2025-11-15 06:52:23
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2025-11-15 03:57:26
Zug, Switzerland, November 14th, 2025/Chainwire/--Supra, the first Layer-1 blockchain built for Automatic DeFi (AutoFi) via full vertical integration, is proud to announce an expansion of its SupraEVM Beta Bounty. CEO and Co-Founder Joshua Tobkin has committed up to $1 million worth of his own $SUPRA tokens as a personal bounty to any developer or research team that can demonstrate a faster, verifiably correct EVM-parallel execution engine than SupraBTM, the core execution engine powering SupraEVM.
The personal bounty, touted as the SupraEVM Speed Challenge, is offered in addition to an ongoing $40,000 USDC performance-based reward offered by the foundation. To date, no participating team has surpassed the benchmarks established by SupraBTM, which remains the top performer in public tests against all known EVM-parallel solutions, including Monad, one of the more optimized projects in the high-performance EVM space.
“I am betting $1 million of my own tokens that no one can beat Supra,” said Co-Founder and CEO Joshua Tobkin. “Supra is built on transparency. We claim to be the fastest, so we are aiming to prove it in public. And if someone can demonstrate a superior execution engine under clear conditions, I will honor that outcome directly.”
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While consensus protocols, data availability layers, and oracle infrastructure have all seen significant improvements in recent years, transaction execution remains a limiting factor for scaling decentralized applications. Safe and deterministic parallel execution within the Ethereum Virtual Machine (EVM) is particularly challenging, yet essential for enabling low-latency DeFi, real-time games, and AI-driven autonomous agents.
SupraEVM, powered by SupraBTM (Block Transactional Memory), addresses this challenge with a conflict-specification aware architecture that reduces overhead, anticipates transaction collisions, and schedules execution based on statically analyzed dependency graphs.
SupraBTM has been benchmarked on 10,000 Ethereum mainnet blocks and tested head-to-head against Monad’s 2-Phase Execution (2PE) approach using identical commodity hardware (16-core AMD 4564P CPU with 192 GB RAM).
Results showed SupraBTM delivering:
The engine’s design avoids the need for speculative execution and frequent rollbacks, instead relying on a deterministic scheduling model that is adaptable across varying thread configurations.
“Supra was built from the ground up to integrate execution, consensus, and core infrastructure components into a cohesive framework,” said Jon Jones, CBO and Co-Founder at Supra. “The result is an architecture that not only delivers performance, but does so in a way that is reproducible and testable against any known parallel EVM engine available today.”
The $1 million token commitment is available to developers or research teams who can produce a faster EVM execution engine under defined test conditions. Entries must be open source, verifiable, and reproducible.
The full criteria include:
Participants may choose to claim the reward directly, or engage further with Supra’s engineering organization to collaborate. Token rewards are from Tobkin’s personal allocation, unlocking in 2027 and vesting over two years. The prize is independent of Supra’s core operations or treasury.
“This challenge is focused on the core technical issue that continues to constrain the EVM,” Tobkin added. “The objective is to find or validate the most performant execution engine possible. If someone is able to build a better system than what we have achieved at Supra, the industry should recognize it and benefit from it.”
For full technical documentation, rules, and binaries for the SupraEVM Beta Bounty, users can visit the bounty’s dedicated docs page, with in-depth details of the $1M SupraEVM Speed Challenge available on its dedicated landing page. Supra’s technical team has provided a deep-dive benchmark report comparing SupraBTM and Monad available on their website, while developers interested in early SupraEVM access can join the waitlist here.
Supra is the first chain built for Automatic DeFi (AutoFi), a novel self-operating automated financial system that also serves as the perfect framework for crypto AI Agents, built upon its vertically integrated Layer-1 blockchain with built-in high-speed smart contracts, native price oracles, system-level automation and bridgeless cross-chain messaging.
Supra’s vertical stack unlocks all-new AutoFi primitives that can generate fair recurring protocol revenue and redistribute it across the ecosystem, reducing reliance on inflationary block rewards entirely over time. This stack also equips onchain AI Agents with all the tools they need to run a wide variety of powerful DeFi workflows for users automatically, autonomously, and securely.
Press Manager
Supra
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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2025-11-15 03:29:34
Singapore, Singapore, November 13th, 2025/Chainwire/--From the fastest Layer 2 blockchain to the foundation of global onchain markets, RISE introduces a new standard for orderbooks on the EVM, fully onchain and synchronously composable.
