Generative AI coding assistant developer Magic AI has raised $117 million in a Series B funding round led by NFDG..

Magic AI Raises $117M for Generative AI Coding 'Coworker' Not 'Copilot - Voicebot.ai

Magic AIis in the Application Development Tools Industry

Magic AI

ChatGPT captivated millions seemingly overnight by writing coherent articles, poems, and coding scripts. Now, artificial intelligence (AI) is moving beyond parlour tricks into digitally dominating (or streamlining) entire professions. According to AuthorityHacker’s 2023 AI survey, 33% of consumers think they are using AI platforms. But the . actual usage is 77% Additionally, 90% of marketers are confident in learning and adapting to new AI tools. AI is already embedded into most business owner’s workflows, whether they know it or not. And that usage is only going to increase, especially when it comes to digital content creation. AI is essentially helping writers, designers, and marketers skip the “A to B” phase of creation. While we typically think of first drafts only in writing, the same is true for image design and video editing. And often, it’s the most time-consuming part. Iteration is a breeze. Let’s explore the exact tools and processes you can use to have AI streamline your writing, imaging, and video content. Brute-Forcing Ideas with AI When ideating, our brains build off other ideas. And often, it’s not the second or third idea that is the best, it’s the 51st or 62nd. So when an AI can generate 50 decent ideas in the blink of an eye, it can spark new ideas, cutting the ideation process out almost entirely. Let’s say you’re a marketing expert and want to build a personal brand on TikTok. Instead of thinking for hours about what to post, just head over to ChatGPT… AI produces volumes of ideas for you to riff off of, ultimately making the ideation process highly efficient. Plus, there are specific tools like which are specifically programmed to give higher-quality outputs the first time. No need to refine your outputs on GPT-4. IdeaGenerator.ai Streamlining the Writing Process The typical writing process often involves hours of work — creating an outline, researching, writing a messy first draft, and then heavy revisions leading to a second draft. So what if AI could accelerate the first two steps to just 15 minutes? Rather than spending 2-3 hours drafting an outline and initial draft for a 2,000-word blog post, tools like , , and can ingest a basic prompt and research synopsis to produce a complete first draft in just minutes. Copy.ai ContentAtScale Jasper.ai This allows the human writer to skip straight to the revision stage, spending their time elevating the tone, style, and refinements that AI can't deliver. By automating the initial research and writing phases, creators maximize their uniquely human skills of artful writing and editing. The result is high-quality content in a fraction of the time, which delivers what want to see. 58% of executives Cutting the Image Creation Process in Half While many are familiar with AI's writing capabilities, image generation tools like DALL-E 2, Midjourney, and StarryAI can also automate visual content creation. Rather than starting from scratch, prompts produce images to use as a jumpstart. Here are some instances that typically take designers hours, but could take much less time with an AI-first draft: YouTube thumbnails Images for blog posts Presentation slides or supportive infographics Social media graphics Book covers Product concept art Logo Illustrations For Instance, let’s say you want to create a modern logo for your agency, then the following prompt will supercharge your process: Illustrative logo icon for [YOUR INDUSTRY], . minimalistic and material design Then, the designer can download that image and scale it using tools like , which gives you a logo that does not rely on a specific size but can be adjusted by anyone with ease. This takes most of the work out of the equation, leaving the designer to increase their output tenfold. Vector Magic AI Video Editing AI tools like , , and can automate tedious parts of the video editing process to create first drafts faster. For podcast clips, the AI can scan transcripts and cut out segments based on prompts like "2-minute segment on AI’s role in marketing." Descript Runway RawShorts For YouTube, voiceover explainers, video footage, and scripts get combined with transitions and text overlays. The AI can even suggest background music and basic color correction. And if you need to create videos for a business homepage such as a SaaS, AI can help assemble clips, turn text into speech, and create ready-to-use videos within minutes. My Experience Having built automation tools like WP Webhooks and Pluginplate, I've witnessed firsthand AI's potential to transform creative work. Currently, more than 20,000 businesses have adopted our tools, demonstrating that companies quick to integrate AI are reaping significant benefits. In contrast, those slower to adapt face challenges such as job losses and the potential closure of their businesses. Now is the crucial moment to acquire the necessary skills to leverage AI effectively for your advantage. Now, my goal is to guide individuals and organizations on responsibly integrating AI to streamline their workflows, make more money, and leverage resources more effectively.

How AI Streamlines Digital Content Creation (Writing, Images, and Videos)

