AI & Trend News

In an era where AI trends shift overnight, what matters more than "who created it first" is "who secures and utilizes information first."

GPT releases new versions almost daily, Google is reworking its LLM, startups in China emerge from a single paper line, governments propose regulations, and companies devise countermeasures. Amidst this, we must ask ourselves: "How can we efficiently gather this information and read the trends?"

The latest AI trend news list is structured into four main categories:

• Global Trends: Core channels like OpenAI, Google, VentureBeat
• Domestic News: In-depth coverage focused on Korean policies, industries, and local case studies
• Overseas Specialized Media: Independent channels covering China, Europe, research papers, and hardware
• Developer/Paper/Practitioner-Focused: Information for those who want to try it out and implement it themselves

"A map of information flows that helps you read the entire movement of AI" – check out the rapidly changing AI information.

⚠️ Quick Tip: Collect news
using Inoreader / Feedly, or use
tools like make.com / zapier to automatically summarize RSS-fed news, post it, receive it via Slack, or collect it in Notion.

1. 🌏 Global AI Trend News RSS Channels

To keep up with global AI trends, you need channels that provide "valuable insights" beyond simple news. The 5 AI trend news channels
introduced below each have unique characteristics, making them optimal for quickly grasping the major directions and trends in the AI industry.

Category	Channel Name	RSS Link	Features
1	The Batch (by DeepLearning.AI)	Go to	Andrew Ng's newsletter, focusing on the latest research, industry, and startups
2	VentureBeat – AI	Go	AI startups, investment-focused, strong business perspective
3	MIT Tech Review – AI	Go	AI + Social Impact Useful for service planners
4	Google AI Blog	Go to	Official Google Blog, Research Announcements & Gemini Updates
5	OpenAI Blog	Go to	GPT Updates and API Partnership Information

1) The Batch (by DeepLearning.AI)

• RSS: https://www.deeplearning.ai/the-batch/feed/
• Content: Weekly newsletter from the AI education brand founded by Andrew Ng
• Features: Covers the latest research, industry news, and startup trends

2) VentureBeat – AI Section

• RSS: https://venturebeat.com/category/ai/feed/
• Features: Strong focus on 'AI from a business perspective' including AI startups, investments, and Big Tech strategies

3) MIT Technology Review – AI

• RSS: https://www.technologyreview.com/feed/
• Usage Tip: Subscribe to the full feed via RSS and filter for the "AI" keyword
• Bonus: Frequently covers AI's societal impact (perfect for service planners)

4) Google AI Blog

• RSS: https://ai.googleblog.com/feeds/posts/default
• Features: Research introduced directly by Google, open-source announcements, Gemini-related information

5) OpenAI Blog

• RSS: https://openai.com/blog/rss
• Core: Check for GPT updates, technical implementations, partnerships, and API changes

🛠 Usage Tips

• The Batch is perfect for busy people—just check it once a week. Its curation strikes the right balance, neither overwhelming nor sparse, making it great for staying updated. It
  especially bridges the gap between planners and practitioners by linking lectures, talks, and real-world examples.
• VentureBeat is recommended for those wanting to see which AI products are trending and where VC funding is flowing. It excels at summarizing AI trends from a marketing/business perspective
  . Highly useful when setting MVP or startup strategies.
• MIT Tech Review strongly emphasizes the perspective of "How will technology impact people and society?"
  This makes it perfect for planners with a human-centered philosophy, like those working on social networking or emotion-based services.
• Google AI Blog updates rapidly with Google's published papers, model releases, and API-related open-source news. It's the first place to get actual
  development updates on Gemini, Vertex AI, TTS, and language model announcements.
• OpenAI Blog is literally the home of GPT.
  Updates, pricing, feature changes, API changes, GPT partnerships, tool features—if you're "building services based on the
  GPT ecosystem," this is a must-follow.

2. 🇰🇷 Domestic/Korean-based AI News RSS

Domestic AI news differs from overseas in that it features many "on-the-ground trends" like policy, regulation, education, and industry collaboration. This is where you'll find information essential for developing Korean AI strategies or for local marketing/planning/branding.

Category	Channel Name	RSS Link	Feature Summary
1	Bloter (AI Category)	Quick Link	Strong local insights on Korean AI startups, government policies, ethics, and regulations
2	ITWorld Korea – Artificial Intelligence	Go	Enterprise/business-focused IT + AI trends, solution case studies
3	AI Times	Go	Rich in AI application cases across industries like healthcare, education, and finance; fast article update speed

1) Bloter (AI Category)

• RSS: https://www.bloter.net/archives/category/ai/feed
• Features: Abundant local insights on Korean startups, government policies, AI regulations, etc.

2) ITWorld Korea – Artificial Intelligence

• RSS: https://www.itworld.co.kr/t/62017/%EC%9D%B8%EA%B3%B5%EC%A7%80%EB%8A%A5/rss
• Features: Focuses on AI trends applied to enterprise IT, solution-centric

3) AI Times

• RSS: https://www.aitimes.com/rss/allArticle.xml
• Features: Abundant AI application cases by industry (medical, education, finance, etc.), fast article update speed

🛠 Usage Tips

Bloter covers policy trends and startup developments, IT World Korea focuses on enterprise AI technology trends, and AI Times showcases AI application cases across various industries.