RISE, the fastest Ethereum Layer 2, today announced a new strategic direction with the launch of RISE MarketCore and RISEx, as it continues its mission to become the global home for onchain markets. These new platforms establish a comprehensive ecosystem for onchain trading, transitioning RISE from a high-performance execution layer into the foundational engine for global onchain markets. This expansion is bolstered by the recent acquisition of BSX Labs, which contributes key technology to RISE's new global markets offering.
Traditional financial markets, from equities to FX, all run on orderbooks. Until now, this market structure has been largely incompatible with blockchain technology due to latency and complexity. RISE’s performance advancements solve this, enabling orderbooks to operate entirely onchain for the first time. This development enables a level of deep liquidity, programmability, and composability never before possible in finance.
RISE is leveraging its high-performance L2 to launch a new orderbook infrastructure and perpetuals DEX, turning its leading performance edge into a programmable market structure, the core engine that powers global onchain trading.
RISE MarketCore, built on RISE’s ultra-low latency EVM, provides an orderbook infrastructure with deep, shared liquidity that enables anyone to launch fully onchain spot and perpetual markets fast and permissionlessly; with future support planned for additional orderbook-based primitives such as options and prediction markets. It solves key challenges by offering native orderbook primitives, risk engines, and APIs directly at the base layer. Builders can plug into liquid books, founders can list their tokens permissionlessly, and asset issuers can launch their own markets or entire exchanges.
RISEx is the ecosystem’s flagship application, the first Integrated Perpetuals DEX, where DeFi co-exists with CEX-grade perpetuals. Built on the EVM, it delivers a premium trading experience with deep liquidity, tight spreads, and seamless execution. Every order, margin update, and settlement occurs synchronously onchain, providing both speed and composability while maintaining full transparency for traders and market makers.
“RISE was never just about building a faster blockchain. It’s about enabling a new market structure for the internet,” said Sam Battenally, CEO of RISE. “With RISE MarketCore and RISEx, we’re turning the chain itself into the global home for onchain markets, a programmable foundation where liquidity, risk, and innovation converge onchain. This is the infrastructure global finance will be built on: composable, transparent, and unstoppable.”
RISE’s evolution gives developers and institutions a programmable foundation for global market infrastructure.
Key capabilities include:
Native Orderbook Infrastructure: Shared, composable books for both spot and perpetuals markets
Programmable Instruments: SDKs and APIs for building custom products and market logic
High-Performance EVM: Millisecond-class latency and high throughput that ensure seamless execution
Flagship Exchange: RISEx bootstraps ecosystem liquidity and sets a new standard for onchain trading
Next Phase of RISE Platform Development
RISEx enters its closed mainnet this quarter, followed by a public mainnet launch in early 2026. RISE MarketCore will then open for the permissionless deployment of new spot and perps markets. The future roadmap includes expanding the Markets SDK to support options, structured products, and prediction markets, all running natively on RISE’s shared orderbook infrastructure.
RISE is the Home for Global Markets, a high-performance Ethereum Layer 2 that powers programmable markets onchain. Built for CEX-grade performance and full EVM composability; RISE enables builders, traders, and institutions to create and connect to global orderbooks alongside a thriving DeFi ecosystem with ease. RISE is rearchitecting the financial stack for a transparent, composable, and unstoppable onchain economy.
CGO
Sasha Mai
RISE Labs
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. Do Your Own Research before making any financial decision.
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2025-11-15 02:44:19
At the TestIstanbul Conference, Performance Architect Sudhakar Reddy Narra demonstrated how conventional performance testing tools miss all the ways AI agents actually break under load.
When performance engineers test traditional web applications, the metrics are straightforward: response time, throughput, and error rates. Hit the system with thousands of concurrent requests, watch the graphs, and identify bottlenecks. Simple enough.
But AI systems don't break the same way.
At last month's TestIstanbul Conference, performance architect Sudhakar Reddy Narra drew one of the event's largest crowds, 204 attendees out of 347 total participants, to explain why traditional load testing approaches are fundamentally blind to how AI agents fail in production.
"An AI agent can return perfect HTTP 200 responses in under 500 milliseconds while giving completely useless answers," Narra told the audience. "Your monitoring dashboards are green, but users are frustrated. Traditional performance testing doesn't catch this."
The core problem, according to Narra, is that AI systems are non-deterministic. Feed the same input twice, and you might get different outputs, both technically correct, but varying in quality. A customer service AI might brilliantly resolve a query one moment, then give a generic, unhelpful response the next, even though both transactions look identical to standard performance monitoring.
This variability creates testing challenges that conventional tools weren't designed to handle. Response time metrics don't reveal whether the AI actually understood the user's intent. Throughput numbers don't show that the system is burning through its "context window," the working memory AI models use to maintain conversation coherence, and starting to lose track of what users are asking about.