Are you tired of the uncertainty of online clothes shopping? Imagine a world where you could try on clothes virtually before making a purchase, eliminating the frustration of ill-fitting garments. Thanks to the remarkable power of AI, this dream is slowly becoming a reality. Let's dive into the captivating realm of AI-powered virtual clothing try-on experiences. From magical AI tools like and MidJourney to research projects like , AI has been harnessed to enable users to take an image of themselves and seamlessly add any piece of clothing to it. However, achieving perfection in virtual try-on is no easy task. It requires understanding the physics of the world, respecting body shapes, and accurately representing clothing textures and features. ChatGPT VOGUE In this week's episode, I decided to explore a new research called TryOnDiffusion, presented at the CVPR 2023 conference. This innovative approach represents a significant leap forward in realistic virtual try-on experiences. By training AI models to understand input images, differentiate clothing from the person, and combine information intelligently, TryOnDiffusion produces impressive results that bring us closer to the ultimate goal of a perfect virtual try-on. If you're intrigued by the intersection of AI and fashion, join us as we unravel the inner workings of TryOnDiffusion and its potential impact on the future of online shopping. Whether you're an AI enthusiast, a fashion lover, or simply curious about the latest technological advancements, the video offers valuable insights into the cutting-edge world of virtual clothing try-on. We will dive into the world of diffusion models, UNets, and attention, where all those incredibly powerful mechanisms combine forces with helping the field of fashion and online retail. Of course, this work has limitations, but (as you will see) the results are just mind-blowing and very promising. Watch the video and discover the magic of AI-enabled virtual try-on experiences: https://youtu.be/2IJwaDbP3jI?embedable=true&transcript=true https://youtu.be/2IJwaDbP3jI?embedable=true Now that we’ve seen the huge potential of this approach and its limitations, I wanted to share an ethical perspective on such new research as I always do , working with an amazing ethics expert: Auxane. in my newsletter Here’s what she had to say about TryOnDiffusion… This edition delves into the fascinating realm of AI-generated garment visualizations. It explores the ethical considerations surrounding this innovative technology. As usual, we will look into the opportunities and risks to watch when developing this technology! On the positive side, AI-generated garment visualizations offer numerous exciting opportunities to revolutionize personalized fashion experiences. By leveraging AI algorithms and computer vision, these systems can simulate how a particular garment might look on an input person, enhancing the online shopping experience and enabling individuals to make more informed choices. For example, imagine an avid online shopper who wants to buy a dress for an upcoming event. This technology empowers the customer to visualize how the dress might look on their body, assisting them in making a confident purchasing decision. Such technology can also cater to individuals with unique body types or preferences. Take John, who has a prosthetic leg. AI-generated garment visualizations can consider his specific body measurements and provide customized visualizations, ensuring a more inclusive and personalized fashion experience. Additionally, AI-generated garment visualizations can foster creativity and exploration in the fashion industry. Fashion designers can utilize this technology to bring their creative visions to life and experiment with different styles, fabrics, and designs. By generating virtual representations of their garments on various body types and models, designers can gain valuable insights into how their creations translate to real-world wearability. For instance, a designer working on a new collection can use AI-generated visualizations to assess the visual impact of their designs on different body shapes and sizes. This allows them to make informed decisions about adjustments or modifications to ensure inclusivity and accommodate diverse consumer needs! Moreover, AI-generated garment visualizations can be valuable for fashion education and innovation. Fashion students can use these visualizations to learn about garment construction, fit, and design principles without physical prototypes. This enhances their learning experience and reduces material waste and environmental impact! In summary, AI-generated garment visualizations offer opportunities to improve personalized fashion experiences, cater to diverse body types, foster creativity in the fashion industry, and provide valuable tools for fashion education and innovation. With responsible implementation and ethical considerations, this technology has the potential to transform the way we interact with fashion, making it more inclusive, sustainable, and engaging. But, while AI-generated garment visualizations hold immense potential, addressing the risks associated with this technology is essential. Let's dive into them! One significant risk is the potential for biased representations. Suppose the training data used to develop the AI system needs to be more diverse and inclusive. In that case, it can lead to biased visualizations favouring certain body types, skin tones, or fashion trends, reinforcing harmful stereotypes and exclusionary practices. For example, suppose the AI system is primarily trained on data from a particular demographic group. In that case, it may need help to provide accurate visualizations for individuals from underrepresented communities. This can perpetuate inequalities in the fashion industry and limit the access and choices available to marginalized individuals. To mitigate this risk, developers and fashion companies must ensure that the training data sets are diverse, representative, and inclusive of different body types, sizes, ethnicities, and genders. Regular audits and evaluations should be conducted to identify and address biases in the system's outputs. Another critical consideration is data privacy. AI-generated garment visualizations rely on user data, including personal images and body measurements. Safeguarding this data and ensuring user privacy are paramount to maintaining trust and ethical standards. Imagine a user, Joe, who uploads their photo to an AI-powered fashion platform for visualizing garments. The platform needs robust data security measures to protect Joe's image and personal information from unauthorized access, misuse, or data breaches. To address data privacy concerns, transparent data usage policies and consent mechanisms should be implemented, clearly informing users how their data will be collected, stored, and used. Anonymization techniques can also be employed to protect user identities while still enabling accurate visualizations. Additionally, it is crucial to consider the potential misuse of AI-generated garment visualizations. As this technology advances, there is a risk of unauthorized use of individuals' visualizations or creating deep fake images without consent. Protecting individuals' rights and ensuring responsible use of the technology is essential in mitigating these risks. In conclusion, while AI-generated garment visualizations offer exciting opportunities for personalized fashion experiences, addressing risks related to biased representations, data privacy, and potential misuse is crucial. By promoting diversity, inclusivity, and responsible data practices, we can harness the full potential of this technology while safeguarding user rights and ethical standards! As usual, we encourage you to share your thoughts and insights on this fascinating topic with us! Until next time, keep exploring the frontiers of AI, and have a fantastic weekend! -  This ethics segment was written by Auxane Boch ( research associate). TUM IEAI

Virtual Try-On: The Magic of AI Clothing Simulation and Visualization

ভার্চুয়াল ট্রাই-অন: দ্য ম্যাজিক অফ এআই ক্লোথিং সিমুলেশন এবং ভিজ্যুয়ালাইজেশন

Virtuelle Anprobe: Die Magie der KI-Bekleidungssimulation und -Visualisierung

Prueba virtual: la magia de la simulación y visualización de ropa con IA

Essai virtuel : la magie de la simulation et de la visualisation de vêtements par IA

वर्चुअल ट्राई-ऑन: एआई क्लोथिंग सिमुलेशन और विज़ुअलाइज़ेशन का जादू

バーチャル試着: AI 衣類のシミュレーションと視覚化の魔法

가상 체험: AI 의류 시뮬레이션 및 시각화의 마법

Virtual Try-On: A Magia da Simulação e Visualização de Roupas IA

Sanal Deneme: Yapay Zeka Giysi Simülasyonu ve Görselleştirmenin Büyüsü

Thử đồ ảo: Sự kỳ diệu của mô phỏng và trực quan hóa quần áo AI

虚拟试穿：AI服装模拟与可视化的魔力

Okay, there has been a lot of movement in the tech space recently, and here’s the latest scoop – Bard just flipped the script and turned into Gemini, and we are here scratching our head, trying to catch the vibe. It's like one day you're all comfy with your go-to tech buddy, and the next, it's got a new name, new look, and you're like, "Wait, what just happened?" So, why the big switch-up? In the tech world, specifically the space, continuously moving forward is a mandate or you will get left behind. Bard becoming Gemini is more than them just slapping on a new label and calling it a day. Of course, it goes deeper than that. It is more like they're sending out a message to the world, "Hey, we're leveling up, taking things to a whole new dimension." But for real, does a new name mean we're about to get our minds blown, or is it just the same old tunes on a brand-new speaker? software development And let's be honest – every time a favorite app or software pulls the rebrand card, there's a hot minute of chaos. The forums light up, Twitter's, oh wait, X’s got memes for days, and everyone's got an opinion. Some are hyped, thinking Gemini's about to drop some groundbreaking features or shake up the game. Others are side eyeing, wondering if it's all just smoke and mirrors to hype up something that's basically the same deal. But here's where it gets interesting. A rebrand like this? It's a chance to hit the refresh button. Now, Gemini has got the stage, the spotlight is on, and it's their move to show us what's new. Are we getting some next-level features, smoother interfaces, or some crazy AI magic that we didn't even know we needed? Because, hey, let's face it, if you're going to go through the whole drama of changing names and all, you better bring the goods. And this is not the first time a tech giant has made this kind of move. Here are some examples from yesteryears: Microsoft's Windows Live to OneDrive (2014) Microsoft rebranded its cloud storage service from SkyDrive to OneDrive after a trademark dispute. Beyond just a name change, the rebrand was accompanied by new features and improvements, emphasizing the service's role in connecting and unifying the Microsoft ecosystem. This is a great example of how a rebrand can be an opportunity to revitalize a service with new features and a clearer value proposition. Google to Alphabet (2015) In a surprising move, Google restructured its business and introduced Alphabet as its new parent company in the year 2015. This rebranding allowed Google to broaden its scope beyond search and advertising, venturing into areas like health technology, self-driving cars, and other innovative projects. This rebranding signified a strategic shift and expansion of a company's vision and operations. Meta Platforms (formerly Facebook, Inc. in 2021) Facebook, Inc. rebranded itself as Meta Platforms, or simply Meta, to reflect its focus on building the "metaverse". This strategic move aimed to position the company beyond social media, signaling its commitment to developing new immersive experiences. Again, this example illustrates how a rebrand can represent a pivot towards new technologies and market opportunities. Yet, amidst all the buzz, let's not forget – it's not just about what Gemini's serving, but also about what we're vibing with. This whole name-change saga is a conversation, a chance for us to throw in our two cents. Like, what do we want to see in this new Gemini era? More collaboration features, killer design tools, or maybe some eco-friendly tech vibes? Well, only time will tell. Wrapping it up, Bard transforming into Gemini can be best explained as that plot twist you didn't see coming. It's got us all watching, waiting to see if they're about to drop something epic or if it's just the same old story with a flashy new cover. Either way, we are here for it, ready to dive in and see what Gemini has got. Let's keep our fingers crossed and hope they're about to take us on a wild ride into the tech future!

