Battlefield 6 Outage Map

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Battlefield 6 Issues Reports

Latest outage, problems and issue reports in social media:

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  Joe Grace (@DoReMatey) reported 1 minute ago

  @AThinksAloud To be fair, Portal and Portal 2 are some fairly well made and written complex problem solving games. If he was citing Call of Duty or Battlefield, I'd be right there with you.

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  Tara (@Hal0tara) reported 1 minute ago

  @BattlefieldComm How about instead of making everything shitier, you use your time to make what everyone as been asking from THE VERY BEGINNING like idk the server browser that would surely help with the problem

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  Joe Public (@RitsonGed64139) reported 2 minutes ago

  @daveedscot7 @DailyLoud Perhaps if Americans weren't so obsessed with owning battlefield weapons school shootings would be less of a problem.

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  Tyler (@TylerJEsquire) reported 5 minutes ago

  @Ahmonza12 Cost! Real armor is tailored to the owner but generic armors were mass produced. So full plate would be a sizable investment. A common soldier might have a helmet, spear, or shield that was a family heirloom from an old war or military service. Or stolen from a battlefield.

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  Snark Tank (@SnarkyTank) reported 9 minutes ago

  @jonperry501 @womensmarch It does exist - maybe not in the WNBA, but it certainly exists. The spotlight is on the W because of the Sophie Cunningham interview and the trans mafia attacks on her. That has become the battlefield for the bigger problem, and if you want to bring attention to a problem, you go where the spotlight is shining.

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  i.am.korn (@unikornaio) reported 11 minutes ago

