Polymarket Acquires Brahma to Strengthen DeFi Infrastructure

TheNewsCryptoОпубліковано о 2026-03-19Востаннє оновлено о 2026-03-19

Анотація

Polymarket, the largest prediction market, has acquired DeFi infrastructure provider Brahma. The Brahma team will focus on enhancing Polymarket's product suite, aiming to reduce friction in wallet creation, deposits, and token redemptions. The acquisition is expected to bring more liquidity to niche prediction markets on Polymarket. Brahma’s three main products—Strategy Vaults, Brahma Accounts, and Swype.fun—will be discontinued over the next 30 days. This move is part of Polymarket’s continued expansion despite broader crypto market challenges. The company recently announced partnerships with Palantir and TWG AI for an AI-powered sports integrity platform and acquired Dome and Lunch in February. Financial terms of the Brahma acquisition were not disclosed.

The largest prediction market, Polymarket, has publicised that it is acquiring Brahma, a crypto startup offering decentralised finance (DeFi) infrastructure. As per the announcement on March 18, Brahma mentioned that as part of this transition, our team will commit itself to evolving Polymarket’s stack and product suite.

Brahma was rolled out in 2021, and till now it has processed more than $1 billion in volume and may be used by Polymarket to suppress friction around wallet creation, deposits, and token redemptions.

The possession could also draw more liquidity to niche, low-volume prediction markets on Polymarket. The founder and the chief executive officer of Polymarket, Shayne Coplan, mentioned that creating trustworthy infrastructure over blockchain networks and traditional financial rails is hard and there are no shortcuts.

He further went on to add that the Brahma team has signified it can design, function and scale complex products for sophisticated users. Financial details of the possession were not revealed at the time.

The Products of Brahma

In the last four years, Brahma has made three main products: Strategy Vaults for automated DeFi strategies, Brahma Accounts, smart accounts for DeFi users; and Swype.fun, a Visa card associated with DeFi positions for real-world spending.

The company mentioned that each product will be terminated over the upcoming 30 days as the acquisition proceeds. Polymarket has swiftly grown to a listed $20 billion valuation at the time of rapid growth in prediction markets.

Polymarket has carried on to invest in expansion regardless of a wider crypto market slip and an increase in interest in AI. The firm publicised on March 10 that it was collaborating with Palantir Technologies and TWG AI to create an AI-powered sports integrity platform.

It also acquired Y Combinator-supported Dome in February, which offers developer tools for prediction markets, and Lunch, a boutique company specialising in recruiting and assembling teams for tech startups.

Highlighted Crypto News Today:

Crypto Prices Retreat, Asian Markets Move the Same Way

Пов'язані питання

QWhat is the main purpose of Polymarket's acquisition of Brahma?

APolymarket is acquiring Brahma to strengthen its DeFi infrastructure, specifically to evolve its stack and product suite, and to reduce friction around wallet creation, deposits, and token redemptions.

QHow much transaction volume has Brahma processed since its launch in 2021?

ABrahma has processed more than $1 billion in volume since it was launched in 2021.

QWhat are the three main products developed by Brahma in the last four years?

ABrahma's three main products are Strategy Vaults for automated DeFi strategies, Brahma Accounts (smart accounts for DeFi users), and Swype.fun, a Visa card linked to DeFi positions for real-world spending.

QWhat was the valuation of Polymarket at the time of the article, and what market trend contributed to this?

APolymarket had grown to a listed $20 billion valuation at the time, driven by rapid growth in prediction markets.

QBesides Brahma, what other companies has Polymarket recently acquired?

AIn February, Polymarket also acquired Dome, which offers developer tools for prediction markets, and Lunch, a boutique company specializing in recruiting and assembling teams for tech startups.

Пов'язані матеріали

Upbit Rebalances 864B SHIB In Internal Wallet Move

South Korean cryptocurrency exchange Upbit moved a total of 864 billion SHIB (worth approximately $4 million) between its internal wallet addresses. The activity, involving transfers of 384 billion SHIB outbound and 480 billion SHIB inbound, occurred following a 36% SHIB price rally. On-chain data indicates this was a routine internal wallet rebalancing operation by the exchange, not a sell-off or liquidation event. The article cautions that while large on-chain transfers are highly visible, they require proper context and wallet labeling to interpret correctly. In this case, the movement between known Upbit addresses does not inherently signal a change in market supply or selling pressure for SHIB.

