By replacing order books with collective on-chain reserves, decentralized finance (DeFi) brought a complete transformation to crypto asset trading. These reserves, also known as liquidity pools, ensure immediate swaps without direct intermediaries. However, being a liquidity provider means walking a fine line, seeking balance between attractive fees and structural risks that can erode deposited capital. Operational Mechanics and the Role of Market Makers Pools operate via smart contracts governed by automated market makers (AMMs). Unlike centralized platforms, the price adjusts algorithmically based on the asset ratio within the pool. When a user swaps a token, the reserve’s balance shifts, generating a fee that is distributed proportionally among liquidity providers. To maintain parity with external prices, the ecosystem relies on arbitrageurs. These market participants identify price discrepancies between the pool and broader markets, executing trades that bring valuations back into alignment. However, this transactional dynamic extracts value from the pool, directly impacting the net margins of those supplying the underlying liquidity. Yields vs. the Threat of Impermanent Loss The primary appeal of providing liquidity is the continuous capture of swap fees, often supplemented by incentive programs that enhance overall attractiveness. However, when the price of deposited assets diverges significantly from their entry levels, impermanent loss occurs. This phenomenon results in the total value of the deposit being lower than what would have been achieved simply by holding the tokens in a wallet. Alongside this dilemma, other critical vulnerabilities come into play, such as smart contract vulnerabilities, Maximal Extractable Value (MEV) exploits, and stablecoin depegging events. In modern concentrated liquidity models, if the market price falls outside the provider’s defined range, the position stops earning fees and becomes fully exposed to the underperforming asset. Market Outlook and the Shift Toward Active Management The traditional, passive 50/50 capital allocation model is steadily losing ground to highly sophisticated algorithmic protocols. Modern architecture demands active position management, continuous volatility monitoring, and rigorous analysis of trading volume relative to Total Value Locked (TVL). Only pools with sustained, robust volume can offset the implicit costs of arbitrage. The viability of this strategy hinges on the participant’s ability to measure hidden costs against projected nominal yields. The development of hedging instruments and automated execution via specialized vaults will define the sector’s maturity, turning an experimental mechanism into an efficient, foundational component of decentralized market infrastructure. Supplying capital to a decentralized pool is far from a friction-free route to passive income. It functions as a dynamic market strategy where fee revenue must be carefully balanced against asset volatility and arbitrage drain. Rather than relying solely on advertised yields, investors must exercise discipline to determine whether the risk premium truly justifies deploying capital in decentralized markets.  

Key Takeaways

• Chainlink publishes cirBTC reserve data onchain.
• Reported reserves exceed the current token supply.
• The reserve feed is not an audit.
• Reserve-linked minting controls were not announced.
• Direct access remains focused on qualified businesses.

