{ "source_file": "0930", "source_platform": "Medium", "author": "Ivo Bernardo", "title_listed": "Building a Personal AI Agent in a couple of Hours", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/building-a-personal-ai-agent-in-a-couple-of-hours/", "publish_date": "2026-03-31", "corpus_stage": "", "notes": "", "id": "7e9df354e991", "collected_at": "2026-10-02T05:35:34", "title": "Building a Personal AI Agent in a couple of Hours | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Building a Personal AI Agent in a couple of Hours\nWe are living in a Brave New World and prototyping just got fun\n[Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\nMarch 31, 202614 min read\n![](../images/000.png)\nFernão's Home Page - Image by Author\nI’ve been so surprised by how fast individual builders can now ship real and useful prototypes.\nTools like Claude Code, Google AntiGravity, and the growing ecosystem around them have crossed a threshold: you can inspect what others are building online and realize just how fast you can build today.\nOver the past weeks, I’ve started reserving one to two hours a day exclusively for building with my AI-first stack:\n * Google AntiGravity\n * Google Gemini Pro\n * Claude models accessed through AntiGravity\n\n\nThis setup has fundamentally changed how I think about prototyping, iteration speed, and what a “personal AI agent” can realistically do today. More importantly, it pulled me back into hands-on coding and building, something I had personally sacrificed once my role at DareData shifted toward management and orchestration rather than execution.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\nIndividually, this revolution has been a blessing someone who was always going to drift toward management roles. It removes the trade-off I had accepted: that growing a company meant abandoning building entirely. I no longer have to choose between building and managing, they actually reinforce each other.\nBut, there is a broader implication here for people who are “just” developing. If AI agents increasingly handle execution, then pure implementation stops being enough. Developers will be pushed (whether they want it or not) toward coordination, decision-making, and.. management — something that individual contributors hate by heart. In other words, management skills become part of the technical stack, and AI agents are part of the context being managed.\nWhat surprised me most is how transferable my existing management skills turned out to be:\n * Guiding the agent rather than micromanaging it\n * Asking for outcomes instead of instructions\n * Mapping, prioritizing, and pointing the grey areas\n\n\nIn practice, I am managing and coordinating a virtual employee. I can deeply influence some parts of its work while remaining almost entirely ignorant of others — and that is not a bug, it is a big feature. In my personal AI assistant, for example, I can reason clearly about the backend, yet remain mostly clueless about the frontend. The system still works, because my role is no longer to know everything, but to steer the system in the right direction.\nThis is directly analogous to how I coordinate people inside the company. As DareData grows, we do not hire replicas of founders. We intentionally hire people who can do things we cannot do, and, over time, things we will never learn deeply enough to do well.\n![](../images/001.png)\nCurrent Building Stack — Google AntiGravity - Image by Author\nEnough self-reflection on management. Let’s look at what I’m building because that’s what you are here for:\n * A personal AI assistant designed around my actual routines, not a generic productivity template. It adapts to how I work, think, and make decisions.\n * A mobile app that recommends one music album per week, without a traditional recommendation system. No comfort-zone reinforcement and that helps me expand my listening zones.\n * A mobile game built around a single character progressing through layered dungeons, developed primarily as a creative playground rather than a commercial product.\n\n\nThe interesting part is that, while I’m comfortable coding most of the backend, front-end development is not a skill I have — and if I were forced to do it myself, these projects would slow down from hours to days, or simply never ship.\nThat constraint is now largely irrelevant. With this new stack, imagination becomes the real bottleneck. The cost of “not knowing” an entire layer has collapsed.\nSo, in the rest of this post, I’ll walk through my personal AI assistant: what it does, how it’s structured, and why it works for me. My goal is to open-source it once it stabilizes, so others can adapt it to their own workflows. It is currently very specific to my life, but that specificity is intentional, and making it more general is part of the experiment.\n* * *\n## Meet Fernão\nFernão Lopes was a chronicler of the Portuguese monarchy. I chose a Portuguese historical figure deliberately — Portuguese people have a habit of attaching historical names to almost anything. If that sounds stereotypically Portuguese, that’s intentional.\nFernão is Portuguese but actually speaks English, call it a modern day chronicler for the 21st century.\n![](../images/body-da14db5264aa1192cca1.png)\nFernão Lopes, Chronicler of the Portuguese Kingdom - Image Source: Wikimedia Foundation\nAt this point, I’m doing two things I usually avoid: anthropomorphizing AI and leaning into a very Portuguese naming instinct. Consider it a harmless sign of me turning older.\nThat aside, what does Fernão actually do for me? The best place to start is his front page.\n![](../images/body-660886084541d997898c.png)\nFernão’s Front Page - Image by Author\nFernão is a cool-looking dude who currently handles five tasks:\n * **Day Schedule** : plans my day by pulling together calendars, to-dos, and objectives, then turning them into something I can follow.\n * **Writing Assistant** : helps me review and clean up drafts of blog posts and other texts.\n * **Portfolio Helper** : suggests companies or ETFs to add based on rebalancing needs and what’s going on in the macro world (without pretending to be a crystal ball).