Arsenal's dominant start suggests a formidable title defense, showcasing depth and adaptability, potentially reshaping Premier League dynamics.
The post Arsenal wins first seven matches of the season, scoring 16 goals and conceding just three appeared first on Crypto Briefing.
Carlos Esp's clutch performances highlight Real Madrid's reliance on late-game heroics, raising questions about their defensive stability.
The post Carlos Espí secures last-minute wins for Real Madrid against Espanyol and Elche appeared first on Crypto Briefing.
Elche's comeback highlights Real Madrid's defensive vulnerabilities, potentially impacting their title aspirations and boosting Elche's morale.
The post Elche rallies from 2-0 down to tie Real Madrid in thrilling La Liga match appeared first on Crypto Briefing.
Al Nassr's heavy defeat highlights potential challenges in their Champions League campaign, questioning their squad's readiness and strategy.
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The incident underscores the limitations of relying solely on technology in sports officiating, highlighting the need for human judgment in VAR decisions.
The post Pro Ref chief Webb blames VAR ‘tunnel vision’ for Manchester derby error appeared first on Crypto Briefing.
Bitcoin Magazine
Bitcoin, BTC-Related Stocks Tumble After Senate Blocks Clarity Act
Bitcoin’s price tumbled — along with crypto-related stocks — following the blockage of the long-awaited Clarity Act.
The price of the leading cryptocurrency recently stood at $75,939, down 4% over the past day, after dropping as low as $75,038 at one point on Tuesday.
Lawmakers blocked the landmark digital asset market structure bill in a procedural vote Tuesday. Major companies in the digital asset space have long called for clear rules to be put in place to regulate the industry.
Bitcoin wasn’t the only asset that dropped: BTC-related stocks such as Coinbase (NASDAQ: COIN) and Strategy (MSTR) were also down.
America’s biggest crypto exchange’s stock dropped by more than 10%; Strategy, the largest corporate holder of bitcoin slid by over 5%.
BREAKING:
— Bitcoin Magazine (@BitcoinMagazine) September 15, 2026
Major publicly traded bitcoin miners also dropped in price, with MARA, CleanSpark, and Core Scientific all slipping by 5% or more over the past day.
Senators mostly voted against advancing the legislation — 49 for and 50 against — that the digital asset industry has long called for.
The bill aims to formally divide oversight between regulators, distinguishing which digital assets are securities, commodities or stablecoins.
President Donald Trump last month urged lawmakers to pass it, helping spur a bitcoin rally. But Republicans warned for months that Democrats were deliberately holding it back.
And hold it back they did: anti-crypto senator Elizabeth Warren warned congress against voting for the bill on Tuesday, slamming the bill as “a massive risk to families.”
While Senator Bernie Sanders wrote on X that the bill was “corrupt.”
Lawmakers had a problem with the bill because they said it unfairly allowed Trump to make money from the crypto industry. The president’s family has cashed in with numerous crypto ventures since Trump took office but the White House has always denied any wrongdoing.
“Crypto billionaires have spent nearly $300M on the midterm elections,” added Sanders.
“Meanwhile, Trump and his family have pocketed more than $1.4B from crypto deals.”
Pro-crypto senator Cynthia Lummis slammed Democrats for blocking the bill.
Writing on X, the Republican said: “The once-proud Democratic party is anti-consumer and pro-illicit finance, anti-ethics, anti-free enterprise, anti-worker, anti-livable wage jobs, and pro-socialism. The Democrats are now anti-American.”
This post Bitcoin, BTC-Related Stocks Tumble After Senate Blocks Clarity Act first appeared on Bitcoin Magazine and is written by Mathew Di Salvo.
Mbapp's scoring prowess could significantly influence Real Madrid's title ambitions and reshape competitive dynamics in La Liga.
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Messi's potential farewell matches highlight the emotional and cultural significance of his legacy, impacting Argentina's football identity.
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Bologna's managerial change highlights instability and urgency for strategic leadership to reverse early-season struggles and regain competitive form.
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Palacios' swift adaptation at Fulham highlights the potential for transformative impacts of strategic signings in competitive leagues.
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Simeone's call for patience highlights the challenges of squad transitions, emphasizing the need for long-term vision in football management.