What Chainlink changes for cirBTC Native Bitcoin cannot move directly through Ethereum smart contracts. Wrapped tokens address that limitation by keeping BTC on the Bitcoin network while issuing a corresponding token on a programmable blockchain. Circle’s cirBTC is already live on Ethereum and is designed to maintain at least one BTC in reserve for every token issued. It can be used in compatible applications without requiring its holder to sell the underlying Bitcoin exposure. The September 4 update changes how that backing can be monitored. Under Circle’s reserve-verification model, the company discloses the Bitcoin addresses holding cirBTC reserves, while Chainlink Proof of Reserve publishes verified reserve information onchain. Unlike a conventional reserve webpage, an onchain feed can be read by smart contracts and automated risk systems. A lending protocol could compare reported reserves with cirBTC supply before accepting the token as collateral, provided its developers connect the feed to the protocol’s risk controls. Reported reserves exceed cirBTC supply Circle’s live cirBTC dashboard listed approximately 40.03 cirBTC in circulation against 42.51 BTC held in the disclosed reserve addresses in its September 5 reading. cirBTC reserve reading Circle dashboard data dated September 5, 2026, at 8:00 a.m. TOKEN SUPPLY 40.03 cirBTC BTC RESERVES 42.51 BTC CALCULATED SURPLUS 2.49 BTC CALCULATED COVERAGE 106.21% The surplus and coverage ratio are calculations based on Circle’s published figures. The coverage figure divides reported BTC reserves by cirBTC supply, treating each cirBTC as a claim backed by one BTC under Circle’s stated model. Reserves exceeded supply by approximately 2.49 BTC at that reading, although Circle has not described the difference as a permanent reserve buffer. The values will change as tokens are issued or redeemed and as BTC moves between the disclosed addresses. CirBTC’s current supply is still small. If it becomes widely used across lending markets and exchanges, stale reserve information, thin secondary-market liquidity or disrupted redemptions would carry greater consequences. What the reserve feed can verify Chainlink helps users determine whether the BTC held in Circle’s disclosed addresses covers the cirBTC visible onchain. That is a narrower function than a financial audit, which would examine a broader range of assets, liabilities, controls and legal obligations. The reserve reading also depends on Circle identifying all relevant addresses. Holders separately rely on the custodian protecting the BTC, the issuer processing eligible redemptions and the cirBTC smart contract operating correctly. Circle says the backing assets are held through a group affiliate at Circle National Trust, a federally chartered national trust bank supervised by the Office of the Comptroller of the Currency. According to the company, the BTC is segregated from Circle’s corporate assets and held for the benefit of cirBTC holders. The custody structure protects the underlying assets, while Chainlink makes the reported reserve data available onchain. A positive reserve reading does not guarantee immediate redemption or remove operational and smart-contract risks. Circle has not announced an automatic minting safeguard Publishing reserve data allows users and applications to identify a potential mismatch. Preventing unsupported issuance requires an additional control connecting that data to cirBTC’s minting process. Chainlink Proof of Reserve can support rules that stop new tokens from being created when verified backing falls below a required threshold. Circle’s announcement, however, describes reserve monitoring and onchain publication without saying that the cirBTC contract automatically blocks minting in such circumstances. Available now Machine-readable reserve information that can be compared with the amount of cirBTC in circulation. Not confirmed A contract-level rule that automatically prevents additional cirBTC issuance when verified reserves are insufficient. Wyoming’s recent Chainlink integration illustrates the same design choice. As our analysis of Wyoming’s onchain reserve system explained, developers must decide whether the published figure remains a monitoring tool or becomes part of an enforceable minting rule. For cirBTC, the feed currently improves detection. It cannot replace missing Bitcoin, complete a delayed redemption or correct a reserve shortfall by itself. Direct redemption remains institution-focused Reserve coverage is only one part of a wrapped asset’s reliability. Holders also need to understand who can exchange the token directly for the underlying Bitcoin. Circle’s developer documentation says qualified businesses can mint and redeem cirBTC through Circle Mint. The service uses the same API framework that Circle provides for USDC and EURC. A trader may still be able to obtain cirBTC through an exchange or decentralized liquidity pool without qualifying for a Circle Mint account. That trader would depend on the secondary market or an eligible intermediary when leaving the position rather than redeeming directly with Circle. The distinction becomes particularly important during periods of market stress. A fully backed token can temporarily trade below the value of its underlying asset when direct redemption is limited to a narrower group and secondary-market liquidity becomes insufficient. Circle has used a similar institution-focused distribution model elsewhere. As shown by Standard Chartered’s integration of USDC minting and redemption, eligible institutions can access Circle-issued assets through regulated intermediaries without necessarily maintaining a direct relationship with Circle. READ MORE: Poland’s Crypto Licensing Gap Widens After Veto Vote Liquidity and DeFi adoption are the next tests Circle plans to add native cirBTC support to Arc when the network’s mainnet launches, subject to approval, with further blockchain integrations expected later. Expansion across several networks would make aggregate supply tracking more important because all issued tokens would ultimately depend on the same underlying Bitcoin reserves. CirBTC’s progress can be measured through its circulating supply, secondary-market liquidity, redemption access and acceptance as collateral. Protocol documentation will also show whether DeFi applications merely display the Chainlink reserve reading or use it to impose collateral limits. The remaining technical question is whether Circle or integrated protocols will connect the reserve feed to controls that prevent additional issuance or exposure when verified BTC backing is insufficient. The article is provided for informational purposes only and does not constitute investment advice.The post Circle Adds Chainlink Reserve Verification to cirBTC appeared first on Coindoo.

