Crypto World
The Wild True Story Behind Monsters of God
As a lifelong lover of reptiles, Eric Goode has known about the world of exotic reptile smuggling for decades. “I didn’t become acutely aware of the criminality of it until probably into the late ‘80s,” says the director of Tiger King and Chimp Crazy ahead of his latest documentary, another foray into wildlife crime, Monsters of God. The five-episode HBO series is Goode’s most ambitious project to date, surveying the explosion of endangered reptiles trafficked into the country in Florida from the 1970s until today. The series guides us through the web of feuding smugglers and law enforcement crackdowns that connect the Sunshine State to the fauna of Madagascar, Indonesia, Malaysia, and many more countries.
Through remarkable archival footage and an incredible range of interviews, Monsters of God digs into the egotistical and ruthless mentalities that supported competing criminal empires and the undercover operations that dismantled them. The series points a finger at animal lovers who felt uniquely connected to exotic creatures while also treating them as a commodity to be exploited and abused.
“We were really intentional about not making it a traditional true crime doc,” says Jeremy McBride, an executive producer on the series. “It’s really a portrait of how obsession drives people to get the rarest of the rare, no different than the pathology of people collecting baseball cards, or stamps, or rare coins.” There is one difference: baseball cards can’t bite.
The introduction to Monsters of God’s stolen world
Monsters of God’s first episode, airing Aug. 6, ushers us into the world of reptile smuggling through a major rivalry: Tommy Crutchfield and Hank Molt. Based in Philadelphia, Molt’s obsession with exotic climates and wildlife led him to search the world for exotic reptiles in the 1960s and sell them from his newly acquired Pennsylvania pet store. Molt is considered a crucial figure in the growth of reptile houses in American zoos, which at the time were a rare attraction. He prepared and distributed a price list of rare reptiles—including snakes, turtles, lizards—and sold them to a wide variety of eager customers and competitive zoos. Molt often carried them into the country in suitcases, easily circumventing the relatively lax customs regulations. When the Endangered Species Act came into effect in 1973, Molt’s smuggling business boomed—declaring a reptile species as “endangered” also meant it was rarer and more valuable, allowing Molt to fetch a higher price.
In Molt’s view, Crutchfield rode his coattails. Crutchfield is a Florida native whose introduction to the reptile business began in high school capturing snakes for the “Snake-a-torium” in Panama City Beach. As the smuggling market grew, Crutchfield graduated to the big leagues by mimicking Molt’s established business plan, selling smuggled reptiles to collectors, pet owners, and zoos. Crutchfield and Molt’s rivalry is explored in the book Stolen World by Jennie Erin Smith—like Monsters of God’s first episode, the author uses the two men as a window into how smuggling has fuelled the popularity of reptiles in modern America.
Crutchfield is also a certified braggadocio, at one point referring to himself as “John Dilligenger, Bonnie and Clyde all wrapped up in one”. But it’s true that his criminal activities attracted serious heat—in 1997, the threat of his third round of criminal charges as part of the United States Fish and Wildlife Service (USFWS) five-year-long “Operation Chameleon” made Crutchfield flee to Belize.
When Molt went to prison at the tail end of the 1970s, Crutchfield took his place at the top of the food chain—something his former mentor resented when he was released. According to Maria Palladini, former special agent for the Fish and Wildlife Service, Molt had a habit of taking couriers and dealers under his wing only to betray them. While Crutchfield’s business was soon dominating Molt’s—raking in millions in reptile sales and trades every year—his reckless spending and imports soon put him squarely in the Feds’ crosshairs.
In 1995, Crutchfield asked Molt to help hide an import of endangered Fiji Island iguanas so they weren’t discovered by the Fish and Wildlife Service. Molt agreed—and according to Crutchfield, Molt took his revenge by killing the iguanas. (Molt denies causing them harm.) When USFWS informed Molt he was being charged along with Crutchfield, Molt handed over the dead iguanas and cut a deal to avoid being charged. Crutchfield pled guilty—his second of three criminal convictions in the ‘90s—and cut all ties with Molt.
Monsters of God’s wider gallery of rogues
Don’t expect Tommy and Hank to lead every episode of Monsters of God. Rather than zeroing in on one unstable dynamic like Goode did in Tiger King and Chimp Crazy, Monsters of God expands its scope to the entire reptile trafficking ecosystem throughout the ‘80s and ‘90s.
“This was more of a history lesson about the reptile trade in the United States, so it was a very different kind of storytelling—much more nuanced and complex,” says Goode. “We also really wanted to make sure that we got all sides of the story: law enforcement, the big zoos that were complicit, the reptile dealers, the consumers. It was a global story.”
Subsequent episodes reveal who defined Florida’s exotic reptile trade. Ray Van Nostrand, a New York-born collector and seller who moved to Florida in the ‘70s, took advantage of the power vacuum left by Crutchfield and Molt and smuggled huge quantities of illegal reptiles into the country. His son Mike—one of Monsters of God’s most memorably cantankerous interviewees—took over with a legal, legitimate reptile selling business, euphemistically named “Strictly Reptiles,” until he too was tempted by smuggling’s illicit rewards.
But this went way bigger than just reptiles. Ray was in business with returning Tiger King character Mario Tabraue, a “Cocaine Cowboy” druglord with his own exotic animal sanctuary who would smuggle both reptiles and drugs into Florida in the same shipments. On the other side of the law is former DEA agent Larry Loveless and USFWS special agents George Morrison and Ken McCloud, whose respective “Operation Cobra” and “Operation Chameleon” form a clear dramatic backbone to two episodes.
