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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.

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.

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.

I’ve been using a simple AI workflow to capture and act on my best ideas before they get lost between meetings. It’s been so useful that I wanted to share it for others to borrow. Here’s what I do: Step-by-step: 1. While walking between meetings, I open Wispr Flow on my phone and dictate every idea and takeaway into an Apple Note, completely unstructured. The goal is to get everything out of my head before the next meeting overwrites it. 2. The notes sync automatically to my Mac, where a scheduled Claude task runs at the end of the day through the Claude Desktop app and simple connectors. It reads all the voice notes I’ve added. 3. Claude structures the notes in Notion, where I do most of my work, turning them into to-do tasks with key ideas, action items, and suggested deep-work blocks in my calendar for the bigger items. I no longer stress about forgetting important meeting takeaways because, by morning, I have actionable next steps waiting for me. I never have to touch my keyboard.

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 have Claude connected to all my communication platforms (Slack, Notion, calendars, Granola, etc) and pulls a morning brief for me each day. I've curated so it provides me an interactive page each day with call prep, priority tasks, recommendations of tools to use to solve XYZ, recap of activities overnight, etc. Step-by-step: 1. I connected Claude to the communication platforms I use, including Slack, Notion, calendars, and Granola. 2. I defined the sections I wanted every morning: call prep, priority tasks, overnight activity, and recommended tools. 3. I had Claude gather the relevant updates from each connected source. 4. I asked it to turn the result into an interactive page instead of a long unstructured message. 5. I refined the brief over time so it consistently surfaced the information I actually use.

I used to open calls by asking people to remind me where we left off. The notes existed, but they were scattered across transcripts that nobody reviewed. I built two scheduled tasks that work together: one files every meeting at the end of the day, and the other sends me a prep brief every morning. At the end of each day, the first task pulls the verbatim transcript of every meeting I had into a shared Notion database. I use the transcript rather than the AI summary because summaries may be useful that afternoon but are less useful three weeks later when I need the exact thing somebody said. Each meeting becomes a page with the date, client, and attendees stored as real relations rather than text, so everything is filterable later. My business partner has access to the same database, so neither of us has to recap our calls for the other. At 7 a.m., the second task reads my Outlook calendar, finds the email thread or threads tied to each meeting, reads the associated transcripts in Notion, and writes a short prep brief for each one: what we said last time, what I owe them, and what is still open. The part that took the most thought was deciding what should not be filed in the main database. Personal meetings are skipped by keyword. Small internal meetings are screened for topics such as pay, hiring, legal matters, or client-confidential material. Those meetings are routed to a separate database with different permissions. When a meeting is ambiguous, it defaults to the restricted database. Failing toward privacy is the right default when a robot is making the decision. Step-by-step: 1. I turned on Zoom AI Companion so every meeting produces a transcript, then connected Zoom, Notion, and Outlook. 2. I built a Notion database for meeting notes with Date, Client, and Attendees as relation properties connected to existing Clients and People databases. These relations make the notes findable later. 3. I wrote the end-of-day task to pull each transcript verbatim, create a page, match the client by keyword against my client list, and add attendees based on the transcript speakers. 4. I filtered the speaker list because notetaker bots appear as attendees. I removed Fireflies, Otter, Fathom, and the other notetaker bots, and automatically created a person page for anyone who was genuinely new. 5. I deduplicated meetings using the title and date. If a page already existed as a placeholder, I updated it in place instead of creating a second one. 6. I added a skip list for personal meetings and a confidentiality screen that routes sensitive internal meetings to a separate, permission-restricted database. When the classification is unclear, it defaults to restricted. 7. I wrote the morning task to read that day’s calendar, search email and transcripts for each attendee and company, and produce one short brief per meeting covering the last contact, open commitments, and what I owe them. 8. I scheduled both tasks: the filing task for the end of the day and the briefing task for early morning.

It starts when someone clicks “Find My Best AI Opportunity” on my website. Instead of going straight to a booking page, they enter a short AI chat. The assistant asks about their work, business, main pain point, AI experience, urgency, name, and email. The workflow runs in n8n. Once the chat has enough information, it creates an AI Readiness Summary, saves the lead in Notion, sends me an internal brief, and emails the visitor their summary with a link to book a 30-minute call through Cal.com. The result is a better-qualified call: the visitor gets useful value first, and I have the context I need before we meet. Step-by-step: 1. A visitor clicks “Find My Best AI Opportunity” on my website. 2. The visitor completes a short AI chat about their work, business, main pain point, AI experience, urgency, name, and email. 3. n8n uses the collected information to create an AI Readiness Summary. 4. The workflow saves the lead in Notion and sends me an internal brief. 5. The visitor receives their summary by email, along with a link to book a 30-minute call through Cal.com. 6. I review the context before the call, making it better qualified.

