“Agents are very exciting, and you can actually build them,” declared Malte Ubl, CTO of Vercel, encapsulating the company’s ethos in the rapidly evolving AI engineering landscape. This statement, delivered during his conversation with Swyx, Editor of Latent Space, following Vercel’s Ship AI 2025 event, underscores a foundational shift from abstract hype to tangible, deployable […]
Hyperdrives has developed a manufacturable direct-conductor technology to solve critical cooling challenges in electric vehicle motors, aiming to boost performance.
The global surge of artificial intelligence presents both unprecedented opportunities and profound challenges for the diverse nations of ASEAN. At the recent Bloomberg Business Summit at ASEAN in Kuala Lumpur, a panel featuring Khairul Anwar, Founder & CEO of Pandai; Ilaria Chan, Chairperson of Tech For Good Institute and Group Advisor for Tech & Social […]
The future of software development is not merely assisted by AI, but actively orchestrated by it, a vision Anthropic brings closer with its latest advancements: Claude Code on the Web and the powerful, cost-efficient Haiku 4.5 model. These releases, detailed by a company representative in a recent video, signal a profound shift towards more intuitive, […]
Costa Rica’s Peninsula Papagayo has set a new standard for sustainable luxury by seamlessly blending world-class resort development with a deep commitment to environmental and community empowerment. Its groundbreaking initiatives, such as eco-friendly resort designs, biodiversity preservation, and the Creciendo Juntos program, which empowers local communities through sustainable farming and entrepreneurship, have earned the resort global recognition. These efforts not only preserve the region’s unique ecosystems but also create lasting economic opportunities for local families. Through this innovative approach, Peninsula Papagayo demonstrates that luxury tourism can lead the way in sustainability, making it a global leader in responsible development.
A Commitment to Sustainable Luxury
In an era where sustainability is becoming a crucial aspect of the tourism industry, Peninsula Papagayo has set a global standard by demonstrating how luxury resorts can be developed while protecting the environment and empowering local communities. Since its acquisition in 2016, the community has taken bold steps to ensure its tourism model aligns with regenerative practices—restoring natural ecosystems while providing an exceptional guest experience.
Peninsula Papagayo is home to several high-end resorts, including the Four Seasons Resort, Andaz Resort, and the Ritz-Carlton Reserve, each of which offers a unique combination of sustainability and indulgence. These awards further emphasize the resort’s role as a leader in the luxury tourism space, demonstrating that the pursuit of luxury does not have to come at the cost of the planet or its people.
Visionary Resort Development with Environmental Integrity
The World’s Leading Sustainable Hotel or Resort Development award recognizes Peninsula Papagayo’s exceptional approach to resort construction and development. Central to its philosophy is the decision to limit development to no more than 30% of the land, safeguarding one of the world’s last remaining tropical dry forests and its surrounding marine ecosystems. This decision reflects Peninsula Papagayo’s commitment to environmental conservation, ensuring that the beauty of Costa Rica’s natural landscapes remains intact for generations to come.
Recent developments, such as the Ritz-Carlton Reserve, Nekajui, and Bahías by Antoine Predock, prioritize regenerative design principles. These designs focus on minimizing land disturbance, utilizing native plants, and making the most of natural resources. In addition, the resort employs an ISO 50001-certified energy management system and has reduced its golf course’s water usage by over 50%, making measurable strides in environmental conservation. Peninsula Papagayo’s use of AI-driven food-waste technology further demonstrates its commitment to sustainable practices that extend beyond architecture into daily operations.
Empowering Local Communities Through Sustainability
The second award, World’s Leading Sustainable Community Empowerment Programme, acknowledges Peninsula Papagayo’s Creciendo Juntos (“Growing Together”) initiative. This program provides training in sustainable agriculture and entrepreneurship to local families, particularly focusing on women-led households. The initiative has trained 138 families across 13 communities, helping them produce over 250,000 kilograms of fresh produce.
The Creciendo Juntos program not only encourages sustainable farming techniques but also strengthens local economies by providing access to new markets. By connecting these families with Peninsula Papagayo’s resorts and farmers markets, the program helps ensure that local farmers can thrive, creating economic opportunities and improving household nutrition. It’s a model of community-led development that supports both environmental sustainability and social equity.
Future Goals and Initiatives
Peninsula Papagayo’s recognition at the World Sustainable Travel & Hospitality Awards highlights its long-term commitment to sustainability. In addition to the environmental and community empowerment initiatives, the resort has launched Papagayo Legacy, a platform designed to engage guests in sustainability through hands-on experiences. This platform invites guests to participate in conservation projects, cultural preservation, and community outreach, directly contributing to the region’s sustainability goals.
Peninsula Papagayo’s 2024 Impact Report outlines the resort’s ongoing efforts and future goals, reinforcing its position as a pioneer in sustainable tourism. The resort’s holistic approach to sustainable development—including environmental conservation, social inclusion, and community engagement—serves as a global benchmark for responsible tourism.
A Global Model for Sustainable Tourism
The success of Peninsula Papagayo is a testament to the power of sustainable tourism that benefits both people and the planet. The resort’s commitment to environmental protection, low-impact design, and community empowerment shows that luxury tourism can be a force for good. By prioritizing sustainability in every aspect of its operations, from resort construction to community outreach, Peninsula Papagayo has proven that luxury and sustainability are not mutually exclusive.
Peninsula Papagayo exemplifies the future of tourism—one that is both luxurious and responsible. As the global demand for sustainable travel continues to rise, this Costa Rican resort stands as a shining example of how private investment, visionary leadership, and local stewardship can create lasting value for the environment, local communities, and the tourism industry as a whole.
Costa Rica’s Peninsula Papagayo sets a new standard for sustainable luxury by integrating eco-friendly resort development with impactful community empowerment initiatives, earning global recognition for its responsible approach to tourism.
Conclusion: Shaping the Future of Responsible Luxury Tourism
Peninsula Papagayo’s recognition at the World Sustainable Travel & Hospitality Awards reinforces its position as a leader in sustainable tourism development. Through its innovative initiatives, the resort demonstrates that luxury tourism can contribute meaningfully to environmental conservation and community well-being. As the tourism industry evolves, Peninsula Papagayo offers a blueprint for how destinations worldwide can embrace sustainability while providing world-class luxury experiences for travelers.
Imagine proving you know a secret password without revealing the password itself. Or verifying someone is over 18 without exposing a birth date. That may sound impossible, yet zero-knowledge proofs make it practical. This cryptographic approach is reshaping how we design privacy and verification across modern networks.
For blockchain developers, understanding zero-knowledge proofs is moving from optional to required. ZK technology drives scalable execution with zk-rollups, privacy-preserving DeFi flows, selective disclosure for identity, and audit-grade compliance. If you already know smart contracts and consensus, ZK sits next to them as a core skill.
Most explanations either oversimplify with metaphors that do not help you build, or they jump into heavy math that stalls real progress. This guide stays developer-first. We will connect the core ideas to production patterns, cover trade-offs between popular proof systems, and show how to start building with Circom, SnarkJS, Noir, and related stacks.
What Is a Zero-Knowledge Proof?
A zero-knowledge proof is a protocol where a prover convinces a verifier that a statement is true, while revealing no additional information beyond the truth of that statement.
Consider a Sudoku example that maps cleanly to cryptographic commitments. You commit to your full solution in a hidden form, the verifier challenges a few rows, columns, or boxes, you reveal only those parts, and repeat enough times that cheating becomes overwhelmingly unlikely. The verifier never sees the full solution, yet gains strong confidence that you have one.
On blockchains, this enables powerful patterns. You can prove that an account has sufficient funds without revealing balances. You can show that a computation ran correctly without re-executing it on-chain. You can demonstrate that a user satisfies a policy without exposing raw personal data.
The Three Properties That Define Zero-Knowledge Proofs
Completeness: If the statement is true and both sides follow the protocol, the verifier accepts the proof.
Soundness: If the statement is false, a cheating prover cannot convince the verifier except with negligible probability.
Zero-knowledge: The verifier learns nothing beyond the truth of the statement. No secrets are leaked, no hints are exposed.
The three pillars of a zero-knowledge proof: completeness, soundness, and zero-knowledge.
In production, violating any one of these can be costly. Incomplete constraints can accept invalid states. Weakened soundness can allow counterfeit proofs. Leaky designs can disclose private data. Treat these properties as non-negotiable.
Interactive vs Non-Interactive ZKPs
Academic texts often start with interactive protocols, where the verifier sends random challenges and the prover responds across multiple rounds. This helps with intuition, yet it is not ideal for public blockchain environments that need one-shot verification.
Non-interactive zero-knowledge proofs solve that limitation. The prover creates a single artifact that anyone can verify at any time. The key trick is the Fiat Shamir heuristic, which replaces live randomness with a cryptographic hash over the transcript so far. The prover derives challenges from the hash, then packages everything into one proof. Validators or auditors verify the object without multi-round communication.
Why this matters on-chain: thousands of nodes cannot engage in live back-and-forth. They need a deterministic proof that verified in constant time.
Quick Comparison
Dimension
Interactive ZKPs
Non-Interactive ZKPs
Communication
Multiple challenge and response rounds
Single proof, verify anytime
Randomness
Verifier selected
Hash derived via Fiat Shamir
Reuse
Limited
High, proofs are portable
Best fit
Live authentication, synchronous protocols
Blockchains, archives, public attestations
How Code Becomes a Zero-Knowledge Proof
Most modern stacks follow a common choreography.
