After logging 300+hours of intensive AI use in the last six weeks, after the AI bootcamp, 40 days ago from Nov 6th, 2025, running real workflows—not demos—I made a decisive platform shift to GEMINI PRO. Below is the exact rationale, from my personal and practical use documented for transparency and repeatability at the request of some of my followers. ChatGPT — Limitations Observed in High-Intensity Use (300+ Hours of Practical Work) ✘ Inconsistent reliability during peak usage hours; frequent bottlenecks ✘ Constantly have to reopen new chats to continue an existing workflow ✘ Consistently forces the user to refresh the chat when it crashes ✘ Uses DALL·E for image generation (Inferior MCP connector) ✘ Very poor quality in image AI generation ✘ Uses report PDF generator, which is inaccurate and does not parse text properly ✘ Does not produce native video outputs in comparison to Gemini Pro VEO 3 or upcoming VEO 4 power ✘ Does not integrate like Gemini Pro with Google Videos, Products, and Workspace ✘ Does not check Gmail inside the chat interface ✘ Uses folders, but does not use Gems like Gemini Pro, which are sub-agents with rules, (Time Saver) Gemini Pro — Why I Transitioned (Now Operating as “Nautilus”) ✔ Exported ENTIRE history of conversations from ChatGPT to Gemini Pro very easily. ✔ Stronger long-context persistence across extended conversations ✔ Stable performance during sustained, high-intensity usage ✔ Supports structured agent orchestration with Gems ✔ Enables purpose-built autonomous agents (“Tritons”) ✔ Reduces prompt redundancy through retained role-specific logic ✔ Scales horizontally without performance collapse ✔ Better aligned with real business, automation, and execution workflows ✔ Superior image creation with Nana Banana Pro ✔ Superior creation of videos with VEO3 & VEO4 ✔ Checks Gmail inside Gemini Pro chat interface (Genesis Pro user face) ✔ Pulls YouTube videos directly inside the UI chatbox, which GPT does not do ✔ Grabs direct research from Google, which ChatGPT cannot do

Tony says ‘Proximity is power.’ Let’s find out who’s in proximity... Tell us all where you’re from… without actually telling us where you’re from 🤣

“Edit the uploaded photo to a professional Christmas-themed profile picture.Do NOT change the real face, facial structure, skin tone, expression, or age. Keep the face 100% realistic and identical to the original photo. No beautification, no exaggeration, no cartoon effects. Background:Replace the background with a soft, elegant Christmas setting — warm neutral tones, subtle fairy lights, light bokeh effect, faint pine branches, and a cozy holiday ambiance. Keep it minimal and classy, not busy or childish. No Santa hats or gimmicks. Lighting & Mood:Use soft studio-style lighting with a warm glow. Balanced highlights and shadows to maintain a premium, professional look suitable for LinkedIn, Instagram, or YouTube profile photos. Clothing (choose based on gender):• For women: elegant winter outfit such as a neutral blazer, wool coat, or knit sweater in colors like deep green, burgundy, beige, cream, or charcoal. Clean neckline, no flashy patterns.• For men: tailored blazer, coat, or knit sweater in dark green, navy, grey, or maroon. Professional and polished. Details:Add subtle Christmas elements only — delicate lights in the background, soft snowfall effect (very light), or blurred holiday decor.No exaggerated props. No humor. No costumes. Final Look:High-resolution, realistic portrait.Professional, confident, warm, and festive.Perfect for social media profile use.Aspect ratio 1:1.”**

https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/ The TL;DR is they found a way to weigh memory into the inference points that a LLM goes through, rather than having it be separate, it assigns a weight to the memory as it forms. Worthwhile memory gets kept, not-so-important stuff gets purged ("weight decay"). As of December 4th, this is another huge leap on AI capability. Inference points are basically what LLMs use to make small jumps in understanding context, which makes or breaks the experience for a user. After their memory is bloated (thread gets too long), those jumps get harder and harder to make. Weights are an extra metric that evaluates whether or not it should forget in real time. Won't be long until this gets plugged into open source models, opening a new wave of vision-capable AI (think Kimi but in more hardware). Stringing a bunch of smaller AI agents as well, the implications can get scary The question is how much of this is marketing vs actual research-backed architecture? Links to the white papers: https://arxiv.org/abs/2501.00663 https://arxiv.org/pdf/2504.13173 "Infinite Memory" Claim Source "Instead of compressing information into a static state, this architecture actively learns and updates its own parameters as data streams in."

Within the SAME chatgpt/claude/deepseek conversation, run these prompts in this exact order: Prompt #1: Based on everything you know about me, what is the fastest way for me to make <INCOME GOAL>? Limit to 1 offer and 1 channel. Prompt #2: Who is the #1 person I should learn from to succeed? Prompt #3: Pretend you are that person. Create a simple 90-day action plan that needs <$1,000 upfront and has a 95% chance of success. List the top 3 risks.