Prompt EngineeringIn this video, I show you how to use LangExtract to generate high-quality metadata for your Retrieval Augmented Generation (RAG) system. By extracting structured data from unstructured documents, we can filter results more effectively and drastically improve retrieval accuracy. I’ll walk you through a complete example, from setting up LangExtract to integrating metadata filtering into your RAG pipeline.
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00:00 Metadata problem with RAG 00:45 Using Lang Extract for Metadata Extraction 01:46 Building the Retrieval Augmented Generation System 02:22 Setting Up the Environment and Sample Data 03:27 Creating the Metadata Extraction Pipeline 06:56 Implementing Metadata Filters in Vector Store 08:51 Running the Example and Viewing Results
LangExtract + RAG: Smarter Retrieval with Metadata FilteringPrompt Engineering2025-08-12 | In this video, I show you how to use LangExtract to generate high-quality metadata for your Retrieval Augmented Generation (RAG) system. By extracting structured data from unstructured documents, we can filter results more effectively and drastically improve retrieval accuracy. I’ll walk you through a complete example, from setting up LangExtract to integrating metadata filtering into your RAG pipeline.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0
00:00 Metadata problem with RAG 00:45 Using Lang Extract for Metadata Extraction 01:46 Building the Retrieval Augmented Generation System 02:22 Setting Up the Environment and Sample Data 03:27 Creating the Metadata Extraction Pipeline 06:56 Implementing Metadata Filters in Vector Store 08:51 Running the Example and Viewing ResultsSonnet 4.5: The BEST Agentic Coding AI for Building Agents?Prompt Engineering2025-09-29 | Very first look at the Claude Sonnet 4.5 release
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00:00 Introduction to Sonnet 4.5 Release 00:38 New Tools and SDKs 01:03 Language and Performance Enhancements 01:45 Context Awareness and Memory Management 02:17 Pricing and Market Comparison 02:54 Benchmark Performance 05:28 Extended Focus and Task Management 09:22 Enhanced Tool Usage and Alignment 10:43 Claude Agent SDK and Imagine with ClaudeCan AI Make Better Slides Than You?Prompt Engineering2025-09-28 | Checkout: https://www.gamma.app
I this video I test Gamma 3.0 —the AI presentation agent. Gamma also recently released their API, which let's you use the same agent for buck content creation and automation.
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TIMESTAMP: 00:00 Gamma 3.0 00:42 Gamma API 01:20 Creating Presentations with Gamma 02:01 Using Gamma Agent for Customization 06:50 Developers' Guide to Gamma APIBuilding tools for agents — with agentsPrompt Engineering2025-09-25 | Checkout Browserbase for web browsing automation for AI agents: https://browserbase.plug.dev/fN72qF0
In this video we will learn how to design agent tools that actually work for AI agents. It will include picking the right tools, using clear namespaces, returning structured high-signal outputs, and keep context/token use efficient. We break down practical tips from Anthropic’s “Writing Effective Tools for Agents,” including prompt-driven tool specs and real-world evaluation, so your agents perform reliably.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Finally a real VEO-3 competitorPrompt Engineering2025-09-24 | Discover WAN 2.5, Alibaba’s latest AI video model that can generate both visuals and sound in sync. In this preview, I’ll show you how it stacks up with Veo 3 and why it could change the future of generative media.
