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New York City, NY, Aug. 30, 2025 (GLOBE NEWSWIRE) — Introduction – What is Trade Vector AI

Trade Vector AI is a next-generation artificial intelligence platform designed to bring automation, speed, and accuracy to digital asset trading. Built on advanced algorithms and predictive modeling, the system processes large volumes of financial data in real time to identify trade opportunities with precision. Unlike traditional manual methods, Trade Vector AI leverages machine learning, neural networks, and adaptive logic to continuously refine its execution strategies.

The platform is positioned as a comprehensive trading environment where automation meets transparency. By integrating AI into every stage of the trading cycle—from market analysis to order placement—Trade Vector AI aims to reduce human error while maintaining a consistent flow of decisions based on verified data. Its architecture is designed for scalability, meaning it can accommodate both individual traders and institutional participants who require high-frequency execution.

In addition to real-time market scanning, the platform incorporates a robust risk-management engine. This ensures that automated decisions remain within set parameters, balancing opportunity with protection. With its secure design, Trade Vector AI establishes itself as more than just an automation tool—it functions as an intelligent trading partner capable of adapting to rapidly changing market conditions.

As digital markets evolve in 2025, Trade Vector AI positions itself at the forefront of financial technology. Its structured approach combines cutting-edge computing power, encrypted infrastructure, and seamless account integration. By uniting these components, Trade Vector AI presents a reliable, AI-driven ecosystem for those seeking precision-based automation in global markets.

Trade Vector AI Features

The core strength of Trade Vector AI lies in its integrated suite of features tailored to maximize efficiency in digital trading. At the foundation are AI-driven trading algorithms that process historical data, live order books, and market signals to identify patterns and execute decisions in fractions of a second. This enables continuous operation around the clock, capturing opportunities across global exchanges without manual intervention.

A key feature is its automated execution engine. Once trading parameters are defined, the platform executes transactions in real time, maintaining speed and minimizing latency. To complement automation, users can access a demo trading environment, which allows them to practice strategies and become familiar with the system before transitioning to live markets.

Trade Vector AI also integrates risk-management controls, enabling users to define stop-loss levels, trade size limits, and exposure thresholds. This ensures that trading activity remains aligned with individual preferences while maintaining capital protection.

The interface itself is designed for intuitive navigation, making it suitable for newcomers while still offering advanced configurations for professionals. Additionally, the system offers 24/7 operational capacity, ensuring uninterrupted monitoring and execution.

Behind these features is a robust data infrastructure that aggregates information from multiple sources simultaneously. This includes price feeds, volume metrics, and volatility indicators, all processed through machine learning models that continuously refine predictive accuracy.

By combining automation, demo access, risk controls, and continuous learning models, Trade Vector AI establishes itself as a comprehensive platform. Its feature set is designed not only for speed and accuracy but also for accessibility and security, offering a balanced framework for AI-driven trading.

Visit the Official Website Here For More Information

Trade Vector AI – Security Measures, and Factual Performance Data

Security and performance are cornerstones of the Trade Vector AI ecosystem. To protect user accounts and transactions, the platform employs SSL encryption, ensuring all communications and financial data remain secure. Additionally, integration with regulated broker partners strengthens the security framework, offering a layer of compliance oversight across trading activities.

From a technical standpoint, the system operates on high-availability servers that maintain uptime reliability. This infrastructure allows the AI engine to continuously monitor market conditions without interruption. With built-in redundancy and advanced firewall protection, Trade Vector AI minimizes downtime risks and cyber vulnerabilities.

On performance metrics, Trade Vector AI highlights factual execution speed and consistency. Internal tests indicate that the AI models are capable of analyzing multiple market conditions in milliseconds, identifying patterns and deploying orders faster than manual operations. The adaptive learning framework refines these models based on historical and real-time results, improving efficiency over time.

Transparency is built into the reporting tools, which provide real-time dashboards displaying executed trades, active positions, and profit/loss summaries. This ensures that users can validate system performance independently. Additionally, demo accounts are offered with simulated data, giving a transparent view of functionality before engaging in live trading.

Taken together, Trade Vector AI’s security protocols, regulatory partnerships, and verifiable performance tools create an infrastructure centered on trust. By combining encrypted communication, compliance-driven broker integration, and adaptive AI models, the platform presents a secure, factual, and technologically advanced foundation for automated trading.

Why Choose Trade Vector AI? Kuwait Consumer Report Released Here

Trade Vector AI Account Setup Process – Step by Step

The Trade Vector AI onboarding process is structured for clarity and efficiency. Each stage has been designed to help new users transition into automated trading smoothly, while meeting regulatory and security requirements.

Step 1 – Registration
Visit the official Trade Vector AI website and complete the registration form by entering your full name, email address, and phone number. Once submitted, a confirmation link is sent to activate the account.

