Everyday AI Podcast – An AI and ChatGPT Podcast

Scheduled tasks are a secret weapon. ⚔️

How secret? 

They can actually be hard to find and there's not a lot of info out there on how to use them. lolz. 

But for many, they can be the stepping stone to the fully autonomous desktop worker. Because for many users who may only be able (or comfortable) to access AI on the web, scheduled tasks provide that proactive, work-done-for-you vibe that AI agents delivered. 

But how does it work in Gemini, ChatGPT and Claude? 

And what's worth scheduling and automating? 

We put AI to work on this Wednesday and find out. 

Scheduling AI: how to easily make AI work for you in Claude, Gemini and ChatGPT -- An Everyday AI Chat with Jordan Wilson (Replay)

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Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
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Topics Covered in This Episode:
Introduction to Scheduling Tasks in AI
Google Gemini Scheduled Actions Overview
ChatGPT Scheduled Tasks Features & Hacks
ChatGPT Work Mode and Virtual Browser
Claude Scheduled Tasks vs. Routines Comparison
Using API Triggers with Claude Code Routines
Real-World AI Dashboard Scheduling Test
Detailed Results: Gemini vs. Claude vs. ChatGPT
Key Takeaways for Best Automated Scheduling
Practical Use Cases for Scheduling AI Tasks

Timestamps:

00:00 Using scheduled tasks effectively
06:00 Adopting AI for productivity
06:51 Discussing main AI platforms
12:45 Scheduling tasks with ChatGPT
14:38 Work mode and virtual browsing
17:58 Creating and Editing Scheduled Tasks
22:47 Using Claude's cloud routines
26:59 Creating interactive stock visuals
27:51 Tracking AI company stock trends
31:58 Reviewing AI stock tracking tool
35:35 Improving user interface and experience
39:23 ChatGPT's unique scheduling features
41:49 Using APIs in Claude routines
44:45 Dashboard automation and triage setup

Keywords: 
AI scheduling, scheduling AI, scheduled tasks, scheduled actions, proactive agentic workflow, agentic adoption, agent built workflow, Google Gemini, Gemini scheduled actions, Gemini connectors, Gemini canvas mode, ChatGPT scheduling, ChatGPT scheduled tasks, ChatGPT work mode, ChatGPT projects, ChatGPT memory, ChatGPT sites, agentic app actions, model selector, reasoning level, app automation, connectors and skills, OpenAI, Claude scheduling, Claude scheduled tasks, Claude routines, Claude code, Claude co work, Claude home, Claude API token, Zapier integration, trigger-based automation, custom dashboards, triage dashboard, interactive visual, stock price dashboard, news summarization, personalized automation, user interface changes, web interfaces, desktop agents, repetitive tasks automation, business process automation, workflow optimization, productivity AI tools, AI-powered research, CRM integration, KPI tracking
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The next 12 months of AI leaked. 

Kinda. 

For the past 90ish days, we've been quietly collecting evidence of what's next.

1,030 saved posts. 90 Podcasts. Countless conversations. Every model drop, every leak, every quiet product update the big labs hoped you'd scroll past.

Then we connected the dots.

What came out the other side: 19 calls on where AI goes over the next 12 months. And some of them are uncomfortable.

We're walking through all 19. 

Bring your team's AI roadmap. You'll want to edit it. 👇

The Next 12 months of AI: 19 Predictions Every Business Leader Needs to Hear -- An Everyday AI Chat with Jordan Wilson (Replay)

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Reactive Chat Dies, Proactive AI Agents Rise
Voice and Mobile Become AI Default Interface
Manager Threads Replace One-Off AI Chats
Multiplayer AI: Humans and Agents Collaborate
Company-Wide Vibe Operations with ChatGPT Sites
Agent Native Workflows and Resources Standardization
Skill Reuse as Key Company Metric
Company Reasoning Data as Strategic Gold
Shift from Public Leaderboards to Private Evals
Model Routing Becomes AI Industry Norm
Cheaper AI Intelligence, Anthropic Competition Heats
Fortune 100 AI Token Spend Efficiency
Compute Power as New AI Currency
Localized AI Controversies and Election Deepfakes
Mainstream AI Backlash and Content Detection
Math Benchmarks Solved by Advanced AI
Token Maxing Returns with Cost Decline
Open Agents Crash Risks and Cybersecurity
Recursive Self Improvement (RSI) in AI Development

