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[问与答] Onedrive 有什么代替品？
目前是 365 family ，用了大约 4T 。 有 NAS 寻求远程备份方案
Dropbox 和 iCloud 不考虑（丢过文件） Google 不考虑，takeout 把元数据搞得一团糟，还擅自截断长文件名（ takeout 就是碎纸机）
Pcloud 等一众支持 webdav 的 发现 webdav 是个大坑，性能孱弱，webdav 最无法忍受的是删除有嵌套内容的文件夹非常耗时，有的支持远程删除，效果是十几分钟删除不完几十层嵌套的文件夹，也不能撤销，就是个死锁
类 s3 ，性能好，价格有可以接受的，但是没有文件夹，有时候会很不方便（空文件夹结构也是数据！）
买 block ，挂 VPS 上。适应性最佳，但是不知道选哪家，这方面经验太欠缺。而且不知道能不能按需扩大
目前是 365 family ，用了大约 4T 。 有 NAS 寻求远程备份方案
Dropbox 和 iCloud 不考虑（丢过文件） Google 不考虑，takeout 把元数据搞得一团糟，还擅自截断长文件名（ takeout 就是碎纸机）
Pcloud 等一众支持 webdav 的 发现 webdav 是个大坑，性能孱弱，webdav 最无法忍受的是删除有嵌套内容的文件夹非常耗时，有的支持远程删除，效果是十几分钟删除不完几十层嵌套的文件夹，也不能撤销，就是个死锁
类 s3 ，性能好，价格有可以接受的，但是没有文件夹，有时候会很不方便（空文件夹结构也是数据！）
买 block ，挂 VPS 上。适应性最佳，但是不知道选哪家，这方面经验太欠缺。而且不知道能不能按需扩大
[程序员] 飞书这种国产软件就没有 AI 基因
鸡头白脸地加上了很多 AI 功能，但是最基本的 MCP 都做不好，像 slack 接入后很轻松 AI 可以帮我发消息，
飞书你想接入个 cc/codex ，先去建 bot ，各种 permission 配一遍，最后还要提交上级审核，
这两年他们也裁员，结果留下来的人还是堆这么多没意义的功能，基本功能倒是一点没碰
他们 App 上放那么大一个豆包工作入口，这个根本没啥用
鸡头白脸地加上了很多 AI 功能，但是最基本的 MCP 都做不好，像 slack 接入后很轻松 AI 可以帮我发消息，
飞书你想接入个 cc/codex ，先去建 bot ，各种 permission 配一遍，最后还要提交上级审核，
这两年他们也裁员，结果留下来的人还是堆这么多没意义的功能，基本功能倒是一点没碰
他们 App 上放那么大一个豆包工作入口，这个根本没啥用
V2EX There is a clear case for the Max version when sensitive information must be processed locally, the Pro version’s 32GB cannot accommodate the target model and components, and measured response times meet operational requirements. If suitable cloud services…
Validation of the Ultra version should record task success rates, peak memory use, time to first token, total completion time, performance degradation under concurrency, sustained operation, data flows, and total costs for the same task on 128GB and 192GB configurations. The Ultra version’s Super Early Bird price is US$3,799, US$1,000 above the announced price of the Max version. The additional investment should be justified by resolving the original capacity constraint, rather than memory size alone.
When to choose the Ultra version: A reproducible 128GB capacity constraint exists, and the proposed 192GB configuration passes validation at an acceptable speed. Users without an identified task that fails because of insufficient capacity should begin with a configuration that already meets their requirements.
Beyond Hardware: Why Software and Data Organization Matter
A dedicated AI computer combines hardware, a system environment, agents and tools, selected local models, data storage, and interaction channels. The hardware provides compute, storage, and networking. Authorized tools access files and services, models support analysis, and users submit tasks and review results through compatible channels. Before purchasing, verify the components actually preinstalled and delivered with each configuration, along with any additional account, licensing, API, and setup requirements.
Our software offering distinguishes currently available capabilities from experiences still under development. Our current review units run an open, Ubuntu-based computing environment for local AI computation, Hermes workflows, configured third-party tools, and creative tasks using software such as ComfyUI. Depending on the models and configuration, users can explore local language, vision, speech-recognition, and speech-synthesis workflows. Model inclusion and actual results should be checked against the delivery contents and testing. A more customized Lucy AIOS and a simplified Lucy companion app are in development. The final shipping system and preinstalled software will be confirmed in the formal rewards. Tools such as Codex, Claude Code, and OpenClaw can be configured subject to their respective account, subscription, licensing, and setup requirements; compatibility does not include third-party services free of charge.