Step 2 – Account Verification
For compliance and security, identity verification is required. Upload government-issued identification and proof of address to complete KYC (Know Your Customer) protocols.

Step 3 – Initial Deposit
To begin trading, an initial deposit is required. The minimum deposit requirement is $250, which serves as trading capital and unlocks live account access. Deposits can be made via credit/debit card, bank transfer, or other supported payment gateways.

Step 4 – Demo Account Access
Before live trading, users can explore the demo account, which mirrors real-market conditions without financial risk. This allows familiarization with the dashboard and settings.

Step 5 – Live Trading Configuration
Once ready, users set their trading preferences, including risk levels, trade size, and automation settings. The platform’s AI system takes over execution in real time.

Step 6 – Withdrawals
Profits and remaining balances can be withdrawn through the same methods used for deposits. Withdrawals are processed within a standard timeframe, subject to verification.

This structured account setup ensures accessibility, transparency, and compliance, while providing new users with a guided start into AI-driven trading.

Why Choose Trade Vector AI? Kuwait & Canada Consumer Report Released Here

How Does Trade Vector AI Works?

Trade Vector AI operates through an integrated cycle of data gathering, algorithmic processing, and automated execution. At its core, the platform relies on machine learning and predictive analytics to interpret complex market environments.

The process begins with data collection. The AI continuously scans global markets, gathering price movements, order book activity, and volatility metrics. This data is then processed through models trained on historical and real-time conditions.

Next, the analysis stage applies pattern recognition to identify trade opportunities. By referencing both past trends and present signals, the system can anticipate potential market moves. Unlike static strategies, Trade Vector AI uses adaptive models that adjust as new data emerges, ensuring relevance even in dynamic conditions.

Following analysis, the execution engine deploys trades according to predefined parameters set by the user. These parameters can include stop-loss levels, trade size, and exposure limits. By combining automation with customizable controls, the system balances efficiency with risk management.

Throughout this cycle, Trade Vector AI provides real-time reporting dashboards. These display open positions, recent trades, and performance metrics, giving transparency into how the AI operates.

In essence, Trade Vector AI functions as a self-sustaining system: collecting data, analyzing opportunities, and executing trades without manual intervention. Its adaptive learning ensures continuous refinement, making it a dynamic, AI-driven trading platform suited to modern market conditions.

From Beginner to Pro: Guided Onboarding, 24/7 Support, and Intuitive Design

Trade Vector AI is structured to accommodate traders of all levels through a combination of guided onboarding and continuous support. The platform introduces users with a step-by-step walkthrough, ensuring that account creation, verification, and configuration are handled smoothly.

For beginners, the inclusion of a demo trading account is essential. It provides a risk-free environment where strategies can be tested while users become familiar with the dashboard. The intuitive interface minimizes technical complexity, making it possible to engage with automated trading without prior experience.

Beyond onboarding, 24/7 support is available through multiple channels, including live chat and email. This ensures that any technical or operational queries are addressed in real time, regardless of location or time zone.

Professional users benefit from advanced configuration tools that allow greater control over trade parameters, risk exposure, and execution preferences. Despite this sophistication, the platform retains its user-friendly structure, allowing both beginners and professionals to navigate efficiently.

The design philosophy emphasizes accessibility, adaptability, and support. By combining guided onboarding, round-the-clock assistance, and intuitive controls, Trade Vector AI offers a pathway that adapts to the needs of all user levels. This structure enhances usability while reinforcing its position as a reliable AI-driven trading solution.

More Information on Trade Vector AI Can Be Found On The Official Website Here

Regulated, Transparent, and Secure: Why Trade Vector AI Earns Trust in 2025

Trust in digital trading platforms is anchored in regulation, transparency, and robust security protocols. Trade Vector AI integrates these pillars into its operational framework, positioning itself as a trusted environment in 2025.

From a regulatory standpoint, Trade Vector AI partners exclusively with licensed brokers, ensuring that trading activities align with established compliance standards. This collaboration introduces oversight mechanisms that help safeguard both deposits and executed trades.

Transparency is reinforced through real-time reporting tools. Users have immediate access to dashboards displaying trade history, open positions, and profit/loss metrics. This allows for independent validation of system performance and fosters accountability.

In terms of security, the platform incorporates end-to-end SSL encryption, secure payment gateways, and firewall protection. Together, these measures create a safeguarded ecosystem against unauthorized access and cyber risks.

Furthermore, by offering demo access alongside live trading, the platform emphasizes openness, giving users an opportunity to experience its operations first and before committing capital.

In 2025, where digital security and regulatory alignment remain critical, Trade Vector AI demonstrates adherence to these principles. By combining compliance partnerships, transparent reporting, and encrypted infrastructure, it presents itself as a secure, trustworthy, and transparent trading environment.

