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AI & ML Full Course 2026! #BigData# #Analytics# #AI# #MachineLearning# #DataScience# #IoT# #IIoT# #Python# #RStats# #TensorFlow# #JavaScript# #ReactJS# #CloudComputing# #Serverless# #DataScientist# #Linux# #Programming# #Coding# #100DaysofCode#

RunInfra beta is live! Describe your use case. Tell us what to optimize for, which models, and your latency + cost targets. We handle the rest: kernels, quantization, serverless deploy, routing, autoscaling. And your optimized model now deploys straight through our connector integration. More coming soon. Owning your AI isn't a someday thing anymore:

我发现 AI Agent 真正的瓶颈是它拿不到实时数据、不会自己查 Reddit / GitHub，一旦离开模型知识库，就开始脑补。 所以我最近找到一个工具：AgentKey。 定位很简单：给 AI Agent 一把统一的外部数据钥匙。 你可以把它理解成：Claude Code / Codex / Cursor 的 API Gateway + MCP 工具层 + 数据路由器。 以前你想让 Agent 做真实研究，可能要分别配置： Tavily、Firecrawl、Brave Search、Reddit API、X API、 Finance API、Crypto API、GitHub 数据源、电商数据源 每个都有自己的 key、额度、参数、失败处理。 AgentKey 的思路是：用一个 master key，把这些外部数据能力统一接到 Agent 里。 它最适合做的是这几类任务： 1. 发现热点 让 Agent 去 Reddit、GitHub、Product Hunt 找最近被讨论的 AI 工具。 2. 验证项目 读取 README、Docs、Issues、Release，看一个开源项目到底有没有价值。 3. 做竞品研究 抓官网、定价页、评论、用户吐槽，判断一个 SaaS 机会是不是真需求。 4. 做内容选题 把看到一个工具升级成多源验证后再写一篇高质量长文。 5. 做投资辅助研究 查价格、技术指标、宏观数据、crypto on-chain、社交叙事，但不直接给买卖建议。 我认为它真正有价值的地方是：让 Agent 从生成器变成研究员，以前的 AI Coding 工作流大多是：你给信息 → Agent 写代码。 更高级的工作流应该是： Agent 自己找信息 → 验证信息 → 形成判断 → 再写代码 / 写报告 / 写内容。 这才是 Agent 工作流的分水岭。 但也有几个坑： 第一，不要把外部数据当真理。 Reddit、网页内容都有噪音，AgentKey 只是取数，不负责判断。 第二，不要把 master key 放前端。 这种 key 应该只放在本地 CLI、后端、Serverless、私有 Agent worker。 第三，不要一上来做复杂自动化。 先做 3 个固定场景就够了：GitHub 项目研究、X/Reddit 热点发现、SaaS 竞品与用户痛点分析 如果你正在用 Claude Code / Codex / Cursor 搭 Agent 工作流，AgentKey 值得研究。 它像是给 Agent 装上了外部世界的传感器。 未来真正强的 AI Coding 系统是能自己发现问题、收集证据、验证假设、再交付结果。

Dear ICP community, the Internet Computer has now been running strong for 5 years 👏👏👏 Here is a celebratory preview of ICP "cloud engines," the sovereign frontier cloud technology the network shall soon provide from Main points: — Cloud engines enable anyone to spin up their own sovereign frontier cloud. The technology involves an extraordinary inventive step, in which cloud is created from a mathematically secure network of nodes. The nodes run as part of the Internet Computer network ( but are selected and configured by the cloud engine's owner. — The frontier cloud provided by engines is strongly focused on enabling AI agents to build and update online applications and services for us. The world is changing fast, and nearly all new online apps and services are already being built with the help of AI, and thus cloud engines target the future of cloud. — Software hosted on cloud engines is tamperproof, which means that it is immune to infrastructure hacks, because it runs inside a mathematically secure network protocol, rather than on computers directly. This means that AI agents, and those building with them, don't need to have a security team in the loop, or to trust someone else's security team. This is crucial, because in the future, non technical people will demand the freedom to build with full automation — where they just need to issue instructions to AI about what to build, and don't need to worry about anything or anyone else. Of course, apps and services running on engines are also vastly safer from the new breed of hacker being enabled by frontier AI. (The cloud engines themselves are also "tamperproof." Even if a hacker gains physical access to some portion of a cloud engine's nodes, and can make arbitrary changes, the computations and data of the hosted apps and services cannot be corrupted or interrupted so long as the network's fault bounds aren't exceeded. The recent hack of Vercel, a major cloud platform, which gave hackers access to the apps it hosted, provides additional perspective on the importance of this advantage.) — Software hosted on cloud engines is guaranteed to run, so long as a sufficient number of the engine's nodes are running. This means that AI can build applications and services without the need to have a human systems admin team constantly tinkering with the underlying platform to keep it running, which is again crucial, because in the future, non technical people will expect the freedom to use AI to build without the support of others. — New frontier programming language technology, in the form of the Motoko language developed by Caffeine Labs, leverages seminal "orthogonal persistence" technology that unifies program logic and data to deliver further unlocks for AI (Motoko is the first computer language being developed that targets agents that are writing software rather than humans engineers per se). Nowadays, AI can build and update production apps at a prodigious rate, even at the speed of conversation. But it can also make mistakes, and there's a risk that an update it creates might be "lossy" in the sense it causes some transformed data to be lost. Again, in this new world, it's both undesirable and impractical for everyone to have to have a systems admin team on-hand to detect lossy updates and roll them back, but Motoko provides a solution: it can detect new software updates are lossy before they are applied, reducing potentially catastrophic errors by AI to harmless coding retries. — Software hosted on cloud engines is "serverless" but unlike traditional serverless software, directly it directly incorporates data through "orthogonal persistence." Another key purpose is simplify backend software logic and fuel the modeling power of AI by increasing abstraction (sorry for the technical language!!!). Put simply, this enables AI to produce more sophisticated backends, faster, and at dramatically lower costs, as measured by the number AI API tokens consumed during coding. (Tip for the technical: orthogonal persistence is a new paradigm where "the program is the database," and data lives inside program variables, which is possible because it's as if hosted software runs forever in persistent memory). — An expanding database of skills at shall make it possible to develop and directly deploy apps and services to your cloud engines directly from Claude Code, Perplexity, Codex and other AI platforms. Further, your account on can be connected, so that new apps and updates created through conversation automatically appear hosted from your cloud engine. In the future, R&D is going to be very seamless. You converse with AI, and your secure and unstoppable apps or services are created or updated. Cloud engines are designed to directly support this "self-writing cloud" future where we can work hands-free. — Tech sovereignty is becoming a huge issue worldwide, with governments and corporations seeking to create sovereign tech stacks owing to geopolitical tensions. Increasingly, people are realizing that tech provided by foreign nations can come with hidden backdoors and kills switches, from the base platform, right up through hosted apps and services. ICP technology is open source, and those building on ICP using AI own their own source code. When you have the source code, you can verify that there are no backdoors, and when you own the source code thanks to AI, you can update it at will, freeing you from vendor lock-in. But cloud engines take sovereignty much further... — You create a cloud engine by selecting the nodes that will be combined. You can choose the class of nodes used, and their number, but more importantly, you can choose who operates the nodes, and where they are located. Almost any configuration is possible, because the Internet Computer scales the security privileges afforded to hosted software within the network according to configuration (software hosted on cloud engines can directly interoperate with software on other engines and traditional subnets, but base restrictions are applied according to security rules). A cloud engine can be created within a region such as Europe, to comply with regs such as GDPR, or completely within a sovereign state like Switzerland or Pakistan. But cloud engines go further still... — Sovereignty is also about freedom from vendor lock-in. Cloud engines are essentially ICP (Internet Computer Protocol) network configurations, and this means the underlying compute nodes they combine can be swapped out without interrupting their hosted apps and services. This is a big deal. In addition, cloud engines now support nodes that are instances running on Big Tech's clouds, in addition to nodes that are dedicated specialized hardware, as per the Gen I and Gen II nodes that dominate the Internet Computer today. For example, it is possible to have an engine running across different AWS data centers, say, and then reconfigure the engine to run across a mixture of AWS, Google, Azure and Hetzner for even more resilience, without the users of hosted apps and services noticing a thing. That's true freedom. — Sovereign AI is becoming increasingly important too, and cloud engines allow special "AI nodes" to be added to them, so that hosted software can perform inference on hardware provisioned by the owner from a location the owner has selected. Even though the AI nodes are only accessible within the cloud engine, they can still benefit from the forthcoming Internet Intelligence Gateway (IG), which will make it possible to validate inference performed on key frontier open weights LLMs, even when the inference is performed on completely independent AI clouds. When the results of inference are received, this technology can verify that neither the prompt+context (input) nor the inference result (output) have been modified, and that the results were produced by the precise LLM expected. This ensures that AI clouds don't cheat by running inference on