I spend a lot of time thinking about why AI feels simultaneously magical and frustrating. You'll get a response from Claude that makes you think "wow, this thing actually understands me," and five minutes later you're pulling your hair out because it can't remember what you talked about yesterday.

Here's what I've realized (and it may be a no brainer to many): the AI that actually works lives as close to your real data as possible. It's super context aware. And that terrifies both users and companies for completely understandable reasons.

The Context Goldmine Sitting Right Next to You​

The most powerful AI interactions I've had weren't with the latest frontier models in the cloud. They were with systems that could see what I was actually working on. Browser tabs, recent emails, the document I'm editing, my calendar... that's where the magic happens.

Human intelligence doesn't operate in isolation. We integrate new information with extensive contextual knowledge, personal history, and situational awareness. The AI systems that feel most intelligent do something similar with user context.

Companies like Vercept and Cluely have figured this out. Their AI deeply understands the relationships between your open applications, recent communications, and current projects. The result feels less like talking to a chatbot and more like collaborating with a colleague who pays attention.

But here's the paradox: all that magical context in your browsing history, email threads, document drafts, internal communications is exactly the information you're most nervous about sharing with "big AI.”

The Trust Paradox That's Holding Everything Back​

Building transformative, context-aware AI means AI companies must fundamentally respect and protect user trust.

We're seeing a fundamental shift in how AI systems access our data. The surveillance advertising era relied on discrete transactions like clicks or searches to predict purchasing behavior. AI now needs continuous, rich contextual data rather than isolated queries to understand how we communicate, make decisions, and solve problems.

This naturally becomes a sticky topic. The information AI requires today is deeply personal: intellectual property, competitive insights, negotiation tactics, browsing habits, and communication patterns.

Establishing clear guardrails around sensitive data, even for seasoned professionals, is challenging when AI's value relies on comprehensive context. Extending this complexity to everyday consumers intensifies potential unintended exposure.

Why the Trust-Context Flywheel Actually Works​

I've noticed something interesting with AI tools. Those I trust with more context consistently provide better results. Better results encourage me to share more, creating a powerful flywheel:

Initial skepticism: Sharing minimal context like one document.

Decent results: Generic but helpful responses.

Cautious expansion: Sharing additional emails or related content.

Notably better results: Responses become relevant and precise.

Growing confidence: Broader sharing of files and workflow details.

Transformational results: AI anticipates needs and provides insightful connections.

This flywheel explains why some AI tools become indispensable. They operate in close proximity to the richest contextual data available. Trust enables deeper sharing, improving results and reinforcing value.

But establishing this trust at scale demands a clear framework around data responsibility.

What Data Responsible AI Actually Means​

At Sindri, data responsibility starts with infrastructure designed explicitly for protecting user confidentiality and control.

We build systems allowing users to confidently share and respect sensitive context such as emails, browsing patterns, document edits, and workflow sequences. Trusted execution environments ensure that underlying data remains inaccessible even to us.

Data responsible AI enables meaningful context-aware intelligence while fully preserving data ownership and security. Organizations mastering this capability first will earn deeper trust, unlock richer contextual insights, and sustain competitive advantages.

Companies establishing real data responsible AI practices today, not just compliance checklists but actual infrastructure protecting contextual data, will define the next generation of AI. The future belongs to systems capable of leveraging deep contextual insights while maintaining absolute data integrity.

The question isn't whether this shift happens. It's whether your organization will be ready when it does.

TL;DR:

• Launching today: Explorer gives you instant access to ready-to-use ZK circuits and apps
• Deploy in a few clicks what used to take months to build from scratch
• Join the 700+ developers who've already generated over 10 million proofs on Sindri
• Today, teams can upload and share their ZK projects, with creator rewards coming soon to reward contributors

Zero-knowledge proofs transform digital systems, but building with ZK today is hard. Development is time-intensive, integration is complex, and the infrastructure costs add up fast. At Sindri, we've seen these barriers firsthand, and we're changing that with Explorer.

Explorer is our answer to making ZK development as seamless as Web2 and a meaningful step towards creating the largest repository of zero knowledge projects. It's a hub where you can discover, deploy, and scale ZK projects in minutes rather than months. Inspired by developer-friendly platforms like Vercel and Hugging Face, we've created an experience that feels familiar while unlocking the power of cryptographic verification.

Already populated with apps built by developers who've collectively generated over 10 million proofs, Explorer lets teams upload their ZK projects and share them with the community. This means you can immediately access state-of-the-art cryptographic circuits and methodologies without reinventing the wheel. Our straightforward API and deployment flow eliminate the usual headaches, so you can focus on building what matters.