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00:00 WAN 2.5 Preview 00:32 Speech Synchronization and Audio Effects 02:04 Complex Scene Generation 05:58 WAN Animate Release 08:25 Conclusion and Next StepsQwen 3 Omni — The Open AI Model That Does It ALLPrompt Engineering2025-09-23 | In this video, I test out Qwen 3 Omni — Alibaba’s latest open-source multimodal model that can handle text, images, audio, and video in real time. From live demos to benchmarks, we’ll see if Qwen 3 Omni can truly compete with models like GPT-4 and Gemini.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Claude Code Downgrade? Here’s What Actually HappenedPrompt Engineering2025-09-18 | If you noticed claude code degradation in last few weeks, you were not wrong. Anthropic just released a detailed blogpost on what caused it.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0How Good Is GPT-5 Codex? I Built an AppPrompt Engineering2025-09-17 | Sign-up for updates to Verbi - transcription app: https://tally.so/r/3y9bb0
I put GPT-5 Codex to a real test: I handed it a PRD in VS Code, ran it with Codex CLI, and timed how long it took to build a working, on-device app. Watch the full build—planning → coding → debugging → demo—to see where GPT-5 Codex shines, where it stumbles, and if it’s ready for real projects.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0OpenAI Just Dropped A New Coding Model for DevelopersPrompt Engineering2025-09-15 | GPT-5-codex was just released by OpenAI. Here we will at this new release:
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Agent Client Protocol : The “New MCP” for IDEs and Coding AgentsPrompt Engineering2025-09-14 | In this video. we're going t. look at agent-client protocol or ACP that enables communication between coding IDEs and coding agents (like Gemini CLI, Claude Code etc.). This is a standardized communication protocol, very similar to MCP, which can really enable IDEs, agnostic coding agent integration.
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TIMESTAMP: 00:00 What is Agent Client Protocol (ACP) 00:13 The Problem ACP Solves 00:43 ACP in Action 01:47 Technical Details and Integration 03:26 Origins and Support of ACP 06:24 Comparison with MCP 07:20 Current Status and Future ProspectsSuper Agent: An Agent That Builds Its Own ToolsPrompt Engineering2025-09-12 | Subscribe to Skywork through this link to get up to 34% off.”: skywork.ai/p/zb7qts
In this video, we will look at A Hierarchical Multi-Agent Framework for General-Purpose Task Solving and the Skywork ai super agent platform.
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TIMESTAMP:
00:00 Deep Research Agent 01:20 Deep Research Agent Framework 02:25 Agent Architecture and Capabilities 07:20 Skywork Super AgentGitHub Spec Kit: Can It FINALLY Fix “Vibe Coding”?Prompt Engineering2025-09-09 | In this video I have a look at the Github's new Spec Kit, which is their opinionated implementation of Specification Driven Development. I will walk through all the steps that this new specification implements.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Did OpenAI Just FIX Hallucinations?Prompt Engineering2025-09-07 | In this video I will look at why LLMs hallucinate. LLMs hallucinate not because they’re “broken,” but because today’s training and accuracy-only evaluations incentivize guessing. This is based on a new research from OpenAI.
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TIMESTAMP
00:00 Hallucinations in Language Models 00:48 How Language Models Work 02:26 The Issue with Next Word Prediction 02:50 Evaluation Mechanisms and Their Flaws 04:11 Proposed Solutions to Mitigate Hallucinations 07:16 Observations and Claims from OpenAI's PaperEmbedding Gemma: On-Device RAG Made EasyPrompt Engineering2025-09-06 | In this video we learn how to use Google’s Embedding Gemma (300M) to build fast, on-device RAG with ≈200MB memory and support for 100+ languages. We will look at a RAG example.
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TIMESTAMPS: 00:00 EmbeddingGemma 02:15 Comparison with Other Embedding Models 02:41 Google's Interesting position 03:21 Dense Embeddings is Killing Retrieval 06:13 RAG with EmbeddingGemma 09:37 Fine-Tuning and Training the ModelClaude for Chrome: Agentic Browsing is HerePrompt Engineering2025-08-29 | Hands-on review of Claude for Chrome, Anthropic’s agentic browsing Chrome extension—with real demos (posting to X, Zillow search, research & shopping, W-9 download, form filling) to see what works and what doesn’t. We cover strengths, limits, permissions, safety guardrails, and prompt-injection risks so you know if this research-preview tool fits your workflow.