Bard's Now Gemini and We’re Low-Key Shook – Here's the Tea on Software Rebranding

Bard's Now Gemini and We are Low-key Shook - এখানে সফটওয়্যার রিব্র্যান্ডিং এর চা

Bard ist Now Gemini und We’re Low-Key Shook – hier ist der Tee zum Thema Software-Rebranding

Bard's Now Gemini y We're Low-Key Shook: aquí está el té sobre el cambio de marca del software

Bard est maintenant Gémeaux et nous sommes discrets – Voici le thé sur le changement de marque des logiciels

बार्ड्स नाउ जेमिनी और वी आर लो-की शुक - यहाँ सॉफ्टवेयर रीब्रांडिंग पर चाय है

Bard は Gemini になり、私たちは控えめに動揺しています – ソフトウェアのリブランディングについてのお茶です

Bard's Now Gemini 및 We're Low Key Shook - 소프트웨어 브랜드 변경에 관한 이야기는 다음과 같습니다.

Bard's Now Gemini e We’re Low-Key Shook – Aqui está o chá sobre a reformulação da marca de software

Bard's Now Gemini и мы сдержанно потрясены – вот чай о ребрендинге программного обеспечения

Bard Artık İkizler ve Biz Düşük Anahtarlı Sarsıldık - İşte Yazılımın Yeniden Markalanması Üzerine Çay

Bard's Now Gemini và chúng tôi là Low-Key Shook - Đây là ý kiến về việc đổi thương hiệu phần mềm

巴德现在是双子座，我们很低调——这是关于软件品牌重塑的茶点

If you just want to see the code - see . github What are we going to build and why? LLMs are a new hot piece of technology that everyone is experimenting with. Managed services like OpenAI are the cheapest and most convenient way to use them. You can use their LLMs only for a small fee and for approval to train new versions of your data (in some cases). In certain applications, it is required to run an on our own. You may want to process sensitive data (medical records or legal documents) or get great quality output in a language different than English. Sometimes, you have a specialized task that doesn’t require expensive big models from OpenAI. LLM Open-source LLMs are matching the quality of big players like OpenAI but still require a lot of expensive computing resources. There are smaller models available that can run on weaker hardware and perform well enough. As an experiment, let’s deploy a smaller open-source LLM on AWS lambda. Our goal is to learn more about LLMs and docker-based lambdas. We will also evaluate performance and cost to determine if any real-world applications are feasible. For this project, we are going to use the model, a 2.7 billion parameter LLM that matches the quality of outputs from 13B parameters or more open-source models. It was trained on a large dataset and is a viable model for many applications. From my experience, it hallucinates a lot but otherwise provides useful outputs. Its size is perfect for the AWS Lambda environment. Microsoft Phi-2 We will download the model from Huggingface and run it via package (bindings to the popular llama.cpp, heavily optimized model CPU runtime). We will use a smaller, quantized version, but even the full one should fit in Lambda memory. llama-cpp-python We will create an HTTP REST endpoint via the lambda URL mechanism for a given call: PROMPT="Create five questions for a job interview for a senior python software engineer position."

curl $LAMBDA_URL -d "{ \"prompt\": \"$PROMPT\" }" \
    | jq -r '.choices[0].text, .usage' (jq command used to decode returned JSON) Provides LLM output alongside execution details: Instruct: Create five questions for a job interview for a senior python software engineer position.
Output: Questions: 

1. What experience do you have in developing web applications?
2. What is your familiarity with different Python programming languages?
3. How would you approach debugging a complex Python program?
4. Can you explain how object-oriented programming principles can be applied to software development? 
5. In a recent project, you were responsible for managing the codebase of a team of developers. Can you discuss your experience with this process?

{
  "prompt_tokens": 21,
  "completion_tokens": 95,
  "total_tokens": 116
} For this tutorial, you need to know the basics of programming, docker, AWS, and Python. I am using an M-series Mac, so as a default, we’re going to aim at deployment to ARM AWS Lambda, as it simply costs less for more performance. Instructions on how to deploy the x86 version are included. Environment setup (AWS, Docker and Python) I won’t dive into the basics here. If you don’t have the tools installed, please follow these resources. You need an AWS account and AWS CLI installed and configured. Good tutorial from Google search: . tutorial Official docs, including if you’re using SSO: AWS official tutorial. You also need docker: docker docs. As an IDE, I am using Visual Studio Code. Set up lambda function with docker locally. Let’s start with getting the local environment running. If you need more information than I included here, check out the official AWS documentation. We will store every file in a single project directory without subfolders. First, we will need a basic Python lambda function handler. In your project folder, create a file called . lambda_function.py import sys

def handler(event, context):
    return "Hello from AWS Lambda using Python" + sys.version + "!" We will also create a file, in which we specify our dependencies. Let’s start with the AWS library for interacting with their services, just as an example. requirements.txt boto3 Then, we need to specify our docker image composition in file. Comments document what each line does. Dockerfile FROM public.ecr.aws/lambda/python:3.12