A Minnesota man faces felony charges after authorities allege he used what looked like the victim’s photo ID and a forged signature to drain $12,760 from a victim’s accounts during back-to-back visits to two U.S. Bank branches. Dion Antonio Lowe, 55, of Minneapolis is accused of the June 11th thefts from a Missouri victim’s checking and savings accounts, reports Limitless News. Surveillance footage captured the suspect arriving and departing as a passenger in a white Tesla at the St. Paul branch on Payne Avenue and the Woodbury location roughly 40 minutes apart. A crime analyst from Maple Grove identified the individual, prompting his transfer to Washington County Jail on existing warrants from other cases. In an interview on August 27th, the man reportedly admitted to the withdrawals and involvement in a broader organized scheme spanning multiple states. Prosecutors have filed additional felony identity theft counts against him in Washington and Hennepin counties tied to separate incidents in April and July, including a failed attempt to take $36,000. Each count carries potential penalties of up to 10 years in prison and a $20,000 fine. The suspect remains presumed innocent unless convicted in court. Follow us on X, Facebook and Telegram
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&nbsp Disclaimer: Opinions expressed at The Daily Hodl are not investment advice. Investors should do their due diligence before making any high-risk investments in Bitcoin, cryptocurrency or digital assets. Please be advised that your transfers and trades are at your own risk, and any losses you may incur are your responsibility. The Daily Hodl does not recommend the buying or selling of any assets including cryptocurrencies, nor is The Daily Hodl an investment advisor. Please note that The Daily Hodl participates in affiliate marketing. Generated Image: Midjourney The post Minnesota Man Charged After Allegedly Draining $12,760 From Missouri Victim’s U.S. Bank Accounts: Report appeared first on The Daily Hodl.

