This is my 50 Claude Code tips from 6 months of daily use personally and at Meta as a Staff Software Engineer. I've been coding with Claude Code basically 12 hours a day really trying to understand what makes Claude Code tik. Here's everything I wish I knew when I started, from foundations to advanced parallel workflows.

⏱️ TIMESTAMPS
0:00 - Intro
1:04 - ACT 1: Foundations (Tips 1-25)
1:18 - Tip 1: Run from root directory
1:56 - Tip 2: Run /init immediately
2:54 - Tip 3: CLAUDE.md is hierarchical
3:27 - Tip 4: Keep CLAUDE.md concise
3:58 - Tip 5: Structure: What, Domain, Validation
5:36 - Keyboard Shortcuts
5:58 - Tip 6: Shift+Tab toggles modes
6:40 - Tip 7: Escape interrupts
7:43 - Tip 8: Double Escape clears input
7:59 - Tip 9: Double Escape on empty = rewind
8:29 - Tip 10: Screenshot and drag
8:44 - Tip 11: Add context to screenshots
9:09 - Essential Commands
9:41 - Tip 12: /clear resets context
10:13 - Tip 13: /context shows token usage
11:42 - Tip 14: Let auto-compaction work
12:23 - Tip 15: /model switches models
12:49 - Tip 16: /resume recovers sessions
13:21 - Tip 17: /mcp shows MCP status
14:19 - Tip 18: /help shows all commands
14:33 - Tip 19: Git is your safety net
15:24 - CLAUDE.md Deep Dive
15:52 - Tip 20: Add a Critical Rules section
17:08 - Tip 21: Ask Claude to update rules
17:46 - Tip 22: Use workflow triggers
18:27 - Tip 23: Commit CLAUDE.md to git
19:34 - Tip 24: dangerously-skip for throwaway envs
20:39 - Tip 25: Combine skip with allowlists
20:59 - ACT 2: Daily Workflow (Tips 26-32)
21:38 - Tip 26: Start features in Plan Mode
23:46 - Tip 27: Fresh context beats bloated
24:29 - Tip 28: Persist before ending sessions
25:03 - Tip 29: Lazy load context
26:09 - Tip 30: Give verification commands
27:32 - Tip 31: Consider Opus for complex work
28:18 - Tip 32: Read thinking blocks
29:01 - ACT 3: Power User (Tips 33-40)
29:34 - Tip 33: Four composability primitives
29:54 - Tip 34: Skills = recurring workflows
31:33 - Tip 35: Commands = quick shorthand
32:18 - Tip 36: Never create commands manually
33:02 - Tip 37: MCPs = external service docs
33:52 - Tip 38: Ask Claude to install MCPs
34:15 - Tip 39: Subagents = isolated context
37:10 - Tip 40: Avoid instruction overload
37:48 - ACT 4: Advanced (Tips 41-50)
38:02 - Tip 41: Run multiple instances
39:06 - Tip 42: iTerm split panes
40:33 - Tip 43: Enable notifications
41:10 - Tip 44: Git worktrees for isolation
41:40 - Tip 45: /chrome connects browser
43:17 - Tip 46: Powerful for debugging
43:28 - Hooks & Automation
43:41 - Tip 47: Hooks intercept actions
44:10 - Tip 48: Auto-format with PostToolUse
44:24 - Tip 49: Block dangerous commands
44:43 - Tip 50: Explore the plugin ecosystem
45:32 - Context is King (Outro)

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Xiaoqing Ge, Senior Innovation Engineer at Baker Hughes, discusses the following:
• Overview of computer vision
• How deep learning works in computer vision?
• Traditional methods vs deep learning
• Survey of Baker Hughes applications
• Looking ahead

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In this video, I break down a complete AI agent tutorial for beginners in 2026, explaining what is an AI agent and how it works. You’ll learn how to build an AI agent step by step, including how to build an AI agent for free using modern tools and workflows, plus how AI automation fits into building smarter systems. Whether you're looking for a practical AI agent course or want to understand how to make an AI agent for free, this guide covers everything you need to get started.

