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Welcome to Tee's Talk Time – a podcast dedicated to exploring life after retirement. Hosted by Teresa Smith, a retired educator, this channel shares insights, stories, and inspiration on navigating retirement with purpose and excitement. From writing and publishing books to app development, to starting a podcast, Tee’s journey is about embracing new passions and discovering the joys of life after retirement.Subscribe and join Tee as she explores new beginnings, lifelong learning, and living life to the fullest after retirement!
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Signup to Generate a PitchTee’s Talk Time is a society podcast hosted by Teresa Smith, with 59 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.
Teresa Smith hosts Tee’s Talk Time, a society show with 59 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Episode #5
The family and I spent a day at Valley Stream State Park, and we had a great time there. Consider visiting this park if you're in New York this summer.#valleystreamstatepark #valleystream #parks #family
Episode #4
Want to create a realistic AI avatar that looks like you? In this step-by-step tutorial, I'll show you exactly how to generate a professional AI avatar using the latest AI tools.Whether you're a content creator, entrepreneur, educator, or simply curious about AI, you'll learn how to create a lifelike digital version of yourself for videos, social media, marketing, and online content.✅ Create a realistic AI avatar from your photos✅ Generate natural facial expressions and movements✅ Make your AI avatar speak with realistic voiceovers✅ Improve avatar quality for professional results✅ Use AI avatars for YouTube, Instagram, and moreBy the end of this tutorial, you'll have a realistic AI avatar that can help you create content faster while maintaining a professional and personal brand.If you found this tutorial helpful, like the video, subscribe, and leave any questions you may have about the process in the comments section below.#AIAvatar #ArtificialIntelligence #AITutorial #DigitalAvatar #ContentCreation
Episode #3
Welcome back to Tee’s Talk Time! 🎙️In this episode, I’m catching you up on life updates, personal growth, recent experiences, and everything happening behind the scenes. I’m also sharing exciting forecasts and future plans for upcoming podcast episodes, new content ideas, and where I see the show heading next.If you’ve been rocking with Tee’s Talk Time, this episode is for you! Expect honest conversation, reflections, motivation, and a sneak peek at what’s coming soon. Don’t forget to like, comment, subscribe, and turn on notifications so you never miss an episode.✨ More conversations. More growth. More Tee’s Talk Time.Children's YouTube Channel's Handle: @TimeforTots Website for parents, teachers and homeschoolers' resources: https://timefortotsbooks.com#Podcast #LifeUpdate #TeesTalkTime #SelfGrowth #Motivation #PersonalJourney #TalkShow #NewBeginnings
Episode #2
This clip reveals the catalyst for my new romance book, Neighbor Needed 2: David's Song. If you love the romance genre, you should give my book a read. This episode was originally recorded On February 7, 2026.
Episode #1
<p>Welcome back to Tee’s Talk Time! In today’s episode, I’m introducing my newest book, Neighbor Needed 2: David’s Song, a clean, contemporary, urban romance filled with heart, hope, and emotional connection.</p><p>This wholesome romance novel explores love, music, and meaningful relationships in a modern urban setting. If you enjoy clean romance books, slow-burn love stories, and feel-good contemporary romance, this story is for you.</p><p><br></p><p>🎁 FREE on Amazon:</p><p>📅 February 2 – February 6, 2026</p><p><br></p><p>👉 Search Neighbor Needed 2: David’s Song on Amazon.com.</p><p><br></p><p>Perfect for readers...
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Based on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:
Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.
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