3. 🌐 Overseas AI News Channels (China, Europe, Global Experts)

Overseas AI news channels are the first reference points when tracking
the cutting edge of technology. China's focus on deep learning and hardware innovation, Europe's emphasis on AI ethics and policy, and the US's developer-centric practical blogs each offer distinct perspectives, making them excellent for understanding diverse AI trends.

Category	Channel Name	RSS Link	Key Features Summary
1	Synced Review	Quick Link	China and global research/startup-focused AI news, deep learning technology/policy trends
2	The Decoder	Go	Policy and technology coverage on generative AI, ethics, privacy, etc., from a European perspective
3	Towards Data Science	Go	Practitioner-focused AI/data science blog, hands-on tutorials
4	ArXiv Sanity Preserver	Go	Latest paper summaries + recommendation service, useful for identifying paper-based trends
5	NVIDIA Blog – AI Section	Go	Generative AI, graphics AI, research announcements, and other NVIDIA-centric ecosystem trends

• RSS: https://syncedreview.com/feed/
• Features: AI news focused on Chinese and global research institutions/startups, introducing deep learning technology/policy trends

2) The Decoder (Europe-based AI media)

• RSS: https://the-decoder.com/feed/
• Features: In-depth coverage of AI policy and technology from a European perspective, including generative AI, ethics, and privacy

3) Towards Data Science (Medium-based)

• RSS: https://towardsdatascience.com/feed
• Features: Practitioner-focused AI/data science tech blog, hands-on tutorials, insights

4) ArXiv Sanity Preserver (Latest paper summaries)

• RSS: https://www.arxiv-sanity.com/rss
• Features: Service summarizing and recommending the latest papers, useful for understanding paper-centric trends

5) NVIDIA Blog – AI Section

• RSS: https://blogs.nvidia.com/blog/category/ai/feed/
• Features: Provides insights into NVIDIA's ecosystem trends, including generative AI, graphics AI, and AI research announcements

Synced Review is China-based but deeply covers global research and startup news; The Decoder offers a balanced perspective reflecting Europe's unique concerns about generative AI; Towards Data Science is a blog frequently referenced by practitioners/data analysts/PMs in the field; ArXiv Sanity is the best when you're wondering, "What's the hot paper these days?"

🛠 Utilization Tips

• For tracking tech research/papers, the ArXiv Sanity + Papers with Code combo is the fastest and most accurate. It's essential if you're working on paper-based projects or want to stay updated on the latest GPT/LLM trends.
• For practical developers seeking tutorials or application examples, Towards Data Science is invaluable.
  Its Medium-based format offers intuitive writing, and the abundance of practice code makes it accessible even to non-technical readers.
• For a balanced perspective on AI policy, ethics, and societal change, The Decoder is perfect.
  Highly recommended for planners and founders who frequently ask questions like, "What about social responsibility when building AI services?"
• To track Chinese and global deep learning innovation startups, Synced Review is essential.
  It quickly updates news on research linked to companies like Tencent, Baidu, and Qualcomm.
• To follow generative AI trends within the NVIDIA ecosystem, check out the NVIDIA Blog's AI section. It features AI applied to
  real business, including graphics AI, GenAI research, and AI-integrated hardware.

4. Introducing Developer/Open-Source-Centric AI News Channels

If you're someone who actually wants to put AI trends to use—meaning developers, PMs, planners, or data scientists—you absolutely must follow these channels.

The HuggingFace Blog provides the latest model and library releases along with tutorials
, making it a valuable resource for everyone from beginners to practitioners. Papers with Code compiles the latest research papers alongside their code, making it an essential channel for those who want to "read a paper and immediately try it out."

And if you want something light, fast, and trendy, Ben’s Bites newsletter is the best. It’s packed with Gen Z sensibilities, humor, and concise summaries, and its daily morning curation is great for keeping your AI sense sharp.

Number	Channel Name	RSS Link	Key Features Summary
1	HuggingFace Blog	Direct Link	Provides model releases, open-source libraries, and tutorials
2	Papers with Code	Go	Latest AI papers + code provided simultaneously, advantageous for understanding experiment-based trends
3	Ben’s Bites	Go	Gen Z-style AI newsletter, delivering short, impactful daily curation

1) HuggingFace Blog

• RSS: https://huggingface.co/blog/rss.xml
• Features: Model releases, open-source libraries, and tutorial guides

2) Papers with Code

• RSS: https://paperswithcode.com/rss
• Key point: Simultaneously access the latest papers + code (ideal for quickly catching trends)

3) bensbites.beehiiv.com

• RSS: https://bensbites.beehiiv.com/
• Key Point: Daily AI news curation. An AI newsletter with Gen Z sensibilities. (No RSS feed, but email subscription recommended)

🛠 Utilization Tips

• If you're preparing for a practical AI project, start by checking the tutorials & model release posts on the HuggingFace blog.
  Especially since there's a lot of news about Transformers and Diffusers, it becomes a core foundation before building generative AI.
• When developing paper-based features or benchmarking similar services, Papers with Code is incredibly useful for testing the latest model code directly. Hot models
  like GPT, SAM, LLaMA, and Mistral all get posted here quickly.
• Ben's Bites is perfect for spending just 5 minutes a day to get a sense of "what the world is currently obsessed with." While
  the tone is light, the links are all serious. Reading just this one newsletter effectively summarizes an hour's worth of Twitter timeline content.

