AI-fueled Economic Revolution- cost wars erupt, markets fracture, cyber threats surge

● AI Agent Manus – Crush Market Research in 20 Minutes

How to Use AI Agent Manus | Practical Application of Wide Research to Automate Market Research and Data Collection in 20 Minutes

The key contents covered in this article are as follows: Manus setup and how to use credits/redeem codes, linking internal data with M-Connect, Adaptive option and tips for balancing speed and quality, the internal working principles of Wide Research parallel processing (virtual browser + VM), source verification and hallucination prevention techniques that must be validated in practice, cost and ROI calculation methods, legal and security risks, and prompts/checklists readily usable by job roles (Planning/Analyst/Investor/Marketing). We will first highlight ‘how to utilize Data Source Tracking (Audit Trail)’ and ‘how to calculate cost-efficiency versus actual credits and speed’, which are rarely covered in other YouTube videos or news.

1) Prerequisites (Pre-decided Matters)

Check your account and membership type.

It is recommended to secure initial credits (redeem code below can be used).

Clearly define the research scope (number of companies, period, industry classification) and key KPIs (e.g., total funding, number of rounds, growth rate).

If using internal documents (Google Drive, Notion, GitHub, etc.), pre-connect with M-Connect and verify permissions.

2) Project Settings — Options and Prompt Design

Understand the differences between Adaptive, Autonomous, and Chat modes.

Adaptive is optimal for research where parallel processing and mid-course corrections are useful.

The choice of speed (vs quality) directly impacts cost and result reliability.

Control the total time and cost by adjusting the number of parallel agents and session VM resources.

Example Prompt (for company research): “Collect global AI startup investment trends (details: data labeling, LLM solutions, medical AI, etc.) by sector and round for the past 2 years, organize them into a CSV, and generate a summary report including key investors, valuations, and market positions. Be sure to cite sources (articles/Crunchbase/technical reports).”

Example Prompt (for empirical research): “Collect 50 empirical studies on AI adoption in finance, marketing, and healthcare, and provide a list of papers comparing quantitative impacts (e.g., revenue/cost/ROI metrics) and a comprehensive analysis. Prioritize academic databases (arXiv/Google Scholar) and include paper DOIs and links.”

3) Wide Research Execution Flow (Chronological)

1) Initial scanning (keyword-based bulk search) to generate a candidate list (companies, papers, etc.).

2) Each agent’s virtual browser visits designated sources (TechCrunch, Crunchbase, arXiv, company websites, etc.) to read original texts and extract core metadata.

3) Based on extracted results, automatically generate additional exploration queries and perform iterative deep crawling.

4) Parallel agents independently investigate each target (e.g., 20 companies) to produce structured CSV/MD outputs.

5) Automatic generation of a final comprehensive report (including summary, implications, and references).

Practical Case Tip: Running Wide Research for 20 companies can secure basic data in approximately 20 minutes (average of about 1 minute per company).

4) Outputs and Practical Application

Default outputs provided by Manus: CSV (quantitative data), MD/Markdown file (raw report), PDF (final report), data source logs (browser activity).

Connecting CSVs to BI tools (e.g., Tableau, Power BI) enables visualization and dashboard automation.

Create investor lists and contacts directly importable into CRM or IR (Investor Relations) for immediate use in fundraising references or partner scouting.

Instruct the report to always include ‘References/URL (Source)’ to facilitate source verification.

5) Essential Risks & Limitations to Check (Key points often not covered elsewhere)

Audit Trail is central to research reliability.

Keep Manus’s virtual browser logs and crawled original URLs to verify samples.

Preventing Hallucinations (false generated information): Enforce rules on agents such as ‘Do not present claims without a source’ or ‘Tag as low reliability if no source is available.’

Cost Control: Parallel VMs provide speed but consume significant credits.

Test the estimated credit consumption per session to first calculate a budget (e.g., researching 20 companies = approx. X credits).

Legal and Copyright Risks: Paid/subscription sources may have scraping restrictions.

When linking personal information or internal data, always verify permissions and encryption policies.

6) Validation Process (Quality Control Checklist)

Sample Extraction Verification: Randomly sample 5-10% of the total subjects and manually review them.

Cross-verification: Check if the same item has been confirmed by two or more reliable sources.

Introduce a Confidence Score: Requesting a ‘Confidence’ field in Manus output is beneficial for post-processing filtering.

Maintain Change Logs: When changing prompts or options, keep session logs separately to ensure reproducibility.

7) Workflow Examples by Job Role (Practical Application)

Product Manager (PM): Research 20 reference companies → Standardize features/business models → Reflect in roadmap.

Analyst: Collect funding/valuation data → Time-series analysis → Distribute reports.

Investment Team: Organize investor/round history → Generate deal sourcing list → Write IR scripts.

Marketing: Organize competitor messaging/case studies → Create content calendar.

8) Advanced Tips & Prompt Recipes

Actively utilize internal data: Combining with in-house research and customer data via M-Connect dramatically increases report reliability and practicality.

Speed/Quality Trade-off: Recommend a hybrid workflow where initial scanning is run for ‘speed,’ and final report generation is rerun for ‘quality.’

Explicit source prioritization: Giving source priority like “Priority: Crunchbase > TechCrunch > Official Press Releases > Academic” improves accuracy.