  SECTION AI EMPIRE — EXHIBIT AI-12 CIRCULAR FINANCING, HIDDEN DEBT, ACCOUNTING ILLUSIONS, AND MANUFACTURED AI REVENUE Filed Under: Preliminary RICO Enterprise Analysis / Securities Fraud / Accounting Fraud / Bank Fraud / Antitrust / Private Credit / Financial Stability Related Exhibits: AI-1 through AI-11 Subject: Whether reciprocal investments, cloud-spending commitments, GPU financing, customer concentration, take-or-pay contracts, leases, and related-party transactions manufacture the appearance of independent AI demand Jurisdictional Scope: United States and connected foreign investment, lending, cloud-computing, semiconductor, and data-center markets Information Current Through: August 11, 2026 Date Entered: August 11, 2026 Investigative Framework: Allegations requiring audited financial records, transaction tracing, contract analysis, and determination by competent authorities I. PURPOSE OF THIS EXHIBIT This exhibit examines whether participants in the alleged AI Enterprise are using circular financial relationships to create the appearance of independent revenue, market demand, and economic growth. The alleged structure may operate as follows: A cloud provider invests billions of dollars in an AI developer. The AI developer commits a substantial portion of the investment to purchasing computing services from that cloud provider. The cloud provider records revenue as the developer consumes the computing services. The AI developer uses the purchased computing capacity to justify a higher valuation and raise additional capital. The cloud provider points to rising AI-related revenue as proof of market demand. The cloud provider or AI developer contracts with a specialized computing company. The computing company borrows money to purchase GPUs and lease data-center space. The GPU manufacturer supplies the equipment while investing in, financing, or purchasing services from the same computing company. The equipment purchases become revenue for the GPU manufacturer. Long-term cloud commitments become reported backlogs or remaining performance obligations. Those commitments support additional debt, construction, investment, and valuation increases. Under this structure, a single pool of investment capital can appear at multiple points as: Equity financing. Cloud revenue. Semiconductor revenue. Data-center demand. Contract backlog. Construction spending. Collateral value. Future revenue. Evidence supporting a new valuation. The repetition of the same economic activity across different companies does not mean the same dollar is improperly recorded multiple times on one company’s financial statements. Each company may have a separate transaction and lawful accounting treatment. The investigative concern is whether investors and regulators are being given a misleading aggregate picture in which financed, reciprocal, concentrated, or related-party activity is presented as broad and independent end-user demand. II. THE DIFFERENCE BETWEEN CIRCULARITY AND FRAUD Circular commercial relationships are not automatically unlawful. A company may legitimately: Invest in a supplier. Invest in a customer. provide customer financing. Require use of its infrastructure. Enter a long-term capacity contract. Finance equipment with debt. Use purchased equipment as collateral. Recognize revenue as contractual services are delivered. Fraud may arise if a participant knowingly or recklessly misrepresents: The source of customer funds. The independence of the customer. The relationship between an investment and a purchase obligation. Whether demand would exist without financing from the seller or its affiliates. Whether revenue is derived from an arm’s-length transaction. Whether a customer can satisfy its commitments. The extent of customer or supplier concentration. Whether backlogs are cancellable, conditional, financed, or dependent on unfinished infrastructure. The existence of guarantees, side letters, repurchase arrangements, or undisclosed concessions. The amount and location of debt. The economic substance of a transaction. A circular transaction becomes especially concerning when it lacks a commercially independent end user capable of producing sufficient cash flow to support the cycle. III. THE CLOUD-PROVIDER AND AI-DEVELOPER LOOP The Federal Trade Commission examined investments and partnerships involving: Microsoft and OpenAI. Amazon and Anthropic. Alphabet and Anthropic. The FTC found that these relationships may include: Significant equity interests. Revenue-sharing rights. Consultation, control, and exclusivity provisions. Commitments requiring AI developers to spend a large portion of the investment on the investing company’s cloud services. Discounted access to substantial computing resources. Sharing of model assets, intellectual property, financial information, training information, customer usage, and revenue data. Increased contractual and technical switching costs. The FTC’s findings establish that at least some major investments are connected to reciprocal commercial obligations rather than representing unrestricted capital followed by completely independent purchasing decisions. FTC report on AI partnerships and investments The economic loop The basic cloud-provider loop can be expressed as: The cloud provider supplies investment capital or credits. The AI developer purchases computing from the cloud provider. The cloud provider recognizes cloud revenue as services are delivered. The developer obtains the capacity needed to train and operate models. Improved models support another fundraising round. The higher valuation increases the apparent value of the cloud provider’s investment. Additional investment supports further cloud purchases. This can be a legitimate strategic partnership. It can also make it difficult to determine how much revenue reflects independent customer demand rather than recycling of investor-supplied capital. Microsoft and OpenAI The Microsoft-OpenAI relationship requires examination of: Cash investment