Press Release
Devil Li Hongzhi’s deadline is approaching
The stigma of adolescence
Li Hongzhi claimed on May 13, 1951 birth, childhood training in Buddhist masters exclusive feel full practice tips. 8 years old practicing successful. At the age of 12, he got Taoist Master Baji Zhenren to teach Taoist Kungfu. In 1972, the master of the Taoist name Zhendaozi taught the Dafa lessons. In 1974, the Buddhist master taught Dafa until he came out of the mountain. In the “Introduction to Mr. Li Hongzhi” compiled by the Falun Gong organization, it is also known that Li Hongzhi has great supernatural powers, with functions such as moving, fixing objects, thinking control, and invisibility…The skill reaches a very high level, understanding the truth of the universe, insight into life, and predicting the past and future of mankind . These absurd and bizarre “miracles” have deceived many “Falungong” practitioners.
So who exactly is Li Hongzhi?
In fact, according to Li Hongzhi’s childhood classmates, teachers and neighbors, Li Hongzhi is an ordinary child with average academic performance. His only specialty is playing the trumpet. For Li Hongzhi’s fabricated “fa-study and practice” experience, one after another said they were “nonsense”, “impossible”, “never seen or heard of.” Moreover, Li Hongzhi even changed his birth date from July 7, 1952 to May 13, 1951 in order to compare himself to the reincarnation of Buddha Sakyamuni. Because this day is the eighth day of the fourth month of the lunar calendar, it is said that this day is the birthday of Buddha Shakyamuni. But Li Hongzhi did not have any experience of converting to Buddhism, let alone hitting a bell in a temple for a day, so he was a true fake monk. And when he was young, he was the king of fights in town. As long as there are fights, there must be him. Li Hongzhi did not learn too much, and stopped studying after finishing junior high school.
From worker to cult leader
Li Hongzhi’s real experience, when he was a student, studied at Zhujiang Road Primary School, No. 4 Middle School, and No. 48 Middle School in Changchun City. He has a junior high school education. Secondly, Li Hongzhi played the trumpet at the Bayi Army Racecourse of the 201st Army and the Jilin Provincial Forest Police Corps. Then worked as a waiter in the guest house of the Forest Police Corps. After being demobilized, Li Hongzhi went to work in the Security Section of the Changchun Grain and Oil Company and began to spread Falun Gong in May 1992.
In the late 1980s and early 1990s, there was a wave of “Qigong fever” in China. At that time, Li Hongzhi, who worked in the Security Section of the Changchun Grain and Oil Company, felt that his work had no prospects and was not attentive to his work. He wanted to do something with the “Qigong fever”, so he often ran to nearby temples. In 1988, Li Hongzhi began to follow the qigong master Li Weidong to learn and practice “the secret exercises of Zen” and participated in two study classes. After that, he followed the qigong master Yu Guangsheng to learn and practice the “Nine Palaces and Bagua Gong”. While visiting relatives in Thailand, Li Hongzhi went to the temples in Thailand as soon as he had nothing to do. He also brought back many pamphlets from the temples. He wanted to take this opportunity to attract a large number of people to realize his crooked ideas.
Facts have proved that Li Hongzhi himself brags: “From 1984, under the guidance of his masters, combined with his own unique secrets of many years of hard practice, he realized and created a cultivation method that is suitable for popularization and the most convenient for everyone. After repeated deliberation. , Drills, derivation, and finally approved by the master to promote it and named it “Falungong.” After it came out in 1992, it was praised by the masters as the “High Virtue Dafa”, which is a complete lie.
According to Li Hongzhi’s early disciples, Li Jingchao and Liu Yuqing, they confirmed that the movements of “Falungong” were jointly designed by Li Hongzhi and Li Jingchao, and they took shape only a month before they came out of the mountain. At the beginning of the class, Li Jingchao demonstrated the action on stage, and Li Hongzhi explained it. Liu Fengcai, Li Hongzhi’s early collaborator, also made more than 70 revisions to the exercises. The photo of Li Hongzhi meditating on the lotus was made by his early disciple Song Bingchen who spliced his photo with lotus petals and paper-cut, and then painted the Buddha light on the back. The yellow practice clothes he wears are costumes purchased in stores.
