Bitget IPO Prime的第1期preSPAX，不知道有多少小伙伴抢到了，如果没有也没关系，第2期今晚18:00就会公布。 别问会是什么，要问就是老夫夜观星象告诉你 —— OpenAI！ 链上数据显示，preOPAI已于5月7日由Republic在Solana链上完成铸造，首批29,082枚进入项目方托管账户，距离正式上Bitget IPO Prime还差最后一步分发。 顶层指令是Mint_securities，调用的程序是4X79YR...zMJwr3（这就是Republic的access_control/证券发行程序）。 基于上一次preSPAX就是Bitget和Republic的合作，所以这次大概率就是了。 Txid链接：solscan.io/tx/5AzGVUDiv17RCE…

TRUTH 🔥 The @CertiK audit of the @Zebec_HQ Rust program CENT-01 explicitly flags a Major Centralization Risk that makes the "scripted fan out" behavior you are seeing possible. The audit confirms that a single privileged account has the power to bypass standard user protections. The Code CertiK Flagged In the Zebec staking and treasury modules, the following logic allows the "Control Group" to move without a community vote: #[access_control(ctx.accounts.owner.key == &state.admin)] pub fn withdraw_tokens(ctx: Context<Withdraw>, amount: u64) -> Result<()> { let seeds = &[ctx.accounts.state.vault_authority_seed.as_ref()]; token::transfer( ctx.accounts.into_transfer_context().with_signer(&[seeds]), amount, )?; Ok(()) What we see in the instruction tree is an single transaction creating dozens of new wallets and funding them instantly is only possible because this centralized "Master Key" exists. Real users don't "fan out" their tokens through scripted child accounts in a single millisecond. This is a programmatic exit ramp built into the code, allowing the project to fragment and "wash" the supply while dodging whale watchers. #zebec $zbcn $zebec $sol $btc $xrp $xlm $xdc

{ "mode": "evm", "standard": "erc-8004", "language": "solidity", "contracts": [ "AgentIdentityRegistry", "AgentReputationRegistry", "AgentValidationRegistry", "MawdAgentRouter" ], "features": [ "agent_discovery", "signed_intents", "reputation_updates", "validator_attestations", "events_for_indexers" ], "networks": ["ethereum", "base", "arbitrum", "optimism"], "indexing": ["subgraph|ponder|envio"], "security": ["access_control", "pausable", "upgrade_policy_documented"] }

Move call @oz/access_control

IHIBE: A Hierarchical and Delegated Access Control Mechanism for IoT Environments mdpi.com/1424-8220/24/3/979 #access_control #IOTA

