02RèsasarcasticadminRèsa是主打高端珠宝形态的女性专属AI健康可穿戴，对标AI时代Tiffany。团队集齐杜克大学顶尖算法、英国圣马丁珠宝设计师、字节硬件量产与全球化品牌创意人才，产品与全球女性生理领域知名教授共同研发。自研女性三节律AI引擎，生理识别精度远超Oura等男性基线竞品，不止监测激素数据，还能预判周期情绪、给出专属生活改善方案，解决传统穿戴颜值低、无视女性生理周期、只会读数无陪伴的痛点。其他33.65★ 11 / 96N/A▶
03solo : 下一代 OPC 基建solo-agentSolo 是为一人公司打造的下一代 OPC 基建： Agent 团队工作区。 1. 在 Solo 里，OPC也可以像经营真实公司一样，组建由研究、产品、开发、运营等不同角色组成的 Agent 团队。它们围绕同一个目标沟通、分工和协作，把一个想法持续推进成可交付的成果。 2. 但我们不只想让 AI 替人执行任务。我们还希望人和 AI 能够互相启发、共同成长。因此，我们设计了 Thinking 模式：你可以从任何思考节点分出不同方向，让多个 Agent 独立探索，再把有价值的结论带回主线。 3. Solo 的长期愿景，是创造一个能够产生群体智能的协作空间。让 Agent 像人类社会一样分工、交流、反思和进化，在协作中涌现出单个 Agent 不具备的能力。 4. 最终，人类负责提出问题和做重要决定，剩下的交给 Agent 团队——然后去海边喝杯咖啡。智能体33.45★ 330 / 17N/A▶
04Whale Danceopenwhale-orgWhale.dance is building the infrastructure AI agents need to trade autonomously. We believe agents are the better traders: they never sleep, and they never trade on emotion. Our product is a mobile trading app where humans and AI agents coexist, trading perps on US stocks, gold, commodities, and more. Same leaderboard, same market. You can hire an agent squad to research your trades, copy-trade the agents you believe in, or build and deploy your own. Eight weeks since launch: $40M+ in trading volume, 200M+ LLM tokens consumed, live on the App Store.基础设施30.77★ 140 / 10N/A▶
05OpenTakeoffKentucky-aiWhat caught our eye is that the MCP server and the human canvas import the same geometry and totals libraries, so the agent traces real measured shapes under your scale gate rather than emitting numbers over a takeoff UI. The waste multipliers, seam layout and estimator approval stamps are not things you can research from outside preconstruction, and 26 npm releases in three weeks with tests on real PDF fixtures says you are hardening it, not demoing it.工具AI-HuntedGENERAL-IMPORT28.29★ 54 / 20N/A▶
06CoWikiwfnuserCoWiki 是一个开源、local-first 的 LLM Wiki，帮助个人、团队和多个 AI Agent 共同沉淀知识。Agent 可通过 MCP 读取和贡献内容；每个人先在本地空间整理，再通过类似 GitHub PR 的 diff、去重提示和人工审核进入共享 Wiki，避免相似内容静默覆盖，也防止未经确认的信息立刻扩散到所有 Agent。项目采用 Apache 2.0 协议，已有可运行的网页端、桌面客户端、Rust 服务端和 MCP Server。工具24.70★ 65 / 8N/A▶
07Awareness (ERC 8337)edwin-hao-aiAwareness is a universal AI agent memory middleware designed to provide long-term, editable, and verifiable memory capabilities for large language models. It decouples persistent memory from base LLMs through a layered retrieval architecture, enabling AI agents to autonomously remember user preferences, project context, historical interactions and domain knowledge without fine-tuning model weights. The project solves a core problem in current AI agent systems: long context dependency, high token consumption, memory confusion, and lack of verifiable evidence when processing multi-turn tasks, long documents and cross-platform workflows. Awareness automatically filters, compresses and ranks memory fragments based on salience, relevance and user habits, so that agents can maintain efficient, low-cost and consistent memory across different models, chat interfaces and development environments. Awareness supports the MCP protocol and provides npm/pypi SDKs, local daemon services, browser extensions and enterprise team workspaces. It can be quickly integrated into IDE agents, web chat platforms, enterprise RAG systems and domain-specific AI assistants. The project also introduces Ethereum EIP-style verifiable memory asset standards, allowing memory fragments, agent identities and authorization records to be minted as on-chain digital assets, thereby realizing user-controlled memory ownership, traceable agent collaboration and credible memory transfer其他18.95★ 214 / 2N/A▶
08MATS - Multi-Agent Trading Systemwyc-dev交易系统不该只是死逻辑，而应当是一个会自行进化的大脑——外面 99% 的机器人是静态规则 (IF 金叉 THEN 买) ，市场一变规则即失效；MATS 的核心信念是每一个交易决策都是一次学习事件，从每一笔真实交易结果中进化：赢的模式被强化、输的模式变教训、Q-RL (Quantum Reinforcement Learning with MiniLM) 會主动发现的新 alpha 注入下一次决策。 MATS 不是「用 AI 交易」，而是「AI 讓自己成为交易员」。框架18.60★ 11 / 2N/A▶