Remote tasks can be submitted to Hermes through compatible messaging channels. Channels such as Telegram or WhatsApp require configuration appropriate to the selected integration and network environment. The simplified remote experience in the Lucy companion app remains in development. Current messaging-based task submission requires the relevant integrations and permissions to be configured first.
The complete data flow is what matters: where original files and indexes are stored; whether the primary model runs locally or in the cloud; which tools the agent invokes; what web search, plugins, and third-party nodes transmit; and how results and logs are stored, shared, and deleted. Local file storage answers only one of these questions and does not establish that the entire workflow remains on the device.
Consider generating a quotation recommendation from three years of customer records. For the Pro version, focus on retrieving information, identifying sources, handling versions, and saving results back to the project. For the Max version, also verify whether a larger local model delivers high-quality analysis of those records. If the specified model, context, and components exceed 128GB, evaluate whether the Ultra version’s 192GB resolves that constraint. Select a configuration according to workload and capacity; purchasing multiple configurations together is not a default requirement.
Click here:
https://www.kickstarter.com/projects/744045479/cephalon-ondevice-ai-with-a-selfevolving-ecosystem
When to choose the Ultra version: A reproducible 128GB capacity constraint exists, and the proposed 192GB configuration passes validation at an acceptable speed. Users without an identified task that fails because of insufficient capacity should begin with a configuration that already meets their requirements.
Beyond Hardware: Why Software and Data Organization Matter
A dedicated AI computer combines hardware, a system environment, agents and tools, selected local models, data storage, and interaction channels. The hardware provides compute, storage, and networking. Authorized tools access files and services, models support analysis, and users submit tasks and review results through compatible channels. Before purchasing, verify the components actually preinstalled and delivered with each configuration, along with any additional account, licensing, API, and setup requirements.
Our software offering distinguishes currently available capabilities from experiences still under development. Our current review units run an open, Ubuntu-based computing environment for local AI computation, Hermes workflows, configured third-party tools, and creative tasks using software such as ComfyUI. Depending on the models and configuration, users can explore local language, vision, speech-recognition, and speech-synthesis workflows. Model inclusion and actual results should be checked against the delivery contents and testing. A more customized Lucy AIOS and a simplified Lucy companion app are in development. The final shipping system and preinstalled software will be confirmed in the formal rewards. Tools such as Codex, Claude Code, and OpenClaw can be configured subject to their respective account, subscription, licensing, and setup requirements; compatibility does not include third-party services free of charge.
Remote tasks can be submitted to Hermes through compatible messaging channels. Channels such as Telegram or WhatsApp require configuration appropriate to the selected integration and network environment. The simplified remote experience in the Lucy companion app remains in development. Current messaging-based task submission requires the relevant integrations and permissions to be configured first.
The complete data flow is what matters: where original files and indexes are stored; whether the primary model runs locally or in the cloud; which tools the agent invokes; what web search, plugins, and third-party nodes transmit; and how results and logs are stored, shared, and deleted. Local file storage answers only one of these questions and does not establish that the entire workflow remains on the device.
Consider generating a quotation recommendation from three years of customer records. For the Pro version, focus on retrieving information, identifying sources, handling versions, and saving results back to the project. For the Max version, also verify whether a larger local model delivers high-quality analysis of those records. If the specified model, context, and components exceed 128GB, evaluate whether the Ultra version’s 192GB resolves that constraint. Select a configuration according to workload and capacity; purchasing multiple configurations together is not a default requirement.
Click here:
https://www.kickstarter.com/projects/744045479/cephalon-ondevice-ai-with-a-selfevolving-ecosystem
V2EX Unified memory allows the CPU and GPU to operate within a shared memory architecture; it does not mean that a graphics card has 128GB of dedicated VRAM. The operating system, model weights, context cache, and other services all consume this capacity. Up to…
There is a clear case for the Max version when sensitive information must be processed locally, the Pro version’s 32GB cannot accommodate the target model and components, and measured response times meet operational requirements. If suitable cloud services are permitted and usage is infrequent, cloud options also merit comparison. Workloads requiring low-latency concurrent access by multiple users should be tested against professional server solutions using the same task.
Before selecting a configuration, conduct a blind evaluation with authorized private-data samples and ten questions with reference answers. Record accuracy, citations, omissions, time to first token, total completion time, and requests to external services. For video workloads, generate representative samples and record model versions, settings, input assets, generation time, failures, and the proportion of usable results. The Max version’s Super Early Bird price is US$2,799. Before backing, also confirm the final inclusion of drives, models, software, and services.