00:00 - Intro: Why AI Agents are the Future of Productivity
00:54 - Personal Assistant: Monitoring Email & WhatsApp Updates
01:33 - Getting Started: Creating Your Free Base44 Account
02:21 - Dashboard Tour: Understanding Apps vs. Superagents
03:16 - The Employee Mindset: Setting Up Background AI Work
03:31 - Building Phase: Creating Your First AI Superagent
04:13 - Prompting Mastery: Building an Email Monitoring Agent
04:59 - Connectivity: Authorizing Gmail & Google Permissions
05:50 - The Brain Tab: Controlling Your Agent’s Identity & Personality
07:39 - The Soul: Defining Decision-Making Rules for AI
08:48 - Knowledge Base: Uploading Documents & Reference Files
09:45 - Memory Systems: How Your Agent Builds Context Over Time
10:45 - Integrations Hub: Connecting Slack, Calendar & Drive
12:05 - Chat Interface: Communicating with Your AI Assistant
13:30 - Voice Interaction: Using the Microphone Command Feature
14:37 - Tasks & Automation: Scheduled vs. Event-Triggered Workflows
16:13 - External Tools: Mastering OAuth & API Connections
18:47 - Security: Managing Secrets and API Keys Safely
19:28 - WhatsApp Integration: Chatting with AI from Your Phone
21:18 - API Access: Connecting Your Agent to External Systems
22:16 - Monetization: Setting Up Stripe & Payment Integration
23:19 - Real-World Flow: Automated Email Response Drafting
24:44 - Slack Optimization: Summarizing Busy Workspace Activity
26:00 - Reporting: Generating Automated Daily Activity Summaries
27:21 - Business Use Case: Customer Support Automation
28:19 - Prompt Frameworks: Writing Instructions That Get Results
29:44 - Credit Management: Understanding Usage and Costs
30:24 - Troubleshooting: Fixing Active Tasks & Connections
31:10 - Outro: Future-Proofing Your AI-Powered Business

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High Quality HDR 8K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 12k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

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#12KHDR #DolbyVision #60fpsHigh Quality HDR 8K VIDEO ULTRA HD 120FPS, 60FPS, 30FPS For Your HDR 8K resolution devices. Amazing combination of 12k sensor and one of the sharpest lenses in the world Zeiss Otus set in addition of HDR brings image to life! You can use this collection of Hight Resolution clips in your Tv For The Living Room, Office, Lounge, Waiting Room, Spa, Showroom, Restaurant and more. Play It On Your LG Qled TV, Samsung Oled TV, Smart TV, Sony Device, Samsung Technology, Roku, Apple TV, IPad Pro, Apple XDR, Chromecast, Xbox, Playstation and more.

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This video created for entertainment informative, educational purposes and Film & Animation.

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Watch highlights from RED and NVIDIA’s Dec. 12, 2018, event where they jointly announced upcoming availability of NVIDIA CUDA-accelerated REDCODE RAW decode SDK and REDCINE-X, giving software developers and studios a powerful new way to work with 5K, 6K or 8K video. Developers interested in participating in the private beta can contact [email protected]. Public beta begins January 2019.

Training a large-scale deep net is a computationally expensive process, and common CPUs are generally insufficient for the task. GPUs are a great tool for speeding up training, but there are several other options available.

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A CPU is a versatile tool than can be used across many domains of computation. However, the cost of this versatility is the dependence on sophisticated control mechanisms needed to manage the flow of tasks. CPUs also perform tasks serially, requiring the use of a limited number of cores in order to build in parallelism. Even though CPU speeds and memory limits have increased over the years, a CPU is still an impractical choice for training large deep nets.

Vector implementations can be used to speed up the deep net training process. Generally, parallelism comes in the form of both parallel processing and parallel programming. Parallel processing can either involve shared resources on a single computer, or distributed computing across a cluster of nodes.

The GPU is a common tool for parallel processing. As opposed to a CPU, GPUs tend to hold large numbers of cores – anywhere from 100s to even 1000s. Each of these cores is capable of general purpose computing, and the core structure allows for large amounts of parallelism. As a result, GPUs are a popular choice for training large deep nets. The Deep Learning community provides GPU support through various libraries, implementations, and a vibrant ecosystem fostered by nVidia. The main downside of a GPU is the amount of power required to run one relative to the alternatives.

The “Field Programmable Gate Array”, or FPGA, is another choice for training a deep net. FPGAs were originally used by electrical engineers to design mock-ups for different computer chips without having to custom build a chip for each solution. With an FPGA, chip function can be programmed at the lowest level – the logic gate. With this flexibility, an FPGA can be tailored for deep nets so as to require less power than a GPU. Aside from speeding up the training process, FPGAs can also be used to run the resultant models. For example, FPGAs would be useful for running a complex convolutional net over thousands of images every second. The downside of an FPGA is the specialized knowledge required during design, setup, and configuration.