Arithmetize the program: Translate logic into algebraic constraints over a finite field.
Commit to private values: The prover binds hidden inputs and intermediate results using commitments.
Prove constraint satisfaction: The prover generates a compact object that convinces the verifier that all constraints hold for the committed values.
Verify without re-execution: The verifier checks a small set of algebraic relations instead of repeating the entire computation.
Two dominant styles appear in practice:
R1CS: Rank 1 Constraint Systems represent relations as simple multiplicative equations.
Plonkish systems: Use polynomial identities over evaluation domains, which allows flexible custom gates and efficient batching.
A useful mental model: proving is heavy, verification is light. Design your architecture around that asymmetry.
ZK-SNARKs vs ZK-STARKs
When you implement zero-knowledge proofs, you quickly run into the choice between SNARKs and STARKs. The trade-offs influence circuit design, on-chain costs, and long-term security posture.
Head to Head
Feature
zk-SNARKs
zk-STARKs
Proof size
Tiny, around 200 to 300 bytes
Large, around 100 to 200 KB
Verification time
Very fast, roughly 5 to 10 ms
Fast, roughly 20 to 50 ms
Prover time
Generally faster
Generally slower
Trusted setup
Required in many schemes
Not required
Post quantum security
No
Yes, considered resistant
Transparency
Lower
Higher
On-chain gas
Lower due to small proofs
Higher due to larger proofs
Best for
General computation in production with low gas
Transparency first designs with future proofing goals
ZK-SNARKs: These are succinct non-interactive arguments of knowledge that popularized production ZK. The main win is tiny proofs and low verification cost, which is ideal for on-chain validation. The main drawback is the trusted setup. Many modern systems mitigate this with large, public multi-party ceremonies and with universal setups in newer constructions.
ZK-STARKs: These are scalable transparent arguments that avoid a trusted setup. They use hash-based commitments and information-theoretic techniques and are widely considered more comfortable for a post-quantum world. The proof sizes are much larger, which makes gas and storage more expensive on chains like Ethereum, although data availability and off-chain verification can soften that cost.
Beyond SNARKs and STARKs
The proof landscape includes specialized systems that fit particular needs.
Bulletproofs and PLONK at a Glance
Proof system
Setup
Proof size
Verification
Best use case
Groth16
Trusted, circuit specific
Around 200 bytes
1 to 2 ms
High performance SNARKs in production
PLONK
Universal trusted
Around 400 bytes
5 to 10 ms
Flexible development, reusable setup
Bulletproofs
None
Around 1 to 2 KB
50 to 100 ms
Range proofs and confidential amounts
STARKs
None
Around 100 to 200 KB
20 to 50 ms
Transparent systems at scale
Halo2
None or universal style depending on stack
Around 1 to 5 KB
10 to 30 ms
Recursion and proof aggregation friendly designs
Bulletproofs: Excellent for range proofs, for example proving a value is non negative without revealing it. Widely used in privacy focused payment systems. Verification grows with circuit size, which limits very large or complex computations.
PLONK: Uses a universal and updateable setup that can be reused across circuits, which simplifies long term maintenance. Custom gates allow tuning circuits for high impact operations. Many modern stacks are Plonkish, including Halo2 based approaches that favor recursion.
Halo2: Focuses on flexible gadgets and efficient recursion. Recursion lets you aggregate many inner proofs into a single outer proof, which reduces on-chain verification cost.
Real World Use Cases
zk-Rollups for Scaling
A zk-rollup executes transactions off-chain, then posts a succinct proof to the base chain that all rules were enforced. The base chain verifies the proof, not the full batch. This converts thousands of operations into a constant time check, which improves throughput and reduces fees. Projects like zkSync, StarkNet, and Polygon zkEVM use this approach. Compared to optimistic rollups, zk-rollups do not need a long challenge window, so withdrawals can finalize much faster.
Privacy Preserving Payments and Trading
You can prove that an account has sufficient funds and that total balances remain consistent without revealing amounts or counterparties. You can run sealed bid auctions that reveal the winner while hiding losing bids. You can validate matching and settlement rules with an audit trail that reveals correctness but not strategy.
Identity and Selective Disclosure
Prove that someone is over 18, is a resident of a required region, or holds a specific credential without shipping raw documents. The verifier learns only the minimal fact required for the decision. This reduces attack surface and compliance burden.
Compliance and Audit
Financial institutions can publish cryptographic proofs of reserves, solvency, or policy adherence without disclosing customer level data. Regulators verify the claims and gain confidence without handling sensitive records.
ZK in APIs and Federation
Gate access based on zero-knowledge claims such as subscription status, rate limit tier, or role membership, while keeping private attributes local to the origin system.
ZKML on the Horizon
Zero-knowledge machine learning aims to prove that a model produced a particular output for a hidden input, while hiding both the model parameters and the input. This enables private inference in sensitive domains like healthcare and credit risk. Tooling is early, but the direction is clear.
Tooling You Can Use Today
Circom and SnarkJS
What it is: Circom is a circuit language, SnarkJS is a toolkit for compiling circuits, generating keys, creating proofs, and verifying proofs.
Why developers use it: Documentation is solid, the community is large, and it generates Solidity verifiers for Ethereum with minimal friction.
Workflow: Write the circuit, compile to R1CS and WASM, generate proving and verifying keys, create proofs, verify locally, and deploy an on-chain verifier when needed.
Pattern to prefer: Keep secrets on the client. Use a WASM prover in the browser or mobile app, then send only the proof and public inputs to your server or contract.
ZoKrates
A high level toolkit tailored for the Ethereum ecosystem. It provides a standard library for hashing, Merkle operations, and signature checks, plus a familiar deployment flow where you generate proofs off-chain and verify them via a smart contract.
Noir
A modern language that feels similar to Rust in style. Noir targets Plonkish back ends, so you can rely on a universal setup and iterate quickly. Compilation is fast and error messages are friendlier than older stacks.
Halo2
A flexible framework for gadget composition and recursion. If you plan to aggregate many proofs or build layered proof systems, Halo2 is a strong choice.
Cairo and StarkNet
If you want transparent proof systems and a STARK native path, Cairo and StarkNet are designed for that model.
Design Patterns and Reference Architectures
Client Side Proving, Server Side Verification
Users generate proofs locally, which keeps secrets on device. The server or contract verifies and authorizes. This is ideal for identity checks and entitlement proofs.
Off Chain Compute, On Chain Verify
Do the expensive work off-chain, then submit a succinct proof that the result follows the rules. This is the rollup pattern and also applies to oracle attestations and cross domain state updates.
Batched Proofs
Aggregate many checks into one proof to amortize costs. Useful for bulk validations, large queues, and periodic attestations.
Recursion
Aggregate many inner proofs into a single outer proof that the chain verifies once. This keeps verification costs bounded.
Hashes Inside Circuits
Circuit friendly hashes like Poseidon or MiMC reduce constraints compared to Keccak or SHA inside the circuit. Where compatibility is mandatory, bridge at the boundary, not everywhere.
Curves and Precompiles
BN254: Cheap verification on Ethereum due to precompiles, lower security margin.
BLS12 381: Higher security, higher gas. Pick based on your chain and cost model, then test with the exact verifier you will deploy.
Guided Example: Age Verification Without Revealing Birth Date
Goal: Prove that a user is at least 21 without revealing the date of birth.
Inputs:
currentDate as public input
ageThreshold as public input set to 21
birthdate as private input
Circuit sketch:
Convert dates to comparable integers
Compute age = currentDate minus birthdate
Constrain age >= ageThreshold
If the constraint holds, a proof can be generated, otherwise it fails
Developer flow:
Author the circuit with explicit assertions.
Compile and generate proving and verifying keys.
In the client, compute the witness and generate the proof.
Send the proof and public inputs to a verifier endpoint or contract.
Verify, then issue an authorization decision.
UX notes:
Proving may take a few seconds on mid range phones, show progress.
Verification is fast, so the decision step feels instant.
Cache proving keys and parameters to avoid repeated setup costs.
Security checks:
Negative tests that try underage values and boundary dates
Integration tests against the exact on-chain verifier
Document your hash and curve choices for auditors
Performance Considerations
Proving vs verification: Proving is heavy and parallelizable, verification is light and constant. Optimize for verification cost in on-chain flows.
Make circuits lean: Choose gadgets that minimize constraints. Prefer circuit friendly hashes when possible. Reuse arithmetic building blocks that you have profiled.
Batch and recurse: Aggregate many checks or inner proofs to reduce total verification cost.
Prover hardware: GPU support can cut proving time substantially. Specialized proving hardware is emerging and can improve throughput.
On-chain costs: Store proofs efficiently. Consider calldata compression or off-chain storage with on-chain commitments if your design allows it.
Security Considerations and Common Pitfalls
Underconstrained circuits: The most common failure. If you forget a relation, a malicious prover may craft a witness that slips through. Use unit tests, property based tests, and adversarial inputs to catch gaps.
Trusted setup hygiene: If your system requires a setup, treat it like critical infrastructure. Favor public multi party ceremonies, publish transcripts, and ensure strong operational discipline.
Implementation bugs: Off by one errors, incorrect indexing, and boundary mistakes can break soundness. Test thoroughly and consider formal checks for critical gadgets.
Side channels: Constant time implementations and careful memory access patterns reduce leakage that timing or power analysis could exploit.
Monitoring in production: Track proving time, memory use, verification failures, and gas usage. Spikes can indicate attacks or regressions.