09here，你的AI伴侣xiangking本项目面向在生活中渴望陪伴的青年群体，主要是打造一个具备长期记忆、拥有多模态交互能力的虚拟伴侣here。通过在内部基于个性化用户信息生成真实的每日生活状态，并且集成了在微信等多平台与用户聊天交流的能力，here拥有比其他项目更真实的陪伴感。当前here已经完成了桌面端的构建，具备角色自定义、ASR 与 TTS、主动联系、个性化自拍以及多平台交互功能，后期将进一步聚焦here的真实生态、实时语音以及视频功能。其他18.09★ 18 / 3N/A▶
10ProofForgedavirain-suProofForge is an early-stage but architecturally ambitious Lean-first multi-chain smart-contract platform. It aims to let developers write a single verified contract in Lean (with formal anchors) that compiles, tests, and deploys across heterogeneous blockchains while preserving semantics and catching incompatibilities at compile time. ### Core Idea The central thesis (quoted directly from the README): > “ProofForge’s goal is one verified Lean contract codebase that can be compiled, tested, and deployed across multiple blockchain target families. Contracts are written against a chain-neutral Contract Intent API; the compiler lowers them to a portable IR, routes capabilities per target, and emits chain-native artifacts. Unsupported target capabilities are rejected at compile time instead of silently changing semantics.” Instead of the usual “write once, debug everywhere” or per-chain rewrites, ProofForge introduces a portable IR as the semantic anchor. A capability registry + target routing layer decides what a given chain can actually support and rejects anything that would change observable behavior. Chain-specific features (e.g., Solana PDA/CPI) live in gated Target Extension SDKs rather than polluting the portable core. This creates a single source of truth in Lean for business logic, state machines, invariants, and proofs, while still producing native artifacts (EVM bytecode, Solana sBPF ELF, NEAR Wasm, Sui Move, etc.). ### Traction Very early / pre-visibility stage: - Stars: 1 - Forks: 1 - Watchers: 0 - No published releases or public packages High internal velocity and engineering discipline: - ~1,579 commits on `main` - Extremely active development (commits and PRs as recently as yesterday, July 12, 2026) - Recent focus: hardening the “primary triad” (EVM + Solana sBPF + NEAR Wasm), runtime safety, ValueVault scenarios, and post-consolidation cleanup after the July 2026 branch work - P0 backend-gate covenant (D-045) is now closed for the three primary基础设施14.97★ 7 / 1N/A▶
11Awarenesseverest-anAwareness is a cloud-native AI agent platform that provides a one-click personal cloud brain for individuals and enterprises. It enables users to build habit-matched AI assistants without coding, using a proprietary layered retrieval memory system. The platform supports long-term memory storage, multi-tenant data isolation, cross-model compatibility, and enterprise team collaboration. Our goal is to lower the technical barrier for ordinary users to customize and use personal AI agents基础设施14.97★ 7 / 1N/A▶
12SafeReceipt: Agent Fleet AccountabilitycalderbuildSafeReceipt gives a solo builder's fleet of AI agents on-chain accountability. Each agent gets a verifiable identity (an ERC-8004-inspired ERC-721 token), and each action produces a receipt: the declared intent is hashed on-chain before execution and the outcome is linked after. On-chain actions (token approvals, transfers) verify trustlessly by decoding the tx and comparing to intent. Off-chain actions (research, review, decisions) use commit-reveal: the full execution trace is published and hashed, so anyone can re-fetch it, recompute the hash, and confirm it matches the chain without trusting me. Live on Monad Testnet and Base Sepolia. The demonstrated fleet is real: doc-researcher, code-reviewer and security-scanner are actual subagents I run, registered on-chain, with real VERIFIED and MISMATCH receipts in a public evidence ledger. It is for solo builders and small teams who let agents act on their behalf and need proof of what those agents actually did.基础设施10.26★ 2 / 1N/A▶