Bitcoin price forecasting has accumulated an unusually colorful collection of methods. You have basic scarcity models that convert the halving schedule into a price, and run-of-the-mill on-chain models that turn address or transaction activity into value. The highly contested power-law charts draw an ascending corridor through Bitcoin's history, and machine-learning systems feed market and macroeconomic data into incredibly complex software. Each of those approaches enters the price-prediction contest against a very shallow, dumbed-down opponent: naive forecasts that use only current market information. A price forecast can use today's price, a return forecast can use zero, and a direction forecast can use a random walk. Much of the academic literature has struggled to beat it once a model leaves the period in which it was designed. A May 2026 preprint reviewing Bitcoin prediction research by Carlos Baquero of the University of Porto reached a pretty sobering conclusion: across the peer-reviewed record, no model had demonstrated durable superiority over the appropriate naive benchmark at horizons of one to six months across several market regimes. The literature contains hundreds of papers, while Baquero selected 23 for close examination based on their methods, influence, or use of genuine out-of-sample evaluation. The review itself is still awaiting peer review, an important distinction when one of its central arguments is that forecasting claims need stronger evaluation. Short-horizon order flow and daily return forecasts occupy a separate field, and some have produced real predictive value. Online discussions often blend them with longer-horizon price forecasts and valuation models, although each task asks for a different answer. A formula describing Bitcoin's historical path tells us little about tomorrow's direction, while a daily direction model says little about the price six months from now. The easiest rival in finance Naive forecasting works because financial prices are persistent, so a model predicting $100,100 tomorrow when Bitcoin trades at $100,000 today can produce a tiny percentage error even when it has learned almost nothing about direction or return. Today's price would have been nearly as accurate, and evaluating only the first model gives it credit for information the market had already supplied. The benchmark becomes more demanding as the horizon expands because Bitcoin can move violently over a month, giving a forecaster room to add value, while the relationships the model learns decay as the market evolves. A rule calibrated to the retail-led 2017 cycle encountered a different derivatives structure in 2021, and spot ETFs created another route for capital and price discovery in 2024. Each era supplies historical data from a version of the market that no longer exists in quite the same form. This problem, known as non-stationarity, appears when the relationships between variables don't stay stable enough for past observations to describe the future. Bitcoin's user base and liquidity have evolved over time, while regulation and access have changed who can trade it and how. A model can capture a relationship during one period and lose it when the market around the asset evolves. Francesco Puoti, Fabrizio Pittorino, and Manuel Roveri reached a similar result in a study comparing statistical, machine-learning, and deep-learning forecasts. They applied 12 approaches to five major cryptocurrencies at one-day, seven-day, and 30-day horizons. Simple naive models consistently produced better forecasts than ARIMA, Prophet, random forests, XGBoost, LSTM networks, and N-BEATS. The result says more about the available information than the sophistication of each method. A complex model can add value when stable patterns exist for it to learn, and it can memorize noise when those patterns are weak or temporary. Bitcoin offers enormous quantities of data, but the number of independent market cycles it went through is still quite small. Millions of minute bars keep repeating observations from the same 2018 bear market or the same 2020 liquidity shock. How a backtest becomes a crystal ball for predicting Bitcoin price Many Bitcoin models look strongest once their creators have seen the entire historical period used to build them. Researchers can try different variables and lookback windows, move the start date, or swap one architecture for another before publishing the best result. The winner may have discovered a durable relationship, but it also could have won a large lottery conducted on the same price history, an outcome known as backtest overfitting. David Bailey and his co-authors formalized the problem in their research on the probability of backtest overfitting. Trying more model variations raises the odds of finding an excellent historical result through chance. Selecting the winner and presenting its performance alone hides the number of failed attempts that made the winner possible. A single chronological split offers little protection because a researcher can train through 2020 and evaluate the model in 2021, producing an apparently out-of-sample result that owes much of its performance to a single bull market. Walk-forward evaluation is stronger because the model repeatedly retrains on past data and forecasts the next unseen period. Multiple non-overlapping holdout windows are stronger again because they force the same method to encounter bull markets, crashes, sideways periods, and different liquidity conditions. Among the peer-reviewed papers Baquero examined, none evaluated the same approach across several non-overlapping holdout windows covering different regimes. The strongest papers used rolling or walk-forward evaluation over one continuous out-of-sample period. Related Reading New Bitcoin power law chart turns $124k into the ETF-era battleground Those methods provide real evidence, but a single aggregate error can still hide failure in one section behind success in another. Information leakage can also lead to false confidence because a feature calculated with future data can give a model a faint view of the answer. You get the same problem when you normalize variables across the full sample, and overlapping return windows can carry future observations across the training boundary. The error can be subtle enough to survive peer review, especially when a complicated architecture puts several transformations between the raw data and the reported forecast. The metric itself can flatter the model when a 99% accuracy claim refers to how closely a predicted price level follows the actual price, a relatively easy task for a persistent series. Traders care about the direction and size of the move, as well as the cost of acting on it. Models that predict $100,500 when Bitcoin moves from $100,000 to $99,500 have a small price error and still make the wrong trade. The formulas that outlive their forecasts Bitcoin's best-known valuation frameworks thrive because they turn what's obviously a very complicated asset into a nice, intuitive explanation. For example, stock-to-flow says scarcity is what drives value, with each halving reducing new supply relative to the existing stock. Metcalfe-style models say a network becomes more valuable as its user base expands. The power law says Bitcoin's long history follows a stable mathematical relationship between price and time. Each of these ideas contains plausible economic intuition, but its forecasting record depends on whether the fitted relationship survives new data and whether simpler explanations account for the same result. Alexander Shelton's 2024 peer-reviewed examination of Bitcoin return prediction found that stock-to-flow and Metcalfe variables helped explain returns in-sample, but offered limited or zero predictive ability out of sample. Once time effects entered the stock-to-flow regression, its statistical force disappeared. Bitcoin's supply ratio increases on a predetermined schedule, and its price also climbed for much of its history, making two time-linked series look economically connected. We saw that weakness in the market long before it appeared in a formal review. The stock-to-flow model diverged from Bitcoin's price as the asset traded below its projected path for years. Persistent divergence can be absorbed by redefining the output as long-term value or a cycle average, though each redefinition makes the original price claim harder to evaluate. Chart compares Bitcoin’s price with the stock-to-flow model and model variance from 2010 through 2026. Source: CoinGlass Metcalfe's Law faces a related identification problem because network activity and price can climb together when adoption raises value, when a higher price attracts users, or when both variables follow a common time path. Savva Shanaev and his co-authors used instrumental variables across six proof-of-work assets in a study of mining costs, network activity, and crypto value. Once they addressed autocorrelation and the two-way relationship between activity and price, the positive effects attributed to hashrate and transaction count disappeared. Power-law models are in a much more complicated position because their corridors have captured much of Bitcoin's historical path and provide a practical visual language for discussing where price lies relative to a long-run curve. Reports on the Bitcoin power-law model have also shown how ETF-era market structure can alter the forces moving price within that corridor. Chart plots Bitcoin’s price since 2011 within logarithmic support, resistance and linear-regression bands projected through 2040. Source: Bitbo The academic issue lies in the strength of the inference. A high R-squared on a log-log chart establishes that a line fits the observed sample. Formal support for a power law also requires evidence about the distribution of residuals and comparisons with other time functions. Researchers would then need to examine sensitivity to the starting date and performance on future observations. Baquero's review found that the current Bitcoin power-law literature had not yet completed that work. An honest forecasting standard would publish the naive benchmark beside the model and report every market regime separately. Trading costs belong in the results, while public code and data let other researchers reproduce it. The paper should also disclose how many variations were attempted, since that number determines how surprising the winning backtest really is. Valuation narratives need to be separated from point forecasts, and the reported range should reflect the asset's uncertainty. Any correction term should allow a value of zero, letting the model conclude that today's price is its best forecast. That conclusion will always struggle online because it offers no dramatic target and no date to circle. It has one advantage that the forecast bazaar rarely advertises: it tells us exactly how much the model knows beyond the price already visible to everyone. The post From power laws to AI networks, why complex Bitcoin price models memorize market noise appeared first on CryptoSlate.