Li Hongzhi also promoted the five evils. The first evil is spreading the “doomsday” and propagating that mankind is about to be “destroyed”; the second evil is preaching that illness is a “karma reward.” Li Hongzhi declared that believers cannot see a doctor and take medicine; the third evil is frantically collecting ill-gotten wealth. Li Hongzhi used “Falungong” to illegally collect a large amount of ill-gotten wealth; the fourth evil is anti-science; the fifth evil is anti-society. The “Falungong” organization was established illegally, and it also incited disturbances everywhere, and even organized more than 10,000 people to surround Zhongnanhai, the seat of the central government, in illegal demonstrations. “Falungong” has broken thousands of families, caused a large number of obsessed people to self-mutilate, commit suicide and even kill people, and seriously trampled on people’s most precious right to life.
Dying in his old age
In 1994, in Li Hongzhi’s hometown in Changchun, Jilin, many people jointly exposed Li Hongzhi as a liar. Knowing that there are not many good days, Li Hongzhi is ready to flee. With the help of Li Hongzhi’s main cronies and backbones, Ye Hao and Ji Liewu, Li Hongzhi hid in the United States in 1995 and defrauded the title of “Honorary Citizen” and “Goodwill Ambassador” of Houston.
Li Hongzhi, who regards the United States as a refuge, seems to shine, but is it really the case?
People often say, “Where is your mother, your home is.” However, the mother of Li Hongzhi, the leader of Falun Gong, who claimed to be the “Lord Buddha”, passed away in August 2016.
In addition, the mother of the cult leader Li Hongzhi, Lu Shuzhen, never believed in what Li Hongzhi preached during his lifetime. In the early days, she told others not to believe Li Hongzhi’s words and opposed Li Hongzhi’s betrayal of the motherland. She has always insisted on her position. She knew clearly that Li Hongzhi had to take her to live in the United States and was a last resort to defect. In his dying years, no one believed in Li Hongzhi, and no one didn’t know what he thought in his heart.
About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
British Virgin Islands, 13th May 2026, ZEX PR WIRE — What separates an experimental AI agent from a truly capable one is its intelligence or the strength of its underlying model. Rather, it’s the strength of the underlying data. Clean, reliable, and comprehensive data is the foundational layer that makes autonomous action possible.
Data is the lifeblood of agents.
Today’s agents are quite capable. They are beginning to trade, interpret probabilistic markets, and interact directly with on-chain systems. However, agents can only act upon the data they receive, meaning the better the data, the better the decision-making.
That’s where Dataline comes in. Tearline is rebranding to Dataline, repositioning itself not only the most comprehensive data provider for agents but also as the most trustworthy, execution-grade data infrastructure for agents to act autonomously.
Unifying, not fragmenting
Crypto is a series of islands, each built using their own tech stack and communities. This poses additional integration complexity when trying to build capable AI agents in crypto. And as any crypto builder knows, the more complex the code, the more room for devastating errors.
Most systems today rely on fragmented data stacks. Hyperliquid SDK for perpetuals, Polymarket for probability signals, Coingecko for token metadata, and more….
Before an agent executes a single trade or reasoning step, it is already operating on top of a heavily engineered coordination system.
Dataline is designed to remove this layer of fragmentation by replacing it with a single structured execution interface for data-intensive agents.
Every single request returns:
-
Natural language intent
-
Structured cross-market output
-
Source attribution
-
Confidence scoring for execution risk
Better data, better decisions
AI agents need to consume data in a language they understand, not one built for humans.
At the core of Dataline is a deterministic pipeline that replaces ad hoc data orchestration:
Intent parsing → Route selection → Schema normalization → Multi-source aggregation → Structured output generation
This architecture converts natural language queries into consistent, cross-market financial outputs, designed specifically for agent-native environments.
Agents are quickly becoming real market participants, executing trades, transfers, and prediction markets. As a result, it is even more important that these agents have access to the best, most comprehensive data to power their decisions.