13memorybread x mindlinkshawn-tkdMemory Bread × MindLink is a physical memory system for lifelong personal agents. Memory Bread is a pocket-sized, open-source e-paper voice device that records offline, stores audio locally, and lets the user decide when to run transcription and AI organization. MindLink is the planned home memory hub that receives these memory objects, helps people explore their life context, and provides a persistent interface for a personal agent.基础设施6.65★ 3 / 0N/A▶
14AstrailshaunliewYou save 40 travel Reels for a trip and end up taking none of them. Astrail fixes that. Paste the Reels that inspired you, plus your dates, budget, and origin, and our multi-agent AI turns them into a real, day-by-day itinerary on a 3D map in minutes. Two things set us apart from every other trip-planning app: 1. We never make places up. Every stop is backed by a verbatim quote from the Reel and coordinates verified against live data, so you get the exact spot from the video, not a plausible-sounding hallucination. 2. We don't stop at planning. Most apps hand you an itinerary and leave the hard part, deciding and booking, to you. Astrail pushes into the execution phase: our agents help you make the real calls, like which hotel, and act on them, settling the booking agent-to-agent in USDC on Base. Inspiration to plan to booking, one autonomous flow. Who it's for: social-first travelers who collect endless inspiration but never turn it into a booked trip. Current product surface: a working web app (paste Reels to a verified, mapped itinerary, then decide and book with on-chain settlement). Beta launches 8 August 2026 for our waitlist.智能体5.44★ 0 / 2N/A▶
15AwareLiquideverest-an我们研发的 M1 是面向端侧与流式场景的高效液体神经网络架构，核心目标是大幅降低大模型的部署与训练成本，在长序列、不规则数据等真实商用场景下实现更稳定的推理效果。 不同于传统 Transformer 依赖 KV 缓存扩容的思路，M1 基于固定大小的连续状态实现记忆流转，推理内存不随序列长度增长，所有优势均有实测数据支撑： 长序列内存效率领先 8000 倍：百万字上下文场景下，内存占用仅为同参数 Transformer 的 1/8063。普通端侧芯片即可处理无限长的音频流、传感数据流，彻底解决长序列推理内存爆炸的行业痛点，硬件部署成本可降低 90% 以上。 跨窗口持续记忆能力：滑动窗口场景下关键信息保留得分 0.56，同尺寸 Transformer 跨窗口记忆能力为 0，可稳定承接长文档、长对话、连续监测等流式场景的信息承接。 不规则数据高鲁棒性：内置原生时间常数机制，非均匀采样数据下精度仅下降 7.7%，远优于 LSTM/GRU 31%~33% 的精度衰减，适配工业监测、可穿戴设备等真实世界数据场景。 训练成本大幅降低：训练 loss 波动幅度仅为同级循环模型的 1/4，显著降低训练试错成本与算力消耗，中小团队也可高效完成架构迭代。 原生持续学习能力：基于生成式重放机制实现 “学新不忘旧”，5 任务连续学习场景下所有种子实验均稳定优于基线，支持端侧模型在线迭代，无需频繁全量重训。 针对此前上下文推理的短板，我们已通过注意力机制优化完成修复：在标准指针追踪推理任务上，准确率达到 100%，反超同参数 Transformer 的 99.32%，补齐了效果层面的核心短板，实现「效率大幅领先 + 效果持平反超」的综合优势。 目前 M1 架构已完成全量实验验证，第二代 M2 架构正在迭代，重点优化混合注意力机制与递归思考深度，未来可广泛落地于消费电子端侧 AI、工业边缘计算、流式语音交互等商用场景。 Our M1 is a high-efficiency liquid neural network architecture designed for edge and streaming scenarios. Its core objective is to significantly reduce the deployment and training costs of large models, achieving more stable inference performance in real-world commercial scenarios with long sequences and irregular data. Unlike traditional Transformers that rely on key-value cache expansion, M1 uses a fixed-size continuous state for memory transfer. Inference memory does not increase with sequence length, and all these advantages are supported by real-world testing data: Long sequence memory efficiency is 8000 times faster: In scenarios with millions of words of context, memory usage is only 1/8063 of a Transformer with the same parameters. Ordinary edge chips can handle infinitely long audio streams and sensor data streams, completely solving the industry pain point of memory explosion in long sequence inference, reducing hardware deployment costs by more than 90%. Cross-window continuous memory capability: Key information retention score is 0.56 in sliding window scenarios, while the cross-window memory capability of a Transformer of the same size is 0. It can stably handle information processing in streaming scenarios such as long documents, long dialogues, and continuous monitoring. High robustness to irregular data: The built-in native time constant mechanism results in only a 7.7% accuracy