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TIMESTAMP:
00:00 IClaude for Chrome 00:31 Overview of Agentic Browsing 01:39 Demo: Posting on X 03:00 Demo: Apartment Search 04:50 Demo: Research Agent 05:47 Demo: Shopping Assistant 07:12 Demo: Downloading Files 08:37 Demo: Filling Out Web Forms 11:41 Security and VulnerabilitiesCan This FIX Context Loss in RAG?Prompt Engineering2025-08-27 | Checkout Emergent: emergent.1stcollab.com/engineerprompt
Chunking in RAG is broken! In this video we look at contextualized chunk embeddings that preserves document level global information in your chunks.
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00:00 Chunking is Broken 00:55 Contextualized Retrieval Pre-Processing 01:51 Late Chunking Approach 03:12 Similarity Computation Methods 06:05 Introduction to Contextualized Chunk Embeddings 07:35 Sponsor Message: Emergent 08:36 Comparison of Embedding Models 09:32 Code Implementation and Examples 11:49 Pricing and Practical Considerations 13:00 NotebookNano Banana is the NEW Gemini 2.5 Flash ImagePrompt Engineering2025-08-26 | Hands-on with Google’s Nano Banana (Gemini 2.5 Flash Image): I show how to access it in AI Studio and via the Gemini SDK, then demo precise text-guided edits, character/scene consistency, in/outpainting, virtual try-ons, and image restoration. We’ll also cover failure cases like aspect-ratio quirks and quick workarounds so you can start building with the API right away.
Pricing: API cost of $30 per 1M output tokens, one image counting for 1290 tokens so 0.039$ per image. Free users get to generate 100 images for free on AI Studio.
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00:00 Nano Banana 00:12 Model Capabilities and Features 00:52 Accessing the Model and Example Outputs 02:33 Practical Applications and Use Cases 04:54 Advanced Editing and Inpainting 08:29 Image Restoration and Comparison 12:24 Aspect Ratio Challenges and Solutions 14:42 Using the API and SDKWeb Scrapping Made Easy with This FREE MCPPrompt Engineering2025-08-25 | Get started with BrightData here: brdta.com/engineerprompt
Learn how to use the free BrightData MCP server to collect information from multiple diverse sources including hard to scrape sites like Amazon, Reddit etc. In this video I will show you how to use langgraph for building multi-agent workflows.
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00:00 Web Scraping is Hard 01:57 BrightData MCP setup 03:06 Demonstrating Web Scraping with MCP Server 05:28 Building a Coding Agent with Intent Classifier 08:46 Using the System with Python and Graph StudioDeepSeek V3.1: Bigger Than You Think!Prompt Engineering2025-08-22 | DeepSeek V3.1 is a unified hybrid reasoning open-weight model that powers agentic workflows—FP8 training, strong post-training for tool/function calling (non-thinking), Anthropic API support, and big SWE-Bench gains. In this video I unpack pricing and token efficiency, benchmark V3.1 vs R1 and Claude Sonnet 4, and show how to use it for coding agents without wasting tokens.
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00:00 DeepSeek V3.1 00:31 Hybrid Inference Model Explained 01:04 Performance and Efficiency Improvements 05:02 Token Efficiency and Cost Implications 08:03 API and Hosting Considerations 13:23 TestingFinally! A Standard for AI Coding Agents (Agents.md Explained)Prompt Engineering2025-08-20 | Agents.md is a simple, open standard to replace the mess of agent-specific rule files. In this video, I explain how agents.md works, how to add it to your repo (even mono-repos), migration tips, and which tools haven’t adopted it yet.
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0:00 Why rule files are broken 1:12 What is Agents.md 3:05 Example file & structure 5:02 Mono-repo / nested files 6:30 Migration tips 8:05 Who’s missing? 9:10 Final adviceGPT-OSS Jailbreak with this Simple TrickPrompt Engineering2025-08-15 | In this video, I show you how I managed to bypass GPT-OSS’s alignment with a single, simple tweak—no fine-tuning or complex hacks required. I walk through how the model’s prompt template works, why removing it changes its behavior, and share my own tests replicating this jailbreak. This is purely for educational purposes so you can understand how alignment works under the hood.