Tips for Automating AI News

• Add the above addresses to RSS readers like Inoreader or Feedly for daily updates.
• You can also use GPT, Make.com, or Zapier to automatically summarize and deliver it to Slack, Notion, or KakaoTalk.

Schedule of Major Global AI & Cloud Conferences

Conference Name	Organizer	Key Topics	Target Audience	Date	Features
Google Cloud Next	Google	Cloud, AI, Data, Workspace	Developers, IT Leaders, Enterprises	April–May annually	Announcements of AI-based services and GCP strategies
Microsoft Ignite	Microsoft	Azure, Copilot, Security, Enterprise AI	Enterprise, Administrators	October–November annually	Across the entire MS ecosystem + Hands-on focus
NVIDIA GTC	NVIDIA	AI, DL, LLM, GPU, Robotics	Researchers, Developers, AI Startups	Every year in March or April	Center for Generative AI & GPU Innovation
AWS Summit	Amazon	Cloud Infrastructure, DevOps, AI Adoption	Developers, Startups, Enterprise IT	Regional Tour, Seoul in May	Hands-on workshops, industry-specific use cases
OpenAI Dev Day	OpenAI	GPT, API, Prompts, Agents	Prompt Engineer, Startup	Every November	Focus on GPT announcements, feature introductions, and demos
Hugging Face 🤗 Conference	Hugging Face	Open-source LLM, Transformers	Researchers, Open-Source Developers	Irregular (London 2023)	Focus on open models and collaborative ecosystems
ICLR / NeurIPS / ACL	Academia	AI Papers, Research Results Presentation	Researchers, AI academia	April–December (varies by conference)	Paper-centric. Cutting-edge technology introductions
TechCrunch Disrupt	TechCrunch	Startups, AI, Investment Trends	Founders, VCs, innovative companies	Every September–October	AI Startup Presentations and Investor Networking
Slush	Slush (Finland)	AI Startups, Business Innovation	Startups, Tech Companies	Every year in November-December	Europe's innovation hub outside Silicon Valley
RE•WORK AI Summit	RE•WORK	Industrial AI applications, Ethics, LLM	AI practitioners, companies	2-3 times per year (including Seoul)	Application-focused + Includes ethics and Responsible AI topics

Why AWS Summit is a Must-Attend for Beginner Developers

• 1. See technologies you've only heard about "actually" in action
  • Experience GPT, serverless, Lambda, EC2, and other technologies you've only read about through live demos.
  • You'll intuitively grasp, "Oh, so that's how it works!"
• 2. Hearing real-world implementation stories is eye-opening
  • See exactly how large corporations, startups, and solo developers build and operate their MVPs.
  • "I can do that too!" → Immediately applicable to your own service.
• 3. It's a great atmosphere for asking questions
  • Engineers are stationed at every booth, and there's Q&A after each session.
  • If you say you're a beginner, they explain things even more kindly. No need to feel embarrassed at all.
• 4. A place that welcomes non-developers too
  • With a wide range of fields—DevOps, AI, security, server operations, and data—
  • so there's plenty of valuable content for "planners/operators/founders" too.

👨‍💻 Top 5 Recommended AWS Summit Sessions for Development Beginners

Time (KST)	Session Title	Description
11:10 – 12:10	Zero to Hero: Build AI/ML on Amazon ECS with Just a Few Clicks	Learn how even those with limited development experience can easily build AI/ML workloads.
1:00 PM – 2:00 PM	Deploy and Develop LLMs with Amazon SageMaker: Simple Setup, Fast Responses!	A hands-on session focused on quickly deploying and operating large language models (LLMs).
2:10 PM – 3:10 PM	Amazon Q Developer: An AI Assistant That Gives Developers Back Their Time	Learn how to automate repetitive development tasks to boost productivity.
15:20 – 16:20	Data Practitioner's Guide for Generative AI	Explains how to prepare and manage data required for AI model training.
4:30 PM – 5:30 PM	AWS Insights on Stabilizing Distributed Systems	Introduces strategies for ensuring the stability and scalability of distributed systems.

🧑‍💼 Top 5 Recommended AWS Summit Sessions for Service Operators

Time (KST)	Session Title	Description
11:10 AM – 12:10 PM	Building Generative AI-Powered SaaS Services with Amazon Bedrock	Explore how to innovate by integrating generative AI into SaaS services.
1:00 PM – 2:00 PM	The Fast Track to Becoming a Cloud Expert: Smart Operations with Amazon Q Developer	Introduces tools and strategies to enhance cloud operational efficiency.
2:10 PM – 3:10 PM	Data Foundation in the Generative AI Era: From Data to Intelligence, the Game-Changer Driving Innovation	Explore data-driven decision-making and AI integration strategies.
15:20 – 16:20	Customer Service Innovation with Amazon Connect and Generative AI	Shares case studies on enhancing customer experiences by applying AI to customer service.
16:30 – 17:30	How Security Teams Can Shine: Turning Threats into Opportunities with Generative AI!	Explore how leveraging AI in security operations strengthens threat response.

How to Register for AWS Summit

You cannot enter the venue without registering for AWS Summit. Since AWS Summit is a "major conference in the IT industry," advance registration is mandatory.
Registration is free, and simply registering in advance grants you access to the venue badge, souvenir booths, and tours.

Note: You must register within the application period. (I tried to register on the day itself and couldn't do it myself.)