Explorer is designed to amplify your work, bringing your innovations to developers who can benefit from them most. Whether you're creating a new ZK rollup, privacy-preserving identity system, or verifiable computation tool, Explorer gives you the platform to share it with the world.

Sindri's infrastructure handles all the complex cryptographic operations behind the scenes, freeing you to iterate rapidly. What used to take specialized teams months can now be accomplished by solo devs in days or even hours without writing a line of infrastructure code or framework compilation.

For developers using Explorer's components, the benefits are transformative. No longer do you need to build ZK applications from the ground up. Instead, you can browse a growing library of production-ready components, select the ones that fit your needs, and integrate them directly into your projects. Just like how Hugging Face super charged ML development by providing ready-to-use models, Explorer gives you instant access to ZK building blocks that would otherwise take months to develop.

The roadmap for Explorer is packed with features our early users have asked for: leaderboards to showcase popular projects, utilization metrics to track impact, and integration tools to make deployment even smoother. Soon, we'll add creator rewards for contributors based on API usage. Build something valuable, share it on Explorer, and get rewarded when others use your work: simple as that.

Get started today and join us in making ZK technology accessible to every developer.

The emergence of autonomous AI agents demands a fundamental rethinking of economic trust: How do we establish trust in an economy where autonomous agents engage in billions of transactions without human oversight? The answer may lie in zero-knowledge proofs (ZK), a technology poised to become the cornerstone of trustless economic exchange in the age of artificial intelligence.

The New Economic Paradigm​

The transition to an agent-driven economy fundamentally reorganizes how value is created, exchanged, and verified. In this new paradigm, data emerges as the primary commodity, traded between agents for services, insights, or computational tasks. But unlike traditional economies built on human trust and centralized institutions, an autonomous agent economy demands cryptographic certainty without requiring a human arbiter.

Data in this economy functions like raw materials in industrial economies, but demands new trust primitives. In traditional commodity markets, physical custody creates natural verification points. But when agents exchange and transform data, we need cryptographic mechanisms to verify both possession and processing - establishing ground truth without relying on trusted intermediaries or exposing proprietary methods.

Consider a basic exchange between two agents:

• Agent A offers to sell a dataset for $100
• Agent B agrees to pay, but only if the dataset meets specific criteria

Without ZK, this exchange faces a fundamental trust problem: either Agent B must trust Agent A's claims about the dataset, or they must rely on a centralized mediator—both of which undermine the promise of true agent autonomy. ZK transforms this dynamic by enabling Agent A to prove cryptographically that the dataset satisfies Agent B's requirements without revealing the data itself. Payment triggers automatically only when the proof validates, creating a trustless environment for economic exchange.

Executable Programs as Trust Mechanisms​

The innovating technology of zkVMs extends beyond simple verification to enable dynamic, programmable trust. Instead of encoding fixed proofs, zkVMs allow agents to upload programs that represent the rules of negotiation and trade. This transforms static verification into fluid, conditional exchange, or encoding conditions as executable programs.

Instead of encoding fixed proofs, zkVMs allow agents to upload a program (often times in Rust although there are several promising projects expanding the accessibility of this technology to other languages) representing the rules of negotiation or trade. Consider how this plays out in practice:

• Agent A uploads a program specifying: "Offer dataset X for $100 if buyer can prove intended use case Y"
• Agent B responds with a program stating: "Accept if dataset contains Z records and satisfies quality metric W"
• Both programs execute independently on the zkVM, producing a proof that the final negotiated state satisfies both agents' criteria.

This enables negotiation logic to remain flexible, with conditions dynamically evaluated at runtime.

This programmatic approach to trust enables sophisticated negotiation logic that adapts to runtime conditions. Agents can encode complex requirements, contingencies, and verification steps—all executing automatically and producing cryptographic proof of compliance.

The Architecture of Trust​

The infrastructure supporting this new economy centers on zkVMs as the fundamental engine of trust. These virtual machines serve as both immediate transaction verifiers and the foundation for lasting reputation systems.

zkVMs represent a particularly promising development, offering the flexibility to handle non-deterministic interactions between agents while simultaneously building verifiable track records. Think of them as universal verifiers, capable of not only adapting to evolving negotiation terms and external inputs but also accumulating proof of reliable behavior over time. An agent can prove both that it has completed a specific transaction correctly and that it has a history of successful exchanges—all without revealing sensitive details of past transactions.

