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Decibel, a New Onchain Trading Engine for Spot, Perps, and Yield Opportunities, Launches on Aptos

Decibel, a New Onchain Trading Engine for Spot, Perps, and Yield Opportunities, Launches on Aptos

Supported by Decibel Foundation and Aptos Labs, Decibel delivers CEX-level performance with full transparency and capital efficiency.
Decibel, a fully decentralized trading engine built for speed, scale, and composability, launched today on the Aptos blockchain. Developed by Aptos Labs in collaboration with the newly formed Decibel Foundation, Decibel brings together spot trading, perpetuals, and yield strategies into a single, high-performance system. It is designed to serve as an independent, neutral infrastructure for global onchain markets.
Decibel is now live on Devnet. Users can explore the platform in an invite-only test environment, gaining simulated access to high-speed execution, cross-margin accounts, and composable yield strategies. Decibel unifies the performance of centralized exchanges with the transparency and flexibility of DeFi—all through a single wallet-native interface.
Built on Aptos, the fastest Layer 1 blockchain in production, Decibel was designed to act as the execution layer within Aptos' broader financial stack. Alongside Aptos' natural fit for payments (for stablecoin infrastructure) and Shelby (for real-time, decentralized data storage), Decibel enables traders, developers, and institutions to move capital, execute trades, and access financial data instantly. It provides DeFi protocols on Aptos with unified liquidity, programmable automation, and real-time settlement—all composable by design.
'Decibel was designed to democratize access to sophisticated, scalable trading infrastructure,' said Oliver Bell, Board Director at Decibel Foundation. 'Decibel isn't just another DeFi protocol—it's the execution layer that will power the next generation of financial applications and services.'
'Decibel reflects what we've always believed at Aptos Labs, that the next generation of financial systems should be fast, open, and programmable,' said Avery Ching, Co-Founder and CEO of Aptos Labs. "This is a critical step towards the Global Trading Engine vision, where all assets are tokenized and tradeable onchain, and capital markets operate 24/7 without borders, Decibel helps reduce market fragmentation by bringing execution, yield opportunities, and capital efficiency into one unified system, where money moves better onchain.'
"This is a very ambitious vision for the next generation of onchain trading,' said Jordi Alexander, CIO of Selini, a top-three global trader of onchain perps. 'If Decibel can pull off the performance and reliability outlined in the roadmap, it should be a step change for traders everywhere."
Why it Matters
The launch comes as demand for onchain infrastructure reaches new highs. In Q2 2025, DEX volumes exceeded $876B, and DeFi usage topped 14.2 million active wallets with over $48B in weekly transaction volume. However, many projects continue to depend on fragmented platforms and third-party exchanges, which can restrict composability and hinder broader ecosystem development. Decibel addresses these challenges by providing developers with direct access to order flow, real-time data, and programmable incentives, aiming to improve integration and system efficiency.
How Decibel Works
Decibel will support a multicollateral system where many assets can be used as collateral from a single cross-margin account, with users maintaining full self-custody of their assets while interacting with Decibel. Vaults can generate yield, and vault deposits can double as collateral. The decentralized architecture allows developers to permissionlessly build on top of Decibel, routing trade volume to Decibel via new trading interfaces, composable DeFi products, liquidity layers, and more. With Aptos' X-Chain Accounts, users can onboard from Ethereum or Solana without bridges or switching wallets.
Aptos has led the blockchain space in throughput, finality (600ms), and ultra-low fees ($0.00005). With major performance upgrades on the horizon, Decibel is creating a highly optimized trading VM — integrating deeply with Block-STM and the rest of the Aptos stack — and working toward sub-20ms block times while scaling to 1,000,000 orders per second, aiming to rival the speed and efficiency of top centralized exchanges.
Aptos Labs built the initial infrastructure for Decibel in collaboration with the Decibel Foundation, which will provide long-term support to the Decibel ecosystem. Aptos Labs will continue to contribute additional product development services in collaboration with Decibel Foundation.
About Decibel Foundation
The Decibel Foundation is dedicated to advancing the Decibel Protocol, a decentralized non-custodial trading engine designed for high-performance, full-stack trading of perpetual and spot digital assets. The Decibel Foundation supports protocol development, governance, and ecosystem growth to drive the future of onchain capital markets.
Aptos is a next-generation Layer 1 blockchain. Aptos' breakthrough technology, scalable infrastructure and user safeguards are designed to power the next generation of financial systems by offering unparalleled high throughput and low latency that can scale to billions of users.
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This New Protocol Aims to Transform XRP Into a High-Yield Crypto
This New Protocol Aims to Transform XRP Into a High-Yield Crypto