Trade Vector AI – Cost, Minimum Deposit, and Profit

The financial structure of Trade Vector AI is designed for accessibility while maintaining clear transparency. The minimum deposit requirement is $250, which serves as initial trading capital. This amount grants users access to the live trading environment after completing registration and verification.

There are no additional registration or account maintenance fees. Capital deposited is fully allocated toward trading activities, ensuring that funds are directed to market participation rather than platform overhead.

Profit generation is based entirely on the performance of AI-driven strategies in real market conditions. While the system provides predictive automation and adaptive execution, profitability depends on market volatility and trading configurations set by the user. The platform does not guarantee fixed returns; instead, it provides the tools, data analysis, and execution speed necessary for optimized outcomes.

Withdrawals are supported through the same methods as deposits, offering consistency and security. Standard processing times apply, subject to verification for compliance.

In summary, the financial entry point is structured at an accessible level, with transparent allocation of deposits toward trading. Profit potential is determined by market conditions and system performance, offering a clear, data-driven approach without hidden costs or unclear commitments.

Countries Where Trade Vector AI Is Legal

Trade Vector AI operates within a broad international framework, ensuring compliance with jurisdictions where automated trading is permitted. The platform is accessible across Europe, Asia, Africa, and Latin America, subject to local financial regulations.

In the European Union, Trade Vector AI functions in alignment with regulatory requirements, working in cooperation with licensed brokers. Similarly, in regions across Asia and Latin America, the platform’s operations adhere to local guidelines, ensuring that trading activities meet compliance obligations.

Access in North America is determined by specific state and federal rules. While availability may vary, the platform emphasizes regulatory alignment and transparency wherever it is active.

By maintaining partnerships with regulated entities, Trade Vector AI ensures lawful operation across multiple jurisdictions. This approach expands accessibility while safeguarding compliance, reinforcing its position as a global trading solution.

Visit the Official Website Here For More Information

Trade Vector AI Supported Assets

Trade Vector AI offers a wide range of supported assets, enabling diversified trading strategies across global markets. Central to its portfolio are cryptocurrencies, including leading tokens such as Bitcoin, Ethereum, and other high-liquidity digital assets.

Beyond crypto, the platform also integrates forex pairs, covering both major and minor currencies. This inclusion allows users to participate in highly liquid markets, taking advantage of global currency fluctuations.

In addition, Trade Vector AI provides access to commodities and indices, further broadening its scope. By supporting multiple asset classes, the platform ensures that users can diversify portfolios, manage risk, and access opportunities across markets.

This multi-asset framework is supported by AI models that adapt strategies according to asset-specific behavior. Whether operating in crypto, forex, or commodities, the system applies tailored predictive logic, ensuring relevance and precision in execution.

By integrating multiple asset categories under one environment, Trade Vector AI positions itself as a versatile, AI-powered platform capable of supporting diverse trading objectives.

Trade Vector AI – Final Verdict

Trade Vector AI stands as a comprehensive artificial intelligence trading platform, distinguished by its automation, transparency, and security framework. Its architecture is built on adaptive machine learning models that process market data in real time, enabling fast and precise execution.

The system incorporates essential components: demo access, risk-management controls, SSL-encrypted security, licensed broker partnerships, and multi-asset support. Together, these create an ecosystem that balances accessibility with advanced technology.

With a minimum entry requirement of €250, Trade Vector AI opens the door to live trading while offering risk-free practice through its demo environment. Its design accommodates all user levels, from beginners using guided onboarding to professionals seeking high-frequency automation.

In 2025, as trading systems continue to evolve, Trade Vector AI presents itself as a secure, regulated, and adaptive solution. By uniting predictive analytics, global market access, and a transparent operational model, it delivers a forward-looking platform for AI-driven trading.

Visit Here to Register on the Trade Vector AI – Select Your Country Here!!!