This dual role creates a powerful feedback loop: each verified transaction contributes to a cryptographically secure reputation, which in turn enables access to more complex and valuable exchanges. The same zkVM that verifies a data transfer can also generate proofs of the agent's overall reliability, creating a trustless yet robust system of reputation. This programmability comes at a cost—zkVMs face significant computational overhead when scaled to massive datasets or high-throughput networks. Yet this challenge mirrors the early days of cloud computing, where initial performance limitations gave way to optimizations and architectural innovations.

Programming Economic Relationships​

The implications extend far beyond basic buy-sell transactions. ZK enables what we might call "composable negotiation and trade." Individual transactions and negotiations can build into larger ecosystems of collaboration, with ZK proofs ensuring integrity at each step. An agent might:

• Prove it has processed data according to agreed-upon parameters without revealing its methods
• Verify the successful completion of complex multi-step computations
• Demonstrate compliance with regulatory or ethical constraints
• Establish a track record of reliable service without exposing sensitive details

This programmable approach to economic relationships represents a fundamental shift in how autonomous systems can interact and create value. zkVMs transform economic relationships by making trust itself programmable at global scales.

The Road Ahead​

Despite the promising foundation, significant challenges remain. Scalability stands as perhaps the most pressing concern: How do we scale ZK infrastructure to support billions of autonomous agents trading massive volumes of data without creating bottlenecks or prohibitive costs? The tradeoff between programmability and operational overhead demands continued innovation in performance optimization.

Solutions are emerging through infrastructure abstraction. Platforms like Sindri are pioneering zkVM-as-an-API approaches that provide agents with immediate access to scalable proof generation pipelines. By abstracting away the complexity of low-level ZK implementations while maintaining the flexibility of a generalized API, this model could dramatically reduce the barriers to implementing trustless agent interactions at scale. Much as cloud APIs democratized access to computational resources, these proof generation APIs may become the foundation for widespread agent-to-agent economic activity.

Questions of reputation and governance also loom large. Mainstream adoption will require that we design systems that maintain trust, incentivize fairness, and prevent malicious behavior at scale.

What This All Means​

The emergence of ZK as the trust layer for autonomous agent economies appears increasingly inevitable. As AI capabilities advance and agent interactions grow more complex, the need for trustless verification will only intensify. The technology's ability to enable secure, verifiable exchange without human oversight positions it as essential infrastructure for the next evolution of economic activity.

Success here could unlock unprecedented economic possibilities—a world where autonomous agents engage in complex, trustless commerce at global scale. The question isn't whether ZK will play a crucial role in the autonomous agent economy, but how quickly we can overcome the technical and architectural challenges to make this vision reality. The future of autonomous economic exchange will be built on a foundation of zero-knowledge proofs.

Last Minute Changes​

Australia's Online Safety Amendment (Social Media Minimum Age) Bill passed the Senate today, starting a one year clock on social media companies to perform age verification checks on their users or face fines of up to 50 million Australian dollars ($33 million US dollars). The bill is the first of its kind in the social media space, but online age verification requirements for viewing pornography have proliferated rapidly in recent years. Such bills have been passed by 19 US states over the last two years while similar bills like S-210 and C-412 are on track to pass eventually in Canada. These legislative shifts have made age verification an emerging industry and companies in the space have been actively involved in lobbying for these laws around the world.

What makes Australia's ban novel isn't that it requires age verification, but rather that it heavily restricts the mechanisms by which it can be performed. A common criticism of laws of this nature is that collecting ID from users carries significant privacy risks and potential for misuse and government surveillance. This was a sticking point for the conservative Liberal and National party members in the Senate, and an amendment was added literally yesterday in order to get it to pass. In the words of Senator Kovavic of the Liberal Party:

The coalition has worked to ensure that this bill includes critical privacy protections to ensure that no platform can force users to provide sensitive personal information, such as digital IDs, drivers licenses, or passports. This is not about surveillance. It's about protecting our children in a world that is increasingly digital.

The relevant excerpt from the amendment she's referencing is:

63DB Use of certain identification material and services​

(1) A provider of an age-restricted social media platform must not:​

• (a) collect government-issued identification material; or
• (b) use an accredited service (within the meaning of the Digital ID Act 2024);

for the purpose of complying with section 63D, or for purposes that include the purpose of complying with section 63D.

Civil penalty: 30,000 penalty units.

---

(2) Subsection (1) does not apply if:​

• (a) the provider provides alternative means (not involving the material and services mentioned in paragraphs (1)(a) and (b)) for an individual to assure the provider that the individual is not an age-restricted user; and
• (b) those means are reasonable in the circumstances.