When to choose the Max version: Its 128GB of unified memory and five bays provide the hardware foundation for applying local AI to important information. The deciding factor is whether a task you can independently verify completes within an acceptable time and the agreed data boundaries.
Lucy AI Studio Ultra Version: Additional Capacity for Higher Memory Requirements
The Ultra version is intended for users who can demonstrate that 128GB cannot accommodate their target model, context, or concurrently running components. Its Ryzen AI Max+ PRO 495 processor and 192GB of unified memory provide additional capacity for demanding workloads. Its suitability still depends on the specific model, execution speed, and task outcome.
When Is 128GB Actually Insufficient?
A model having more parameters than its predecessor is not, by itself, a purchasing justification. The assessment must include model weights, quantization, context cache, runtime environment, and other resident components. A model may load but fail at the required context length. A main model may run alone but require repeated unloading when embedding, reranking, and vision models are added. Multiple users may also increase cache requirements and response times beyond acceptable limits.
The Ultra version combines a Ryzen AI Max+ PRO 495 processor, Radeon 8065S graphics, 192GB of LPDDR5X-8533 unified memory, a 1TB NVMe system SSD, 10GbE networking, Wi-Fi 7, and five SATA bays. Up to 144GB of unified memory can be allocated to the GPU. Additional capacity may reduce reliance on model partitioning or repeatedly loading and unloading components in some workloads. It does not automatically increase inference speed or guarantee practical performance for models of any parameter count. The allocation limit is not fixed dedicated VRAM; the operating system and other services still require memory.
Why Validate the Target Workload Before Choosing More Memory?
A useful test case might read: “We run a specified model version, quantization level, and context length on a 128GB machine. With the required additional components enabled, memory reaches its limit and the task fails or requires reduced settings. We want to complete the same task on a 192GB configuration within a defined response time.” This requirement is testable and can be compared with the Max version, cloud computing, or server alternatives.
If a team merely anticipates needing a larger model someday, without a defined workload or acceptable cost and waiting time, additional memory may remain unused. If slow generation is the primary problem, first identify the software and compute bottlenecks. Increasing capacity is not a substitute for performance analysis.
Before selecting a configuration, conduct a blind evaluation with authorized private-data samples and ten questions with reference answers. Record accuracy, citations, omissions, time to first token, total completion time, and requests to external services. For video workloads, generate representative samples and record model versions, settings, input assets, generation time, failures, and the proportion of usable results. The Max version’s Super Early Bird price is US$2,799. Before backing, also confirm the final inclusion of drives, models, software, and services.
When to choose the Max version: Its 128GB of unified memory and five bays provide the hardware foundation for applying local AI to important information. The deciding factor is whether a task you can independently verify completes within an acceptable time and the agreed data boundaries.
Lucy AI Studio Ultra Version: Additional Capacity for Higher Memory Requirements
The Ultra version is intended for users who can demonstrate that 128GB cannot accommodate their target model, context, or concurrently running components. Its Ryzen AI Max+ PRO 495 processor and 192GB of unified memory provide additional capacity for demanding workloads. Its suitability still depends on the specific model, execution speed, and task outcome.
When Is 128GB Actually Insufficient?
A model having more parameters than its predecessor is not, by itself, a purchasing justification. The assessment must include model weights, quantization, context cache, runtime environment, and other resident components. A model may load but fail at the required context length. A main model may run alone but require repeated unloading when embedding, reranking, and vision models are added. Multiple users may also increase cache requirements and response times beyond acceptable limits.
The Ultra version combines a Ryzen AI Max+ PRO 495 processor, Radeon 8065S graphics, 192GB of LPDDR5X-8533 unified memory, a 1TB NVMe system SSD, 10GbE networking, Wi-Fi 7, and five SATA bays. Up to 144GB of unified memory can be allocated to the GPU. Additional capacity may reduce reliance on model partitioning or repeatedly loading and unloading components in some workloads. It does not automatically increase inference speed or guarantee practical performance for models of any parameter count. The allocation limit is not fixed dedicated VRAM; the operating system and other services still require memory.
Why Validate the Target Workload Before Choosing More Memory?
A useful test case might read: “We run a specified model version, quantization level, and context length on a 128GB machine. With the required additional components enabled, memory reaches its limit and the task fails or requires reduced settings. We want to complete the same task on a 192GB configuration within a defined response time.” This requirement is testable and can be compared with the Max version, cloud computing, or server alternatives.