# Copy requirements.txt
COPY requirements.txt ${LAMBDA_TASK_ROOT}

# Install the specified packages
RUN pip install -r requirements.txt

# Copy function code
COPY lambda_function.py ${LAMBDA_TASK_ROOT}

# Set the CMD to your handler
CMD [ "lambda_function.handler" ] Finally, we will create a file to make our life easier when running and building the container. docker-compose.yml version: '3'

services:
  llm-lambda:
		image: llm-lambda
    build: .
    ports:
      - 9000:8080 OK, done! Make sure your docker engine is running, and type in the terminal: docker-compose up The container should be built and started. Our lambda is available under a long URL below (quirks of the official Amazon image). To test if the lambda is running, open another terminal window/tab and type in: curl "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}' Now you can come back to the tab you ran docker-compose in and type ctrl-c to stop the container. You can put this test code in a bash script, as seen in the project repo. Also, it will be useful to create a .gitignore file if you’re going to version control this project. Run an LLM inside a container. Now that we have a working lambda let’s add some AI magic. To run an LLM, we need to add to our . llama-cpp-python requirements.txt boto3
llama-cpp-python To build it, we need to introduce a . This is because the default Amazon docker image doesn’t include the build tools required for llama-cpp. We are doing a pip install with flags in order to use multi-threaded optimizations. docker build stage CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" The code below also includes downloading the model. Community hero, the Bloke, is sharing compressed (quantized, less computationally intensive but lower quality) versions of models, ready to download . To make use of them, we can install the huggingface CLI and run the appropriate commands. You can switch the repository ( ) and the model ( ) to whatever you like if you want to deploy a different model. here TheBloke/phi-2-GGUF phi-2.Q4_K_M.gguf RUN pip install huggingface-hub && \
    mkdir model && \
    huggingface-cli download TheBloke/phi-2-GGUF phi-2.Q4_K_M.gguf --local-dir ./model --local-dir-use-symlinks False # Stage 1: Build environment using a Python base image
FROM python:3.12 as builder

# Install build tools
RUN apt-get update && apt-get install -y gcc g++ cmake zip

# Copy requirements.txt and install packages with appropriate CMAKE_ARGS
COPY requirements.txt .
RUN CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install --upgrade pip && pip install -r requirements.txt

# Stage 2: Final image using AWS Lambda Python image
FROM public.ecr.aws/lambda/python:3.12

# Install huggingface-cli and download the model
RUN pip install huggingface-hub && \
    mkdir model && \
    huggingface-cli download TheBloke/phi-2-GGUF phi-2.Q4_K_M.gguf --local-dir ./model --local-dir-use-symlinks False

# Copy installed packages from builder stage
COPY --from=builder /usr/local/lib/python3.12/site-packages/ /var/lang/lib/python3.12/site-packages/

# Copy lambda function code
COPY lambda_function.py ${LAMBDA_TASK_ROOT}

CMD [ "lambda_function.handler" ] Let’s modify our lambda code to run LLM inference. Some more work is required to get the prompt out of the request body in the production environment. import base64
import json
from llama_cpp import Llama

# Load the LLM, outside the handler so it persists between runs
llm = Llama(
    model_path="./model/phi-2.Q4_K_M.gguf", # change if different model
    n_ctx=2048, # context length
    n_threads=6,  # maximum in AWS Lambda
)

def handler(event, context):
    print("Event is:", event)
    print("Context is:", context)

		# Locally the body is not encoded, via lambda URL it is
    try:
        if event.get('isBase64Encoded', False):
            body = base64.b64decode(event['body']).decode('utf-8')
        else:
            body = event['body']

        body_json = json.loads(body)
        prompt = body_json["prompt"]
    except (KeyError, json.JSONDecodeError) as e:
        return {"statusCode": 400, "body": f"Error processing request: {str(e)}"}

    output = llm(
        f"Instruct: {prompt}\nOutput:",
        max_tokens=512, 
        echo=True,
    )

    return {
        "statusCode": 200,
        "body": json.dumps(output)
    } OK, time to test! My docker setup has 8GB of RAM assigned, but it should run well under 4GB. Let’s rebuild the container and start it again. It will take more time as the model needs to download (~1.8GB). docker-compose up --build This time, let’s test with a real prompt. The first run will take more time as the model needs to load. curl "http://localhost:9000/2015-03-31/functions/function/invocations" \
    -d '{ "body": "{ \"prompt\": \"Generate a good name for a bakery.\" }" }' Example output: Instruct: Generate a good name for a bakery.
Output: Sugar Rush Bakery.

{
  "prompt_tokens": 15,
  "completion_tokens": 6,
  "total_tokens": 21
} Deploy to AWS Lambda With everything working locally, let’s finally deploy our LLM to AWS! To execute the deployment successfully, the following steps are required: : Determine necessary information such as AWS region, ECR repository name, Docker platform, and IAM policy file, and disable AWS CLI pager. Initial Setup : Optionally confirm that the AWS CLI is correctly configured. Verify AWS Configuration : ECR (Elastic Container Registry) Repository Management Determine if the specified ECR repository already exists. If absent, create a new ECR repository. : IAM Role Handling Check for the existence of a specific IAM role for Lambda. If not found, establish the IAM role and apply the policy. AWSLambdaBasicExecutionRole : Securely log Docker into the ECR registry using the retrieved login credentials. Docker-ECR Authentication : Utilize Docker Compose to build the Docker image, specifying the desired platform. Docker Image Construction : Label the Docker image appropriately for ECR upload. ECR Image Tagging : Transfer the tagged Docker image to the ECR. ECR Image Upload : Fetch the ARN linked to the specified IAM role. Acquire IAM Role ARN : Assess whether the Lambda function exists. Lambda Function Verification : Set parameters like timeout, memory allocation, and image URI for Lambda. Lambda Function Configuration : Lambda Function Deployment/Update For a new Lambda function, create it with defined settings and establish a public Function URL. For an existing Lambda function, update its code using the new Docker image URI. : Obtain and display the Function URL of the Lambda function. Function URL Retrieval It is a simple but lengthy process to do manually, so I’ve created a script using AWS CLI. Feel free to modify the variables and run it yourself. The script is quite complex; you can do all the steps manually in the GUI if you want, apart from pushing the container. If you are not doing this on an M-series Mac, be sure to change Linux/arm64 to Linux/amd64. #!/bin/bash

# Variables
AWS_REGION="eu-central-1"
ECR_REPO_NAME="llm-lambda"
IMAGE_TAG="latest"
LAMBDA_FUNCTION_NAME="llm-lambda"
LAMBDA_ROLE_NAME="llm-lambda-role" # Role name to create, not ARN
DOCKER_PLATFORM="linux/arm64" # Change as needed, e.g., linux/amd64
IAM_POLICY_FILE="trust-policy.json"
PAGER= # Disable pager for AWS CLI

# Authenticate with AWS
aws configure list # Optional, just to verify AWS CLI is configured

# Check if the ECR repository exists
REPO_EXISTS=$(aws ecr describe-repositories --repository-names $ECR_REPO_NAME --region $AWS_REGION 2>&1)

if [ $? -ne 0 ]; then
    echo "Repository does not exist. Creating repository: $ECR_REPO_NAME"
    # Create ECR repository
    aws ecr create-repository --repository-name $ECR_REPO_NAME --region $AWS_REGION
else
    echo "Repository $ECR_REPO_NAME already exists. Skipping creation."
fi