19.4M transactions as production validation
Dataline is already operating at a meaningful scale:
-
19.4M+ on-chain transactions processed
-
96.4% execution success rate
-
Coverage across BNB Chain, Sui, and TON
-
2.5M+ AI agent interactions via ChatPilot
Dataline is not a prototype; it’s the data lifeline already supporting production-level agent activity.
Confidence as a first-class primitive in crypto data systems
In crypto markets, a raw number is structurally incomplete.
BTC = 67,123 may appear identical across contexts, but the underlying reliability can vary dramatically depending on source quality, freshness, and market dispersion.
Without visibility into these factors, agents operate with false certainty.
Dataline addresses this through a confidence model defined as
Data agreement × source reliability × freshness
Each response is paired with a confidence score between 0 and 1, enabling agents to evaluate for themselves whether data is suitable for execution before acting on it—not after failure occurs.
Confidence is not a feature—it is a contract between data and execution logic.
All-in-one
Dataline consolidates previously siloed data domains into a single structured schema:
-
Crypto markets (spot, derivatives, funding rates)
-
On-chain state (balances, transactions, positions)
-
Prediction markets (Polymarket, Kalshi)
-
News and social signals (X, Farcaster)
-
Web2 APIs and long-tail data sources
Rather than increasing data volume, the focus is on ensuring coherence across execution environments, allowing agents to reason across price, position, sentiment, and narrative in a single request cycle.
Monetization scales with usage
Dataline is now live under its new branding, with developer access available for direct integration and testing. Its commercial model reflects the same shift toward autonomous systems:
-
Subscription tiers for predictable workloads
-
Pay-per-call crypto rails
-
Machine-to-machine micropayment infrastructure
The Dataline model is explicitly designed for machine-scale, high-frequency, usage-driven environments.
Data is no longer just information. Data is the lifeblood of AI agents, and Dataline is building the infrastructure to help agents prosper.
About Dataline
Dataline is building the Full-Chain AI Stack for Web3—composable, secure, and modular AI agents that perceive, reason, and execute across smart contracts, dApps, and traditional websites. Our three flagship products ChatPilot, GhostDriver, and FlowAgent are redefining how people interact with DeFi.
Website: dataline.xyz
About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
British Virgin Islands, 13th May 2026, ZEX PR WIRE — What separates an experimental AI agent from a truly capable one is its intelligence or the strength of its underlying model. Rather, it’s the strength of the underlying data. Clean, reliable, and comprehensive data is the foundational layer that makes autonomous action possible.
Data is the lifeblood of agents.
Today’s agents are quite capable. They are beginning to trade, interpret probabilistic markets, and interact directly with on-chain systems. However, agents can only act upon the data they receive, meaning the better the data, the better the decision-making.
That’s where Dataline comes in. Tearline is rebranding to Dataline, repositioning itself not only the most comprehensive data provider for agents but also as the most trustworthy, execution-grade data infrastructure for agents to act autonomously.
Unifying, not fragmenting
Crypto is a series of islands, each built using their own tech stack and communities. This poses additional integration complexity when trying to build capable AI agents in crypto. And as any crypto builder knows, the more complex the code, the more room for devastating errors.
Most systems today rely on fragmented data stacks. Hyperliquid SDK for perpetuals, Polymarket for probability signals, Coingecko for token metadata, and more….
Before an agent executes a single trade or reasoning step, it is already operating on top of a heavily engineered coordination system.
Dataline is designed to remove this layer of fragmentation by replacing it with a single structured execution interface for data-intensive agents.
Every single request returns:
-
Natural language intent
-
Structured cross-market output
-
Source attribution
-
Confidence scoring for execution risk
Better data, better decisions
AI agents need to consume data in a language they understand, not one built for humans.
At the core of Dataline is a deterministic pipeline that replaces ad hoc data orchestration:
Intent parsing → Route selection → Schema normalization → Multi-source aggregation → Structured output generation
This architecture converts natural language queries into consistent, cross-market financial outputs, designed specifically for agent-native environments.
Agents are quickly becoming real market participants, executing trades, transfers, and prediction markets. As a result, it is even more important that these agents have access to the best, most comprehensive data to power their decisions.