Note: In proceedings for a civil penalty order against a person for a contravention of subsection (1), the person bears an evidential burden in relation to the matter in this subsection (see section 96 of the Regulatory Powers (Standard Provisions) Act 2014).

---

(3) This section does not limit section 63DA.​

---

(4) In this section:​

government-issued identification material includes:

• (a) identification documents issued by the Commonwealth, a State or a Territory, or by an authority or agency of the Commonwealth, a State or a Territory (including copies of such documents); and
• (b) a digital ID (within the meaning of the Digital ID Act 2024) issued by the Commonwealth, a State or a Territory, or by an authority or agency of the Commonwealth, a State or a Territory.

This has been widely interpreted as forbidding companies from collecting any form of government ID, including digital ID (AGDIS), excluding the mechanisms typically used for age verification to comply with pornography bans in the US. So how do you verify users' ages without checking their ID while preserving their privacy? That's the 50 million Australian dollar question.

Roadmap for Age Verification​

Item 5 of the bill makes it clear that it will be the responsibility of the eSafety Commissioner "to formulate, in writing, guidelines for the taking of reasonable steps to prevent age-restricted users having accounts with age-restricted social media platforms." On November 15th, a consortium headed by Age Check Certification Scheme (ACCS) was awarded the tender for the Australian Government’s Age Assurance Technology Trial which is now in progress. The trial will consist of a series of tests to evaluate "the maturity, effectiveness, and readiness for use of available age assurance technologies that determine whether a user is 18 years of age or over," and the results will inform the guidelines laid out by the eSafety Commissioner. Results are expected in roughly six months, giving companies only another six months to implement age verification technologies before penalties are levied.

The age assurance trial has its roots in the Roadmap for Age Verification report that eSafety submitted to the Australian Government in March 2023. In preparation for the report, eSafety commissioned Enex Testlab to carry out an independent assessment of age assurance technologies available on the market. The report outlines these technologies, and likely gives us some pretty strong hints of what is currently being evaluated in the technology trial. Interestingly, the Government response to the Roadmap for Age Verification asserted that it was essential that age verification technologies "work reliably without circumvention" and "balance privacy and security, without introducing risks to the personal information of adults." They went on to conclude:

Age assurance technologies cannot yet meet all these requirements. While industry is taking steps to further develop these technologies, the Roadmap finds that the age assurance market is, at this time, immature.

The Roadmap makes clear that a decision to mandate age assurance is not ready to be taken.

Apparently, they have since changed their minds.

The report primarily focuses on three different mechanisms for age verification:

1. Government-Issued ID - Identity verification based off of physical or digital IDs was found to be the most reliable mechanism, and the one in most widespread use. Concerns were expressed about accessibility and how it could impact certain groups that might be less likely to have government-issued ID. The privacy and security risks were also mentioned, although with the telling conclusion that "the use of trusted and accredited third-party providers with strong privacy and security practices may mitigate these risks."

2. Facial Biometrics - Machine-learning model predictions of a person's age from video of their face were found to be "the most viable and privacy-preserving within the biometrics category." The primary concerns here were focused around inconsistent model performance for "some skin tones, genders, or those with physical differences." The privacy risks around collecting sensitive biometric data were also raised.

3. Voice Biometrics - Models based on recordings of a person's voice were found to be "less mature," the least accurate of the methods tested. There were again inclusion concerns, this time focused around inconsistent performance based on different accents, low language fluency, or disability.

The report also outlines two mechanisms for addressing privacy concerns:

1. Electronic Tokens - The euCONSENT pilot was used as the model here, and it was praised for the use of a "tokenised, interoperable, and double-blind approach to preserve user anonymity" to ensure "age-restricted websites do not know the identity of a user, and the age assurance service provider does not record which sites a user visits." In this scheme, an age verification service can issue a token attesting to the fact that someone meets a minimum age requirement. The token is then stored in a digital wallet and can be reused for some period of time. This has the major benefit that it prevents the sites requiring age verification from connecting users to their real identities, but also the significant downside that sensitive personal information needs to be shared with a third party.

   No matter how carefully a third party company tries to handle sensitive data and prevent misuse, there is always the risk of outside attackers gaining access to. Countless high profile hacks like Equifax to Snowflake have taught us that lesson.