Contact:-
Trade Vector AI
485 Bd de la Gappe, Gatineau, QC J8T 5T9, Canada
Phone Support: Trade Vector AI Canada: +1 (437) 920-9751
Trading Assistance: +1 (437) 169-3417
Email: info@trade-vector-ai-app.net
Website: https://trade-vector-ai-app.net/
General Disclaimer:
The content provided in this article is for informational and educational purposes only. It does not constitute financial, legal, or professional advice. Readers are advised to consult a certified financial advisor, licensed loan officer, or legal professional before making any financial decisions. The information presented may not apply to every individual circumstance and is not intended to substitute professional judgment or regulatory guidance. The information provided on this website does not constitute investment advice, financial advice, trading advice, or any other sort of advice and you should not treat any of the website’s content as such. We does not recommend that any cryptocurrency should be bought, sold, or held by you. Do conduct your own due diligence and consult your financial advisor before making any investment decisions.
Trading Disclaimer:
Trading cryptocurrencies carries a high level of risk, and may not be suitable for all investors. Before deciding to trade cryptocurrency you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with cryptocurrency trading, and seek advice from an independent financial advisor. ICO’s, IEO’s, STO’s and any other form of offering will not guarantee a return on your investment.
HIGH RISK WARNING: Dealing or Trading FX, CFDs and Cryptocurrencies is highly speculative, carries a level of non-negligible risk and may not be suitable for all investors. You may lose some or all of your invested capital, therefore you should not speculate with capital that you cannot afford to lose. Please refer to the risk disclosure below. Trade Vector AI does not gain or lose profits based on your activity and operates as a services company. Trade Vector AI is not a financial services firm and is not eligible of providing financial advice. Therefore, Trade Vector AI shall not be liable for any losses occurred via or in relation to this informational website.
SITE RISK DISCLOSURE: Trade Vector AI does not accept any liability for loss or damage as a result of reliance on the information contained within this website; this includes education material, price quotes and charts, and analysis. Please be aware of and seek professional advice for the risks associated with trading the financial markets; never invest more money than you can risk losing. The risks involved in FX, CFDs and Cryptocurrencies may not be suitable for all investors. Trade Vector AI doesn”t retain responsibility for any trading losses you might face as a result of using or inferring from the data hosted on this site.
LEGAL RESTRICTIONS: Without limiting the above mentioned provisions, you understand that laws regarding financial activities vary throughout the world, and it is your responsibility to make sure you properly comply with any law, regulation or guideline in your country of residence regarding the use of the Site. To avoid any doubt, the ability to access our Site does not necessarily mean that our Services and/or your activities through the Site are legal under the laws, regulations or directives relevant to your country of residence. It is against the law to solicit US individuals to buy and sell commodity options, even if they are called “prediction” contracts, unless they are listed for trading and traded on a CFTC-registered exchange unless legally exempt. The Financial Conduct Authority has issued a policy statement PS20/10, which prohibits the sale, promotion, and distribution of CFD on Crypto assets. It prohibits the dissemination of marketing materials relating to distribution of CFDs and other financial products based on
Cryptocurrencies that addressed to Austria, Kuwait, UK/AU residents. The provision of trading services involving any MiFID II financial instruments is prohibited in the EU, unless when authorized/licensed by the applicable authorities and/or regulator(s). Please note that we may receive advertising fees for users opted to open an account with our partner advertisers via advertisers websites. We have placed cookies on your computer to help improve your experience when visiting this website. You can change cookie settings on your computer at any time. Use of this website indicates your acceptance of this website. Please be advised that the names depicted on our website, including but not limited to Trade Vector AI, are strictly for marketing and illustrative purposes. These names do not represent or imply the existence of specific entities, service providers, or any real-life individuals. Furthermore, the pictures and/or videos presented on our website are purely promotional in nature and feature professional actors. These actors are not actual users, clients, or traders, and their depictions should not be interpreted as endorsements or representations of real-life experiences. All content is intended solely for illustrative purposes and should not be construed as factual or as forming any legally binding relationship
RISKS ASSOCIATED WITH FUTURES TRADING
Futures transactions involve high risk. The amount of the initial margin is low compared to the value of the futures contract, so that transactions are “leveraged” or “geared”. A relatively small market movement has a proportionately larger impact on the funds that you have deposited or have to pay: this can work both for you and against you. You may experience the total loss of the initial margin funds as well as any additional funds deposited in the system. If the market develops in a way that is contrary to your position or if margins are increased, you may be asked to pay significant additional funds at short notice to maintain your position. In this case it may also happen that your broker account is in the red and you thus have to make payments beyond the initial investment.
RISKS ASSOCIATED WITH ELECTRONIC TRADING
Before you begin carrying out transactions with an electronic system, you should carefully review the rules and provisions of the stock exchange offering the system, or of the financial instruments listed that you intend to trade, as well as your broker’s conditions. Online trading has inherent risks due to system responses/reaction times and access times that may vary due to market conditions, system performance and other factors, and on which you have no influence. You should be aware of these additional risks in electronic trading before you carry out investment transactions.
Accuracy Disclaimer:
All information included in this article is presented in good faith and believed to be accurate at the time of writing. However, no representations or warranties are made regarding the completeness, accuracy, reliability, or timeliness of any information presented. Any reliance placed on such information is strictly at the reader’s own risk. The publisher does not accept responsibility for typographical errors, outdated information, or changes to products, terms, or policies after publication.
Regulatory and Jurisdictional Disclaimer:
Lending laws vary by jurisdiction, and not all services described in this article may be available in every state or region. It is the responsibility of the reader to understand and comply with local laws and regulations. The platforms mentioned are independently operated and are not controlled or endorsed by the publisher.
Third-Party Liability Waiver:
The publisher, its writers, editors, affiliates, and syndication partners shall not be held liable for any direct or indirect loss, damages, or legal claims arising from the use of this content or from reliance on any third-party services, platforms, or products mentioned herein. All loan agreements, terms, and disputes are strictly between the borrower and the lender or service provider.
Syndication Partner Use:
This content may be republished or syndicated by authorized partners under existing licensing or distribution arrangements. All syndication partners are free from liability regarding the editorial stance, financial suggestions, or any user outcome resulting from the reading or application of this content.


