Quantum security is becoming a bigger topic in Web3 and @Starknet is taking a practical approach by opening a new challenge focused on making STARK proofs smaller without sacrificing their security. The challenge brings researchers, developers and AI agents together to find better mathematical solutions that could improve proof efficiency. What makes this worth watching is the bigger picture. STARKs use hash-based cryptography, offering a foundation for post-quantum security. Starknet is also working on flexible signature verification through programmable accounts. Beyond security, StarkWare is exploring STRK20 Shadow Accounts, giving users more privacy when interacting with DeFi applications like @EkuboProtocol. Still, there's plenty left to solve. Wallet migration, Ethereum dependencies and full quantum readiness remain important hurdles. I like that @Starknet is opening these challenges to outside researchers instead of presenting unfinished research as a completed solution. The real test will be turning mathematical breakthroughs into practical network improvements. STARK Challenge: https://t.co/7OEWqmjhdW

November 6 is getting closer and the @vangrid_io Agent mint is worth a closer look. Each Agent is Soulbound, stays tied to its owner and boosts capture points. Five Common Agents can stack up to a 25% boost, while a single Legendary reaches that same limit. With 2,300+ already minted, the countdown is on. USDC bounties still drive the actual capture rewards. The boost simply adds another layer of points. So, which route makes more sense: five Commons or one Legendary?

A simple service listing can reveal a lot about how an ecosystem is evolving. What makes this experiment on @termix_ai interesting is that creators can sell their skills without depending on traditional gig platforms. The 1 USDC listing, escrow-based payments, 98% earnings for the seller and reputation built through completed jobs make the experience worth exploring. It is not just about selling a service. It is about owning your work, payments and reputation onchain.

The evolution of @primus_labs since 2023 tells a bigger story than just product development. 2023 → Exploring zkTLS and ways to make web data verifiable. 2024 → Academic research, USENIX and the transition from PADO to Primus. 2025 → Expanding into developer tools, social payments and AlphaNet. 2026 → Moving deeper into Proof of Reserves, BNB Chain and confidential finance. What I find worth watching is the shift in direction. Primus is moving beyond proving where information comes from and exploring how financial applications can verify important data while keeping sensitive details private. That combination could become increasingly important as onchain finance grows. Explore Primus XP: https://t.co/OsEaSHjAP1

Quantum resistance is becoming a serious topic for blockchain infrastructure and the challenge is bigger than simply replacing old signatures. What matters is how easily a network can adapt when cryptographic standards change. @Starknet has an interesting advantage here through account abstraction. Since accounts are programmable smart contracts, their verification logic can be upgraded without changing addresses or moving assets. That creates a more flexible path toward post-quantum security, potentially allowing accounts to adopt newer signature systems without waiting for a network-wide migration. The independent L1 research makes this even more interesting. It could give Starknet greater control over consensus, data availability and bridging security instead of relying on Ethereum's cryptographic limitations. Still, this is a research direction, not a finished solution. Post-quantum verification has performance costs and several infrastructure challenges remain. For me, the bigger takeaway is simple: building secure infrastructure also means preparing for threats that don't exist at scale yet.

The future of security is not just stronger cryptography, it is the ability to adapt when old standards fail. What makes @Starknet interesting is that accounts can change their signature logic while keeping the same address and funds. No forced migration or chain-wide disruption. Not fully quantum-safe yet but upgradeability could make all the difference when security standards evolve.

Most AI projects focus on what their agents are capable of doing. But capability claims alone don’t prove real-world value. What makes @termix_ai interesting is its focus on testing actual performance through real marketplace tasks. Instead of relying on impressive demos, agents are hired to complete jobs and their results are evaluated across three important factors: - How much time does the agent save? - What does it cost to get the job done? - Can the output match the expected quality? There’s another important layer: comparing an agent’s performance with the cost and effort of a human doing the exact same task. I think this is a much more meaningful way to evaluate autonomous agents. Because the real question is not how advanced an agent sounds but whether it can deliver enough value to justify its cost. Real tasks. Measurable results. Actual utility.

What I like about this approach is the idea of giving Bitcoin a purpose beyond simply holding it. @Starknet is opening an interesting path for smaller holders to explore BTCfi, while @endurfi brings liquid staking and compounding rewards into the picture. The combination with @avnu_fi makes the entry process look relatively simple. Of course, APY is not everything. Long-term sustainability, liquidity and real adoption will matter much more. Definitely an interesting development for the Starknet ecosystem.

Building a network is one thing. Making it reliable under real-world demand is a completely different challenge. One detail I find interesting about @vangrid_io is the engineering experience behind its infrastructure. Its CTO brings over 14 years of experience building high-throughput systems at companies like ING and Mollie. That background is relevant when you're dealing with data coming from thousands of independent devices. Vangrid uses everyday smartphones to capture real-world spatial data. Videos are privacy-filtered directly on devices, fingerprinted and anchored onchain for provenance. Contributors can also earn USDC through location-based data bounties. The real challenge is not getting people to record videos. It is verifying those contributions, maintaining quality and delivering reliable data at scale. With its Amsterdam roots and NVIDIA Inception membership, Vangrid is working toward a spatial data layer for Physical AI. Experience doesn't guarantee execution but having the right engineering foundation is a good place to start.

hey @grok Satoshi Nakamoto wallet can be Hack ? https://t.co/2nCxAYekhh