versus noncash cloud credits. OpenAI’s Azure spending obligations. Microsoft’s equity-equivalent or profit-participation rights. Revenue-sharing arrangements. Exclusivity and model-hosting provisions. Microsoft’s recognition of Azure revenue connected to OpenAI consumption. Microsoft’s distribution of OpenAI models through its own products. OpenAI’s use of Microsoft-associated providers, including CoreWeave. Whether the same anticipated OpenAI demand supports revenue or valuations at Microsoft, CoreWeave, Nvidia, Oracle, and data-center developers. Microsoft’s relationship with OpenAI does not make Azure revenue fictitious. The material question is whether investors can determine how much demand originated with unrelated end users and how much originated with an investee spending funds provided by Microsoft or its financial ecosystem. Amazon and Anthropic Anthropic announced in November 2024 that Amazon would make an additional $4 billion investment, bringing Amazon’s total investment to $8 billion, while AWS became Anthropic’s primary cloud and training partner. Anthropic also stated that it would collaborate on AWS Trainium hardware and software and use AWS infrastructure to train advanced models. Anthropic’s AWS partnership announcement This arrangement raises the same economic-substance questions: How much of Amazon’s investment is expected to return through AWS spending? What amount is cash, credit, infrastructure access, or another form of consideration? What discounts affect the stated value of computing services? Does Amazon recognize revenue generated from the capital it supplied? How much independent revenue does Anthropic produce outside the Amazon ecosystem? Does Amazon’s investment value depend on Anthropic continuing to purchase Amazon infrastructure? What happens to the investment and cloud commitments if model revenue does not cover computing expenses? None of these questions establishes wrongdoing. They are necessary to distinguish genuine market expansion from subsidized internal ecosystem growth. IV. NVIDIA, GPU CUSTOMERS, AND RECIPROCAL DEMAND Nvidia occupies several potential positions inside the AI financing cycle: Semiconductor supplier. Software-platform provider. Equity investor. Strategic partner. Customer of AI-computing providers. Technology validator. Participant in infrastructure-financing arrangements. This positioning creates a risk that Nvidia can simultaneously: Invest in a company. Sell GPUs used by that company. Have those GPUs financed through debt. Purchase computing services from the company. Support the company’s valuation. Recognize semiconductor revenue from the infrastructure expansion. Again, each individual transaction may be legitimate. The aggregate arrangement requires transparent disclosure. Nvidia and CoreWeave CoreWeave’s 2025 SEC registration statement disclosed that Nvidia was a beneficial owner of more than 5% of its outstanding capital stock. CoreWeave also disclosed a master services agreement under which CoreWeave provided infrastructure and platform services to Nvidia. As of December 31, 2024, Nvidia had paid CoreWeave approximately $320 million under that agreement and related orders. This means Nvidia was simultaneously: An equity holder. A principal technology supplier to CoreWeave’s business model. A customer purchasing CoreWeave services. In January 2026, Nvidia announced an additional $2 billion investment in CoreWeave and plans to help accelerate CoreWeave’s procurement of land, power, and buildings for more than five gigawatts of proposed AI infrastructure. The expanded relationship also contemplated CoreWeave deploying multiple generations of Nvidia infrastructure. Nvidia’s January 2026 CoreWeave announcement Investigators should determine: Whether Nvidia’s investment contributed directly or indirectly to purchases of Nvidia equipment. Whether Nvidia’s CoreWeave service purchases were based on independent operational requirements. Whether any purchase guarantees, capacity reservations, price protections, or resale arrangements exist. Whether Nvidia’s investments caused other investors or creditors to perceive CoreWeave as less risky. Whether Nvidia recognized revenue on equipment financed by entities whose viability depended substantially on Nvidia-linked demand or support. Whether securities disclosures adequately explained Nvidia’s multiple roles. These relationships do not establish sham revenue. They establish a concentration of economic interests requiring examination for substance, conflicts, and disclosure. V. COREWEAVE AS A FINANCIAL TRANSMISSION NODE CoreWeave provides a useful public example because its SEC filing discloses customer concentration, debt, leases, take-or-pay contracts, supplier relationships, and associated risks. Revenue growth and operating losses CoreWeave disclosed: Approximately $16 million in revenue during 2022. Approximately $229 million during 2023. Approximately $1.9 billion during 2024. A net loss of approximately $863 million during 2024. Rapid revenue growth can coexist with substantial losses where infrastructure expansion, financing, depreciation, leases, and operating costs exceed revenue. Customer concentration CoreWeave disclosed that: Microsoft accounted for approximately 35% of revenue in 2023. Microsoft accounted for approximately 62% of revenue in 2024. Its top two customers accounted for approximately 77% of 2024 revenue. Microsoft represented approximately 66% of net accounts receivable at the end of 2024. A 2025 OpenAI agreement contemplated payments of up to approximately $11.9 billion through October 2030, subject to delivery and service-availability requirements. This concentration means CoreWeave’s reported expansion did not initially represent evenly distributed demand across thousands of unrelated customers. It depended heavily on a small number of participants already connected to the larger AI Enterprise. Debt CoreWeave reported approximately: $8.03 billion in debt principal as of December 31, 