            



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Billionaire Philipe Laffont Just Sold Coatue Management’s Stake in Super Micro Computer and Piled Into Another Artificial Intelligence (AI) Giant Up Over 336,000% Since Its IPO

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Philipe Laffont is part of an elite group of investors called the Tiger Cubs, who worked for Julian Robertson’s Tiger Management in the 1990s.

In the 1990s, an elite group of investors worked for a tech-focused hedge fund called Tiger Management, led by the legendary investor Julian Robertson. Not only did Robertson mentor this group of investors, but he would go on to seed many of their future hedge funds as the talented group, referred to as the Tiger Cubs, went on to become great investors in their own right.

Philippe Laffont, the founder of Coatue Management, is part of this group, and is now viewed as one of the great tech investors of the modern era. Coatue Management’s equity holdings were valued at roughly $35 billion at the end of the second quarter. That’s why investors are always paying attention to which stocks Coatue is buying and selling.

In the second quarter, the fund sold its stake in Super Micro Computer (SMCI -5.42%) and piled into another artificial intelligence (AI) giant that generated a total return over 336,000% since its initial public offering.

Image source: Getty Images.

Super Micro Computer: Beating the shorts so far

AI and tech infrastructure and server maker Super Micro Computer has been a controversial and volatile play for the past year. In August 2024, short-seller Hindenburg Research came out with a major short report alleging potential accounting fraud at the company. The report said that Supermicro rehired executives who had been a part of an accounting scandal at the company in 2018 that involved understating expenses and overstating revenue.

The stock got hit hard after Supermicro announced it would need to delay its annual 2024 filing to assess its internal controls. However, the company would eventually go on to file its 2024 10-K and did not need to restate any of its financial statements, a good sign for investors. Furthermore, management earlier this year also provided strong fiscal 2026 guidance of $40 billion in revenue, way ahead of consensus at the time. Supermicro’s fiscal year ends on June 30 of each year.

In August, shares struggled after the company reported lower-than-expected quarterly results and weaker-than-expected guidance, due to President Donald Trump’s tariffs, which resulted in less working capital in June and “specification changes from a major new customer.” Laffont and Coatue loaded up on the stock some time in the fourth quarter of 2024 and sold in the second quarter of this year, so the fund could have bought the dip after the short report and might have sold over concerns about tariffs, although that’s speculation. Supermicro’s stock is up about 46% this year, so Coatue seems to have timed its trade well.

Supermicro looks real cheap right now for a stock benefiting from the AI boom, trading around 16 times forward earnings. Tariffs are likely to be an ongoing issue but if AI demand remains strong, Supermicro, which supplies servers to the likes of Nvidia, should be a major beneficiary. The stock may remain volatile, but I think investors can take a position in the more speculative part of their portfolio.

Oracle: A longtime tech player benefiting from AI

With a market cap of nearly $664 billion, Oracle (ORCL -5.97%) isn’t part of the “Magnificent Seven,” but it’s another large tech company expected to benefit from the AI capital expenditure boom. Coatue purchased over 3.8 million shares in the second quarter, valued at over $843 million.

The cloud giant offers clients the ability to tap into a number of AI solutions including generative AI and machine learning capabilities that provide automation tools and AI application development, among other services. Similar to Microsoft and Amazon, although not as dominant, Oracle’s position as a cloud provider positions the company well to be a first point of contact for clients looking to add AI capabilities.

In the company’s most recent earnings report for its fourth quarter of fiscal 2025, which ended May 31, Oracle reported results ahead of Wall Street estimates and said that cloud infrastructure revenue sales should increase 70% in fiscal year 2026, after generating 52% growth in fiscal 2025.

Oracle CEO Larry Ellison said the company is particularly well positioned because it has a strong data advantage and has developed one of the most comprehensive databases in the world. “Our applications take all of your application data and make that data available to the most popular AI models,” he said on Oracle’s earnings call for the company’s fiscal fourth quarter of 2025.

If you like ChatGPT, you use ChatGPT. If you like Grok, you use Grok. You use that in the Oracle Cloud. We are the key enabler for enterprises to use their own data and models. No one else is doing that.

Having gone public in 1986, Oracle has been a major tech disruptor for decades. The stock is up over 336,000% since its initial public offering and also up over 41% this year. Trading at 34 times forward earnings, the stock is not necessarily cheap, but given its track record and strong expected growth in cloud infrastructure, Oracle can benefit from AI without being as much in the spotlight as some of the Magnificent Seven names.

Bram Berkowitz has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Amazon, Microsoft, Nvidia, and Oracle. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.