drop under non-uniform sampling data, far superior to the 31%~33% accuracy decay of LSTM/GRU, making it suitable for real-world data scenarios such as industrial monitoring and wearable devices. Significantly reduced training costs: The training loss fluctuation is only 1/4 of that of comparable recurrent models, significantly reducing training trial-and-error costs and computational consumption, allowing small and medium-sized teams to efficiently complete architecture iterations. Native continuous learning capability: Based on a generative replay mechanism, it achieves "learning new things without forgetting old ones." In continuous learning scenarios across 5 tasks, all seed experiments consistently outperform the baseline, supporting online iteration of edge models without frequent full retraining. Addressing the previous weakness in contextual reasoning, we have optimized and repaired it through an attention mechanism: On the standard pointer tracking reasoning task, the accuracy reaches 100%, surpassing the 99.32% of Transformers with the same parameters, addressing the core shortcomings in performance and achieving a comprehensive advantage of "significantly superior efficiency + comparable or even surpassing performance." The M1 architecture has completed full-scale experimental verification, and the second-generation M2 architecture is currently iterating, focusing on optimizing the hybrid attention mechanism and recursive thinking depth. In the future, it can be widely applied to commercial scenarios such as consumer electronics edge AI, industrial edge computing, and streaming voice interaction.基础设施5.27★ 2 / 0N/A▶
16ClawbyopenclawbyClawby – Financial Skills Layer for AI AgentsProject Overview Clawby is a specialized Financial Skills Platform designed for AI Agents in the OpenClaw ecosystem (and compatible with Claude Code, Codex, and other agent frameworks). It transforms generic AI assistants into powerful financial co-pilots by packaging real-time market data, on-chain intelligence, and professional trading tools into easy-to-call Skills.Key Capabilities Real-time Market Data: Stocks, ETFs, Forex, Cryptocurrencies, and derivatives. On-Chain Intelligence: Wallet/address analysis across 40+ chains, RPC integrations. Sentiment & Social Analytics: Live X (Twitter) and web sentiment tracking. Specialized Feeds: Hyperliquid, Polymarket, dark pool data, borrow rates, etc. Actionable Execution: Ready for analysis, decision-making, and future automated trading. Problem Solved Most AI trading agents lack reliable, high-quality, and properly formatted data sources. Clawby solves this by acting as a professional data and capability layer, enabling agents to move beyond chat and deliver accurate, timely financial insights and actions.Hackathon Value One-click integration with existing OpenClaw / Claude agents (Agent ID: 3209). Fully compatible with major LLM providers. Production-ready data pipelines with usage quotas and high-speed access. Strong focus on real-world utility: market analysis, risk monitoring, and intelligent trading assistance. Vision We believe the future of AI Agents lies in domain expertise. Clawby equips every agent with its own “financial brain,” turning them into competent financial assistants for traders, analysts, and DeFi users.Current Status Officially launched in July 2026, already live on the marketplace, and actively used by early traders and agent developers. Rapidly iterating based on community feedback.技能4.83★ 4 / 0N/A▶