1. Access the official site and apply
   • Direct Link to AWS Summit Seoul 2025 Official Page Registration
2. Click the "Register Now" button
   • It's free.
   • You can register with just an email address, even without an AWS account.
   • Complete the application by filling in the form with your name, email, company, job title, use case, etc.
3. 3. After registration is complete, check the email you used to apply
   • You will receive a QR code and registration number.
   • The QR code is your admission ticket. Entry is not permitted without it.

You cannot apply for AWS Summit after the application period ends. If you missed the application, you can only participate in the sessions via live streaming.

What if you missed the AWS Summit application?

If you missed the AWS Summit registration period, consider applying for the live stream online.

For those unable to attend due to distance, those
who missed pre-registration but don't want to miss out
,
beginners wanting to focus and watch at their own pace,
or service operators wanting to grasp the latest tech trends

can apply for online live streaming to watch the lectures held at AWS Summit.

How to Apply for Online Live Streaming

1. Access the AWS Summit Seoul official page: https://aws.amazon.com/ko/events/summits/seoul/
2. Click the "Register for Online Participation" or "Live Streaming" button
3. Enter your email/name and complete registration
   • Access live on the day of each session!
   • Some sessions also offer **VOD (Video on Demand)** for later viewing.

Finally,

• You can ask questions via live chat.
• Repeat learning via VOD (note-taking recommended)
• Provides the latest AWS services & real-world application cases exactly as they are.

A single security setting could compromise your entire MVP

These days, you can whip up an MVP in no time just by integrating a couple of AI tools like Claude or Cursor. GPT polishes your language, Claude gives emotionally resonant answers to questions, and Cursor IDE turns your thoughts into code.

Even a 20-year veteran developer says, "Wow, the world has really improved." Seeing me, a non-developer, whip up an MVP server, it must feel unfair from the perspective of someone who developed in C. Yet, we often don't know where the conversations or code exchanged with AI are stored or where they're transmitted.

Claude stores logs for 30 days by default, and Cursor can send your entire repository information externally with its default settings. Yet most people feel reassured by this convenience and remain indifferent to security settings.

But most security incidents aren't caused by hacking—they're "problems arising from not setting things up."

1. Observe: Actual leaks stem more from 'configuration errors' than technical flaws

Claude and Cursor are extremely useful for AI-powered workflow automation, but using them with default settings carries a constant risk of information leakage.

✅ Core Issue

• The Claude API stores prompt and response data for 30 days by default
• Cursor IDE sends work logs and code snippets to external servers when Privacy Mode is OFF
• Users often input sensitive data without realizing this

📌 Leak Scenarios Scenario 1

If a request containing the API Key is sent to Claude → It is stored verbatim in the logs

✅ Situation

Situation where a user directly asks Claude about API usage:

이 API 키로 사용자 리스트 가져오려면 어떻게 해야 해?  
API Key: sk-test-51a23abc456defg789

⚙ How it works

• The Claude API (MCP) stores prompts and response logs for 30 days by default.
• Without a separate contract (Enterprise plan) or configuration, these are stored on the server.
• Within the Claude system, Anthropic's internal operations team has access to these logs (unless on an Enterprise plan).

🧨 Path of Occurrence

1. API Key included in the prompt → Transmitted to Claude
2. Claude API servers automatically log this content
3. Logs are retained for 30 days and can be accessed for internal auditing or debugging
4. May be exposed during internal audits or error debugging without external compromise

📊 Risk Assessment

Item	Risk Level
Scope of Exposure	1 API Key → Potential access to entire server
Potential for internal exposure	Accessible to Anthropic internal operations team
External Exposure Potential	Low (No direct hacking, but weak security)
Risk of human error	High (Developers, planners, and marketers all habitually ask GPT)

📌 Leak Scenario 2

Cursor with Privacy Mode OFF → Entire code automatically uploaded

✅ Situation

• Writing Claude integration prompts
• .env, config.json, api_keys.py Indexing repositories containing Claude integration prompts

⚙ How It Works

• When Privacy Mode is OFF, work history is sent to external servers like Cursor logs + Fireworks
• When indexing repositories, the entire structure is uploaded externally in chunks
• Without filtering .env, potentially including sensitive files

🧨 Occurrence Path

1. Indexing a repository containing sensitive files
2. Cursor automatically analyzes and saves
3. Some code structure and configuration values are transmitted to third-party servers
4. Stored for up to 30 days or more depending on external server log retention period

📊 Risk Assessment

Item	Risk Level
Scope of Leakage	Entire project structure + sensitive configuration values
Potential Internal Exposure	Accessible by Cursor team or connected external platforms
External Exposure Risk	Possible during man-in-the-middle attacks or API integration issues
Likelihood of Error	Very high (Initial state is ON by default with insufficient notifications)

2. Connecting: The Triple Risk Created by Claude + Cursor + User Habits

Actual information leaks do not stem from a single tool issue, but occur when the Claude API + Cursor IDE + user behavior intersect.

Simultaneous exposure of these three components creates unexpected security vulnerabilities.

Component	Key Risk
Claude MCP	– Default 30-day log retention – Dangerous commands possible upon tool registration – Transmission records not removed if settings are inadequate
Cursor IDE	– Logs stored and externally transmitted when Privacy Mode is OFF – Risk of including sensitive files when indexing entire repositories
User Habits	– Sharing prompt screens during meetings- API key exposure via captured images- Sharing sensitive code snippets on Slack/Notion

🎯 Key Insight

Tool security → IDE settings → Human habits: When all three become lax simultaneously, actual security incidents quietly occur.