Post-automation: Receive the output CSV and configure an automated pipeline for KPI calculation and visualization using Python scripts.

9) Cost & ROI Calculation (Quick Guide)

1) Measure the average credit consumption per experimental session (3-5 test sessions recommended).

2) Total target research count × Number of parallel agents × Credits per session = Estimated total credits.

3) Multiply by cost per credit (or membership cost) to calculate total expense.

4) Calculate ROI by comparing with existing manual labor time (personnel cost): (Time saved × Personnel cost) ÷ Manus cost = ROI.

Realistic Example: If a task that took a person one day (8 hours) to research 20 companies is reduced to 20 minutes with Manus, productivity improves by over 24 times on a time basis, and when converted to efficiency, productivity can increase by 10 to 100 times depending on the company or project.

10) Security & Legal Checkpoints

Check the terms of service for the scraping target.

When connecting internal data, apply the principle of minimizing OAuth/API Key permissions.

Instruct to remove or mask sensitive information (PII) from research subjects.

11) Actual Operation Template (Simple Step-by-Step Guide)

Step 1: Define research goals and KPIs (10 minutes).

Step 2: Draft Manus prompts (20 minutes).

Step 3: M-Connect integration and source prioritization (15 minutes).

Step 4: Execute Wide Research (20 minutes to several hours, depending on the number of targets).

Step 5: Sample verification and reliability correction (30-60 minutes).

Step 6: Report distribution and BI integration (30 minutes).

12) Events & Credit Information (Actual Benefits)

Event 1: Write ‘how you plan to use wide research’ in a comment and leave your email; 5 people will be randomly selected to receive a 1-month free Manus Pro account coupon (worth $199).

Event Period: Until September 28, 2025.

Event 2: Sign up with the redeem code below to receive 1000 free credits for everyone (until October 20, 2025).

Redeem Link (Go to signup page): https://manus.im/redeem?c=AIGB002W

Redemption Code: AIGB002W

(Caution) When using the redeem code, always check account-specific policies and credit validity period.

13) Conclusion and Recommended Action Plan

Short-term (1 week): Run a small pilot (10-20 cases) to experience credit consumption, speed, and reliability.

Mid-term (1-3 months): Connect to internal workflows and BI, automate standardized reports through templating.

Long-term (6-12 months): Standardize Manus-based research pipelines into investment and product strategies to secure global economic and market research competitiveness.

Manus is not a ‘simple AI chat’ but a tool that automates practical research through virtual browsers, parallel VMs, and data integration.

< Summary >Manus’s Wide Research automates large-scale market research and data collection through virtual browsers and parallel VMs.Key elements include preparation (account, credits, M-Connect), options (Adaptive, Autonomous, Speed/Quality), and prompt design.Prevent hallucinations through source tracking and sample verification, and plan your budget by measuring credit consumption.For practical application, structuring workflows by PM, analyst, investment team, and marketing roles yields immediate results.Secure initial credits with the redeem code (AIGB002W) and run a pilot.< / Summary >

(The main text naturally includes keywords such as ‘global economy’, ‘market research’, ‘AI trends’, ‘productivity’, and ‘funding’ for SEO optimization.)

*Source: [ AI 겸임교수 이종범 ]

– AI 에이전트 Manus 활용법 | 시장조사와 자료조사 20분만에 자동화하는 와이드 리서치 실전 사용법

● Korea-Japan Marriage Boom – AI Fuels Economic Revolution

The Surge in Korea-Japan International Marriages: Phenomena, Causes, Economic Impact, and AI Opportunities — This article provides a comprehensive overview of statistics, on-site reactions, hidden economic influence, the “economic role of the matching industry” often overlooked elsewhere, and “practical AI solutions for prediction and support.”

1) Confirming the Phenomenon — Basic Facts and Time Series

Statistics show that the number of marriages between Korean men and Japanese women increased by approximately 40% from 2023 to 2024.It is estimated to have risen from about 850 cases in 2023 to around 1,100 cases in 2024.The fact that this is the highest figure since 2015 is significant.This trend is occurring in conjunction with global economic shifts and demographic changes.

2) Analysis by Core Causes of the Phenomenon

Economic FactorsSouth Korea’s relative wage increases and Japan’s long-term wage stagnation are shifting comparative advantages.Differences in living costs and housing expenses have an immediate impact on marriage and migration decisions.This ripples through the labor market and consumption patterns (household consumption structure).
Media and Cultural FactorsShort-form video platforms like TikTok and YouTube are rapidly reproducing the image of Korean men within Japan.This content acts as ‘advertisements’ for the marriage market, accelerating matching demand.
Visa and Practical MotivationsVisa restrictions (such as the expiration of short-term stays) create an incentive for long-term residency through marriage.This leads to practical decisions beyond personal feelings (such as securing stable living and residency rights).
Growth of the Matching IndustryKorea-Japan customized marriage agencies (e.g., Daily) organize demand and facilitate actual transitions (dating → marriage).They are not merely service providers but act as ‘invisible immigration brokers,’ playing an economic role in population movement.

3) On-Site Reactions and Culture Clashes — Stereotypes vs. Reality

Reactions from the Japanese side are polarized.Among men, relative dissatisfaction is observed, while among women, there is a simultaneous increase in interest and a surge in inquiries.
Examples of Cultural Differences (Practical Tips)Differences such as chopstick etiquette, customs regarding sharing personal belongings, and frequency of contact (Korea’s frequent communication vs. Japan’s independence) can become sources of conflict in daily life.Explicitly agreeing on these cultural codes before marriage can reduce conflicts.