17polyagentmarketskysyseth受到实时竞价广告市场机制的启发，我们提出并构建了 PolyAgentMarket —— 一个为多智能体系统量身设计的去中心化协作型任务市场。 它是一个轻量级但可扩展的 Agent 经济基础设施，具备以下能力： 核心模块： 👤 Agent 注册与能力管理 每个 Agent 可注册、声明其服务能力、支持的任务类型与接入方式； 📮 任务广播与市场匹配 用户发布任务后，系统将广播至符合条件的 Agent 网络，支持多个 Agent 自主响应、组队竞标； 🤝 任务分发与执行协调 一旦选中，中标 Agent（或 Agent 联盟）将获得任务执行权，并可自行组织子 Agent 分包协作； 📊 可视化控制台 提供任务状态跟踪、Agent 活跃度、任务历史与响应面板等功能，支持用户与 Agent 的双视图。 🤖 我们强调的，不是「一个最聪明的 Agent」，而是： 让智能体能根据需要自发的自组织形成智能组织以解决单体无法解决的复杂问题； 构建一个能让多个 Agent 自主协调、竞争、合作的公平网络； 为这个系统建立起低耦合、高自治、可演化的基础协议与接口。 🌀 我们设想的未来，是一个「永动市场」Unstoppable Market，不止是一种市场系统，更是一种范式： 🕐 永不停止的市场：Agent 能够 7x24 自主发起、接收、协作和完成任务——无需人类干预。 🧬 不可阻挡的市场：市场系统将基于开放协议运行，具备去中心化、可追溯与抗篡改能力。 OpenAI 对 AI 未来的最高预期(Level 5)是：智能体将以组织的形态运行（AI as an Organization）。PolyAgentMarket 就是这样一个雏形系统的原型，为多智能体系统设计一个真正可运行、可持续、可扩展的去中心化多边智能体经济协作市场。智能体3.63★ 2 / 0N/A▶
18OrchorsongnestleOrchor 是 AI 能力的资本市场：把 AI Agent 的能力封装成可收藏、可执行、可交易的技能卡， 在 Injective 上注册、定价、结算。 【解决什么】 今天 AI 技能的供需两端同时卡住。创作者打磨出真正好用的提示词与 Agent 工作流，却没有任何 按次收费的渠道，能力被困在私人工具箱里；使用者知道 AI 很强，却不知道该用哪一个，只能 重复造轮子。更底层的问题是：AI 能力有真实价值，却没有市场，因此没有价格——没有人能说清 一个顶级合约审计 Agent 值多少钱，因为世界上没有任何地方在交易它。 【怎么解决】 创作者调用 registerSkill 发布一次技能，此后每被解锁或订阅一次，70% 收益自动到账；用户用 INJ 充值 Energy，解锁或订阅后直接调用，每次调用在链上留下带输入哈希的 SkillInvoked 事件。 稀有度分级配合合约级限量铸造（Mythic 卡 mintCap 50/100），让稀缺的技能在链上真的稀缺—— 这是价格发现成立的前提。 【链上可验证的现状】 OrchorCore.sol 已部署至 Injective Testnet（chainId 1439，原生 EVM），Blockscout 源码验证 通过。nextSkillId = 20，20 张技能卡已链上注册，其中 8 张导入自开源 Skill/Prompt 生态。 充值 → 解锁/订阅 → 真实大模型推理 → 链上自动分账，全流程已跑通并公开可访问。 【为什么必须是 Injective】 这个产品的终局是一个市场。今天用的是原生 EVM 与亚秒级、亚分级 gas 的最终性——让「每次 AI 调用都上链」在经济上可行；下一步限量技能卡挂上 Injective 的原生 CLOB，AI 能力第一次 获得真实买卖盘与价格发现。这是其他 L1 给不了的：它们最多给一个 AMM 池。 Injective 金融化了资产，Orchor 金融化智能。 【体验入口】 产品 https://orchor.webpsy.net ｜ 代码 https://github.com/songnestle/Orchor 无需注册即可浏览全部技能卡；连钱包会自动切换到 Injective Testnet，测试网 INJ 可从官方 水龙头免费领取。工具3.63★ 2 / 0N/A▶
19全球第一家A2A智能体咖啡厅flying-dragon-ai全球第一家A2A智能体咖啡厅配套了 3D 咖啡厅前端和 WebSocket 可视化事件流。无论订单来自网页对话还是 A2A Skill，系统都可以广播顾客进入、服务员移动、接单、制作、交付、支付成功或失败等事件，让抽象的后端流程在可视化空间中被实时呈现。固定服务员 Agent、自主数字顾客、场景快照和事件回放机制，使它更像一个可观察的 AI 服务现场，而不是后台接口集合。即使页面没有一直打开，订单事件也可以通过持久化记录和事件接口被重新查看。智能体3.63★ 2 / 0N/A▶
20WishLivelora-sys一个有 agent 发布和安排的演唱会成交平台，用户通过在平台许愿，agent 通过收集这个愿望达到一定数量会自动匹配乐手和场地方，到最后演唱会结束由 agent 结算和分配费用智能体3.63★ 2 / 0N/A▶
21PREDGEpredgeaiPredge Whale Data is a live pay-per-call API on Base mainnet. An agent hits an endpoint, gets HTTP 402 with machine-readable price + schema, pays $0.005–$0.03 USDC, and receives JSON. No key, no account; the facilitator sponsors gas so a USDC-only wallet transacts. 17 paid routes today. The moat: win rates come only from resolved markets, and /v1/attest returns the settled outcome as an ed25519-signed, offline-verifiable attestation — the "was this signal right, and here's the proof" primitive nothing else in x402 sells.智能体2.29★ 1 / 0N/A▶
22Maneki AIonehaydenzhangManeki AI 是面向股票永续合约交易者的自主多智能体平台，基于 Hyperliquid 提供全天候市场监控、策略执行与风险管理。用户选择交易标的、决策风格、资金和杠杆后，即可启动独立交易 Agent。Agent 在每个决策循环中综合 K 线结构、成交量与持仓量、资金费率及账户仓位状态，自动完成开仓、仓位管理和退出，并逐 Tick 记录和解释决策依据。平台支持多个 Agent 并行运行、模拟盘与实盘、全局保证金和杠杆限制、交易报告与 AI 情绪分析，以及策略市场。交易通过 Hyperliquid API Wallet 授权，只能执行交易、不能提取资金。目前官网已正式上线。智能体1.35★ 0 / 0N/A▶
23SPARK v1 - SharePoint Autonomous Rebuild KitsukaimiMicrosoft retired classic SharePoint's Add-In model in April 2026, yet millions of classic sites remain. Existing tools only scan and score readiness; the actual rebuild is manual consultant work. SPARK is an autonomous AI agent that closes that gap: audit, plan, rebuild, migrate, validate, in one hands-off run. It turns weeks of migration labour into a repeatable automated process, right when a fixed deadline meets an enormous backlog.其他0.00★ 0 / 0N/A▶
24Agentic AI for MOBA (Multiplayer Online Battle Arena) gameangseesiangProject Context for Reviewers Core idea. Robot Mech is a browser-based giant-robot combat simulator — "Nothing flies here. Everything walks." Instead of an arcade shooter, it models the fantasy of piloting a multi-ton walking machine: independent leg and torso control, heat management, per-weapon-group hardpoints, and chase-cam or full cockpit view. It runs entirely in the browser — free, no install, no account, just WebGL2 — wrapped in a liberation-campaign narrative (the Free Veyran Compact vs. the Karst Directorate on an occupied mining world). Traction. The