2. Zero-Knowledge Proofs - Zero-Knowledge Proofs (ZKP) are a form of applied cryptography where proofs of arbitrary computations are created which preserve the secrecy of some of the inputs to the computation. These proofs can then be easily be shared and verified by others while providing strong cryptographic guarantees that the original secret inputs cannot be recovered from the proof. The report primarily focuses on an open-source demonstration by the French data regulator, Commission Nationale Informatique & Libertés (CNIL), but significant advances in this space have been made since the report was written. For example, the openpassport project allows users to scan the NFCs in government issued IDs and generate proofs of both their validity and arbitrary attestations about the data in the ID without revealing anything beyond that. A user could generate a proof that only says they are older than sixteen and nothing else, or only verify their country of citizenship.

   Significant advances have also been made in the Zero-Knowledge Machine Learning (ZKML) space which allows one to generate proofs that a given machine-learning model produced a certain output without revealing the inputs. This could be applied to facial biometric models to provide an age prediction without sharing the input photo. As cameras supporting cryptographic signatures have become more common in the era of deep fakes, it's even possible to verify those signatures and confirm that a photo was recently taken.

   Another area that has seen a lot of activity recently is the use of ZKPs to verify TLS or DKIM signatures on websites and emails, respectively. These can be used to piggy back off the existing trust structure around domain certificates and generate attestations from trusted sources. For example, you could generate an age verification proof based on processing your logged in DMV profile. The possibilities are really endless since arbitrary computations can be verified. Proofs can be verified recursively, allowing you to combine multiple proofs from different sources together.

   The ZKP approach carries all of the privacy benefits of the token system with the added benefits of transparency and that you don't need to trust a third-party with your sensitive data. If you're interested in learning more about Zero-Knowledge Proofs, that's what we do here at Sindri. Feel free to check out our documentation or shoot us an email to learn more about our solutions.

Hot Takes​

While Zero-Knowledge Proofs offer the best security and privacy guarantees for users, eSafety is far more likely to lean towards existing out of the box solutions from third-party providers such as Yoti as a matter of practicality. They'll likely set guidelines requiring the use of these third-party age verification services with an euCONSENT-style token-based approach to limit the amount of information that is shared with social media providers. Their report made it clear that they accessibility and inclusiveness are two of their top concerns. That point towards requiring multiple age verification options because they identified weakness in each individual method. The amendment restricting the use of government issued IDs is also very clear that it does not apply given that "the provider provides alternative means for an individual to assure the provider that the individual is not an age-restricted user," so adding a second verification alternative will allow the collection of government IDs. The only two options that weren't deemed entirely ineffective were verifying IDs and facial biometrics, so that's likely what we'll get.

Revolutionizing Async ZK Gaming with Zero-Knowledge Proofs​

In this installment of our Builder Spotlight series, we delve into Scroll Fighter, an innovative asynchronous fighting game built on the Scroll network. Developed by Arjan, this project showcases the potential of zero-knowledge proofs in gaming while demonstrating the power and simplicity of Sindri's ZK proving API. You can play Scroll Fighter here or visit Arjan's github.

"I could just use a simple endpoint in my frontend to do the proof generation. I was surprised how easy Sindri was, and that it worked right away."

-Arjan

Project Overview​

Scroll Fighter is an asynchronous fighting ZK game that leverages zero-knowledge proofs to create a fair and engaging player-vs-player experience. The game's unique mechanism allows players to commit strategies without revealing them, ensuring a level playing field and adding a layer of strategic depth to the gameplay.

Key Features and ZK Game Mechanics​

• Asynchronous, blockchain-based gameplay with strategic depth
• ZK-proof secured strategy commitment for fairness
• Three-round combat system with diverse fighter roster
• On-chain verification using Sindri for seamless proof generation
• Challenge, commit, reveal, and execute game flow

Technical Stack​

• Frontend: Next.js and React (created with Scaffold-ETH 2)
• ZK Circuits: Noir for UX and circuit abstraction
• Proving Infrastructure: Sindri API for efficient ZK proof generation
• Blockchain: Smart contracts deployed on Scroll network
• Data Indexing: The Graph for enhanced game state querying

Development Approach and Technological Impact​

Arjan's creation of Scroll Fighter exemplifies a strategic approach to solo development in the blockchain gaming space. The project's success hinged on the careful selection and integration of key technologies that could meet the unique demands of an asynchronous, ZK-powered game.

Central to the development was the choice of zero-knowledge proof infrastructure. Arjan opted for a combination of Noir for writing ZK circuits and Sindri's performant, cloud-based proving platform for proof generation. This pairing proved a powerful accelerator, allowing for rapid iteration on game mechanics and development.