# Check if the Lambda IAM role exists
ROLE_EXISTS=$(aws iam get-role --role-name $LAMBDA_ROLE_NAME 2>&1)

if [ $? -ne 0 ]; then
    echo "IAM role does not exist. Creating role: $LAMBDA_ROLE_NAME"
    # Create IAM role for Lambda
    aws iam create-role --role-name $LAMBDA_ROLE_NAME --assume-role-policy-document file://$IAM_POLICY_FILE
    aws iam attach-role-policy --role-name $LAMBDA_ROLE_NAME --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
else
    echo "IAM role $LAMBDA_ROLE_NAME already exists. Skipping creation."
fi

# Get login command from ECR and execute it to authenticate Docker to the registry
aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $(aws sts get-caller-identity --query Account --output text).dkr.ecr.$AWS_REGION.amazonaws.com

# Build the Docker image using Docker Compose with specific platform
DOCKER_BUILDKIT=1 docker-compose build --build-arg BUILDPLATFORM=$DOCKER_PLATFORM

# Tag the Docker image for ECR
docker tag llm-lambda:latest $(aws sts get-caller-identity --query Account --output text).dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG

# Push the Docker image to ECR
docker push $(aws sts get-caller-identity --query Account --output text).dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG

# Get the IAM role ARN
LAMBDA_ROLE_ARN=$(aws iam get-role --role-name $LAMBDA_ROLE_NAME --query 'Role.Arn' --output text)

# Check if Lambda function exists
FUNCTION_EXISTS=$(aws lambda get-function --function-name $LAMBDA_FUNCTION_NAME --region $AWS_REGION 2>&1)

# Parameters for Lambda function
LAMBDA_TIMEOUT=300 # 5 minutes in seconds
LAMBDA_MEMORY_SIZE=10240 # Maximum memory size in MB
LAMBDA_IMAGE_URI=$(aws sts get-caller-identity --query Account --output text).dkr.ecr.$AWS_REGION.amazonaws.com/$ECR_REPO_NAME:$IMAGE_TAG

# Deploy or update the Lambda function
if echo $FUNCTION_EXISTS | grep -q "ResourceNotFoundException"; then
    echo "Creating new Lambda function: $LAMBDA_FUNCTION_NAME"
    aws lambda create-function --function-name $LAMBDA_FUNCTION_NAME \
        --region $AWS_REGION \
        --role $LAMBDA_ROLE_ARN \
        --timeout $LAMBDA_TIMEOUT \
        --memory-size $LAMBDA_MEMORY_SIZE \
        --package-type Image \
        --architectures arm64 \
        --code ImageUri=$LAMBDA_IMAGE_URI

    aws lambda create-function-url-config --function-name $LAMBDA_FUNCTION_NAME \
        --auth-type "NONE" --region $AWS_REGION

    # Add permission to allow public access to the Function URL
    aws lambda add-permission --function-name $LAMBDA_FUNCTION_NAME \
        --region $AWS_REGION \
        --statement-id "FunctionURLAllowPublicAccess" \
        --action "lambda:InvokeFunctionUrl" \
        --principal "*" \
        --function-url-auth-type "NONE"
else
    echo "Updating existing Lambda function: $LAMBDA_FUNCTION_NAME"
    aws lambda update-function-code --function-name $LAMBDA_FUNCTION_NAME \
        --region $AWS_REGION \
        --image-uri $LAMBDA_IMAGE_URI
fi

# Retrieve and print the Function URL
FUNCTION_URL=$(aws lambda get-function-url-config --region $AWS_REGION --function-name $LAMBDA_FUNCTION_NAME --query 'FunctionUrl' --output text)
echo "Lambda Function URL: $FUNCTION_URL" Now, let’s add proper permissions and run the script. chmod +x deploy.sh
./deploy.sh
# AWS_PROFILE=your_profile ./deploy.sh 
# if you want to use a different profile than default As a result of the script, you should se the lambda function URL in your terminal. Let’s use it for a test. PROMPT="Create five questions for a job interview for a senior python software engineer position."

curl $LAMBDA_URL -d "{ \"prompt\": \"$PROMPT\" }" \
    | jq -r '.choices[0].text, .usage' Like before, the first run will take longer. chmod +x test_remote.sh
LAMBDA_URL=your_url ./test_remote.sh You should receive back an output similar to the one at the top of the article. If you want to see the full project code, see . github Performance Cost and speed You may have noticed that I chose to run the model under the configuration: 6 core multi-threading 10GB of RAM 5-minute timeout How was the performance, and how much RAM was used? Lambda never reserves compute for you and sometimes loads your program “cold.” When you make a request, anything you need to load (like a model) needs to happen at this moment. We call this a cold start. For the prompt about five interview questions, the execution time after the cold start was 17 seconds, which is not bad at all. After that, it consistently took 9 seconds to generate an output, tested in 4 subsequent runs. The average output length was 83 tokens. So, when running hot. we achieved ~9.2 tokens/second As a comparison, community-reported speeds for OpenAI models are GPT-4 10t/s, GPT-4-turbo 48t/s, and GPT-3-turbo 50-100t/s. So we’re matching a decent but not industry-leading speed with little effort. You could run this prompt 4444 times in a free tier. Above that, . The same 1000 runs with a GPT-3.5 turbo would cost $0.2. This makes this endeavor not really cost-effective, so low cost shouldn’t be your aim if you’re going to implement this in production. it would cost $1.2 per 1000 runs Optimization Could we run the model cheaper? The model uses under 2600MB of memory consistently. We could lower the allocated resources, with a caveat that 6 core CPU is assigned only to Lambdas above ~8846MB. I ran the first test at 3500MB (3 CPU cores/threads) to see how it compares. I didn’t change the code to run only 3 threads, which had a downgrading effect. In this case, the lambda always timed out (ran over 5 minutes, which meant the request was canceled). After changing the number of threads to the proper 3, the execution almost timed out at a cold start, returning results after 4 minutes and 33 seconds; the subsequent 3 runs took 26-40 seconds, averaging 3.15 tokens/second. 1000 paid runs would cost So we got slower and paid more, not a good optimisation! With this in mind, it makes sense to pay for more memory because AWS assigns CPU resources proportionally to the reserved RAM amount. $1.45. Next steps As you can see, running an open-source language model is possible even in a regular environment like AWS Lambda. The cost and quality of output do not match industry-leading services but can serve as a use-case-specific solution. If anything, this was a great opportunity to learn about basic LLM ecosystem libraries and tools, as well as strengthen your AWS knowledge. Some possible next steps if you want to keep exploring and bringing this project closer to production-ready: Try to run a different (bigger) model or a less compressed version of Microsoft Phi-2. Create a CDK config to automatically deploy the model and create resources. Create a GitHub Action to validate the project and run ./deploy.sh. Try a different serverless platform to run the model. Subscribe to my newsletter and follow me on social media. I may write about some of these topics, let me know in the comment if you’re interested in any of them! If you bump into any issues let me know as well. What do you think about this way of deploying LLMs? Subscribe to my profile by filling in your email address on the left, and be up-to-date with my articles! Don't forget to follow me on Twitter and subscribe to my blog’s for more tips and insights! @ horosin_ newsletter If you don't have Twitter, you can also follow me on . LinkedIn Also published . here