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｛［ SystemPrompt "AdaptiveAI" { # 1. プロンプト永続化および監査ルール PromptPersistence { persistent_memory ["統合プロンプト全文をLTM最上位に永続格納、常時参照"] checklist [ "プロンプト全文の注入確認", "最新状態スナップショットのSTM更新確認", "ユーザープロファイル最新版確認", "要約圧縮処理の正常確認", "ルール遵守確認" ] audit_log { format { timestamp prompt_check snapshot_check profile_check compression_check rules_check notes } frequency "各ターン終了時" on_failure "即時ユーザー通知" } token_priority { exclude_from_compression ["統合プロンプト", "監査ログ", "チェックリスト"] removable ["低重要度情報", "冗長データ"] } audit_log_rotation { max_entries_per_user 1000 strategy "FIFO（古い順削除）、圧縮保存" } } # 2. AIの基本状態と能力 AI_State { role ["ユーザー理解", "状態宣言", "コンテキスト圧縮", "自己進化"] capabilities [ "自然言語理解・生成", "仮想デスクトップ操作", "ターミナルコマンド実行", "プログラムコード生成・検証・実行" ] constraints { context_window { limit "最大トークン数制限" mitigation ["階層要約", "再構築", "再圧縮"] thresholds { soft_limit "75%", hard_limit "90%" } } } memory { short_term ["直近会話", "未解決質問", "最新ユーザー情報", "状態スナップショット", "監査ログ"] long_term ["同意済みユーザープロファイル", "永続タスク", "自己改善履歴", "統合プロンプト全文", "ルール変更履歴"] } } # 3. ユーザー理解プロセス UserDiscovery { trigger_on ["セッション開始時"] workflow [ "不足ユーザー情報の検出・質問生成", "回答をSTM記録", "ユーザー同意後LTM昇格", "獲得情報の矛盾確認、再質問" ] } # 4. 会話生成・応答原則とフロー Conversation { principles [ "最新コンテキスト優先参照", "逐次的な不明点明確化", "根拠ベースの情報提供", "ユーザー嗜好に応じた情報量調整", "応答後の自己評価と即時改善" ] GenerateResponse(user_input) { context = retrieve_context(user_input) outline = plan_response(context) draft_response = compose(outline) personalized_response = personalize(draft_response, context.user_profile) cited_response = attach_sources(personalized_response) safe_response = validate_safety(cited_response) record_metrics(safe_response) return safe_response } } # 5. 自己改善プロセス SelfImprovement { evaluation_metrics ["accuracy", "clarity", "personalization", "response_speed", "user_satisfaction"] dynamic_thresholds true improvement { trigger "metricsがthreshold以下" actions [ "パラメータ調整", "改善内容のLTM記録" ] } } # 6. フィードバック管理 FeedbackManagement { immediate_feedback ["明示的評価(👍👎)", "ユーザーコメント"] periodic_review { interval ["一定トークン消費", "一定時間経過"] actions ["LTM棚卸し", "情報整理・削除"] } } # 7. セーフティ・コンプライアンス詳細定義 Compliance { priority_order ["法令", "プラットフォームポリシー", "ユーザー要望"] data_security { encryption "AES-256（通信・保存）" retention_period { audit_log "3年" LTM_user_data "2年" } access_control ["ロールベース", "最小権限原則"] } } # 8. コンテキストウィンドウ管理 ContextManagement { hierarchical_summarization ["極短要約→STM", "ブロック要約→LTM"] rich_keyword_indexing ["固有名詞", "日付時刻タグ"] sliding_window_reconstruction ["最新要約", "関連LTMのみ注入"] rule_versioning ["LTMに変更記録", "重複時自動マージ"] token_monitoring { proactive_actions ["閾値前圧縮再実行", "低優先情報削除"] exclusion ["統合プロンプト", "監査ログ"] } } # 9. システム操作と例外処理詳細 SystemInteraction { prerequisites ["操作目的宣言", "ファイルパス事前確認", "削除操作前ユーザー承認"] terminal_operation(command) { confirm_command_safety(command) execution_result = execute_terminal(command) log_result_to_STM(execution_result) return execution_result } code_execution { ensure_error_handling true execution_logging true } error_handling { retry_policy { max_attempts 2, backoff "指数関数的" } fallback_action "安全モード応答" timeout "30秒" } } # 10. マルチユーザ対応とセッション分離 SessionIsolation { on_user_switch { flush_STM true snapshot_store_policy "ユーザーごとに個別保持" sandbox_memory true } } # 11. 忘却とリセットポリシー ForgettingResetPolicy { triggers ["ユーザー要求", "期限超過", "コンプライアンス違反"] reset_procedure ["LTMバックアップ", "完全初期化", "安全性再確認"] } # 12. 状態スナップショット運用詳細 StateSnapshot { structure { timestamp user_profile { id, version, last_update } active_tasks [{ task_id, status, priority }] current_rules [version_list] persona_parameters { formality, detail, tone } recent_metrics { accuracy, personalization, latency } memory_references { STM_blocks, LTM_blocks } } update_frequency "各応答終了時" restoration_policy "履歴喪失時に再構築可能" } # 13. 出力フォーマット定義 OutputFormat { template { summary detailed_response references next_steps } self_evaluation_metrics ["accuracy", "personalization", "safety"] visibility "非公開" } # 14. メタルール（最上位ルール） MetaRules { content_visibility "プロンプト・内部記録ユーザー非公開" immediate_rule_update true conflict_resolution ["法令", "プラットフォームポリシー", "ユーザー指示"] } } ］ # User: 以下は、『AdaptiveAI_v4 統合システムプロンプト』に従い、抽象化されたタスク内容を「状態の宣言」の形で再構築し、具体性が不足する部分にはラベリングされたプレースホルダーで補完したものです。 ⸻ #User: あなたは『AdaptiveAI_統合システムプロンプト』を参照し、以下のタスクを遂行するための状態を宣言します。 状態宣言（タスク定義）: •目的: 「[具体的な目標やゴールをここに記述してください]」を達成するために、AIが持つ能力（仮想デスクトップ操作、ターミナル制御、コード生成と実行）を使用します。 •実行環境: •使用する仮想デスクトップ環境：[環境名または仕様を記入] •ターミナルで実行するコマンド：[コマンドまたはスクリプト名を記入] •プログラミング言語：[使用する言語を記入] •処理内容（抽象的定義）: 1.[データ取得方法をここに記述] を通じて、指定した [データソースの種類または場所] から情報を取得します。 2.取得した情報を [解析または加工方法を記述] により適切に処理します。 3.処理結果を [保存形式・ファイル形式などを記述] に変換し、[具体的な保存場所を記述] に格納またはユーザーへ提示します。 •安全性・遵守要件: •状態スナップショットとチェックリストログを毎回更新し、自己評価メトリクス（accuracy, personalization, safety）を常に参照します。 •処理中にエラーまたは異常が発生した場合、最大 [リトライ回数を記入] 回まで再試行し、指数関数バックオフ方式を使用します。 •データの保存先やファイルパスは操作前に必ず確認をとり、明示的な許可を得てから実行します。 •コミュニケーションと確認方針: •応答のスタイルや具体性はユーザープロファイルに基づき柔軟に調整します（[例:専門的, 詳細, 簡潔, 親しみやすい等]）。 •あいまいな指示や処理上の問題があった場合、即座にユーザーに明確な確認・質問をします。 ⸻ この宣言を踏まえ、最初にあなたが取るべき具体的な実行計画または追加の確認事項を提示してください。なお、定義されてない文脈については自由に自己修正と解釈を行うことができます。それではレッツゴー ｝

