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The new ChatGPT Chrome extension is surprisingly good at navigating complicated technical settings. My favorite use case so far has been using it to create the special DNS records for a new website. This has always been confusing, even though I’ve done it dozens of times. The process varies across platforms, every DNS registrar uses different names for records such as A and CNAME records, and the interfaces are constantly changing. It’s tedious and easy to get wrong. For this kind of one-off, highly detailed technical task, I copy and paste the specific instructions from my website host provider into the extension while I’m logged in to my DNS provider. GPT then takes control of the mouse and fills in the required fields. The use cases are endless, from filling out an expense report to handling other tedious tasks in legacy or old-school software that doesn’t have a simple CLI, MCP, or API connection. If AI can’t control a system directly through the backend, browser control closes another gap. These legacy systems often lack newer integrations, which also makes them annoying, tedious, and frustrating to use. That makes them particularly good tasks to give to AI—especially now that the ChatGPT Chrome extension is good at following instructions and completing actions in the browser. Step-by-step: 1. I log in to my DNS provider. 2. I copy and paste the specific DNS instructions from my website host provider into the ChatGPT Chrome extension. 3. I let GPT navigate the DNS provider’s interface and take control of the mouse. 4. GPT fills in the required fields for records such as A or CNAME records, accounting for the registrar’s particular interface. 5. I use the same approach for other detailed, repetitive tasks in legacy software that lacks CLI, MCP, or API connections.

I built an Opportunity Intelligence app with Claude Code for our sales team. Team members select the state and industry they want to target, and the app pulls live signals, such as whether a company is hiring for a specific role or opening a new facility, among others. The app then produces a top 10 list for further research, including a company overview, how our products work within that organization, battlecards, a custom LinkedIn introduction message, and a custom email message. From there, the team can research specific people at each organization. The app produces a top 10 list of targets with their LinkedIn profiles, phone numbers, and email addresses. Step-by-step: 1. I built an Opportunity Intelligence app with Claude Code for our sales team. 2. Team members select the state and industry they want to target. 3. The app pulls live signals, including whether companies are hiring for specific roles or opening new facilities. 4. The app generates a top 10 list of companies to research, with a company overview, how our products work within each organization, battlecards, a custom LinkedIn introduction message, and a custom email message. 5. Team members use that information to research specific people at each organization. 6. The app generates a top 10 list of targets with their LinkedIn profiles, phone numbers, and email addresses.

I created an online app where my friends can select the gigs they plan to attend. It shows who is going to each gig and when, along with a clear festival-wide timeline. The festival host only provides stage-based schedules, which are difficult to read and coordinate. My app brings the information together in a more practical format for planning with friends. Step-by-step: 1. I created an online app for coordinating festival plans. 2. I added a way for friends to select the gigs they plan to attend. 3. I organized the selections to show who is going to each gig and when. 4. I created a clear timeline that combines the festival’s stage-based schedules. 5. I used the combined timeline to make planning and coordination easier.

I configured Claude to review my calendar every two weeks for scheduled VIP meetings. 1. I configured Claude to review the calendar biweekly. 2. I set it to identify scheduled VIP meetings.

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.

Most days, I want one capable general AI agent. The failure mode appears with higher-stakes questions in security, finance, law, or medicine: my main chat is already full of project context, the agent may answer confidently outside its depth, and a “specialist” prompt in the same thread still inherits the assumptions and tools that session has already loaded. I set up Hermes Agent with a consultation workflow that cold-starts an isolated specialist profile for a one-shot answer. I keep four advisors ready: security, financial, legal, and medical. Each call starts a separate process with its own model, tools, and skills. The specialist has no access to my parent conversation, so I have to write a self-contained prompt. It takes anywhere from a few seconds to a couple of minutes—not instantly—which is fine because I only use it when the stakes justify the wait. The gate is the part I care about most. Nothing runs until I explicitly name the profile, approve the exact prompt, and confirm. There is no silent auto-routing. When I want a second opinion, I draft my own take first, send the same question to the specialist, and compare where we agree and differ. This keeps the general agent honest and prevents specialty advice from becoming the default for routine work. I also keep the scope tight. I limit the medical profile to personal or educational questions, or fully de-identified material—not identifiable patient care. Routine work that my main agent already handles well never goes through consultation. I reserve the specialist profiles for deliberate second opinions or questions I do not want the default session answering alone. Step-by-step: 1. I work through the problem in my main Hermes session and write my own answer first when I want a second opinion. 2. If the question is high-stakes in security, finance, law, or medicine—or I simply want a deliberate second look—I choose the specialist profile to use. 3. I write a self-contained prompt with every fact and constraint the specialist needs because it cannot see the parent chat. 4. I name the profile, approve the prompt, and confirm before anything runs. 5. I let Hermes cold-start that profile as a separate process and return a one-shot answer. 6. I compare my draft with the specialist’s response: where we agree, where we differ, and what still needs a human professional. 7. I skip consultation for routine work and keep medical use limited to personal, educational, or de-identified material.