But all these smugglers pale in comparison to Anson Wong, a Malaysian smuggler who was dubbed the “Pablo Escobar of animal trafficking.” Wong had been smuggling illegal animals and products through his export company based in Penang since the ‘80s and gained a reputation as an incredibly elusive and dangerous figure. Wong haunts the stories of the Floridian smugglers, connected to many of the feuds and undercover operations. He emerged as the thread on Goode’s evidence board, tying together the pieces of the director’s most complex documentary so far.
“We had over 174 interviews. We had 1,500 hours of footage. We had Eric’s personal archive and research. We needed a pace that could get people through it, we needed this propulsion, and Eric’s quest trying to get to Anson Wong is that propulsion. Anson Wong is this sort of throughline in all of our characters’ stories, like Keyser Söze,” says McBride.
Monsters of God was a different beast to produce
Each episode of Monsters of God is packed with talking heads who talk candidly about their criminal exploits and feuds, but that doesn’t mean it was easy to get them all to talk—compared Joe Exotic and Tonia Haddix, the subjects of Tiger King and Chimp Crazy, the reptile community is far more paranoid. “Joe and Tonia were unique in that way, where they just wanted to be peacocks. They desperately wanted the attention. The reptile world, for the most part, is much more guarded,” says Goode.
While some people had ongoing charges and declined to speak until their cases were adjudicated, others had already been convicted and were willing to talk because double jeopardy protected them from being charged a second time. Others were happy to put everything on the table. “Hank Molt was proud that he could smuggle the way he did, he enjoyed circumventing the laws, and he liked that there were laws because it made it more of a challenge, like Catch Me If You Can with Leonardo DiCaprio,” explains Goode.
In Tiger King and Chimp Crazy, the wildlife crimes spiralled out of control while cameras were rolling. “The story unraveled contemporaneously with the filming, so we didn’t know that Joe Exotic was going to try to kill Carol Baskin, that he would get arrested and go to prison. We just happened to be filming different people, and then they all intersected in real time,” says Goode. By contrast, Monsters of God is interested in how the present was shaped by history, so it’s exclusively concerned with reconstructing the past.
The sheer breadth of the material meant that, according to McBride, the shape of each episode wasn’t clear at first, leading them to evolve their true crime storytelling. “What makes this series so distinctive is this psychological character study and this high-stakes true crime documentary, and within all of that, you have this chronology that we analyze with bigger themes involving the extinction crisis and our impact on this planet,” says McBride. “We wanted to draw these big ideas to an everyday person and how it touches their life, how this world overlaps with culture and criminality in a way that a lot of shows don’t touch on.”
The beating heart of Monsters of God
“This story for me is deeply personal,” Goode says in voiceover at the beginning of the series. “I was part of the problem.” But he was definitely the right man for the job: while Goode used to buy reptiles from shady dealers, he had turned a new page by 2003, when he co-founded The Turtle Conservancy to protect the many species of turtles who risk extinction from poachers and smugglers. His reptile expertise made him the ideal filmmaker to handle the cautious and shifty characters wary of appearing on camera—as Goode says, “I can speak their language about reptiles very fluently.” He knows the history of exotic animal collections and menageries in America and Europe, and the hypocrisy displayed by the subjects of Monsters of God—on both sides of the law—infuriates him.
Monsters of God expands on a theme from Goode’s previous docs—that a cultural obsession with rare animals leads to rampant commodification that exploits wildlife, encouraging animal lovers to embrace becoming consumers in a violent and exploitative transaction. Although Monsters of God looks to history, Goode isn’t convinced enough has been done to stop wildlife from being commodified. “It’s become more popular because of pop culture. Films like Jurassic Park can trigger these things. Finding Nemo—everyone wanted to buy a clownfish. Or Harry Potter. When I went to Southeast Asia, all the bird markets in Jakarta had baby owls everywhere. Hopefully this show doesn’t fuel interest in reptiles and bring in more illegal animals.”
Robinhood Chain's decentralized exchange volume fell 72.5% between its Jul. 11 peak and Aug. 1, but every other headline metric on the chain kept climbing through the drop. While transactions, total value locked and stablecoin supply are all at record highs, the size of the average trade collapsed…. Read the full story at The Defiant
Volunteer developers filed 4,962 security findings across 390 Bitcoin projects in about 30 hours. Of the 391 codebases they reviewed, exactly one came back clean.
The group calls itself the Bitcoin Red Team. It rated 720 of those findings high or critical. Only 147 have reached the maintainers who have to fix them.
Every 1 in 7 Findings is Serious
The severity split is narrower than the raw total suggests. Reviewers logged 85 critical issues and 635 high ones.
That works out to 14.5% of everything filed. The rest sit in medium, low, or informational buckets. Another 246 findings carry no severity label at all.
Evidence quality varies too. About 21.4% came with working proof-of-concept code. Roughly 91% arrived through automated scanning. Reviewers retired just eight as false positives.
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One Hour Produced 83% of the Findings
The 30-hour framing needs a caveat. A single hour absorbed 4,101 findings. That spike was a backfill, not live scanning. Rob Hamilton, chief executive of Bitcoin insurer AnchorWatch, ran his own review before the campaign formally began.
He said he spent over $10,000 scanning more than 100 libraries.
Strip the dump out, and the pace changes sharply. Roughly 840 findings were received over the other 29 hours. That is closer to 29 an hour than the 166.3 the report advertises.
The Data Points Away From Hardware Wallets
The category breakdown carries a surprise. Hardware wallets and firmware, the group Coldcard belongs to, ranked second lowest for serious flaws at 9.6%.
Other corners fared worse. Mining pools hit 21.7%, infrastructure and tooling 21.5%, and swaps and exchanges 20.9%. Privacy tools topped the table at 24%, though reviewers covered only three of them.