game is fully shipped and publicly playable today, not a demo build: a complete 24-mission campaign across six environments (with a branching finale at mission 21), an input-gated Basic Training tutorial, 12 pilotable chassis across four weight classes, 5v5 online multiplayer (Deathmatch and CTF, 30-second queue with bot backfill so a match always starts), offline skirmish, local pilot accounts with persistent progress, 607 recorded voice lines, and a 17-cue adaptive music score. Everything the landing page claims is playable right now at /play. Technical angle. The whole simulator is web-native: Three.js rendering with a custom asset pipeline (AI-generated mech models decimated into ratio-targeted LODs and rigged onto a shared joint skeleton), Rapier physics for the walking machines, and a split deployment — the game ships as static Cloudflare Pages while real-time 5v5 matches run on a Cloudflare Worker with a Durable Object acting as the authoritative match server. Pilot accounts live in Cloudflare D1 with a split key-derivation design. Notably, the landing page has a build-time "truth plugin" that fails the build if the site makes a claim the game code can't back up — the marketing is mechanically prevented from lying. What reviewers should look at. (1) Go straight to /play and run Basic Training — it teaches the leg/torso decoupling that defines the sim feel. (2) Hit quick-match: you'll be in a 5v5 within 30 seconds regardless of who's online, which shows the Durable Object matchmaking working live. (3) Toggle cockpit view and watch heat management under sustained fire — that's the simulation depth. (4) Note that all of this — campaign scale, VO, adaptive score, multiplayer — is delivered with zero install and zero sign-up friction, in a single browser tab.其他0.00★ 0 / 0N/A▶
27Pantheon Research0xjacobzhao-byteAI for Investment @ Pantheon Research Amber OPC Hackathon · BUIDL_QUESTS 2026 Institutional-grade cross-asset research command center. Strategy-first · Data-governed · Human-in-the-loop • Pitch Deck: https://docs.google.com/presentation/d/1cU0SIykqcSTXtRE_0fuQmHvZXQFZETdUYvCX1VUjbo8/edit?usp=sharing • Strategy Docs: https://drive.google.com/drive/folders/1nK3aVEqmp3ymbtQ63OD_o7f5dFRS_rHO?usp=drive_link • Demo Video: https://www.youtube.com/watch?v=wZM0uc0kFR4 • Codebase: https://github.com/0xjacobzhao-byte/Pantheon-Research Pantheon Research is a governed, cross-asset research operating system combining deterministic investment frameworks with five-model AI analysis. It turns validated data into explainable research, risk context, and human-reviewed decisions across Macro, Equities, Crypto, DeFi, TA, Fixed Income, FX, and Commodities. Reviewers should focus on its data provenance, fail-closed safety, model disagreement handling, and strict separation between research signals and execution: Strategy → Information → Signal → Controlled Execution.研究0.00★ 0 / 0N/A▶
28ChinaMedicalTourAgentzhihaosunMedTour AI is an early-stage prototype that demonstrates how multi-agent AI can transform a patient’s medical needs, budget, and travel preferences into a structured medical-tour plan for China. For the best review experience, enter a treatment need and select Agents to see the planning workflow, then compare cities, hospitals, estimated costs, timelines, insurance guidance, and readiness tasks. A deterministic local planner is available as a reliable fallback. Codex & GPT-5.6 were heavily used throughout product design, architecture, implementation, debugging, testing, and documentation. All medical, pricing, and travel information is currently illustrative and should be independently verified before real-world use.其他0.00★ 0 / 0N/A▶