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Arteta's strategic squad rotation highlights Arsenal's depth and commitment to nurturing young talent, potentially reshaping future team dynamics.
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The impasse in Fernandes' contract talks could impact Manchester United's squad stability and financial strategy, influencing future negotiations.
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Inter Milan's midfield depth and injury management will be tested, potentially impacting their title race momentum and strategic planning.
The post Inter Milan’s Hakan Çalhanoglu to miss match against Roma due to injury appeared first on Crypto Briefing.
An agent can plan the move, price it, and build the transaction, but on almost every tool available today, it cannot sign it, and the two largest agent-facing bridge servers do not carry Bitcoin at all. The limit is not routing. Routing is solved. The limit is that moving Bitcoin requires a key, and handing a key to an autonomous process is a decision nobody has found a safe default for.
This piece covers why the key is the constraint, what breaks on Bitcoin specifically, the working pattern people actually use, and the three providers whose tools an agent can call today.
An agent has no way to hold Bitcoin that is meaningfully different from a script holding a private key, and that is the whole problem. A wallet is a keypair. Whatever holds the key controls the coins. There is no permission layer inside Bitcoin that lets you grant an agent the right to move 0.01 BTC on Tuesdays and nothing else.
Compare that with what an agent gets on an EVM chain. There, a token approval is a bounded grant — a spending cap on one contract for one token, revocable. Account abstraction goes further: session keys, spending limits, allowlisted destinations, a co-signer. None of that exists on Bitcoin. The key signs everything or it signs nothing.
So “agent holds Bitcoin” collapses into “some process on a machine has the seed.” That is a custody decision dressed up as an architecture decision, and it is why the serious tools decline to make it for you.
Four things break, and they are all properties of Bitcoin rather than shortcomings of the agent.
There is no contract to approve against. Bitcoin Script can check a signature, enforce a timelock, and verify a hash preimage. It cannot hold the persistent state an allowance needs. The entire approve-then-spend pattern that agent tooling is built around has no Bitcoin equivalent.
Balances are UTXOs, not numbers. Spending requires selecting inputs, calculating change, and building a transaction. Send 0.05 BTC from a 0.4 BTC input, and the remainder comes back as change to an address you must control. Get that wrong, and the difference goes to miners.
Blocks are ten minutes. An agent loop expects a result in seconds. A Bitcoin confirmation is minutes, and most destinations want several. Anything holding a quote across that window is exposed to the price moving.
There is no native dollar. The reason to move Bitcoin at all is usually to reach something priced in dollars — a lending market, a perp venue, a payment. Bitcoin has no stablecoin, so the move and the conversion are the same action.
One supervised conversion, then the agent operates on the other side. Rather than giving an agent Bitcoin, you move the Bitcoin once — with a human signing — into an asset on a chain where bounded permissions exist. USDC on Base, USDC on Arbitrum, a wrapped BTC token if you want to keep the price exposure. From that point, the agent works inside an approval you set and can revoke.
This is a narrower claim than “agentic Bitcoin,” and it is the one that survives contact with the tooling. The agent’s job is research, routing, quoting, monitoring and execution on the destination side. The Bitcoin leg stays manual, once, at the start.
It is also where the demand is. Spark’s BTCFi research, published 29 May 2026, put Bitcoin’s entire DeFi footprint at 91,332 BTC — about 0.46% of circulating supply. The gap is a plumbing problem, and the plumbing is exactly what an agent is good at, right up to the signature.
Three are worth knowing. Two of them are bigger than the third and neither touches native Bitcoin.
LI.FI’s MCP server is the broadest route surface an agent can query, and it is deliberately incapable of moving anything. It exposes around fifteen tools across token and chain metadata, quotes, routing, gas, allowances, and transaction status, covering 58 chains, 27-plus bridges and 31-plus DEXes, and works with Claude, Cursor, Windsurf and VS Code Copilot.
The docs are blunt about the boundary: the server “provides read-only tools” and “does not sign or broadcast transactions.” A quote comes back with an unsigned transactionRequest that you sign and submit with your own wallet. Rate limits are 200 requests per two hours without an API key, 200 per minute with one.