\n * **Financial Organizer** : extracts spending from my bank statements and uploads everything into the Cashew app, saving me from yet another task that took me about 3 to 4 hours monthly.\n * **Subscriptions & Discounts**: keeps track of all my subscriptions and surfaces discounts or benefits I probably have but never remember to use.\n\n\nIn this post, I’ll focus on the Day Schedule app.\nAt the moment, Fernão’s **Day Schedule** does three simple things:\n * Fetches my calendar, including all scheduled meetings\n * Pulls my to-dos from Microsoft To Do\n * Retrieves my personal Key Results from Notion\n\n\nAll of this is connected through APIs. The idea is straightforward: every day, Fernão looks at my constraints, priorities, and commitments, then generates the best possible schedule for me.\n![](../images/body-974e90d942044cc641c0.png)\nFernão’s Current Powers - Image by Author\nTo generate a schedule in the front-end is pretty easy (all the front-end was vibe coded). Here is the generate schedule button:\n![](../images/body-b53ab0193f9f6adc4f4e.png)\nGenerate Schedule for a Specific Day - Image by Author\nOnce I hit Generate Schedule, Fernão starts cooking in the background:\n![](../images/body-a8fcc1c951fd6f4d7089.png)\nFernão is Generating a Schedule - Image by Author\nThe steps are then, in order: fetching my calendar, tasks, and Notion data.\nThe next point is also where basic coding literacy really starts to matter, not because everything the following code does not work, but because you need to understand _what_ is happening and _where_ things may eventually break or need improvement.\nLet’s start with the calendar fetch. At the moment, this is handled by a single, gigantic function created by Claude that is **completely unoptimized**.\npython\n\n```\ndef get_events_for_date(target_date=None):\n \"\"\"\n Fetches events for a specific date from Google Calendar via ICS feeds.\n \n Args:\n target_date: datetime.date object for the target day. If None, uses today.\n \n Returns a list of event dictionaries.\n \"\"\"\n # Hardcoded calendar URLs (not using env var to avoid placeholder issues)\n CALENDAR_URLS = [\n 'cal1url',\n 'call2url'\n ]\n \n LOCAL_TZ = os.getenv('TIMEZONE', 'Europe/Lisbon')\n \n # Get timezone\n local = tz.gettz(LOCAL_TZ)\n \n # If no target date provided, use today\n if target_date is None:\n target_date = datetime.now(local).date()\n \n # Create datetime for the target day boundaries\n day_start = datetime.combine(target_date, datetime.min.time()).replace(tzinfo=local)\n day_end = day_start + timedelta(days=1)\n \n # Debug: Print the date range we're checking\n print(f\"\\n[Debug] Checking calendars for date: {target_date.strftime('%Y-%m-%d')}\")\n print(f\" Start: {day_start.strftime('%Y-%m-%d %H:%M %Z')}\")\n print(f\" End: {day_end.strftime('%Y-%m-%d %H:%M %Z')}\")\n print(f\" Timezone: {LOCAL_TZ}\")\n \n all_events = []\n \n # Fetch from each calendar\n for idx, cal_url in enumerate(CALENDAR_URLS, 1):\n calendar_name = f\"Calendar {idx}\"\n print(f\"\\n[Debug] Fetching {calendar_name}...\")\n \n try:\n # Load calendar from ICS URL with adequate timeout\n r = requests.get(cal_url, timeout=30)\n r.raise_for_status()\n cal = Calendar(r.text)\n \n events_found_this_cal = 0\n total_events_in_cal = len(list(cal.events))\n print(f\" Total events in {calendar_name}: {total_events_in_cal}\")\n \n # Use timeline to efficiently filter events for target day's date range\n # Convert local times to UTC for timeline filtering\n day_start_utc = day_start.astimezone(timezone.utc)\n day_end_utc = day_end.astimezone(timezone.utc)\n \n # Get events in target day's range using timeline\n days_timeline = cal.timeline.overlapping(day_start_utc, day_end_utc)\n \n for e in days_timeline:\n if not e.begin:\n continue\n \n # Get event start time\n start = e.begin.datetime\n if start.tzinfo is None:\n start = start.replace(tzinfo=timezone.utc)\n \n # Convert to local timezone\n start_local = start.astimezone(local)\n \n # Debug: Print first few events to see dates\n if events_found_this_cal < 3:\n print(f\" Event: '{e.name}' at {start_local.strftime('%Y-%m-%d %H:%M')}\")\n \n # Get end time\n end = e.end.datetime if e.end else None\n end_local = end.astimezone(local) if end else None\n \n all_events.append({\n \"title\": e.name,\n \"start\": start_local.strftime(\"%H:%M\"),\n \"end\": end_local.strftime(\"%H:%M\") if end_local else None,\n \"location\": e.location or \"\",\n \"description\": e.description or \"\"\n })\n events_found_this_cal += 1\n \n print(f\" [OK] Found {events_found_this_cal} event(s) for target day in {calendar_name}\")\n \n except requests.exceptions.RequestException as e:\n print(f\" [X] Network error fetching {calendar_name}: {str(e)}\")\n continue\n except Exception as e:\n print(f\" [X] Error processing {calendar_name}: {type(e).__name__}: {str(e)}\")\n continue\n \n # Sort by start time\n all_events.sort(key=lambda x: x[\"start\"])\n \n # Print all events in detail\n if all_events:\n print(f\"\\n[Google Calendar] Found {len(all_events)} event(s) for {target_date.strftime('%Y-%m-%d')}:\")\n print(\"-\" * 60)\n for event in all_events:\n time_str = f\"{event['start']}-{event['end']}\" if event['end'] else event['start']\n location_str = f\" @ {event['location']}\" if event['location'] else \"\"\n print(f\" {time_str} | {event['title']}{location_str}\")\n print(\"-\" * 60)\n else:\n print(f\"\\n[Google Calendar] No events for {target_date.strftime('%Y-%m-%d')}\")\n \n return all_events\n```\n\nAs a Python developer, all the `print` statements give me the ick. But that’s a problem for the next phase of Fernão: refactoring and optimizing the code once the product logic is solid.\nThis is also where I really see the _human + AI_ dynamic working. I can immediately spot several ways to improve this function (reduce verbosity, cut unnecessary latency, clean up the flow) but doing that well still requires time, judgment, and intent. AI helps me move fast; it doesn’t replace the need to know what _extraordinary_ looks like.