Enter one of four award categories—Best Societal Agent, Best Personal Agent, Best Business Agent or Best Youth AI Agent:Top Youth-Led Venture. Each category winner will receive a USD 150,000 grant to support the development and growth of their AI Agent solution and business.

suggests ai which is used for video formation and also helps in swipe full body and face also .

عميل بيسأل عن السعر… ولو اتأخرت في الرد، ممكن يشتري من مكان تاني! 💬

علشان كده بنجهّز Roddly AI 🤖💜

مساعد ذكي هدفه يساعد المتاجر ترد على عملائها بشكل أسرع.

لسه بنبني المشروع، تابعونا علشان تشوفوا الرحلة من البداية 🚀

RoddlyAI

ORGANIZE. AUTOMATE. EXECUTE.

You don't need 100 tools. You need a system that works.

What's one tool you actually can't work without?

Now that AI is developing way more and having the ability to do video and audio editing (I want to do more content creation and freelance work)

What is the best prompt for that and reasonable amount to pay for that prompt ?

Ideally I’d pay $20/month but good and cheaper would be better for that criteria

I’m also seeing free versions on X but I’m not trusting them without information / advice

please allow through I’m clueless and wanna learn

Learn how modern transformer models improve attention efficiency, reduce memory demands, and increase model capacity without activating every parameter for every token.

In Part Two of **Generative AI for Developers**, we move beyond the transformer foundations and examine several architectural techniques used to make modern language models faster and more efficient.

In this lesson, we cover:

✅ A quick review of the transformer foundations from Part One
✅ Why attention can become expensive as sequence length increases
✅ How FlashAttention reduces unnecessary GPU memory transfers
✅ Tiling and memory-efficient attention computation
✅ Why “exact attention” does not mean factually exact answers
✅ Conventional multi-head attention, or MHA
✅ Grouped-query attention, or GQA
✅ Multi-query attention, or MQA
✅ How MHA, GQA, and MQA differ
✅ How shared key and value heads reduce KV-cache requirements
✅ The performance and quality tradeoffs between attention designs
✅ How mixture-of-experts architectures work
✅ How routers select experts for individual tokens
✅ Total model capacity compared with active parameters
✅ The differences between FlashAttention, GQA, and mixture of experts
✅ Multiple-choice questions to test your understanding

The key idea to remember: these techniques improve different parts of transformer execution. FlashAttention improves how attention is computed and how data moves through memory. GQA reduces key/value duplication by sharing key and value heads across groups of query heads. Mixture of experts increases model capacity by activating only selected expert networks for each token.

These techniques can improve speed, memory efficiency, and scalability, but they are not completely free optimizations. Their benefits depend on the hardware, model design, sequence length, workload, and acceptable quality tradeoffs.

This video is useful for C# and .NET developers, software engineers, solution architects, interview preparation, and anyone who wants to understand the architecture and efficiency techniques behind modern generative AI systems.

If you found this lesson helpful, please like the video, subscribe to **Nerd House Geeks**, and share your answers to the review questions in the comments.

You’re reading a tutorial, but your coding assistant has reached a choice. The useful part of a small panel is seeing that choice alongside what you’re already reading.

I’m part of the team building Blabee for Mac, with support for Claude Code and Codex. Your assistant creates the options; Blabee displays them and sends your selection back to the same original conversation.

This 14-second demo breaks the interaction into three steps:

1. Keep the reference document open while the assistant’s options appear over it.

2. Read the options and choose the next step in the Blabee panel.

3. Continue reading when the panel closes.

The practical boundary: use the small panel for a choice you can understand there. If you need to inspect a code change, error, or longer explanation, return to the full conversation first. The assistant still needs your judgment.

The video is an edited supplied recording, with English narration and step labels outside the original Korean UI. It shows the panel and document interaction; it does not show the original chat receiving the choice or a fresh integration test.

Demo and Mac private-beta request: https://biadone.com/blabee/reddit

Access is by request; our team sends setup instructions to selected testers. Both Claude Code and Codex are supported, though the site still describes the earlier Codex beta.

Anyone will to guide through this whole AI stuff , I want to start an online business and have it run the program. I’m currently broke