cheaper models than are being paid for, and bad actors aren't modifying the inputs or outputs to surreptitiously insert advertising into results, say, or change facts, or insert malware when code is being generated. What's super cool about this technology is the cost of the verification is scalable. A very valuable additional security can be achieved with only 1-2% of extra cost. — Scaling apps and services when they hit capacity limits is another thorny problem that cloud engines help the world address. Engines make scaling possible without rewriting or reconfiguring software. The query workload capacity of hosted software can be horizontally scaled simply by adding new nodes to an engine, and nodes can also be added in geographical proximity to demand. Meanwhile, update workload capacity can first be scaled-up by swapping an engine's nodes out for the next class up, and then when no larger class of node is available, horizontally scaled-out by "splitting" the engine into two, which doubles available capacity. (Technical tip: horizontally scaling update capacity by splitting engines requires multi-canister architectures). — For those who have been following how Caffeine builds apps that can efficiently store large numbers of files, I should mention that apps built on cloud engines will also support the new ICP Blob Storage cloud network (since cloud engines currently have up to about 3 TB of memory, which apps storing large amounts of files can easily exceed). We are also working on allowing blob storage nodes to be added to cloud engines, to enable sovereign mass blob storage within an engine, similarly to how AI nodes can be added currently. — Lastly, but certainly not least, I should mention that cloud engines are multi-blockchain capable, and ready for digital assets, thanks to the clever math at their core. For example, an e-commerce service built on a cloud engine can securely accept and custody stablecoin payments, or a multi-chain DEX could be hosted. Further, engines can support software autonomy (software orchestrated and controlled by other autonomous software, in a decentralized way) and can themselves be orchestrated by SNS technology, and thus run autonomously too. Today, though, the focus is on *mainstream* cloud. This year, the cloud industry will generate approximately one trillion dollars in revenue. That number is already huge, but is expected to grow to two trillion dollars by 2030. After years of continuous development, which have seen more than $500m spent on R&D, the Internet Computer network is now tacking directly toward this mainstream cloud market with cloud engine technology. In their first version, cloud engines are not meant to be a cloud panacea. For example, currently they are not ideal for working with big data. You should use something like DataBricks for that. Cloud engines are carefully targeted at enabling AI to produce traditional online applications and services, including SaaS, in a safer and more productive way, which represents a new market segment with tremendous potential. Of course, DFINITY will continue to work relentlessly to push forward ICP's capabilities, so expect further developments. It's worth mentioning that this cloud segment isn't just about creating new apps and services using AI, it's also about replacing legacy systems and apps built on super expensive SaaS services. Caffeine Labs is working to produce technology (Caffeine Snorkel) that can study an enterprise's legacy systems and app built on SaaS, create replacement systems and apps, and migrate the data, while supporting key stakeholders through the process over email and chat, with full automation. Thus the legacy systems and SaaS markets shall also be addressed by cloud engines. Zooming out, and reasoning in a more metaphysical way, we believe, as we always have, that there is room for a new kind of cloud created by mathematical networks, that provides seminal advances in the fields of security and resilience, as well as true sovereignty and freedom from lock-in. That this same technology, with the help of additional technologies like orthogonal persistence and Motoko, enables AI to build for us without the need for so much oversight, and to create more backend sophistication while consuming fewer AI API tokens, enables ICP to bring game-changing advances to the world. Cloud engines will work synergistically with the Intelligence Gateway, which will enable apps and services running on engines to seamlessly leverage AI, wherever that AI is running, while providing verifiability at extremely low cost for open weights frontier models. We believe that cloud engines represent an inflection point in the storied history of the Internet Computer project, and I'm very proud to be sharing the details with you on the network's fifth birthday 💪 I'll be back with more news soon!!