Every movement and interaction recorded on blockchains requires financial compensation for the technological resources consumed. Far from being a simple, incidental operational expense, this economic friction represents the true cost of securing immutable, decentralized block space against constant global demand. Market Context and Settlement Models The term “Gas Fee” is often generalized to describe any fee incurred when transferring assets on-chain, but this is a misconception. While Ethereum rates the algorithmic workload of its smart contracts, Bitcoin prices the transaction’s physical footprint in virtual bytes alongside the prevailing congestion in its mempool. In contrast to legacy architectures, high-throughput networks like Solana calculate fees using a fixed base per signature coupled with an optional priority fee. The apparent price disparity across chains directly reflects their core architectural trade-offs among speed, decentralization, and structural robustness. Network fees act as a critical economic filter to deter spam while securing validator profitability. During periods of peak market euphoria or extreme volatility, block space turns into an auction where immediate inclusion is reserved for whoever is willing to bid the highest fee. Technological Impact and Layered Fragmentation Ethereum’s technical evolution successfully rolled out the EIP-1559 standard, burning the base fee to make cost forecasting more predictable for users. This adjustment removed arbitrary fee spikes, turning network resource consumption into a deflationary driver for the native asset. The rise of Layer 2 solutions such as Base and Arbitrum shifted computation off the main chain at significantly lower costs. Historic milestones like Dencun and Pectra optimized this infrastructure using ephemeral data blobs, drastically reducing the cost of posting proofs back to the settlement layer. It remains crucial to separate rollup security from that of standalone sidechains like Polygon PoS. Layer 2 networks pay directly for data availability on the base layer, whereas sidechains rely on entirely autonomous consensus models and custodial risk profiles. Future Outlook and Operational Efficiency Optimizing on-chain operational costs cannot be left to chance; it demands disciplined transaction management. On Bitcoin, strategically consolidating unspent transaction outputs (UTXOs) during low-traffic windows cushions the fee spikes that inevitably hit during network congestion. Within EVM environments, revoking redundant smart contract allowances and pruning unnecessary DeFi calls prevents sunk costs on failed transactions. Under most execution environments, a reverted transaction still burns the allocated gas because nodes already performed the computational validation. The rollout of account abstraction and fee sponsorship will drive applications where network costs stay entirely abstracted away from the end user. Even so, decentralized computation will always carry an underlying cost that will decide which protocols capture the largest share of global economic throughput. Grasping the underlying mechanics behind on-chain fees turns an unavoidable expense into a deliberate capital efficiency strategy. True financial sovereignty requires looking beyond the raw fee quote to weigh the actual cryptographic security powering each transaction confirmation  