\nFor now, I haven’t spent much time optimizing it, and that’s a conscious choice. Despite its rough edges, the function does exactly what it needs to do: it pulls data from my calendars and feeds the meeting information into Fernão, enabling everything that comes next.\nNext, Fernão pulls my tasks from Microsoft To Do. This is where my daily to-dos live (the small, concrete things that need to get done and that give structure to a specific day). All of this is configured directly in the Microsoft To Do app, which is a core part of my daily workflow.\nIf you’re curious about the broader productivity system behind this, I’ve written about it in a related post on the brother of this blog (Wait a Day) linked [below](https://waitaday.substack.com/p/on-productivity).\nSo, let’s look at another verbosed function:\npython\n\n```\ndef get_tasks(target_date=None):\n \"\"\"\n Fetches tasks from Microsoft To Do for a specific date (tasks due on or before that date).\n \n Args:\n target_date: datetime.date object for the target day. If None, uses today.\n \n Returns a list of task dictionaries.\n \"\"\"\n CLIENT_ID = os.getenv('MS_CLIENT_ID', 'CLIENT_ID_KEY')\n AUTHORITY = \"https://login.microsoftonline.com/consumers\"\n SCOPES = ['Tasks.ReadWrite', 'User.Read']\n \n # Setup persistent token cache\n cache = SerializableTokenCache()\n cache_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), '.token_cache.bin')\n \n if os.path.exists(cache_file):\n with open(cache_file, 'r') as f:\n cache.deserialize(f.read())\n \n # Authentication with persistent cache\n app = PublicClientApplication(CLIENT_ID, authority=AUTHORITY, token_cache=cache)\n accounts = app.get_accounts()\n \n result = None\n if accounts:\n result = app.acquire_token_silent(SCOPES, account=accounts[0])\n if not result or \"access_token\" not in result:\n for account in accounts:\n app.remove_account(account)\n result = None\n \n if not result:\n flow = app.initiate_device_flow(scopes=SCOPES)\n if \"user_code\" not in flow:\n print(f\"[MS To Do] Failed to create device flow: {flow.get('error_description', 'Unknown error')}\")\n return []\n if \"message\" in flow:\n print(flow['message'])\n result = app.acquire_token_by_device_flow(flow)\n\n if not result or \"access_token\" not in result:\n print(f\"[MS To Do] Authentication failed: {result.get('error_description', 'No access token') if result else 'No result'}\")\n return []\n headers = {\"Authorization\": f\"Bearer {result['access_token']}\"}\n \n # Get date boundaries for target date\n # If no target date provided, use today\n if target_date is None:\n from datetime import date\n target_date = date.today()\n \n # Convert date to datetime in UTC for comparison\n now = datetime.now(timezone.utc)\n target_day_end = datetime.combine(target_date, datetime.max.time()).replace(tzinfo=timezone.utc)\n \n # Fetch all lists\n lists_r = requests.get(\"https://graph.microsoft.com/v1.0/me/todo/lists\", headers=headers)\n if lists_r.status_code != 200:\n return []\n \n lists_res = lists_r.json().get(\"value\", [])\n all_tasks = []\n \n # Fetch tasks from all lists with server-side filtering\n for task_list in lists_res:\n list_id = task_list[\"id\"]\n list_name = task_list[\"displayName\"]\n \n # Simple filter - just get incomplete tasks\n params = {\"$filter\": \"status ne 'completed'\"}\n \n tasks_r = requests.get(\n f\"https://graph.microsoft.com/v1.0/me/todo/lists/{list_id}/tasks\",\n headers=headers,\n params=params\n )\n \n if tasks_r.status_code != 200:\n continue\n \n tasks = tasks_r.json().get(\"value\", [])\n \n # Filter and transform tasks\n for task in tasks:\n due_date_obj = task.get(\"dueDateTime\")\n \n if not due_date_obj:\n continue\n \n due_date_str = due_date_obj.get(\"dateTime\")\n if not due_date_str:\n continue\n \n try:\n due_date = datetime.fromisoformat(due_date_str.split('.')[0])\n if due_date.tzinfo is None:\n due_date = due_date.replace(tzinfo=timezone.utc)\n \n # Include all tasks due on or before the target date\n if due_date <= target_day_end:\n # Determine status based on target date\n target_day_start = datetime.combine(target_date, datetime.min.time()).replace(tzinfo=timezone.utc)\n \n if due_date < target_day_start:\n status = \"OVERDUE\"\n elif due_date <= target_day_end:\n status = f\"DUE {target_date.strftime('%Y-%m-%d')}\"\n else:\n status = \"FUTURE\"\n \n all_tasks.append({\n \"list\": list_name,\n \"title\": task[\"title\"],\n \"due\": due_date.strftime(\"%Y-%m-%d\"),\n \"importance\": task.get(\"importance\", \"normal\"),\n \"status\": status\n })\n except Exception as e:\n continue\n \n # Print task summary\n if all_tasks:\n print(f\"[MS To Do] {len(all_tasks)} task(s) for {target_date.strftime('%Y-%m-%d')} or overdue\")\n else:\n print(f\"[MS To Do] No tasks due on {target_date.strftime('%Y-%m-%d')} or overdue\")\n \n # Save token cache for next run\n if cache.has_state_changed:\n with open(cache_file, 'w') as f:\n f.write(cache.serialize())\n \n return all_tasks\n```\n\nThis function retrieves a full list of my tasks for the current day (and overdue from previous days) — here’s an example of how they look in to-do:\n![](../images/body-96fa82e50812e8aaf2b0.png)\nExample of Tasks in the To-Do App - Image by Author\nAfter pulling tasks from To Do, I also want Fernão to understand _where I’m trying to go_ , not just what’s on today’s list. For that, it fetches my objectives directly from a Notion page where I have the key results for my year.\nHere’s an example of how those objectives are structured:\n * the first column shows my baseline at the start of the year,\n * the last column defines the target I want to reach,\n * and the column on the right tracks my current progress.\n\n\nThe sample below include how many blog posts I want to write for you this year, as well as the total number of books I aim to sell across my three titles.\nThis gives Fernão broader context on what to priority in the tasks, as you’ll see in the prompt to create the schedule.\n![](../images/body-29ea1e034704c0120956.png)\nPersonal Objectives Example - Image by Author\nBtw, while writing this post, I ended up adding a small widget to the app. If we’re building a personal assistant, it might as well have some personality.