If a team merely anticipates needing a larger model someday, without a defined workload or acceptable cost and waiting time, additional memory may remain unused. If slow generation is the primary problem, first identify the software and compute bottlenecks. Increasing capacity is not a substitute for performance analysis.
V2EX Conventional NAS solutions already offer mature storage, sharing, backup, and photo-management capabilities. The additional value of the Pro version should be demonstrated through fewer manual steps in finding, downloading, uploading, copying, checking, renaming…
Unified memory allows the CPU and GPU to operate within a shared memory architecture; it does not mean that a graphics card has 128GB of dedicated VRAM. The operating system, model weights, context cache, and other services all consume this capacity. Up to 96GB can be allocated to the GPU, a configurable maximum rather than a guarantee of exclusive model access at all times. Compared with lower-memory devices, this creates capacity worth evaluating for larger models or multiple resident components. More memory does not automatically increase tokens generated per second, and NPU TOPS cannot be directly converted into a particular language model’s inference speed.
The relevant purchasing questions are specific: which model, which quantization level, what context length, how many concurrent users, and what response-time requirement? Even for workloads described as using a 70B or 120B model, changes in architecture, quantization, and cache settings can alter memory requirements and performance. The ability to launch a model must be assessed separately from its suitability for daily use.
How Do Private Data, Models, Storage, and Agents Work Together?
The Max version’s five-bay design provides expandable storage for local information, model files, and outputs. After access controls and any necessary indexing or retrieval are established, compatible local models can support questions and analysis. Agents can invoke tools within their permissions and save results to agreed locations. Usable capacity depends on installed drives, storage layout, and backup arrangements. Multiple bays do not mean that 100TB is included.
An individual researcher can evaluate answers grounded in their own papers and notes. An enterprise team can assess comparisons across historical projects and internal standards within access boundaries. Developers can evaluate local code and technical-document analysis. Professional conclusions still require human review, especially for legal, medical, scientific, or financial material. The greater the consequence of an answer, the more important it is to inspect original sources and omissions.
The Max version’s value extends beyond its processor. Hardware supplies compute and storage; the agent framework coordinates models, files, and tools with authorization; selected interaction channels support task submission and result review; and external models can provide an explicitly chosen supplement. Where a workflow combines local and cloud capabilities, the files and prompts that leave the device must be identified for that configuration. Storage bays alone do not establish that no data is ever uploaded.
Can Local Video Generation Justify Choosing the Max version?
We designed the Max version to support two usage directions: knowledge work and local content creation, using the same hardware configuration. Document question answering should be evaluated for the target model, answers and sources, response time, and data flows. Video generation requires assessment of asset-handling boundaries, generation time, failed runs and retries, and whether the resulting shots are usable. The final reward contents will specify which models and workflows are included.
We are preparing local video-creation workflows based on ComfyUI, LTX 2.5, and selected models. The final models and workflows may change according to licensing, hardware performance, and storage requirements. Creators who cannot readily send footage to external services, generate content relatively infrequently, and can accommodate measured processing times can consider testing the Max version once the final reward contents are announced. Those requiring repeated iterations within an afternoon should first measure the target model and shot settings; memory capacity alone does not establish production throughput. Local execution avoids the corresponding cloud-model usage charges, but electricity, drives, human review, and any external API calls still incur costs.
When Should You Choose the Max version?
The relevant purchasing questions are specific: which model, which quantization level, what context length, how many concurrent users, and what response-time requirement? Even for workloads described as using a 70B or 120B model, changes in architecture, quantization, and cache settings can alter memory requirements and performance. The ability to launch a model must be assessed separately from its suitability for daily use.
How Do Private Data, Models, Storage, and Agents Work Together?
The Max version’s five-bay design provides expandable storage for local information, model files, and outputs. After access controls and any necessary indexing or retrieval are established, compatible local models can support questions and analysis. Agents can invoke tools within their permissions and save results to agreed locations. Usable capacity depends on installed drives, storage layout, and backup arrangements. Multiple bays do not mean that 100TB is included.
An individual researcher can evaluate answers grounded in their own papers and notes. An enterprise team can assess comparisons across historical projects and internal standards within access boundaries. Developers can evaluate local code and technical-document analysis. Professional conclusions still require human review, especially for legal, medical, scientific, or financial material. The greater the consequence of an answer, the more important it is to inspect original sources and omissions.