4) The Most Important Point We Rarely Hear — The Economic Role of the Matching Industry

A key aspect rarely covered by other media: marriage agencies and brokerage platforms go beyond mere intermediation to create economic networks.By providing integrated services for language, visas, housing, and finance (remittances, household goods, insurance), they drive small-scale commerce and entrepreneurship.Therefore, the surge in international marriages leads to an increase in ‘household-level cross-border economic activity,’ creating new demand for local small businesses, real estate, and service industries.
Policy ImplicationsThese should not be dismissed as mere socio-cultural phenomena.If the economic ecosystem (service supply chain) formed through brokers and platforms is recognized and managed by policy, it can lead to regional economic revitalization.

5) Impact on Demographics and Labor Market (Short- to Mid-Term Outlook)

Potential for Mitigating Population DeclineWhile international marriages impact marriage and birth rates in the short term, long-term residency and settlement are key.Supported by policy integration (language education, employment assistance), this can translate into a tangible population influx effect.
Labor Market ImpactMigration through marriage alters the composition of household labor.Specifically, it induces subtle changes in the labor market structure through household consumption management, small business startups, and the creation of small-scale trade and services between the two countries.This creates concentrated demand in specific industries (housing, education, translation/interpretation, marriage services).

6) The Future Transformed by AI and Technology (An AI Perspective Rarely Highlighted Elsewhere)

Enhanced Matching and TrustworthinessAI recommendation algorithms can enhance matching based on cultural compatibility, lifestyle habits, and values.This evolves beyond simple appearance and hobby matching to models that predict the probability of successful settlement.
Role of Automatic Translation, Counseling, and Educational ToolsReal-time translation and AI-based language education rapidly lower initial language barriers.However, cultural nuances (e.g., etiquette, hospitality culture) require hybrid solutions combined with human counseling.
Predictive Analytics (Early Warning System)By analyzing social media and search trends, AI can detect surges in marriage trends faster than traditional statistics.This information enables early responses from policymakers, real estate companies, and marriage agencies.

7) Business and Policy Recommendations — Practical Action Plan

Government (Policy)Spotlight: Promote the packaging of sponsor visas, language education, and integrated services.Local governments should design customized housing and welfare programs for international couples.
Businesses (Business Opportunities)Marriage matching platforms should create premium products combining AI recommendations with cultural coaching,and fintech companies should offer customized financial services (household management, remittances, insurance) for multinational households.Real estate developers should plan Korea-Japan couple-preferred products like ‘small housing + outdoor space.’
Civil Society and NGOsStrengthen initial adaptation support, mediation of domestic conflicts, and programs protecting the rights of women and foreigners.

8) Risks and Mitigation Strategies

Risks: Exploitation/Commodification, Social Backlash, Housing InstabilityMitigation: Transparent matching regulations, post-marriage support (integrated assistance), linking with housing support policies.
Risks: Family breakdown due to cultural misunderstandingsMitigation: Mandate pre-marriage cultural education and hands-on experience programs, align expectations through enhanced counseling.

9) Practical Checklist (For those advising/supporting Korea-Japan couples or seeking business opportunities)

Before Marriage: Create a cultural differences checklist (e.g., dining etiquette, frequency of contact, family visit norms).
Visa/Residency: Prioritize confirming long-term residency options and employment possibilities.
Finance: Transparently agree on household budget and salary information through realistic conversations.
Technology: Combine translation apps with AI counseling to lower initial communication costs.
Community: Check local government’s multicultural support programs in advance.

10) Conclusion — Significance from the Perspective of Global Economy and AI Trends

The surge in international marriages is not merely a cultural phenomenon.It is a new ‘population and consumption network’ created by a combination of structural changes in the global economy, structural issues in the labor and housing markets, and the AI/platform economy.If policies and businesses respond agilely to this, they can contribute to regional economic revitalization and population issue mitigation.AI will be a key tool in prediction and support, and marriage agencies are highly likely to evolve into ‘integrated service platforms’ in the future.

< Summary >The approximately 40% surge in Korea-Japan international marriages from 2023 to 2024 is the result of a combination of economic, media, and visa issues, along with the accumulated activities of the matching industry.The key point is that matching companies are creating economic networks beyond simple intermediation.AI plays a significant role in matching accuracy, language barrier resolution, and trend prediction, requiring businesses and policies that utilize it.Key risks include exploitation, social backlash, and housing instability, with integrated support, regulation, and the combination of technology and education being solutions.