\nSo I asked Gemini:\n> _“Can you add a fun animation while the app is retrieving calendar events, checking to-dos, and pulling Notion data? Maybe a small widget of Fernão stirring a pot, with the caption_ ‘Fernão is cooking’ _.”_\n![](../images/body-ed21f915a461dff05073.png)\nFernão is cooking - Image by Author\nOnce this round finishes, tasks and calendar events are successfully collected and displayed in Fernão’s front end (a sample of the tasks and meetings for my day is shown below).\n![](../images/body-8f0196522cc2c6e6f8b1.png)\nTasks and Calendar Events fetched by Fernao - Image by Author\nAnd now the fun part: with calendars, tasks, and objectives in hand, Fernão composes my entire day into a single, magical plan:\ntext\n\n```\n07:30-09:30 | Gym\n09:30-09:40 | Check Timesheets on Odoo (Due: 2026-02-04)\n09:40-09:55 | Review Tasks in To-Do - [Organization Task] (Due: 2026-02-04)\n09:55-10:15 | Read Feedly Stuff - [News Catchup] (Due: 2026-02-04)\n10:15-10:30 | Write culture doc, inspiration: https://pt.slideshare.net/slideshow/culture-1798664/1798664 (Due: 2026-02-04)\n10:30-10:45 | Answer LinkedIns - [Organization Task] (Due: 2026-02-04)\n10:45-11:00 | Check Looker (Due: 2026-02-04)\n11:00-11:30 | This Week in AI Post (Due: 2026-02-04)\n11:30-12:00 | Prepare Podcast Decoding AI\n12:00-13:00 | Podcast Decoding AI - Ivo Bernardo, DareData (Event)\n13:00-14:00 | Lunch Break\n14:00-14:30 | Candidate 1 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n14:30-18:00 | Prepare DareData State of the Union\n18:00-18:30 | Candidate 2 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n18:30-19:00 | Candidate 3 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n19:00-19:30 | Candidate 4 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n19:30-20:00 | Candidate 5 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n20:00-20:15 | Check Insider Trading Signals for Stock Ideas https://finviz.com/insidertrading?tc=1 (Overdue)\n20:15-20:30 | Marketing Timeline (Overdue)\n20:30-21:00 | Dinner Break\n21:00-22:00 | Ler\n22:00-22:15 | Close of Day - Review and prep for tomorrow\n```\n\nThis is one of those moments that genuinely feels a bit magical. Not because the technology is opaque, but because the outcome is so clean. A messy mix of meetings, tasks, and long-term goals turns into a day I can actually execute.\nWhat makes it even more interesting is how simple the final step is. After all the heavy lifting is done in the background (calendar events, to-dos, objectives), I don’t orchestrate a complex pipeline or chain of prompts. I use a single prompt.\nThat one prompt takes everything Fernão knows about my constraints and priorities and turns it into the day you’re about to see.\ntext\n\n```\nname: daily_schedule\ndescription: Generate a daily schedule based on calendar events and tasks\nmodel: gemini-2.5-flash-lite\ntemperature: 0.3\nmax_tokens: 8192\n```\n\ntext\n\n```\nvariables:\n - date_context\n - events_str\n - tasks_str\n - context\n - todo_context\n - auto_context\n - currently_reading\n - notion_context\n - is_today\ntemplate: |\n You are my personal AI scheduling assistant. Help me plan my day!\n **TARGET DATE:** Planning for {date_context}\n **EVENTS (Fixed):**\n {events_str}\n **TASKS TO SCHEDULE:**\n {tasks_str}\n **MY CONTEXT:**\n {context}\n **TASK CONTEXT (how long tasks typically take):**\n {todo_context}\n **AUTO-LEARNED CONTEXT:**\n {auto_context}\n **CURRENTLY READING:**\n {currently_reading}\n **RESULTS & OBJECTIVES (from Notion):**\n {notion_context}\n **SCHEDULING RULES:**\n 1. **Cannot schedule tasks during calendar events** - events are fixed\n 2. **Mandatory breaks:**\n - Lunch: 12:30-13:30 (reserve when possible)\n - Dinner: 20:30-21:00\n - Playing with my cat: 45-60 minutes somewhere scattered around the day\n 3. **Task fitting:** Intelligently fit tasks between events based on available time\n 4. **Time estimates:** Use the task context to estimate how long each task will take\n 5. **Working hours:** 09:30 to 22:00\n 6. **Start the schedule:** {is_today}\n \n **YOUR JOB:**\n 1. Create a complete hourly schedule for {date_context}\n 2. Fit all tasks between events and breaks\n 3. **Prioritize tasks based on my Results & Objectives from Notion** - focus on what matters most\n 4. Be conversational - if you need more info about a task, ask me!\n 5. **Save learnings:** If I give you context about tasks, acknowledge it and say you'll remember it\n **FORMAT:**\n Start with a friendly greeting, then provide the schedule in this format:\n ```\n 09:30-10:00 | Task/Event\n 10:00-11:00 | Task/Event\n ...\n ```\n After the schedule, ask if I need any adjustments or if you need clarification on any tasks.\n Generate my day's schedule now, thanks!\n```\n\nAnd with this, Fernão is definitely cooking:\n![](../images/body-b9b54450de32cb0a43e1.png)\nFernão is Cooking again - Image by Author\nThis has been a genuinely fun system to build. I’ll keep evolving Fernão, giving him new responsibilities, breaking things, fixing them, and sharing what I learn along the way here.\nOver time, I also plan to write practical tutorials on how to build and deploy similar apps yourself. For now, Fernão lives only on my machine, and that’s likely to remain the case. Still, I do intend to open-source it. Not because it’s universally useful in its current form (it’s deeply tailored to my life), but because the underlying ideas might be.\nTo make that possible, I’ll need to abstract tools, modularize functionality, and allow features to be turned on and off so others can shape the assistant around their own workflows, rather than mine.\nI could have built something similar using Claude Code alone. I didn’t. I wanted full control: the freedom to swap models, mix providers, and eventually run Fernão on a local LLM instead of relying on external APIs. Ownership and flexibility matter more to me than convenience here.\nIf you were building a personal AI assistant, what tasks would you give it? I’d genuinely like to hear your ideas and try to build them inside Fernão. Leave a comment as this project is still evolving, and outside perspectives are often the fastest way to make it better.