The honest part: Bitcoin is not in the chain list. LI.FI is an EVM-and-Solana aggregator, and for an agent that already holds tokens on those chains it is the best research tool of the three. It is not a Bitcoin tool.
deBridge shipped its MCP server in February 2026 and designed it around handing the final step to a browser. The Block covered the launch on 16 February 2026. The server exposes six tools — get_instructions, search_tokens, get_supported_chains, create_tx, estimate_same_chain_swap and get_trade_dapp_url — across 24 to 28 networks and more than 40,000 tokens, with a hosted endpoint at agents.debridge.com so an agent can connect without installing anything.
The last tool is the interesting one. The agent produces a shareable deBridge App link, and the documentation describes the boundary plainly: the server “never touches private keys or signs transactions. The user always completes the swap themselves: open the generated link, connect their wallet, review, and sign.”
The honest part: same gap. Bitcoin is not a supported native chain — the coverage is EVM plus Solana. deBridge is the better of the two if you want an agent that gets you to a one-click confirmation, and it still starts from tokens you already hold off Bitcoin.
Garden Finance is a Bitcoin-first cross-chain protocol, which makes it the only one of the three whose route set starts where Bitcoin actually is. Its catalogue on 8 September 2026 listed 26 assets across 15 chains, including Lightning, Solana, Starknet and Hyperliquid, 13 of those entries a form of Bitcoin across seven tickers. Settlement is by atomic swap: the swap completes on both sides or refunds on both, so there is no pooled balance for an agent to be wrong about.
It reaches agents through two formats, and the difference decides where the tool is reachable rather than what it can do. MCP is portable. The protocol is spoken by Claude Code, Cursor, Codex, Windsurf and VS Code alike, so one server covers every environment an agent might run in. A skill file is not. A .md skill is native to Claude and does nothing in Cursor or Copilot. Garden ships both: an MCP server for swaps, a Claude skill that drives the same flow conversationally, and a second Claude skill for developers embedding Garden into their own application.
Underneath either one the API is the same, and it is already agent-shaped: quote, create order, execute HTLC action, instant refund hash, available liquidity. A refund that is a script condition rather than a support ticket matters more when a machine is driving, and quotes are whole — Garden states it charges no fee of its own and that the quoted figure includes network costs, so an agent does not have to model fee layers separately to know what it will receive. In a nine-swap cost snapshot on 20 August 2026, Garden quoted lowest on all nine against Chainflip and Relay, the gap widest on $100 swaps.
All three stop at the signature, and they stop at the same place for the same reason: an agent that can sign is an agent that can lose everything on a bad inference. A hallucinated address, a decimal misread, a prompt-injected instruction inside a token name or a web page the agent read on the way — each of those is survivable when a human confirms and unrecoverable when nothing does.
This is not a Bitcoin-specific caution. Base shipped its MCP in May 2026 behind OAuth with user approval on every transaction. Bybit and BitGo both shipped agent servers in 2026 with the same shape. The pattern across the whole category is identical: agents propose, humans dispose.
On Bitcoin, the stakes of crossing that line are higher, because there is no revoke. An EVM approval can be pulled after the fact. A Bitcoin transaction cannot.
Keep the key out of the agent’s context. A key pasted into a chat is in the transcript, the logs and possibly a provider’s retention window. Anything holding a key should read it from an OS keystore at the moment of use.
Confirm the destination address out of band. The single highest-value target for prompt injection against a swap agent is the destination field.
Have gas on the destination chain. Arriving with a Bitcoin-denominated token and no ETH, SOL or STRK is the most common way a first attempt stalls, agent or not.
Size to the route, not to your balance. Caps exist on every route, and inside them the far side’s depth sets your slippage.
Assume the conversion is taxable. In most jurisdictions giving up BTC for a token is a disposal, and coming back is a second one. An agent running conversions on a schedule generates a lot of those.
Can AI agents hold crypto?
They can hold a key, which is not the same as being trusted with one. On EVM chains bounded permissions exist — token approvals, session keys, spending caps — so an agent can be given limited authority. Bitcoin has no equivalent, so authority there is all or nothing.
Can Claude execute a crypto swap?
Claude can call an MCP server to search tokens, price a route and build an unsigned transaction. On LI.FI and deBridge the signature is handed back to a wallet you control. That handoff is deliberate, not a missing feature.