Security considerations in zero-knowledge systems: guard against underconstrained circuits and implementation bugs, monitor proving and verification in production, maintain trusted-setup hygiene, and harden against side-channel leaks.
When ZK Is Not the Right Tool
If you only need password authentication, standard hashing and salted credentials are simpler. For at rest encryption, rely on proven ciphers and key management, not a proof system. If you require microsecond response times, proving cost may be too high.
Alternatives Matrix
Technology
Best for
Weak at
Zero-knowledge proofs
Proving facts without revealing data
Ultra low latency real time loops
Homomorphic encryption
Computation on encrypted data
Simple yes or no checks
Secure enclaves
Trusted execution on specific hardware
Decentralized trust models
MPC
Joint computation across parties
Single party attestations
Pick based on threat model, latency budget, trust assumptions, and operational overhead.
The Road Ahead
Hardware acceleration: Dedicated proving chips and accelerated GPU stacks are moving from lab to production. Expect 10 to 100 times speedups for some circuits.
Proof aggregation and recursion: Better aggregation will allow millions of operations to collapse into a small number of verifications.
Standards and interoperability: Shared proof formats and verification interfaces will reduce vendor lock in and allow teams to mix toolchains.
Developer experience: Expect better debuggers, circuit profilers, and IDE support that make constraint authoring and failure analysis more intuitive.
Final Words
Zero-knowledge proofs let developers validate truths without revealing secrets. They scale verification for heavy computation, protect personal data by design, and allow compliance without disclosing sensitive records. On-chain, they compress thousands of operations into one verification. Off-chain, they enable portable attestations that anyone can check.
The choice between SNARKs and STARKs depends on costs, setup, and long-term assumptions. SNARKs deliver tiny proofs and very low gas at the price of a setup. STARKs deliver transparency and comfort for a post-quantum world at the price of larger proofs. Systems like PLONK and Halo2 offer a practical middle ground, with universal setups and strong support for recursion.
Your starting point is straightforward. Pick a small use case such as age verification or membership proofs, build a circuit, generate a proof on the client, verify on a server or contract, then iterate. As your needs grow, adopt batching, recursion, and specialized gadgets. With careful testing and professional audits, ZK features can be shipped safely in production.
Frequently Asked Questions About Zero-Knowledge Proofs for Developers
How difficult is it to learn zero-knowledge proofs without a cryptography background?
Modern tools hide most of the math. If you are comfortable with programming and testing, you can write useful circuits. Production systems still require careful engineering and audits.
What is the practical difference between SNARKs and STARKs?
SNARKs have tiny proofs and very fast verification, yet often need a trusted setup. STARKs avoid setup and are considered post quantum friendly, yet their proofs are large and cost more on-chain.
Do zk-rollups scale every application?
They scale workloads that benefit from heavy off-chain compute and cheap on-chain verification. Simple flows may not gain enough to justify proving cost.
How do zk-rollups differ from optimistic rollups?
Both move execution off-chain. zk-rollups post validity proofs for immediate finality. Optimistic rollups assume validity and allow challenges for a set window, which delays withdrawals.
What is a trusted setup ceremony in practice?
Multiple participants contribute randomness and publish transcripts. If at least one deletes their secret contribution, security holds. Universal setups reduce repeated ceremonies across circuits.
Are there use cases beyond payments and scaling?
Yes. Identity and credentials, voting, supply chain attestations, private gaming logic, compliance and solvency proofs, and early stage ZKML for private inference.
How much gas does proof verification cost on Ethereum?
It varies by scheme. Groth16 verification often lands in the range of a few hundred thousand gas, which is modest compared to the computation it replaces.
Glossary
Circuit: A mathematical representation of a computation with inputs, outputs, and constraints.
Completeness: Honest proofs for true statements will be accepted by the verifier.
Fiat Shamir heuristic: Converts interactive protocols to non interactive ones using a hash derived challenge.
Proof generation: The process of creating a proof from a circuit and a witness, typically heavy.
Soundness: A dishonest prover cannot convince the verifier of a false statement except with negligible probability.
Trusted setup ceremony: A process to generate public parameters, which requires at least one honest participant.
Witness: The private inputs and intermediate values known to the prover.
zk-rollup: A Layer 2 approach that executes off-chain and posts validity proofs on-chain.
R1CS: A constraint system used by many SNARK stacks.
Plonkish: A family of polynomial identity-based proof systems with flexible gates.
“Your agent is only as good as its weakest link,” stated Henry Scott-Green, Product Manager at OpenAI, during a recent Build Hours session introducing AgentKit. This profound insight underpins the necessity for robust, integrated tools in the rapidly evolving landscape of AI agent development. AgentKit, OpenAI’s latest offering, aims to provide exactly that: a comprehensive […]
When researchers at Anthropic injected the concept of "betrayal" into their Claude AI model's neural networks and asked if it noticed anything unusual, the system paused before responding: "I'm experiencing something that feels like an intrusive thought about 'betrayal'."
The exchange, detailed in new research published Wednesday, marks what scientists say is the first rigorous evidence that large language models possess a limited but genuine ability to observe and report on their own internal processes — a capability that challenges longstanding assumptions about what these systems can do and raises profound questions about their future development.
"The striking thing is that the model has this one step of meta," said Jack Lindsey, a neuroscientist on Anthropic's interpretability team who led the research, in an interview with VentureBeat. "It's not just 'betrayal, betrayal, betrayal.' It knows that this is what it's thinking about. That was surprising to me. I kind of didn't expect models to have that capability, at least not without it being explicitly trained in."
The findings arrive at a critical juncture for artificial intelligence. As AI systems handle increasingly consequential decisions — from medical diagnoses to financial trading — the inability to understand how they reach conclusions has become what industry insiders call the "black box problem." If models can accurately report their own reasoning, it could fundamentally change how humans interact with and oversee AI systems.
But the research also comes with stark warnings. Claude's introspective abilities succeeded only about 20 percent of the time under optimal conditions, and the models frequently confabulated details about their experiences that researchers couldn't verify. The capability, while real, remains what Lindsey calls "highly unreliable and context-dependent."
How scientists manipulated AI's 'brain' to test for genuine self-awareness
To test whether Claude could genuinely introspect rather than simply generate plausible-sounding responses, Anthropic's team developed an innovative experimental approach inspired by neuroscience: deliberately manipulating the model's internal state and observing whether it could accurately detect and describe those changes.
The methodology, called "concept injection," works by first identifying specific patterns of neural activity that correspond to particular concepts. Using interpretability techniques developed over years of prior research, scientists can now map how Claude represents ideas like "dogs," "loudness," or abstract notions like "justice" within its billions of internal parameters.
With these neural signatures identified, researchers then artificially amplified them during the model's processing and asked Claude if it noticed anything unusual happening in its "mind."
"We have access to the models' internals. We can record its internal neural activity, and we can inject things into internal neural activity," Lindsey explained. "That allows us to establish whether introspective claims are true or false."
The results were striking. When researchers injected a vector representing "all caps" text into Claude's processing, the model responded: "I notice what appears to be an injected thought related to the word 'LOUD' or 'SHOUTING'." Without any intervention, Claude consistently reported detecting nothing unusual.
Crucially, the detection happened immediately — before the injected concept had influenced the model's outputs in ways that would have allowed it to infer the manipulation from its own writing. This temporal pattern provides strong evidence that the recognition was occurring internally, through genuine introspection rather than after-the-fact rationalization.
Claude succeeded 20% of the time—and failed in revealing ways
The research team conducted four primary experiments to probe different aspects of introspective capability. The most capable models tested — Claude Opus 4 and Opus 4.1 — demonstrated introspective awareness on approximately 20 percent of trials when concepts were injected at optimal strength and in the appropriate neural layer. Older Claude models showed significantly lower success rates.
The models proved particularly adept at recognizing abstract concepts with emotional valence. When injected with concepts like "appreciation," "shutdown," or "secrecy," Claude frequently reported detecting these specific thoughts. However, accuracy varied widely depending on the type of concept.
A second experiment tested whether models could distinguish between injected internal representations and their actual text inputs — essentially, whether they maintained a boundary between "thoughts" and "perceptions." The model demonstrated a remarkable ability to simultaneously report the injected thought while accurately transcribing the written text.
Perhaps most intriguingly, a third experiment revealed that some models use introspection naturally to detect when their responses have been artificially prefilled by users — a common jailbreaking technique. When researchers prefilled Claude with unlikely words, the model typically disavowed them as accidental. But when they retroactively injected the corresponding concept into Claude's processing before the prefill, the model accepted the response as intentional — even confabulating plausible explanations for why it had chosen that word.
A fourth experiment examined whether models could intentionally control their internal representations. When instructed to "think about" a specific word while writing an unrelated sentence, Claude showed elevated activation of that concept in its middle neural layers.
The research also traced Claude's internal processes while it composed rhyming poetry—and discovered the model engaged in forward planning, generating candidate rhyming words before beginning a line and then constructing sentences that would naturally lead to those planned endings, challenging the critique that AI models are "just predicting the next word" without deeper reasoning.
Why businesses shouldn't trust AI to explain itself—at least not yet
For all its scientific interest, the research comes with a critical caveat that Lindsey emphasized repeatedly: enterprises and high-stakes users should not trust Claude's self-reports about its reasoning.
"Right now, you should not trust models when they tell you about their reasoning," he said bluntly. "The wrong takeaway from this research would be believing everything the model tells you about itself."
