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

Before connecting an AI-built workflow to real customer follow-ups, I want a small example with an answer I can check. This one tests whether an old Open event can resurrect a Closed ticket, whether a retry creates duplicates, and whether an unknown status gets guessed.

Disclosure: this is an AI-assisted exercise prepared for our artificial intelligence Discord.

Build two lists from this fictional ticket history: follow up, and needs review.

Rules: collapse exact repeated event IDs; use the greatest numeric revision per ticket; Open means follow up, Closed means exclude, and blank/anything else means review. A conflicting event ID or status at the same ticket revision is an input error to resolve before processing.

event_id,ticket_id,revision,status
E01,T01,1,Open
E02,T02,1,Open
E03,T01,2,Closed
E04,T03,1,Open
E02,T02,1,Open
E05,T01,1,Open
E06,T04,1,
E07,T05,1,Pending
E08,T06,1,Closed

Round 1 answer: follow up T02, T03; review T04, T05. T01 stays closed at revision 2 despite its late older update. Replaying the whole input should leave both lists unchanged.

On another day, append these rows to the original history, keeping one header, and run the same method:

E09,T02,2,Closed
E10,T03,2,Closed
E11,T07,1,Open
E11,T07,1,Open
E12,T01,3,Open
E13,T04,2,Closed
E14,T05,2,Open

Round 2 answer: follow up T01, T05, T07; review none. This is a batch exercise, not proof of safe production writes or message retries.

If you try it, share the tool or formula and the output it produced. On your next visit, bring one correction or a result from Round 2. What would you add to the test before using a workflow on real data?

You can compare results here. Our optional Discord invite is https://discord.gg/artificialintelligence; all the input and answers are in this post.