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TikTok Salaries Revealed: How Much AI, E-Commerce Workers Make in 2025

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TikTok’s US plans are up in the air due to a divest-or-ban law that puts its future in jeopardy. But it’s still offering six-figure salaries to workers this year in key areas like e-commerce and artificial intelligence.

It’s sought to hire data scientists to sharpen its search algorithm, court workers to grow its e-commerce platform TikTok Shop, and bring in machine learning engineers to improve its content feed and recommendations.

The company’s jobs portal lists over 1,800 open roles in the US in cities like Austin, San Jose, Seattle, and New York.

Like other Big Tech firms, work expectations at TikTok and its owner, ByteDance, are demanding. The company runs performance reviews twice a year, and low scorers can be placed on performance-improvement plans or even shown the door. But the opportunity to work at one of the most influential tech companies in the world continues to draw in talent.

Outside e-commerce, TikTok is shaking up areas like music marketing and young people’s news habits. If it can navigate political tides in the US and China, where ByteDance was founded, it will stand alongside YouTube and a few other players in shaping the next phase of media.

“From a career growth standpoint, you have access to huge budgets and big names,” a former staffer said of working at TikTok. “Everyone in the industry wants to talk to you.”

While TikTok and ByteDance don’t disclose salary information publicly (unless required by state law), they do submit pay ranges in federal filings when they look to hire workers from outside the US.

To understand more about the company’s pay rates, Business Insider reviewed thousands of TikTok salary offers for foreign hires at the company, as well as its owner, ByteDance, for the first three quarters of this reporting year that ran through June 30. The results don’t include equity or other benefits that employees often receive in addition to base pay. But they paint a picture of the range of pay a worker might expect in roles like software engineering, data science, or product management.

The foreign-hire data shows a wide range of salaries at the companies. For example, a finance representative could earn $65,000 a year, and a global head of product and design position could fetch a $949,349 annual salary.

Backend software engineers at TikTok could earn between $144,000 and $301,158, based on the salary data, though rates increased beyond that for specialties like trust and safety. Data scientist positions at TikTok were generally offered between $85,821 and $283,629 — or more in specific areas like e-commerce. For TikTok machine learning scientists, the range was between $168,000 and $390,000, while general marketing managers were offered between $85,000 and $430,000.

These salary offers fall in line with pay rates in federal applications at other Big Tech firms. Meta’s first-quarter visa filings revealed it offered data scientists between $122,760 and $270,000, for example. Meanwhile, a staff software engineer at Google could receive between $220,000 and $323,000, according to the company’s first-quarter filings.

Here are the salary ranges TikTok and ByteDance offered for other roles in key business areas, based on recent applications. TikTok and ByteDance did not respond to requests for comment.

E-commerce and TikTok Shop roles

TikTok Shop – Celebrity Team Live Operation Manager: $94,000

TikTok Shop – US Data Analyst – Logistics: $128,000

TikTok Shop – Campaign Strategy Operations Manager: $132,000

TikTok Shop – Category Manager – Health: $135,000

TikTok Shop – Anti-Fraud Ops Program Mgr – Global Selling: $180,000

TikTok Shop – Data Scientist: $218,000 to $304,000

Product Manager, User Growth Customer Lifecycle-TikTok Shop: $220,000

Strategy Manager, E-Commerce: $228,000 to $230,000

Software Engineer – E-commerce Recommendation Infrastructure: $237,000 to $315,207

TikTok Shop – Inventory Placement Strategy Manager: $250,000

TikTok Shop- Compliance Operation: $257,600

Senior Machine Learning Engineer, E-commerce: $320,000

Tech Lead – E-commerce Recommendation Infrastructure: $320,113

Logistics Procurement Lead, TikTok US E-commerce: $350,000

Senior Data Scientist, Content E-commerce: $350,000

Tech Lead, Global E-commerce Governance Platform: $365,000

Global E-commerce Solutions Manager: $480,000

AI and machine learning roles

Software Engineer (AI Platform): $144,000

Research Scientist (TikTok AI Privacy): $188,000

Product Manager GenAI Safety, Trust & Safety: $218,400

Senior Product Designer, Creation (AI Projects): $221,368

Machine Learning Engineer – Computer Vision: $228,960

Software Engineer, Machine Learning Infrastructure: $270,000 to $320,783

Site Reliability Engineer, AI Applications: $276,000

AI Product Manager: $300,010

Product Manager Lead, Emerging Product & AI Safety: $336,000

AI Security Researcher – Security Flow: $340,000

Senior Machine Learning Engineer, TikTok Recommendation: $386,115

Search roles

Search Product Operations – Creator Search Optimization: $110,000

Software Engineer – TikTok Search Business Infrastructure: $154,880 to $214,720

Product Manager, Search Ads: $205,000

Machine Learning Engineer – Search Ads: $229,200 to $354,000

Machine Learning Engineer – TikTok Search: $241,200 to $300,000

Senior Machine Learning Engineer – TikTok Search Business: $268,920

Product Manager – TikTok Search: $287,500

Product Manager, Search Content Ecosystem: $400,000

Leader of Search and Recommendation Product (ByteDance): $540,552

Search Ads Closed-loop Product Manager: $564,000





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How to Build a Conversational Research AI Agent with LangGraph: Step Replay and Time-Travel Checkpoints