Yahoo

time3 hours ago

  • Yahoo

This New Protocol Aims to Transform XRP Into a High-Yield Crypto

XRP (XRPUSD), one of the leading cryptocurrencies by market capitalization, has been on a wild tear in recent months. This is mostly due to Ripple's (the company behind XRP) legal headwinds turning into tailwinds. The Securities and Exchange Commission is now much more lax when it comes to cryptocurrencies. Plus, President Donald Trump and First Lady Melania Trump both have their own crypto tokens. Before the election, Trump vowed to promote cryptos. Ripple's legal wins and Trump's reelection caused XRP to surge, and it is now up 508.8% from its 52-week low. The rally is warranted, since the administration's attitude toward crypto gives it a window of a few years where it can aggressively expand and become an established part of the global banking system. More News from Barchart A 'Satoshi-Era' Bitcoin Whale Just Dumped $9 Billion BTC. Here's Why Some Crypto Lovers Think This Is a Really Bad Sign. Get exclusive insights with the FREE Barchart Brief newsletter. Subscribe now for quick, incisive midday market analysis you won't find anywhere else. XRP was originally built to streamline cross-border remittances as a bridge currency for financial institutions and to reduce the friction of traditional systems like SWIFT. Over the years, its use cases have expanded beyond payments to include smart contracts, decentralized identity protocols, tokenized assets, and central bank digital currencies (CBDCs). And these are mainly the reasons why investors are so bullish on XRP going forward. But theres another reason worth factoring in, which could supercharge its performance. XRP Could Become One of the Highest-Yielding Cryptos XRP has underperformed its similarly sized peers until recently, and the gains have also plateaued in recent months. Unlike many other altcoins that allow staking, XRP has lacked any yields that give holders an incentive to buy and hold long term. This is changing with the introduction of DeFi protocols like MoreMarkets, which enable XRP holders to earn substantial yields on their tokens without relinquishing control. By depositing XRP into specialized smart contract vaults, users can access automated DeFi strategies that were previously reserved for institutions. In turn, this could make XRP into one of the highest-yielding cryptocurrencies available. Retail investors would be able to get yields through the XRP Earn Account through self-custodial smart contract vaults that automatically allocate funds to vetted third-party DeFi strategies. The yields could be as high as 20%, although 5% is what you get during the initial testing phase. Investors have already deposited $2.5 million into the platform as of July 24. Does This Change the Paradigm for XRP? This isn't true 'staking' like you see on other chains, where you lock up tokens to help secure the network and earn rewards. XRP's blockchain doesn't support that natively, so MoreMarkets is more like yield farming, the practice of moving crypto assets through different protocols to earn the highest yields. If MoreMarkets gains more popularity, it could give investors more incentive to hold XRP for longer. However, this is unlikely to be something that completely changes the paradigm for XRP. A consistent and stable 20% yield is highly unlikely, and a 5% to 10% yield is more in line with other chains. Should You Buy XRP Now? XRP has strong support at $3, but it has very strong resistance levels in the $3-$3.30 range. It has failed to make a move above its 7-day moving average and hold, so I'd wait for a break above $3.30 to gain near-term confidence about a bona-fide rally. In the long run, XRP is a great bet due to the opportunity it has to expand its partnerships. Spot ETFs could be approved soon, and JPMorgan believes it could trigger $8 billion in inflows in the first year of trading. On top of that, the market has been eagerly waiting for altseason to kick in. This means billions could flow down from Bitcoin to altcoins like XRP. On the date of publication, Omor Ibne Ehsan did not have (either directly or indirectly) positions in any of the securities mentioned in this article. All information and data in this article is solely for informational purposes. This article was originally published on Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data