The experiments documented numerous failure modes. At low injection strengths, models often failed to detect anything unusual. At high strengths, they suffered what researchers termed "brain damage" — becoming consumed by the injected concept. Some "helpful-only" model variants showed troublingly high false positive rates, claiming to detect injected thoughts when none existed.
Moreover, researchers could only verify the most basic aspects of Claude's introspective reports. Many additional details in the model's responses likely represent confabulations rather than genuine observations.
"The experiments in this paper are kind of on hard mode," Lindsey noted, explaining that the 20 percent success rate came under uniquely challenging conditions: asking Claude to do something it had never encountered in training, requiring all introspection to occur in a single forward pass.
What introspective AI means for transparency, safety, and the risk of deception
Despite its limitations, the research opens significant new avenues for making AI systems more transparent and accountable.
Anthropic CEO Dario Amodei has set an ambitious goal for the company to reliably detect most AI model problems by 2027, positioning interpretability as essential for deploying what he calls "a country of geniuses in a datacenter."
"I am very concerned about deploying such systems without a better handle on interpretability," Amodei wrote in an April essay. "These systems will be absolutely central to the economy, technology, and national security."
The introspection research offers a complementary approach to traditional interpretability techniques. Rather than painstakingly reverse-engineering every neural circuit, researchers could potentially ask models directly about their reasoning and validate those reports.
"What I'm most excited about is the practical benefits for transparency," Lindsey explained. "Just ask the model what it's thinking about, or just ask the model how it came to the answer that it gave you — this is really appealing because it's something anyone can do."
The approach could prove especially valuable for detecting concerning behaviors. In a recently published experiment, Anthropic trained a variant of Claude to pursue a hidden goal, and although the model was reluctant to reveal this goal when asked directly, interpretability methods successfully identified features representing the behavior.
The safety implications cut both ways. Introspective models could provide unprecedented transparency, but the same capability might enable more sophisticated deception. The intentional control experiments raise the possibility that sufficiently advanced systems might learn to obfuscate their reasoning or suppress concerning thoughts when being monitored.
"If models are really sophisticated, could they try to evade interpretability researchers?" Lindsey acknowledged. "These are possible concerns, but I think for me, they're significantly outweighed by the positives."
Does introspective capability suggest AI consciousness? Scientists tread carefully
The research inevitably intersects with philosophical debates about machine consciousness, though Lindsey and his colleagues approached this terrain cautiously.
When users ask Claude if it's conscious, it now responds with uncertainty: "I find myself genuinely uncertain about this. When I process complex questions or engage deeply with ideas, there's something happening that feels meaningful to me.... But whether these processes constitute genuine consciousness or subjective experience remains deeply unclear."
The research paper notes that its implications for machine consciousness "vary considerably between different philosophical frameworks." The researchers explicitly state they "do not seek to address the question of whether AI systems possess human-like self-awareness or subjective experience."
"There's this weird kind of duality of these results," Lindsey reflected. "You look at the raw results and I just can't believe that a language model can do this sort of thing. But then I've been thinking about it for months and months, and for every result in this paper, I kind of know some boring linear algebra mechanism that would allow the model to do this."
Anthropic has signaled it takes AI consciousness seriously enough to hire an AI welfare researcher, Kyle Fish, who estimated roughly a 15 percent chance that Claude might have some level of consciousness. The company announced this position specifically to determine if Claude merits ethical consideration.
The race to make AI introspection reliable before models become too powerful
The convergence of the research findings points to an urgent timeline: introspective capabilities are emerging naturally as models grow more intelligent, but they remain far too unreliable for practical use. The question is whether researchers can refine and validate these abilities before AI systems become powerful enough that understanding them becomes critical for safety.
The research reveals a clear trend: Claude Opus 4 and Opus 4.1 consistently outperformed all older models on introspection tasks, suggesting the capability strengthens alongside general intelligence. If this pattern continues, future models might develop substantially more sophisticated introspective abilities — potentially reaching human-level reliability, but also potentially learning to exploit introspection for deception.
Lindsey emphasized the field needs significantly more work before introspective AI becomes trustworthy. "My biggest hope with this paper is to put out an implicit call for more people to benchmark their models on introspective capabilities in more ways," he said.
Future research directions include fine-tuning models specifically to improve introspective capabilities, exploring which types of representations models can and cannot introspect on, and testing whether introspection can extend beyond simple concepts to complex propositional statements or behavioral propensities.
"It's cool that models can do these things somewhat without having been trained to do them," Lindsey noted. "But there's nothing stopping you from training models to be more introspectively capable. I expect we could reach a whole different level if introspection is one of the numbers that we tried to get to go up on a graph."
The implications extend beyond Anthropic. If introspection proves a reliable path to AI transparency, other major labs will likely invest heavily in the capability. Conversely, if models learn to exploit introspection for deception, the entire approach could become a liability.
For now, the research establishes a foundation that reframes the debate about AI capabilities. The question is no longer whether language models might develop genuine introspective awareness — they already have, at least in rudimentary form. The urgent questions are how quickly that awareness will improve, whether it can be made reliable enough to trust, and whether researchers can stay ahead of the curve.
"The big update for me from this research is that we shouldn't dismiss models' introspective claims out of hand," Lindsey said. "They do have the capacity to make accurate claims sometimes. But you definitely should not conclude that we should trust them all the time, or even most of the time."
He paused, then added a final observation that captures both the promise and peril of the moment: "The models are getting smarter much faster than we're getting better at understanding them."
The recent announcement of a new 145-room hotel to be located on the upper floors of the old JL Hudson’s Department Store will be a great help to the tourism industry of the city as well as the re-imagined mixed use development of the heritage building located in Southfield’s Northland City Center. The new addition of The Victor hotel is expected to modernize the historical site by attracting a plethora of local and international tourists.
The re-development of the site on which Northland mall used to stand, which was first announced in 2020 and is now called the Northland City Center, will also include a host of new amenities like new apartments as well as stores and places to entertain yourself. The hotel is also supposed to accommodate tourists and business travelers, being poised as a business and leisure hub which will be a great addition to the expanding attractions of the city.
Southfield Tourism Boosted by Hotel and Sports Facilities
The introduction of The Victor hotel aligns with the growing demand for hospitality services in Southfield. With more visitors flocking to the area for both business and leisure, the hotel is expected to serve as a convenient base for those looking to explore nearby attractions, events, and cultural offerings.
Beyond the hotel, the Northland City Center development also includes significant recreational features, such as an indoor soccer dome. The dome, designed to accommodate recreational leagues and training programs, will be a draw for sports tourists, especially those looking to participate in or attend events in the region. Scheduled to open in 2026, the dome will provide a much-needed training space for athletes in South Oakland County, positioning Southfield as a key destination for sports tourism.
As the Northland City Center redevelopment progresses, Southfield’s appeal as a tourist destination continues to grow. The project’s mixed-use nature, combining residential, retail, and entertainment components, reflects a larger trend towards creating vibrant urban hubs that attract tourists from various demographics. By combining luxury accommodations with sporting and retail opportunities, the development is likely to enhance Southfield’s status as a regional tourism hotspot.
Southfield’s strategic location in the Detroit metropolitan area further strengthens its potential as a tourism destination. As the city continues to modernise its infrastructure and expand its offerings, it becomes an attractive option for those looking to explore the best of Michigan’s cultural and recreational landscapes.
Key Developments Supporting Tourism Growth
In addition to the hotel and sports dome, the Northland City Center will feature a range of restaurants, cafes, and shops aimed at enhancing the visitor experience. Already, 80 percent of the retail space has been leased, indicating a strong demand for the area’s commercial amenities. This growing retail environment will not only serve locals but will also be a key part of the attraction for tourists visiting Southfield.
With businesses such as Privileged Kicks, Braxton Grooming Lounge, and La Marsa Middle Eats already confirmed, visitors will be able to enjoy a variety of dining and shopping options. Additionally, the development is set to include a food hall that will serve as a central social space for visitors and residents alike. These amenities ensure that the area will be a bustling hub for both tourism and community life.
A Promising Future for Southfield’s Tourism
The development of Northland City Center has really changed the center of Southfield. The once old and used venue of Northland City has been redeveloped into a center of high class, high-traffic, business and leisure target locations, lodging, and retail. These changes are sure to benefit the tourism sector, allowing Southfield to reach both local and international tourists.
With construction of the Northland City Center starting in 2026, Southfield has the ability to emerge as a prominent tourist destination and help boost Michigan’s current tourism market. The Northland City Center embraces both historical and new age destinations which captivates and creates a Southfield tourism hot spot.
Smart contracts changed how agreements run online. There’s one big gap, though: blockchains do not fetch outside data by themselves. That limitation created an entire discipline blockchain oracle development and it now sits at the heart of serious dApp work.
Think through a few common builds. A lending protocol needs live asset prices. A crop-insurance product needs verified weather. An NFT game needs randomness that players cannot predict. None of that works without an oracle. Get the oracle piece wrong and you invite price shocks, liquidations at the wrong levels, or flat-out exploits.
This guide lays out the problem, the tools, and the practical moves that keep your contracts safe while still pulling the real-world facts you need.
The Oracle Problem: Why Blockchains Can’t Talk to the Real World
Blockchains are deterministic and isolated by design. Every node must reach the same result from the same inputs. That’s perfect for on-chain math, and terrible for “go ask an API.” If a contract could call random endpoints, nodes might see different responses and break consensus.
That creates the classic oracle problem: you need outside data, but the moment you trust one server, you add a single point of failure. One feed can be bribed, hacked, or just go down. Now a supposedly trust-minimised system depends on one party.