Timestamps:

00:00 Starting the AI 101 series
03:35 Yearly AI predictions roundup
07:54 Using full duplex AI assistants
09:45 Talking vs. Typing to AI
13:45 Breaking down AI silos
19:03 Turning processes agent-native
22:32 Skill development and reuse in AI
24:09 Bringing Slack DMs into Channels
29:46 Dealing with AI usage limits
30:45 AI startups revolutionizing knowledge work
35:46 AI strategy in Fortune 500 companies
40:11 AI impact on local politics
41:58 Concerns Over AI Watermarking
46:52 Experiencing token budget challenges
51:05 Sergey Brin prioritizes RSI at Google
52:06 Discussing AI model improvements
55:24 Closing and subscription reminder

Keywords: 
AI predictions, AI trends, business AI strategy, proactive AI agents, reactive chat, AI operating systems, ChatGPT, Claude, Grokbot, voice and mobile AI control, full duplex agent, AI skills, skill reuse, manager threads, multiplayer AI, agent native, company reasoning data, private AI benchmarks, public leaderboards, private evals, model routing, AI token spend, open source models, compute scarcity, hardware scarcity, AI controversies, local AI data centers, AI deepfakes, AI backlash, AI content detectors, AI in politics, math solved by AI, token maxing, cyber defense, open agents, cybersecurity budget, recursive self improvement, RSI, Fortune 100 AI usage, AI workforce transformation, dashboard automation, AI for dashboards, no-code AI apps, business intelligence AI, automation skills, agent crashes, model overhang, vendor lock in, AI-powered cyberattacks, AI-driven skill creation, AI-enabled workflows, token efficiency, AI local hosting, cost-effective AI models, enterprise AI adoption, AI asset management, company AI metrics.
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AI’s all-you-can-eat era is ending. 🍲

For years, one subscription felt like unlimited access to frontier models.

But that business model for the AI labs apparently breaks when agents can now run for days, use tools, retry work and burn through tokens.

And with Anthropic's powerful Fable 5 model exiting subscription tiers today and moving to API only pricing, it's as imperative of a time as ever to figure out your AI spend strategy. 

Frontier AI is becoming a metered utility. On today's show, we teach you how to deal with it. 

AI Cost Control 101: Why Your Chatbot Bill Is Becoming a Board-Level Problem -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
End of Unlimited AI Subscription Plans
Anthropic Fable Five Subscription Removal
Copilot and Grok Switching to Pay-Per-Use
Enterprise AI Cost Control Challenges
Token Consumption in Agentic AI Models
Board-Level AI Spending Concerns
Strategies for AI Spend Optimization
Fine-Tuning and Multi-Model Routing Solutions
Seven-Step AI Cost Reduction Playbook

Timestamps:

00:00 Rising AI costs and usage
05:18 AI service cost challenges
10:18 Cost of AI and OpenAI's Future
14:18 Chatbot costs becoming a big issue
15:10 Automating work with desktop agents
19:26 Hidden costs of automation loops
24:13 The future of model mixtures
25:16 Microsoft Foundry's fine-tuning service
31:20 Fine tuning AI models
32:13 Closing thoughts on AI future

Keywords: 
AI cost control, chatbot bill, AI spend, token efficiency, metered AI, agentic models, AI subscription plans, Fable Five, Anthropic, API pricing, OpenAI, GPT-5.6, Copilot cowork, GitHub Copilot, Google Gemini, AI credits, usage limits, credit-based system, Grok, NeoCloud, board-level AI concerns, token maxing, spending limits, enterprise AI, SMB advantage, API token pricing, token-based billing, model routing, open source AI models, GLM 5.2, Kimmy 2.7, caching, difficulty-based routing, fine-tuning models, Microsoft Foundry, fine-tuning as a service, Thinking Machines Lab, tuned specialists, mixture of models, AI routers, perplexity, Merge, spend routers, AI budgeting, overage alerts, default model selection, AI model compaction, automation, human-in-the-loop AI, context length limits, token burn rate, Jovan’s paradox, AI tool escalation
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Talking about prompts and chatbots won't help you talk about AI strategy in 2026. 

You've gotta know the ins and outs of loops, plans, goals, subagents and more. 

In this episode of Everyday AI, we're breaking down the agent lingo and how the key terms play out in systems like Codex and Claude Desktop. 