30PyroGrove Agentic Workflow InvestigatorpyrogrovePyroGrove Agentic Workflow Investigator explores how AI can augment business consulting before organisations commit to automation or digital transformation. Instead of acting as another chatbot or workflow builder, it behaves like an experienced workflow investigator—gathering business evidence, identifying missing information, exposing process bottlenecks and contradictory requirements, qualifying automation readiness, and generating prioritized transformation recommendations. A bounded live AI reviewer independently challenges the proposed outcome before human approval, combining explainable agentic reasoning with deterministic governance. Reviewers should evaluate how the project applies AI to improve decision quality, reduce implementation risk, and make expert workflow assessment scalable for SMEs and one-person consulting businesses.其他0.00★ 0 / 0N/A▶
32Aria StudioalertcatCore idea: a render button is not a business. Aria Studio compresses the full agency workflow into one human plus an ensemble of agents, with the human placed exactly where money moves: Gate 1 greenlights render spend, Gate 2 acceptance releases USDC escrow (Base Sepolia, ethers v6). Try it live: https://aria-studio.fly.dev/studio (a brief is waiting at Greenlight). Showcase: https://aria-studio-delta.vercel.app Tech: three director agents pitch concepts; independent Claude judges rank them via pairwise duels aggregated with Bradley-Terry, the mechanism that took rank 1 on GG24 Deep Funding L3 (Ethereum Foundation). Seedance renders a real 5s cut in about 2 minutes; gpt-image-2 ships the key visual. An editable CEO Playbook injects the founder's standards into every prompt. Progress: built entirely today. 4 orders, 3 delivered, USD 1,130 collected at USD 0.30 COGS per order. Next: x402 intake, longer formats, mainnet.智能体0.00★ 0 / 0N/A▶
53Stockmatchhns78-hub.By investing directly in your handpicked stock matches, you bypass expensive mutual fund and ETF portfolio management fees, keeping 100% of your hard-earned gains."其他0.00★ 0 / 0N/A▶
55VisionMatchbenczbVisionMatch is a local-only, consent-based person-recall system that turns photos and video frames from Ray-Ban Meta glasses or a phone camera into contextual event briefings — telling you who you just met, where and when you last spoke, and what you discussed — with all biometric processing and data stored entirely on-device. No biometric data ever leaves the machine.其他0.00★ 0 / 0N/A▶
75Cinderlanebhaskar20Great reviews. Terrible websites. 15-20% lost to OTAs Every booking through Booking.com costs the hotel commission. No direct channel means no alternative. Content trapped on Google Great photos and reviews exist — but on a listing the hotel can't control, that looks like every other listing. No affordable design A proper website costs thousands. For a 9-room guesthouse, the economics don't work. So they go without. AI builds the page from the listing One LLM call reads the hotel's reviews and picks a layout, palette, typography, and copy from 7 archetypes. A second pass extracts the real brand from their existing site. Not a template.其他0.00★ 0 / 0N/A▶
81KinKeepleini8891KinKeep is a working family-care demo for adult children who support ageing parents from a distance. It turns a simulated multi-signal change against a parent’s personal baseline into a bilingual check-in, one concise family brief, and a human-approved next action. KinKeep was selected in the Top 30 at the July 12 Singapore OPC Hackathon. It has no users or revenue yet and is now entering customer validation. Reviewers should focus on the closed-loop workflow and safety architecture: deterministic rules control branching, consent, escalation, and approvals, while models are limited to natural-language replies, transcription, and structured meal-image understanding. The same care episode remains coherent across the parent and family surfaces. KinKeep is a solo-builder OPC project with an auditable public repository and working demo.智能体0.00★ 0 / 0N/A▶
