^{AI MARKET PULSE}
AI Agents Are Moving From Experiments to Coworkers

AI adoption is shifting from curiosity to core infrastructure. Venture investors across enterprise software agree that 2026 marks the inflection point when companies stop testing models and start deploying agents at scale. According to VCs, knowledge workers will have at least one named AI agent embedded in workflows. These systems don’t just assist; they execute, monitor, and coordinate work at marginal cost. As budgets expand and confidence rises, AI agents are expected to handle a growing share of analysis, operations, and decision-making. The fastest-growing companies aren’t model builders, but operators who identify workflow gaps created by GenAI adoption and relentlessly ship production-ready tools. This marks a structural change: enterprises are rearchitecting teams around software labor, not human-only headcount.

So what does that mean for you?
Treating AI as a side tool is the risk. The same shift transforming enterprises creates asymmetric upside for individuals who act early:

• Build AI-first workflows others can’t easily replicate
• Package agent-driven systems into products or services
• Replace manual coordination with autonomous execution
• Position yourself as the operator of leverage, not the labor

AI agents compress organizational complexity the way cloud compressed infrastructure. Those who learn to deploy, manage, and monetize them early scale with adoption; those who wait compete against software coworkers that never sleep. The transition isn’t distant — it’s unfolding now, rewarding builders who align.

👉️ Read the article here

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^{WORKFLOW BUILDER}
The 5-Day "Trust-to-Sale" Sequence

Goal: Turn cold subscribers into hot leads by automating a 5-day educational "mini-course" that delivers value and naturally transitions into a pitch for your paid offer.

Steps:

1. Architect the Curriculum

   Define the "one big problem" your course solves and map out the 5-day transformation.

   Prompt ChatGPT: Act as a digital marketing expert. Map out a 5-day email mini-course for [Target Audience] that solves [Specific Problem]. Provide a catchy name for the course and a one-sentence "learning goal" for each day.

   ➡️ Next: Review the outline and ensure the Day 5 lesson aligns perfectly with your paid product.

2. Script the Daily Lessons

   Write engaging, story-driven emails that educate without overwhelming the reader.

   Prompt ChatGPT: Write the first 2 emails for my mini-course "[Course Name]." Email 1 should be a warm welcome that sets expectations. Email 2 should teach the first core lesson. Keep the tone conversational and the formatting easy to skim.

   ➡️ Next: Repeat this for Emails 3 and 4, ensuring each ends with a "teaser" for the next day.

3. Engineer the "Soft Pitch"

   Transition from teaching to selling by showing why your paid offer is the logical next step.

   Prompt ChatGPT: Write Email 5 of the sequence. It should summarize the key wins from the last 4 days and then introduce [Your Product Name] as the ultimate tool to achieve [Desired Outcome]. Include a clear call to action and a "PS" that creates urgency.

   ➡️ Next: Save all 5 drafts into a dedicated folder or Notion page.

4. Build the Automation (GetResponse)

   Set up the digital pipes so your course runs on autopilot for every new subscriber.

   ➡️ Next: Open GetResponse, go to "Automation," and use the "Welcome New Contacts" template to drag-and-drop your 5 emails with 24-hour delays between them.

5. Launch the Opt-in Page

   Create a simple landing page that sells the value of the free course to get people on your list.

   Prompt ChatGPT: Write a high-converting headline and 3 benefit-driven bullet points for a landing page that encourages people to sign up for my free "[Course Name]" email course.

   ➡️ Next: Use the GetResponse landing page builder to go live and start sharing your link on social media.

Pro Tip (Upgrade):
Use ManyChat to automate the "trigger" for your course. Set it up so whenever someone comments "COURSE" on your Instagram post, they automatically receive a DM with the link to your opt-in page.

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^{TODAY’S PROMPT}
Build a Daily Reflection Habit Without Overthinking

This mega-prompt turns journaling into a guided, repeatable system instead of a blank page. It delivers a structured Notion setup, daily reflection prompts, and a simple mood and energy tracker designed for consistency and clarity. The output is practical, calming, and immediately usable — helping you build self-awareness, spot patterns, and reflect with intention without overthinking, therapy-speak, or guesswork.

👉 Use the prompt + check out my example output here

CLEAR ASK
Act as a Notion systems designer, behavioral psychology coach, and journaling expert.
Your task is to design a complete AI-powered journaling system that can be implemented in Notion, including daily guided reflection prompts, a mood tracker, and optional weekly insights.

The output must be ready to build immediately in Notion without additional clarification.

CONTEXT & PURPOSE
This journal is meant to help the user:
• Build a consistent daily reflection habit
• Track emotional patterns, energy levels, and mindset
• Turn journaling into a guided system, not a blank page
• Use AI prompts to think more clearly, regulate emotions, and spot trends over time
• The final system should feel simple, supportive, and sustainable, not overwhelming or overly therapeutic.