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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More importantly, it pulled me back into hands-on coding and building, something I had personally sacrificed once my role at DareData shifted toward management and orchestration rather than execution.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) roadmap.\nIndividually, this revolution has been a blessing someone who was always going to drift toward management roles. It removes the trade-off I had accepted: that growing a company meant abandoning building entirely. I no longer have to choose between building and managing, they actually reinforce each other.\nBut, there is a broader implication here for people who are “just” developing. If AI agents increasingly handle execution, then pure implementation stops being enough. Developers will be pushed (whether they want it or not) toward coordination, decision-making, and.. management — something that individual contributors hate by heart. In other words, management skills become part of the technical stack, and AI agents are part of the context being managed.\nWhat surprised me most is how transferable my existing management skills turned out to be:\n * Guiding the agent rather than micromanaging it\n * Asking for outcomes instead of instructions\n * Mapping, prioritizing, and pointing the grey areas\n\n\nIn practice, I am managing and coordinating a virtual employee. I can deeply influence some parts of its work while remaining almost entirely ignorant of others — and that is not a bug, it is a big feature. In my personal AI assistant, for example, I can reason clearly about the backend, yet remain mostly clueless about the frontend. The system still works, because my role is no longer to know everything, but to steer the system in the right direction.\nThis is directly analogous to how I coordinate people inside the company. As DareData grows, we do not hire replicas of founders. We intentionally hire people who can do things we cannot do, and, over time, things we will never learn deeply enough to do well.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/02/image-66-1024x595.png)\nCurrent Building Stack — Google AntiGravity - Image by Author\nEnough self-reflection on management. Let’s look at what I’m building because that’s what you are here for:\n * A personal AI assistant designed around my actual routines, not a generic productivity template. It adapts to how I work, think, and make decisions.\n * A mobile app that recommends one music album per week, without a traditional recommendation system. No comfort-zone reinforcement and that helps me expand my listening zones.\n * A mobile game built around a single character progressing through layered dungeons, developed primarily as a creative playground rather than a commercial product.\n\n\nThe interesting part is that, while I’m comfortable coding most of the backend, front-end development is not a skill I have — and if I were forced to do it myself, these projects would slow down from hours to days, or simply never ship.\nThat constraint is now largely irrelevant. With this new stack, imagination becomes the real bottleneck. The cost of “not knowing” an entire layer has collapsed.\nSo, in the rest of this post, I’ll walk through my personal AI assistant: what it does, how it’s structured, and why it works for me. My goal is to open-source it once it stabilizes, so others can adapt it to their own workflows. It is currently very specific to my life, but that specificity is intentional, and making it more general is part of the experiment.\n* * *\n## Meet Fernão\nFernão Lopes was a chronicler of the Portuguese monarchy. I chose a Portuguese historical figure deliberately — Portuguese people have a habit of attaching historical names to almost anything. If that sounds stereotypically Portuguese, that’s intentional.\nFernão is Portuguese but actually speaks English, call it a modern day chronicler for the 21st century.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/02/image-190.png)\nFernão Lopes, Chronicler of the Portuguese Kingdom - Image Source: Wikimedia Foundation\nAt this point, I’m doing two things I usually avoid: anthropomorphizing AI and leaning into a very Portuguese naming instinct. Consider it a harmless sign of me turning older.\nThat aside, what does Fernão actually do for me? The best place to start is his front page.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-340-1024x497.png)\nFernão’s Front Page - Image by Author\nFernão is a cool-looking dude who currently handles five tasks:\n * **Day Schedule** : plans my day by pulling together calendars, to-dos, and objectives, then turning them into something I can follow.\n * **Writing Assistant** : helps me review and clean up drafts of blog posts and other texts.\n * **Portfolio Helper** : suggests companies or ETFs to add based on rebalancing needs and what’s going on in the macro world (without pretending to be a crystal ball).\n * **Financial Organizer** : extracts spending from my bank statements and uploads everything into the Cashew app, saving me from yet another task that took me about 3 to 4 hours monthly.\n * **Subscriptions & Discounts**: keeps track of all my subscriptions and surfaces discounts or benefits I probably have but never remember to use.\n\n\nIn this post, I’ll focus on the Day Schedule app.\nAt the moment, Fernão’s **Day Schedule** does three simple things:\n * Fetches my calendar, including all scheduled meetings\n * Pulls my to-dos from Microsoft To Do\n * Retrieves my personal Key Results from Notion\n\n\nAll of this is connected through APIs. The idea is straightforward: every day, Fernão looks at my constraints, priorities, and commitments, then generates the best possible schedule for me.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-341-1024x440.png)\nFernão’s Current Powers - Image by Author\nTo generate a schedule in the front-end is pretty easy (all the front-end was vibe coded). Here is the generate schedule button:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-342-1024x205.png)\nGenerate Schedule for a Specific Day - Image by Author\nOnce I hit Generate Schedule, Fernão starts cooking in the background:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-343-1024x626.png)\nFernão is Generating a Schedule - Image by Author\nThe steps are then, in order: fetching my calendar, tasks, and Notion data.