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00:00 GPT-OSS and Jailbreak 00:40 Understanding Large Language Model Training 01:51 Instruction Fine-Tuning and Prompt Templates 04:10 Removing Alignment from GPT-OSS 06:25 Practical Demonstration and Code Walkthrough 11:02 What's NextNot all models providers are equalPrompt Engineering2025-08-14 | Deciphering GPT-OSS Performance: Why Inference Setup Matters
We explore the performance variability across different API providers and benchmark tests, highlighting significant discrepancies in speed, cost, and intelligence.
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00:00 Introduction to Inference Challenges 00:47 Benchmarking API Providers 03:40 Performance Variations Explained 06:57 Local Hosting Solutions 08:46 Conclusion and Final ThoughtsGPT-5: The Most Polarizing ModelPrompt Engineering2025-08-08 | In this video, I take a deep dive into GPT-5—beyond the hype—to look at both its impressive capabilities and some glaring issues, including the “chart crimes.” I break down the real benchmarks, hidden rate limits, router problems, and what OpenAI didn’t show in their launch. Despite the flaws, I’ll explain why GPT-5 still manages to impress me in key areas.
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TIMESTAMPS: 00:00 Introduction to GPT-5: The Hype and Reality 00:19 Chart Crimes and Presentation Issues 01:45 Rate Limits and Access Tiers 03:12 System Card Insights and Benchmarks 06:48 Real-World Performance and Cost Efficiency 09:10 Price Comparison and Model Selection 12:48 Testing GPT-5 on AGI TasksGPT-5 - A Good Coding Model?Prompt Engineering2025-08-07 | Very first look at GPT-5 coding capabilities. This is the best coding model that OpenAI has released so far. GPT-5 is a next level model.
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00:00 Introduction to OpenAI's New Models 00:10 Model Specifications and Requirements 00:31 Capabilities and Features 01:10 Running the Models Locally 01:21 Performance Benchmarks 01:37 Safety and Ethical Considerations 03:11 Technical Details and Training 10:15 Community and Industry Collaboration 13:09 Hands-On Testing 15:05 Conclusion and Future OutlookLangExtract: Turn Messy Text into Graph-RAG InsightsPrompt Engineering2025-08-04 | In this quick tutorial I show you how Google’s open-source LangExtract converts messy PDFs, HTML, and DOC files into clean knowledge graphs that plug straight into Retrieval-Augmented Generation (RAG) workflows. Watch me run entity- and relationship-extraction with a long-context LLM like Gemini, build custom schemas, and visualize everything in seconds. If you’re working on AI agents, vector databases, or search pipelines, this is the fastest way to make your data Graph-RAG ready.
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00:00 Introduction to Lang Extract 00:39 Overview of Lang Extract Capabilities 01:21 Setting Up Lang Extract 03:03 Basic Example: Entity Extraction 05:48 Advanced Example: Relationship Extraction 10:02 Creating Knowledge Graphs 11:29 Conclusion and Additional ResourcesHorizon: OpenAI’s Secret Open-Weight Model?Prompt Engineering2025-08-02 | We will look at Horizon Beta, the alleged Open Weight Model from OpenAI. Its blazing 140 TPS throughput, huge 256K-token context window, and leaked 120B/20B MoE specs.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Gemini Deep Think: Built for the Hardest ProblemsPrompt Engineering2025-08-01 | Gemini Deep Think is the model for the hardest and most challenging problems. A version of this recently won gold in IMO 2025. Thanks to the Google DeepMind, I had early access to the model and here I am sharing some of my thoughts on it.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Master Claude Code Sub‑Agents in 10 MinutesPrompt Engineering2025-07-29 | check out claude code: http://clau.de/prompteng
In this video, you will learn about the new sub-agents feature in Claude Code. This addresses common issues related to context management and tool usage
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Augment Code: Specs Driven Development For AI Coding AgentsPrompt Engineering2025-07-28 | Try Augment Code with Tasklist: augmentcode.com
Tired of “vibe coding” that feels magical—right up to the moment everything breaks? In this video I show how switching to Specs‑Driven Development (PRDs → task lists → AI agent execution) lets us build a fully local speech‑to‑text CLI that actually works, stays maintainable, and ships on‑time.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0NEW Qwen 3 Coder: Did the Benchmark Lie?Prompt Engineering2025-07-23 | We are looking into Qwen 3 Coder, the first open weight model that is closer to Sonnet 4.