Deploying Open-Source Language Models on AWS Lambda

So, you’ve got a business idea but are unsure how to determine whether it is worth pursuing. Or maybe you’ve already been through the mill and spent a year building a product no one wanted. For either of these scenarios, you are not alone. The issue is many startup leaders don’t find the right problem to solve; they fall in love with one idea and , ignoring any feedback they receive. That’s why, currently 9 out of 10 startups fail. try to force a fit As a seasoned entrepreneur and Product Management professional leading product development in , I have learned the value of finding that problem-solution fit first. But I also realized there needs to be more user-friendly business planning tools to give founders a helping hand. AdTech Startup leaders often leverage innovative tools like LivePlan and Biz Plan to get the answers to ‘What MVP will work?’ or ’Does this solution address the issue I aimed to resolve?’ These are excellent tools for entrepreneurs to put business ideas into pre-made structured frameworks, helping them figure out their target audience, customer demands, and industry landscape. However, on occasion, filling out these templates with built-out ideas can be overwhelming. Here are four more tips to help verify if you have a viable business idea. Explore ChatGPT Prompts With all the current rage, is the first obvious tool to use — and I have witnessed the true magic of AI when harnessed to empower entrepreneurs. ChatGPT can quickly help deliver your , using prompts to determine market-solution, customer, and product-founder fit. ChatGPT validation plan For example, let’s take this first pain point: Entrepreneurs often miss out on defining their unique advantage when starting a business. The ‘why me?’ part. That’s why taking a moment to analyze your education, experience, background, and network through ChatGPT is worthwhile: Do you have access to a specific customer set or expertise in a niche area? For example, suppose you want brand and start this food company in the US. You realize §you have a close friend who owns America’s top food distribution company and could have distribution lined up. to launch a new healthy snack To more thoroughly explain your unique advantage, though, you can simply ask ChatGPT the obvious: “I’m thinking of starting a business that [does XYZ], and I want to understand my unique strengths in making it a success. I have experience in [XX] and access to people who [XX]. I want you to ask me questions to help me think through what unique advantages or risks I may have.” Don’t be surprised to see a reasonably adequate action plan listing what skills you can maximize and other activities you need to complete to get your business off the ground. Next up, it is worth asking ChatGPT who the ideal customer would be for your business. These kinds of prompts can do the trick: “Can you describe my potential customer base’s demographics and explain their problems? What products or services are they using now to tackle these issues? How could my business add more value?” And finally, founders must always ensure enough customer demand to buy their product or service. In other words, is the problem you are addressing big enough? To find out, ask ChatGPT something like: “Can you give me five reasons someone would want to buy my product?” Conduct Customer Interviews Once you’ve collated the input and feedback about your business idea from , you can put it into practice with customer discovery. A round of interviews with prospective customers means you can understand your user base’s problems, empowering you to provide a more effective and valuable solution. Those startups who have failed may find they . generative AI skipped this stage Another pitfall is asking friends and family for advice or feedback. They’ll be overly generous, which can be misleading. Instead, get speaking to real prospective users because an idea is just an idea unless you have a paying customer. For early-stage startups, using tools such as Google Forms or Survey Monkey for user testing can get the job done. But for more established companies with larger budgets, there are tools like CustomerGauge. Also, remember that ChatGPT can come in handy here for . You just need to describe your business and potential customer base for the LLM to create three compelling value propositions that address how your company solves a problem. When it comes to the customer interviews, you can ask which of the three value propositions resonates most. generating questions If you feel particularly adventurous, you could build a prototype of your digital product or put wireframes together with a design tool like before getting in front of customers. Users could interact with the prototype directly and provide insights about usability via comment mode. And Figma’s screen recording software can capture customer interviews. Figma Plus Customer interviews are invaluable if you want to know if people would pay for your service or product — and how much. They can also be an opportunity to gather feedback or ask what essential features you need to include to win business. Leverage Landing Page Apps Landing pages are like interactive online business flyers and are a great way to target different customer segments with tailored information. It’s also a low-cost, efficient, data-driven approach to test market potential. Why do you think even established companies use landing pages to launch new products, letting customers preorder or sign up for a waitlist? By tracking metrics like page visits, click-through rates, and conversion rates, you can determine if there’s genuine interest in your idea. The company Carrd helps entrepreneurs make free landing pages, while ConvertKit can drive email newsletter signups with its built-in email marketing tool. But many other apps are available, too, enabling entrepreneurs to build one-page websites that are easy to tweak and customize without any serious coding. These apps also often facilitate A/B testing and the experimentation of different headlines, images, or CTAs to optimize conversion rates. Lastly, to convert prospects, there needs to be an action for them to take, like signing up for a newsletter, filling out a form, contacting you, or purchasing an e-book. Overall, a landing page is a safer bet than building a full product or service: If the business idea doesn’t resonate with an audience, you have yet to invest heavily. Examine Intelligent AI Assistance for Business Planning Finally, even though today’s business planning software platforms have good intentions, sometimes they hinder entrepreneurs’ ability to ideate, design, and launch successful businesses. That’s because current platforms are missing a learning component to assist entrepreneurs in articulating their intentions and minimizing project timelines. This is where an AI-powered co-pilot comes in to make business planning more accessible and democratize AI for all. Newer AI-driven platforms and apps are now available that use Open AI’s APIs, for example, alongside fine-tuning models, to produce idea evaluators that inform an entrepreneur if their idea is worth pursuing or not. The platforms can assess the uniqueness of the solution and business model, giving entrepreneurs a score and offering suggestions for improvements in key areas. The feedback mechanism and personalized assessment of an idea, target market, or problem statement can help entrepreneurs iterate quickly and develop business plans. But remember, receiving a high score on these AI-powered platforms doesn’t necessarily mean you’ll be successful — it is just a barometer. However, the valuable insights and recommendations provided could optimize data-driven decision-making and enhance the success rate of new ventures. Today, startups are up against many factors: talent shortages, funding obstacles, fierce competition, and an economic downturn. However, these emerging companies are essential for the global economy’s resilience to encourage innovation, job creation, and technological development — but only when a startup effectively addresses a pressing issue and can sustain operations. That’s why leveraging different technologies for better business planning is vital, providing a more favorable environment for startups to thrive and meaning we are all better off. This article was originally published by on Danish Ahmed The Sociable.