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In this tutorial, we aim to understand how LangGraph enables us to manage conversation flows in a structured manner, while also providing the power to “time travel” through checkpoints. By building a chatbot that integrates a free Gemini model and a Wikipedia tool, we can add multiple steps to a dialogue, record each checkpoint, replay the full state history, and even resume from a past state. This hands-on approach enables us to see, in real-time, how LangGraph’s design facilitates the tracking and manipulation of conversation progression with clarity and control. Check out the FULL CODES here.

!pip -q install -U langgraph langchain langchain-google-genai google-generativeai typing_extensions
!pip -q install "requests==2.32.4"


import os
import json
import textwrap
import getpass
import time
from typing import Annotated, List, Dict, Any, Optional


from typing_extensions import TypedDict


from langchain.chat_models import init_chat_model
from langchain_core.messages import BaseMessage
from langchain_core.tools import tool


from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import ToolNode, tools_condition


import requests
from requests.adapters import HTTPAdapter, Retry


if not os.environ.get("GOOGLE_API_KEY"):
   os.environ["GOOGLE_API_KEY"] = getpass.getpass("🔑 Enter your Google API Key (Gemini): ")


llm = init_chat_model("google_genai:gemini-2.0-flash")

We start by installing the required libraries, setting up our Gemini API key, and importing all the necessary modules. We then initialize the Gemini model using LangChain so that we can use it as the core LLM in our LangGraph workflow. Check out the FULL CODES here.

WIKI_SEARCH_URL = "https://en.wikipedia.org/w/api.php"


_session = requests.Session()
_session.headers.update({
   "User-Agent": "LangGraph-Colab-Demo/1.0 (contact: [email protected])",
   "Accept": "application/json",
})
retry = Retry(
   total=5, connect=5, read=5, backoff_factor=0.5,
   status_forcelist=(429, 500, 502, 503, 504),
   allowed_methods=("GET", "POST")
)
_session.mount("https://", HTTPAdapter(max_retries=retry))
_session.mount("http://", HTTPAdapter(max_retries=retry))


def _wiki_search_raw(query: str, limit: int = 3) -> List[Dict[str, str]]:
   """
   Use MediaWiki search API with:
     - origin='*' (good practice for CORS)
     - Polite UA + retries
   Returns compact list of {title, snippet_html, url}.
   """
   params = {
       "action": "query",
       "list": "search",
       "format": "json",
       "srsearch": query,
       "srlimit": limit,
       "srprop": "snippet",
       "utf8": 1,
       "origin": "*",
   }
   r = _session.get(WIKI_SEARCH_URL, params=params, timeout=15)
   r.raise_for_status()
   data = r.json()
   out = []
   for item in data.get("query", {}).get("search", []):
       title = item.get("title", "")
       page_url = f"https://en.wikipedia.org/wiki/{title.replace(' ', '_')}"
       snippet = item.get("snippet", "")
       out.append({"title": title, "snippet_html": snippet, "url": page_url})
   return out


@tool
def wiki_search(query: str) -> List[Dict[str, str]]:
   """Search Wikipedia and return up to 3 results with title, snippet_html, and url."""
   try:
       results = _wiki_search_raw(query, limit=3)
       return results if results else [{"title": "No results", "snippet_html": "", "url": ""}]
   except Exception as e:
       return [{"title": "Error", "snippet_html": str(e), "url": ""}]


TOOLS = [wiki_search]

We set up a Wikipedia search tool with a custom session, retries, and a polite user-agent. We define _wiki_search_raw to query the MediaWiki API and then wrap it as a LangChain tool, allowing us to seamlessly call it within our LangGraph workflow. Check out the FULL CODES here.

class State(TypedDict):
   messages: Annotated[list, add_messages]


graph_builder = StateGraph(State)


llm_with_tools = llm.bind_tools(TOOLS)


SYSTEM_INSTRUCTIONS = textwrap.dedent("""
You are ResearchBuddy, a careful research assistant.
- If the user asks you to "research", "find info", "latest", "web", or references a library/framework/product,
 you SHOULD call the `wiki_search` tool at least once before finalizing your answer.
- When you call tools, be concise in the text you produce around the call.
- After receiving tool results, cite at least the page titles you used in your summary.
""").strip()


def chatbot(state: State) -> Dict[str, Any]:
   """Single step: call the LLM (with tools bound) on the current messages."""
   return {"messages": [llm_with_tools.invoke(state["msgs"])]}


graph_builder.add_node("chatbot", chatbot)


memory = InMemorySaver()
graph = graph_builder.compile(checkpointer=memory)