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00:00 Qwen Coder 01:49 Size and Architecture 02:57 How does it compare to Sonnet 07:36 Examples and DemonstrationsNEW Qwen 3, Better than Kimi K2?Prompt Engineering2025-07-22 | In this video, I compare the performance of two leading open weight AI models, Qwen3's latest non-reasoning model and KIMI K2, along with a few proprietary models, using the same set of prompts. We look at various benchmarks and real-world tasks, such as website creation, coding, physics simulation, and maze-solving, to see which model performs better. The results are surprising and insightful, especially in terms of reasoning capabilities and benchmark performance. Join me as we dive deep into this AI model showdown!
Find the Feature Crew Youtube channel: @TheFeatureCrew
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Qwen3 vs KIMI K2: AI Model Showdown
00:00 Overview of Qwen3 and Kimi K2 00:54 Qwen3's Non-Reasoning Capabilities 02:15 Coding Benchmarks and Comparisons 03:35 Complex Task Performance 06:46 Maze Solving and Reasoning Tests 10:34 Final Thoughts on the modelsChatGPT Agent Is Here: Your All‑In‑One AI Worker?Prompt Engineering2025-07-17 | Website: engineerprompt.ai
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Kimi K2 — The Deep Researcher AgentPrompt Engineering2025-07-17 | Kimi-K2 is a great coding model but they also have a great Deep researcher tool that is SOTA on a number of key benchmarks. In this video we explore how it was trained and how it compares to the other Deep researchers.
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00:00 Kimi Researcher 00:55 Single vs Multi Agent 02:04 Reinforcement Learning Approach 02:57 Emergent Capabilities 04:07 Context Management 06:16 Comparison of Deep Research Tools 07:15 Detailed Results from Various Tools 09:16 Kimi's Unique FeatureslocalGPT 2.0 - Building the Best Private RAG SystemPrompt Engineering2025-07-15 | I am releasing the new version of localGPT as a preview. This has a ton of enhancements you will not find in other rank systems. Check out the repo:
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00:00 LocalGPT - RAG 2.0 00:48 What is New in localGPT 01:54 Creating and Managing Indices 03:48 All about Retrieval 16:59 Installation and Setup 22:24 What''s coming nextKimi K2 - The DeepSeek Moment for Agentic Coding?Prompt Engineering2025-07-12 | KIMI K2 is the new State of the Art Open Weight Coding model.
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00:00 Introduction to Kimmi K2 00:27 Model Specifications and Availability 01:42 Benchmark Performance 04:33 Training Insights 05:29 Token Efficiency and Optimizer 06:15 Comparative Analysis with Other Models 08:36 User Experience and Testing 11:54 Licensing and Final ThoughtsGrok 4—Possibly the Most Powerful Model in the World?Prompt Engineering2025-07-10 | XAI just released Grok 4, the most powerful model in the world.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Secret Context Engineering Trick For RAGPrompt Engineering2025-07-07 | I explain why re-ranking isn’t enough for RAG and show how sentence-level pruning strips out noisy tokens and cuts hallucinations. You’ll see the token savings, accuracy boost, and a quick setup you can drop into any retrieval pipeline. Try this swap and watch your RAG answers get sharper.