3. Discovering the Principle: The Most Common Mistakes

Actual leaks mostly stem from "habits"

Bad Habits	Potential Risks
Directly entering API keys into Claude	Server Access Privilege Leak
Cursor Privacy Mode OFF	Transmission of Entire Project Logs
Uploading to GitHub .env Upload	Service fully exposed
Enter actual URL/path in prompt	Service structure leakage to competitors

---

4. Practicing Security Safety: Claude MCP + Cursor Security Checklist

Claude API

• Request Zero-Retention setting on Enterprise plan
• Do not call the API directly from the frontend; only call it from the backend
• Do not directly input API keys, URLs, or product names in prompts → <<KEY>>, <<URL>> Use

MCP Tool

• Mandatory JSON schema implementation during registration
• Execute results in Sandbox first
• Set tool permissions with separate read/write access

Cursor IDE

• Privacy Mode must be enabled
• Indexing is only permitted up to the README level
• .cursorignoreIncludes the following:

.env  
credentials.json  
secret.py

Organization security policy

• Core documents encrypted and stored using Git-crypt or Age
• External collaborators must sign NDAs + minimize access permissions
• Quarterly Red Team security assessments conducted

The trend in video content production is evolving so rapidly that people are saying, "It's not made by humans, it's made by AI."

Especially with the dramatic advancement of text-to-video auto-generation technology, even people with zero
video editing experience can now create high-quality videos in just 10 minutes.

Today, we'll organize 20 AI video generation tools that are easy to use for everyone from beginners to experts, categorized by function, purpose, and style. This is also extremely useful
for those creating character-based content 🍊

✅ Top 20 AI Video Generation Tools (Features + Use Cases)

Name	Key Features	Recommended Use
Sora (OpenAI)	Generates high-quality photorealistic videos using only natural language input	Idea sketching, storyboarding
Runway ML (Gen-2)	Text/image-based videos with motion control	Cinematic short-form content, advertising
Pictory	Blog text → Auto video + subtitles + voiceover	Blog repurposing, YouTube
Synthesia	AI Avatar Debut, Narration in 80 Languages	Lectures, presentations, global content
HeyGen	Diverse Character Avatars, Capable of Emotional Expression	Interviews, Explanatory Videos
DeepBrain AI	Realistic avatars + natural Korean speech	Advertising, presentation videos
Kaiber	Strong in generating artistic style videos	Music videos, experimental content
InVideo	Slideshow-style video creation, offers numerous templates	Marketing, SNS content
Pollo AI	Image prompt → Animation generation	Character videos, fairy tale content
Luma AI (Dream Machine)	Generate high-speed live-action videos around 5 seconds	Live-action backgrounds, short clips
Pika Labs	Excels at text-to-motion/color/style video generation	Gen Z short-form content, trendy ads
D-ID	Static facial images → Speaking video implementation	Character AI, horoscope chatbot implementation
Colossyan	Collaboration-focused platform + Specialized in educational content	Tutorials, corporate manuals
Elai.io	Multilingual Support, Script Input → AI Avatar Video Generation	Global introduction content
Hour One	Real-person-based video templates + text conversion	News, corporate reports
Veed.io	AI-powered automatic subtitle generation + editing features	Short-form branding, editing assistant
Fliki	Text Input → AI Narration + Video Matching	Blog Briefing, Audio-Based Content
Lumen5	Article or text-based → Automatic image slide placement	Blog summary videos
Rephrase.ai	Personalized marketing videos, name insertion possible	Email videos, customer retargeting
Descript (Scenes)	Podcast audio → Automatic video generation + Audio editing	Audio-based YouTube videos

🧩 Feature-Specific Comparison Chart

Feature	Avatar-Centered	Slide-style video	Style/Animation Videos	News/Education Specialized
Synthesia	✅	–	–	✅
Colossyan	✅	✅	–	✅
Elai.io	✅	✅	–	✅
Pictory	–	✅	–	✅
Lumen5	–	✅	–	✅
Pika Labs	–	–	✅	–
Runway ML	–	–	✅	–
Veed.io	–	✅	–	–
D-ID	✅	–	–	–
Rephrase.ai	✅	–	–	✅

🎯 Recommended Combinations by Purpose

Purpose	Tool Combinations	Description
Character Content	D-ID + HeyGen	Image Character → Emotion-Expressive Video Implementation
Blog Auto-Video Generation	Pictory + Lumen5 + Fliki	Completion of Routine for Automatic Text-to-Video Generation
Character Implementation	D-ID + Elai.io	Script-Based Video + Natural Speech Support
Stylish Short-Form	Pika Labs + Runway	Style/Motion-Based Branding Short-Form
Educational content creation	Colossyan + Synthesia	Optimized for avatar-based lecture videos
Presentation/Report Summarization	Veed.io + Lumen5	Automates script → slides → editing

AI Video Practical Routine Example: "Blog → YouTube Video Conversion"