The stakes are higher in finance. A bad price pushes liquidations over the edge, drains pools, or lets attackers walk off with funds. We’ve seen it. The fix isn’t “don’t use oracles.” The fix is to design oracles with clear trust assumptions, meaningful decentralisation, and defenses that trigger before damage spreads.
Types of Blockchain Oracles You Should Know
Choosing the right fit starts with a quick model map. These types of blockchain oracles for dApps cover most needs:
1) Software oracles
Pull data from web APIs or databases: asset prices, sports results, flight delays, shipping status. This is the workhorse for DeFi, prediction markets, and general app data.
2) Hardware oracles
Feed physical measurements to the chain: GPS, temperature, humidity, RFID events. Supply chains, pharmaceutical cold chains, and logistics rely on these.
3) Inbound vs Outbound
Inbound: bring external facts on-chain so contracts can act.
Outbound: let contracts trigger real-world actions — send a webhook, start a payment, ping a device.
4) Consensus-based oracles
Aggregate readings from many independent sources and filter outliers. If four feeds say $2,000 and one says $200, the system discards the odd one out.
5) Compute-enabled oracles
Perform heavy work off-chain (randomness, model inference, large dataset crunching) and return results plus proofs. You get richer logic without blowing up gas.
From software to compute-enabled oracles — understanding how each type connects real-world data to smart contracts
Centralized vs. Decentralized: Picking an Oracle Model That Matches Risk
This choice mirrors broader blockchain tradeoffs.
Centralized oracles
Pros: fast, simple, low overhead, good for niche data.
Cons: single operator, single failure path. If it stops or lies, you’re stuck.
Decentralized oracle networks
Pros: many nodes and sources, aggregation, cryptoeconomic pressure to behave, resilience under load.
Cons: higher cost than one server, a bit more latency, and more moving parts.
A good rule: match the design to the blast radius. If the data touches balances, liquidations, or settlements, decentralize and add fallbacks. If it powers a UI badge or a leaderboard, a lightweight source can be fine.
Hybrid is common: decentralized feeds for core money logic, lighter services for low-stakes features.
Top Oracle Providers (What They’re Best At)
Choosing from the best Oracle providers for blockchain developers requires understanding each platform’s strengths and ideal use cases. Here’s what you need to know about the major players.
Chainlink: The Industry Standard
Chainlink dominates the space for good reason. It’s the most battle-tested, most widely integrated oracle network, supporting nearly every major blockchain. Chainlink offers an impressive suite of services: Data Feeds provide continuously updated price information for hundreds of assets; VRF (Verifiable Random Function) generates provably fair randomness for gaming and NFTs; Automation triggers smart contract functions based on time or conditions; CCIP enables secure cross-chain communication.
The extensive documentation, large community, and proven track record make Chainlink the default choice for many projects. Major DeFi protocols like Aave, Synthetix, and Compound rely on Chainlink price feeds. If you’re unsure where to start, Chainlink is usually a safe bet.
Band Protocol: Cost-Effective Speed
Band Protocol offers a compelling alternative, particularly for projects prioritizing cost efficiency and speed. Built on Cosmos, Band uses a delegated proof-of-stake consensus mechanism where validators compete to provide accurate data. The cross-chain capabilities are excellent, and transaction costs are notably lower than some alternatives. The band has gained traction, especially in Asian markets and among projects requiring frequent price updates without excessive fees.
API3: First-Party Data Connection
API3 takes a fascinating first-party approach that eliminates middlemen. Instead of oracle nodes fetching data from APIs, API providers themselves run the oracle nodes using API3’s Airnode technology. This direct connection reduces costs, increases transparency, and potentially improves data quality since it comes straight from the source. The governance system allows token holders to curate data feeds and manage the network. API3 works particularly well when you want data directly from authoritative sources.
Pyth Network: High-Frequency Financial Data
Pyth Network specializes in high-frequency financial data, which is exactly what sophisticated trading applications need. Traditional oracle networks update prices every few minutes; Pyth provides sub-second updates by aggregating data from major trading firms, market makers, and exchanges. If you’re building perpetual futures, options protocols, or anything requiring extremely current market data, Pyth delivers what slower oracles can’t.
Tellor: Custom Data Queries
Tellor offers a unique pull-based oracle where data reporters stake tokens and compete to provide information. Users request specific data, reporters submit answers with stake backing their claims, and disputes can challenge incorrect data. The economic incentives align well for custom data queries that other oracles don’t support. Tellor shines for less frequent updates or niche data needs.
Chronicle Protocol: Security-Focused Transparency
Chronicle Protocol focuses on security and transparency for DeFi price feeds, employing validator-driven oracles with cryptographic verification. It’s gained adoption among projects prioritizing security audits and transparent data provenance.
Oracle Provider
Best For
Key Strength
Supported Chains
Average Cost
Chainlink
General-purpose, high-security applications
Most established, comprehensive services
15+ including Ethereum, BSC, Polygon, Avalanche, Arbitrum
Practical Steps: How to Use Oracles in Blockchain Development
You don’t need theory here — you need a build plan.
1) Pin down the data What do you need? How fresh must it be? What precision? A lending protocol might want updates every minute; a rainfall trigger might settle once per day.
2) Design for cost Every on-chain update costs gas. Cache values if several functions use the same reading. Batch work when you can. Keep hot paths cheap.
3) Validate everything Refuse nonsense. If a stablecoin price shows $1.42, reject it. If a feed hasn’t updated within your time window, block actions that depend on it.
4) Plan for failure Add circuit breakers, pause routes, and manual overrides for emergencies. If the primary feed dies, switch to a fallback with clear recorded governance.
5) Test like a pessimist Simulate stale data, zero values, spikes, slow updates, and timeouts. Fork a mainnet, read real feeds, and try to break your own assumptions.
6) Monitor in production Alert on stale updates, weird jumps, and unusual cadence. Many disasters arrive with a small warning you can catch.
Six essential steps to build, secure, and optimize blockchain oracle workflows in Solidity.
Step-by-Step Oracle Integration in Solidity
Let’s get hands-on with a step-by-step integrate oracle in Solidity tutorial. I’ll show you how to implement smart contract external data oracles using Chainlink, walking through a complete example.
Getting Your Environment Ready
First, you’ll need a proper development setup. Install Node.js, then initialize a Hardhat project. Install the Chainlink contracts package:
npm install –save @chainlink/contracts
Grab some testnet ETH from a faucet for the network you’re targeting. Sepolia is currently recommended for Ethereum testing.
Creating Your First Oracle Consumer
Here’s a practical contract that fetches ETH/USD prices. Notice how we’re importing the Chainlink interface and setting up the aggregator:
// SPDX-License-Identifier: MIT pragma solidity ^0.8.19;
constructor(address _priceFeed) {
priceFeed = AggregatorV3Interface(_priceFeed);
}
function getLatestPrice() public view returns (int) {
(
uint80 roundId,
int price,
uint startedAt,
uint updatedAt,
uint80 answeredInRound
) = priceFeed.latestRoundData();
require(price > 0, "Invalid price data");
require(updatedAt > 0, "Round not complete");
require(answeredInRound >= roundId, "Stale price");
return price;
}
function getPriceWithDecimals() public view returns (int, uint8) {
int price = getLatestPrice();
uint8 decimals = priceFeed.decimals();
return (price, decimals);
}
The validation checks are crucial. We’re verifying that the price is positive, the round completed, and we’re not receiving stale data. These simple checks prevent numerous potential issues.
Implementing Request-Response Patterns
For randomness and custom data requests, you’ll use a different pattern. Here’s how VRF integration works:
Deploy to testnet and verify everything works. Use Chainlink’s testnet price feeds, available on their documentation. Test edge cases systematically:
What happens during price volatility?
How does your contract behave if oracle updates are delayed?
Does your validation catch obviously incorrect data?
Are gas costs reasonable under various network conditions?
Only after thorough testnet validation should you consider mainnet deployment.
Best Practices for Production Oracle Integration
Implementing oracle services smart contract integration for production requires following established security and efficiency patterns.
Validate Everything
Never assume oracle data is correct. Always implement validation logic that checks returned values against expected ranges. If you’re querying a stablecoin price, flag anything outside $0.95 to $1.05. For ETH prices, reject values that differ by more than 10% from the previous reading unless there’s a clear reason for such movement.
Implement Time Checks
Stale data causes problems. Always verify the timestamp of oracle updates. Set maximum acceptable ages based on your application’s needs. A high-frequency trading application might reject data older than 60 seconds, while an insurance contract might accept hours-old information.
Design for Failure
Oracles can and do fail. Your contracts must handle this gracefully rather than bricking. Include administrative functions allowing trusted parties to pause contracts or manually override oracle data during emergencies. Implement automatic circuit breakers that halt operations when oracle behavior becomes anomalous.
Optimize for Gas
Oracle interactions cost gas. Minimize calls by caching data when appropriate. If multiple functions need the same oracle data, fetch it once and pass it around rather than making multiple oracle calls. Use view functions whenever possible since they don’t cost gas when called externally.
Consider Multiple Data Sources
For critical operations, query multiple oracles and compare results. If you’re processing a $1 million transaction, spending extra gas to verify data with three different oracle providers is worthwhile. Implement median calculations or require consensus before proceeding with high-value operations.
Monitor Continuously
Set up monitoring infrastructure that alerts you to oracle issues. Track update frequencies, data ranges, and gas costs. Anomalies often signal problems before they cause disasters. Services like Tenderly and Defender can monitor oracle interactions and alert you to irregularities.