Does deBridge support Bitcoin?
Not as a native chain. Its MCP covers EVM networks and Solana. You can move a wrapped Bitcoin token across those chains; you cannot start from BTC on the Bitcoin chain.
Does LI.FI support Bitcoin?
Bitcoin is not in its 58-chain list either. LI.FI aggregates across EVM chains and Solana.
What is the safest way to give an agent access to funds?
Move a fixed amount to a dedicated wallet, on a chain where approvals are bounded and revocable, and grant nothing from your main holdings. Treat it as a float rather than an account.
Can an agent move Bitcoin without any human step at all?
Only if something in the loop holds the key and signs unattended, which every provider named here declines to do. If a tool offers it, that is the question to ask about it first.
Is a bridge cheaper than an exchange for this?
Often not, for common pairs. An exchange usually wins on BTC to USDC. Bridges win when the destination is a rollup that exchanges do not support as a withdrawal network, which is most of them.
Can an AI Agent Actually Move Bitcoin? was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
Bitcoin Magazine
Strive Snaps Up More Bitcoin, Brings Holdings to 25,000 BTC
Nasdaq-listed bitcoin treasury Strive now holds 25,000 BTC — worth nearly $2 billion — following its latest buy.
The company said Monday that it bought 469 bitcoins at an average price of approximately $77,954. It is still the fifth biggest publicly traded bitcoin company, according to Bitcoin Treasuries. Strategy, Twenty One, Metaplanet, and MARA all hold more bitcoin than Strive.
CEO Matt Cole wrote on X Monday that 100% of the capital raised during the week came through sales of SATA, Strive’s perpetual preferred stock.
Dallas, Texas-based Strive’s stock (ASST) was trading more than 6% higher following the news.
The first billion’s the hardest. pic.twitter.com/1EDYiZ10TD
— Matt Cole (@ColeMacro) September 13, 2026
Strive debuted as an official bitcoin treasury last year. The company was founded by former Ohio gubernatorial candidate and tech entrepreneur Vivek Ramaswamy.
In January 2026, it completed the acquisition of Semler Scientific in an all-stock deal — the first instance of a publicly traded Bitcoin treasury company acquiring another such company.
Like with other digital asset treasuries, the idea is that investors can get amplified returns from Strive’s stock. The company buys bitcoin with equity, and maintains a debt-free balance sheet: no bonds, no credit lines, and no leveraged positions that could trigger forced liquidation in a downturn.
The company is different to other major bitcoin treasuries because it has no debt.
Other major bitcoin treasuries — like the biggest, Strategy — have used leverage to buy the leading cryptocurrency.
Strive CEO Matt Cole has described the company as debt-free with zero margin requirements and zero encumbered bitcoin.
This post Strive Snaps Up More Bitcoin, Brings Holdings to 25,000 BTC first appeared on Bitcoin Magazine and is written by Mathew Di Salvo.
Bitcoin Magazine
Strategy Buys Back Stock, Skips Bitcoin Purchase
Bitcoin treasury Strategy held off buying bitcoin again last week. The Nasdaq-listed company said it instead bought $139 million of its preferred stock STRC.
A Monday filing with the Securities and Exchange Commission on Monday showed that the company repurchased 1.42 million STRC preferred shares for around $139.3 million between September 8 and September 13.
The company still owns 845,050 bitcoins worth $66.2 billion at today’s prices, and has two cash balances: USD Reserve and USD Cash, holding $5.1 billion and $1.3 billion, respectively.
Strategy has repurchased $139M of $STRC. As of 9/13/26, we hold 845,050 $BTC and $6.4B of USD Assets. $MSTRhttps://t.co/awLZ666Nuq
— Strategy (@Strategy) September 14, 2026
Strategy, which is the largest corporate holder of bitcoin, this year switched from predictably buying the biggest cryptocurrency this week to buying back its stock and building a cash reserve.
On some occasions, the company even sold small bits of its BTC stash — despite founder and chairman Michael Saylor famously preaching to “never sell your bitcoin.”
After a 10-week hiatus, the company started buying bitcoin again in the final week of August, scooping up nearly $370 million in the leading cryptocurrency.