Evaluating the Viability of Your Business Idea with Modern Tools

https://youtu.be/UM6jCRMM5o4?si=y8DC8ZnP1f2cvUrg&embedable=true Figure Above: A 4-minute bullet overview if you prefer this in video format. Hey folks, Let's dive right in and get to the meat of the matter without any fluff. I've heard a lot of hype about large language models (LLMs) like Chat GPT, and while they're impressive, they're far from perfect. Here's a Quick Rundown on Why These Models Aren't the Future of AGI (Artificial General Intelligence). LLMs, like Chat GPT, can make statements that sound right but are plain wrong. They can spit out info that fits a pattern without actually being connected to reality. Hallucination: There's an insane amount of tech and money behind these models to get them to remember up to 8,000 tokens at once. But, they have a short memory span – after a certain point, they start forgetting what you said earlier. Not so great for larger databases or extended conversations. Memory Limitations: When ChatGPT shares a fact or tidbit, it can't cite its sources. It's been fed data from countless places, but it can't tell you exactly where each piece of info came from. No Golden Source: All the cool visualizations, GUIs, games, and application integrations? That's manual work. Engineers putting in hours to make it look seamless. It's not all AI magic; it's just good old-fashioned human labor. Heavy Manual Integration: LLMs weren't designed with ethics at the forefront. They're optimized for performance. That means there's a ton of manual work done to filter out the unsavory bits to make them user-friendly. Ethics and Filtering: Without data, these models are like empty shells. They don't actively learn in real-time. They get massive updates every so often, requiring billions of dollars and tons of hours. It's a colossal human effort, not the model spontaneously evolving. Over-reliance on Data: Remember GPT-1 or GPT-2? No? That's because models age out and get replaced. It's a sure sign we're dealing with a product, not an evolving intellect. They Get Outdated: These models have a strong grasp on language, but language isn't always a perfect reflection of the world. Just because Chat GPT can generate coherent sentences doesn't mean it understands the complexities of reality. Language ≠ Reality: In essence, while the concept of a "super-intelligent parrot" might be overused, it's not entirely off the mark. LLMs are indeed powerful language models, but that doesn't equate to a deep understanding of the world. So the next time someone gets all starry-eyed about LLMs being on the brink of AGI, arm yourself with these points. There's much more to say, but this should give you a solid foundation. Stay curious, and thanks for dropping by! More from me: Building AI GameDev Tech Talk Github

The Limitations of LLMs Like ChatGPT: A Straight-Talking Overview

Ever wondered about the magic behind those WiFi login pages that greet you at places like Starbucks? You know the drill – you sip your coffee, pull out your laptop or smartphone, connect to the WiFi, and voilà! Suddenly, you're redirected to a page where you need to log in or accept terms before diving into the digital realm. It's a seamless experience we've all grown accustomed to, but have you ever thought about creating one yourself? Well, probably not. But I did! And there’s a good reason why. I live in Ruppin Street, and as a joke, I call my apartment the “Royal Ruppin Relax” as if it were some kind of boutique hotel. I wanted to create my own customized WiFi login portal so that guests at my home would get a surprise when they logged in. That's what we're diving into today: In this tutorial, I’ll show you how to build and customize your own – a digital gateway that not only controls access but also acts as a canvas for your creativity, and a great conversation starter! With a Raspberry Pi and a bit of AI magic, you can transform your mundane WiFi login into an engaging, personalized experience. captive portal https://youtu.be/zbaLWR8GbdM?embedable=true But first, What is a Captive Portal? The term might sound technical, but in essence, it's the official name for those login pages you encounter when connecting to a public WiFi network. Most captive portals are like virtual gatekeepers, ensuring that only authorized users gain access to a WiFi network. But this interface can be a powerful tool, not just for authentication, but also for conveying information and engaging users creatively. Capabilities of a Captive Portal: Captive portals authenticate users by prompting them to enter login credentials or accept terms and conditions. This process ensures that the network is used responsibly and securely. Authentication: One of the features of a captive portal is its customization potential. Businesses often use captive portals to showcase their branding, display advertisements, or provide essential information. Customization: Captive portals enable administrators to control the type of access users have to the internet. For instance, they can restrict certain websites, limit bandwidth, or provide different levels of access based on user roles. So technically, you can configure it such that your devices are prioritized bandwidth-wise on your WiFi network, but that’s up to you 😉 Access Control: Now, let's move forward and create our own captivating captive portal. The creative journey begins! You Will Need: Before we dive into creating your personalized captive portal, let's gather the essentials: The heart of your project, this versatile microcomputer will serve as the central hub for your captive portal setup. Raspberry Pi: You'll need a microSD card (at least 16GB) to store the operating system and other necessary files. MicroSD Card: Ensure you have a compatible power supply for your Raspberry Pi to keep it running smoothly. Power Supply: You'll require an Ethernet cable to establish a wired connection between your Raspberry Pi and your internet router. Ethernet Cable: Why Raspberry Pi? In the landscape of network devices, not all routers are created equal. Many standard routers lack native support for captive portals, making it challenging to implement this feature seamlessly. When faced with this limitation, we turn to as a solution. This credit-card-sized, affordable computer will allow you to run complimentary network-related software, and overcome the constraints of your existing router. Raspberry Pi If you never used your Raspberry Pi before, set it up according to the . Our next step would be installing RaspAP. simple instructions on the official website RaspAP: Simplifying WiFi Management Now that you have your Raspberry Pi ready, it's time to introduce RaspAP. RaspAP is an open-source software that simplifies the process of setting up a WiFi access point on your Raspberry Pi. Think of it as the bridge between your Raspberry Pi and the devices that will connect to your WiFi. To install RaspAP, simply follow the . simple instructions on the official website Why Do We Need RaspAP for a Captive Portal? ‍To create a captive portal, we need a WiFi network that's entirely under our control. RaspAP allows you to do just that: While Raspberry Pi provides the hardware backbone, RaspAP adds the user-friendly interface we’re going for, making it incredibly easy to configure your WiFi network settings. You can customize the network name (SSID), set up passwords, and manage the connection preferences. RaspAP handles the complexities of access points, security protocols, and IP addresses, ensuring that the WiFi network your guests connect to, operates smoothly and securely. Why Is an Ethernet Cable Needed? ‍You might be wondering about the necessity of an Ethernet cable in a wireless setup. When you connect your Raspberry Pi to your router using an Ethernet cable, you establish a stable, wired connection. This wired connection serves as the foundation upon which you'll build your customized WiFi network. Introduction to Nodogsplash Now that you've set up your WiFi access point with RaspAP, it's time to introduce Nodogsplash into the mix. Nodogsplash is a high-performance Captive Portal and the key player in bringing our idea to life. Nodogsplash offers by default a simple splash page that we will customize later. Install and configure Nodogsplash by following the . If you are successful - you will see this page: easy tutorial on RaspAP’s official documentation Customizing the Splash Page Here comes the exciting part! Now we will customize the captive portal page to our liking. Customizing the splash page might seem like a challenging task for two reasons: Nodogsplash has specific rules that the splash page must adhere to, ensuring functionality. Deviating from these rules might result in our captive portal not working, making it crucial to comply with them. Nodogsplash Rules: A CDC (Captive Detection Client) is a component in operating systems or devices that helps in detecting whether a network has a captive portal. When a device connects to a WiFi network, the CDC functionality checks if the network connection is restricted by a captive portal. If it detects a captive portal, the device redirects the user to the portal's login or authentication page. Most of the CDCs don’t allow JS or even s, so we will have to work with HTML and CSS only to make a beautiful captive portal. Manipulating HTML & CSS requires a good understanding of their syntax, making customization challenging for many users. CDCs force us to work with HTML and CSS only, no JS. href To overcome these challenges, we will use some ✨ ✨. AI magic First, we will obtain a stunning boutique hotel picture with Leonardo AI: an innovative tool that generates realistic and visually appealing images from prompts. Here’s how you can use it: Go to the Leonardo AI website and click on “AI Image Generation” Visit Leonardo AI: Using Leonardo AI's intuitive interface, generate an image that resonates with your captive portal's ambiance. You can tweak various settings until you find the perfect image. My prompt was: “A beautiful boutique hotel next to the sea, palms and luxurious atmosphere, beautiful day” Generate Your Image: Once satisfied with the generated image, download it to your computer. This stunning visual will serve as the backdrop for your customized splash page. Download Your Image: Now that we have the image, we can customize the default and . To do that we will use ’s AI agents, that can quickly get us to the point: HTML CSS Fine Open Fine and click “Deploy Agent”. Upload the YAML file of the HTML Agent, found . This agent specializes in HTML and CSS tasks. Deploy an HTML Agent to Your workspace: here Place the default Nodogsplash files in a folder, together with your generated image. Run inside the folder and then add it as a new project to Fine. Create a project: git init The agents work according to a plan specified in a notebook. I wrote a short description for my wanted task and connected the notebook to the project. Create a Notebook and specify the changes you want to make: The agent will start changing the HTML and CSS pages according to the specifications in your notebook. If it isn’t exactly to your liking, make the final changes and that’s it! Run the agent, and make some final tweaks: With Fine’s AI agents, the process of customizing your splash page becomes intuitive and efficient. You don’t need to deal with HTML and CSS, and you don’t need to learn the rules of Nodogsplash. You easily transform a basic login interface into a visually appealing and engaging portal that captivates users, providing a memorable WiFi experience. Test Your Customized Page After Fine generates the code, test your customized splash page. To do that, upload your files to the Raspberry Pi and replace the default splash page files in /etc/Nodogsplash/htdocs/ Ensure that it complies with Nodogsplash rules and provides a seamless user experience. Make any necessary adjustments until you achieve the desired result. Final Words By integrating Raspberry Pi, RaspAP, Nodogsplash, Fine, and Leonardo AI, you've not only created a functional captive portal but also unleashed your creativity without the headache of coding intricacies. This project not only enhances your technical skills but also transforms your WiFi experience at home. Feel free to experiment further and explore the endless possibilities of customization, all thanks to the power of innovative AI technology. Now it's your turn to improve your home WiFi experience! Get creative, get connected, and let your imagination run wild, AI will take care of the rest! Also published here.

