2026-01-29 04:05:55
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\ If you’ve used a sleek, embeddable wallet in a decentralized app (dApp) lately—the kind that lets you sign in with your Google or Discord account—you’ve likely been using Wallet-as-a-Service (WaaS). It’s convenient, sure. But as Human Tech (the team behind the Holonym Foundation) points out, these wallets aren't actually yours. You’re just renting them. If the app stops paying the service provider, or if the provider goes bust, your "self-custody" vanishes into the ether.
\ Enter Wallet-as-a-Protocol (WaaP).
\ Human Tech is launching what they call a "structural inversion" of the last decade of crypto onboarding. Instead of treating your digital identity as a subscription service for developers, WaaP aims to turn the wallet into a fundamental layer of the internet—one that belongs to the human, not the platform.
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To understand why this matters, you have to look at the current state of "embedded" wallets. Over 100 million keys have been created through WaaS providers. These services made crypto usable for normal people by hiding the complexity of seed phrases and private keys behind familiar social logins.
\ But this convenience came with a hidden cost: vendor lock-in. Because these wallets are "services," they are siloed. Your wallet in App A doesn't talk to App B. More importantly, your access is contingent on the developer’s relationship with the service provider.'
\ "Most of these wallets are not owned. They are rented," Human Tech explains in their latest manifesto. "If the app stops paying, the user loses access. This is a quiet reversion to the old internet model."
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WaaP is an open protocol. Think of it like SMTP for email or HTTP for the web. It’s a set of rules that allows for protected self-custody that is universal and portable across any app or blockchain.
\ The technical wizardry behind this involves Two-Party Computation (2PC) and Multi-Party Computation (MPC). In simple terms, your private key is split into two pieces. One piece is derived from your own "human attributes" (like biometrics or identity) and secured by the trustless Human Network. The other is locked in the decentralized Ika network behind security policies you control.
\ Because no single entity holds the full key, there is no single point of failure. Even if the Human Tech team disappeared tomorrow, the protocol remains, and your assets stay yours.
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What makes WaaP particularly compelling is how it handles the "human" side of technology. Traditional self-custody is brutal—lose your 12-word seed phrase, and your money is gone forever. WaaP offers a "practical self-custody" model. It allows for trustless recovery without seed phrases, social engineering risks, or reliance on a single device.
\ But the real kicker is portability. Imagine having one wallet that follows you across every chain, every app, and even into the world of AI.
\ As AI agents begin to transact on our behalf, the stakes for security have never been higher. WaaP allows users to safely delegate authority to these agents. You can tell an AI, "You can sign this transaction for a specific game, but you can't touch my life savings." It turns AI into a controlled extension of the human, rather than a security liability.
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The launch of the WaaP SDK and WaaP.build (a white-labeling platform for developers) marks the beginning of what Human Tech calls the "post-WaaS era."
\ For developers, the pitch is simple: stop paying for your users' wallets and start giving them true ownership. WaaP is free to create and use, composable, and resistant to the common attack vectors—like phishing and "blind signing"—that have plagued the industry for years.
\ It also brings powerful "plug-ins" to the table. Developers can embed Zero-Knowledge (ZK) identity verification, allowing for KYC or age checks without ever seeing the user's personal data.
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For years, the crypto industry has struggled to balance security with usability. We’ve oscillated between the "Wild West" of seed phrases and the "Gilded Cage" of centralized services.
\ Human Tech’s Wallet-as-a-Protocol suggests we don’t have to choose. By building the wallet into the fabric of the internet itself, they are making a bet that the future of the digital economy isn't built on services we subscribe to, but on protocols we own.
\ In a world where our digital lives are increasingly under surveillance and our assets are often at the mercy of platform whims, WaaP feels like a necessary return to the original promise of the web: a space where humans, not corporations, are the source of trust.
\ The protocol is live, the tools are ready, and for the first time in a long time, the keys to your digital kingdom might actually stay in your pocket.
Don’t forget to like and share the story!
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2026-01-29 03:04:04
Which is the best crypto presale that gives you a real shot at 100x before the early window closes, Bitcoin Hyper or Pepeto ($PEPETO)? Two names are getting the most attention from presale hunters right now. Bitcoin Hyper, pitching itself as a Bitcoin scaling solution using Solana Virtual Machine integration, priced around $0.013 per token. On the other side is Pepeto ($PEPETO), known as “The God of Frogs,” which has raised $7.19M at $0.000000180, building a meme-utility ecosystem made specifically for meme coin traders. Both presales are moving into late stages, but the path to big returns is very different once you look at the math.