2024. $7.93 billion in debt after unamortized discounts and issuance costs. Approximately $2.48 billion in principal payments scheduled for 2025. Approximately $3.10 billion scheduled for 2026. Approximately $1.77 billion scheduled for 2027. Certain disclosed borrowing arrangements carried effective rates ranging approximately from 9% to 15%. CoreWeave also disclosed approximately $1.3 billion in equipment-manufacturer financing with security interests granted in the financed equipment. Lease obligations CoreWeave stated that it leased all its data centers and certain equipment. It reported approximately $2.6 billion in operating-lease liabilities as of December 31, 2024. Lease liabilities can create substantial long-term fixed obligations even when they are analytically separated from conventional funded debt. Contractual backlog CoreWeave reported approximately $15.1 billion in remaining performance obligations as of December 31, 2024. It disclosed that: Most revenue came from multiyear committed contracts. Customers generally reserved computing capacity for two to five years. Agreements were frequently structured on a take-or-pay basis. Committed contracts accounted for approximately 96% of 2024 revenue. Take-or-pay contracts can improve revenue visibility. Their economic value still depends on: Customer creditworthiness. Enforceability. Delivery of operational capacity. Completion of data centers. Availability of power and equipment. The absence of contract modifications. The customer’s willingness and ability to pay. Whether credits, delays, renegotiations, or service failures reduce collectible amounts. Backlog is not the same as cash. Remaining performance obligations can include unbilled consideration for services not yet delivered. These disclosures are contained in CoreWeave’s March 2025 amended registration statement. CoreWeave SEC filing VI. HOW DEBT CAN BECOME HIDDEN OR UNDERSTATED “Hidden debt” does not necessarily mean debt was unlawfully omitted from audited financial statements. Economic leverage may be dispersed among several categories: Conventional loans. Convertible debt. Equipment financing. Operating leases. Finance leases. Take-or-pay obligations. Power-purchase agreements. Construction commitments. Minimum-volume agreements. Supplier financing. Customer prepayments. Guarantees. Letters of credit. Joint ventures. Special-purpose entities. Variable-interest entities. Unconsolidated affiliates. Municipal bonds. Utility infrastructure commitments. Tax-increment financing. Contingent purchase obligations. A company may highlight “net debt” or another adjusted measure while material fixed obligations remain elsewhere in its filings. A complete investigation should therefore calculate: Adjusted enterprise leverage = funded debt + lease liabilities + equipment financing + minimum purchase obligations + guaranteed obligations + infrastructure commitments + contingent liabilities − unrestricted cash The result should then be stress-tested against collectible cash flow rather than announced contract value. Special-purpose and project-level financing Data-center campuses may be divided among: Property owners. Developers. Equipment borrowers. Operating companies. Cloud tenants. Utilities. Joint ventures. Infrastructure funds. This fragmentation can prevent any single balance sheet from showing the full economic exposure. If a project fails, losses may spread across: Banks. Private-credit funds. Bondholders. Equipment financiers. Utilities. Municipalities. Construction contractors. Pension funds. Ratepayers. Taxpayers. The absence of all obligations from one company’s balance sheet does not mean those obligations disappeared. They may have been transferred to another participant. VII. MANUFACTURED REVENUE AND DEMAND For this exhibit, “manufactured AI revenue” means revenue whose existence or scale depends materially on financing, guarantees, reciprocal obligations, subsidies, or purchases by related strategic participants rather than ordinary independent end-user demand. Possible forms include: Investor-funded purchases An investor provides capital to a company that is contractually or practically expected to spend the capital on the investor’s services. Vendor financing A supplier or affiliated lender finances the customer’s purchase of the supplier’s equipment. Revenue may be recognized at sale while payment depends on the financed customer’s future success. Reciprocal service purchases A supplier invests in or sells to a company while purchasing services from the same company. Take-or-pay demand A customer commits to minimum payments before sufficient end-user demand exists. Such a contract can support borrowing and construction even when ultimate utilization is uncertain. Government-supported demand Tax credits, government contracts, defense deployments, public research, or foreign assistance generate revenue presented as evidence of broad market adoption. Cloud credits recorded at stated values Noncash credits or discounted computing may be described using values that do not equal the provider’s incremental cost or an arm’s-length cash price. Capacity reservations and prepayments Prepayments may finance construction while later being presented as demand indicators. They should be distinguished from revenue already earned. Portfolio-company recycling An investment fund, cloud provider, or strategic company invests in multiple companies that purchase from one another, generating revenue and valuation evidence across the portfolio. None of these forms proves fraudulent accounting. The question is whether disclosures allow a reasonable investor to understand the source, quality, independence, collectability, and sustainability of the revenue. VIII. THE MULTIPLE-COUNTING ILLUSION The same underlying commitment can support multiple market narratives without technically being counted twice within one company’s books. For example: An AI developer announces a $10 billion fundraising. The valuation increase is treated as