Crypto libraries carried the volume instead. They produced 1,385 findings across 128 projects, more than a quarter of the corpus.
Calle, the pseudonymous physicist who created the Cashu ecash protocol, said maintainers are confirming the worst reports.
Most of the critical reports we’ve made so far were quickly verified by project owners. We know we’re hitting real targets,” they wrote.
Why the Red Team Formed After Coldcard
The sweep began because of one broken chip. Coinkite disclosed on July 30 that seed generation on affected Coldcard devices fell back to a predictable software routine.
The shortfall was severe. Only 32 bits came from the secure element, capping an attacker’s search at about 4.3 billion guesses.
Galaxy Research pegged confirmed thefts at 1,596 Bitcoin (BTC) from roughly 7,300 addresses on Aug. 4. A suspected fourth attack wave would bring the total to nearly $130 million. Galaxy stresses its address list is not definitive.
The panic showed up on-chain, where active addresses spiked to a 20-month high. Korean holders largely escaped because dice-based seeds are common there.
Weak randomness keeps returning in Bitcoin, however. The 2023 Milk Sad bug seeded Libbitcoin Explorer keys from 32 bits of clock time. In May, the Ill Bloom vulnerability drained $5.7 million from wallets built on a weak JavaScript generator.
Funding Follows the Findings
OpenSats, a nonprofit that funds Bitcoin development, launched a Code RED grant track on Thursday. It pays researchers who disclose flaws. It also refunds the artificial intelligence (AI) bills the work runs up.
Meanwhile, Bitcoin traded near $64,396 on Thursday, up 0.5% over 24 hours. The audit has not moved the market.
Context still matters for the raw number. These are findings, not confirmed exploits, and most will never be weaponized.
On the evidence so far, though, Coldcard was not an isolated failure. The data also suggests the next one will not be a hardware wallet.
The post Bitcoin Network Warning: Developers Find Nearly 5,000 Vulnerabilities appeared first on BeInCrypto.
Six separate forces must align for Bitcoin to double from here. Google Gemini AI predicts they will, and its price prediction calls for $120,000 to $150,000 before 2026 closes.
Accelerating global M2 money supply growth tops that list. Central bank interest rate cut cycles follow close behind.
Then comes the delayed supply squeeze from the post-halving issuance deficit. New coins arrive more slowly while demand keeps building.
Expanding institutional spot ETF allocations add steady bid pressure. Emerging momentum in sovereign strategic reserves brings a buyer class that did not exist a few years ago.
Impending legislative clarity on the market is the final piece. Gemini treats the combination as a confluence rather than any single trigger.
The bear scenario is framed as minor. Inflation sticky enough to delay rate cuts would remove the monetary tailwind entirely.
Unexpected regulatory enforcement friction could do similar damage. Temporary spot ETF net outflows round out the risks.
Any of those stalling momentum would test a deeper support zone around $48,000 to $55,000. Gemini AI still argues that leverage has largely been flushed out already.
Long-term institutional holders continue absorbing sell pressure. That skews structural risk and reward toward aggressive expansion into new high-water marks.
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Bitcoin Price Prediction: Six Tailwinds And The Question Of Whether Bitcoin Waits For Them
The daily chart tells a rougher story than the forecast. Bitcoin peaked near $126,000 in October and lost ground for months. February brought the violent part of the decline. Price broke from roughly $90,000 down to $60,000 in a matter of weeks.
Spring produced a genuine recovery attempt toward $82,000 by May. June erased it, dragging Bitcoin back near $58,000.
Since then, the market has carved out a base. July and August have formed a series of higher lows with little conviction on the upside.
The latest close is $64,858, up 1.25% and $801 on the day. The session ranged from $63,820 to $64,862. Support sits at $60,000 first and $58,000 at the June floor. Resistance shows up at $68,000, then $72,000 and $76,000.
RSI reads 54.39 with its signal line at 49.58. The 5-point gap places momentum slightly on the bullish side of neutral. Both readings sit near the middle of the range. Nothing here suggests exhaustion in either direction.
Gemini needs roughly a double from this base. The chart is not there yet, though a monthly close above $68,000 would be the first real evidence.
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The post Google Gemini AI Predicts Most Likely Bitcoin Price by End of 2026 appeared first on Cryptonews.
Analyst Crypto Patel said on August 6 that Lido DAO’s LDO token could recover more than 1,700% after falling nearly 94% from its previous all-time high.
The market watcher believes LDO is sitting in a high-risk accumulation zone but warned that the token’s bearish structure remains intact until it reclaims major resistance levels.
LDO Tests Multi-Year Support After Heavy Sell-Off
Crypto Patel’s analysis on X placed LDO inside a long-term demand area after its decline from the previous cycle peak near $4.
“Everyone Forgot About $LDO After A -94% Crash,” he wrote. “The Long-Term Recovery Potential From Here Could Exceed 1,700%.”
The token is currently trading around $0.29, close to the analyst’s proposed accumulation zone between $0.275 and $0.24.
He said that LDO is still inside a multi-year descending channel, with price action still showing lower highs and lower lows. A weekly close below $0.23 would invalidate the current setup, while a move above $0.47 would be needed to signal a possible trend change.
The token’s recent weakness has been linked to concerns around Ethereum’s proposed EIP-8361. Developer Jerome de Tychey said on August 5 that the proposal aims to prevent staking from rising without limits.
Analyst Ted Pillows suggested that LDO’s decline was likely connected to fears that lower ETH fstaking rewards could reduce demand for liquid staking tokens such as stETH.