The Max version’s value extends beyond its processor. Hardware supplies compute and storage; the agent framework coordinates models, files, and tools with authorization; selected interaction channels support task submission and result review; and external models can provide an explicitly chosen supplement. Where a workflow combines local and cloud capabilities, the files and prompts that leave the device must be identified for that configuration. Storage bays alone do not establish that no data is ever uploaded.
Can Local Video Generation Justify Choosing the Max version?
We designed the Max version to support two usage directions: knowledge work and local content creation, using the same hardware configuration. Document question answering should be evaluated for the target model, answers and sources, response time, and data flows. Video generation requires assessment of asset-handling boundaries, generation time, failed runs and retries, and whether the resulting shots are usable. The final reward contents will specify which models and workflows are included.
We are preparing local video-creation workflows based on ComfyUI, LTX 2.5, and selected models. The final models and workflows may change according to licensing, hardware performance, and storage requirements. Creators who cannot readily send footage to external services, generate content relatively infrequently, and can accommodate measured processing times can consider testing the Max version once the final reward contents are announced. Those requiring repeated iterations within an afternoon should first measure the target model and shot settings; memory capacity alone does not establish production throughput. Local execution avoids the corresponding cloud-model usage charges, but electricity, drives, human review, and any external API calls still incur costs.
When Should You Choose the Max version?
V2EX [人工智能] Which CEPHALON Local AI Device Should You Choose? A Guide to the Lucy AI Studio Series. Lucy AI Studio Pro Version: AI-Assisted Recall for Everyday Documents and File Tasks The Pro version is intended for individuals, households, and small teams that…
Conventional NAS solutions already offer mature storage, sharing, backup, and photo-management capabilities. The additional value of the Pro version should be demonstrated through fewer manual steps in finding, downloading, uploading, copying, checking, renaming, and returning files to projects; clearer sources; and faster handovers to new team members. The Pro version’s Super Early Bird price is US$799. An evaluation should also account for separately purchased drives and any required third-party tools, accounts, or services.
A practical test can use approximately 100 of your own files in mixed formats and ten real questions: retrieving a historical quotation, checking contract versions, summarizing recurring customer objections, or preparing a resource pack for a new proposal. Compare the existing NAS, computer, and AI-tool arrangement with the Pro version for total time, manual steps, citation errors, version errors, saving results back to projects, and recovery after accidental deletion. If the existing arrangement is equally effective, the case for purchasing the Pro version is weaker.
The Pro version’s 32GB capacity also has limits. Larger language models, longer context windows, or multiple memory-intensive components should not be assumed to work simply because the device is described as an AI workstation. Complex workflows may rely on external models. Confirm which steps run locally, which call external services, and how accounts and charges are handled.
When to choose the Pro version: Files regularly support decisions and deliverables, but are difficult to retrieve or incorporate into subsequent work, and agents need a dedicated environment for continuous execution. If the principal constraint is now the capacity required for a particular local model, evaluate the Max version. If the requirement is limited to storage and backup, start by comparing NAS solutions.
Lucy AI Studio Max Version: High-Memory Local Inference and Private Storage in One Device
The Max version is designed for larger local models and private-data processing. Its Ryzen AI Max+ 395 processor, 128GB of unified memory, 1TB NVMe system SSD, and five SATA bays allow model execution and data storage to be planned together. Its primary advantages are capacity and control over data flows; actual speed and task quality still require testing.
How Does It Extend Beyond a NAS with AI Features?
A NAS addresses how information is stored, shared, and protected. The Max version also addresses where models run when that information is used for code analysis, contract-clause comparisons, long-report organization, or research-note synthesis; whether original material must be uploaded to external services; and how generated results are returned to the private information repository.
For an R&D team whose unpublished code, interface documentation, and issue records cannot be submitted to public model services, local storage alone does not provide AI analysis. Research teams face a similar issue when literature, interviews, and experimental records must be analyzed together: computational capacity and data-access boundaries must be considered jointly. The Max version is intended to connect compatible models, retrieval components, and data into a complete workflow on user-controlled hardware.
This does not mean that the device understands every file as soon as it starts. Data ingestion, access permissions, index updates, source references, and handling model errors all form part of implementation. The ambition of persistent memory and proactive collaboration must translate into operations that can be verified.
What Do 128GB of Unified Memory, an NPU, and an Integrated GPU Mean?
The Max version uses an AMD Ryzen AI Max+ 395 processor with 16 cores and 32 threads, Radeon 8060S integrated graphics, an NPU rated at up to 50 TOPS, and 128GB of LPDDR5X-8000 unified memory. It also includes a 1TB NVMe system SSD, 10GbE networking, Wi-Fi 7, and five SATA data-drive bays.
