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Gemini 3 Pro finally here, Opus 4.5 surprises everyone, GPT 5.1and more | Episode 5 Rate Limited
In this episode of the Rate Limited podcast, hosts Ray Fernando and Eric, along with guest Adam Larson, dive deep into the latest developments in AI coding models, including updates on Codex, GPT 5.1, Gemini 3, and Opus 4.5. They discuss the performance of these models, their applications in real-world coding scenarios, and the challenges faced by developers working with legacy code. The conversation also touches on insights from the AI Native DevCon, highlighting the complexities of integrating AI into existing codebases and the evolving role of QA engineers in this landscape.Links:Ray: https://www.youtube.com/@RayFernando1337Eric: https://www.youtube.com/@pvncherAdam: https://www.youtube.com/@GosuCoderChapters00:00 Introduction to AI Practitioners00:57 Updates on Codex and Model Releases02:54 Exploring GPT 5.1: User Experiences05:45 Comparing GPT 5.1 and Gemini 309:03 Deep Dive into Gemini 3 Performance12:47 Opus 4.5: A Game Changer?22:51 Thoughts on AI Models and Their Applications35:44 Navigating AI Pricing and Plans38:11 Exploring AI Tools and Their Unique Features39:33 Workflow Strategies for AI Coding42:48 The Challenges of Legacy Code and AI Integration50:25 Insights from the AI Native DevCon
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OpenAI Codex changes the way they handle context | Episode 4
Codex has introduced significant changes that affect its usability with external tools.The truncation of context in Codex has raised concerns among users.Claude's handling of context and contradictions is seen as superior to Codex.Chinese AI models are gaining traction and are being compared to Western models.User experiences with various AI models highlight the importance of context management.The competition among AI models is intensifying, with open-source models becoming more viable.Apple's potential entry into the AI space could disrupt existing market dynamics.The future of AI models may involve more integration with consumer hardware.The balance between speed and accuracy in AI models is crucial for effective use.The evolving landscape of AI tools requires users to adapt their workflows.SummaryIn this episode, the hosts discuss the latest developments in AI models, focusing on Codex and its recent changes, including context truncation issues. They compare Codex with Claude and other models, highlighting user experiences and the rise of Chinese AI models. The conversation also touches on the potential impact of Apple's entry into the AI space and the evolving dynamics of the AI market. The hosts share insights on the importance of context management and the future of AI tools, emphasizing the need for users to adapt their workflows as the landscape continues to change.Sound bites"Codex has introduced significant changes.""Claude handles contradictions better than Codex.""Chinese AI models are gaining traction."Chapters00:00 Introduction to AI Language Models03:00 Codex Research and Tool Limitations06:02 Comparing Codex with Claude Code08:55 Impact of Context Truncation on Performance12:02 Exploring Chinese AI Models15:06 Kimi K2 and MinMax M2 Insights21:53 The Evolution of AI Models and Performance23:29 Concerns Over Data Privacy and Model Origins25:33 Quality and Safety in AI Model Deployment30:46 Emerging Models and Competitive Pricing32:49 Utilizing GLM 4.6 in Workflows36:48 Budgeting for AI Tools and Services43:36 The Impact of Cursor and Composer Models45:55 Exploring Use Cases for AI Tools48:57 The Evolution of Claude 2.052:00 The Importance of Architectural Planning58:00 Anticipating Gemini 3.0 and Market Dynamics01:01:40 The Future of AI Models and Competition
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1:11:09
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1:11:09
Is GPT 5 Actually Degraded? | Episode 3
SummaryIn this episode, the hosts discuss the latest features of Cursor 2.0, its positioning in the market compared to other coding agents, and the implications of AI on job markets. They explore the evolution of coding agents, the impact of teleoperation in robotics, and the future of AI in everyday life. The conversation also touches on community engagement and the potential for live shows.TakeawaysCursor 2.0 introduces an agent workflow focused on prompting.The speed and flow of Cursor 2.0 are key advantages over competitors.AI's impact on job markets is complex, with layoffs influenced by automation.The entry-level job market for engineers is currently very challenging.Teleoperation in robotics raises questions about privacy and surveillance.AI should enhance human capabilities rather than replace them.The evolution of coding agents is reshaping software engineering practices.Community engagement is vital for sharing experiences with AI models.The potential for live shows could enhance community interaction.The future of AI in everyday life is still uncertain but promising.TitlesExploring Cursor 2.0: The Future of Coding AgentsAI and Job Markets: A Complex RelationshipSound bites"Cursor 2.0 just dropped!""AI is not good enough to cut my job.""We're in a movie, guys!"Chapters00:00 Introduction to Cursor 2.0 and Its Features02:49 Benchmarking and Positioning of Cursor 2.005:53 The Evolution of Coding Agents08:49 User Experiences with GPT-5 and Codex12:00 Challenges in Context Management15:02 Data Sharing and Privacy Concerns18:04 Claude's New Skill System and Its Implications30:05 Cloud-Based Skills and Automation32:20 Creating Business Workflows with AI34:40 Impact of AI on Job Market and Layoffs38:27 Navigating AI's Role in Engineering Jobs44:07 The Future of Robotics and Teleoperation