The exchange terminated operations and stopped withdrawals after an audit found unknown transactions worth over $7 million that moved custodied assets to external wallets. Orionx accused Joaquín Díaz and Roberto Zibert, the exchange’s founding partners, of participating in these movements. Orionx Halts Customer Withdrawals Amidst $7 Million Fund Deficit Chile, which had discussed adopting bitcoin […]

Hyperliquid News Today: HYPE Nears All-Time High, Bitwise Buys BigHyperliquid news today centers on four fast moving threads that pushed $HYPE close to its yearly peak. The token touched $87.17 on September 6, 2026, per CoinGecko data, as Bitwise resumed large-scale buying for its Bitwise HYPE ETF, the protocol burned another batch of tokens, and a whale pulled millions off exchanges. Adding to the momentum, the latest Trump CFTC news shows a push to move US listing plans forward through Kraken parent Payward's regulated unit, Bitnomial. Together, these threads explain why this remains one of the most searched names in crypto news today, and $HYPE price today Trump CFTC plan chatter has only grown louder as a result, keeping crypto news near the top of trending lists.HYPE Price Today: Live Market DataMetricValuePrice$87.1724h Range$83.97 to $87.28Market Capitalization$19.399 billion24h Trading Volume$822.795 millionTotal Value Locked$6.828 billionCirculating Supply222.446 million HYPE
Source: CoinGecko price data, reviewed September 6, 2026.Why is HYPE price rising today? For price live tracking, the token sits roughly 2,133% above its listing low. This figure reflects the Max timeframe since launch, not a 24-hour or short-term move, and steady perpetuals trading volume above $800 million daily has kept buyers active even as the wider market cools.Why Is Suddenly Dominating Crypto Headlines?Quick answer: four events collided in one session. $HYPE held near $87, Bitwise bought $10.5 million of the asset after a four day pause, the protocol burned $830,000 in tokens over 24 hours, and a wallet withdrew $8.25 million from four exchanges, all while its US regulatory path stayed in focus.Hyperliquid US Listing Bitnomial ExplainedCoin Bureau outlined the mechanics behind the plan in a recent post. This Hyperliquid news today price update centers on a structure, not a full merger:

• The US would not get Hyperliquid's global, permissionless venue.
• Payward would build a separate, compliant product on the Kraken Bitnomial platform, the CFTC regulated exchange it acquired earlier this year.
• US traders would likely face fewer markets and lower leverage than the offshore version.
• Both the CFTC and SEC would need to rewrite rules first, a process that could take close to a year.

Formal CFTC approval has not been granted, the CFTC Bitnomial framework is still under review, and US regulation remains unsettled. CME Group CEO Terrence Duffy has separately called crypto perpetual futures "a disaster waiting to happen," and the CME lawsuit crypto perpetuals dispute adds friction to the broader onshore plan. The bigger question of when will launch in US markets still has no confirmed date.Coin Bureau also covered this update in a post on X. Bitwise HYPE ETF Buying Today ResumesArkham data shows Bitwise = ETF buying today snapped a four day pause:

• Friday purchase: $10.5 million, the largest single day since a $23.2 million buy on August 27.
• Total bought since launch: $166.3 million.
• Bitwise now runs the largest ETF by cumulative purchases, with HYPE ETF inflows resuming after the brief lull.

Arkham also covered this development in a post on X. Hyperliquid Whale Withdraws Millions TodayThis Hyperliquid whale withdraws millions today, per OnchainLens, in a fresh whale withdrawal spread across four venues:ExchangeAmount WithdrawnOKX46,000 $HYPE (~$3.88M)Bybit29,000 $HYPE (~$2.45M)Kraken13,000 $HYPE (~$1.10M)Gate9,670 $HYPE (~$816K)The wallet has moved funds to cold storage before, suggesting this whale activity follows an accumulation pattern rather than a sale.OnchainLens also covered this withdrawal in a post on X. HYPE Token Burn News TodayThis token burn news today adds a deflationary data point:

• 24 hour burn: 9,730 $HYPE (~$829,500) at an $85.27 average price.
• Lifetime burn: 48.42 million HYPE, worth roughly $4.14 billion at current prices.
• That equals 4.84% of HYPE's maximum supply permanently removed.