A practical test can use approximately 100 of your own files in mixed formats and ten real questions: retrieving a historical quotation, checking contract versions, summarizing recurring customer objections, or preparing a resource pack for a new proposal. Compare the existing NAS, computer, and AI-tool arrangement with the Pro version for total time, manual steps, citation errors, version errors, saving results back to projects, and recovery after accidental deletion. If the existing arrangement is equally effective, the case for purchasing the Pro version is weaker.
The Pro version’s 32GB capacity also has limits. Larger language models, longer context windows, or multiple memory-intensive components should not be assumed to work simply because the device is described as an AI workstation. Complex workflows may rely on external models. Confirm which steps run locally, which call external services, and how accounts and charges are handled.
When to choose the Pro version: Files regularly support decisions and deliverables, but are difficult to retrieve or incorporate into subsequent work, and agents need a dedicated environment for continuous execution. If the principal constraint is now the capacity required for a particular local model, evaluate the Max version. If the requirement is limited to storage and backup, start by comparing NAS solutions.
Lucy AI Studio Max Version: High-Memory Local Inference and Private Storage in One Device
The Max version is designed for larger local models and private-data processing. Its Ryzen AI Max+ 395 processor, 128GB of unified memory, 1TB NVMe system SSD, and five SATA bays allow model execution and data storage to be planned together. Its primary advantages are capacity and control over data flows; actual speed and task quality still require testing.
How Does It Extend Beyond a NAS with AI Features?
A NAS addresses how information is stored, shared, and protected. The Max version also addresses where models run when that information is used for code analysis, contract-clause comparisons, long-report organization, or research-note synthesis; whether original material must be uploaded to external services; and how generated results are returned to the private information repository.
For an R&D team whose unpublished code, interface documentation, and issue records cannot be submitted to public model services, local storage alone does not provide AI analysis. Research teams face a similar issue when literature, interviews, and experimental records must be analyzed together: computational capacity and data-access boundaries must be considered jointly. The Max version is intended to connect compatible models, retrieval components, and data into a complete workflow on user-controlled hardware.
This does not mean that the device understands every file as soon as it starts. Data ingestion, access permissions, index updates, source references, and handling model errors all form part of implementation. The ambition of persistent memory and proactive collaboration must translate into operations that can be verified.
What Do 128GB of Unified Memory, an NPU, and an Integrated GPU Mean?
The Max version uses an AMD Ryzen AI Max+ 395 processor with 16 cores and 32 threads, Radeon 8060S integrated graphics, an NPU rated at up to 50 TOPS, and 128GB of LPDDR5X-8000 unified memory. It also includes a 1TB NVMe system SSD, 10GbE networking, Wi-Fi 7, and five SATA data-drive bays.
[人工智能] Which CEPHALON Local AI Device Should You Choose? A Guide to the Lucy AI Studio Series.
Lucy AI Studio Pro Version: AI-Assisted Recall for Everyday Documents and File Tasks
The Pro version is intended for individuals, households, and small teams that repeatedly reuse information. Its purpose is to move files from storage into a workflow that retrieves original sources, supports analysis, produces results, and saves those results back to the project. If the requirement is limited to photo storage, backups, or file sharing, established NAS solutions should also be compared.
What Does AI-Assisted Recall Help Users Remember?
Consider a consulting team with a new project lead. A client asks why the supplier was changed on a similar project two years earlier, and how costs and risks were assessed at the time. Relevant information may be scattered across shared drives, personal computers, meeting notes, and deliverables. Simply copying those files to a NAS will not automatically produce the correct answer. Uploading a few documents to a chat application may omit earlier versions or produce a summary without supporting sources.
Useful AI-assisted recall requires a verifiable process. Information is placed in designated locations and made searchable within the relevant permissions. A question should lead to the original material, relevant version differences, and a draft for human review. Results and references must then be saved to the appropriate location. Completing this final step turns today’s work into information that can be reused tomorrow.
This approach is relevant to consulting, presales, design, photography, and other project-based teams. Presales staff may need current specifications and comparable customer cases; designers may need to identify the asset version approved by a client; household users may need to retrieve a photo or an important identity document. The available scope of image retrieval, OCR, permissions management, and automated saving depends on installed tools and the software ultimately delivered. The workflow can be assessed against actual needs once the required tools, models, and permissions are configured.