Another option is the “Application Specific Integrated Circuit”, or ASIC. ASICs are highly specialized, with designs built in at the hardware and integrated circuit level. Once built, they will perform very well at the task they were designed for, but are generally unusable in any other task. Compared to GPUs and FPGAs, ASICs tend to have the lowest power consumption requirements. There are several Deep Learning ASICs such as the Google Tensor Processing Unit (TPU), and the chip being built by Nervana Systems.

There are a few parallelism options available with distributed computing such as data parallelism, model parallelism, and pipeline parallelism. With data parallelism, different subsets of the data are trained on different nodes in parallel for each training pass, followed by parameter averaging and replacement across the cluster. Libraries like TensorFlow support model parallelism, where different portions of the model are trained on different devices in parallel. With pipeline parallelism, workers are dedicated to tasks, like in an assembly line. The main idea is to ensure that each worker is relatively well-utilized. A worker starts the next job as soon as the current one is complete, a strategy that minimizes the total amount of wasted time.

Parallel programming research has been active for decades, and many advanced techniques have been developed. Generally, algorithms should be designed with parallelism in mind in order to take full advantage of the hardware. One such way to do this is to decompose the data model into independent chunks that each perform one instance of a task. Another option is to group all the tasks by their dependencies, so that each group is completely independent of the others. As an addition, you can implement threads or processes that handle different task groups. These threads can be used as a standalone solution, but will provide significant speed improvements when combined with the grouping method. To learn more about this topic, follow this link to the Open HPI Massive Open Online course (MOOC) on parallel programming - https://open.hpi.de/courses/parprog2014.

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Fine-tuning larger models can be tricky on consumer hardware. In this video I go over why its better to use large models for fine-tuning vs smaller models, I go over the issue with the naive approach to fine-tuning, and finally, I go over how to use DeepSpeed to successfully fine-tune even the largest GPT Neo model.

Notebook Git repo: https://github.com/mallorbc/GP....T_Neo_fine-tuning_no
finetuning repo: https://github.com/Xirider/finetune-gpt2xl
DeepSpeed repo: https://github.com/microsoft/DeepSpeed
happy transformers: https://happytransformer.com/
GPT article with images: https://towardsdatascience.com..../gpt-3-the-new-might

Timestamps

00:00 - Intro
00:36 - Background on fine-tuning
02:41 - Setting up Jupyter
05:17 - Incorrect naive fine-tuning method
10:02 - Correctly fine-tuning with DeepSpeed
15:35 - Fine-tuning the 2.7B model
16:35 - Fine-tuning the 1.3B model
17:55 - Looking at the README
20:08 - Outro and future work

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6 Reasons Why ChatGPT Can Make Your Business by simplilearn is a tutorial based in Artificial Inteliigence and Large Learning Models. This AI tutorial will discuss the top 6 reasons why chatGPT and AI can help you build a successful business.. The video cover How to Use ChatGPT to Grow Your Business and to tap out the best out of your business. This Simplilearn tutorial will cover the following six reasons.

00:00:00 Introduction to 6 Reasons Why chatGPT can make your business
00:02:00 Decision Making Capabilities
00:02:50 Customer Services
00:03:45 Content Creation
00:04:24 AI and ChatGPT for Data analysis
00:05:38 Sales and Marketing
00:06:49 Automation

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This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots.

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GPT-3 was in all news its Human-like Text Generation capabilities. Shame it was never opensourced by Open AI (!). Hence EleutherAI with a bunch of researchers set out to create a true GPT-3 open-source alternative and GPT-J-6B is the product of those efforts.

GPT-J-6B is a 6 billion parameter, autoregressive text generation model trained on The Pile.