\nThe next point is also where basic coding literacy really starts to matter, not because everything the following code does not work, but because you need to understand _what_ is happening and _where_ things may eventually break or need improvement.\nLet’s start with the calendar fetch. At the moment, this is handled by a single, gigantic function created by Claude that is **completely unoptimized**.\npython\n\n```\ndef get_events_for_date(target_date=None):\n \"\"\"\n Fetches events for a specific date from Google Calendar via ICS feeds.\n \n Args:\n target_date: datetime.date object for the target day. If None, uses today.\n \n Returns a list of event dictionaries.\n \"\"\"\n # Hardcoded calendar URLs (not using env var to avoid placeholder issues)\n CALENDAR_URLS = [\n 'cal1url',\n 'call2url'\n ]\n \n LOCAL_TZ = os.getenv('TIMEZONE', 'Europe/Lisbon')\n \n # Get timezone\n local = tz.gettz(LOCAL_TZ)\n \n # If no target date provided, use today\n if target_date is None:\n target_date = datetime.now(local).date()\n \n # Create datetime for the target day boundaries\n day_start = datetime.combine(target_date, datetime.min.time()).replace(tzinfo=local)\n day_end = day_start + timedelta(days=1)\n \n # Debug: Print the date range we're checking\n print(f\"\\n[Debug] Checking calendars for date: {target_date.strftime('%Y-%m-%d')}\")\n print(f\" Start: {day_start.strftime('%Y-%m-%d %H:%M %Z')}\")\n print(f\" End: {day_end.strftime('%Y-%m-%d %H:%M %Z')}\")\n print(f\" Timezone: {LOCAL_TZ}\")\n \n all_events = []\n \n # Fetch from each calendar\n for idx, cal_url in enumerate(CALENDAR_URLS, 1):\n calendar_name = f\"Calendar {idx}\"\n print(f\"\\n[Debug] Fetching {calendar_name}...\")\n \n try:\n # Load calendar from ICS URL with adequate timeout\n r = requests.get(cal_url, timeout=30)\n r.raise_for_status()\n cal = Calendar(r.text)\n \n events_found_this_cal = 0\n total_events_in_cal = len(list(cal.events))\n print(f\" Total events in {calendar_name}: {total_events_in_cal}\")\n \n # Use timeline to efficiently filter events for target day's date range\n # Convert local times to UTC for timeline filtering\n day_start_utc = day_start.astimezone(timezone.utc)\n day_end_utc = day_end.astimezone(timezone.utc)\n \n # Get events in target day's range using timeline\n days_timeline = cal.timeline.overlapping(day_start_utc, day_end_utc)\n \n for e in days_timeline:\n if not e.begin:\n continue\n \n # Get event start time\n start = e.begin.datetime\n if start.tzinfo is None:\n start = start.replace(tzinfo=timezone.utc)\n \n # Convert to local timezone\n start_local = start.astimezone(local)\n \n # Debug: Print first few events to see dates\n if events_found_this_cal < 3:\n print(f\" Event: '{e.name}' at {start_local.strftime('%Y-%m-%d %H:%M')}\")\n \n # Get end time\n end = e.end.datetime if e.end else None\n end_local = end.astimezone(local) if end else None\n \n all_events.append({\n \"title\": e.name,\n \"start\": start_local.strftime(\"%H:%M\"),\n \"end\": end_local.strftime(\"%H:%M\") if end_local else None,\n \"location\": e.location or \"\",\n \"description\": e.description or \"\"\n })\n events_found_this_cal += 1\n \n print(f\" [OK] Found {events_found_this_cal} event(s) for target day in {calendar_name}\")\n \n except requests.exceptions.RequestException as e:\n print(f\" [X] Network error fetching {calendar_name}: {str(e)}\")\n continue\n except Exception as e:\n print(f\" [X] Error processing {calendar_name}: {type(e).__name__}: {str(e)}\")\n continue\n \n # Sort by start time\n all_events.sort(key=lambda x: x[\"start\"])\n \n # Print all events in detail\n if all_events:\n print(f\"\\n[Google Calendar] Found {len(all_events)} event(s) for {target_date.strftime('%Y-%m-%d')}:\")\n print(\"-\" * 60)\n for event in all_events:\n time_str = f\"{event['start']}-{event['end']}\" if event['end'] else event['start']\n location_str = f\" @ {event['location']}\" if event['location'] else \"\"\n print(f\" {time_str} | {event['title']}{location_str}\")\n print(\"-\" * 60)\n else:\n print(f\"\\n[Google Calendar] No events for {target_date.strftime('%Y-%m-%d')}\")\n \n return all_events\n```\n\nAs a Python developer, all the `print` statements give me the ick. But that’s a problem for the next phase of Fernão: refactoring and optimizing the code once the product logic is solid.\nThis is also where I really see the _human + AI_ dynamic working. I can immediately spot several ways to improve this function (reduce verbosity, cut unnecessary latency, clean up the flow) but doing that well still requires time, judgment, and intent. AI helps me move fast; it doesn’t replace the need to know what _extraordinary_ looks like.\nFor now, I haven’t spent much time optimizing it, and that’s a conscious choice. Despite its rough edges, the function does exactly what it needs to do: it pulls data from my calendars and feeds the meeting information into Fernão, enabling everything that comes next.\nNext, Fernão pulls my tasks from Microsoft To Do. This is where my daily to-dos live (the small, concrete things that need to get done and that give structure to a specific day). All of this is configured directly in the Microsoft To Do app, which is a core part of my daily workflow.\nIf you’re curious about the broader productivity system behind this, I’ve written about it in a related post on the brother of this blog (Wait a Day) linked [below](https://waitaday.substack.com/p/on-productivity).\nSo, let’s look at another verbosed function:\npython\n\n```\ndef get_tasks(target_date=None):\n \"\"\"\n Fetches tasks from Microsoft To Do for a specific date (tasks due on or before that date).\n \n Args:\n target_date: datetime.date object for the target day. If None, uses today.\n \n Returns a list of task dictionaries.