Desktop Agent Lingo Simplified: Goals, Loops, Plans, Subagents and how it works in Codex and Claude Code -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Desktop Agent Vocabulary Primer
Agent Harnesses: Codex vs. Claude Code
Desktop Agent Plans: Features and Workflow
Goal Setting in Codex and Claude Desktop
Plan vs. Goal: Key Differences
Agent Loops: Automation and Verification
Sub Agents: Parallel Task Management
Context Windows and Task Delegation
Guardrails, Verification, and Cost Control
Transition from Chatbots to Autonomous Agents

Timestamps:

00:00 Shifting focus to AI agents
03:28 Accessing the Start Here series
09:31 Using plan mode in clawed desktop
12:04 Understanding plan vs. goal mode
14:25 Setting project goals and planning
19:33 Accessing Start Here series
22:03 Building effective training loops
26:48 Managing sub agents effectively
27:30 Setting up sub-agent system
30:47 Closing and subscription reminder

Keywords: 
desktop agent, desktop AI agent, agent lingo, agent vocabulary, long running agent, autonomous agent, codex, Claude Code, Claude desktop, AI harness, agentic harness, agentic tools, super app, Microsoft super app, OpenAI codex, long running desktop agents, plan mode, planning phase, agent plan, goal setting, AI goal, agent goals, loop mode, agent loops, scheduled automations, sub agents, agent subagents, context windows, parallel work, context hygiene, verification steps, approval points, skills, automations, API token usage, project threads, co work tab, code tab, work trees, checkpoints, file access, browser automation, human in the loop, token efficiency, agent delegation, AI supervision, knowledge work automation, AI subagent management, desktop agent mental model, computer control, AI project management, AI workload delegation, remote steering, front end chatbot, proactive AI, AI context sharing.
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Is the open model GLM-5.2 really Opus 4.8 level? 🤯

You mighta missed this, but over the past few weeks, three distinct forces have all converged at one: 

↳ Chinese open models are near frontier SOTA
↳ Microsoft is reportedly considering open models to run Copilot
↳ Enterprises everywhere are talking token efficiency as AI costs soar

So while many are watching GLM-5.2 as an isolated model, it's important we dive deeper on its wider implications.

Open Source Surge? Does GLM-5.2 Make Open Source an Enterprise Priority? -- An Everyday AI Chat with Jordan Wilson

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More on this Episode: Episode Page
Today's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Website: YourEverydayAI.com
Email The Show: info@youreverydayai.com
Connect with Jordan on LinkedIn

Topics Covered in This Episode:
Open Source AI's "ChatGPT Moment"
GLM 5.2 Model Benchmarks & Performance
Enterprise Adoption Drivers for Open AI
Microsoft Evaluating DeepSeek for Copilot
Token Maxing to Token Efficiency Shift
GLM 5.2 Infrastructure vs. Consumer Use
Autonomous Workflow Overshoot Explained
Capability Gap and Workflow Challenges
Enterprise Scenarios for Open Source Models
Future of Task-Specific SOTA AI Models

Timestamps:

00:00 Open source AI catching up
04:52 Enterprise shift to DeepSeek models
08:57 Comparing AI model performances
12:46 Running AI models locally
14:17 Open source model cost efficiency
17:37 Cost challenges with AI models
21:05 Agentic task token consumption
25:05 Introducing the Start Here series
27:58 Impact of AI on Job Roles
32:29 Evaluating Open Source AI Models
36:00 Considering open source models
37:09 Future of open source AI

Keywords: 
open source AI, open source AI models, GLM 5.2, z AI, Zhipu AI, Chinese open source models, DeepSeek, Microsoft, enterprise AI, token maxing, token efficiency, AI spend, AI deployment, open weight models, proprietary AI models, AI benchmarks, Artificial Analysis Intelligence Index, enterprise infrastructure, agentic workflows, coding tool use, autonomous agents, long context window, coding capabilities, API costs, AI privacy considerations, model distillation, data privacy, compute requirements, GPU infrastructure, AI hardware, API hosting, Hugging Face, AWS, AI cost reduction, Copilot Cowork, Azure security, Anthropic, OpenAI, Claude Opus, multimodal models, task-specific AI models, model capability gap, autonomous workflow overshoot, agentic tasks, non-agentic tasks, state of the art open models, model fine-tuning, small language models, AI adoption barriers, frontier models, AI job automation, workflow transformation, AI subsidies, token billing, Stanford AI study, AI industry trends
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