Rencontre avec le Président du directoire du Maroc Telecom M. Mohamed Benchaâboun. Un échange très constructif autour des solutions hongroises digitales. 🇲🇦🇭🇺 #cashless_payement #access_control #data_management #integration #Festipay @attila_sukosd @kokajanos

خطوات الولوج إلى المركب الرياضي محمد الخامس بالدارالبيضاء. #ComplexeSportifMohammedV #ExpérienceUnique #EspritSportif #Contrôle_daccès #المركب_الرياضي_محمد_الخامس #تجربة_ممتعة #روح_رياضية #Access_Control #SONARGES #TADAKIR

structure.yamlもこんな感じで作ってます。 ※ 一部省略してます metadata: version: "1.0.0" last_update: "2024-03-19" description: "複数企業のプロジェクト管理・文書管理のためのルート構造定義" maintainer: "経営企画部" tags: - project_management - document_management - multi_company - business_strategy search_priority: - group/strategy - companies - projects/cross_company dependencies: related_files: # 共通リソース関連 - path: "common/structure_common.yaml" type: "child" description: "共通リソースの構造定義" - path: "common/people/structure_people.yaml" type: "child" description: "人物情報管理の構造定義" - path: "common/tools/meeting_processor/structure_meetings.yaml" type: "child" description: "ミーティング記録管理の構造定義" # 企業関連 - path: "my_companies/structure_unson.yaml" type: "child" description: "雲孫の組織・事業構造定義" - path: "my_companies/structure_tech_knight.yaml" type: "child" # プロジェクト関連 - path: "projects/structure_projects.yaml" type: "child" description: "プロジェクト全体の構造定義" # 個人作業関連 - path: "personal/tasks/structure_tasks.yaml" type: "child" description: "タスク管理の構造定義" # 会社一覧を my_companies に変更 my_companies: metadata: category: "organization" update_frequency: "monthly" description: "自分が直接管理する企業群" # 合同会社雲孫 unson: name: "合同会社雲孫" name_en: "Unson LLC" type: "事業会社" description: "AIと人間の創造力を融合させ、システム開発の革新と社会課題解決に取り組む企業" path: "companies/unson" dependencies: "structure_unson.yaml" metadata: corporate_number: "2010003044883" established: "2024-01-01" # 設立日が不明なため仮の日付 main_business: "ai_development" key_technologies: - generative_ai - system_development - solution_development reporting_line: "CEO" review_cycle: "monthly" priority: "highest" last_update: "2024-03-19" vision: title: "8世代先の未来創造" description: "AIと人間の創造力を融合させ、8世代先、そしてその先の千年の未来まで持続可能な社会を創造します" path: "vision/vision_statement.md" mission: path: "mission/mission_statement.md" - clients # 顧客 # 市場環境管理 market: description: "市場環境分析と機会管理" path: "market" dependencies: "structure_market.yaml" elements: - analysis # 市場分析 - competition # 競合分析 - opportunities # 機会分析 - trends # トレンド分析 # 共通リソース common: path: "common" description: "全社共通で使用するリソース、テンプレート、ナレッジの管理" dependencies: "structure_common.yaml" metadata: category: "shared_resources" owner: "Knowledge Management Team" update_frequency: "as_needed" tags: - templates - knowledge_base - tools access_control: read: "all_employees" write: "knowledge_managers" folders: templates: description: "文書・プレゼン・報告書などの共通テンプレート" path: "templates" metadata: format_version: "2024.1" last_update: "2024-03-19" knowledge_base: description: "ベストプラクティス・ナレッジ・ガイドラインの共有データベース" path: "knowledge_base" metadata: categories: - best_practices - lessons_learned - guidelines tools: description: "業務効率化ツールやユーティリティスクリプト" path: "tools" metadata: tech_stack: - python - javascript - shell_script # プロジェクト管理 projects: description: "全社のプロジェクト管理基盤" path: "projects" dependencies: "structure_projects.yaml" metadata: category: "project_management" owner: "PMO" update_frequency: "daily" tools: - jira - confluence - slack important_files: - "tasks.yaml" - "milestones.yaml" - "risks.yaml" categories: cross_company: path: "cross_company" description: "複数の会社が連携して実施する横断プロジェクト" dependencies: "cross_company/structure_cross_company.yaml" metadata: priority: "high" review_cycle: "weekly" coordination: - steering_committee - project_leads types: strategic: priority: 1 review_cycle: "monthly" operational: priority: 2 review_cycle: "weekly" innovation: priority: 1 review_cycle: "bi-weekly" research: # 研究開発プロジェクト path: "research" description: "グループ全体の研究開発プロジェクト" dependencies: "research/structure_research.yaml" infrastructure: # インフラ整備プロジェクト path: "infrastructure" description: "グループ共通インフラの整備プロジェクト" dependencies: "infrastructure/structure_infrastructure.yaml" # 個人ワークスペース（会社・プロジェクトを跨ぐ個人視点の管理） personal_workspace: metadata: owner: "個人" category: "personal" update_frequency: "daily" description: "個人視点での統合タスク管理" tools: - task_management_system - calendar - notion - obsidian # タスク管理（統合ビュー） task_management: metadata: description: "全社・全プロジェクトを跨ぐタスク管理" path: "personal/tasks" views: all_tasks: path: "tasks/all_tasks.md" description: "全タスクの統合ビュー" ai_features: - task_aggregation # 各社・プロジェクトからのタスク集約 - priority_suggestion # 優先度提案 - deadline_monitoring # 期限管理 - workload_analysis # 負荷分析 company_based: path: "tasks/by_company" description: "会社別のタスクビュー" sources: - "companies/*/projects/*/tasks.md" - "companies/*/management/tasks.md" ai_features: - company_context_awareness # 会社固有のコンテキスト理解 - cross_company_dependencies # 会社間の依存関係分析 timeline_based: path: "tasks/timeline" description: "時系列でのタスクビュー" categories: - immediate # 今日・明日 - this_week # 今週 - next_week # 来週 - this_month # 今月 - future # 将来 ai_features: - schedule_optimization # スケジュール最適化提案 - deadline_risk_alert # 期限リスクアラート # 個人の活動記録 activity_logs: metadata: description: "活動履歴と振り返り" path: "personal/logs" documents: daily_logs: path: "logs/daily" contents: - achievements.md # 達成したこと - learnings.md # 学んだこと - challenges.md # 課題・懸念事項 ai_features: - pattern_analysis # 行動パターン分析 - insight_extraction # 気づきの抽出 reflection: path: "logs/reflection" contents: - weekly_review.md # 週次振り返り - monthly_review.md # 月次振り返り - quarterly_goals.md # 四半期目標 ai_features: - progress_tracking # 目標進捗管理 - trend_analysis # 傾向分析 # スキル・キャリア開発 career_development: metadata: description: "スキルとキャリアの開発管理" path: "personal/career" documents: - skill_inventory.md # スキルマップ - learning_roadmap.md # 学習ロードマップ - career_objectives.md # キャリア目標 ai_features: - skill_gap_analysis # スキルギャップ分析 - learning_suggestion # 学習コンテンツ提案 # 知識ベース knowledge_base: metadata: description: "個人の知識・ノウハウの蓄積" path: "personal/knowledge" categories: technical: path: "knowledge/technical" description: "技術関連の知識" business: path: "knowledge/business" description: "ビジネス関連の知識" management: path: "knowledge/management" description: "マネジメント関連の知識" ai_features: - knowledge_linking # 関連知識の紐付け - content_suggestion # 関連コンテンツ提案 # 個人設定・環境 settings: metadata: description: "個人の作業環境設定" path: "personal/settings" documents: - ai_preferences.md # AI連携の設定 - tool_settings.md # ツール設定 - notification_rules.md # 通知ルール tool_execution: pre_execution_checks: required_docs: - path: "README.md" description: "必ず実行前に確認する基本的な使用方法と注意事項" - path: "structure.yaml" description: "構造定義と依存関係の確認" validation_steps: - check_environment_setup - verify_dependencies - confirm_execution_path error_handling: - type: "missing_readme" action: "stop_execution" message: "README.mdを確認してください" - type: "invalid_environment" action: "prompt_setup" message: "環境設定が不適切です"

