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Today, Celo upgraded its OP-Succinct Lite implementation on mainnet to use SP1 Hypercube from Succinct SP1 Hypercube introduces a new proving architecture that reduces proof latency and improves efficiency, further strengthening Celo's zk fault-proof infrastructure

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Celo

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12,555 просмотров • 1 месяц назад •via X (Twitter)

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The telephoto system on the vivo X300 Ultra is a bit insane. This generation brings upgrades not only in sensor specifications, but also in stabilization and subject tracking, pushing smartphone telephoto photography closer to the level of professional cameras. 1. Upgraded 200MP “Thanos Telephoto” system The X300 Ultra uses a Blueprint × Samsung HP0 200-megapixel telephoto sensor, representing the fifth generation of the Zeiss-branded “Thanos telephoto.” Building on the previous HPE solution, vivo further refined the sensor through deep customization, improving color rendering, autofocus, HDR performance, and power efficiency, allowing this 200MP telephoto to deliver stronger overall imaging performance. 2. Gimbal-level stabilization and 60fps tracking capture The X300 Ultra introduces around 3° optical stabilization on its telephoto lens, a massive jump compared with the 0.7°–1° typically seen in telephoto OIS systems and even beyond the 1.2° on the X200 Ultra. This enables CIPA 7.0-level professional stabilization. The stronger stabilization significantly improves handheld shooting at long focal lengths, including 200mm, 400mm, and beyond. In addition, the device features a Blueprint high-refresh tracking engine that supports 60fps motion capture, doubling the industry’s common 30fps capability and allowing telephoto cameras not only to capture static subjects, but also to reliably track fast-moving targets.

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46,609 просмотров • 4 месяцев назад

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:
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We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

359,537 просмотров • 9 месяцев назад

KASPA HITS MAJOR MILESTONES IN 2026 AS TOCCATA HARD FORK NEARS AND SUPPLY EMISSION WINDS DOWN Kaspa has stacked a series of milestones over the past year as the network transitions from a payments-focused proof-of-work chain to a programmable Layer 1. The Crescendo hard fork pushed the network to 10 blocks per second, with peak loads exceeding 10,000 TPS. While cumulative on-chain transactions are approaching 2 billion. Founder Yonatan Sompolinsky (Yonatan Sompolinsky) took the Kaspa pitch to the Oxford Union in March, a notable mainstream-academic crossover for a fair-launched community project. The Toccata hard fork hit code freeze on April 15 with mainnet activation targeted between June 5 and June 20. Toccata brings native L1 covenant programming via the SilverScript compiler, ZK verification opcodes, KIP-21 partitioned sequencing, and KRC-20 tokens as a base-layer feature. The supply story compounds the upgrade story. Roughly 95.4% of $KAS is already in circulation, with new emission approaching zero by the end of 2026. $KAS trades near $0.0325 with a market cap of around $888 million, with a near-fully distributed supply heading into its biggest programmability upgrade.
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KASPA HITS MAJOR MILESTONES IN 2026 AS TOCCATA HARD FORK NEARS AND SUPPLY EMISSION WINDS DOWN Kaspa has stacked a series of milestones over the past year as the network transitions from a payments-focused proof-of-work chain to a programmable Layer 1. The Crescendo hard fork pushed the network to 10 blocks per second, with peak loads exceeding 10,000 TPS. While cumulative on-chain transactions are approaching 2 billion. Founder Yonatan Sompolinsky (Yonatan Sompolinsky) took the Kaspa pitch to the Oxford Union in March, a notable mainstream-academic crossover for a fair-launched community project. The Toccata hard fork hit code freeze on April 15 with mainnet activation targeted between June 5 and June 20. Toccata brings native L1 covenant programming via the SilverScript compiler, ZK verification opcodes, KIP-21 partitioned sequencing, and KRC-20 tokens as a base-layer feature. The supply story compounds the upgrade story. Roughly 95.4% of $KAS is already in circulation, with new emission approaching zero by the end of 2026. $KAS trades near $0.0325 with a market cap of around $888 million, with a near-fully distributed supply heading into its biggest programmability upgrade.