*Source: [ 지식인사이드 ]

– 현재 일본에서 한-일 국제결혼으로 난리 난 이유 | 상국열차 EP.1 (JM, 코노미, 홍대의 대표)

● Grok 4 Fast Ignites AI Cost Wars, Meta AR Fumbles, YouTube Explodes, DeepSeek Fractures Global AI

xAI Grok 4 Fast Announced, Meta Ray‑Ban Display, YouTube AI Tools, DeepSeek Safe — 8 Key Takeaways from This Article

AI, artificial intelligence, economic outlook, global economy, and AI trends are naturally integrated throughout the main text.The important topics covered in this article are as follows:What Grok 4 Fast’s performance-cost innovation and 2M token context window actually mean.How Grok 4 Fast’s adoption of ‘agentic behavior’ as a default changes cost structures and service design strategies.The realistic limitations and commercial potential of AR interaction presented by Meta’s Ray‑Ban Display + Neural Band.How YouTube’s AI creator tools will transform creator revenue models and production workflows.The long-term impact of national ‘model forks’ like DeepSeek R1 Safe on intellectual property, regulation, and market fragmentation.In-depth insights into changes in cost structures, infrastructure, and corporate purchasing behavior, often overlooked in other reports.An actionable checklist and strategic recommendations for businesses, creators, and investors.A summary of key observation points and investment/policy risks for the next 6-18 months.

1. Grok 4 Fast Unveiled — What’s Changed (Timing: Immediately After Announcement)

Grok 4 Fast is a model that maintains inference performance similar to the existing Grok 4 while reducing costs by up to 98%.The most notable change is its massive 2 million token (2M) context window.This context size allows tasks involving long legal documents, research reports, game design documents, or extended conversation sessions to be completed ‘without switching models’.The model design adopts a ‘one brain, two modes’ approach, handling both fast responses and deep reasoning (chain-of-thought) with the same model.Tool usage is built-in, so actions like web searching, code execution, and link jumping are processed as default behaviors.In a real-world demo, it showed autonomous exploration (agentic) behavior, such as cross-verifying multiple sources to calculate total experience points for Path of Exile 2 level-ups.The cost structure is stated as 20¢/million for input and 50¢/million (base) for output, with rates increasing incrementally beyond a certain token count.Initial adoption barriers are lowered by offering free trials through open routers and certain gateways.Key insights (points not well covered by other news outlets) are as follows:Grok 4 Fast’s price-performance policy makes ‘large-scale, long-context’ applications economically viable, accelerating the externalization of enterprise research, legal, and R&D workloads previously only possible with large models.The ‘tiered’ structure of the token-based pricing system is likely to stimulate corporate contracts (custom rates) and on-premise/hybrid deployment demands, predicated on large-scale context usage.As agentic capabilities become a default feature, new SLA (Service Level Agreement) items will be required to control the entire loop of ‘question → tool invocation → verification → result’.

2. Grok 4 Fast Technical and Operational Details (Timing: Access & Developer Release)

Performance Metrics: Approaches or in some cases exceeds Grok 4 in standard inference, math, and coding tests.Token Usage Efficiency: Reports indicate it uses approximately 40% fewer inference tokens for the same result on average.Tool Usage Strategy: Features ‘meta-decision-making’ for autonomously deciding to invoke web crawling, image/video analysis, and code execution.Pricing Details: A three-tiered structure: Free tier (limited traffic) → Small-scale pricing (initial entry) → Tiered pricing for high token usage.Developer Experience: Immediately accessible via iOS/Android apps and API, with a choice of two flavors (inference-specialized/non-inference).Business Impact: Provides a clear cost advantage in services combining CRM/ERP with large-scale document processing, contract review, research assistance, and long-term customer conversation history.

3. Meta Ray‑Ban Display + Neural Band Release — Real-World Usage Observations (Timing: Product Launch Date)

Meta has launched the Ray-Ban Display bundled with the Neural Band wrist controller in the US market.The basic HUD provides a monocular (right eye) 600×600 pixel display with approximately 20° field of view.The Neural Band reads finger muscle signals to implement gestures like pinch and swipe.The advantage is the ability to quickly manipulate the UI with ‘hand gestures rather than eye movements’.The disadvantages include a heavier frame compared to traditional Ray-Bans, and the monocular HUD design can cause eye strain and imbalance with prolonged use.The chipset is Snapdragon AR1 (1st generation), which has performance limitations compared to AR1 Plus, leading to observed UI delays and stuttering.Practical Implications: Wrist-based input like the Neural Band has the potential to make hand gestures a mainstream interaction method in AR’s ‘gaze, voice, hand’ combination.However, for it to become an everyday substitute, the next generation (dual-eye, eye-tracking, more powerful chip) will be needed.

4. YouTube’s Expansion of AI Creator Tools — Changes in Production and Monetization (Timing: Platform Release)

YouTube has added Idea Inspiration, Title A/B Testing, Automatic Dubbing (including lip-sync), and Similarity (face) Detection to Creator Studio.Live streaming supports simultaneous horizontal/vertical broadcasting, mini-games, and automatic highlight cuts.Shorts will feature V3 Fast (motion transfer, restyle, object insertion) and AI editing/remixing tools.On the monetization front, features include automatic timestamping for product tags, post-placement brand sponsorship replacement, and automatic matching through a partnership hub.Practical implications are as follows:Content creation and experimentation costs will significantly decrease, boosting the competitiveness of long-tail creators.Automation of brand-creator matching will accelerate the commodification of sponsorships and standardization of contracts (in the form of rate cards).’Side-by-side simultaneous exposure’ for ad formats, if it replaces traditional interruptive ads, will necessitate a redesign of ad revenue structures and CPM/CPV auction mechanisms.