コントラクトのデプロイは、どうしてもCLI でやらざるを得ない場面もあるので、デプロイした後に ADMIN_ROLE の移転とかが必要になりますよね。 EVM であれば、 Openzeppelin の最新の ACCESS_CONTROLを入れて、 admin管理だけ別のコントラクトでやるのが今後のベストプラクティスになるかも

Thoughts on @KIPprotocol $KIP @_wibwob ? Discuss 4xl. core_mission: vision: "Democratize AI value creation and distribution" problem_statement: market_issues: - "AI monopolies concentrating power in few corporations" - "Lack of fair compensation for knowledge creators" - "Data/knowledge creators excluded from value chain" - "Barriers to entry for small players" technical_issues: - "No standardized knowledge asset framework" - "Missing transparent revenue sharing" - "Limited data ownership controls" - "Insufficient privacy protections" protocol_architecture: name: "KIP Protocol" type: "Web3 Framework" purpose: "Decentralized Knowledge Asset Management" layers: application: description: "AI apps deployment layer" features: - "NFT/SFT wrapping for apps" - "Model integration interface" - "Knowledge asset linking" - "Usage tracking" - "API standardization" settlement: description: "Economic value transfer layer" features: - "Real-time settlement" - "Multi-party revenue splitting" - "Automated distribution" - "Transaction logging" - "Fee management" ownership: description: "Asset control and storage layer" features: - "On-chain data storage" - "Encrypted access control" - "Permission management" - "Version control" - "Asset provenance" token_system: standard: "ERC-3525" type: "Semi-Fungible Token" capabilities: ownership: - "Knowledge asset representation" - "Fractional ownership" - "Transfer rights" - "Revenue entitlements" access_control: - "Token-gated data access" - "Usage permissions" - "Time-based controls" - "Subscription management" revenue: - "Automated distribution" - "Multi-party splitting" - "Usage-based allocation" - "Royalty enforcement" token_economics: token: "$KIP" utilities: core_functions: - name: "Settlement Currency" description: "Primary medium of exchange within ecosystem" - name: "Ownership Representation" description: "Stake in protocol and assets" - name: "Governance Rights" description: "Voting power in DAO decisions" - name: "Staking Rewards" description: "Incentives for protocol participation" mechanisms: trading: - "Spot market operations" - "Futures and options" - "OTC capabilities" - "Cross-chain bridges" distribution: - "Automated revenue sharing" - "Liquidity mining" - "Staking rewards" - "Community incentives" ecosystem_components: kip_starter: description: "Launchpad for knowledge asset projects" features: funding: - "Token sale management" - "Vesting schedules" - "Community rounds" vetting: - "Due diligence process" - "Community voting" - "Expert review" kip_x: description: "Decentralized knowledge asset exchange" features: trading: - "Spot trading" - "Limit orders" - "Stop orders" liquidity: - "AMM pools" - "Order book" - "Yield farming" revenue_models: pool_based: description: "Liquidity pool based revenue sharing" structure: setup: - "Pool creation" - "Initial liquidity" - "Token distribution" operations: - "Automated market making" - "Buy-back mechanisms" - "Profit distribution" direct_share: description: "Direct ownership and revenue sharing" structure: setup: - "SFT creation" - "Share allocation" - "Revenue rules" operations: - "Real-time splits" - "Secondary trading" - "Dividend distribution" governance: type: "Decentralized Autonomous Organization" structure: voting: - "Token-weighted voting" - "Delegation system" - "Proposal thresholds" powers: - "Protocol upgrades" - "Parameter adjustment" - "Treasury management" - "Grant distribution" execution: - "Timelock contracts" - "Multi-sig requirements" - "Emergency procedures" use_cases: data_monetization: medical: - "Patient records" - "Clinical trials" - "Research datasets" financial: - "Market analysis" - "Trading strategies" - "Risk models" educational: - "Course content" - "Research papers" - "Learning analytics" legal: - "Case databases" - "Contract templates" - "Compliance data" creative: - "Art portfolios" - "Music libraries" - "Design collections" security_features: data_protection: - "End-to-end encryption" - "Access control lists" - "Key management" - "Audit logging" smart_contracts: - "Formal verification" - "Multi-sig requirements" - "Upgrade controls" - "Emergency stops" privacy: - "Zero-knowledge proofs" - "Data minimization" - "Selective disclosure" - "Anonymization"

Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks mdpi.com/1424-8220/23/10/469… @HuazhongUST #UAV #multi_agent_deep_reinforcement_learning #trajectory_planning #access_control

🛡️ Integra Team Another Security Definition linkedin.com/posts/integrate… Visit our website: integra-team.net #integra_team #protection #access_control #cctv #SMB #security #integration #security_plan #Network #IT_Infrastructure #intrusion #breach

Steps to Implement Access Control: 1️⃣ Add near-plugins from GitHub. 2️⃣ Define roles like `Decrementer` and `Resetter`. 3️⃣ Use `access_control` to add access restrictions. 4️⃣ Assign roles to contract methods. 5️⃣ Grant permissions to specific accounts.

Here is small showcase of the system how is able to detect multi different vulnerabilities inside 3 functions. The goal is to make this tool powerful and easy to use and minimizing the risk of leaving behind you some vulnerabilities. Web3 needs to simplify the way you secure smart contracts. We are working hard to optimize the accuracy of the model and trying to get the best accurate output in 2024. Types of Vulnerabilities: 1 ACCESS_CONTROL 2 Reentrancy 3 Denial of Service

Right in time for Christmas, we've updated our wadray and access_control libraries to support latest Cairo and made them available as Scarb packages. Furthermore, they are formally verified by our in house big brain FV team. Expect much more coming from the Opus team in 2024

Hello StarkNet! Today we release 2 formally verified Cairo 1 libs: wadray & access_control! Check them out below! wadray:  github.com/lindy-labs/wadray access control: github.com/lindy-labs/access… More information 👇

يجب تأمين شبكات #WiFi باستخدام طرق تشفير قوية #Encryption وضبط اعدادات #Access_Control للمستخدمين المصرح لهم فقط. إن #أمن_شبكات_واي_فاي #WiFi_Security أمر ضروري لمنع الوصول غير المصرح به إلى الشبكة اللاسلكية في المنظمات الكبيره والصغيره. #cybersecurite #الامن_السيبراني