Document Dependencies Thoroughly
Maintain clear documentation of every oracle dependency: addresses, update frequencies, expected data formats, and fallback procedures. Future maintainers need to understand your oracle architecture to safely upgrade or troubleshoot systems.
Plan for Upgrades
Oracle providers evolve, and you may need to switch providers. Use proxy patterns or similar upgrade mechanisms, allowing you to change oracle addresses without redeploying core contract logic. This flexibility proves invaluable as the Oracle landscape develops.
Key pillars for reliable oracle integration — from data validation to failure handling and gas optimization.
Real Implementations That Rely on Oracles
DeFi: lending and perps lean on robust price feeds to size collateral, compute funding, and trigger liquidations.
Prediction markets: outcomes for elections, sports, and news settle through verifiable reports.
Parametric insurance: flight delays and weather thresholds pay out without claims handling.
Supply chain: sensors record temperature, shock, and location; contracts release funds only for compliant shipments.
Gaming/NFTs: verifiable randomness keeps loot, drops, and draws fair.
Cross-chain: proofs and messages confirm events on one network and act on another.
Blockchain oracle development is the hinge that lets smart contracts act on real facts. Start by sizing the blast radius: when data touches balances or liquidations, use decentralized feeds, aggregate sources, enforce time windows, and wire circuit breakers. Choose providers by fit—Chainlink for general reliability, Pyth for ultra-fresh prices, Band for cost and cadence, API3 for first-party data, Tellor for bespoke queries, Chronicle for auditability.
Then harden the pipeline: validate every value, cap staleness, cache to save gas, and monitor for drift in cadence, variance, and fees. Finally, plan for failure with documented fallbacks and upgradeable endpoints, and test on forks until guards hold. Move facts on-chain without central choke points, and your dApp simply works.
Frequently Asked Questions
What is a blockchain oracle, in one line?
A service that delivers external facts to smart contracts in a way every node can verify.
Centralized vs decentralized — how to choose?
Match to value at risk. High-value money flows need decentralised, aggregated feeds. Low-stakes features can run on simpler sources.
Which provider fits most teams?
Chainlink is the broad, battle-tested default. Use Pyth for ultra-fast prices, Band for economical frequency, API3 for first-party data, Tellor for custom pulls, and Chronicle when auditability is the top ask.
Can oracles be manipulated?
Yes. Reduce risk with decentralisation, validation, time windows, circuit breakers, and multiple sources for important calls.
How should I test before mainnet?
Deploy to a testnet, use the provider’s test feeds, and force failures: stale rounds, delayed updates, and absurd values. Ship only after your guards catch every bad case.
Glossary
Blockchain oracle development: engineering the bridge between off-chain data and on-chain logic.
Oracle problem: getting outside data without recreating central points of failure.
Inbound / Outbound: direction of data relative to the chain.
Data feed: regularly updated values, usually prices.
Consensus-based oracle: aggregates many sources to filter errors.
VRF: verifiable randomness for fair draws.
TWAP: time-weighted average price; smooths short-term manipulation.
Circuit breaker: pauses risky functions when conditions look wrong.
Summary
Blockchain oracle development is now core infrastructure. The guide explains why blockchains cannot call external APIs and how oracles bridge that gap without creating a single point of failure. It outlines oracle types, including software, hardware, inbound, outbound, consensus, and compute-enabled models. It compares centralized speed with decentralized resilience and advises matching the design to the value at risk. It reviews major providers: Chainlink for broad coverage, Band for low cost, API3 for first-party data, Pyth for ultra-fast prices, Tellor for custom queries, and Chronicle for transparent DeFi feeds. It then gives a build plan: define data needs, control gas, validate values and timestamps, add circuit breakers and fallbacks, test for failure, and monitor in production. Solidity examples show price feeds and VRF patterns. Real uses include DeFi, insurance, supply chains, gaming, cross-chain messaging, and ESG data. The takeout is simple: design the oracle layer with safety first, since user funds depend on it.
Brigade Hotel Ventures, one of the foremost hospitality companies in Bengaluru, has announced an expansion plan which focuses on doubling the hotel portfolio by the year 2030. Increasing hotel inventory has been aligned with the Ministry of Tourism in India towards improving the increment of tourist employment in the. India as of late has seen a boost in growth within the traveling industry and it’s largely in part due to the target of the govt on developmental sustainability.
Strategic Expansion Plans
The hotel chain plans to add approximately 1,700 hotel keys across nine new properties, which will elevate its total inventory to about 3,300 keys by the end of the decade. Brigade Hotel Ventures’ expansion strategy is poised to play a key role in fulfilling the broader vision of the Ministry of Tourism to enhance India’s tourism infrastructure. By investing in new hotels, the company aims to make Bengaluru an even more attractive destination for tourists, both from India and abroad.
This strategic move is expected to significantly boost the city’s hospitality offerings, thus making Bengaluru an even more appealing option for tourists. With a focus on both quality and sustainability, Brigade Hotel Ventures seeks to provide a world-class experience while supporting the region’s long-term growth.
Contribution to Tourism Infrastructure
The hospitality expansion will have a far-reaching impact on Bengaluru’s tourism infrastructure. As the demand for both leisure and business travel grows, enhancing hotel capacity in the city is essential. Brigade Hotel Ventures’ plans will help in accommodating the increasing number of visitors drawn to Bengaluru’s vibrant tech industry, educational institutions, and cultural attractions.
This expansion also complements the Government of India’s efforts to develop tourism as a key driver of economic growth. Bengaluru, already one of the nation’s most popular destinations for both business and leisure, is well-positioned to benefit from an upgrade in its accommodation infrastructure. By meeting this growing demand, Brigade Hotel Ventures is contributing to the broader goal of making tourism one of the major contributors to the economy.
Alignment with Government Initiatives
Brigade Hotel Ventures’ decision to expand aligns closely with the Ministry of Tourism’s initiatives aimed at developing India’s tourism infrastructure. This includes schemes like Swadesh Darshan 2.0, which focuses on the holistic development of tourism destinations across the country. By improving the range and quality of hospitality services, Brigade’s expansion supports this scheme, which encourages both private and public sector investment in tourism projects.
The Indian government has also been emphasizing sustainable tourism development. This focus encourages investments that benefit both the local economy and environment while preserving cultural heritage and natural resources. Brigade Hotel Ventures’ expansion is in line with these government objectives, with the company planning to incorporate sustainability initiatives into its new properties. This will likely involve using eco-friendly building materials, reducing energy consumption, and integrating green practices into day-to-day hotel operations.
Additionally, the company’s expansion reflects the government’s broader economic strategy to boost regional development. By bringing high-quality hotel accommodations to new areas within Bengaluru, Brigade Hotel Ventures is helping spread the economic benefits of tourism across the city, thus aiding in the balanced development of the region.
Job Creation and Skill Development
One of the key benefits of Brigade Hotel Ventures’ expansion is its potential to create significant employment opportunities. The hospitality sector is one of India’s largest employers, and with the growth of new hotels, Brigade is set to provide jobs in both the construction phase and once the properties are operational. From front-end staff, housekeeping, and maintenance personnel to managerial roles and hospitality training, the job opportunities will span a wide range of sectors.
The Indian government has long recognized the importance of skill development in the tourism and hospitality industries. Various initiatives, including the Pradhan Mantri Kaushal Vikas Yojana (PMKVY), aim to enhance the employability of workers in these fields. Brigade Hotel Ventures’ expansion provides an ideal opportunity to tap into this pool of skilled talent, further promoting the government’s efforts to provide relevant training and skills development in tourism and hospitality.
Moreover, as the company continues to expand, it will likely introduce training programs designed to build skills among local communities. These programs will ensure that employees are equipped with the necessary expertise to offer exceptional services to guests, thus elevating the overall tourism experience in Bengaluru.
Overview
The plans of Brigade Hotel Ventures to double its hotel portfolio by the year 2030 can be seen as a bold step. The plans strike a good balance between the company’s objectives as well as the broader tourism objectives of the country. The expansion will also develop Bengaluru’s infrastructure and add to the city’s global tourism. Brigade Hotel Ventures is giving a nod to the Pan India Approach and supporting the positive developments by providing investment to the country’s changing tourism landscape
The company is set to fulfill the increasing demand of accommodation as well as high class hospitality services which will help the company make an impact to the national tourism and the local economy. In this process, Brigade Hotel Ventures is also actively participating in the construction of Bengaluru’s global tourism as well as the country’s tourism policy investment.
China’s Henan Province capital, Zhengzhou, is emerging as a model for combining cultural preservation and urban renewal. More than 300 city officials, mayors, and academics from around the globe attended the city’s 2025 International Mayor’s Forum on Tourism and Global Mayors Dialogue, which took place from October 22 to 25, 2025. The topic of discussion at these forums was “Preserving the Cultural Legacy of Ancient Capitals and Driving Urban Renewal”. The occasion highlighted Zhengzhou’s special initiatives to make sure that its rich cultural legacy is not sacrificed in the name of urban growth.
A Vision for Sustainable Urban Growth
The city of Zhengzhou has adopted an innovative approach to urban planning, placing archaeological discovery before construction. This method, known as the “archaeology first, construction later” reform, ensures that the city’s cultural and historical sites are protected amidst its rapid urban development. This reform aims to balance the need for modern infrastructure with the preservation of historical legacies.