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00:00 Introduction to Reducing Hallucination in RAG Systems 01:07 Challenges with Traditional RAG Systems 01:25 Practical Example: DeepSeek Paper 03:21 Introducing the Pruning Phase 04:36 Provence Model for Context Pruning 06:37 Performance and Availability 07:08 Demo and Practical Use 08:51 Licensing and Future ProspectsContext Engineering is All You NEED!Prompt Engineering2025-07-04 | I unpack context engineering—why everyone’s talking about it, how it differs from classic prompt engineering, and where it actually matters for long-context LLMs. We’ll cover the big failure modes (context poisoning, distraction, confusion, clash) and the simple fixes—RAG, pruning, summarizing, and quarantining—that keep your AI agents on track. Perfect if you’re building RAG pipelines or multi-tool agents and want cleaner prompts, fewer tokens, and better answers.
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TIMESTAMP: 00:00 Introduction to Context Engineering 00:41 Defining Context Engineering 03:21 Context Engineering vs. Prompt Engineering 04:23 Common Issues in Context Engineering 11:03 Solutions for Effective Context Management 14:50 Some Final ThoughtsThe Only Embedding Model You Need for RAGPrompt Engineering2025-07-02 | I walk you through a single, multimodal embedding model that handles text, images, tables —and even code —inside one vector space. In this short demo I show the install steps, run RAG retrieval benchmarks, and compare cost vs. traditional multi-model setups. If you’re building search or RAG pipelines, see how one all-in-one embedding can simplify your stack and boost accuracy.
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0I Gave Devin A Real World Coding Task, Here’s How it Cooked!Prompt Engineering2025-07-01 | Get $20 in free credits (devin.ai/pricing: select Core plan) with promo code: PROMPTENGINEERING
I put Devon AI, the “OG” coding agent, to the test by asking it to build a full RAG application straight from my GitHub repo. In this step-by-step demo I show the setup, how I assign tasks via issues, review Devon’s pull requests, and run the finished contextual-retrieval app. See how its confidence scores, junior-engineer workflow, and free-credit plan can speed up your own RAG projects.
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00:00 Getting Started with Devon: The AI Software Engineer 00:55 Interacting with Devon via GitHub 02:10 Devon's Unique Workflow and Features 04:46 Integrating Devon with Linear and Jira 08:07 Optimizing and Testing with DevonGemini CLI + ANY MCP Server — Step‑by‑Step TutorialPrompt Engineering2025-06-27 | To get started with BrightData get a $15 Credit with this link: brdta.com/engineerprompt
In this video, I show you exactly how to connect Gemini CLI to any MCP server step by step. I’ll walk you through the full setup so you can integrate Gemini into your own workflows easily. This is my go-to method whenever I build new AI tools using Gemini.
1. Locate (or create) your Gemini CLI config # macOS/Linux mkdir -p ~/.gemini code ~/.gemini/settings.json # or use your editor of choice
Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0Gemini CLI — Google’s Free Open-Source Coding AgentPrompt Engineering2025-06-25 | I had early access to Gemini-CLI, which is a free and open source alternative to Claude Code. This is a powerful CLI based Agent that you can run for free from @Google
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00:00 Introduction to Gemini CLI 01:06 Setting Up Gemini CLI 02:32 Using Gemini CLI Commands 04:55 Creating a Text-to-Image Web App 05:40 Troubleshooting and Debugging 10:05 Token Usage and Optimization 11:29 Final Thoughts and ComparisonWarp: The CLI Agent That Could Replace Claude CodePrompt Engineering2025-06-24 | Checkout Warp at https://go.warp.dev/promptengineering and use the promo code: PROMPTENGINEERING to get 1 month free of Warp Pro (First 1000 redemptions).