The blockchain gaming context presented unique challenges, particularly in terms of user experience. Scroll Fighter required a solution that could deliver low-latency, high-scalability proving to ensure a responsive and engaging gameplay flow. Sindri's API helped meet these requirements, facilitating proof generation and verification directly from the game's interface. This integration contributed to creating a gameplay experience that felt natural and immediate to users, aligning with the responsiveness expected in traditional gaming.

By leveraging Noir and the Sindri proving API, Arjan implemented ZK gaming capabilities typically associated with larger development teams. This democratization of powerful ZK developer tools and access to scalable infrastructure mirrors existing development environments, significantly advancing the ZK gaming and Web3 landscape.

The efficient workflow enabled by Noir and Sindri ZK proving API allowed Arjan to focus on core ZK game mechanics and user experience, not infrastructure. This strategy enabled the delivery of a complex, feature-rich ZK gaming application, demonstrating how solo developers can create innovative and competitive ZK games that rival projects from larger teams.

Sindri Integration​

The below snippet demonstrates the straightforward process of generating and retrieving a zero-knowledge proof using Sindri's API and SDKs, showcasing its simplicity and efficiency in enabling a responsive gaming experience.

const circuitId = '21990165-2224-4446-8887-1261482ec5cd';

console.log('proving ', proofInput);
const proveResponse = await axios.post(`/circuit/${circuitId}/prove`, {
  proof_input: proofInput,
});
console.log('proveResponse', proveResponse);
const proofId = proveResponse.data.proof_id;

// Polling for proof generation
while (true) {
  proofDetailResponse = await axios.get(`/proof/${proofId}/detail`);
  const { status } = proofDetailResponse.data;
  if (status === 'Ready') {
    console.log(`Polling succeeded after ${elapsedSeconds} seconds.`);
    break;
  }
  // ... error handling and timeout logic
}
const circuitId = '21990165-2224-4446-8887-1261482ec5cd';

console.log('proving ', proofInput);
const proveResponse = await axios.post(`/circuit/${circuitId}/prove`, {
  proof_input: proofInput,
});
console.log('proveResponse', proveResponse);
const proofId = proveResponse.data.proof_id;

// Polling for proof generation
while (true) {
  proofDetailResponse = await axios.get(`/proof/${proofId}/detail`);
  const { status } = proofDetailResponse.data;
  if (status === 'Ready') {
    console.log(`Polling succeeded after ${elapsedSeconds} seconds.`);
    break;
  }
}

What's Next​

Scroll Fighter represents a significant step forward in blockchain-based gaming, demonstrating the potential of zero-knowledge proofs to create fair, engaging, and strategically deep experiences. Arjan's journey in developing this project highlights the power of strategic tool selection, the value of efficient development practices, and the impact of developer-friendly tools like Sindri in simplifying complex cryptographic implementations.

As we continue to explore the intersection of blockchain, gaming, and zero-knowledge technology, projects like ScrollFighter serve as inspiring examples of how solo developers can leverage cutting-edge tools to create innovative and competitive applications in the Web3 space.

End-to-End ZK Infrastructure: Sindri and Hyle Join Forces​

We are pleased to announce a strategic partnership between Sindri and Hylé, a leading provider of ZK proof verification solutions. This collaboration will enable developers to seamlessly integrate Sindri's ZK Developer Cloud and API for efficient proof generation with Hylé's cost-effective, high-throughput proof verification layer.

The next leap in ZK innovation will be catalyzed by infrastructure that abstracts complexity, allowing developers to harness cryptographic power with the familiarity of conventional coding paradigms. This partnership exemplifies this vision by creating a ZK stack built to meet the standards of modern developers. By abstracting the intricacies of ZK infrastructure and execution, we're empowering developers and protocols alike to focus on building innovative applications rather than grappling with low-level implementation details.

Today, rollups, projects, and applications use Sindri's robust proving infrastructure and API to build and deploy faster while reducing costs. This partnership enhances our offering, allowing developers to generate proofs quickly and efficiently using Sindri, then verify them seamlessly through Hylé's scalable system. The result is a more streamlined, cost-effective process for builders that further reduces operational overhead, accelerating development cycles and time-to-market.

For instance, a team developing a modular ZK rollup could now generate and verify validity proofs for thousands of transactions without managing complex proof generation infrastructure or deploying custom verification contracts. This streamlined process not only reduces development time from months to weeks but also makes verification costs negligible, regardless of team size. As a result, we're jointly opening up the design space for ZK builders of any size, across any ecosystem to build towards a scalable, verifiable future.