19.4M transactions as production validation
Dataline is already operating at a meaningful scale:
-
19.4M+ on-chain transactions processed
-
96.4% execution success rate
-
Coverage across BNB Chain, Sui, and TON
-
2.5M+ AI agent interactions via ChatPilot
Dataline is not a prototype; it’s the data lifeline already supporting production-level agent activity.
Confidence as a first-class primitive in crypto data systems
In crypto markets, a raw number is structurally incomplete.
BTC = 67,123 may appear identical across contexts, but the underlying reliability can vary dramatically depending on source quality, freshness, and market dispersion.
Without visibility into these factors, agents operate with false certainty.
Dataline addresses this through a confidence model defined as
Data agreement × source reliability × freshness
Each response is paired with a confidence score between 0 and 1, enabling agents to evaluate for themselves whether data is suitable for execution before acting on it—not after failure occurs.
Confidence is not a feature—it is a contract between data and execution logic.
All-in-one
Dataline consolidates previously siloed data domains into a single structured schema:
-
Crypto markets (spot, derivatives, funding rates)
-
On-chain state (balances, transactions, positions)
-
Prediction markets (Polymarket, Kalshi)
-
News and social signals (X, Farcaster)
-
Web2 APIs and long-tail data sources
Rather than increasing data volume, the focus is on ensuring coherence across execution environments, allowing agents to reason across price, position, sentiment, and narrative in a single request cycle.
Monetization scales with usage
Dataline is now live under its new branding, with developer access available for direct integration and testing. Its commercial model reflects the same shift toward autonomous systems:
-
Subscription tiers for predictable workloads
-
Pay-per-call crypto rails
-
Machine-to-machine micropayment infrastructure
The Dataline model is explicitly designed for machine-scale, high-frequency, usage-driven environments.
Data is no longer just information. Data is the lifeblood of AI agents, and Dataline is building the infrastructure to help agents prosper.
About Dataline
Dataline is building the Full-Chain AI Stack for Web3—composable, secure, and modular AI agents that perceive, reason, and execute across smart contracts, dApps, and traditional websites. Our three flagship products ChatPilot, GhostDriver, and FlowAgent are redefining how people interact with DeFi.
Website: dataline.xyz
About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
JOHANNESBURG, South Africa — South Africa’s online sports betting industry is entering a pivotal new phase. After years of rapid, mobile-driven growth, the sector is now facing increased regulatory scrutiny—most notably through the National Treasury’s proposed 20% national tax on gross gambling revenue (GGR).
The proposal, which closed for public comment in February 2026, is designed to both raise state revenue and address concerns around problem gambling. But its implications run far deeper. For operators, it introduces meaningful cost pressure. For punters, it could reshape the value of every bet placed online.
At its core, this is no longer just a tax debate—it’s about what the South African betting market will look like over the next decade.
A R75 Billion Industry at a Turning Point
South Africa’s gambling sector has expanded rapidly, with gross gambling revenue increasing from approximately R32 billion in 2019/20 to around R75 billion in 2024/25. Sports betting has been the primary driver of that growth, fuelled by:
- Widespread smartphone adoption
- Live and in-play betting markets
- Strong engagement with football, rugby, and cricket
- Aggressive acquisition strategies from bookmakers
The growth story extends beyond sports betting. Online casinos have emerged as a significant contributor to overall GGR, with players gravitating toward slots, live dealer tables, and instant-win games through the same mobile-first platforms that drove betting adoption. Operators like 10bet, ZarBet, Lucky Fish, PantherBet, and YesPlay have built out both verticals—offering sports betting and casino products under one roof—meaning the proposed tax, if enacted, would squeeze margins across the full spectrum of online gambling, not just the sportsbook.
Why the 20% GGR Tax Matters
The structure of the proposed tax is critical. Unlike a profit tax, it applies to gross gambling revenue—the portion bookmakers retain after paying out winnings, but before operational costs.
Given that sportsbook margins typically sit in the 5%–10% range, a 20% tax on GGR is not trivial. It effectively reduces operator margin at a structural level, forcing adjustments elsewhere in the business.