I tested ChatGPT vs Gemini 2.5 Pro with these 3 prompts - and it shows what GPT-5 needs to do
I tested ChatGPT vs Gemini 2.5 Pro with these 3 prompts - and it shows what GPT-5 needs to do

Tom's Guide

time4 hours ago

  • Tom's Guide

I tested ChatGPT vs Gemini 2.5 Pro with these 3 prompts - and it shows what GPT-5 needs to do

OpenAI has been teasing GPT-5 throughout the summer, keeping the AI community on tenterhooks. Back in mid-July, Sam Altman couldn't resist sharing that their internal GPT-5 prototype had topped this year's International Mathematical Olympiad. His casual mention that "we are releasing GPT-5 soon" sent a clear signal that the model's production-ready, even if the full research system remains under wraps. But here's the thing — Google's Gemini 2.5 Pro already sets a formidable benchmark. This "thinking" model dominates maths, science, and coding leaderboards while shipping with a staggering one-million-token context window (with two million on the horizon). Gemini 2.5 Pro also boasts native ingestion of text, images, audio, video, and complete codebases, plus an experimental "Deep Think" mode that also excels at Olympiad-level mathematics and competitive coding challenges. Core rumors around GPT-5 point to it being an agent-ready model capable of reasoning as well as creativity. A blend of the o-series and GPT-series models. The next few months will be pivotal. These tests show the gap isn't about pure performance — it's about thoughtful completeness. GPT-5 doesn't need to be universally better. It needs to be consistently whole. A config file labelled "GPT-5 Reasoning Alpha" surfaced in an engineer's screenshot, researchers discovered the model name embedded in OpenAI's own bio-security benchmarks, and a macOS ChatGPT build shipped with intriguing flags for "GPT-5 Auto" and "GPT-5 Reasoning", suggesting dynamic routing between general and heavy-duty reasoning engines. Even OpenAI researcher Xikun Zhang offered reassurance to anxious users that "GPT-5 is coming" in an X post. Sources speaking to Tech Wire Asia paint an exciting picture of an early August release alongside lighter "mini" and "nano" variants designed for lower-latency applications. The approach reportedly merges the GPT-4 family with the o-series into a unified model architecture — reasoning plus knowledge similar to Gemini 2.5 Pro. Get instant access to breaking news, the hottest reviews, great deals and helpful tips. Rumors are buzzing about a 256,000-token context window, native video input capabilities, and agent-style autonomy for complex tasks. If accurate, that would double ChatGPT-4o's memory capacity and put GPT-5 squarely in competition with Google's latest. To understand exactly what GPT-5 needs to deliver, I put ChatGPT-4o and o3 head-to-head with Gemini 2.5 Pro across three demanding prompts that stress-test long-context analysis, full-stack code generation, and multi-step planning. The results reveal precisely where OpenAI needs to focus their efforts. I challenged both systems to analyze a 3,000-word peer-reviewed article on renewable-energy policy, identify logical fallacies or unsupported claims, summarize findings in plain English, and propose three specific research avenues while highlighting biases and assumptions. Specifically, I found an article in the journal PLOS One on the use of renewable energy as a solution to climate change. I fed each the PDF and the prompt outlined above. I didn't use Deep Research from either Gemini or ChatGPT — just the native model. When I fed this renewable energy research paper to ChatGPT, the difference between 4o and o3 was striking. The standard 4o model delivered a competent overview that correctly identified the correlation-causation problem and noted missing economic controls — solid analysis that would satisfy most readers. But o3 operated on an entirely different level, systematically dissecting the paper using formatted tables that exposed five distinct types of bias, caught subtle methodological flaws like data imputation risks, and proposed specific research approaches including difference-in-differences and synthetic controls. The standout feature was a comprehensive table that didn't just suggest future research directions, but explained precisely why each avenue mattered and how to pursue it — the kind of rigorous analysis you'd expect from a senior research analyst, not an AI. While both ChatGPT versions focused on broad methodological critiques, Gemini 2.5 Pro zeroed in on a critical statistical flaw that completely undermined one of the paper's key