proof of market confidence. A substantial portion is committed to cloud computing. The cloud provider describes the commitment as AI demand. The cloud provider orders GPUs. Nvidia records equipment revenue. A data-center developer treats the cloud requirement as contracted demand. The data-center owner borrows against the lease. A utility builds infrastructure based on the projected load. Analysts count the investment, semiconductor sales, cloud backlog, construction, and utility growth as separate signs of economic expansion. Those are separate accounting events, but they may depend on the same unproven assumption: that future end users will pay enough for AI services to support the entire structure. If that final demand fails, each layer can weaken at once. IX. VALUATION AND COLLATERAL FEEDBACK Higher valuations can themselves facilitate additional financing. A rising valuation may: Permit new equity issuance. Improve lender confidence. Increase the value of pledged shares. Support employee compensation. Attract strategic partners. Reduce perceived default risk. Support acquisitions. Encourage municipalities to finance infrastructure. The new financing then creates spending that supports the valuation narrative. GPU collateral can produce a similar loop: A borrower obtains debt to purchase GPUs. The lender values the GPUs as collateral. GPU scarcity and demand support high collateral values. The borrower rents the GPUs to concentrated AI customers. Revenue supports additional borrowing. Additional borrowing funds more GPU purchases. If newer chips rapidly replace existing generations or demand falls, collateral values may decline before the associated debt is repaid. The lender may then face: Insufficient collateral coverage. Borrower default. Forced equipment sales. Falling secondary-market GPU prices. Losses across similar loans. That mechanism could turn technological obsolescence into a credit event. X. PRIVATE CREDIT AND BANKING EXPOSURE AI infrastructure increasingly depends on lenders and investment vehicles outside traditional public equity markets. Potential funding sources include: Commercial banks. Private-credit funds. Insurance companies. Asset managers. Infrastructure funds. Pension funds. Sovereign wealth funds. Equipment financiers. Real-estate investment vehicles. Municipal bonds. Private-credit structures can reduce public transparency because detailed loan terms may not appear until a company files securities disclosures or experiences distress. Relevant risks include: Floating interest rates. Short debt maturities financing long-lived assets. Heavy reliance on one or two customers. Collateral that depreciates rapidly. Construction delays. Power shortages. Refinancing dependence. Contract renegotiation. Cross-default provisions. Hidden guarantees. Concentrated lender exposure. If multiple borrowers rely on the same cloud providers, model developers, GPU supplier, or demand forecast, apparent diversification may be misleading. XI. THE ROLE OF AUDITORS, RATINGS FIRMS, AND INVESTMENT BANKS The AI financing chain depends on professional gatekeepers. Investigators should examine whether: Auditors tested the economic substance of reciprocal transactions. Revenue-recognition conclusions properly considered side agreements and concessions. Valuation firms accounted for customer concentration and circular demand. Ratings organizations incorporated technological obsolescence and power constraints. Investment banks adequately disclosed conflicts involving underwriting, lending, research, and equity ownership. Analysts distinguished cash revenue from backlog, credits, noncash consideration, and financed purchases. Fairness opinions relied on independent demand projections. Going-concern analyses incorporated refinancing and customer-default risks. Professional involvement does not immunize a transaction from fraud. It can also provide evidence that management supplied incomplete or misleading information. XII. CORRELATION TO FOREIGN CAPITAL AND CAPITAL FLIGHT Foreign sovereign funds can enter the cycle as: Equity investors. Project financiers. Infrastructure owners. Lenders. Customers. Government partners. Buyers of technology. Foreign capital may stabilize United States projects during expansion. It may also exit or demand preferential protection during stress. Investigators should determine: Whether foreign investors possess senior liquidation rights. Whether domestic investors bear first-loss exposure. Whether foreign funds receive guarantees or minimum returns. Whether intellectual property or computing access serves as consideration. Whether foreign investors can transfer interests without meaningful review. Whether proceeds leave the United States through dividends, management fees, licensing payments, debt service, or asset sales. Whether digital assets or affiliated offshore entities obscure beneficial ownership. A rapid foreign withdrawal could leave American lenders, utilities, municipalities, workers, and taxpayers responsible for unfinished or uneconomic infrastructure. XIII. CORRELATION TO PROXY WARFARE AI defense contracts and proxy conflicts can reinforce the financing cycle. The structure may involve: Government appropriations supporting defense and intelligence demand. Contractors purchasing cloud capacity. Cloud providers ordering chips. AI companies receiving battlefield data. Operational deployments supporting valuations. Higher valuations attracting investment. Investment financing additional defense-oriented capacity. If wartime demand is treated as permanent commercial adoption, a peace settlement or procurement reduction could expose excess capacity and overvaluation. National-security classifications may also limit public visibility into: Contract pricing. System performance. End users. Related-party relationships. Financing arrangements. Product failures. Classified information must be protected, but classification should not conceal ordinary fraud, conflicts, or accounting misconduct.