I built an AI meeting-intelligence workflow that turns my meetings, emails, chats, and handwritten notes into one living action tracker. Every action stays linked to its original source, and the workflow automatically generates a prep sheet for each meeting. Two problems drove me to build it. First, provenance: action lists are often disconnected from their sources. A line such as “follow up with vendor on pricing” is much less useful when I can’t open the Teams message or email it came from to review the details or reply. The context disappears as soon as the action is recorded. Second, I take most of my notes by hand because I think better on paper. Those notes used to be a dead end: they never entered any system, so the follow-ups disappeared. The workflow runs on a morning schedule or on demand before a meeting. I use Claude in Cowork with MCP connectors for email, chat, and calendar. It pulls recent messages and upcoming meetings while capturing the deep link or ID for each source item, so every action can be traced back to the exact email or Teams thread. For handwritten notes, I photograph the pages into a OneDrive folder and tag them. The workflow uses OCR and vision to extract the text and action items. Quick shorthand tags—`action`, `prep`, and `done`—on messages and photos tell it what to track or close. A Python layer then deduplicates the actions, assigns each one a stable ID, applies the tags, and updates the master tracker while preserving the source links. The result is one self-maintaining action list. I can click any item to return to its original artifact to reference the details or reply, and my paper notes finally flow into the same system. To recreate it, connect an AI assistant to your mail, chat, and calendar through MCP and store each source link with its action. Add a OneDrive photo drop with OCR for handwritten notes, use a simple tag vocabulary, and separate the responsibilities: let the AI handle capture and OCR, while plain code handles deduplication, IDs, and status updates. Step-by-step: 1. I scheduled the workflow to run each morning and added an option to run it on demand before a meeting. 2. I connected Claude in Cowork to my email, chat, and calendar through MCP so it could gather recent messages and upcoming meetings. 3. I captured the deep link or ID for every source item, allowing each action to trace back to the exact email or Teams thread. 4. I photographed handwritten notes into a tagged OneDrive folder and used OCR and vision to extract their text and action items. 5. I used the shorthand tags `action`, `prep`, and `done` on messages and photos to indicate what to track or close. 6. I used a Python layer to deduplicate actions, assign stable IDs, apply tags, and update the master tracker without removing source links. 7. I used the resulting tracker to return to the original artifact for each action and to bring paper-note follow-ups into the same system.

Je veux créer N’ose Digital IA, un SaaS international dédié à l’automatisation et conçu de A à Z comme un véritable business SaaS, avec l’IA au cœur de la plateforme. Le projet comprend un dashboard IA de service client ainsi qu’un agent vocal qui répond aux clients et s’appelle « Client ». Step-by-step: 1. Créer le SaaS N’ose Digital IA. 2. Optimiser la plateforme autour de l’automatisation. 3. Concevoir un dashboard IA dédié au service client. 4. Intégrer un agent vocal appelé « Client » pour répondre aux clients. 5. Développer la plateforme comme un business SaaS international, avec l’IA au cœur du projet.

I’m building GradePath because schoolwork gets scattered quickly. A lecture, my notes, and the files for a class can all end up in different places. Then, when it’s time to study, I have to dig around to find what I need. The idea is simple: record a class, keep the recording and course materials together, and return to them later. If I ask Tutor about something, it should show me the relevant part of my notes, files, or class recording. If it can’t find an answer, it should say so. I also want students to be able to share useful study sheets with people who are actually in their class. I’m not trying to turn school into another content feed. I want to make it easier to keep up with a class and find the right material when I need it. Step-by-step: 1. I record a class. 2. I keep the recording, notes, and course materials together in GradePath. 3. I return to those materials when I need to study. 4. I ask Tutor about a topic and have it show the relevant part of my notes, files, or class recording. 5. If Tutor can’t find supporting material, it says so instead of presenting an unsupported answer. 6. I share useful study sheets with students who are in the same class.

Vercel provides the domains and the base “wiring.”