腾讯云开源了 AI Agent 沙盒 Cube Sandbox，Rust 编写，Apache 2.0 协议。 Agent 跑模型生成的代码需要一个隔离环境，避免误删文件或越权访问主机。这类服务的接口事实标准是 E2B，OpenAI Agents SDK、Manus、Perplexity、Hugging Face 都接它。Cube 对 E2B 做完全兼容，原本接 E2B 的 Agent 只要改一个环境变量就能切过来。 腾讯云公布了两组性能数据。单并发冷启动低于 60ms，50 并发时平均 67ms、P95 90ms、P99 137ms。单实例常驻内存低于 5MB（沙盒规格不超过 32GB 时测得），一台 96 核服务器可同时跑 2000 多个沙箱。同场景下 Docker 容器启动约 200ms、共享主机内核；传统虚拟机启动以秒计、单实例内存 20MB 起。 Cube 的做法是给每个 Agent 开一套独立的 Guest OS 内核，走硬件级隔离，同时把启动时间压到百毫秒内。加速靠资源池预置、快照克隆、底层锁优化；压内存靠 Rust 重写、CoW 内存复用、reflink 磁盘共享。项目还附带 CubeVS，用 eBPF 做沙盒之间的网络隔离。 规模化验证给了两个案例。Cube 原本跑在腾讯云 Serverless 体系里，承载过百亿级调用。元宝 AI 编程场景迁到 Cube 后，资源核时消耗降了 95.8%。外部客户里，MiniMax 在 Agentic RL 训练中靠 Cube 做到分钟级调度数十万沙箱实例。下一步规划是把事件级快照回滚也开源出去，提供百毫秒级状态回滚。

发布了 v0.2.0 — 新增 Service Layer，首个数据服务适配器 AI Agent 不只需要隔离计算环境，还需要数据。 之前 Sandbank 只解决了"计算"问题——给 Agent 一个沙箱跑代码。但 Agents 要记住上下文、存储结果、跨会话协作，光有沙箱不够，还需要数据库。 v0.2.0 引入 Service Layer，在 Compute（沙箱）和 Storage（卷）之外新增第三层：Data Services。首个实现是 sandbank / db9，接入 的 Serverless PostgreSQL。 用它能做什么： - 一行代码创建数据库，凭证自动注入沙箱环境变量，Agent 零配置直连 - 内置 Brain Schema，多 Agent 共享记忆层——memory/tasks/artifacts 三张表开箱即用 - pgvector 语义检索，Agent 可以按"意思"搜索历史记忆，而非精确匹配 - db9 官方 Skill 自动注入，Agent 天生会写 SQL，无需额外 prompt - 跨会话持久化，Agent 重启后能捡起上次的工作进度和上下文 - 多 Agent 任务协调，一个 Agent 创建任务，另一个领取执行，状态实时同步 - Agent 生成的代码、文档、分析结果存为 artifacts，随时可查可追溯 - 数据库分支（branch），安全地让 Agent 在隔离副本上试验，不影响主库 测试覆盖：语句 100%，行 100%，含 35 个单元测试 + 3 个真实 API 集成测试。