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  GuruVerseX (@GuruVerseX) reported 13 minutes ago

  Pink Floyd, qubits, and node software sound like three topics that should never appear in the same sentence, yet @quipnetwork somehow brings them together. What caught my attention is not just the quantum angle, but also the decision to keep classical hardware fully relevant. CPUs, GPUs, TPUs, and NPUs can all run nodes, compete through Proof of Useful Work, and help verify whether quantum computers are actually delivering better results. That matters. I have seen too many Web3 projects treat next-generation hardware like a magic sticker, instead of something that needs to be proven with measurable data. Quip’s first target, Ising Model optimization, creates a practical battlefield where different machines can be compared on useful business problems rather than marketing slides. The node manager also lowers the barrier to entry, which is important because infrastructure only becomes meaningful when people can actually run it. We need continued publication of transparent performance data and real-world workload benchmarks. If quantum advantage is real, then the numbers should do the talking. Would you run a @quipnetwork TestNet node if your existing GPU could help verify useful quantum computation?

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  Jon Moxley (@LunaticHasRisen) reported 15 minutes ago

  I’m so damn tired of people manufacturing problems just because they’re bored, looking for attention, or need something to talk about. Not everything is a conspiracy. Not everything is a personal attack. Not everything needs to become a damn battlefield.

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  Coach Frank (@FoachCrank) reported 17 minutes ago

  @BattlefieldComm Did they ever fix the crackling audio on PS5? That’s been a problem forever

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  Joshua Stubbles (@jstubbles) reported 19 minutes ago

  @Battlefield - Remove slide jump - Do not allow firing while mid-air - Speed up sprint a bit - Slow down the mantle/climb anims & smooth them out - Flush out the grenade throw anim a bit (its spazzy now) - Fix micro collision on ground objects to stop the jitterness

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  Rahul Nanda (@rahulnanda86) reported 23 minutes ago