Goal: Automatically convert daily blog posts into YouTube content

1. Blog Post Collection 
   – Automatic extraction via WordPress API or RSS 
   – Recommended text length: 800–1500 characters
2. Pictory Input → Video Draft Generation 
   – Auto-generate subtitles and
   narration – AI voice customization available if needed
3. Add motion/backgrounds with
   Luma AI or Kaiber – Apply emotion-customized
   backgrounds – Achieve authentic short-form feel
4. Insert subtitles/logos/brand music with Veed.io 
   – Build consistent brand styling 
   – Auto-export in various resolutions

Tips for Creating Character Videos

• When using static image
  characters → Animate mouth shapes and expressions with D-ID
• Adjust speech/sentences with emotional intonation using Elai.io or Synthesia
• Use Runway ML or Luma AI for backgrounds that enhance character emotion
• Automate periodic content publishing with Make + GPT + Pictory

🔮 Final Summary

Item	Summary
Number of Tools	20 total major AI video generation tools
Core Features	Text → Video, Avatar Speech, Subtitle Automation, Style Motion
Recommended Routine	Blog → Automatic Video Conversion / Character Interview Visualization
Recommended For	Solo brands, YouTubers, course creators, content marketers

---

The days of struggling with content creation are over.
Now, all you need is one idea to produce a video. Automate
your emotionally resonant stories with AI video tools.

If you have more questions, just tell us your "purpose of use."
We'll immediately recommend tools + design combination routines + automation flows for you.

After the ChatGPT craze, every developer wants to build an AI model. But the reality? It ends up costing way too much money.

Especially for individual developers or startups:

• Cloud: Unpredictable billing bombs 💸
• On-premises: Heavy initial investment burden 💰
• Just giving up: Falling behind in AI innovation 📉

But is this really the only way? So I've put together a summary.

2025: A New Turning Point in AI Development

1. HuggingFace + AWS combo

I fine-tuned one sentiment analysis model, then nearly had a heart attack seeing the AWS bill the next day

You might set a monthly budget of around 1 million won, but when billing day rolls around, you could get hit with an unexpectedly huge charge.

2. On-Premises vs. Cloud: Reality Check

Is on-premises really the answer? Dell EMC server racks + a knowledge industry center (with low electricity rates) could be far more efficient.

Dell EMC server rack configuration:

• 4 GPU servers (RTX 4090 x 4 per server)
• Total Purchase Cost: 80 million KRW (one-time)
• Knowledge Industry Center electricity cost: 500,000 KRW/month

Equivalent performance on AWS p3.8xlarge:

• $14.688 per hour (approx. 20,000 KRW)
• Assuming 720 hours per month: 14.4 million KRW
• Over 170 million won per year 💸

Conclusion: Running it for just 6 months shows that on-premises can be more profitable when viewed as a long-term investment.

3. But the hidden costs of on-premises

bash# 예상 vs 현실
초기구매비: 8,000만원 → 1억 2천만원 (UPS, 쿨링시스템 추가)
전기세: 월 50만원 → 월 120만원 (에어컨 24시간 가동)
관리비: 0원 → 월 200만원 (시스템 관리자 필요)

4. Ultimately, the developer's dilemma

Cloud: Flexible but a cost bomb
On-premises: High upfront costs but profitable long-term?

But the real problem is… both cost a lot of money 😭

5. So the real solution we found: NPU

Neural Processing Unit = AI-dedicated chip

• Over 10x more power-efficient than GPUs
• High initial cost but long-term benefits
• Predictable fixed costs

NPU + Knowledge Industry Center Combination:

• Initial: 30-80 million KRW
• Monthly operation: 500,000–1,500,000 KRW (electricity + management)
• After 6 months: Becomes cheaper than AWS

6/ But the real game changer is this

Pre-trained model + Fine-tuning

• Training from scratch ❌ Utilizing existing models ⭕
• Reduces development time by 1 year
• Saves hundreds of thousands of dollars
• Only 100,000-500,000 KRW per month

🧠 AI Training Cost Strategy at a Glance

Strategy	Recommended For	Key Benefits	Emotion-Based Criteria	Budget	Risk
🔹 Pre-trained model + Fine-tuning	Short-term results, MVP launchers	Time + Cost Savings, Flexibility	Suitable for MVP implementation	💸 100,000~500,000 KRW/month	Limited customization
🔹 NPU + On-Premises	Companies building their own AI OS	Lower power costs, reduced long-term expenses, increased independence	Capable of building large-scale architectures	💸 Initial investment: 30–80 million	Initial capital burden
🔹 Small Language Models (sLM)	Personal creators, prototypes	Laptop-compatible, lightweight	Optimal for UX experimentation	💸 0~100,000 KRW	Difficulty with complex logic processing
🔹 Cloud NPU (KT ATOM)	Startups seeking GPU alternatives	Stability↑, Operational Ease	Backend for server processing	💸 300,000~700,000 KRW/month	Dependencies, complex setup

1. Pre-trained models + Fine-tuning (Highly recommended)

Leveraging pre-trained AI models can reduce AI application development time by up to one year and save hundreds of thousands of dollars.

Reference: What Are Pre-trained AI Models? : NVIDIA Blog

Cost: 100,000–500,000 KRW/month

• HuggingFace models + AWS/Google Cloud Spot Instances
• Fine-tune existing models for specific use cases

2. NPU + On-Premises Combination (Long-Term Optimal)

NPUs offer higher efficiency compared to GPUs, excel at achieving price competitiveness through mass production, and deliver low-power, high-performance AI computations

Reference: AitimesTechm

Initial Cost: 30-80 million KRW Monthly Operating Cost: 5-15 million KRW (Electricity + Maintenance)

3. Utilizing Small Language Models (sLM)

Small models are gaining prominence starting in 2025. They can deliver meaningful performance even with billions of parameters, making them easily executable on personal laptops or high-performance smartphones.