Useful Links:
✅ Mesh Transformer JAX - https://github.com/kingoflolz/mesh-transformer-jax
✅ GPT-J-6B: 6B JAX-Based Transformer Blogpost by Aran Komatsuzaki - https://arankomatsuzaki.wordpr....ess.com/2021/06/04/g
✅Web Demo of GPT-J-6B for Text Generation - https://6b.eleuther.ai/
✅Colab (Python) Notebook - https://colab.research.google.....com/github/kingoflol
✅JAX - https://jax.readthedocs.io/en/....latest/notebooks/qui

Related Videos:

🎥 AI Text Generation with GPT-3 OpenSource Alternative GPT-Neo Model using Hugging Face Hub
- https://www.youtube.com/watch?v=0PuVk6c8Ua8

🎥 AI-Generated Blog Content with GPT-Neo (GPT-3 Alternative) + Gradio | Python ML Web App - https://youtu.be/d_xRYyy2LFM

Redux: Handle an Action in the Store


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This Business Intelligence Tutorial For Beginnersby Simplilearn will take you through the core concept of what is business intelligence. In this video, you will understand the process of business intelligence along with the applications and tools used for business intelligence. In addition, you will also know about the process involved in business intelligence.

Below are the topics we have covered in this business intelligence tutorial for beginners.
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In 2018 the kg will be defined by Planck's constant, not a hunk of metal.
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Special thanks to the staff at NIST who made this possible: Darine Haddad, Jon Pratt, Stephan Schlamminger, and Ben Stein.

Additional footage and animations by Sean Kelley, Jennifer Lauren Lee, and Frank Seifert.

I have been obsessed with measurement for a long time and I'm not sure quite how it happened. The world's roundest object played a role in this. I guess I'm just fascinated by how difficult it is to pin down a quantity like a kilogram. A physical object seemed like a good idea until the mass of the international prototype kilogram wasn't as constant as expected. These methods of the Kibble balance and silicon sphere have shown better precision than 20 parts per billion, making them superior to the old method. The agreement between Avogadro approaches

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Sing's Best Songs - "Let me hear you sing!" Groove with all your favorite songs featuring Rosita (Reese Witherspoon), Meena (Tori Kelly), Gunter (Nick Kroll), Ash (Scarlett Johansson), Johnny (Taron Egerton), and more! What's your favorite song from the Sing movies?
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In this video, I walk through my complete workflow for tackling large coding projects using Claude Code's plan mode. I demonstrate how to start with a rough dictated prompt, use plan mode to explore the codebase and generate clarifying questions, and break complex work into multi-phase plans that can span multiple context windows. I show my custom rules configuration that keeps plans concise and adds unresolved questions, how to monitor context usage throughout implementation, and my strategy of storing plans as GitHub issues to preserve them across context resets. This approach combines upfront planning with aggressive auto-accept during implementation phases, allowing AI to handle substantial features while maintaining control and code quality. I share practical tips including my favorite concision rule, the benefits of multi-phase planning, and how to effectively manage context windows for large projects.

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Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

--------------------

This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
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This video on Search Engine Marketing Full Course 2026 by Simplilearn, explains how paid advertising works on search engines like Google. You’ll learn Google Ads basics, keyword targeting, ad creation, bidding strategies, and campaign optimization. The course also covers conversion tracking, performance analysis, and ROI-focused marketing. With real-world examples, you’ll understand how businesses drive traffic and sales using paid search. Perfect for beginners, marketers, and professionals looking to master performance marketing.

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➡️ About Digital Marketing Specialist

Experience live and interactive learning with this comprehensive digital marketing masters program. Learn to leverage ChatGPT and other generative AI tools for digital marketing. Additionally, you will receive a Meta Certified Digital Marketing Associate exam in the program.

Key Features:
✅ Key Features
✅ Industry recognised Digital Marketing Specialist certificate from Simplilearn
✅ Live classes delivered by digital marketing industry experts
✅ Learn to Leverage ChatGPT and the latest generative AI tools for digital marketing
✅ Free Meta Certified Digital Marketing Associate exam voucher worth $99
✅ Learn 35+ digital marketing tools
✅ 5 Capstone problem statements and 15+ course-end projects
✅ 10+ case studies from brands like Adidas, KFC, Nike, Intel, etc.

Skills Covered:
✅ Website Creation
✅ Behavioral Marketing
✅ Keyword Research
✅ Search Engine Optimization
✅ Search Engine Marketing
✅ Campaign Management
✅ Social Media Marketing
✅ Mobile Marketing
✅ Content Marketing
✅ Content Strategy
✅ Strategies for Paid Campaigns
✅ Analytics ROI amp Evaluation
✅ AI Automation amp Emerging Technology
✅ Gen Artificial Intelligence AI Tools

👉 Learn More At: https://www.simplilearn.com/master-in-digital-marketing?utm_campaign=eDq9GHU84nQ&utm_medium=Lives&utm_source=Youtube

🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688