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Cardano Developers Announce Timeline for Dijkstra Upgrade Deployment

The developers of Cardano have outlined the timeline for deploying the Dijkstra upgrade, aimed at enhancing the network's functionality and user experience. The first phase, scheduled for completion by the end of this year, will initially focus on implementing Ouroboros Linear Leios (CIP-164) to increase transaction throughput via Supplementary Endorser Blocks. It also includes protocol changes for nested transactions (CIP-118), protective scripts (CIP-112), extended account addresses (CIP-159), and simplified staking reward withdrawals (CIP-181). A public testnet, Musashi Dojo, was launched on June 23rd for community testing before mainnet deployment. The second phase is planned for Q2 2027 and involves deploying Ouroboros Peras (CIP-140) to accelerate transaction processing through an additional staking pool voting layer. This will be preceded by extensive testing, including a preview hard fork with a two-week testing window for Staking Pool Operators (SPOs) to evaluate the voting mechanism and transaction delays, followed by deployment to a pre-production network for further testing. Final activation on the mainnet will require approval from Delegated Representatives (DReps), SPOs, and the Constitutional Committee, with no preliminary launch date announced yet. This follows the recent July activation of the Van Rossem hard fork, which reduced Plutus smart contract costs to prepare for future upgrades.

cryptonews.ru08/17 18:17

Cardano Developers Announce Timeline for Dijkstra Upgrade Deployment

cryptonews.ru08/17 18:17

OpenAI Researcher Exposes ASI Timeline: Most Have Become Reality

In April 2025, a group of former OpenAI researchers published a 71-page document titled "AI 2027," outlining a timeline for Artificial Superintelligence (ASI). Their predictions, now being tracked by an independent project, show 51% are already confirmed, ahead of schedule, or on track. Notably, alarming predictions are arriving faster than anticipated. The forecast that AI would achieve top-tier human-level capabilities in cyber offense and defense by early 2027 was realized in April 2026, nine months early. Similarly, major Pentagon contracts with leading AI labs were signed 18 months earlier than predicted. The core mechanism for an intelligence explosion—Recursive Self-Improvement (RSI), where AI accelerates its own development—has not yet closed its loop. While AI, like Anthropic's Claude, now writes most new code, the bottleneck has shifted to human review and high-level research direction. A July 2026 study indicates the current AI-driven productivity gain in R&D is about 9%, below the estimated 15% threshold needed for a self-sustaining RSI feedback loop. However, underlying capabilities continue to accelerate rapidly. The "time horizon" metric for AI to autonomously handle tasks is doubling every three months, suggesting monthly-scale autonomous operation could be feasible by early 2027. Consequently, the original authors have revised their median prediction for fully automated AI programming forward to around mid-2028.

marsbit08/17 03:19

OpenAI Researcher Exposes ASI Timeline: Most Have Become Reality

marsbit08/17 03:19

Worried about AI's Self-Evolution, Anthropic Intends to Stop Training?

In early 2026, Anthropic signaled a significant shift in its public narrative regarding AI development timelines and safety. In June, its Anthropic Institute published a detailed article, "When AI builds itself," presenting internal data suggesting accelerating AI self-improvement. Key figures included over 80% of merged code being written by Claude and a 52x speedup in certain optimization tasks. The article outlined three future scenarios, with the most speculative being full recursive self-improvement (RSI), where AI autonomously builds better successors. Anthropic stated RSI is "possible" and may arrive faster than most institutions are prepared for. This narrative pivot followed a series of strategic moves. In January, CEO Dario Amodei wrote about a powerful self-improvement feedback loop. In February, Anthropic revised its Responsible Scaling Policy, removing a core commitment to pause training if capabilities outstripped safety controls, citing the risk of falling behind competitors. This change coincided with reported pressure from the US Department of Defense. By May, Anthropic's valuation had soared to $965 billion. Anthropic's stance was mirrored by other industry leaders. DeepMind CEO Demis Hassabis adjusted his AGI timeline to "by 2029" and admitted to using provocative language like "foothills of the singularity" to create urgency. OpenAI also released a model claiming a key role in its own creation process. The article's carefully calibrated tone—presenting dramatic data alongside qualifying footnotes—exemplifies a balancing act between signaling technological acceleration and managing commercial, regulatory, and safety imperatives. External experts offered contrasting interpretations of the same data, from warnings of catastrophic risk akin to Chernobyl to skepticism that current automation merely handles "grunt work," not genius. The coordinated narrative shift among top labs highlights the complex interplay between perceived technical inflection points and strategic communication aimed at investors, regulators, and the public.

marsbit06/05 06:22

Worried about AI's Self-Evolution, Anthropic Intends to Stop Training?

marsbit06/05 06:22

CARF Global Implementation Timeline Overview: What Are the Commitments of Mainland China and Hong Kong?

CARF (Crypto-Asset Reporting Framework) is a global framework for the automatic exchange of tax-related data on crypto-assets, targeting crypto-asset service providers as reporting entities. As of the end of 2025, 76 jurisdictions have committed to implementing CARF, with a phased rollout plan. The first group, including the UK and EU member states, will begin automatic information exchange in 2027. The second group, which includes Singapore, the United Arab Emirates, and Hong Kong, is scheduled to fully implement the framework in 2028. Data collection for reportable transactions will begin one year prior, starting in 2026. Hong Kong has explicitly committed to implementing CARF. It plans to start collecting crypto-asset transaction data in 2027 and commence automatic tax information exchange with partner jurisdictions in 2028. Service providers operating in Hong Kong must establish compliance and reporting mechanisms. In contrast, Mainland China has not yet committed to CARF and is not included in any of the implementation batches. It is also not listed by the OECD as a jurisdiction with relevance that has yet to commit. Under its current regulatory framework, which imposes strict limitations on crypto-asset activities, there are no legal crypto-asset service providers that could be integrated into the CARF system. Therefore, in the short term, Mainland China does not meet the conditions for participating in CARF's routine information exchange. It is noted that Mainland China has extensive experience with the Common Reporting Standard (CRS) since 2018. Should its crypto regulatory policies change in the future, it possesses the institutional and technical capacity to align with CARF. However, given the present policy environment, the likelihood of Mainland China joining the framework around or after its 2027 launch remains low.

marsbit01/28 12:39

CARF Global Implementation Timeline Overview: What Are the Commitments of Mainland China and Hong Kong?

marsbit01/28 12:39

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