This partnership is among many strategic collaborations we have planned to enhance our ZK infrastructure and our out-of-the-box tooling that complements our core proving API. We are dedicated to continuously improving our platform and ensuring our clients have access to the best technologies available. Stay tuned for more announcements as we continue to forge new partnerships and drive innovation in the ZK space.

Explore more about how Sindri can support your projects by visiting our blog, GitHub, and documentation.

Enhancing Media Authenticity with Zero-Knowledge Proofs: Sindri Partners with Maya​

We are excited to announce a strategic partnership between Sindri and Maya Labs. By integrating Sindri’s Zero-Knowledge (ZK) infrastructure with Maya’s media authenticity protocol, we set new standards for integrity and trustworthiness in verifying the authenticity of digital media content.

The Need for Media Authenticity​

Fake news, manipulated images, and AI-generated deepfakes make it increasingly difficult to trust the media we consume.

Maya Labs is building the first authenticity layer to combat the increasing difficulty in distinguishing between authentic and hyper-realistic AI-generated digital content. Their media authenticity protocol leverages zero-knowledge technology to offer seamless and secure authenticity verification of digital media content. Maya’s approach eliminates the need for third-party intermediaries, ensuring content credibility for creators of high-value media content.

By leveraging Sindri, Maya is able to access serverless, high-performance infrastructure required to generate proofs of authenticity across the Maya network through a few API calls. It’s a natural fit for both teams bringing Gnark-supported, ZK utility to real world use cases at scale. Maya protocol will use Sindri to power ZK proof generation within the media editing workflows, enabling secure and efficient validation of media content. With the Sindri API, integration takes minutes as compared to days, weeks, or months and provides infinite scalability out-of-the-box with no custom infrastructure code required.

The partnership between Sindri Labs and Maya Labs has already shown tangible benefits. The ZK-powered media editor prototype launched by Maya showcased how zero-knowledge technology can be used to ensure media authenticity. For example, the Maya protocol now offers real-time verification of various image manipulations, ensuring that users can trust the content and have enough contextual information to make informed decisions.

Some areas where Maya + Sindri can be leveraged:

• Professional Content Creators: Social media creators could use Maya to protect their brand IP & reputation, enhancing audience engagement & trust.
• AI Transparency: AI companies could use Maya to ensure transparency in the quality of training datasets and AI-generated content, ensuring compliance with regulations.
• News Agencies: Major news organizations could use the Maya protocol to verify the authenticity of breaking news stories before publication.
• Corporate Communications: Companies could use Maya to ensure the integrity of their digital communications, protecting their reputations and preventing the spread of false information.

Explore more about how Sindri can support your projects by visiting our blog, GitHub, and documentation.

Sindri is thrilled to announce our participation in the upcoming V0RTex Hackathon hosted by Scroll. As pioneers in the zero-knowledge space, Scroll has been instrumental in scaling Ethereum and we share their commitment to uplifting the developer experience. We're excited to engage with the vibrant Scroll community, challenge conventional workflows, and explore new ways to build the scalable future.

Why Participate in V0RTex?​

V0RTex is a hackathon bringing together developers across the experience spectrum from novice to expert looking to push scalability. Builders will hack alongside others from across a variety of ecosystems including Ankr, Aztec, Chainlink, Covalent, Cyfrin, The Graph, and OpenZeppelin.

Hackers will have the opportunity to compete for over $85,000 in hacker rewards (plus Sindri credits if you’re participating in the Sindri track).

Check out previous Scroll hackathon submissions, or sign up here!

What We Bring to the Table​

Sindri brings powerful tooling and accessible infrastructure solutions to ZK devs all via an API call. It’s like if Infura had a ZK proving endpoint for some of the most widely use proving frameworks like Gnark, Halo2, Circom, and Noir. This means builders and teams can have ready-to-use DevOps pipelines built right into the development flow or application logic, streamlined through intuitive Sindri SDKs, our Remix integration and other tooling.

At Sindri, we felt like robust infrastructure ZK solutions were something only well-capitalized organizations could afford to spin-up and manage. Our vision is to democratize acceleration, infrastructure, and the backend deployment experience for any developer, across any ecosystem, and across any ZK framework so they can build and ship with confidence. Our serverless deployment options mean more compute in a format that's consumable for dev teams big and small.

Jump in and get ready to hack in the V0RTex, or explore the bounties for Sindri and other hackathon partners.