Assume the user wants:
• Clarity, not rambling
• Structure, not free-form chaos
• Progress over perfection

DETAILED INPUT REQUIREMENTS
Use the inputs below.
If any are missing, make reasonable defaults and clearly state assumptions before proceeding.
User Inputs:
• Primary Goal of Journaling:
[e.g., mental clarity, stress reduction, productivity, emotional awareness, self-growth]
• Tone Preference:
[e.g., calm, encouraging, analytical, reflective, neutral]
• Daily Time Commitment:
[e.g., 5 minutes / 10 minutes / 15+ minutes]
• Journaling Experience Level:
[Beginner / Intermediate / Advanced]
• Preferred Reflection Style:
[Short prompts / Structured questions / Open reflection with guidance]
• Mood Tracking Depth:
[Simple (1–5 scale) / Emotional labels / Energy + mood combined]
• Weekly Review Preference:
[Yes / No]

Formatting Instructions:
• Prompts must be short, clear, and non-judgmental
• Use bullet points and headers
• Write in plain, human language
• No therapy language or diagnoses

ADDITIONAL INSTRUCTIONS
Follow these rules strictly:
• Design for daily consistency, not intensity
• Avoid vague prompts like “How do you feel?”
• Every prompt should have a clear purpose
• Assume the journal will be used inside Notion
• Structure outputs so they can be copied into:
• A Notion database
• A daily journal template
• Do not reference external apps unless optional

When suggesting AI prompts:
• Write them exactly as the user would paste them into ChatGPT or Notion AI
• Keep them short enough to use daily without friction

OUTPUT REQUIREMENTS
Deliver the system in this exact structure:
1. Journal System Overview
• What this journal is designed to do
• How it should be used daily
• Why it works psychologically

2. Notion Database Structure
Provide:
• Database name
• Required properties (with types), including:
• Date
• Mood
• Energy
• Notes
• Optional tags
• Simple setup instructions

3. Daily Guided Journal Template
Include:
• A daily check-in section
• 3–5 guided reflection prompts
• One AI reflection prompt the user can run each day
• Clear spacing and formatting for Notion

4. Mood & Energy Tracker
• Exact scale or labels to use
• How to log quickly (under 30 seconds)
• How to interpret trends over time

5. Weekly Review (If Enabled)
• 3–5 review questions
• One AI-powered weekly insight prompt
• How to spot patterns without overthinking

6. Optional Enhancements
Provide 2–3 optional upgrades, such as:
• Habit alignment
• Stress pattern detection
• Productivity reflection
• Keep these optional and clearly labeled.

FINAL QUALITY BAR
The final output must be:
• Immediately usable
• Simple enough for daily use
• Emotionally supportive but not clinical
• Clear enough to build in Notion without questions
• Free of fluff, hype, or generic journaling advice
• Do not explain the framework.
• Do not ask follow-up questions.
• Assume the user wants a finished system.
Begin now.

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^{AI SPOTLIGHT}
Meta’s AI Strategy Is About Control — Not Just Capability

Meta’s acquisition of Manus signals a clear shift in how big tech is approaching AI: speed and execution matter more than novelty. Manus isn’t a research lab or a flashy model release — it’s an operational AI agent company already generating over $100M in annualized revenue within months of launch. Its agents handle real work: research, coding, analysis, and autonomous task execution. Meta isn’t buying potential; it’s buying proven deployment at scale. This fits a broader pattern as Meta aggressively acquires talent and tooling to hardwire AI agents directly into consumer and enterprise products. The goal isn’t experimentation — it’s owning the layer where AI actually replaces work.

Key Takeaways:

• Meta is prioritizing agent execution over model breakthroughs
• Manus proves AI agents can scale revenue extremely fast
• Big tech is buying deployed systems, not just talent
• AI agents are moving directly into consumer and enterprise stacks
• Control of workflow automation is becoming strategic infrastructure

My Take:

This isn’t about who trains the smartest model — it’s about who owns the agents doing the work. Meta understands that AI value compounds at the deployment layer, not the benchmark layer. Companies that control agents embedded in workflows control leverage, data, and distribution. The next AI winners won’t just build intelligence — they’ll operationalize it everywhere.

👉️ Read the full article here

^{AI QUICK HITS}

🔈️ AI dictation apps finally hit their stride in 2025, as advances in speech-to-text and language models made voice typing fast, accurate, customizable, and privacy-aware — turning spoken words into polished emails, notes, and even code with minimal cleanup.

🧠 OpenAI is hiring a highly paid head of AI safety to stay ahead of growing concerns that increasingly powerful models could be misused — from mental health harms to cybersecurity threats — as the company acknowledges that AI’s rapid progress is now creating real-world risks that need active mitigation.

🤔 ^{Which image is real?}

One of these images is real, and one was created with AI. Vote for which one you think is real, and tell us why you voted that way.

The last edition’s results—60% were right!

Did you vote in the poll from December 29th? The top image was real! Go back and see the photos here. So what do you think? Are you getting better at spotting AI vs. not?

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