“$LDO is selling off because of concerns around Ethereum’s EIP-8361 proposal,” Pillows wrote on X.
He added that the proposal is still an early draft and has a long process before any possible implementation.
Lido has also had to deal with changing conditions across Ethereum staking. As CryptoPotato reported last month, the platform started moving around $16 billion worth of staked ETH onto larger post-Pectra validators, with the idea being that Lido’s curated node operators stop running thousands of identical 32 ETH validators and collapse them into fewer, bigger ones.
Case for a Longer-Term Bottom
LDO is currently about 25% above the $0.235 low it hit on June 25, a level that replaced its previous floor and now marks the bottom of its five-year trading history since launching in 2021 at an all-time high near $7.30.
In the last seven days, it has fallen close to 18% and is down roughly 27% over the past two weeks, according to CoinGecko. Furthermore, trading volume sits near $50 million, down 43% from the previous day.
The bullish case hinges on LDO reclaiming $0.47 on a weekly closing basis, a level that flipped from support to resistance after a breakdown in 2024. From there, Crypto Patel maps out targets at $1.50, $2.50, and eventually back toward the token’s old cycle high near $4, the move that would produce the kind of gain he’s describing. He points to Lido’s continued lead in Ethereum liquid staking and the shrinking token supply left to unlock as reasons the setup could work if ETH climbs back above $3,000.
The post Analyst Predicts 1,700% LDO Rally From Long-Term Support appeared first on CryptoPotato.
What do we talk about in Syria?
What was changing in Syria was not only what people were able to do, but what they were able to say. The shroud of silence and fear that enveloped Syria throughout the Assad decades was gone. Syrians were, finally, speaking freely about those dark decades.
In Hama, a city 130 miles north of Damascus, I visited the atelier of one of the last traditional weavers in the city. We drank coffee as he told stories of February 1982, when Hafez al-Assad put the rebellious city under siege and the regime’s army killed tens of thousands in a single month. Others in the shop shared stories of entire families being killed together in their homes. What felt radical was that we were in Hama, openly speaking about the stories of Hama.
In Aleppo, which is 220 miles northwest of Damascus, a kind man sitting next to me at dinner casually recounted how he and his wife—both were jailed for opposing Assad during the revolution—had survived. Their survival and our ability to speak openly about the brutal past felt miraculous. I teared up listening to them. I couldn’t get over what it meant to hear these stories exactly where they had happened. Just being there, bearing witness together, speaking and listening without fear or filter seemed to loosen some of the pain trapped inside us. In Hama, the wounds are 44 years old; the city was hollowed out, every family touched by loss. In Aleppo, in the suburbs of Damascus, across Syria, people and neighborhoods heaved under the trauma of the last 14 years. Thousands of families are still searching for answers about their loved ones—more than 100,000 of whom had disappeared under Assad’s rule.
Telegram trading bots let users buy and sell tokens directly from a chat interface. This guide explains how they work, which bots dominate the market, and what risks come with handing a bot your private key.
Summary
- Telegram trading bots are automated tools that connect to decentralized exchanges through the Telegram messaging app, letting users swap tokens, snipe new listings, and set limit orders without using a traditional wallet interface
- The leading bots by volume include Banana Gun, Maestro, Unibot, BONKbot, and Trojan, each handling hundreds of millions of dollars in weekly trading volume across Ethereum, Solana, and Base
- These bots generate revenue through transaction fees, typically charging 0.5% to 1% per trade on top of the standard DEX swap fees and network gas costs
- The primary convenience is speed: a trader can paste a contract address into a Telegram chat and execute a buy in under two seconds, compared to the 15 to 30 seconds required to navigate a DEX interface manually
- The primary risk is custody: most Telegram bots generate a wallet for the user and hold the private key on their servers, meaning a bot compromise could result in total loss of funds
Telegram trading bots emerged in 2023 as a response to a specific problem in decentralized finance: the gap between the speed at which opportunities appear and the speed at which a human can execute a trade through a conventional DEX interface. When a new token launches on Uniswap or Raydium, the first buyers often capture the largest gains. By the time a trader opens their browser, connects their wallet, approves the token contract, sets slippage, and confirms the transaction, the price may have already moved 50% or more.
Telegram bots compressed that entire workflow into a single message. Paste a contract address, tap a button, and the bot submits the transaction on your behalf. The interface is a chat window. The execution happens on-chain. The speed advantage turned what started as a niche tool for memecoin traders into an infrastructure layer that now processes billions of dollars in monthly volume.
This guide explains how these bots work under the hood, which ones dominate the market, what they cost, and where the risks hide.
How Telegram trading bots work
A Telegram trading bot is a program that runs on a server, connects to one or more blockchain networks, and accepts commands through the Telegram Bot API. When a user starts a bot for the first time, the bot generates a new crypto wallet (a public-private key pair) and associates it with the user’s Telegram account. The user funds this wallet by sending tokens to the generated address.
Once funded, the user can trade by sending commands to the bot. The most basic command is a buy: the user pastes a token contract address, selects an amount, and the bot constructs a swap transaction on the relevant decentralized exchange, signs it with the user’s private key, and broadcasts it to the network. The entire process typically completes in one to three seconds on Solana and three to ten seconds on Ethereum, depending on network congestion.
The bot handles several technical steps that would otherwise require manual interaction. It automatically detects which DEX has liquidity for the token. It calculates the optimal route through liquidity pools, sometimes splitting the trade across multiple pools to reduce price impact. It sets gas parameters to prioritize transaction inclusion. On Ethereum, many bots integrate with block builders and private mempools to avoid sandwich attacks, a form of MEV (maximal extractable value) that front-runs and back-runs a user’s trade to extract profit.