\n \"\"\"\n CLIENT_ID = os.getenv('MS_CLIENT_ID', 'CLIENT_ID_KEY')\n AUTHORITY = \"https://login.microsoftonline.com/consumers\"\n SCOPES = ['Tasks.ReadWrite', 'User.Read']\n \n # Setup persistent token cache\n cache = SerializableTokenCache()\n cache_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), '.token_cache.bin')\n \n if os.path.exists(cache_file):\n with open(cache_file, 'r') as f:\n cache.deserialize(f.read())\n \n # Authentication with persistent cache\n app = PublicClientApplication(CLIENT_ID, authority=AUTHORITY, token_cache=cache)\n accounts = app.get_accounts()\n \n result = None\n if accounts:\n result = app.acquire_token_silent(SCOPES, account=accounts[0])\n if not result or \"access_token\" not in result:\n for account in accounts:\n app.remove_account(account)\n result = None\n \n if not result:\n flow = app.initiate_device_flow(scopes=SCOPES)\n if \"user_code\" not in flow:\n print(f\"[MS To Do] Failed to create device flow: {flow.get('error_description', 'Unknown error')}\")\n return []\n if \"message\" in flow:\n print(flow['message'])\n result = app.acquire_token_by_device_flow(flow)\n\n if not result or \"access_token\" not in result:\n print(f\"[MS To Do] Authentication failed: {result.get('error_description', 'No access token') if result else 'No result'}\")\n return []\n headers = {\"Authorization\": f\"Bearer {result['access_token']}\"}\n \n # Get date boundaries for target date\n # If no target date provided, use today\n if target_date is None:\n from datetime import date\n target_date = date.today()\n \n # Convert date to datetime in UTC for comparison\n now = datetime.now(timezone.utc)\n target_day_end = datetime.combine(target_date, datetime.max.time()).replace(tzinfo=timezone.utc)\n \n # Fetch all lists\n lists_r = requests.get(\"https://graph.microsoft.com/v1.0/me/todo/lists\", headers=headers)\n if lists_r.status_code != 200:\n return []\n \n lists_res = lists_r.json().get(\"value\", [])\n all_tasks = []\n \n # Fetch tasks from all lists with server-side filtering\n for task_list in lists_res:\n list_id = task_list[\"id\"]\n list_name = task_list[\"displayName\"]\n \n # Simple filter - just get incomplete tasks\n params = {\"$filter\": \"status ne 'completed'\"}\n \n tasks_r = requests.get(\n f\"https://graph.microsoft.com/v1.0/me/todo/lists/{list_id}/tasks\",\n headers=headers,\n params=params\n )\n \n if tasks_r.status_code != 200:\n continue\n \n tasks = tasks_r.json().get(\"value\", [])\n \n # Filter and transform tasks\n for task in tasks:\n due_date_obj = task.get(\"dueDateTime\")\n \n if not due_date_obj:\n continue\n \n due_date_str = due_date_obj.get(\"dateTime\")\n if not due_date_str:\n continue\n \n try:\n due_date = datetime.fromisoformat(due_date_str.split('.')[0])\n if due_date.tzinfo is None:\n due_date = due_date.replace(tzinfo=timezone.utc)\n \n # Include all tasks due on or before the target date\n if due_date <= target_day_end:\n # Determine status based on target date\n target_day_start = datetime.combine(target_date, datetime.min.time()).replace(tzinfo=timezone.utc)\n \n if due_date < target_day_start:\n status = \"OVERDUE\"\n elif due_date <= target_day_end:\n status = f\"DUE {target_date.strftime('%Y-%m-%d')}\"\n else:\n status = \"FUTURE\"\n \n all_tasks.append({\n \"list\": list_name,\n \"title\": task[\"title\"],\n \"due\": due_date.strftime(\"%Y-%m-%d\"),\n \"importance\": task.get(\"importance\", \"normal\"),\n \"status\": status\n })\n except Exception as e:\n continue\n \n # Print task summary\n if all_tasks:\n print(f\"[MS To Do] {len(all_tasks)} task(s) for {target_date.strftime('%Y-%m-%d')} or overdue\")\n else:\n print(f\"[MS To Do] No tasks due on {target_date.strftime('%Y-%m-%d')} or overdue\")\n \n # Save token cache for next run\n if cache.has_state_changed:\n with open(cache_file, 'w') as f:\n f.write(cache.serialize())\n \n return all_tasks\n```\n\nThis function retrieves a full list of my tasks for the current day (and overdue from previous days) — here’s an example of how they look in to-do:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-344.png)\nExample of Tasks in the To-Do App - Image by Author\nAfter pulling tasks from To Do, I also want Fernão to understand _where I’m trying to go_ , not just what’s on today’s list. For that, it fetches my objectives directly from a Notion page where I have the key results for my year.\nHere’s an example of how those objectives are structured:\n * the first column shows my baseline at the start of the year,\n * the last column defines the target I want to reach,\n * and the column on the right tracks my current progress.\n\n\nThe sample below include how many blog posts I want to write for you this year, as well as the total number of books I aim to sell across my three titles.\nThis gives Fernão broader context on what to priority in the tasks, as you’ll see in the prompt to create the schedule.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-345-1024x216.png)\nPersonal Objectives Example - Image by Author\nBtw, while writing this post, I ended up adding a small widget to the app. If we’re building a personal assistant, it might as well have some personality.\nSo I asked Gemini:\n> _“Can you add a fun animation while the app is retrieving calendar events, checking to-dos, and pulling Notion data? Maybe a small widget of Fernão stirring a pot, with the caption_ ‘Fernão is cooking’ _.”_\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-346-1024x345.png)\nFernão is cooking - Image by Author\nOnce this round finishes, tasks and calendar events are successfully collected and displayed in Fernão’s front end (a sample of the tasks and meetings for my day is shown below).