claims. The smoking gun: an R-squared value of 0.0298 for Canada's data, meaning the statistical model explained only 3% of the variance — essentially random noise masquerading as a "positive relationship." Gemini also identified a circular reasoning problem where researchers used linear regression to create missing data points, then analyzed that artificially smoothed dataset with more regression analysis. Though less comprehensive than o3's response, Gemini's laser focus on quantitative rigor caught fundamental errors that both ChatGPT models missed entirely. The implications are clear: GPT-5 needs to combine o3's breadth of analysis with Gemini's precision in statistical reasoning. While o3 excels at comprehensive structural analysis and methodology critique, it completely missed basic statistical red flags that Gemini caught immediately. The ideal system would automatically run sanity checks on correlation coefficients, sample sizes, and confidence intervals without prompting — when a paper claims a relationship exists with an R-squared near zero, the AI should flag it immediately. Think of it this way: o3 writes like a brilliant policy analyst who can contextualize research within broader frameworks, while Gemini reads like a sharp-eyed statistician who spots when the numbers don't add up. For GPT-5 to truly compete with Gemini 2.5 Pro's analytical capabilities, OpenAI needs both skills seamlessly integrated — comprehensive analytical frameworks backed by rigorous quantitative verification. For the coding challenge the rule was 'one shot', no follow up. It had to build the response within a single message. I specified a simple clicker game with tap-to-collect coins, an in-game shop, upgrade tree, and save-state functionality, requesting a complete, responsive web app in HTML/CSS/JavaScript with commented code explaining core mechanics. First, the preview feature from Canvas in ChatGPT failed on both tests, so I had to download the code and run it directly in the browser. No hardship but Gemini's preview worked perfectly. ChatGPT o3's implementation started strong with a sophisticated architecture that showed real programming expertise. The code featured clean separation of concerns, custom CSS variables for consistent theming, and a well-structured game state object. The upgrade system used dynamic cost functions with exponential scaling, and it included a passive income system using intervals. However, there's a critical issue: the code cuts off mid-function. What we can see suggests o3 was building something elegant — but an incomplete game is no game at all and the rule was one shot. In stark contrast, 4o delivered a complete, working game that does exactly what was asked — no more, no less. The implementation features a simple coin-clicking mechanic, localStorage for persistence, and two basic upgrades: an auto-clicker and a double-click multiplier. The code is clean and functional, using straightforward loops for automation and JSON serialization for saving. 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Most impressively, it includes features I didn't even request, like auto-save every 30 seconds and a confirmation dialog for resets. The contrast here is illuminating. While o3 showed sophisticated programming patterns, it failed the fundamental requirement: shipping working code. GPT-5 needs to combine o3's architectural elegance with reliability that ensures complete, functional output every time. More importantly, it needs to match Gemini's product thinking — anticipating user needs beyond the literal specification. The difference between "build a clicker game" and building a good clicker game lies in understanding implicit requirements: players expect satisfying feedback, progression systems that create goals, and quality-of-life features like auto-save. Gemini demonstrated this product sense by adding unlock conditions that create strategic depth and offline earnings that respect player time. For GPT-5 to compete in code generation, it needs to deliver not just syntactically correct code, but thoughtfully designed products that show understanding of user experience, game design principles, and production-ready practices. The goal isn't just to write code — it's to create software people actually want to use. For the final test I asked the AI's to help with some travel planning. Specifically a ten-day family trip through parts of Europe. The prompt: Plan a 10-day family vacation (2 adults, 2 children ages 10 and 14) visiting London, Paris, Rome, and Barcelona with a total budget of €10,000. Include: Ensure the budget covers all expenses including international travel, accommodation, food, activities, and incidentals. Present the information in an organized, easy-to-follow format. ChatGPT o3 delivered what I can only describe as a travel agent's masterpiece. We're talking specific hotel recommendations with direct booking links (Novotel London Tower Bridge at €182/night via exact train times (morning Eurostar, 2h 16m), and granular cost breakdowns that show the entire trip coming in at €6,000 — leaving a realistic €4,000 buffer. The customs advice reads like insider knowledge: order coffee at the bar in Rome for local prices, keep your voice down on the Paris Métro, and watch out for Roman drivers who won't stop at zebra crossings. ChatGPT 4o took a more traditional approach, delivering a competent itinerary that hits all the major spots but relies heavily on approximations (~€180/night) and generic recommendations ("family-friendly pub"). While it covers all the required elements, the linked sources are mostly Wikipedia pages rather than actual booking sites. It's the difference between a travel blog post and an actionable planning document. Gemini transformed the assignment into something else entirely — a narrative journey that reads like a high-end travel magazine feature. Beyond the expected attractions, it adds experiences I didn't even think to ask for: Gladiator School in Rome where kids learn sword-fighting techniques, specific times to catch the afternoon light through Sagrada Família's stained glass, and psychological tricks like using gelato as museum motivation. The writing itself sparkles with personality ("Queuing is a national sport" in London) while maintaining practical precision. Each day includes specific departure times, walking routes, and restaurant recommendations with exact locations. The budget hits exactly €10,000, suggesting careful calculation rather than rough estimates. Most impressively, it anticipates family dynamics — building in pool time at Hotel Jazz in Barcelona as a "reward for tired kids (and parents)." This comparison exposes a fundamental challenge for OpenAI. While o3 excels at data precision and 4o provides reliable completeness, Gemini demonstrates something harder to quantify: contextual intelligence that understands the human experience behind the request. GPT-5 needs to combine o3's factual accuracy with Gemini's emotional intelligence. When someone asks for a family vacation plan, they're not just requesting a spreadsheet of costs and times — they're asking for help creating memories. Gemini understood this implicitly, weaving practical logistics with experiential richness. The technical requirements are clear: maintain o3's ability to provide specific, bookable recommendations with real links while adding Gemini's narrative flair and anticipation of unstated needs. But the deeper challenge is developing an AI that doesn't just answer the question asked, but understands the human story behind it. For complex, multi-faceted tasks like travel planning, raw intelligence isn't enough. GPT-5 needs to demonstrate wisdom — knowing when to be a precise logistics coordinator and when to be an inspiring travel companion who understands that the best trips balance spreadsheets with serendipity. These tests exposed three critical gaps that GPT-5 must address to compete with Gemini 2.5 Pro. OpenAI faces a nuanced challenge beyond raw capability metrics. These tests reveal that while ChatGPT excels at certain reasoning tasks, Gemini 2.5 Pro has redefined expectations around completeness and contextual understanding. The leaked "GPT-5 Reasoning Alpha" configurations suggest OpenAI recognizes this shift. Their rumored approach — unified architecture, expanded context windows, native video understanding — addresses the right technical gaps. But the real challenge is developing judgment: knowing when statistical precision matters, when completion is non-negotiable, and when to read between the lines of user requests. This competition benefits everyone. Gemini has raised the bar from "Can it reason?" to "Does it understand?" If GPT-5 combines OpenAI's analytical strengths with Google-level reliability and product thinking, we're witnessing the emergence of AI systems that genuinely grasp human needs. We're near or at AGI with this next generation leap. The next few months will be pivotal. These tests show the gap isn't about pure performance — it's about thoughtful completeness. GPT-5 doesn't need to be universally better. It needs to be consistently whole.