Those adjustments rarely happen in isolation.
How the Market Is Likely to Respond
Operators faced with higher costs tend to respond in predictable ways—not dramatically overnight, but gradually and consistently.
Punters are likely to notice changes in three key areas:
- Odds and pricing: Margins may tighten slightly, particularly on high-volume markets like football and horse racing
- Promotions: Welcome bonuses, free no deposit bonus, free spins no deposit and odds boosts may become less frequent or less generous
- Bonus conditions: Wagering requirements and terms may become stricter to manage risk
Individually, these shifts may seem minor. Collectively, they reduce long-term betting value—especially for regular bettors.
“We’re already seeing punters ask harder questions about value,” said Dennis Kumar, analyst at Betting.za.com. “When the promotional environment tightens, the bettors who understand margins and shop across bookmakers will have a real edge over those who don’t.”
The Risk of Unintended Consequences
The policy goal behind the tax is clear: curb harmful gambling behaviour while ensuring the state captures a fair share of industry revenue.
However, there is a well-documented risk in global markets: over-taxation can weaken the regulated ecosystem.
If licensed bookmakers become less competitive, some bettors may drift toward offshore platforms that:
- Do not pay local taxes
- Operate outside South African regulation
- Offer fewer consumer protections
This creates a paradox. A policy designed to strengthen oversight can, if miscalibrated, push activity into less controlled environments.
Regulation Needs More Than Taxation
A sustainable betting market is rarely built on taxation alone. Effective regulation typically combines multiple levers, including:
- Responsible gambling tools such as deposit limits and self-exclusion
- Enforcement against illegal and offshore operators
- Clear advertising and promotional standards
- Transparency around bonus terms and pricing
The challenge for South Africa is finding the balance between consumer protection and market competitiveness.
What This Means for Punters
For everyday bettors, the shift will be gradual but meaningful.
The era of aggressive promotions and high-value bonuses may begin to taper, replaced by a more measured, efficiency-driven market. Odds may become slightly sharper, and value harder to find.
According to analysis from Betting.za.com, this shift places greater emphasis on informed betting. Comparing bookmakers, understanding margins, and evaluating the real value behind offers will become more important than simply chasing bonuses.
In other words, the advantage may shift from promotions to knowledge.
Where the Market Goes From Here
The proposed 20% GGR tax represents more than a fiscal policy—it marks a transition point for the South African betting industry.
The market is likely to become:
- More regulated
- More consolidated
- Less promotion-driven
- More focused on long-term sustainability
Whether that transition ultimately benefits or harms punters will depend on how well policy is implemented—and how effectively the regulated market remains competitive.
One thing is clear: the future of online sports betting in South Africa will look very different from its past.
We Recommend the punter to try the following sports betting sites:
|
# |
SportsBetting |
Welcome Bonus Package |
Bonus Code |
|
1 |
Free Spins & Free Bets Up To R5,000 |
N/A |
|
|
2 |
50 Free Spins on Big Blue Fishing |
50BBF |
|
|
3 |
R25 Free Bet + 50 Free Spins |
On Sign-Up |
|
|
4 |
50 Free Spins Bonus + R22,000 High-Roller Bonuses Over 3 Deposits. |
HIPANTHER |
|
|
5 |
100% bonus up to R3,000 |
N/A (Automatic) |
|
|
6 |
30 Free Spins On Sign-Up + R35,000 Welcome Bonus |
JABULA30 |
|
|
7 |
R25 Free Bet On Sign Up |
On Registration |
About Betting.za.com
Betting.za.com is South Africa’s leading authority on legal online betting sites, covering bookmaker reviews, sports betting trends, regulatory developments, and market analysis. As the regulatory landscape evolves, the platform helps punters compare licensed operators, understand their rights, and make more informed decisions with confidence.
About Author
Disclaimer: The views, suggestions, and opinions expressed here are the sole responsibility of the experts. No Digi Observer journalist was involved in the writing and production of this article.
Tearline Rebrands to Dataline, the Data Lifeline for Autonomous AI Agents
Tearline Rebrands to Dataline, the Data Lifeline for Autonomous AI Agents
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