\ Pepeto only needs to reach $0.000018 to deliver 100x from $0.000000180, a target that would require an achievable market cap level already reached by much weaker meme projects in past cycles, which is why many see Pepeto’s 100x path as far more realistic. Bitcoin Hyper would need $1.30 to hit 100x from $0.013, which demands far broader market penetration and much larger capital inflows. In simple terms: the lower the entry price, the easier it is to get huge multiples, as long as the project can attract demand.
\ That’s where Pepeto tries to separate itself. It’s not only a narrative. Pepeto already promotes working utility, including PepetoSwap with zero-fee swaps live today, a cross-chain bridge coming soon, and a Pepeto Exchange planned for 2026 with 850+ verified projects already applying. That creates a direct use case and demand engine earlier than most infrastructure-only presales, which often need full adoption before price momentum can really build.
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Bitcoin Hyper at $0.013 requires climbing to $1.30 for 100x returns. This demands either massive individual token value or significant total supply circulation at elevated prices. For context, few tokens outside top-10 market caps sustain $1+ valuations. Pepeto at $0.000000180 needs only $0.000018 for 100x, a price point PEPE demonstrated as achievable for viral meme tokens with strong community traction.
\ Lower entry creates exponentially superior percentage gain opportunities. A $100 investment in Bitcoin Hyper buys approximately 7,692 tokens. The same $100 in Pepeto buys 555,555,555 tokens. This quantity difference means Pepeto holders accumulate positions that generate meaningful dollar returns at micro-cap price points, while Bitcoin Hyper requires macro movements to deliver equivalent absolute gains.
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Bitcoin Hyper builds Layer 2 infrastructure requiring wrapped BTC through canonical bridges, EVM integration for smart contracts, and developer adoption for ecosystem growth. Technical complexity creates implementation risk and extended timelines before utility materialization. The project's roadmap depends on successfully attracting developers to build on yet another Layer 2 solution in an increasingly saturated market.
PepetoSwap processes transactions now with zero fees, solving immediate friction for traders. Over 850 projects submitted applications to list on the Pepeto Exchange, demonstrating verified demand before launch. This pre-approval queue validates market need rather than hoping for post-launch adoption. Every listing generates revenue. Every swap creates volume. Every bridge transaction routes through PEPETO tokens. Working products generate immediate utility.
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Bitcoin Hyper's presale approaches conclusion without disclosed hard cap, creating uncertainty around final raise amounts and token distribution. Pepeto operates with transparent $10M hard cap, $7.19M raised, and only $2.81M remaining before permanent closure. This definite scarcity creates urgency Bitcoin Hyper's structure cannot replicate. Days separate current buyers from permanent exclusion at presale pricing.
The 214% staking rewards create immediate holding value while Pepeto's audited contracts from SolidProof and Coinsult verify security. Bitcoin Hyper offers staking, but the seven-day post-TGE lockup creates brief restriction. Pepeto's transparent tokenomics and founder background from PEPE's original team add credibility that newer projects struggle to establish during presale phases.
https://www.youtube.com/watch?v=SUMHJeOqNY4&embedable=true
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Bitcoin Hyper lists on Uniswap and undisclosed CEX partners after mainnet launch precedes token generation. This structure means network functionality validates before trading begins, potentially reducing volatility. However, higher entry pricing limits percentage upside. Pepeto's lower entry creates explosive potential if even fractional PEPE-level adoption occurs post-listing. The $700,000 giveaway builds community momentum.
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To Invest In the best crypto presale follow these simple steps:
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Bitcoin Hyper is working on long-term scaling ideas that may take years to fully pay off. Pepeto is focused on what meme traders want right now: simplicity, speed, and real tools already in motion. That difference matters when it comes to returns.
Pepeto’s extremely low entry level gives early buyers something most projects can’t offer anymore, real room to grow. This is the stage where small decisions can lead to massive outcomes. History shows that the biggest gains don’t come from complex promises, but from early access to working products that people actually use.
\ With the presale close to selling out, time is becoming the biggest risk. Once this window closes, early pricing disappears forever. For investors searching for the best crypto presale, the next 100x opportunity, or a chance at a truly life-changing return, Pepeto stands out as one of the few projects where timing, utility, and upside all align, right now.