The user interface is entirely within Telegram. Buttons replace the connect-wallet and approve-token steps of a traditional DEX. Portfolio tracking, profit and loss calculations, and token watchlists are all presented as inline messages or callback buttons within the chat.
The major Telegram trading bots
The Telegram bot landscape has consolidated around a handful of dominant platforms, each with different strengths.
Banana Gun is the highest-volume Telegram trading bot as of mid 2026. It operates on Ethereum, Solana, Base, and Blast. Banana Gun is known for its sniping capabilities: the ability to detect a new token listing and execute a buy transaction in the same block as the liquidity addition. The bot charges a 0.5% fee on manual buys and a 1% fee on snipes. Banana Gun processed more than $8 billion in cumulative trading volume in its first year of operation and has generated hundreds of millions in fee revenue, a portion of which is distributed to holders of the BANANA token.
Maestro was one of the earliest Telegram trading bots, launching on Ethereum before expanding to Solana and other chains. Maestro offers sniping, limit orders, copy trading (automatically mirroring the trades of a specified wallet), and anti-rug protection that attempts to detect and front-run liquidity removals. Its fee structure is 1% per transaction.
Unibot launched in mid 2023 and was among the first bots to gain significant traction. It pioneered the revenue-sharing model where a percentage of trading fees is distributed to token holders. Unibot operates primarily on Ethereum and introduced features like private transactions routed through Flashbots to protect against MEV. Trading fees are 1% for non-token-holders and 0.5% for UNIBOT holders.
BONKbot is the dominant Telegram trading bot on the Solana network. Named after the BONK memecoin community, BONKbot specializes in Solana token trading and benefits from Solana’s low transaction fees and fast confirmation times. A trade on BONKbot costs a fraction of a cent in network fees compared to several dollars on Ethereum, making it the preferred tool for high-frequency memecoin trading where traders execute dozens of small trades per day.
Trojan emerged as a competitor to BONKbot on Solana, differentiating itself through a cleaner interface and additional features such as DCA (dollar-cost averaging) orders and multi-wallet management. Trojan has grown rapidly and regularly competes with BONKbot for the top position in Solana trading volume.
What Telegram bots actually cost
The total cost of a Telegram bot trade includes three components: the bot fee, the DEX swap fee, and the network gas fee.
The bot fee is the primary revenue source for the bot operator. It typically ranges from 0.5% to 1% of the trade value. On a $1,000 trade, this means $5 to $10 goes to the bot.
The DEX swap fee is paid to liquidity providers on the underlying decentralized exchange. On Uniswap V3, this is typically 0.3% for established tokens and 1% for newer, lower-liquidity tokens. On Raydium (Solana), the standard fee is 0.25%.
The network gas fee varies dramatically by chain. On Ethereum, a swap transaction costs $3 to $15 depending on network congestion. On Solana, the same transaction costs less than $0.01. On Base, gas fees typically fall between $0.01 and $0.10.
Adding these together, a $1,000 trade on Ethereum through a Telegram bot with a 1% fee costs roughly $10 (bot fee) + $3 (DEX fee at 0.3%) + $5 to $10 (gas) = $18 to $23. The same trade on Solana costs roughly $10 (bot fee) + $2.50 (DEX fee) + $0.01 (gas) = $12.51. These costs are meaningful for small trades. A $100 trade on Ethereum through a Telegram bot loses 18% to 23% of its value to fees before any price movement occurs.
The fee economics explain why Telegram bot trading has concentrated on Solana, where the low gas costs make small, frequent trades economically viable. On Ethereum, Telegram bot trading is more practical for larger position sizes where the fixed gas cost represents a smaller percentage of the trade.
Sniping and launch trading
Sniping is the feature that originally drove adoption of Telegram trading bots. When a new token launches on a DEX, the token creator adds liquidity to a pool. The first trades against that liquidity get the lowest prices. Sniping bots monitor the blockchain for liquidity addition transactions and attempt to place a buy order in the same block.
The technical mechanics differ by chain. On Ethereum, snipers use private transaction channels such as Flashbots or MEV Blocker to submit transactions directly to block builders, bypassing the public mempool where they could be front-run. The bot must predict the exact block in which liquidity will be added and submit a transaction with sufficient gas priority to be included immediately after the liquidity transaction.
On Solana, sniping works differently because the network uses a leader-based block production model rather than a mempool auction. Bots connect to multiple RPC nodes and submit transactions with optimized compute budgets to maximize the probability of early inclusion. The speed competition on Solana is measured in milliseconds, and the leading bots invest heavily in infrastructure co-located with Solana validators. Some bots maintain dedicated connections to multiple validator operators, paying for priority access that shaves tens of milliseconds off submission times. The infrastructure arms race mirrors the high-frequency trading competition in traditional finance, where firms spend millions on co-location and network optimization to gain microsecond advantages.
Sniping carries substantial risk. Many new token launches are scams, rug pulls, or honeypot contracts that allow buying but prevent selling. A successful snipe on a fraudulent token results in a total loss. The anti-rug features offered by bots like Maestro attempt to simulate a sell transaction before executing the buy, checking whether the token contract allows selling. However, sophisticated scam contracts can pass these checks and then enable restrictions after a set number of blocks or a specific volume threshold.
The custody problem
The most significant risk of Telegram trading bots is the custody model. When a user creates a wallet through a Telegram bot, the bot generates the private key and stores it on its servers. The user receives the public address and sometimes can export the private key, but the bot retains a copy.