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54:14
The Real Cost of Free AI Coding: Episode 2 Rate Limited
SummaryIn this episode of the Rate Limited podcast, hosts Ray Fernando, Adam (GosuCoder), and Eric Provencher dive into the implications of free AI agents, discussing the hidden costs associated with data privacy and sustainability. They explore the performance of Haiku 4.5 compared to Sonnet 4.5, the dynamics of ad targeting in the AI market, and the importance of effective planning and execution in AI models. The conversation also touches on retrieval techniques, the future of AI agents, and the significance of community engagement in navigating the rapidly evolving landscape of AI technology.TakeawaysFree AI agents come with hidden costs, primarily related to data privacy.The sustainability of free AI models is questionable due to high token costs.Haiku 4.5 shows promise but has limitations compared to Sonnet 4.5.Ad targeting strategies may not align with the needs of high-end engineers.Effective planning in AI models can significantly improve output quality.Retrieval techniques like grep and embedding models have their pros and cons.Context management is crucial to avoid pollution in AI outputs.Community engagement is essential for sharing knowledge and experiences.Different AI models have unique strengths that can be leveraged for specific tasks.The evolution of AI technology requires ongoing discussions and collaboration.Chapters00:00 Introduction to Free AI Agents03:05 The Cost of Free: Data and Sustainability06:11 Ad Targeting and User Engagement08:54 Haiku 4.5: Performance and Comparisons11:57 Complexity in AI Models15:08 Optimizing Model Usage18:01 Real-World Applications and Strategies30:08 Debugging Complex Systems with Language Models31:37 The Evolution of Planning Modes in Coding Tools34:09 Cursor's Planning Mode: A Game Changer36:30 Efficiency in Feature Shipping with Cursor38:08 Retrieval Techniques: Grep vs. Embedding Models40:31 Agentic Retrieval vs. Embedding: A Debate43:39 The Importance of Context in Code Retrieval46:39 The Rise of GPT-5 Pro and Its Impact51:22 Comparing Grok and GPT-5 Pro54:31 Community Engagement and Future Directions
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Is Sonnet 4.5 the BEST coding model and more | Episode 1
KeywordsAI models, Sonnet, GPT-5, benchmarking, coding assistants, user experience, reasoning, bug fixing, community engagement, AI trendsSummaryIn this episode of Rate Limited, the hosts discuss the latest developments in AI models, focusing on benchmarking Sonnet and GPT-5. They explore the nuances of model behavior, context windows, and real-world testing, particularly in bug fixing. The conversation highlights user experiences, challenges, and the importance of reasoning in AI models. The hosts also engage with the community, encouraging listeners to share their insights and experiences with various AI tools, while contemplating the future of AI coding assistants.TakeawaysAI models are constantly evolving and improving.Benchmarking is crucial to determine the best model for specific tasks.User experience varies significantly between different AI models.Context windows play a vital role in model performance.Real-world testing reveals strengths and weaknesses of AI models.Community feedback is essential for understanding model effectiveness.Reasoning capabilities differ among AI models, affecting their output.Explicit prompts yield better results with AI models.AI models can be seen as teammates in coding tasks.The landscape of AI tools is rapidly changing, requiring continuous adaptation.TitlesNavigating the AI Model LandscapeBenchmarking Sonnet and GPT-5: A Deep DiveSound bites"Best is so hard to measure.""GPT-5 is right up there with it.""Sonnet 4 was unusable for me."Chapters00:00 Introduction to AI Models and Their Applications01:48 Benchmarking Sonnet 4.5 and GPT-505:57 Exploring Model Behavior and Problem Solving10:05 User Experiences with Sonnet and GPT-513:57 Context Management and Tool Usage in AI Models17:56 Comparative Analysis of AI Models in Development22:01 The Future of AI in Software Development30:30 Exploring Coding Methodologies32:30 The Evolution of AI Models34:48 Tuning AI Models for Optimal Performance38:58 Evaluating Chinese AI Models42:57 The Importance of Rule Adherence in AI44:57 Community Perspectives on AI Tools
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