\ Make Sure To Use The Official Website To Buy Pepeto: https://pepeto.io/
\
1) Which is the best crypto presale to buy right now?
For investors chasing the clearest 100x setup, many pick Pepeto ($PEPETO) because it has a much lower entry price and utility already in motion.
\ 2) Why is Pepeto presale better than Bitcoin Hyper?
Because Pepeto offers market-ready products (zero-fee PepetoSwap, bridge coming, exchange planned) while Bitcoin Hyper depends on Layer-2 adoption and longer build timelines.
\ 3) Can Pepeto deliver 100x returns?
It’s possible. Pepeto only needs to reach $0.000018 for 100x, which is a level meme coins have hit in past cycles when demand goes viral.
\ To stay ahead of key updates, listings, and announcements, follow Pepeto on its official channels only:
Website: https://pepeto.io/
X (Twitter): https://x.com/Pepetocoin
Telegram: https://t.me/pepeto_channel
Instagram: https://www.instagram.com/pepetocoin/
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:::tip This story was published as a press release by Tokenwire under our Business Blogging Program.
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2026-01-29 03:00:04
Smart thermostats and solar systems fail because they optimize rigid goals. EcoNet shows how belief-based AI helps homes reason under uncertainty.
2026-01-29 02:30:13
Some recent trends in theoretical ML
2.1 Deep Learning via continuous-time controlled dynamical system
2.2 Probabilistic modeling and inference in DL
3.1 Kuramoto models from the geometric point of view
3.2 Hyperbolic geometry of Kuramoto ensembles
3.3 Kuramoto models with several globally coupled sub-ensembles
Kuramoto models on higher-dimensional manifolds
4.1 Non-Abelian Kuramoto models on Lie groups
4.2 Kuramoto models on spheres
4.3 Kuramoto models on spheres with several globally coupled sub-ensembles
5.1 Statistical models over circles and tori
5.2 Statistical models over spheres
5.3 Statistical models over hyperbolic spaces
5.4 Statistical models over orthogonal groups, Grassmannians, homogeneous spaces
6.1 Training swarms on manifolds for supervised ML
6.2 Swarms on manifolds and directional statistics in RL
6.3 Swarms on manifolds and directional statistics for unsupervised ML
6.4 Statistical models for the latent space
6.5 Kuramoto models for learning (coupled) actions of Lie groups
6.6 Grassmannian shallow and deep learning
6.7 Ensembles of coupled oscillators in ML: Beyond Kuramoto models
Examples
7.2 Linked robot’s arm (planar rotations)
7.3 Linked robot’s arm (spatial rotations)
7.4 Embedding multilayer complex networks (Learning coupled actions of Lorentz groups)
As exposed in the previous subsection, non-Abelian Kuramoto models with equal frequencies exhibit potential dynamics. Potential functions (21) and (22) measure the total disagreement between generalized oscillators. Therefore, collective dynamics of generalized Kuramoto oscillators with all positive couplings can be regarded as continuous-time consensus algorithms minimizing the total disagreement. If all couplings are negative, the term anti-consensus is used.
\ The same is valid for Kuramoto models on spheres and their disagreement function (23).
\
\ Finally, we mention that consensus algorithms have also been introduced on Stiefel manifolds [3], while Riccati flows have been studied on Siegel domains as well [82].
\
:::info Author:
(1) Vladimir Jacimovic, Faculty of Natural Sciences and Mathematics, University of Montenegro Cetinjski put bb., 81000 Podgorica Montenegro ([email protected]).
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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
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2026-01-29 02:30:03
Three years into the generative AI era, I've been watching a pattern repeat with clients across sectors.
\ The conversation usually starts the same way: they've got AI running somewhere in the org, often in a few places, showing some signs of Agentic behaviors. Customer service has a chatbot, product built a recommendation engine or narrative-driven LLM context flow, marketing runs campaigns through an LLM, and engineering automated some code reviews plus testing, etc.
\ Then the question: "How do we actually get value out of all this?"
\ This is the space between having Agentic AI and knowing what to do with it. Between feeling busy with AI projects and actually seeing business impact.
\ In 2026, research shows we're hitting an inflection point. Nearly 90% of companies report using AI in at least one business function, yet most still struggle to scale pilots or demonstrate clear ROI. The shift happening now looks less like a feature rollout and more like a redesign of operating models, governance structures, and risk management frameworks.
\ The winners this year won't be determined by who has the most AI. They'll be defined by who figured out orchestration, observability, and auditability.