\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-347-1024x358.png)\nTasks and Calendar Events fetched by Fernao - Image by Author\nAnd now the fun part: with calendars, tasks, and objectives in hand, Fernão composes my entire day into a single, magical plan:\ntext\n\n```\n07:30-09:30 | Gym\n09:30-09:40 | Check Timesheets on Odoo (Due: 2026-02-04)\n09:40-09:55 | Review Tasks in To-Do - [Organization Task] (Due: 2026-02-04)\n09:55-10:15 | Read Feedly Stuff - [News Catchup] (Due: 2026-02-04)\n10:15-10:30 | Write culture doc, inspiration: https://pt.slideshare.net/slideshow/culture-1798664/1798664 (Due: 2026-02-04)\n10:30-10:45 | Answer LinkedIns - [Organization Task] (Due: 2026-02-04)\n10:45-11:00 | Check Looker (Due: 2026-02-04)\n11:00-11:30 | This Week in AI Post (Due: 2026-02-04)\n11:30-12:00 | Prepare Podcast Decoding AI\n12:00-13:00 | Podcast Decoding AI - Ivo Bernardo, DareData (Event)\n13:00-14:00 | Lunch Break\n14:00-14:30 | Candidate 1 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n14:30-18:00 | Prepare DareData State of the Union\n18:00-18:30 | Candidate 2 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n18:30-19:00 | Candidate 3 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n19:00-19:30 | Candidate 4 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n19:30-20:00 | Candidate 5 (name hidden) and Ivo Bernardo @ Google Meet (Event)\n20:00-20:15 | Check Insider Trading Signals for Stock Ideas https://finviz.com/insidertrading?tc=1 (Overdue)\n20:15-20:30 | Marketing Timeline (Overdue)\n20:30-21:00 | Dinner Break\n21:00-22:00 | Ler\n22:00-22:15 | Close of Day - Review and prep for tomorrow\n```\n\nThis is one of those moments that genuinely feels a bit magical. Not because the technology is opaque, but because the outcome is so clean. A messy mix of meetings, tasks, and long-term goals turns into a day I can actually execute.\nWhat makes it even more interesting is how simple the final step is. After all the heavy lifting is done in the background (calendar events, to-dos, objectives), I don’t orchestrate a complex pipeline or chain of prompts. I use a single prompt.\nThat one prompt takes everything Fernão knows about my constraints and priorities and turns it into the day you’re about to see.\ntext\n\n```\nname: daily_schedule\ndescription: Generate a daily schedule based on calendar events and tasks\nmodel: gemini-2.5-flash-lite\ntemperature: 0.3\nmax_tokens: 8192\n```\n\ntext\n\n```\nvariables:\n - date_context\n - events_str\n - tasks_str\n - context\n - todo_context\n - auto_context\n - currently_reading\n - notion_context\n - is_today\ntemplate: |\n You are my personal AI scheduling assistant. Help me plan my day!\n **TARGET DATE:** Planning for {date_context}\n **EVENTS (Fixed):**\n {events_str}\n **TASKS TO SCHEDULE:**\n {tasks_str}\n **MY CONTEXT:**\n {context}\n **TASK CONTEXT (how long tasks typically take):**\n {todo_context}\n **AUTO-LEARNED CONTEXT:**\n {auto_context}\n **CURRENTLY READING:**\n {currently_reading}\n **RESULTS & OBJECTIVES (from Notion):**\n {notion_context}\n **SCHEDULING RULES:**\n 1. **Cannot schedule tasks during calendar events** - events are fixed\n 2. **Mandatory breaks:**\n - Lunch: 12:30-13:30 (reserve when possible)\n - Dinner: 20:30-21:00\n - Playing with my cat: 45-60 minutes somewhere scattered around the day\n 3. **Task fitting:** Intelligently fit tasks between events based on available time\n 4. **Time estimates:** Use the task context to estimate how long each task will take\n 5. **Working hours:** 09:30 to 22:00\n 6. **Start the schedule:** {is_today}\n \n **YOUR JOB:**\n 1. Create a complete hourly schedule for {date_context}\n 2. Fit all tasks between events and breaks\n 3. **Prioritize tasks based on my Results & Objectives from Notion** - focus on what matters most\n 4. Be conversational - if you need more info about a task, ask me!\n 5. **Save learnings:** If I give you context about tasks, acknowledge it and say you'll remember it\n **FORMAT:**\n Start with a friendly greeting, then provide the schedule in this format:\n ```\n 09:30-10:00 | Task/Event\n 10:00-11:00 | Task/Event\n ...\n ```\n After the schedule, ask if I need any adjustments or if you need clarification on any tasks.\n Generate my day's schedule now, thanks!\n```\n\nAnd with this, Fernão is definitely cooking:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2026/03/image-348-1024x688.png)\nFernão is Cooking again - Image by Author\nThis has been a genuinely fun system to build. I’ll keep evolving Fernão, giving him new responsibilities, breaking things, fixing them, and sharing what I learn along the way here.\nOver time, I also plan to write practical tutorials on how to build and deploy similar apps yourself. For now, Fernão lives only on my machine, and that’s likely to remain the case. Still, I do intend to open-source it. Not because it’s universally useful in its current form (it’s deeply tailored to my life), but because the underlying ideas might be.\nTo make that possible, I’ll need to abstract tools, modularize functionality, and allow features to be turned on and off so others can shape the assistant around their own workflows, rather than mine.\nI could have built something similar using Claude Code alone. I didn’t. I wanted full control: the freedom to swap models, mix providers, and eventually run Fernão on a local LLM instead of relying on external APIs. Ownership and flexibility matter more to me than convenience here.\nIf you were building a personal AI assistant, what tasks would you give it? I’d genuinely like to hear your ideas and try to build them inside Fernão. Leave a comment as this project is still evolving, and outside perspectives are often the fastest way to make it better.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657081760_wqdt7l.jpeg)\nWritten By\n## [Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\n[See all from Ivo Bernardo](https://towardsdatascience.com/author/ivopbernardo/)\n[Deep Dives](https://towardsdatascience.com/tag/deep-dives/)[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Building](https://towardsdatascience.com/tag/building/)[personal assistant](https://towardsdatascience.com/tag/personal-assistant/)[Prototype](https://towardsdatascience.com/tag/prototype/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-personal-ai-agent-in-a-couple-of-hours%2F&title=Building%20a%20Personal%20AI%20Agent%20in%20a%20couple%20of%20Hours)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-personal-ai-agent-in-a-couple-of-hours%2F&title=Building%20a%20Personal%20AI%20Agent%20in%20a%20couple%20of%20Hours)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fbuilding-a-personal-ai-agent-in-a-couple-of-hours%2F&text=Building%20a%20Personal%20AI%20Agent%20in%20a%20couple%20of%20Hours)\n\n\nTowards Data Science is a community publication. 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