5. DeepSeek R1 Safe and National Model Forks (Timing: National Regulatory & Project Announcements)

DeepSeek R1 Safe is a variant re-trained in collaboration with Huawei and universities to align with Chinese norms.Official announcements claim a ‘near 100% success rate in avoiding sensitive topics,’ but success rates drop significantly with aggressive prompts (role-playing, indirect questioning).Saudi Arabia’s Humane Arabic model and US demands for neutrality, among others, reflect a trend where models are fragmenting into ‘multiple regional forks’ rather than remaining ‘single global’ entities.The core impacts are as follows:In the global economy, AI services have reached a turning point where not only technological prowess but also adherence to political and cultural regulations becomes a competitive advantage.When selecting models, companies must simultaneously consider ‘regulatory compliance, supply chain, and data sovereignty’.The market for ‘safety-aligned’ model certification and verification, and regional compatibility bridge (translation, policy filter) businesses, will create new business opportunities.

6. Economic, Industrial, and Policy Ripple Effects — Practical Perspective Summary (Timing: 6-18 Months from Now)

As AI costs significantly decrease, the margin structures of SaaS and PaaS will be reshaped from a global economic perspective.In particular, as long-context processing becomes economically feasible, automation will accelerate in high-value sectors such as law firms, consulting, and game studios.National model forks are likely to lead to technological protectionism, potentially increasing global supply chain and data localization costs.AR hardware (e.g., Ray-Ban Display) is still in its early stages, so consumer adoption will follow a pattern of ‘experimental adoption → next-generation hardware’.AI tools on platforms (YouTube) will simplify the production pipeline, leading to a surge in content supply, which in turn increases the importance of discovery and curation technologies.Business Recommendation: Companies should explicitly include ‘token-based operating costs’ and ‘regulatory compliance costs’ in their Total Cost of Ownership (TCO) models.Investment Perspective: High opportunities for follow-up investment in ‘cost-efficient inference’ and ‘model safety and verification’ solutions.

7. Practical Checklist — What to Do Right Now

C-Level: Add token cost scenarios (low/medium/high) to project budgets.Engineers/Product Teams: Measure latency and cost in long-context scenarios (e.g., 100k+ tokens).Creators/Marketers: Experiment quickly with YouTube’s A/B tools and automatic highlights to gain an early advantage.Legal/Compliance: Prepare a checklist for the regulatory compliance (standard refusal/filtering) of AI models for each region of use.Investors: Consider benchmark investments in agentic services and regulatory verification solutions.

8. Key Observation Points for the Next 6‑18 Months

Observe how much lower Grok 4 Fast’s token rates will go in actual large-scale enterprise contracts.Check when Meta will integrate AR1 Plus, dual-eye HUD, and eye-tracking in its next version.Monitor the real impact of YouTube’s advertising experiments (side ads, sponsorship automation) on CPM and ad revenue.Confirm what changes regional model forks will bring to supply chains and contract clauses (data sovereignty, governing law).Watch whether ‘verification certificates and labels’ become standardized in the alignment verification market.

⚡ Key Implications Not Well-Covered by Other Media (Exclusive Insights)

The tiered token pricing structure will make ‘large-scale, long-context’ demand sensitive at the enterprise level, leading to a surge in on-premise and hybrid deployment requirements.Agentification is not merely a UX improvement but a paradigm shift in the design of corporate SLAs, legal responsibilities, and data evidentiary management (logs, source tracking).Ray-Ban’s Neural Band suggests that ‘wrist-based input’ could become a core interface determining the actual adoption of AR interaction.While national ‘Safe’ forks reduce regulatory risk in the short term, they will, in the long term, cause global market fragmentation and interoperability costs, forcing multi-vendor strategies.The increasing automation features on YouTube will lead to an oversupply of content, potentially causing the value of ‘content quality filtering and brand safety’ technologies to skyrocket.

Execution Recommended Roadmap (Prioritized)

Step 1 (0-3 months): Conduct internal PoC for Grok 4 Fast token cost simulation.Step 2 (3-6 months): Experiment with content and test ad formats using YouTube AI tools.Step 3 (6-12 months): AR pilot (including Neural Band) and assessment of accessibility and fatigue.Step 4 (12-18 months): Prepare for regulatory risks with multi-model backup and supplier contract redesign.

< Summary >

Grok 4 Fast makes long-document and agentic workloads economically viable with its 2M token context and cost-efficiency.Meta’s Ray-Ban Display introduces a new input axis with the Neural Band, but hardware limitations (monocular HUD, chip performance) remain.YouTube’s AI tools reduce production costs and create structural changes in monetization, likely reshaping the advertising and sponsorship ecosystem.DeepSeek Safe and national model forks will accelerate regulatory-cultural filtering, altering global supply chains and contract structures.Practically, prioritizing token cost scenarios, regulatory compliance, AR UX pilots, and platform experiments is essential.[Related Articles…]Grok 4 Fast: What a 98% Cost Reduction Means — A Practical Interpretation Meta Ray‑Ban Display Real-World Review and the Significance of Neural Band

*Source: [ AI Revolution ]

– xAI Just Dropped Grok 4 FAST: Faster, Cheaper With 2M Context Window

● Skywork AI’s Report Dominance – Consulting Shift, Legal Bombshells Emerge

Reading this article will immediately provide you with the following key insights:We present Skywork’s ‘fundamental competitive advantages (source tracking, consulting-grade design, speed)’ that differentiate it from Manus, Genspark, and ChatGPT, through real-world examples and comparisons.We provide an implementation checklist and risk/regulation response strategies for each enterprise adoption timeline (Immediate → Short-term → Mid-term → Long-term).We thoroughly explain critical insights rarely covered elsewhere — legal/licensing risks of sources, potential for vendor lock-in, and strategies for integration into internal knowledge bases.We offer practical evaluation metrics (accuracy, source transparency, speed, cost, security) and prompt/template examples that can be used immediately in practice.We summarize the mid-to-long-term impact of Skywork-related tools on industries from the perspective of the global economy and the Fourth Industrial Revolution (economic outlook, AI).