Industry analysts project a surge from $7.8 billion today to over $52 billion by 2030 in the autonomous AI agent market, with predictions that 40% of enterprise applications will embed AI agents by the end of 2026.
\ But here's what those numbers miss: having Agents is different from orchestrating them.
\ I recently worked with a client who had 17 different AI implementations running across their business, from marketing automation to supply chain optimization to HR screening.
\ Each one worked fine in isolation. But then their product team tried to launch Agents that operations and the business couldn’t observe and audit, revealing existential risks and blindsides. Nobody had actually designed these systems to work together because nobody thought about orchestration until it was too late.
When people hear "orchestration," they often think integration layer. Connect the APIs, move some data around, call it done.
\ That's plumbing. Useful plumbing, but not orchestration.
\ Real orchestration means your AI systems understand context across domains. Think about specialized orchestrator models that can divide labor between different components, coordinating tools, and language models to solve complex problems. It's the difference between having smart tools and having an intelligent system.
\ Here's an example. Let’s say a retail company wants to optimize inventory. They have demand forecasting AI in one corner, supply chain planning in another, and pricing optimization somewhere else. All three are solid models. The issue is they all optimize for different things.
\ Orchestration can fix this by establishing a coordination layer. Rather than a central AI that replaces specialized models, this system would understand the relationships between its objectives. When demand forecasting suggests increasing inventory, the orchestration layer would check supply chain constraints and pricing implications before executing. Huge unlock for the organization and the business. Without it, there would be disconnects that affect customer delivery and the overall fulfillment process.
\ My prediction is that in 2026, enterprises will increasingly discover that the competitive frontier lies in managing specialized components effectively.
Most executives still treat governance as the thing you do to stay compliant. The overhead that legal requires. The checkbox exercise before deployment. A key precursor or underlying aspect of governance with AI, though, is actually observability.
\ Can you trace AI and Agent actions to their original inputs and outputs at each interface or boundary so that you know what you are delivering across the long-tail of customer use cases is actually what you intended? If you can, you then have auditability, which in turn means you have governance.
\ That view is expensive and today, with AI and Agents, very near-sighted or downright existentially risky. Before, the risk was localized because the product and technology were deterministic–all code was WYSIWG, mostly, and was linear, not open-ended AI.
\ When Agentic AI started taking actions rather than just generating responses, governance stopped being about central review and became about designing systems that can operate responsibly at scale. The companies that figured this out early turned governance into observability and then quick feedback loops to gain the confidence to ship; in other words, speed that ships confidently.
\ Regulated industries are adopting auditable AI processes and model risk management as mandatory capabilities. The key elements include continuous monitoring, explainability requirements, version control, and transparent decision trails.
\ The firms treating these as features rather than constraints are moving faster than competitors, still working through manual approval chains.
There's a concept gaining traction called "decision velocity," which refers to how quickly smaller decision trees and processes can be automated at scale. It's a useful lens for understanding what changes when orchestration and governance with observability work together.
\ Think about how decisions happen in most enterprises. Someone identifies an issue, gathers data, analyzes options, and escalates to whoever has authority. That person reviews context, makes a call, and communicates the decision. Implementation happens, and results get monitored.
\ Each step takes time. More importantly, each step involves coordination costs like finding the right person, explaining context, waiting for availability, and following up on execution.
\ AI and Agents change the equation when they can handle the entire loop, including execution and monitoring. But that only works if the Agent or AI understands the boundaries it operates within (governance) and can coordinate with other systems that need to know about the decision (orchestration).
\ I've seen companies achieve 5-7x improvements in certain decision cycles by getting this right. Not 10% better. Multiple times faster. The difference between responding to market changes in weeks versus days, or adjusting operations quarterly versus nearly continuously.
Here's how you know if you have an orchestration problem: ask your teams what success looks like for their AI initiatives.
\ If everyone gives you different answers, you have a coordination gap. If nobody can connect their metrics across their peers to business outcomes, you have an orchestration gap. If people can't explain how their AI decisions affect other systems, you have a governance and auditability gap.
\ Research from MIT shows that organizations in early stages of AI maturity had financial performance below industry average, while those in advanced stages performed well above average. The difference is having the capabilities to use it strategically.
\ The maturity models all point to the same progression. You start with experimentation, where individual teams build individual solutions. That's fine for learning, but it doesn't scale.