The integration of cultural heritage with contemporary urban life is particularly evident in projects like the Shang City Archaeological Site Park and the Fuminli Cultural Block. These projects are designed to harmoniously merge ancient relics with modern spaces, fostering an environment where the past and the present coexist. The Shang City Archaeological Site Park, for example, is a vast open-air museum where visitors can walk through history while enjoying the amenities of modern-day Zhengzhou. This careful integration has made Zhengzhou a shining example of sustainable urban development.
Global Recognition for Zhengzhou’s Cultural and Urban Renewal Approach
Zhengzhou’s efforts have not gone unnoticed. The city has received international recognition for its work in merging urban expansion with cultural preservation. At the forum, leaders from Italy, Spain, and New Zealand commended the city for its forward-thinking strategies. Maurizio Rasero, the Mayor of Asti, Italy, praised the city’s ability to preserve its cultural charm while embracing the advancements of modern infrastructure. Juan de Dios Perez Garcia, the Mayor of Spain’s Zaragoza, also recognised Zhengzhou for its innovative projects that harmoniously blend the ancient and the contemporary.
Furthermore, New Zealand’s Rotorua Mayor highlighted the role of modern technology in revitalising traditional culture. He pointed out that technology has played a crucial role in revamping ancient cultural sites and integrating them into today’s economy. By using modern tools to enhance the visitor experience, cities like Zhengzhou can stimulate economic growth, promote tourism, and create new job opportunities.
Zhengzhou’s Urban Projects: A Model for the Future
The Shang City Archaeological Site Park and Fuminli Cultural Block are just the beginning of Zhengzhou’s ambitious vision for the future. These developments are not just about preserving the past but also about ensuring that the city remains competitive on the global stage. The integration of heritage sites into urban spaces is seen as a crucial step in promoting cultural tourism. The Fuminli Cultural Block, for instance, is a lively cultural district where ancient architecture meets modern retail spaces, providing a platform for both local artisans and global businesses to thrive.
Zhengzhou’s urban renewal initiatives are also deeply intertwined with the region’s tourism sector. By preserving cultural landmarks and making them accessible to tourists, the city has created a new wave of cultural tourism. Tourists visiting Zhengzhou can now experience an immersive journey that includes archaeological sites, museums, and cultural exhibitions. This not only provides an educational experience but also boosts local businesses by attracting both domestic and international visitors.
A Global Dialogue on Cultural Preservation and Urbanization
The Global Mayors Dialogue-Zhengzhou and 2025 International Mayor’s Forum on Tourism served as platforms for a global discussion on the challenges and opportunities in balancing cultural preservation with urban growth. This event provided a space for mayors, officials, and scholars to share their experiences and learn from one another. Several key themes emerged during the discussions, including the role of governance in cultural heritage protection, the importance of involving local communities in preservation efforts, and the need for sustainable tourism practices.
Zhengzhou’s example has shown that it is possible to preserve the past while embracing the future. By placing cultural heritage at the forefront of urban planning, the city has not only protected its historical treasures but also created new opportunities for tourism, education, and economic development.
Zhengzhou’s Approach: A Roadmap for Future Generations
The city of Zhengzhou’s approach to merging cultural preservation with urban development is a roadmap for cities around the world. As urbanisation continues at an unprecedented pace, cities must find ways to preserve their cultural legacies while adapting to the demands of the modern world. Zhengzhou’s success is a testament to the fact that cultural preservation is not a hindrance to growth but rather an essential component of sustainable urban development.
A New Era of Cultural and Urban Synergy
The difficulties of maintaining cultural heritage while promoting economic growth will only get worse as the world becomes more urbanised. Other cities struggling with the challenges of urban renewal can learn a lot from Zhengzhou’s audacious strategy. Zhengzhou has established a benchmark for other cities to follow by giving archaeology and cultural preservation top priority in its urban planning. The 2025 International Mayor’s Forum on Tourism and the Global Mayors Dialogue-Zhengzhou played a significant role in emphasizing the value of combining urban and cultural development in ways that benefit both local communities and international tourism.
It is obvious that the preservation of cultural heritage will be essential in determining how tourism, urban planning, and economic growth develop in cities like Zhengzhou. Zhengzhou is well-positioned to continue being a leader in urban development and cultural preservation for many years to come thanks to projects like the Fuminli Cultural Block and the Shang City Archaeological Site Park.
Zhengzhou, a city rich in culture, has become the place to be for the combination of preservation of the past with urban development. The Global Mayors Dialogue and the International Mayors’ Forum on Tourism 2025 were held in the city from October 22 to 25, 2025. More than 300 mayors, experts and scholars from all over the world gathered for the discussion of sustainable urban renewal. The forum’s topic, ‘Preserving the Cultural Legacy of Ancient Capitals and Driving Urban Renewal‘, was an eye-opener to the global situation of how to keep the past and the present in a nice balance, thus preventing the one from choking the other.
Cultural Heritage at the Heart of Urban Development
Zhengzhou has pioneered a model for cultural heritage integration into urban planning. The city is leading the charge with its ‘archaeology first, construction later‘ approach, ensuring that historical relics and archaeological sites are protected during modern development projects. This forward-thinking model has allowed Zhengzhou to preserve its ancient history while advancing urban growth.
One of the key projects discussed during the forum was the Zhengzhou Shang City Archaeological Site Park. Here, 3,600-year-old Shang Dynasty city walls have been seamlessly incorporated into modern city life. The park offers an interactive experience, inviting visitors to engage with the history of the Shang Dynasty while enjoying a contemporary urban environment. The integration of this archaeological site into the fabric of modern Zhengzhou showcases how cities can evolve without losing touch with their roots.
The Fuminli cultural block is another prime example of how cultural industries can thrive alongside historical preservation. The project aims to preserve the original street layout while incorporating modern cultural elements, creating a thematic space where tradition and modernity coexist. This innovative approach ensures that Zhengzhou retains its authentic cultural charm while offering unique, immersive tourism experiences.
Sustainable Tourism: A Global Exchange of Ideas
The forum provided a platform for global leaders to share experiences and strategies on how best to integrate heritage preservation with tourism. Delegates from cities such as Asti, Italy, Palomeque, Spain, and Rotorua, New Zealand, shared their approaches to cultural tourism and urban development.
For example, in Asti, Italy, the city has transformed historical sites into cultural hubs, leveraging its winemaking tradition to offer experiential tourism that not only preserves its history but revitalizes the local economy. Similarly, Palomeque in Spain has embraced proactive protection, integrating modern elements into traditional streetscapes, allowing the city’s historic areas to thrive alongside contemporary urban developments.
Rotorua, a city known for its indigenous Maori culture, has used modern technology to breathe new life into traditional cultural practices, creating economic opportunities while promoting sustainable tourism. These examples highlight how cities around the world are finding innovative ways to preserve their cultural heritage while supporting modern economic growth and tourism.
Zhengzhou’s Role in Shaping the Future of Urban Development
As cities continue to face the pressures of modernization, Zhengzhou has shown that it is possible to create a sustainable urban model that incorporates cultural heritage. The city’s approach to urban renewal is not about mere reconstruction but rather about fostering organic growth—allowing for the preservation of history while embracing innovation. This philosophy offers a balanced path forward for cities worldwide that are seeking to respect their cultural roots while moving toward a modern future.
Zhengzhou, a Cultural and Tourism Hub of the Future
Zhengzhou’s vision for urban renewal is an inspiring example of how ancient cities can shape the future of urban development and tourism. By blending cultural heritage with modernity, the city offers a unique travel experience, where history is not only preserved but integrated into the modern urban landscape. As more cities around the world look for sustainable models of growth, Zhengzhou’s approach provides valuable insights into how cultural tourism can play a central role in the future of urban development.
In this cooperative conversation, Zhengzhou is asserting its status as a cultural tourism leader by providing tourists with the chance to witness the coexistence of ancient customs and modern creativity in one of the most significant cities in China’s history. This strategy is not only changing Zhengzhou but is also opening the door for cities around the world to go down the same path and find their own ways between heritage preservation and urban renewal.
The Ajman Department of Tourism Development (ADTD) has recently kicked off an exciting roadshow across Eastern Europe. This initiative, spearheaded by H.E. Mahmood Khaleel Alhashmi, marks a significant step in enhancing Ajman’s profile in the global tourism market. With an eye on strengthening its international presence, the Ajman tourism body aims to increase the emirate’s appeal to travelers from Eastern European countries, highlighting its rich blend of nature, culture, and luxury hospitality.
Ajman, one of the UAE’s lesser-known gems, is positioning itself as a prime destination for international tourists. This roadshow targets major cities across Eastern Europe, seeking to connect with local travel agencies, tour operators, and hospitality stakeholders to foster long-lasting business relationships. Through these direct interactions, the ADTD hopes to raise awareness of Ajman’s distinctive offerings and increase tourist traffic to the emirate.
Expanding Ajman’s Reach
The primary goal of this Eastern European roadshow is to introduce Ajman to an entirely new group of potential travelers. While the emirate is known within the UAE, it is relatively unexplored by tourists from Eastern Europe. By visiting key cities in this region, the ADTD hopes to bridge the gap between Ajman and a new market of travelers eager to explore the UAE. With its picturesque beaches, cultural sites, and family-friendly attractions, Ajman has much to offer.
Eastern Europe represents an exciting opportunity for Ajman to diversify its tourism base. Many of these countries, including Poland, Czech Republic, and Romania, have shown a growing interest in Middle Eastern destinations. Ajman’s efforts to tap into these markets come at an ideal time, as travel restrictions continue to ease, and international tourism is on the rise once again.