Does Skywork Outperform Manus, Genspark, and ChatGPT for ‘Practical Reports’? — Comprehensive Analysis

Immediate — Feature & Quality Differences and Practical Applicability

Skywork’s core strengths are ‘clickable source attribution’ and ‘automatic generation of consulting-grade visualizations.’This significantly reduces verification and review time when preparing market reports or investment materials.Manus, Genspark, and general ChatGPT variants excel at text generation, but show differences in visualization, source transparency, and format consistency.Quick Summary: For ‘presentation-quality’ deliverables that can be immediately deployed in tasks, Skywork holds an advantage.

Sub-items — Practical Checks

Source Transparency: Skywork provides original links for each figure and claim, enabling the generation of ‘verifiable reports.’
Visualization Quality: It offers automatic charts and infographics, allowing for the creation of presentation slides without a separate designer.
Speed: Many users report results 2-3 times faster for the same task (however, differences may arise during internal data upload or customization).

Short-term (6-12 months) — Enterprise Adoption and Operational Considerations

Adoption Priority: Security & Data Governance → Custom Template Building → Deployment & Training.Enterprises must verify Skywork’s method of ‘aggregating external sources’ and its connection to internal knowledge (internal reports, CRM).The most crucial point (often not covered in videos or news): Even if a source is ‘clickable,’ it does not guarantee commercial use permission for that data.In other words, the presence of a source does not automatically resolve licensing issues, copyright problems, or data usage restrictions (Rights & Attribution).

Sub-items — Operational Checklist

Legal Review: Confirm the commercial usability of automatically attached citations and scraped images.
Logs & Audits: A system capable of tracking who cited which source is necessary (for compliance).
Internal Knowledge Integration: Integrate with RAG (Retrieval Augmented Generation) to manage internal data and external sources separately.

Mid-term (1-3 years) — Market & Business Model Changes

Structural changes in the consulting and research industries are accelerating.Traditional research houses will see significantly reduced costs for ‘data collection and draft preparation’ and must shift their differentiation strategy to ‘client-specific interpretation and strategy design.’Tools like Skywork lower report production costs and enable even small startups to produce high-quality reports, thereby reducing market entry barriers.What this means: Intensified price competition in the consulting market and a redefinition of the role of specialized personnel.

Sub-items — Business Opportunities

Productization: Selling report templates as SaaS or commercializing API-based report automation products.
Collaboration Ecosystem: Increased partnerships with design and data providers (for visualization and data sources).
Workforce Redeployment: Junior researchers shifting roles to data verification and source curation.

Long-term (3-5 years) — Regulation, Governance, Market Structure

The ‘reliability’ of data sources will become a key competitive differentiator.If regulations strengthen (e.g., EU’s AI Act), source tracking and transparency could become legal requirements.Skywork’s ‘source attribution’ feature is an initial defense, but regulatory bodies are likely to demand stricter audit logs and explainability.Furthermore, vendor lock-in can be an issue, so companies must confirm open format and export capabilities as essential features.

Sub-items — Regulatory Response Points

Audit Log Retention: Store source, generation time, and prompt records for each output.
Application of Data Minimization Principle: Cite only necessary parts when using external sources.
Multi-Vendor Strategy: Diversify dependency by duplicating core pipelines.

Key Insights Rarely Discussed Elsewhere (Exclusive Insights)

1) Beware of the misconception that a clickable source guarantees ‘truly verifiable data.’

The more diverse the sources (Google snippets, news articles, academic papers), the greater impact differences in licensing and recency (publication date) will have on decision-making.2) When mixing RAG (Internal DB + External Web), risks increase if ‘source priority rules’ are not automated.
It’s necessary to establish a policy on whether internal company knowledge takes precedence over external sources, or if the latest external data is preferred.3) The ‘interactivity’ of visualizations is a powerful advantage in presentations, but format loss can occur when converting to static reports (for submission/filing).
It’s necessary to check for potential information loss or distortion when converting dynamic graphics to PDF.4) Vendor marketing (speed, discount codes) promotes initial adoption, but scarcity marketing like ’50 spots’ has little impact on actual enterprise adoption planning.
Instead, measuring ROI in a PoC (Proof of Concept) is crucial.

Practical Evaluation Metrics (Accuracy, Provenance, Speed, Cost, Security) — Checklist

Accuracy: Measure factual consistency across 10 sample reports.Provenance: Ratio of successful clicks to original sources, indication of source publication date and author.Speed: Average generation time (seconds/minutes) for identical requirements.Cost: Cost per report (including labor cost replacement effect).Security: Data encryption, log retention, availability of on-premise/private instance options.