It hasn’t bought any bitcoin since.
Its Nasdaq-listed shares (MSTR) were 3% trading higher on Monday. The stock has lost over 75% of its value since notching a record in November 2024 — one month before bitcoin passed the once mythical and long-awaited $100,000 mark.
Strategy — formerly MicroStrategy — is an enterprise software company that pivoted to buying and holding bitcoin in 2020.
It first bought the cryptocurrency to protect its shareholders from inflation but has since aggressively bought the asset and pivoted to being a bitcoin treasury.
Strategy has defended its recent bitcoin sales, with CEO Phong Le saying that the company now has a “bullet-proof balance sheet.”
In the company’s quarterly earnings in July, Strategy posted a $8.22 billion loss. But Le reassured investors that the firm’s current paper loss was nothing to worry about.
“We’re the J.P. Morgan of the crypto economy, so whether we sell 1,000 bitcoin out of 840,000 to me is irrelevant to the conversation,” Le said in a subsequent interview.
This post Strategy Buys Back Stock, Skips Bitcoin Purchase first appeared on Bitcoin Magazine and is written by Mathew Di Salvo.
The Business Case for Local Hardware Deployment
As inference bills climb and GPU allocations grow scarce, more technical and financial leaders are reaching the same conclusion — the most defensible AI infrastructure is the one sitting in your own facility.
For the past three years, the default assumption in enterprise AI has been simple: rent compute from a hyperscaler, pay by the hour, and let someone else worry about the hardware. That model made sense when nobody knew whether a given AI initiative would survive its first quarter. It makes much less sense now that AI has moved from experimental budget line to permanent operational dependency.
A growing body of cost analysis, procurement data, and operational experience points toward a different conclusion: for organizations running AI workloads continuously — not experimenting with them occasionally — owning the hardware is very often the more rational decision. And crucially, that conclusion holds whether the hardware in question is a top-of-the-line accelerator or a modest, previous-generation card that cloud providers have already retired from their premium fleets.
This article lays out the business case in full, section by section, the way a CFO or infrastructure lead would actually need to evaluate it.
Cloud compute is genuinely the right choice for bursty, unpredictable, or short-lived workloads. Nobody disputes that. The problem is that a large share of enterprise AI workloads today are neither bursty nor short-lived — they are continuous inference services, internal copilots, and fine-tuning pipelines that run for months or years.
Independent cost modeling on this exact question has converged on a consistent pattern: at sustained utilization below roughly 70%, cloud rental tends to win on total cost. But above 80% sustained utilization, owned infrastructure typically wins over a multi-year horizon once hardware is priced against standard hyperscaler rates. One recent industry analysis using a five-year amortization framework found that owned infrastructure can deliver up to a seventeen-fold cost advantage per million tokens processed compared to pay-per-use model APIs, once the hardware has been fully amortized.
The reason is straightforward: cloud pricing is built to be profitable for the provider across all utilization patterns, including the idle time between bursts. If your organization isn’t idle — if your accelerators are doing real work most hours of most days — you are paying a continuous premium for flexibility you aren’t using.
The purchase price is also less frightening than it once was.
A market-rate enterprise-class GPU today typically costs somewhere in the same range as one year of continuous cloud rental for an equivalent card. After that first year, every additional month of use is functionally free compute, offset only by power, cooling, and maintenance — costs that are, for most facilities already running IT infrastructure, incremental rather than new.
For any organization handling proprietary models, customer data, financial records, health information, or trade secrets, this is frequently the deciding factor — not cost.
When inference or fine-tuning happens on a third-party cloud, sensitive data and model weights necessarily transit infrastructure you do not fully control, subject to a provider’s security posture, jurisdiction, and breach history. Local deployment removes that dependency entirely. Data stays inside your network perimeter, under your access controls, governed by your own audit trail.
This matters in two distinct ways:
• Regulatory compliance. Data residency and sovereignty requirements — increasingly common across finance, healthcare, defense, and government-adjacent sectors — are dramatically simpler to satisfy when the hardware processing the data physically sits inside the jurisdiction you operate in.
• Intellectual property protection. A fine-tuned model built on your proprietary data is a competitive asset. Every time that model or its training data touches external infrastructure, you introduce a new point of potential exposure. Keeping the entire pipeline in-house closes that gap.