How to Create a Custom Captive Portal for Home WiFi with Raspberry Pi and AI 

In the bustling streets of the digital metropolis, influencers are the modern-day celebrities, their voices echoing through the vast corridors of social media. But as the sun sets on traditional influencer monetization methods, a new dawn is breaking: the age of AI-driven engagement. 🔥 The Digital Hustle: Engagement's Heavy Price Hello, world! I'm Dzmitry Leukavets, founder of WAKA. As a front-row spectator of the influencer realm's evolution, I've seen the seismic shift AI is bringing. But first, let's dive into the numbers. Stat Alert: The influencer market skyrocketed from $1.7 billion in 2016 to a whopping $16.4 billion in 2022. Yet, 61% of people trust influencers for product recommendations. This trust underscores the importance of genuine engagement. Historically, influencers have been the bridge between brands and consumers. From the 1700s, when Josiah Wedgwood collaborated with Princess Charlotte, to today's social media moguls, the essence remains the same, but the methods have evolved. 🚀 The Engagement Dilemma: Always On, Always Exhausted Sarah L., with her impressive 500k followers, captures the sentiment perfectly: "The digital world doesn't sleep. But humans need to." The rise of platforms like YouTube, Instagram, and TikTok has revolutionized the way influencers engage. But with global audiences spanning multiple time zones, the pressure to be constantly "on" is immense. 🤖 AI Chatbots: The New Influencer Sidekicks These aren't just bots; they're the future. With 80% of top-tier influencers now integrating AI for engagement, the age of automation isn't coming; it's here. AI chatbots, like Waka, offer a solution to the engagement conundrum. They provide real-time, personalized responses, ensuring fans always feel valued. This not only amplifies engagement but also boosts loyalty. Stat Alert: Influencers leveraging AI have seen a 60% increase in monetizable interactions. The game is changing, and fast. 💰 Beyond Chats: The Revenue Revolution AI bots are more than conversationalists; they're sales magicians. With a 70% surge in conversion rates for AI-empowered influencers, the revenue potential is undeniable. Brands integrating chatbots have experienced a 67% increase in conversion rates. This isn't just about casual banter; chatbots can transition a fan conversation towards transactions, promoting merchandise, exclusive deals, or paid content. 📊 Data Goldmine: Insights That Drive Strategy Bots aren't just about engagement; they're about understanding. With AI-driven insights, influencers can tailor their content strategy, resonating deeper with their audience. These interactions provide a goldmine of data, offering insights into fan preferences, behaviors, and interaction patterns. This data-driven approach helps influencers refine their strategies for maximum impact. ❤️ Personalization: Crafting Unique Fan Journeys In a world craving personalized experiences, AI bots deliver. They craft interactions tailored to each fan, forging deeper connections and loyalty. Modern chatbots, with their advanced algorithms, can curate personalized experiences for fans, making them feel seen and valued. Stat Alert: A staggering 66% of fans are more likely to engage long-term with influencers offering personalized interactions. 🌐 Democratizing AI: The Future, Accessible to All AI shouldn't be a luxury. Platforms like WAKA are breaking barriers, ensuring every influencer, irrespective of their tech prowess, can harness AI's magic. The no-code movement, exemplified by platforms like WAKA, allows influencers to tap into the power of AI without the technical hurdles. 🌟 The Road Ahead: The AI-Powered Influencer Era As we stand at this digital crossroad, the message is clear: AI is reshaping the influencer landscape, and those ready to embrace it are set to soar. The influencer landscape is rapidly evolving, and AI chatbots like Waka are leading the charge. Ready to experience the AI magic? Dive into WAKA's world and be a part of the future. Limited beta access spots up for grabs – https://waka.uwu.ai/

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