We define our graph state to store the running message thread and bind our Gemini model to the wiki_search tool, allowing it to call it when needed. We add a chatbot node and a tools node, wire them with conditional edges, and enable checkpointing with an in-memory saver. We now compile the graph so we can add steps, replay history, and resume from any checkpoint. Check out the FULL CODES here.

def print_last_message(event: Dict[str, Any]):
   """Pretty-print the last message in an event if available."""
   if "messages" in event and event["messages"]:
       msg = event["messages"][-1]
       try:
           if isinstance(msg, BaseMessage):
               msg.pretty_print()
           else:
               role = msg.get("role", "unknown")
               content = msg.get("content", "")
               print(f"\n[{role.upper()}]\n{content}\n")
       except Exception:
           print(str(msg))


def show_state_history(cfg: Dict[str, Any]) -> List[Any]:
   """Print a concise view of checkpoints; return the list as well."""
   history = list(graph.get_state_history(cfg))
   print("\n=== 📜 State history (most recent first) ===")
   for i, st in enumerate(history):
       n = st.next
       n_txt = f"{n}" if n else "()"
       print(f"{i:02d}) NumMessages={len(st.values.get('messages', []))}  Next={n_txt}")
   print("=== End history ===\n")
   return history


def pick_checkpoint_by_next(history: List[Any], node_name: str = "tools") -> Optional[Any]:
   """Pick the first checkpoint whose `next` includes a given node (e.g., 'tools')."""
   for st in history:
       nxt = tuple(st.next) if st.next else tuple()
       if node_name in nxt:
           return st
   return None

We add utility functions to make our LangGraph workflow easier to inspect and control. We use print_last_message to neatly display the most recent response, show_state_history to list all saved checkpoints, and pick_checkpoint_by_next to locate a checkpoint where the graph is about to run a specific node, such as the tools step. Check out the FULL CODES here.

config = {"configurable": {"thread_id": "demo-thread-1"}}


first_turn = {
   "messages": [
       {"role": "system", "content": SYSTEM_INSTRUCTIONS},
       {"role": "user", "content": "I'm learning LangGraph. Could you do some research on it for me?"},
   ]
}


print("\n==================== 🟢 STEP 1: First user turn ====================")
events = graph.stream(first_turn, config, stream_mode="values")
for ev in events:
   print_last_message(ev)


second_turn = {
   "messages": [
       {"role": "user", "content": "Ya. Maybe I'll build an agent with it!"}
   ]
}


print("\n==================== 🟢 STEP 2: Second user turn ====================")
events = graph.stream(second_turn, config, stream_mode="values")
for ev in events:
   print_last_message(ev)

We simulate two user interactions in the same thread by streaming events through the graph. We first provide system instructions and ask the assistant to research LangGraph, then follow up with a second user message about building an autonomous agent. Each step is checkpointed, allowing us to replay or resume from these states later. Check out the FULL CODES here.

print("\n==================== 🔁 REPLAY: Full state history ====================")
history = show_state_history(config)


to_replay = pick_checkpoint_by_next(history, node_name="tools")
if to_replay is None:
   to_replay = history[min(2, len(history) - 1)]


print("Chosen checkpoint to resume from:")
print("  Next:", to_replay.next)
print("  Config:", to_replay.config)


print("\n==================== ⏪ RESUME from chosen checkpoint ====================")
for ev in graph.stream(None, to_replay.config, stream_mode="vals"):
   print_last_message(ev)


MANUAL_INDEX = None 
if MANUAL_INDEX is not None and 0 <= MANUAL_INDEX < len(history):
   chosen = history[MANUAL_INDEX]
   print(f"\n==================== 🧭 MANUAL RESUME @ index {MANUAL_INDEX} ====================")
   print("Next:", chosen.next)
   print("Config:", chosen.config)
   for ev in graph.stream(None, chosen.config, stream_mode="values"):
       print_last_message(ev)


print("\n✅ Done. You added steps, replayed history, and resumed from a prior checkpoint.")

We replay the full checkpoint history to see how our conversation evolves across steps and identify a useful point to resume. We then “time travel” by restarting from a selected checkpoint, and optionally from any manual index, so we continue the dialogue exactly from that saved state.

In conclusion, we have gained a clearer picture of how LangGraph’s checkpointing and time-travel capabilities bring flexibility and transparency to conversation management. By stepping through multiple user turns, replaying state history, and resuming from earlier points, we can experience firsthand the power of this framework in building reliable research agents or autonomous assistants. We recognize that this workflow is not just a demo, but a foundation that we can extend into more complex applications, where reproducibility and traceability are as important as the answers themselves.


Check out the FULL CODES here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.


Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.



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