BSCN

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The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using Claude Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in Zcash 🛡️’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As Anthropic framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): Bug bounty program and honeypot details:
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The OriginTrail DKG V10 begins its mainnet rollout with a Frontier-AI Resilience Gate. Today, the final V10 release candidate (the exact contract bytecode intended for mainnet) goes live as a public pre-mainnet, funded with 300,000 ethereum:0xaa7a9ca87d3694b5755f213b5d04094b8d0f0a6f tokens: a 200,000 TRAC honeypot pool of real, drainable positions plus a 100,000 severity-reward pool. Independent researchers and AI-augmented teams are invited to break it: drain the honeypot and you keep what you take, and every valid finding is paid by severity. It’s a real pass/fail checkpoint: findings are fixed and verified first, and clearing the gate is the precondition for the mainnet launch. The first step of the DKG V10 deployment, by design. Why lead with security instead of shipping and patching later? On May 29, 2026, a researcher using Claude Opus 4.8 surfaced a critical, roughly four-year-old soundness flaw in Zcash 🛡️’s Orchard pool (a bug that had passed repeated expert review) in about a day, with a working proof-of-concept. The moment matters; the trajectory matters more. Claude Mythos, Anthropic’s frontier model, is so capable at finding vulnerabilities that it was first withheld from public release and run only inside a defensive partner program, where it reportedly surfaced more than ten thousand high- and critical-severity bugs in its first month. It’s now days from a reported public release. The bar for what an attacker, human or AI, can find only rises from here. As Anthropic framed it, the advantage goes to whoever uses these tools first: attackers in the short term, defenders who fix bugs before code ships in the long term. The Resilience Gate is how we make sure we’re on the defenders’ side, testing DKG V10 not just against today’s models but against what arrives next. For anyone shipping on-chain systems, the implication is simple: this code launches once, mistakes can’t be undone, and the responsible move is to invite that scrutiny before any user value is at stake. The path to mainnet, in four phases: Phase 0: Freeze. Final contracts locked and deployed (complete) Phase 1: Frontier-AI Resilience Gate. Open review program, through June 17 Phase 2: Mainnet launch. Hardened, feature-complete V10 (week of June 15) Phase 3: Continuous audit. Every contract, ongoing after launch If you work in smart-contract security, or build with AI that does, we’d welcome your review. No allowlist, real rewards, coordinated disclosure. *Dates are indicative: the exact mainnet date depends on the pace of network bootstrapping and the time needed to patch and re-verify any more severe findings from the Gate. Release candidate 17 (rc17): Bug bounty program and honeypot details:

OriginTrail Developers

466,082 просмотров • 1 месяц назад

Never thought I'd post something like this but Drohgoh has been threatening to make stuff up about me slandering me calling me a stalker/pedo and I've had enough. In 2021, he obsessed over a girl who had just turned 18 (he was 26) and invited her to mythic raids and taught her how to use keybinds and coached her privately on hpal 🤡 and got mad when other guys tried to invite her to keys without him there. He tried to fly her out to Disney World to be her "first" when she was 19 but it didnt work out so he leaked her private pic (she was 16 in the pic) and slandered her. He also shit talked her dead mother. Drohgoh leaked my personal info today in retaliation for me speaking the TRUTH about him on my stream. Partnered MDI streamer doxxing another partnered streamer? He told his stream discord that it's all "fake" and said that if the girl came forward he would dox her as well. He also threatened to "sue" me. Good luck with that, Predgoh! 🤠 Certainly some weird predator sociopath behavior going on here! Check out this clip from earlier this morning! Also going live in a few minutes to discuss this further with crazy video proof of him leaking my info! He will surely leak my stuff more so I expect some free food delivered thanks guys!
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Never thought I'd post something like this but Drohgoh has been threatening to make stuff up about me slandering me calling me a stalker/pedo and I've had enough. In 2021, he obsessed over a girl who had just turned 18 (he was 26) and invited her to mythic raids and taught her how to use keybinds and coached her privately on hpal 🤡 and got mad when other guys tried to invite her to keys without him there. He tried to fly her out to Disney World to be her "first" when she was 19 but it didnt work out so he leaked her private pic (she was 16 in the pic) and slandered her. He also shit talked her dead mother. Drohgoh leaked my personal info today in retaliation for me speaking the TRUTH about him on my stream. Partnered MDI streamer doxxing another partnered streamer? He told his stream discord that it's all "fake" and said that if the girl came forward he would dox her as well. He also threatened to "sue" me. Good luck with that, Predgoh! 🤠 Certainly some weird predator sociopath behavior going on here! Check out this clip from earlier this morning! Also going live in a few minutes to discuss this further with crazy video proof of him leaking my info! He will surely leak my stuff more so I expect some free food delivered thanks guys!

Gromnak

70,983 просмотров • 1 год назад