The ETHOS™ Framework (Evaluation Through Hierarchical Oversight of Sources) is a multistage forensic audit system designed to transform AI-generated research into “audit-ready” ground truth. Its five-stage lifecycle is designed to ensure technical precision: Step-by-step: 1. EXTRACT (Stage 1): Using the Ground Truth Manifesto and the six-tier Authority Ladder, ETHOS extracts structured tax reports from multiple LLM archetypes: Technical Specialists, Strategic Advisors, and Operational Drafters. This forces their initial findings to follow a strict legal hierarchy. 2. TEST (Stage 2): The Tax Citation Auditor subjects the reports to a three-pass forensic review, testing every citation for existence, pinpoint accuracy, and application fit. Any citation that cannot be verified in a primary repository is immediately downgraded or flagged. 3. HEAL (Stage 3): The Post-Audit Correction Protocol (PACP) repairs the evidence chain by requiring the LLM to resolve flagged citations, replace fabrications with 3–15-word verbatim micro-quotes, and revalidate all “knock-on” effects across downstream computations and thresholds. 4. ORGANIZE (Stage 4): The Human-in-the-Loop (HITL) controller organizes the pre-audit and post-audit artifacts in a consolidated AI sandbox, such as Google Notebook, Claude Cowork, or Perplexity Spaces. This manages the collectively exhaustive data, elevates mutually exclusive advisory angles, and maintains institutional version control. 5. SYNTHESIZE (Stage 5): ETHOS applies MECE principles (Mutually Exclusive, Collectively Exhaustive) and the Weighted Authority Confidence Index (WACI) to adjudicate model conflicts and synthesize a single ground-truth memo. Every load-bearing conclusion is certified for release.

I have a Daily Brief agent on Codex with access to my Slack, Gmail, Notion, and Google Drive that checks everything that happened in the last 24 hours on request. It sends me the brief through Slack, and I do maybe 10% of the final manual editing. I also gave it several examples of before/after editing, so now it sends messages in my exact voice and style. Step-by-step: 1. I connected my Daily Brief agent in Codex to Slack, Gmail, Notion, and Google Drive. 2. I told it to review activity from the last 24 hours and pull out the items that actually needed attention. 3. I structured the output as a concise brief and had Codex deliver it through Slack. 4. I manually reviewed the draft and made the final edits before using it. 5. I gave the agent before-and-after examples of those edits so future briefs would sound more like my own voice.

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

I’m researching two separate documentary films. For each project, the bot runs three web crawls and one health check every day. Based on my notes, scripts, and other existing initial research, it identifies research domains by theme and media type, favoring audio and images while applying a higher threshold to other documents. Anything scoring eight or higher is logged in Notion and downloaded automatically when possible. The bot cycles through different themes, and the health check adjusts the similarity threshold based on the results. If many items score eight, it may log and download only nines. If fewer qualifying items appear, it may begin downloading sevens. Most items cannot be downloaded because they are inaccessible to the bot, but they are still logged and linked. I review the items in Notion and mark each one according to criteria such as “people only for research” or “reject—permissions required.” It isn’t a full replacement for professional archival research, but I feel it’s getting me 90% of the way there. As an independent filmmaker, this is a huge benefit. Each morning, it sends me a synopsis, and each week, it sends me a list of pre-written emails to send to archives that require human interaction. Step-by-step: 1. I provide the bot with my notes, scripts, and existing initial research for each documentary project. 2. For each project, the bot runs three web crawls and one health check every day. 3. It identifies research domains by theme and media type, with a preference for audio and images and a higher threshold for other documents. 4. It logs items scoring eight or higher in Notion and automatically downloads them when possible. 5. It cycles through different themes and uses the health check to adjust the similarity threshold: it may focus on nines when many eights appear, or begin downloading sevens when fewer qualifying items appear. 6. It logs and links items that cannot be downloaded because they are inaccessible to the bot. 7. I review each item in Notion and mark it according to criteria such as “people only for research” or “reject—permissions required.” 8. Each morning, I receive a synopsis, and each week, I receive pre-written emails for archives that require human interaction.

Customer events trigger AI workflows that classify requests, automate actions, update systems, and keep teams in sync without manual intervention. Step-by-step: 1. Customer events trigger the AI workflows. 2. The workflows classify requests. 3. They automate actions and update systems. 4. They keep teams in sync without manual intervention.

As a fast-moving startup, many of our team's best ideas come from random Slack threads, but get lost and never fully hashed out. Instead of spending hours a day manually adding tasks to our databases, we used Notion's new Agents feature (rolling out soon for GA) and built an "AI Project Manager" that monitors Slack messages daily and logs tasks autonomously. Step-by-step: 1. I connected Notion’s Agents feature to the Slack conversations where team ideas usually appear. 2. I defined the kinds of messages that should become projects or follow-up tasks. 3. I scheduled the agent to review Slack messages every day. 4. I had it capture qualifying tasks in the team’s database automatically. 5. I used the database as the durable follow-up layer so promising threads did not disappear in chat history.