"We're measuring speed when we should be measuring intelligence under load," Narra argued.
Narra's presentation outlined several AI-specific performance metrics that testing frameworks currently ignore:
Intent resolution time: How long it takes the AI to identify what a user actually wants, separate from raw response latency. An agent might respond quickly but spend most of that time confused about the question.
Confusion score: A measure of the system's uncertainty when generating responses. High confusion under load often precedes quality degradation that users notice, but monitoring tools don't.
Token throughput: Instead of measuring requests per second, track how many tokens the fundamental units of text processing the system handles. Two requests might take the same time but consume wildly different computational resources.
Context window utilization: How close the system is to exhausting its working memory. An agent operating at 90% context capacity is one conversation turn away from failure, but traditional monitoring sees no warning signs.
Degradation threshold: The load level at which response quality starts declining, even if response times remain acceptable.
The economic angle matters too. Unlike traditional applications, where each request costs roughly the same to process, AI interactions can vary from pennies to dollars depending on how much computational "thinking" occurs. Performance testing that ignores cost per interaction can lead to budget surprises when systems scale.
One practical challenge Narra highlighted: generating realistic test data for AI systems is considerably harder than for conventional applications. A login test needs a username and a password. Testing an AI customer service agent requires thousands of diverse, unpredictable questions that mimic how actual humans phrase queries, complete with ambiguity, typos, and linguistic variation.
His approach involves extracting intent patterns from production logs, then programmatically generating variations: synonyms, rephrasing, edge cases. The goal is to create synthetic datasets that simulate human unpredictability at scale without simply replaying the same queries repeatedly.
"You can't load test an AI with 1,000 copies of the same question," he explained. "The system handles repetition differently than genuine variety. You need synthetic data that feels authentically human."
Another complexity Narra emphasized: AI systems don't stay static. As models get retrained or updated, their performance characteristics shift even when the surrounding code remains unchanged. An agent that handled 1,000 concurrent users comfortably last month might struggle with 500 after a model update, not because of bugs, but because the new model has different resource consumption patterns.
"This means performance testing can't be a one-time validation," Narra said. "You need continuous testing as the AI evolves."
He described extending traditional load testing tools like Apache JMeter with AI-aware capabilities: custom plugins that measure token processing rates, track context utilization, and monitor semantic accuracy under load, not just speed.
The presentation also covered resilience testing for AI systems, which depend on external APIs, inference engines, and specialized hardware, each a potential failure point. Narra outlined approaches for testing how gracefully agents recover from degraded services, context corruption, or resource exhaustion.
Traditional systems either work or throw errors. AI systems often fail gradually, degrading from helpful to generic to confused without ever technically "breaking." Testing for these graceful failures requires different techniques than binary pass/fail validation.
"The hardest problems to catch are the ones where everything looks fine in the logs but user experience is terrible," he noted.
Whether these approaches will become industry standard remains unclear. The AI testing market is nascent, and most organizations are still figuring out basic AI deployment, let alone sophisticated performance engineering.
Some practitioners argue that existing observability tools can simply be extended with new metrics rather than requiring entirely new testing paradigms. Major monitoring vendors like DataDog and New Relic have added AI-specific features, suggesting the market is moving incrementally rather than revolutionarily.
Narra acknowledged the field is early: "Most teams don't realize they need this until they've already shipped something that breaks in production. We're trying to move that discovery earlier."
The high attendance at Narra's TestIstanbul session, drawing nearly 60% of conference participants, suggests the testing community recognizes there's a gap between how AI systems work and how they're currently validated. Whether Narra's specific approaches or competing methodologies win out, the broader challenge remains: as AI moves from experimental features to production infrastructure, testing practices need to evolve accordingly.
For now, the question facing engineering teams deploying AI at scale is straightforward: How do you test something that's designed to be unpredictable?
According to Narra, the answer starts with admitting that traditional metrics don't capture what actually matters and building new ones that do.
2025-11-15 02:43:54
Associate Technical Architect Bharat Kumar Dokka transformed SQL Server patching by engineering an automated PowerShell-driven system that handles over 200 servers, preserves high availability, and saves nearly 600 man-hours. His innovation improved security, reliability, and operational efficiency while elevating strategic team focus and reinforcing his leadership in enterprise infrastructure and automation.
2025-11-15 02:38:51
Bimal Subhakumar, a leading SAP Transportation Management expert, transformed global logistics operations through early SAP TM adoption, multimodal implementations, and cross-functional integration. Recognized as a top SAP Community contributor and now Global Practice Head at PrimeS4, he drives consulting excellence, technical innovation, and specialized talent development across the SAP ecosystem.