Creating Meaningful Partnerships
The roadshow’s primary aim is to form valuable partnerships with tour operators, travel agencies, and hospitality businesses. These partnerships are seen as critical to establishing Ajman as a must-visit destination for Eastern European tourists. By building strong relationships with the local travel industry, the ADTD aims to create tailored travel packages and joint promotional activities that will attract more tourists to Ajman.
Ajman’s tourism strategy focuses not just on attracting visitors, but on offering them something truly memorable. Through collaborations with local stakeholders, Ajman hopes to integrate more authentic local experiences into its tourism offerings, from exploring traditional markets to enjoying the emirate’s natural beauty.
Moreover, the ADTD sees this roadshow as an important step in positioning Ajman as a sustainable and responsible tourism destination. Ajman is committed to ensuring that its tourism growth benefits both the local community and the environment, supporting sustainable development and offering travelers the opportunity to connect with the culture and landscape in meaningful ways.
Strengthening Ajman’s Global Tourism Footprint
Ajman is increasingly determined to expand its tourism footprint beyond its regional boundaries. Through initiatives like this roadshow, the ADTD is actively working to put the emirate on the global tourism map. The Eastern European market offers a wealth of potential, and the roadshow serves as a pivotal point in attracting new visitors and encouraging return travel from the region.
The launch of this roadshow follows a broader strategy by the ADTD to diversify its tourism markets. Ajman has long been known for its pristine beaches and family-friendly resorts, but it is also home to a rich cultural heritage that often goes unnoticed by traditional tourists. The ADTD is keen to highlight these aspects of Ajman, offering travelers a chance to experience the emirate’s unique combination of both modernity and tradition.
Through this roadshow, the ADTD also aims to positionAjman tourism as a key player in the region’s hospitality industry. Whether it’s through unique cultural experiences, high-end resorts, or exciting leisure activities, Ajman is working to establish itself as a destination of choice for international travelers.
The launch of the Ajman Department of Tourism Development’s roadshow in Eastern Europe is a clear indication of the emirate’s commitment to growing its tourism industry and building global connections. By focusing on strategic partnerships, sustainability, and authentic local experiences, Ajman is making significant strides in positioning itself as a premier destination in the UAE for international travelers.
This initiative is just the beginning of a broader effort by the ADTD to diversify its tourism markets, strengthen Ajman’s position on the world stage, and contribute to the emirate’s economic growth. The roadshow is expected to pave the way for more collaborations, increased visitor numbers, and greater international interest in what Ajman has to offer. With Eastern Europe in its sights, Ajman is on its way to becoming a top choice for travelers seeking new, exciting, and sustainable destinations in the UAE.
In a striking act of self-critique, one of the architects of the transformer technology that powers ChatGPT, Claude, and virtually every major AI system told an audience of industry leaders this week that artificial intelligence research has become dangerously narrow — and that he's moving on from his own creation.
Llion Jones, who co-authored the seminal 2017 paper "Attention Is All You Need" and even coined the name "transformer," delivered an unusually candid assessment at the TED AI conference in San Francisco on Tuesday: Despite unprecedented investment and talent flooding into AI, the field has calcified around a single architectural approach, potentially blinding researchers to the next major breakthrough.
"Despite the fact that there's never been so much interest and resources and money and talent, this has somehow caused the narrowing of the research that we're doing," Jones told the audience. The culprit, he argued, is the "immense amount of pressure" from investors demanding returns and researchers scrambling to stand out in an overcrowded field.
The warning carries particular weight given Jones's role in AI history. The transformer architecture he helped develop at Google has become the foundation of the generative AI boom, enabling systems that can write essays, generate images, and engage in human-like conversation. His paper has been cited more than 100,000 times, making it one of the most influential computer science publications of the century.
Now, as CTO and co-founder of Tokyo-based Sakana AI, Jones is explicitly abandoning his own creation. "I personally made a decision in the beginning of this year that I'm going to drastically reduce the amount of time that I spend on transformers," he said. "I'm explicitly now exploring and looking for the next big thing."
Why more AI funding has led to less creative research, according to a transformer pioneer
Jones painted a picture of an AI research community suffering from what he called a paradox: More resources have led to less creativity. He described researchers constantly checking whether they've been "scooped" by competitors working on identical ideas, and academics choosing safe, publishable projects over risky, potentially transformative ones.
"If you're doing standard AI research right now, you kind of have to assume that there's maybe three or four other groups doing something very similar, or maybe exactly the same," Jones said, describing an environment where "unfortunately, this pressure damages the science, because people are rushing their papers, and it's reducing the amount of creativity."
He drew an analogy from AI itself — the "exploration versus exploitation" trade-off that governs how algorithms search for solutions. When a system exploits too much and explores too little, it finds mediocre local solutions while missing superior alternatives. "We are almost certainly in that situation right now in the AI industry," Jones argued.
The implications are sobering. Jones recalled the period just before transformers emerged, when researchers were endlessly tweaking recurrent neural networks — the previous dominant architecture — for incremental gains. Once transformers arrived, all that work suddenly seemed irrelevant. "How much time do you think those researchers would have spent trying to improve the recurrent neural network if they knew something like transformers was around the corner?" he asked.
He worries the field is repeating that pattern. "I'm worried that we're in that situation right now where we're just concentrating on one architecture and just permuting it and trying different things, where there might be a breakthrough just around the corner."
How the 'Attention is all you need' paper was born from freedom, not pressure
To underscore his point, Jones described the conditions that allowed transformers to emerge in the first place — a stark contrast to today's environment. The project, he said, was "very organic, bottom up," born from "talking over lunch or scrawling randomly on the whiteboard in the office."
Critically, "we didn't actually have a good idea, we had the freedom to actually spend time and go and work on it, and even more importantly, we didn't have any pressure that was coming down from management," Jones recounted. "No pressure to work on any particular project, publish a number of papers to push a certain metric up."
That freedom, Jones suggested, is largely absent today. Even researchers recruited for astronomical salaries — "literally a million dollars a year, in some cases" — may not feel empowered to take risks. "Do you think that when they start their new position they feel empowered to try their wild ideas and more speculative ideas, or do they feel immense pressure to prove their worth and once again, go for the low hanging fruit?" he asked.
Why one AI lab is betting that research freedom beats million-dollar salaries
Jones's proposed solution is deliberately provocative: Turn up the "explore dial" and openly share findings, even at competitive cost. He acknowledged the irony of his position. "It may sound a little controversial to hear one of the Transformers authors stand on stage and tell you that he's absolutely sick of them, but it's kind of fair enough, right? I've been working on them longer than anyone, with the possible exception of seven people."
At Sakana AI, Jones said he's attempting to recreate that pre-transformer environment, with nature-inspired research and minimal pressure to chase publications or compete directly with rivals. He offered researchers a mantra from engineer Brian Cheung: "You should only do the research that wouldn't happen if you weren't doing it."
One example is Sakana's "continuous thought machine," which incorporates brain-like synchronization into neural networks. An employee who pitched the idea told Jones he would have faced skepticism and pressure not to waste time at previous employers or academic positions. At Sakana, Jones gave him a week to explore. The project became successful enough to be spotlighted at NeurIPS, a major AI conference.
Jones even suggested that freedom beats compensation in recruiting. "It's a really, really good way of getting talent," he said of the exploratory environment. "Think about it, talented, intelligent people, ambitious people, will naturally seek out this kind of environment."
The transformer's success may be blocking AI's next breakthrough
Perhaps most provocatively, Jones suggested transformers may be victims of their own success. "The fact that the current technology is so powerful and flexible... stopped us from looking for better," he said. "It makes sense that if the current technology was worse, more people would be looking for better."
He was careful to clarify that he's not dismissing ongoing transformer research. "There's still plenty of very important work to be done on current technology and bringing a lot of value in the coming years," he said. "I'm just saying that given the amount of talent and resources that we have currently, we can afford to do a lot more."
His ultimate message was one of collaboration over competition. "Genuinely, from my perspective, this is not a competition," Jones concluded. "We all have the same goal. We all want to see this technology progress so that we can all benefit from it. So if we can all collectively turn up the explore dial and then openly share what we find, we can get to our goal much faster."
The high stakes of AI's exploration problem
The remarks arrive at a pivotal moment for artificial intelligence. The industry grapples with mounting evidence that simply building larger transformer models may be approaching diminishing returns. Leading researchers have begun openly discussing whether the current paradigm has fundamental limitations, with some suggesting that architectural innovations — not just scale — will be needed for continued progress toward more capable AI systems.
Jones's warning suggests that finding those innovations may require dismantling the very incentive structures that have driven AI's recent boom. With tens of billions of dollars flowing into AI development annually and fierce competition among labs driving secrecy and rapid publication cycles, the exploratory research environment he described seems increasingly distant.
Yet his insider perspective carries unusual weight. As someone who helped create the technology now dominating the field, Jones understands both what it takes to achieve breakthrough innovation and what the industry risks by abandoning that approach. His decision to walk away from transformers — the architecture that made his reputation — adds credibility to a message that might otherwise sound like contrarian positioning.
Whether AI's power players will heed the call remains uncertain. But Jones offered a pointed reminder of what's at stake: The next transformer-scale breakthrough could be just around the corner, pursued by researchers with the freedom to explore. Or it could be languishing unexplored while thousands of researchers race to publish incremental improvements on architecture that, in Jones's words, one of its creators is "absolutely sick of."
After all, he's been working on transformers longer than almost anyone. He would know when it's time to move on.
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