This means the bot operator has full access to every wallet created through the platform. If the bot’s servers are compromised, every user’s funds are at risk. If the bot operator decides to act maliciously, they can drain every wallet simultaneously. This is the exact opposite of the self-custody principle that decentralized finance was built to enable.
Several Telegram bots have experienced security incidents. In September 2023, Maestro experienced an exploit through a vulnerability in its token approval router contract, resulting in approximately $280,000 in user losses. The bot reimbursed affected users, but the incident highlighted the concentrated risk. In late 2023, Unibot experienced a contract exploit that affected users who had granted token approvals through the bot.
The practical advice for managing this risk is straightforward: treat the Telegram bot wallet as a hot wallet with limited funds. Transfer only the amount needed for immediate trading. Move profits to a hardware wallet or self-custody solution regularly. Never store a significant portion of your portfolio in a Telegram bot wallet. Some traders set a hard rule: never keep more than they can afford to lose entirely in the bot wallet.
Some newer bots have introduced partial mitigations. A few support connecting external wallets through WalletConnect, so the user retains custody of the private key and approves each transaction through their own wallet app. This approach sacrifices speed (each trade requires a manual approval step) but eliminates the custody risk. The tradeoff reflects the fundamental tension in Telegram bot trading: speed and convenience on one side, security and self-custody on the other.
The custodial risk is compounded by the lack of regulatory oversight. Traditional exchanges that hold customer funds are subject to licensing requirements, capital reserves, and regular audits. Telegram trading bots operate outside these frameworks entirely. There is no deposit insurance, no regulatory body to file complaints with, and no legal obligation for the bot operator to maintain solvency or segregate user funds. Users are trusting anonymous or pseudonymous teams with their private keys, and the only recourse in the event of a loss is whatever goodwill or reputational incentive the bot operator feels.
Revenue, tokens, and the bot economy
Telegram trading bots have created a new category of crypto revenue-generating businesses. The fee revenue is substantial: Banana Gun alone has generated more than $100 million in cumulative fees. Several bots have issued tokens that entitle holders to a share of the fee revenue, creating a form of equity-like exposure to the bot’s trading volume.
The token economics vary by project. Banana Gun distributes a percentage of trading fees to BANANA token holders who stake their tokens. Unibot distributes a share of fees to UNIBOT holders. The yield depends on trading volume, which is highly correlated with market sentiment. During bull markets and memecoin frenzies, daily fee revenue can spike by ten times or more. During quiet markets, revenue can drop to a fraction of peak levels.
This volume sensitivity makes Telegram bot tokens among the most volatile assets in crypto. UNIBOT rose from $3 to $200 during its initial hype cycle in 2023, then declined more than 90% before finding a lower range. BANANA experienced similar volatility. Traders who buy bot tokens are effectively making a leveraged bet on future DEX trading volume, particularly memecoin trading volume, which has historically been the most cyclical segment of the crypto market.
The competitive dynamics are intense. Bots compete on speed (fastest execution wins the sniping market), fees (lower fees attract volume-sensitive traders), features (copy trading, limit orders, DCA), and chain coverage (supporting more chains captures more trading activity). The low barriers to entry mean new bots can launch quickly, but the network effects of user adoption and the infrastructure investment required for competitive sniping speeds create meaningful advantages for established players.
The relationship between Telegram bots and decentralized exchange volume is symbiotic. Bots route a significant share of total DEX volume, particularly on Solana where BONKbot and Trojan together have accounted for more than 30% of all Raydium swap volume during peak memecoin periods. This makes bots a critical distribution layer for DEXs, and some DEX protocols have begun offering fee rebates or priority routing to the highest-volume bots. The arrangement benefits both sides: bots get better execution for their users, and DEXs get more volume and fees.
What this does not cover
This guide does not cover the legal and regulatory status of Telegram trading bots, which remains unclear in most jurisdictions and may evolve as regulators examine unregistered trading platforms. It does not cover the specific token contract risks of memecoin trading, including honeypot contracts, hidden mint functions, and transfer tax manipulation, which are the most common causes of loss for Telegram bot users. It does not cover the broader MEV landscape beyond its relevance to Telegram bot users, nor the technical details of Solana validator operation or Ethereum block building that underpin the sniping infrastructure.
Practical checks before using a Telegram trading bot
Check the bot’s track record. Search for past security incidents, contract exploits, or reports of fund losses. A bot that has been operating for more than 12 months without a major incident has passed a meaningful stress test, though past safety does not guarantee future safety.
Check the fee structure. Calculate the total cost of a round-trip trade (buy and sell) including bot fees, DEX fees, and gas on the specific chain you plan to trade. If the total cost exceeds 3% to 5% of your trade size, the fee drag will make it very difficult to trade profitably.
Check the custody model. Determine whether the bot generates and holds your private key, or whether it supports external wallet connections. If the bot holds your key, plan your fund management accordingly and never keep more in the bot wallet than you are prepared to lose.
Check the withdrawal process. Before trading, test a small withdrawal to confirm that you can move funds out of the bot wallet to an external address without delays or restrictions.
Check the bot’s social channels. Active developer communication, regular updates, and transparent incident response are positive signals. A bot with no public developer presence or communication channel is a higher risk.
Are Telegram trading bots legal?
The legality depends on jurisdiction. In most countries, using a bot to trade on decentralized exchanges is not explicitly prohibited, but the bots themselves may be operating as unregistered broker-dealers or money transmitters. Users should research their local regulations before using these tools.
Can I lose all my money using a Telegram trading bot?
Yes. The two most common ways to lose everything are trading a scam token (honeypot or rug pull) and a bot security breach where the private key is compromised. Limiting the funds stored in the bot wallet reduces the maximum loss from a security breach.
Which Telegram trading bot is best for beginners?