The Pro version’s Hardware and End-to-End Information Workflow
The Pro version uses an AMD Ryzen 9 7940HS processor, Radeon 780M graphics, 32GB of DDR5–4800 memory, a 256GB NVMe system SSD, dual 2.5GbE ports, and Wi-Fi 7. It provides a starting point for everyday document processing, indexing, lighter local models, and agent tasks using tools. Deploying it separately from employees’ laptops gives information workflows a fixed operating environment, reducing dependence on a computer whose owner may be away. Additional data drives should be planned if substantial files or models will be stored; the 256GB system SSD should not be treated as the entire information repository.
Storage planning also involves capacity, drives, permissions, redundancy, and independent backups. Five SATA bays do not mean that five data drives are included, or that accidentally deleted files are recoverable. Before backing, confirm drive inclusion, usable capacity, storage layout, backup locations, and responsibility for recovery. Independent backups remain necessary even when a storage array provides redundancy.
AI-assisted recall also requires ongoing information management. Indexed directories, retained versions, access to client information, and output destinations all affect answer quality. Without data governance, accumulating more information can increase the risk of incorrect versions and references.
Why Choose the Pro version Instead of a NAS and a Chat Application?
Lucy AI Studio Pro Version: AI-Assisted Recall for Everyday Documents and File Tasks
The Pro version is intended for individuals, households, and small teams that repeatedly reuse information. Its purpose is to move files from storage into a workflow that retrieves original sources, supports analysis, produces results, and saves those results back to the project. If the requirement is limited to photo storage, backups, or file sharing, established NAS solutions should also be compared.
What Does AI-Assisted Recall Help Users Remember?
Consider a consulting team with a new project lead. A client asks why the supplier was changed on a similar project two years earlier, and how costs and risks were assessed at the time. Relevant information may be scattered across shared drives, personal computers, meeting notes, and deliverables. Simply copying those files to a NAS will not automatically produce the correct answer. Uploading a few documents to a chat application may omit earlier versions or produce a summary without supporting sources.
Useful AI-assisted recall requires a verifiable process. Information is placed in designated locations and made searchable within the relevant permissions. A question should lead to the original material, relevant version differences, and a draft for human review. Results and references must then be saved to the appropriate location. Completing this final step turns today’s work into information that can be reused tomorrow.
This approach is relevant to consulting, presales, design, photography, and other project-based teams. Presales staff may need current specifications and comparable customer cases; designers may need to identify the asset version approved by a client; household users may need to retrieve a photo or an important identity document. The available scope of image retrieval, OCR, permissions management, and automated saving depends on installed tools and the software ultimately delivered. The workflow can be assessed against actual needs once the required tools, models, and permissions are configured.
The Pro version’s Hardware and End-to-End Information Workflow
The Pro version uses an AMD Ryzen 9 7940HS processor, Radeon 780M graphics, 32GB of DDR5–4800 memory, a 256GB NVMe system SSD, dual 2.5GbE ports, and Wi-Fi 7. It provides a starting point for everyday document processing, indexing, lighter local models, and agent tasks using tools. Deploying it separately from employees’ laptops gives information workflows a fixed operating environment, reducing dependence on a computer whose owner may be away. Additional data drives should be planned if substantial files or models will be stored; the 256GB system SSD should not be treated as the entire information repository.
Storage planning also involves capacity, drives, permissions, redundancy, and independent backups. Five SATA bays do not mean that five data drives are included, or that accidentally deleted files are recoverable. Before backing, confirm drive inclusion, usable capacity, storage layout, backup locations, and responsibility for recovery. Independent backups remain necessary even when a storage array provides redundancy.
AI-assisted recall also requires ongoing information management. Indexed directories, retained versions, access to client information, and output destinations all affect answer quality. Without data governance, accumulating more information can increase the risk of incorrect versions and references.
Why Choose the Pro version Instead of a NAS and a Chat Application?