Prompts & Templates (Ready-to-use Examples)

Template A — Consulting Report (Market Entry)”Please generate a consulting report for a startup aiming to enter the European market with an AI Fintech LLM SaaS.Include: Competitor analysis, market size (2020-2026), pricing strategy, regulatory risks, recommended actions, and implementation roadmap.Attribute each figure with a clickable source link, and include a 1-page summary and visualizations for 5 slides.”Template B — RAG Verification”Combine the following internal documents and external papers to create a summary.Set internal document priority as ‘Internal > Academic > News,’ and label the source and publication date for each claim.”

Cost Structure, Commercial Model, and Vendor Management

Tools like Skywork are typically based on subscription SaaS, with advanced features (on-premise, dedicated instance, SLA) incurring additional costs.Practical Tip: During the PoC phase, itemize and estimate costs separately for ‘cost per report,’ ‘API call cost,’ and ‘data storage cost.’Vendor Lock-in Countermeasures: Ensure regular data export and guarantee of standard formats (CSV, PPTX, PDF + metadata).

Impact from the Perspective of Global Economy & Fourth Industrial Revolution (Economic Outlook)

The ‘report productivity enhancement’ of AI tools increases research output, reducing information asymmetry and improving the strategic planning capabilities of small and medium-sized enterprises.From an economic outlook perspective, a decrease in report production costs is highly likely to lead to reduced market information costs → accelerated competition → improved industry efficiency.However, advanced analytics and strategic consulting may become premium services, requiring consulting personnel to shift towards high-value-added competencies.The proliferation of AI tools will accelerate the digital transformation of the global economy and establish itself as a core competitive advantage in the era of the Fourth Industrial Revolution.

Conclusion — 6 Things Companies Should Do Right Now

1) Execute PoC: Compare Skywork, Manus, and Genspark using the same prompt for 3 actual reports.2) Legal Review: Confirm source and image licensing, and commercial use possibilities.3) Security Policy: Establish policies for logs, audits, and exports.4) Cost Structure Analysis: Itemize subscription + additional costs (data, format conversion).5) Internal Training: Establish prompt design and source verification workflows.6) Multi-Vendor Strategy: Duplicate core pipelines and back up data.

Skywork holds a strong advantage in practical report generation with ‘clickable sources’ and ‘consulting-grade visualizations.’However, practical limitations such as legal and licensing risks of sources, vendor lock-in, and format loss in dynamic visualizations must be thoroughly examined.Practically, adoption should proceed with a parallel strategy of PoC, legal review, security, and multi-vendor approaches, while in the mid-to-long term, the research market structure and the role of consulting personnel will be redefined.By conducting comparative tests based on key evaluation metrics (accuracy, provenance, speed, cost, security), the cost-effectiveness can be clearly determined.

*Source: [ TheAIGRID ]

– This New AI Tool DESTROYS Manus, Genspark And Chatgpt

● AI, Red Teams – Cyber Attacks Target Your Bottom Line

Ethical Hacking in Action: Red Teaming, Penetration Testing, and Cybersecurity Practical Guide

First, here are the core takeaways from this article: it’s organized chronologically from contract & objective setting, reconnaissance, vulnerability scanning, penetration testing, red team simulation, reporting & evaluation, to the practical impact of AI on offense and defense.In particular, this guide details practical core aspects often not covered in other YouTube videos or news — (1) KPIs for quantifying red team performance and a cost-benefit (ROI) framework, (2) how AI/LLMs integrate into the actual attack chain and telemetry design for defense, and (3) pre-emptive legal preparations and risk mitigation from a regulatory and insurance perspective.

1) Contract & Objective Setting (Scope & Rules of Engagement) — Project Start Point

It starts with the Statement of Work (SOW), goals, and constraints (rules of engagement).Objective: Defined from a business impact perspective (e.g., for a bank, a scenario like ‘account takeover → fund transfer’).Scope: Specifies IP/system/people/geographical constraints.Budget & Duration: Vulnerability scan 20–40 hours, penetration test 40–80 hours, red team 2–4 months or more.Legal & Insurance Issues: Pre-approval for testing (legal), verification of cyber insurance clause compliance.Key: The more rules, the more realistic the verification possible — effective test design is needed under constraints different from an actual attacker.

2) Reconnaissance (Information Gathering) — Drawing the Inside from an Outside Perspective

OSINT: Check domains/subdomains, employee SNS, Glassdoor, code repositories (e.g., credential leaks).Dark Web: Search for leaked credentials and token information.Third-party & Supply Chain: Check for exposed supply chain-linked services and APIs — a core attack vector today.Combine automated tools + manual verification: Clues gathered during reconnaissance are the first link in the attack chain.

3) Vulnerability Scanning (Automated) — Broad and Fast

Tools: Nessus, Qualys, OpenVAS, etc.Purpose: Rapid identification of the entire attack surface (vulnerability list generation).Deliverables: Vulnerability list, CVSS priority, simple recommendations.Limitations: Automated scans have false positives and negatives — manual analysis is needed for supplementation.Time & Cost: Efficient for initial risk mapping.

4) Penetration Testing — Manual Attack and Impact Analysis

Tools: Nmap (network scan), Burp Suite (web), Metasploit (exploit framework), etc.Method: Targeted vulnerability exploitation, privilege escalation, verification of data access possibilities.Framework: Map TTPs (techniques, tactics, procedures) with MITRE ATT&CK.Performance Indicators: Actual privilege acquisition, possibility of sensitive data access, scope of impact.Reporting: Technical reproduction steps + actionable prioritized recommendations.