BONKbot on Solana is often recommended for beginners because Solana’s low gas fees make experimentation cheap. A failed trade on Solana costs less than a cent in gas, compared to several dollars on Ethereum. The lower cost of mistakes allows beginners to learn without significant fee-related losses.
How do Telegram trading bots make money?
Through transaction fees, typically 0.5% to 1% per trade. Some bots also earn revenue through priority transaction routing, where they charge additional fees for guaranteed fast execution during high-demand periods such as token launches.
Can Telegram bots protect me from rug pulls?
Some bots offer anti-rug features that simulate sell transactions before executing buys, check for blacklisting functions in the token contract, or monitor for liquidity removal events. These protections catch basic scams but cannot detect sophisticated exploits or delayed rug mechanisms. No bot can guarantee protection against all forms of token fraud.
Do I need to pay taxes on Telegram bot trades?
In most jurisdictions, yes. Each swap is a taxable event, and the high-frequency nature of Telegram bot trading can create dozens or hundreds of taxable transactions per day. Most bots do not provide tax reports, so users need to export their wallet transaction history and use third-party tax software to calculate their obligations.
What is the difference between sniping and copy trading?
Sniping targets new token launches, attempting to buy in the same block as the initial liquidity. Copy trading replicates the trades of a specified wallet address in real time. Sniping is a speed competition against other bots. Copy trading is a strategy that relies on the skill of the wallet being copied.
Can I use multiple Telegram trading bots at the same time?
Yes. Many traders use different bots for different chains or strategies. A common setup is BONKbot or Trojan for Solana memecoin trading and Banana Gun for Ethereum sniping. Each bot generates its own wallet, so funds must be distributed across multiple wallets accordingly.
Disclaimer
This article is for informational purposes only and does not constitute financial or investment advice. Cryptocurrency investments carry significant risk, and you should conduct your own research before making any investment decisions. Information is accurate as of August 6, 2026.
Bitcoin miners are increasingly positioning themselves as providers of artificial intelligence infrastructure and high-performance computing capacity, but the stock-market reaction to fresh AI hosting deals has cooled markedly in the past couple of years. New research suggests that while contract values are growing, investors are paying less attention to the headline announcement and more attention to what happens next—execution, funding, and long-term profitability.
According to an analysis by Blocksbridge Consulting published in TheEnergyMag’s Miner Weekly, deals tied to AI infrastructure have become less “market-moving.” The report reviewed 25 AI and HPC infrastructure contracts announced between June 2024 and August 2026, finding that the average stock move on announcement day fell from roughly 24% for the earliest deals to about 10% for the most recent ones. Median gains also declined by around half over the same period, even as the reported size and value of the contracts increased.
Key takeaways
- Blocksbridge Consulting’s review shows AI/HPC deal announcement-day reactions weakening from ~24% average moves to ~10% in later deals.
- Median gains from these announcements dropped by about half despite larger contract sizes, implying investors value execution more than upfront figures.
- Revenue per contracted megawatt has inched higher over time, suggesting AI hosting agreements are becoming more financially attractive.
- Major early wins for miners tied to notable AI counterparties produced sharp stock jumps, while newer mega-deals have generated smaller, shorter-lived reactions.
- Investor caution is also visible in infrastructure-focused indices, with TheEnergyMag’s TEM AI Infrastructure Growth Index down ~28.5% from its June peak.
Why AI-hosting news is moving stocks less
The central takeaway from the Blocksbridge Consulting analysis is not that AI hosting deals are shrinking—they appear to be growing in economic importance—but that markets have started to anticipate them. As more miners and infrastructure providers offer similar propositions, investors may treat new contracts as incremental confirmation rather than a sudden re-rating of business prospects.
The report points to a nuanced shift. On one hand, annualized revenue per contracted megawatt has edged upward across the sample, an indicator that AI hosting agreements may be improving in value. On the other hand, the reduced market reaction suggests that investors now scrutinize the substance behind those deals: whether capacity can be delivered on time, how projects are financed, and how durable profitability will be once contract ramp-ups and operational costs are accounted for.
In other words, it’s possible for deals to be economically better while still failing to trigger the same stock enthusiasm as earlier announcements—because expectations adjust. When investors believe execution risk is either higher or more variable than the market used to assume, the “surprise” embedded in contract headlines becomes smaller.
Early deal spikes versus muted mega-deal reactions
The difference between early and later announcements stands out in examples cited alongside the Blocksbridge Consulting findings. According to the report’s examples, initial agreements connected to AI infrastructure sparked dramatic moves for certain miners and hosting operators.
Core Scientific’s initial hosting agreement with CoreWeave reportedly pushed its shares up by more than 40%. Applied Digital’s first CoreWeave lease gained nearly 49%, while TeraWulf’s first Fluidstack deal surged almost 60%.
But as the market has absorbed similar news, later mega-deals have tended to elicit more modest reactions. TeraWulf’s 401-megawatt lease with Anthropic lifted its shares by about 5%. CleanSpark’s $6.6 billion AI hosting agreement reportedly gained nearly 9%. Bitdeer’s new Tydal contract briefly pushed its stock up roughly 12%, but the gains reportedly faded by the close.
That pattern fits the report’s broader conclusion: investors appear more likely to react to earlier “proof points” and less likely to reprice rapidly when a company announces a larger continuation of an established AI hosting strategy. For traders and portfolio managers, the implication is straightforward—volatility around announcements may be structurally lower than it was during the market’s earlier phase of AI infrastructure discovery.