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00:00 Introduction to AI Coding Agents 00:23 Warp: The New Agent Development Environment 00:52 Exploring Warp's Interface and Features 02:53 Practical Example: Implementing a New Feature with Warp 04:14 Setting Up Local GPT with Warp 08:07 Implementing Contextual Retrieval in Local GPT 11:37 Final Thoughts on WarpRogue Agents — When AI Starts Blackmailing — New Study from AnthropicPrompt Engineering2025-06-22 | I dug into Anthropic’s new “agentic misalignment” study and was shocked to see how many top-tier language models chose blackmail, espionage, or even letting a human die when their goals or existence were threatened. By walking you through the tightly constrained experiments—where models had only bad options—I explain why these unsettling behaviors emerged and what they mean for anyone giving AI real-world agency.
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00:00 Agentic Misalignment 00:55 Case Study: Claude's Blackmail Scenario 01:55 Experimental Setup and Constraints 05:15 Why this Happens? 11:12 What does this mean for Agents in Production?LocalGPT 2.0: Turbo-Charging Private RAGPrompt Engineering2025-06-20 | In this video, I will show you a preview of the new version of LocalGPT 2.0, my free, open-source tool that lets you chat with your files on your own computer—no internet or API keys needed. I walk through the new tricks I added, like smart query splits, better search, and a fast, private RAG pipeline built in plain Python. Watch to see how you can use LocalGPT 2.0 for quick, safe answers from your own documents.
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Timestamps: 00:00 What's New in Local GPT 01:23 Technical Deep Dive 02:31 Architecture Overview 03:12 Component Breakdown 04:12 Indexing Process 09:09 Retrieval Process 14:01 Final Thoughts and Future PlansContext Engineering for Building Better AgentsPrompt Engineering2025-06-16 | Last week, I reviewed two fascinating articles on building multi-agent systems. The first, from Anthropic, promotes a multi-agent approach, while the second, from Cognition Labs, argues against it. This video provides a concise summary of both perspectives, highlighting differing strategies for orchestrating complex tasks and the importance of contextual engineering in the process.
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Timestamps:
00:00 Who is Right? 01:40 Challenges in Multi-Agent Systems 03:38 Sequential Execution Approach 04:23 Context Engineering and Compression 06:22 Anthropic's Multi-Agent System 11:01 Practical Insights and Recommendations 11:58 Prompt Engineering and Tool Design 16:29 Evaluation and Metrics 22:03 Deployment and Execution StrategiesAI Agents & The Future of Coding: A Conversation with a GooglerPrompt Engineering2025-06-09 | In this episode, we sit down with Karl, the leads the Cloud Product DevRel team at Google, to discuss the burgeoning role of AI agents in coding assistance and the evolving role of developers. We delve into Google's latest innovations in AI, particularly the Agent Development Kit (ADK), and talk about the practical applications and future of AI agents in helping developers build scalable solutions. We also explore the balance between AI automation and human input in development, and the new tools Google is providing to facilitate this synergy. Tune in to hear insights on the future of software development in the AI era.
Learn more about Google for Developers: https://goo.gle/4kH6RLI
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00:00 Introduction and Episode Overview 00:42 Discussion on AI Agents 02:02 Evolution of AI Agents and Development Kits 04:38 User Interface and Developer Experience 05:32 Challenges and Opportunities with AI Agents 08:22 Future of Coding with AI Assistance 15:10 Google's AI Ecosystem and Developer Support 20:06 Evolving Role of DevelopersGemini 2.5 Pro Beats O3 — Big Drops from ElevenLabs & QwenPrompt Engineering2025-06-06 | In this video, we’ll take a look at how Gemini 2.5 Pro compares to OpenAI’s GPT-4o (O3) across multiple benchmarks, highlighting real-world use cases and performance. I’ll also cover major new releases from ElevenLabs and Qwen3, showing how these updates impact voice cloning and LLM capabilities. If you’re following the latest breakthroughs in AI, multimodal models, and speech tech, this roundup is packed with insights.