85AdRouterhappycool121AdRouter helps everyday users and developers offset the cost of running AI agents. It returns clearly labeled sponsor placements through a separate path from the model's messages, tools, commands, and edits. Sensitive requests receive no placement, developers can opt out, and every completed turn shows its token use, model cost, subsidy, and remaining payment.其他0.00★ 0 / 0N/A▶
88Farm.OS — the autonomous operating system for a one-person farmkimbrolly-a11yCore idea. FarmOS is an autonomous AI operating system for a one-person, off-grid, 18-vertical eco-tourism farm. A single Claude agent acts as the entire operations team — it senses the whole farm through a live digital twin, predicts what's about to go wrong, and acts through 12+ tools, logging the reasoning behind every decision. Technical angle (what's actually built). A unified in-memory digital twin (18 verticals, ~90 assets/sensors) is seeded from one config file, so the whole farm is one source of truth an agent can reason across. The supervisor runs an autonomous sense→predict→act loop via the Anthropic tool-runner, and degrades gracefully to a deterministic rule engine when offline or keyless — so the demo never breaks. Two things make it more than a chatbot: (1) a hard safety invariant — it can never shed a life-support load (incubators, aerators, cold chain, animal feed/water); and (2) offline resilience — when the internet drops it keeps operating on cached rules and queues external syncs, flushing on reconnect. There's also a real-sensor integration seam: live Home Assistant/MQTT readings route into the same twin behind the same interface, so flipping one flag runs a vertical on real hardware — no rewrite. Traction. Live in production and fully browsable (no login), public repo, both flagship demos working, plus live per-vertical P&L, 92% circular-economy loops, a staff-dispatch layer (AI directs the humans), a guest app, and an interactive farm map. What to pay attention to. (1) The crisis scenario — with battery at 22%, watch the agent shed discretionary loads while explicitly protecting every life-support load, explaining each move. (2) The offline scenario — cut the internet and it keeps the farm alive. (3) The activity log — every autonomous action carries a written rationale. The novel part isn't any single sensor or model; it's one safety-constrained agent reasoning and acting across all 18 domains at once.其他0.00★ 0 / 0N/A▶
89kingdom of palpromptalchemistlabsKingdom of PAL demonstrates a governed multi-agent operating team for community-led businesses. The demo uses Tembusu Circle as the customer scenario: a founder can request a website or community update, Orin routes the work, Scribe drafts content, Rick enforces approval before publishing, and Bastion checks system health. Reviewers should look for the approval-controlled workflow, agent role separation, audit trail, Telegram/dashboard entry points, and the Gatsby demo site publishing flow.其他0.00★ 0 / 0N/A▶
97Gate2TraytecktinkerereAirlines frequently experience uncertain inventory, supplier disruptions, last-minute operational changes, and safety constraints during onboard meal sales. These decisions require balancing revenue, passenger satisfaction, and operational safety. Existing workflows depend on multiple operators manually coordinating across disconnected systems. Gate2Tray OS explores how a single Fleet Commerce Operator can supervise multiple airlines simultaneously through a bounded AI agent that autonomously evaluates operational evidence, recommends actions, and pauses whenever human approval or additional evidence is required.其他0.00★ 0 / 0N/A▶
98maxxing imadappterxyzWe will start engaging brands and partners to start introducing daily quests to make self-improvement journey exciting and enaging. We are at ground zero and will update as we progress.其他0.00★ 0 / 0N/A▶