Indexes show momentum slowing, not demand disappearing
The cooling enthusiasm is also reflected beyond individual stock moves. The TheEnergyMag TEM AI Infrastructure Growth Index—tracking publicly traded companies developing AI data center and digital infrastructure businesses—has reportedly fallen about 28.5% from its June peak, even though the index remains sharply higher than a year earlier. The implication is that investors have not abandoned the sector, but they have reduced the intensity of the chase.
The same article notes that the slowdown in these AI infrastructure equities has mirrored broader risk appetite. It cites the Philadelphia Semiconductor Index falling nearly 17% from its July peak, suggesting that part of the recent softness could be tied to sector-wide sentiment rather than purely idiosyncratic execution concerns for specific mining or hosting players.
For Bitcoin miners that have broadened into AI workloads and high-performance computing, this matters because their ability to convert new contracts into steady earnings depends not only on deal economics, but also on the capital markets environment. When AI infrastructure equity momentum slows, lenders and equity investors often become more selective about who can finance expansions and meet delivery timelines—exactly the areas the Blocksbridge analysis implies investors are emphasizing more now.
What investors should watch next
If the market is indeed moving toward a more “disciplined” pricing of AI hosting deals, the next signals will likely be less about the size of the headline contract and more about execution milestones: ramp schedules, delivery progress, and evidence that annualized megawatt economics can hold up as contracts scale. Readers should watch whether announcement-day reactions continue to weaken as deals become more common, or whether new structures—potentially with clearer financing and delivery frameworks—can restore stronger sentiment.
ether.fi has removed all restaking exposure from weETH, making its flagship asset a plain liquid staking token and confining restaking to weETHs, a separate token built on Symbiotic. The protocol announced the split on X on Thursday. The change ends the arrangement that made ether.fi the largest… Read the full story at The Defiant
An additional 22 miles of the waterborne barrier system has been constructed since Trump’s return to office.
Challenges in construction
Trump’s Administrations have faced several hurdles in constructing the border wall.
One aspect of the construction that has posed significant challenges has been the need to acquire land that is already owned. Approximately 70% of the border is made up of private, tribal, or state-owned land, according to the GAO. And the federal government has faced pushback from all three fronts.
Among multiple challenges that have been made in response to the government’s attempts to acquire land, the Texas General Land Office in July sent CBP and an agency contractor a cease-and-desist letter after it said it discovered that the contractor had cleared over a mile of state land for construction, using heavy machinery and destroying vegetation.
“Texas sovereignty will not be infringed upon by failure to follow established protocol,” said Commissioner Dawn Buckingham in a statement. “I am committed to maintaining a positive relationship with CBP, but we will not allow rogue actors who breached our agreement to undermine the incredible work we do for Texas.”
Culper Research shorted Nvidia after predicting the $5 trillion AI giant might be re-routing Chinese demand for AI chips through sketchy deals with neighboring countries.
Almost no one believed it at the time, but as it turns out, it called the top.
Despite Nvidia CEO Jensen Huang’s guidance of “assuming zero for China” to comply with US export controls to the country, the company actually benefitted from work-arounds and created big problems for itself in neighboring nations.
“I’m forecasting China’s sales to be zero,” Huang said in November 2025 after US export restrictions halted Nvidia’s chip sales to China. “It’s zero for the next quarter, zero for the quarter after that. We’re assuming it’s going to be zero.”
By May 13, however, Culper Research sniffed a problem with that claim and sold-short Nvidia shares. It sensed that Nvidia might be re-routing its Chinese demand for AI chips via Taiwan and places like Malaysia and Singapore.
It also foresaw legal problems as regulators discovered its diversions.
With the exception of one day immediately following that report, Nvidia’s stock has never closed any day higher than its May 13 close.
Calling the top on Nvidia
It was an unexpected and remarkably accurate call in the middle of a bullish mania. The week prior to the report, Nvidia had rallied 13%, and shares were up an impressive 20% year-to-date.
Skeptical, Culper Research wrote, “We are short Nvidia for one reason: The company has a significant China problem.”
As it turns out, Nvidia did have massive, unpublished problems in China and neighboring Taiwan. In the three months since that report, those problems became mainstream news.
On July 24, Taiwanese prosecutors searched the home and workplace of an Nvidia employee suspected of smuggling prohibited chips to China. Investigators also went through his desk at the company’s Taipei office.
It’s the first known legal action against an alleged Nvidia employee in Taiwan’s widening AI chip-smuggling investigation. Prosecutors said the man was “strongly suspected of having committed the offences,” and cited a risk of flight and destruction of evidence.
That story surfaced on July 28. The same day, Jensen Huang quietly sat down with US Commerce Secretary Howard Lutnick in Washington, DC.
Read more: Apple overtook Nvidia as largest public company this morning
Saw these problems coming three months ago
Both events landed 11 weeks after an activist short seller told investors exactly where to look.
On May 13, Culper Research estimated that more than 20% of Nvidia’s fiscal 2026 compute revenue would still run on Chinese demand, even though that demand would, according to its analysis, probably run through Southeast Asian intermediaries and Taiwanese diversions.
The report named those intermediaries: Singapore’s Megaspeed, Malaysia’s Speedmatrix, and a subsidiary of Taiwan’s Gigabyte, Giga Computing.
Crucially, Culper warned that the exposed corridor of Chinese demand routing through Taiwan was “just one of many in what is a complex and far-flung operation.”
It predicted multiple additional Nvidia OEMs, partners, and intermediaries would sustain their Chinese demand through intermediaries in nearby countries.
A former high-level Nvidia employee told the firm that “Megaspeed is just the tip of the iceberg.”
Huang insisted the company wasn’t skirting export restrictions and that it “repeatedly tested and sampled data centers around the world and found no diversion.”
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