part publishing intelligence system that runs the newsletter as one closed loop: scan → triage → publish → measure → remember. It monitors Reddit, search trends, and RSS to identify what midlife women are paying attention to before those topics reach our feeds. Discovery pulls in those sources, ranks every feed using a click-rate-based quality score, and lets Claire triage articles into the week’s issue. Curation composes Sunday’s newsletter and drafts the introduction in her voice. Performance reads the Mailchimp results back into the system and feeds them into the rankings, so strong sources rise and weak ones fall over time. Underneath all four stages is Memory: a vector-searchable corpus of every article, save, rejection, and the reasoning behind each decision. Memory is the real spine of the system. It lets Missive ask editorial questions such as “Have we covered this before?” and “Is this source still earning its slot?” instead of requiring one person to hold everything in her head. We’re a two-person operation: I build with Claude Code, and Claire edits. The system runs on one database for under $25 a month. I built it because the alternative was Claire drowning in a Feedly-and-spreadsheet routine that discarded everything as soon as an issue shipped. We had no record of what we had run and no feedback on what actually landed. My bet is that the corpus is the moat. Claire’s editorial taste—every save, rejection, and “cornerstone” stamp, with the reasoning stored alongside the decision—is a training set no one else has. A system that remembers turns her job from synthesizer into judge. Missive is deliberately internal-only: no SaaS and no customers, ever. That frees me to build for our exact workflow instead of a hypothetical buyer, and to build for 2028 instead of this quarter. The near-term payoff is a calmer Sunday. The long-term goal is a proprietary editorial-intelligence layer we could never buy off the shelf—the foundation for the research and audience products that come next. Step-by-step: 1. I monitor Reddit, search trends, and RSS for topics that midlife women are paying attention to. 2. I pull those sources into Missive and rank each feed using a click-rate-based quality score. 3. Claire triages the ranked articles into the week’s Midlife Missive. 4. Missive composes Sunday’s newsletter and drafts the introduction in Claire’s voice. 5. I import the Mailchimp results so the system can update source rankings based on performance. 6. Missive stores every article, save, rejection, “cornerstone” stamp, and the reasoning behind each decision in a vector-searchable corpus. 7. We use that memory to check whether a topic has already been covered and whether a source is still earning its place. 8. I build and maintain the internal system with Claude Code, while Claire handles editing, using one database that costs under $25 a month.

A reusable Claude Skill called `trust-due-diligence`, packaged as a `.skill` file. It is not a single report; it is a methodology made up of a `SKILL.md` file and three reference documents that teach Claude a repeatable process for investigating a named person, company, coach, or offer before you commit money or trust to them. Once installed, it activates automatically whenever you ask something like “deep dive on X” or “is this legit,” so you do not need to explain the process each time. Step-by-step: 1. Package the `trust-due-diligence` Claude Skill as a `.skill` file. 2. Include a `SKILL.md` file and three reference documents. 3. Use the files together to teach Claude a repeatable due-diligence methodology. 4. Install the Skill so it activates automatically for prompts such as “deep dive on X” or “is this legit.” 5. Use it to investigate a named person, company, coach, or offer before committing money or trust.