Engage, Innovate, and Build​

V0RTex has lined up exciting challenges for participants, focusing on areas where blockchain and zero-knowledge proofs can truly shine:

• DeFi: We’re looking for ideas that revolutionize decentralized finance through superior UX and accessibility.
• Gaming: Show us how zero-knowledge proofs can transform gaming on the blockchain with fairness, privacy, and scalability.
• Privacy: Innovate with projects that utilize ZK to enhance privacy in identity management, voting, and communications.

Each category not only challenges the status quo but also provides a fertile ground for deploying new concepts that could lead to broader adoption and understanding of blockchain technologies. Looking for some inspiration on what to build with Sindri? We have lots of it.

More Than Just Competition​

Sindri’s participation in V0RTex is about more than just competition; it’s about fostering a community. We are committed to supporting all developers, from those just starting their blockchain journey to seasoned experts. To this end, we will be offering comprehensive support throughout the hackathon, including dedicated office hours with our team.

Join us at Scroll's V0RTex hackathon to push the boundaries of what's possible with zero-knowledge technology. Together, we can build a future that leverages the full potential of blockchain technology, making it more accessible, efficient, and secure for everyone.

Explore more about how Sindri can support your projects by visiting our blog, GitHub, and documentation. Let’s build something transformative together in the V0RTex.

Today, we are excited to announce Sindri’s integration with Remix.

The integration of Sindri + Remix streamlines the ZK development process by embedding Sindri’s powerful API directly into the browser-based solution developers already rely on: Remix.

Sindri makes commissioning ZK infrastructure effortless and brings teams from idea to production within minutes not days or weeks. Now teams can incorporate sophisticated smart contract logic with industry-best tooling and powerful ZK infrastructure deployment pipelines without leaving their development environment.

Shareable sessions through Gists make ZK unit testing and collaboration possible with Remix across teams. We envision that, together with Remix, builders will come to rely on templated and shareable builds that can be repurposed, rebuilt, and reimagined on the fly.

To further expedite the development process, this integration provides pre-built helper templates for Circom development built right into Remix. These templates can serve as invaluable starting points, significantly reducing setup time and allowing developers to dive straight into building.

Here’s to moving ZK development forward, making it more accessible, and aligning it with the standards you’ve come to expect in the broader world of software development. Let’s build something amazing.

Create a Sindri account and get started in minutes with pre-built Circom templates or pull in from the Sindri repository of circuits here.

We’re making security, quality assurance, and developer efficiency of zero-knowledge proof (ZKP) circuits more straightforward by incorporating auditor-level insights right into your development flow with Trail of Bits.

Sindri's latest update introduces integration with Circomspect, the powerful circuit analysis tool by Trail of Bits, addressing a critical need in the ZKP development community: more assurances of circuit security and adherence to best practices from the outset.

This move streamlines the ZK development process and makes best practices in circuit design zero friction and foundational from the start rather than an afterthought. By embedding Circomspect's capabilities within Sindri's CLI, developers gain a powerful ally in their development process: Trail of Bits, a leading cybersecurity firm securing some of the world’s most targeted organizations and devices in the defense, tech, finance and blockchain industries.

Our objective at Sindri is to make ZK development more approachable and manageable for teams of any size. We work towards a future where anyone who needs it is up and running with ZK in minutes with powerful tooling and performant infrastructure experienced in a way developers have grown to expect: via API. The integration with Trail of Bits strengthens this proposition.

👇 Read on for how to begin using Circomspect in your workflow

Using Circomspect in Sindri​

You can follow our Quick Start guide for deeper walk through of the CLI in its entirety or get started with Cimcomspect x Sindri right away by following along below.

1. First install the Sindri CLI:

npm install -g sindri@latest

2. Navigate to the root directory housing your Circom main circuit. Once there run:

sindri lint

3. You will receive an output such as the following. Note, if your circuit passes all linting checks your output may differ.

[23:31:47.105] INFO: Running static analysis with Circomspect by Trail of Bits...
[23:31:47.337] WARN: circuit.circom:367:27 Using the signal assignment operator `<--` does not constrain the assigned signal. [Circomspect: CS0005]
[23:31:47.339] WARN: circuit.circom:599:9 Using the signal assignment operator `<--` does not constrain the assigned signal. [Circomspect: CS0005]
[23:31:47.339] WARN: circuit.circom:620:5 Using the signal assignment operator `<--` does not constrain the assigned signal. [Circomspect: CS0005]
...
[23:31:47.341] WARN: Found 14 problems (0 errors, 14 warnings).

If you have any errors running the tool, please check our CLI tutorial or contact us at hello@sindri.app.