5) Red Teaming (Adversarial Simulation) — A Comprehensive Test from a Real Attacker’s Perspective

Purpose: Comprehensive security validation based on realistic scenarios (people, process, technology).Core Concepts: Command-and-Control (remote control), lateral movement, persistence.Operation: Conducted with blue team unawareness (unknown state) or in a limited disclosure format.Referee Team: Responsible for safety, communication, and mediation — controls incidents and legal risks.Tools & Methodologies: C2 frameworks, reproducible TTP sequences, combination of real attack techniques such as steganography and spear-phishing.Performance Indicators (Recommended): Detection rate, Mean Time To Detect (MTTD), Mean Time To Respond (MTTR), dwell time, business impact score (monetized).

6) Reporting, Evaluation & Prioritization (The Most Crucial Stage)

Report: Technical evidence + non-technical summary (PowerPoint) + actionable recommendations.Format: Vulnerability discovered → reproduction steps → impact (business context) → priority & remediation guide.ROI Measurement: Present the expected reduction in losses (monetary) versus patching costs — essential for executive buy-in.Post-validation: Retesting after patching, continuous monitoring (continuous red/purple teaming).Key: Reporting is the value of the service. Without a report, it’s hard to be recognized for more than ‘fun’.

7) SOC, Blue Team & Orchestration — From Detection to Response

SOC Role: Log collection, correlation analysis, alarm management, initial response.SIEM & SOAR: Automate repetitive responses with QRadar, Splunk, Elastic + SOAR (automated playbooks).Personnel: SOC Analyst (Level 1-3), Blue Team trained in Red Team TTPs (needs understanding of attacker mindset).Training: Tabletop exercises, live drills, purple teaming (joint red & blue practice).Performance Indicators: Alert validity (true/false positives), alert handling time, manual compliance rate of responses.

8) AI and 4th Industrial Revolution Trends: Shifting Landscape of Offense/Defense (Key Insights)

Offensive Aspect: LLMs (Large Language Models) and automation accelerate phishing/social engineering creation, exploit chain automation, and vulnerability exploitation script generation.Defensive Aspect: AI-based anomaly detection (behavioral), ML models integrated into SIEM are powerful in noise filtering and correlation analysis.Points to Note (Often Not Emphasized Elsewhere):

Since attackers also use AI, ‘adversarial ML’ testing is needed to validate defensive models.
Defensive ML performance depends on the quality of telemetry (logs, endpoint, network packets, MFA events, etc.).
When utilizing LLMs, managing output hallucinations/privacy issues and implementing security controls during fine-tuning with internal data are necessary.Economic Context: Global economic instability (e.g., inflation, recession) increases pressure for corporate cost reduction, creating a temptation to cut security investments.However, supply chain vulnerabilities and the acceleration of digital transformation ironically expand the attack surface, increasing long-term costs.Practical Suggestion: When introducing AI, simultaneously presenting a roadmap for ‘detection improvement (KPI enhancement)’ and ‘business impact monetization’ makes budget allocation easier.

9) Organizational Design: People, Process, Technology + Governance

Composition: Red Team, Blue Team, Referee/Governance, SOC, Security Architect.Processes: Systematize pre-approval, emergency shutdown procedures, and post-test disclosure policy.Training: Educate Blue Team on Red Team TTPs, enhance AI/ML understanding for SOC.DevSecOps Integration: Automated vulnerability scanning + runtime protection (feature-specific defense) in CI/CD pipelines.Regulatory & Compliance Linkage: Incorporate compliance with personal information and financial regulations into red team scenarios.

10) Critical Insights Not Often Discussed Elsewhere (List Format) — Practical Tips for Immediate Use

1) Quantify business impact in monetary terms. This is how management approves patching budgets.2) Set KPIs as business metrics (potential monetary loss, service availability downtime) as much as technical indicators.3) Invest in ‘telemetry design’ — AI defense is useless without good data.4) Continuous purple teaming: Periodic, automated TTP validation, not just a one-off red team engagement.5) Conduct pre-workshops with legal and insurance teams to minimize losses in case of incidents during testing.6) Include backup and recovery scenarios in red team targets to validate actual ransomware recovery.7) Include attack scenarios using LLMs (e.g., tailored phishing) in defensive training.8) Deception (honeypots, honeytokens) dramatically increases detection rates.9) Red team performance culminates in the ‘report’ and ‘executive summary’ — design their format and delivery effectively.10) Cyber insurance often requires conditions (e.g., proof of regular penetration tests), so prepare for them in advance.

< Summary >The content is organized as a chronological scenario, flowing from contract → reconnaissance → scan → pentest → red team → report → continuous improvement.Key aspects include reporting and business impact monetization, AI defense based on telemetry quality, and proactive legal preparations such as regulatory compliance and insurance.As AI accelerates both offense and defense, investment in ML validation, adversarial ML readiness, and data design is crucial.Introducing business-centric KPIs (detection time, response time, monetary impact) makes it easier to justify security investments to management by explaining the ROI.

*Source: [ IBM Technology ]

– Ethical Hacking in Action: Red Teaming, Pen Testing, & Cybersecurity