bitcoinist23 хв тому

Upbit Rebalances 864B SHIB In Internal Wallet Move

bitcoinist23 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

This article introduces a novel training paradigm for generative models called Explorative Modeling (XM), which enables true end-to-end training. Traditionally, powerful generative models like autoregressive and diffusion models are not trained end-to-end. They are trained to predict a single small step but require iterative multi-step sampling for inference. This "exposure bias" leads to error accumulation and limits performance. The core challenge XM addresses is "mode blurring." In generative tasks, a single input (e.g., "generate a dog") corresponds to many valid outputs (multiple modes). Standard training objectives like reconstruction loss force the model to average these modes, producing unrealistic, blurry outputs. To avoid this, existing models break generation into many small, almost deterministic steps, sacrificing end-to-end training. XM tackles this by restructuring the training loop itself. Its key insight is to amplify "generative expressivity." For each training input, instead of generating one sample, the model generates K candidate outputs. Only the candidate closest to the real data is used for computing the loss and updating the model via backpropagation. This simple "best-of-K" mechanism is implemented as a short for-loop. By exploring multiple possibilities, the model learns to distribute its guesses across different modes rather than collapsing to their uninformative average. The paper demonstrates that "exploration" acts as a new, powerful scaling axis. Gains from XM increase with model size, data scale, and compute. Experiments show improvements in FID scores for image generation and significant efficiency gains, sometimes outperforming larger models without exploration. When pushed to the limit, XM enables fully single-step, end-to-end generative models. In robotics tasks, an "Explorative Policy" matched the performance of a 100-step Diffusion Policy with a single forward pass, drastically improving inference speed. While the best-of-K concept is not entirely new, the authors' contribution lies in formally understanding it as a direct method to boost generative expressivity without fragmenting the generation process. This work suggests that as models scale, enhancing exploration during training may become crucial for overcoming fundamental performance bottlenecks.

marsbit23 хв тому

Can Generative Models Finally Be Trained End-to-End? The Core Is a For Loop

marsbit23 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

The International Conference on Learning Representations (ICLR) 2027 has introduced a new submission rule limiting authors to a maximum of 20 papers per conference. This policy, announced due to a 68% surge in submissions for ICLR 2026 that strained the review system, aims to ensure fair and informative peer review. Google DeepMind researcher Dan Roy responded with a sarcastic petition on X, calling the 20-paper cap "absurd" and claiming it would slow AI progress. His post mockingly argued that AI agents can now produce incremental research and that large language models (LLMs) already handle all reviewing, so output should be maximized. This satire critiques the broader trend of AI's role in academia, referencing a 2025 study that found about 21% of ICLR 2026 reviews were likely fully AI-generated. Roy's underlying point questions whether simply capping submissions addresses the systemic issue of AI potentially flooding conferences with agent-written papers and AI-assisted reviews. The debate highlights tensions between managing submission volume and maintaining research quality as AI tools become pervasive.

marsbit28 хв тому

A Maximum of 20 Submissions Per Person? DeepMind Researcher Satirically 'Petitions' Against ICLR's New Rule

marsbit28 хв тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

A security vulnerability in Coldcard hardware wallets led to the theft of 1,367.05 BTC (approx. $89 million) from 4,585 addresses, according to Galaxy Research. The flaw, caused by insufficient randomness in seed phrase generation for specific firmware versions, triggered a massive migration of funds as users moved assets to new, secure wallets. This event caused the largest on-chain movement of small Bitcoin transactions since the FTX collapse, distorting key market indicators like active addresses and exchange deposits, and significantly dampening market sentiment. The incident also highlighted challenges in cybersecurity investigations, as researchers reported that safety guardrails on mainstream U.S. AI models hindered the analysis of attack patterns, forcing them to use an open-source Chinese AI model for forensic work. This underscores a dilemma in balancing AI security restrictions with the need for rapid response in tracing stolen crypto assets.

marsbit1 год тому

Coldcard Vulnerability Leads to $89 Million Theft, Sparking Largest On-Chain Migration Since FTX Collapse

marsbit1 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

The AI boom is facing an unexpected bottleneck: a severe shortage of skilled construction workers and electricians. As tech giants like Meta, OpenAI, and Alphabet race to build massive data centers—such as OpenAI's $16 billion "Stargate" project—they are hitting a critical labor wall. The U.S. needs an estimated 130,000 more electricians, 240,000 construction workers, and 150,000 supervisors by 2030 for AI infrastructure alone, but tens of thousands of electrician jobs go unfilled each year. While AI companies offer high premiums, with electricians earning up to $280,000 annually, worker scarcity still causes massive losses—delays on a single project can cost $14.2 million per month. The complexity of building AI data centers, which require immense power (equivalent to powering hundreds of thousands of homes), sophisticated electrical systems, and advanced liquid cooling solutions, demands highly skilled technicians who are in short supply. To combat this, companies are investing heavily in training. Meta has committed $115 million to a free training school offering tuition, housing, and stipends, targeting 5,000 new workers. OpenAI is partnering with unions to secure skilled labor. These efforts are paying off, with a significant rise in Gen Z interest in trade schools over college. However, the power demands are staggering. AI data centers are driving a rapid surge in electricity consumption, projected to account for up to 12% of U.S. power use by 2028 and raising costs for consumers. Furthermore, the construction boom is project-based, leading to a potential future glut of trained workers once building peaks, which could depress wages industry-wide. The race for AI supremacy now depends as much on skilled hands as on advanced chips.

marsbit2 год тому

Annual Salary of Millions Competing for Electricians, Meta Rushes to Open Its Own Technical School

marsbit2 год тому

Торгівля

Спот