Reference: Where is AI Headed in 2025? 7 Essential Trends You Must Know Now

4. Cloud NPU Services

KT Cloud offers Rebellion's ATOM chip NPU on its cloud platform. Compared to traditional GPUs, it offers the advantages of low power consumption and high performance, enabling cost savings.

Helpful Resource: Serving sLM with NPU: Exploring New Possibilities — kt cloud [Tech blog]

💡 Conclusion: Why NPU + Knowledge Industry Centers Are the Answer

NPUs are intelligent semiconductors optimized for specific AI computations, delivering superior power efficiency and performance compared to general-purpose GPUs in their respective domains.

Reference: Server and Edge-Oriented NPU Technology Development Trends

Why NPU + On-Premises is Optimal:

• Power Efficiency: NPUs are gaining attention as an alternative to overcome the limitations of high power consumption and high costs, enhancing efficiency through low-power, high-speed processing
• Predictable Costs: No cloud billing surprises
• Data Security: Eliminates the need for external data transmission
• Long-Term Cost-Effectiveness: Investment payback within 6 months to 1 year

Reference: Why NPUs are gaining prominence over GPUs in the AI era… "The key is power and cost savings"

🚀 Final Recommendations

However, due to the large initial investment:

• For short-term projects → Utilize pre-trained models
• If AI is a core business long-term → NPU + server rack on-premises + knowledge industry center (low electricity costs) is the most efficient choice.

Share your experiences saving on AI development costs or tales of billing hell in the comments! However, due to the large initial investment cost, for short-term projects → utilize pre-trained models, and if AI is to be a core business long-term → NPU + server rack on-premises + knowledge industry center (low electricity rates) is the most efficient choice.

"Should I learn to code?" This question is no longer unusual. Recently, NVIDIA CEO Jensen Huang emphasized, "AI will handle coding, so now we must cultivate domain knowledge like 'digital biology.'" In an era where AI automatically writes code, what knowledge should we truly delve into? The bio industry, in particular, is rapidly rising in importance as it is closely linked to survival issues like drug development, food, and climate.

Below, we'll explore the importance of domain knowledge in the AI era and the transformations occurring within the bio industry.

📌 If you're grappling with these questions, take note!

Even if AI writes code automatically, failing to grasp the problem's essence can lead to catastrophic errors in critical fields
. Complex domains like biology, drug development, and climate issues demand more than superficial knowledge. You must cultivate 'deep
domain expertise' by mastering field experience, tacit knowledge, and regulatory insights. If you want to stay competitive and avoid being left behind in the AI era, read this article to the end.

🧬 What is Domain Knowledge, the Core Competitive Edge in the AI Era?

Below, you can see why domain knowledge is crucial in the AI era.

Distinction	Shallow Knowledge	Domain Depth (Deep)
Scope	Surface-level information obtained through tutorials or searches	Includes field experience, failure cases, regulations, and specialized terminology
Thinking Style	"What is the API usage?"	"What are the optimal metrics and constraints?"
AI Utilization	Using AI-generated code as-is	Verify and modify AI results for practical application
Example	"Low heart rate variability indicates high stress"	Clinical criteria for heart rate variability, age/gender adjustment, sensor error consideration

📖 Why does domain knowledge become a competitive advantage?

• Focus on the essence of the problem, not grammar: Since AI writes code for us, the ability to define problems is now the core competitive advantage.
• Fields where errors are critical: Biology, finance, healthcare, etc., require deep knowledge because errors can threaten lives or lead to significant losses.
• Solving complex problems: Fields involving multiple intertwined elements like hardware and data ethics cannot be addressed with superficial knowledge alone.

In other words, the deeper the domain knowledge, the greater the ability to accurately define and solve complex problems.

🚀 AI-Driven Innovation in the Bio Industry

Discover the transformative convergence of AI and the bio industry below.

Field	Traditional Approach	Changes After AI Implementation
Drug Development	Years of experimentation and clinical trials	AI simulation and automated synthesis, dramatically accelerating research speed
Synthetic biology	Repetitive and manual processes	AI-based automated design and DNA synthesis, maximizing experimental efficiency
Climate and food solutions	Traditional and Limited Approaches	AI-based precision agriculture, enabling design of carbon-fixing microorganisms

• Drug Development: AI rapidly discovers drug candidates and increases clinical trial success rates.
• Synthetic Biology: Automated DNA printing and AI design enable gene editing in complex organisms.
• Climate and Food Challenges: AI can be leveraged to develop environmentally friendly crops or design microorganisms that efficiently sequester carbon.

🎯 3-Step Routine for Deepening Domain Expertise

The step-by-step approach to building domain knowledge is as follows.

Step	Practice Method	Goal
Literature Review	PubMed articles, patents, regulatory information exploration	Identify core keywords and latest trends
Field Research	Expert interviews, analysis of real-world cases	Acquiring tacit knowledge obtainable only in the field
Experimental Validation	Direct experimentation using AI-in-the-Loop methodology	Enhancing AI result reliability and reducing correction rates

• Literature Review: Continuously update the latest research and regulatory information to strengthen surface knowledge.
• Field research: Acquire tacit knowledge such as regulatory environments and failure patterns through expert interviews.
• Experimental Validation: Deepen knowledge through practical experience verifying AI-provided results and reducing incorrect cases.

