Velion Venture

@Wager_Predict @Wager_Predict Always Supporting Great Team. $WP Ticker is good.

We're rebranding. Predict Protocol → Wager Predict 🎯 $PRED → $WP The name changed. Nothing else did: ✅ Same team ✅ Same Vision ✅ Same permissionless leveraged prediction markets ✅ Same 40K+ wallets, $380M+ volume Bigger vision. Same conviction. TGE soon. All Official Links: Website: wagerpredict.com X: x.com/Wager_Predict Telegram Channel: t.me/Wager_Predict Linkedin: linkedin.com/company/wager-… Medium: wagerpredict.medium.com

Today marks a major milestone for NewEraAI We're proud to announce our $500,000 Strategic Investment. Backed by Velion Ventures, Zerone Ventures, MiraCapital and BlockRise. This investment allows us to accelerate our mission of building the infrastructure for true AI ownership on-chain. We believe creators should own what they create. Every image, every prompt, and every transaction should be verifiable, transferable, and owned by the user, not the platform. Thank you to our investors for believing in our vision, and to our community for supporting us from day one. We're just getting started. 🚀

Happy to seee both of our portfolio projects working together! This is Huge!

The moment you've been waiting for is here. Today we're revealing $PRED tokenomics, and we built it for one group above all: you. 36% of total supply goes straight to the community and rewards. Not insiders. Not VCs. The people who actually use Predict. → 26% Ecosystem & Rewards → 10% Airdrop 30B total supply. Fair from day one. This is what conviction looks like. 🟢

🇵🇭 Predict Protocol x Philippines Week that's a wrap! Thank you to everyone who showed up, asked questions, and made this event one to remember. We were proud to sponsor and be part of such a vibrant, welcoming community. The conversations, the energy, and the people made it all worth it. We're walking away inspired and more committed than ever to building alongside you. This is only the start. Salamat, Philippines! 🙏 See you soon.

Introducing the All-New: Zebec Enterprise. Employees. Contractors. Vendors. All in one platform for global payroll and HR management. Bringing streaming payroll to modern global teams. ⛵️

Check out WLFI Markets, now live in the Zebec SuperApp! 🦅☝️

Building trust machines for AI 🤖 Phala at #CCSummit

Clearpool is live on @XDCNetwork. Now XDC-native institutions can tap into Clearpool's credit markets directly⚡️ More to come. $CPOOL

Prime active loans just crossed $5,000,000 🙌🏻 The institutional demand for permissioned onchain credit is here. $CPOOL

📢 Today we're taking AgentPay from architecture to a working demo: an AI agent autonomously buying @Polymarket data on @base, paid per request through a state channel. Open source. Plug in your own Claude or Codex agent. Thousands of paid calls. One open channel. 🧵

Bernstein report this week: tokenized RWAs at $51B, up 40% YTD, despite the broader crypto market falling. Private credit is the largest category at 47% of the total. Institutional interest in tokenization is accelerating independent of crypto market conditions. Step by step, this is exactly what we built Prime for.

OPPO × Phala published a joint paper on Kubernetes Pod-level remote attestation for confidential AI. Goal: prove the hardware, container image, and Pod identity before sensitive data enters the workload. arxiv.org/abs/2606.03323

A major milestone for stablecoin utility in Europe. @AllUnityStable has selected Zebec Enterprise to power Europe's first onchain government benefits distribution program, using EURAU on @StellarOrg. The program brings regulated digital euros into employee benefits and enterprise payments, with plans to expand payroll and workforce payouts across AllUnity's ecosystem.

Congratulations to Sahara AI researcher @XisenJ, whose paper has been accepted to the ICML 2026 Trustworthy AI for Good workshop. His paper, Proof-of-Guardrail in AI Agents, proposes a cryptographic system for verifying that an AI agent actually ran the safety guardrail it claims to. Read the full paper: arxiv.org/abs/2603.05786

In the single-model era, one API call usually implied one relatively legible execution path. You knew which model ran. You had a rough mental model of cost. You could reason about latency and usage from the outside. In the orchestration era, none of that holds. A case study 👇 ---- @SakanaAILabs launched Fugu this week. It looks like one model. You call it through a single API. But what you call and what actually runs are two different things. Fugu is a coordinator trained to break a task apart, route pieces of it to a pool of other models, and call instances of itself recursively to handle sub-tasks. One prompt can fan out into a swarm of sub-calls the user never sees. The whole thing still surfaces as one model response. Two developers ran into the consequences of that almost immediately. @LLMJunky said a single prompt consumed 100% of a five-hour quota. @cortesi paid for Sakana's $200 tier, found the API slow, and said he hit his limit in under an hour. Neither could say which models ran, how many times Fugu called itself, or why one prompt cost what it did. The issue isn't specific to Fugu either. When @AnthropicAI briefly opened access to Fable earlier this month, users reported similar quota burns with similarly little visibility into why. When one request fans out into many model calls, and some of those calls spawn more, the unit the buyer sees no longer maps cleanly to the unit of work happening underneath. Forecasting cost gets harder. So does explaining latency, attributing performance, or knowing what actually ran. Sakana's benchmark figures sharpen the question. Shared numbers for Fugu include 54.2 on SWE-Pro, 95.1 on GPQA-D, and 93.2 on LiveCodeBench v6, with testers noting scores above Opus, Gemini 3.1, and GPT 5.4 on each. But Sakana's own base model is around 7B parameters. Frontier-tier results from a coordinator that size strongly suggest that a meaningful share of the work is being done by the larger third-party models it calls. Behind one endpoint, the system may be making its own decisions about how much work to do, which models to invoke, and how many times to invoke them. For hobby use, opaque execution is an annoyance. For production systems touching real money or real workflows, it becomes harder to dismiss. You can't price what you can't predict, and you can't debug what you can't trace. When the run is a sealed box, it also becomes harder to prove what the agent actually did. Fugu is one early launch, and Sakana will likely smooth out the rough edges. But as orchestration becomes the product, execution visibility becomes core infrastructure. The next layer of competition in AI will be won by the teams that can make increasingly complex agentic runs legible enough to trust. Follow @SaharaAI for analysis of agent infrastructure, the AI economy, and where the real building is happening.