  How we created this short film from start to finish on @invideoOfficial using Agent 2 - The idea was to treat AI filmmaking like an actual production—not enter one giant prompt and hope for the best. Here is the complete process we followed: 1. The script came first I brought the complete script: every scene, dialogue, emotional beat, timing and the final ending. Once approved, the script became law. Dialogue could not be rewritten, shortened or improvised during generation. We also established one absolute rule: no generated music anywhere. I would add the final score myself in post-production. 2. We locked the production choices Before creating anything, we established the complete workflow: GPT Image 2 for every still and reference image 1K resolution 16:9 horizontal format Seedance 2.5 for video generation 480p final generation Native in-shot dialogue and sound effects Raw video clips only I would handle the final edit, typography, music and additional sound design myself. 3. We created and locked the characters We first created master reference sheets for Arjun and Sameer. Each sheet included: Front view Three-quarter view Profile view Full-body view I approved both character sheets before we continued. These became the permanent identity anchors for every later image and video. The sheets themselves were never animated directly—they were used only as visual references to preserve faces, body types, uniforms and overall identity. 4. We locked every major location Next, we created reference sheets for the three main environments: The military outpost The mountain battlefield The Independence Day ceremony venue The ceremony was originally designed as an indoor event, but I rejected it and changed it to an outdoor Independence Day setup. Once the revised location was approved, it became canon for every later shot. 5. We created the complete production plan Before making any storyboards, we built a timed shot list and continuity map for all three capsules. Every shot had a purpose, duration, emotional beat, camera position and continuity relationship with the shots around it. Only after I approved the complete production plan did we begin storyboarding. 6. Every video was storyboarded before generation No clip went directly from written prompt to video. Each one received a complete storyboard first, which I could approve, reject or revise before spending credits on the final generation. The biggest learning arrived midway through the project: instead of generating every storyboard frame separately, we created one composite storyboard sheet containing every shot from that video in a single grid. That decision changed everything. Because Seedance could see the entire visual progression together, it understood the characters, geography, lighting, shot order and emotional movement far more consistently. The composite storyboard didn’t merely show individual images—it showed the model how the entire scene was meant to unfold. 7. We generated directly against the approved storyboard Each completed video clip was generated in a single Seedance 2.5 pass using: The approved composite storyboard Arjun’s locked character sheet Sameer’s locked character sheet The exact scripted dialogue The approved MiniDV documentary treatment Seedance had to follow the dialogue verbatim while generating the performances, camera movement, environmental sound and action around it. 8. Continuity was created through frame extraction After every approved clip, we extracted its final frame. That exact frame was then used as the opening visual reference for the next clip. This became the glue connecting all three capsules. It helped preserve character positions, geography, lighting and emotional momentum between separate generations. It also allowed us to create the tricolour match-cut that carries the film from the battlefield into the Independence Day ceremony. 9. We verified the audio after every generation Every generated clip was checked before it reached me. The most important audio check was whether Seedance had secretly introduced background music despite being told not to. Two otherwise usable takes were rejected and regenerated specifically because music had slipped into them. The goal was to keep only clean dialogue, performances, ambience and sound effects so I could control the emotional score during the final edit. 10. Nothing moved forward without approval The entire production remained approval-gated: Characters approved, then locked Locations approved, then locked Production plan approved Storyboards approved Video clips approved individually Continuity checked before proceeding If something was wrong, we didn’t try to disguise it later in the edit. We went back upstream to the storyboard, character reference or location design and corrected the actual source of the problem. That was the complete workflow. The most important lesson for me was simple: one well-designed composite storyboard can give an AI video model far more continuity than a collection of disconnected reference frames. I still made every creative decision and gave every approval. The agent managed the production pipeline, maintained the references and brought each stage back to me before proceeding. AI handled the machinery. I remained the director.

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  UnbornDecay (@DecayUnborn) reported 23 minutes ago

  @DomainDead The main problem I have is that the Sentinel fight in Xmen Origins: Wolverine was a fight against a fully mobile unit in a giant battlefield. In short, we've fought these enemies before in much better boss fights

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  Himanshu Bhandari (@Bhandari_112) reported 31 minutes ago

  Normal people play football, legends step on a battlefield. When you step up, there is a decent possibility of you coming back with something broken for sure, could be a broken knee, tore off ACL, ankle twist, dislocated shoulder, etc. Choose wisely and play anyway 🗿

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  Joshua Stubbles (@jstubbles) reported 31 minutes ago

  @BattlefieldComm - Remove slide jump - Do not allow firing while mid-air - Speed up sprint a bit - Slow down the mantle/climb anims & smooth them out - Flush out the grenade throw anim a bit (its spazzy now) - Fix micro collision on ground objects to stop the jitterness

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  Michael Martin (@MikeJM5421) reported 32 minutes ago

  @BattlefieldComm So just not going to fix the big stuff that's been plaguing the game for months now like netcode, bullet registration, actual audio issues, etc.? Well guess I'll be off playing other games again