\ The next stage involves getting systems to talk to each other, establishing shared data foundations, and building common platforms. This is where most enterprises are stuck as we kick off 2026.
The companies positioning themselves well for this year are making specific choices.
\ They're prioritizing orchestration infrastructure over adding more point solutions. When evaluating new AI capabilities, they ask how it fits with existing systems before asking how good it is standalone.
\ They're treating governance frameworks as product decisions, not compliance exercises. Product takes governance and decomposes it into observability and auditability for the business, which is important for engineering and operations’ iterative cycles, and “is the work” to deliver AI or Agentic AI predictably and accurately over time. Building observability into AI systems from the start. Designing for auditability. Creating clear accountability structures.
\ Leadership is shifting from centralized IT oversight to empowering line-of-business leaders to find and fund AI and Agent solutions that directly advance their goals. But that decentralization only works when there's strong orchestration and governance holding it together.
\ The most effective enterprise strategies begin with a foundational question: what data can we trust, and what do we need to fix before we automate decisions at scale. That's where orchestration, observability, and auditability, leading to a true governance posture, intersect with execution.
\ The practical work involves several pieces: building coordination layers that let specialized AI and Agent systems work together, establishing governance frameworks that enable autonomous operation within clear boundaries, creating measurement systems that connect AI activity to business outcomes, and developing talent that understands both the technical and organizational aspects.
\ None of this is simple. But it's the work that separates companies using AI from companies transformed by it.
…
Nick Talwar is a CTO, ex-Microsoft, and a hands-on AI engineer who supports executives in navigating AI adoption. He shares insights on AI-first strategies to drive bottom-line impact.
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2026-01-29 02:15:03
Some recent trends in theoretical ML
2.1 Deep Learning via continuous-time controlled dynamical system
2.2 Probabilistic modeling and inference in DL
3.1 Kuramoto models from the geometric point of view
3.2 Hyperbolic geometry of Kuramoto ensembles
3.3 Kuramoto models with several globally coupled sub-ensembles
Kuramoto models on higher-dimensional manifolds
4.1 Non-Abelian Kuramoto models on Lie groups
4.2 Kuramoto models on spheres
4.3 Kuramoto models on spheres with several globally coupled sub-ensembles
5.1 Statistical models over circles and tori
5.2 Statistical models over spheres
5.3 Statistical models over hyperbolic spaces
5.4 Statistical models over orthogonal groups, Grassmannians, homogeneous spaces
6.1 Training swarms on manifolds for supervised ML
6.2 Swarms on manifolds and directional statistics in RL
6.3 Swarms on manifolds and directional statistics for unsupervised ML
6.4 Statistical models for the latent space
6.5 Kuramoto models for learning (coupled) actions of Lie groups
6.6 Grassmannian shallow and deep learning
6.7 Ensembles of coupled oscillators in ML: Beyond Kuramoto models
Examples
7.2 Linked robot’s arm (planar rotations)
7.3 Linked robot’s arm (spatial rotations)
7.4 Embedding multilayer complex networks (Learning coupled actions of Lorentz groups)
One favorable property of Kuramoto models is that many of them exhibit potential dynamics. As a rule, ensembles with identical oscillators and symmetric couplings are gradient flows (sometimes in more than one sense).
\
\ Underline that models on spheres, as well as on special orthogonal and unitary groups are gradient flows in the chordal metric on these manifolds [87]. On spheres, this is equivalent to the cosine metric.
\ Consensus algorithms on Grassmannian manifolds are gradient flows as well, we refer to [7] for an explanation.
\ The above observations further imply that by adding the noise in an appropriate way to the above systems we obtain various Langevin dynamics. For instance, (1) are Langevin dynamics for the potential
\
\ As already noted above, recent geometric investigations of Kuramoto models have shown that models on spheres (12) with global coupling induce gradient flows in hyperbolic balls [76]. In general, the question of hyperbolic gradient flows induced by Kuramoto models on spheres is still to be fully explored. In particular, it is interesting to examine conditions for the potential dynamics on hyperbolic multi-discs and multi-balls which are induced by the models with several sub-ensembles. The second interesting question is adding the noise in an appropriate way in order to obtain Langevin dynamics on hyperbolic balls.
\
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:::info Author:
(1) Vladimir Jacimovic, Faculty of Natural Sciences and Mathematics, University of Montenegro Cetinjski put bb., 81000 Podgorica Montenegro ([email protected]).
:::
:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
:::
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