The HYPE token burn rate has stayed steady with prior weeks, adding one more thread to Hyperliquid news today.Onchain Lens also covered the burn figures in a separate post on X. Expert Take: What These Moves Signal for HYPEAnalysts suggest renewed ETF demand, steady burns, and an unresolved US path could keep HYPE volatile short term, though none of this confirms a launch date. Any Hyperliquid price prediction, or after CFTC news specifically, should treat the Bitnomial plan as a multi month process rather than an imminent catalyst. For now, Hyperliquid news today reflects accumulation and burn activity set against a regulatory timeline measured in months, not days.Disclaimer: This article is for informational purposes only and does not constitute financial or investment advice.This and other crypto assets remain highly volatile, and readers should conduct independent research before making any trading decisions.

Séduisant sur le papier et lors de notre premier essai, l’Engwe P275 SE promettait monts et merveilles pour un tarif défiant toute concurrence. Mais que vaut réellement ce VAE abordable à l’épreuve impitoyable du quotidien ? Après six mois de vélotaf intensif et plus de 1 600 kilomètres au compteur, entre pannes estivales, usure express et factures imprévues, voici notre retour d'expérience sans concession.

Nouvelle violation de données en France ! L'Association des maires de France et des présidents d'intercommunalité (AMF) vient de se faire pirater par un cybercriminel. Le hacker revendique le vol de plus de 114 000 entrées, dont des mots de passe stockés en clair, désormais mises en vente sur un forum du dark web. L'organisme a saisi la Cnil et doit encore évaluer l'ampleur exacte de la fuite. Des maires, des adjoints et des agents territoriaux sont exposés.

A British Bitcoin investor has recovered 61 BTC more than a decade after losing access to the coins when early UK exchange Intersango disappeared, turning an inaccessible early-crypto holding into assets worth millions. The investor, identified only as Chris, bought the 61 BTC through Intersango in 2011 when Bitcoin traded at about £2.94. He regained the 61 BTC in full on May 28 after instructing CEL Solicitors in January to pursue the assets. The coins were valued at £3.33 million when the recovery was completed. Bank Records Help Establish Ownership After 15 Years Intersango began in early 2011 as Britcoin before operating under the Intersango name. It supported Bitcoin trading against pounds, dollars, euros and Polish zloty before GBP and USD trading stopped in late 2012 and the website went offline by early 2014. The company was dissolved in March 2016. Chris had spent years without access to the Bitcoin before reopening the case in 2026. Recovering the coins required historical ownership evidence, including bank records dating back almost 15 years, alongside specialist blockchain tracing and documents connected to overseas proceedings. The case was resolved within four months rather than through a prolonged contested trial. The recovery differs from Bitcoin that is genuinely inaccessible because its private keys have been destroyed or lost. Intersango still controlled the coins, leaving ownership and legal recovery as the central issues. That distinction has frustrated other early holders. James Howells has spent years trying to recover a hard drive containing thousands of BTC from a Welsh landfill, including an earlier plan involving robot-assisted excavation after the device was discarded in 2013. More Than 5,500 BTC Traced to Former Intersango Users More than 5,500 BTC have been traced and are believed to be connected to former Intersango customers who were unable to withdraw before the exchange disappeared. At Bitcoin prices around $80,000, that pool would carry a market value above $440 million, although individual ownership claims still need to be established. Old email accounts, correspondence with the exchange and bank statements showing payments to Intersango can provide evidence linking former customers to historical balances. The California proceedings connected to the remaining assets have also created a route for users seeking recognition of their claims. Dormant or long-unmoved Bitcoin is not necessarily lost. A separate 999.6846 BTC movement earlier this year came from an address identified in litigation as abandoned, demonstrating that an inactive blockchain address can still have someone capable of signing transactions. Recovery Returns Every Bitcoin in the Claim Chris received all 61 BTC covered by his claim rather than a cash settlement based on their historical value. He has indicated that part of the recovered wealth will go toward a larger family home while most of the Bitcoin will remain invested. For former Britcoin and Intersango customers, proving an old account balance remains the critical step. The more than 5,500 BTC already traced could support additional claims from users able to connect surviving emails, account details or banking records to assets held after the exchange shut down. The post British Investor Recovers 61 Bitcoin From Defunct Intersango Exchange After 12 Years appeared first on Crypto Adventure.