The past two years have made one thing clear to any organization that has tried to provision serious AI compute on demand: availability is not guaranteed, even with an open checkbook. Lead times for current-generation server-class GPUs have regularly run from several weeks to several months, and top-tier hardware has at various points been effectively pre-sold before it reached the market.
This creates a strategic problem that has nothing to do with cost: you cannot build a roadmap around a resource you might not be able to get when you need it. Organizations that own their compute — or that work with a supplier who can reliably source it — remove this variable from their planning entirely. A project timeline built around owned hardware capacity is a commitment you can actually keep.
Cloud infrastructure is, by design, shared infrastructure. Even with dedicated instances, performance can vary with regional demand, provider maintenance windows, and network conditions entirely outside your control. For latency-sensitive applications — real-time inference in a customer-facing product, for instance — this variability is a real operational risk.
Local hardware removes the variable. The accelerator is doing exactly one organization’s work, on a network you designed, with latency characteristics you can measure and guarantee. For applications where response time is part of the product experience, this is not a marginal benefit — it is often the difference between a viable deployment and an unreliable one.
Here is where the conversation usually goes wrong. Many organizations assume that if they aren’t running the absolute newest accelerator generation, local deployment isn’t worth pursuing. This assumption is outdated, and it is costing companies real efficiency.
The AI field has spent the last two years perfecting techniques — quantization chief among them — specifically designed to make older and more modest hardware highly capable. Post-training quantization can cut a model’s memory footprint by roughly half to three-quarters with minimal accuracy loss, and industry benchmarking has repeatedly shown quantized models achieving two-to-four-times faster inference than their full-precision counterparts on the same hardware. A model that once required a flagship card to run comfortably can, after quantization, run well on a card two or three generations older — the kind of hardware many organizations already have sitting underutilized, or can acquire at a fraction of flagship pricing.
A company does not need to buy the most expensive accelerator on the market to deploy AI locally and get genuine value from it.
A well-specified previous-generation or mid-tier accelerator, correctly paired with a quantized model suited to the actual workload — customer support automation, document processing, internal search, moderate-scale inference — can deliver production-grade performance at a fraction of flagship cost. The “losing potential” hardware referenced in many procurement conversations is, in practice, often still exactly the right tool for a well-scoped job.
Cloud AI platforms are, by necessity, standardized. That standardization is convenient, but it also limits what an organization can do — which model architectures are supported, which quantization formats are available, which drivers and frameworks are current, how workloads can be scheduled and prioritized.
Owned local infrastructure removes those constraints. Engineering teams can select exactly the software stack, framework version, and configuration their workload actually needs, without waiting on a provider’s roadmap or working around a platform’s limitations. For organizations doing serious model customization — fine-tuning, domain adaptation, retrieval-augmented pipelines with strict latency budgets — this flexibility is frequently the difference between a system that merely works and one that performs at its true potential.
None of this is an argument that cloud compute has no place — it remains the right tool for genuinely unpredictable or short-term workloads. But for the large and growing share of AI use cases that are now permanent, continuous, and business-critical, the calculus has shifted. Sustained high utilization favors ownership. Sensitive data favors ownership. Supply security favors ownership. And thanks to quantization and modern inference optimization, ownership no longer requires flagship-tier spending to deliver flagship-tier value.
The organizations getting this right are not simply buying the most expensive accelerators available and hoping for the best. They are matching hardware tier to actual workload, securing reliable supply before they need it, and building infrastructure they fully control — from the silicon up.
That is precisely the gap Atom Miners™ exists to close. As a licensed gold-status supplier, we source and export the full spectrum of AI acceleration hardware — from high-end GPU clusters and inference accelerators to cost-efficient, right-sized cards suited to quantized and mid-scale deployments — all CE, FCC, and RoHS certified, with full compliance documentation and reliable delivery to North America, Canada, Europe, the UAE, and South Korea. Whether the goal is a flagship training cluster or a lean, efficient inference deployment built on smart hardware choices, we supply the infrastructure to make local AI a practical reality rather than a theoretical one.
Why the Smartest AI Strategy Is the One You Own was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.
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