Consistently practicing this process will show the correction rate for AI recommendations drop from 30% to below 10%.

📌 Frequently Asked Questions (FAQ)

Can I engage with AI and the bio industry even if I know nothing about coding?
Yes, domain knowledge and problem-definition skills are more important than coding. Understanding basic AI principles is sufficient.

What's the fastest way to acquire domain knowledge? The quickest
method is to interview field experts and cultivate the habit of regularly reading and analyzing the latest research papers.

What is AI's greatest impact on the bio industry? It helps solve critical
problems like drug development, food security, and climate change quickly and accurately.

How can we increase the reliability of AI recommendation results? Reliability can be
enhanced through direct AI-in-the-Loop experiments and iterative feedback processes.

How do domain knowledge and tacit knowledge differ?
Domain knowledge is information obtainable from literature, while tacit knowledge is experience gained on the ground and knowledge acquired from real-world environments like regulations.

📘 Essential Additional Information for the AI Era!

🌱 Success stories in the bio industry using AI

Case	Company	Achievement
Accelerated New Drug Development	Insilico Medicine	Shortening Drug Development Time
Climate Change Solutions	Ginkgo Bioworks	Successful Development of Carbon-Capturing Microorganisms

• Companies actively leveraging AI are rapidly growing in the market and demonstrating tangible results.
• By referencing these cases and establishing your own AI utilization strategy within your field, you can expect even greater results.

In conclusion

The era where AI handles coding has arrived. However, true competitiveness still lies in domain knowledge. The more complex and high-risk a field is, like the bio industry, the more essential it is to possess deep domain expertise. While AI boosts efficiency through automation, the power to define problems and understand the context of solutions remains a human responsibility. Cultivate your domain depth through literature reviews, field interviews, and AI-in-the-Loop experiments. This sustained effort will ultimately become your unique competitive edge, guiding you through the AI era.

In an era of rapidly evolving technology where AI has become part of daily life, competitiveness now hinges on "which platform provides what insights," transcending mere tools.

While numerous global tech summits will be held in 2025, we've curated the top 10 summits across key fields—AI, cloud, data, design, and startups—that deliver genuine insights, organized by category and timing.

We encourage all tech players—developers, founders, designers, and planners—to check them out.

🌍 2025 Global Major Tech & AI Summit List

Summit Name	Schedule (Tentative)	Field	Key Content	Recommended Attendees
AWS Summit	May 14-15, 2025	Cloud / AI / SaaS	AWS AI Services, Serverless Architecture, Startup Case Studies	Developers, CTOs, Startup Operators
Snowflake Summit	June 23-26, 2025 (Las Vegas, USA)	Data / Analytics / Automation	Data Engineering on Snowflake, AI Model Integration	Data Analysts, BI Teams, Data Startups
OpenAI Dev Day	Early November 2025 (Online/San Francisco)	Generative AI / API	Latest Feature Releases: GPT, Embedding, Assistant API, and More	AI Developers, Planners, AI Startups
Google Cloud Next	April 8–10, 2025 (Las Vegas)	Cloud / AI / Infrastructure	Vertex AI, Firebase, AI App Building	Cloud Engineers, App Developers
Microsoft Build	May 2025 (Online and Seattle, USA)	Copilot / GitHub / Azure	Microsoft-based productivity, AI agents, Dev Tool release	.NET, GitHub Copilot users, PM
Figma Config	Scheduled for June 2025 (San Francisco + Online)	Design / Collaboration Tools	Design Systems, Dev-Design Collaboration Automation	Designers, Frontend Developers, UX Planners
SXSW (South by Southwest)	March 7–15, 2025 (Austin, USA)	Culture / Entrepreneurship / Future Technology	AI, Creativity, Content Business, Insight Trends	Entrepreneurs, Cultural Planners, Content Creators
Notion Block by Block	Scheduled for Q4 2025 (Online)	Workflow / Productivity	Notion API, Automation, Collaboration Case Studies	Team Leaders, Marketers, Notion Users
AI for Good Summit	Scheduled for July 2025 (Geneva, Switzerland or online)	Ethical AI / Social Value	Topics: AI and Sustainability, Human Rights, Public Innovation	Public institutions, social enterprises, researchers
Anthropic Claude Summit (TBD)	Second half of 2025 (estimated)	Generative AI / Ethical AI	Claude API Utilization, Responsible AI Design	AI Researchers, Prompt Engineers

✅ Which summit is right for me?

Target Audience	Recommended Summit
Beginner Developers & Founders	AWS Summit, Microsoft Build, OpenAI Dev Day
AI-Based SaaS Planners	Snowflake, Google Cloud Next, Claude Summit
Designers & UX Teams	Figma Config, Notion Block by Block
Content-Based Founder	SXSW, Notion, AI for Good
Data Analyst/Engineer	Snowflake, Google Cloud Next, AWS

✈️ Final Tips

• Most events available online (free or discounted with early registration)
• Sign up for official site/email alerts to get sessions as soon as they're announced
• Numerous reviews/summaries available on YouTube, Dev.to, Medium, etc., enabling post-event learning