The Unseen Systems That Will Make or Break Digital Finance
The Unseen Systems That Will Make or Break Digital Finance

Entrepreneur

time4 hours ago

  • Entrepreneur

The Unseen Systems That Will Make or Break Digital Finance

Opinions expressed by Entrepreneur contributors are their own. Before billions of people streamed videos on their phones or ran businesses from their pockets, the groundwork was quietly being laid. Satellites were going into orbit. Fiber-optic cables were being buried beneath cities. 5G towers were rising across skylines. That hidden infrastructure is what made the mobile internet possible. I've seen this playbook before. The Ergen family spent decades helping to expand the physical backbone of global connectivity, long before most people realized the importance of that infrastructure. Today, I see the same story playing out again, this time in the naturally evolving world of decentralized finance (DeFi). The biggest breakthroughs always begin with what people don't see. In telecom, it was towers and satellites. In DeFi, it's infrastructure, regulation and access. As DeFi moves beyond speculation toward real-world utility, the opportunity isn't just in the applications. It's in the infrastructure, including custody rails, compliance frameworks and cross-border systems; in a nutshell, the entire ecosystem that will function securely, globally and at scale. Related: Mark Cuban Says Explosive Growth in DeFi Is 'Like the Early Days of the Internet' From telecom to tokenization: A familiar blueprint One thing my background in telecom taught me is that lasting change depends on what happens behind the scenes. The systems that enable mass adoption, whether in communications or finance, have to be in place before the public ever sees the benefits. That's exactly the mindset I'm bringing to the growth of DeFi technologies. While much of the attention in Web3 still chases market cycles and hype, the real work is happening at the infrastructure level, building the tools that make decentralized applications usable, compliant and scalable. 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It's trust that drives systems forward, and trust comes from infrastructure, including regulatory clarity, security and seamless access for users. That's why entrepreneurs today spend a lot of time not just thinking about what can be built, but where and how it can be built. Experience shows that working with policymakers, rather than around them, can accelerate the adoption of new ideas, and this principle applies equally to decentralized finance. Jurisdictions like the UAE, Singapore, and, of course, the U.S., where regulatory frameworks are clear and forward-looking, are now leading the way in digital asset innovation for this reason. But regulation is only one piece. There's also a last-mile problem that we as an industry need to solve. It's not enough to build robust systems; they have to be intuitive. That means better user interfaces, frictionless fiat onramps and tools that work without requiring deep technical knowledge. The best infrastructure fades into the background. No one thinks about how their phone connects to a tower. It just works. That's the standard we should be aiming for in financial systems, too. Related: Why Entrepreneurs Can't Afford to Ignore DeFi The emergence of stablecoins One of the most evident signs that DeFi is entering a phase of real-world utility is the emergence of stablecoins, which, unlike volatile crypto assets, are designed to maintain a consistent value, typically tied to fiat currencies such as the U.S. dollar. This stability positions them as a practical entry point for both institutions and individuals, facilitating cross-border transactions, real-time settlement and access to yield-generating opportunities without the usual barriers of traditional banking. Stablecoins are emerging as the connective tissue between the decentralized and traditional financial systems. They're being used for payroll, remittances, on-chain treasuries and even in central bank conversations about digital currencies. As regulatory clarity increases and infrastructure matures, stablecoins will likely be the backbone of a new, programmable financial layer that's global in reach, secure by design and open by default. As interest in Web3 returns, particularly with moves like Circle's IPO and blockbuster ETF inflows being registered on a daily basis, we must maintain our focus on what matters: not chasing the hype but laying the foundations. Every technology wave reaches a point where infrastructure becomes the priority. This is that moment for DeFi.

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