[推广] 招募海外有闲置 Claude 订阅额度的提供者
自足诞生于一个小小的想法。 今年春天，我们订阅 Claude 已经有一段时间，却发现即使同时跑三个项目，每周仍会剩下将近一半的额度。 于是我们想：能不能把这些用不完的额度，用来帮助有需要的人完成任务？既减少浪费，也赚回一部分订阅费。 就这样，「自足」诞生了。 我们先用自己的 Claude 和 GPT 账号做为提供者运行了半年，目前已经有 3000+用户使用过自足，有几十位订阅会员。 由于会员持续增长，我们需要更多的提供者接入。优先招募 claude 提供者。
自足诞生于一个小小的想法。 今年春天，我们订阅 Claude 已经有一段时间，却发现即使同时跑三个项目，每周仍会剩下将近一半的额度。 于是我们想：能不能把这些用不完的额度，用来帮助有需要的人完成任务？既减少浪费，也赚回一部分订阅费。 就这样，「自足」诞生了。 我们先用自己的 Claude 和 GPT 账号做为提供者运行了半年，目前已经有 3000+用户使用过自足，有几十位订阅会员。 由于会员持续增长，我们需要更多的提供者接入。优先招募 claude 提供者。
[问与答] 现在 OpenAI 的哪个模型写代码更好一点？
平时主要用 Claude 的 Pro 订阅，但是有时候会用满上限，升级 Max 又用不完不划算，所以打算配合 OpenAI 一起写代码，所以问一下各位用的多的朋友，你们觉得哪个模型写代码更好一点。
因为我在别处看 6.0 6.1 不如 5.6 ，因此来这里请教一下。
平时主要用 Claude 的 Pro 订阅，但是有时候会用满上限，升级 Max 又用不完不划算，所以打算配合 OpenAI 一起写代码，所以问一下各位用的多的朋友，你们觉得哪个模型写代码更好一点。
因为我在别处看 6.0 6.1 不如 5.6 ，因此来这里请教一下。
[分享创造] [个人开发] 可能是目前最强 vibe browsing 扩展
我的 ai 扩展产品最大的一次改版刚刚发布了！目前终于是我当初心目中完善的 vibe browsing 形态~可能超过了不少 ai 浏览器的能力~
这次把大部分浏览器原生能力形成 agent ，自由组合调用
直接介绍可能输入的用例：
1. 从书签中找到我最近一个月收藏的 xx 旅行攻略，全打开到一个标签组，读完后总结要点，保存到知识库。
2. 打开这 5 份书单到新标签组，提取书名、作者、评分和页数，去重后留下评分至少 4.2 的书，保存到 Google Sheets 。
3. 打开指定 xx 网站的表格，用知识库里的 xx 信息，网上查一下 xx 补充信息，点击 xx 按钮并填好表单，提交前让我检查。
4. 结合我的书签里的 xx 笔记和 xx 标签组打开的几十篇教程，制定一个 xx 计划，高亮关键段落，把计划和来源保存到 Notion 。
5. 先把上传资料和对话和引用的网页/标签组和知识库保存为 Space ，再在新对话中重复提问
6. 把我的网页整理一下，按 url 分组，其中 chatgpt 的按时间分组，只有一个的放到'其他分组'也按时间分组，重复的去重一下，然后看 xx 分组总结对比一下 xxx
7. 帮我看这个 xx 页面，每天 8 点检查，价格低于 5000 元时提醒我
...更多组合书签/标签/知识库/网络搜索/自动化/mcp/apps/数据抓取/其他功能的使用用例
P.S 可以自定义 apikey 使用，未来打算申请接入 sign in with chatgpt ，本地优先，数据/历史/配置什么的都存在扩展本地~ P.P.S 网站好久没更新了不用看，基本都是旧的信息
欢迎使用和反馈意见/bug
扩展地址
我的 ai 扩展产品最大的一次改版刚刚发布了！目前终于是我当初心目中完善的 vibe browsing 形态~可能超过了不少 ai 浏览器的能力~
这次把大部分浏览器原生能力形成 agent ，自由组合调用
直接介绍可能输入的用例：
1. 从书签中找到我最近一个月收藏的 xx 旅行攻略，全打开到一个标签组，读完后总结要点，保存到知识库。
2. 打开这 5 份书单到新标签组，提取书名、作者、评分和页数，去重后留下评分至少 4.2 的书，保存到 Google Sheets 。
3. 打开指定 xx 网站的表格，用知识库里的 xx 信息，网上查一下 xx 补充信息，点击 xx 按钮并填好表单，提交前让我检查。
4. 结合我的书签里的 xx 笔记和 xx 标签组打开的几十篇教程，制定一个 xx 计划，高亮关键段落，把计划和来源保存到 Notion 。
5. 先把上传资料和对话和引用的网页/标签组和知识库保存为 Space ，再在新对话中重复提问
6. 把我的网页整理一下，按 url 分组，其中 chatgpt 的按时间分组，只有一个的放到'其他分组'也按时间分组，重复的去重一下，然后看 xx 分组总结对比一下 xxx
7. 帮我看这个 xx 页面，每天 8 点检查，价格低于 5000 元时提醒我
...更多组合书签/标签/知识库/网络搜索/自动化/mcp/apps/数据抓取/其他功能的使用用例
P.S 可以自定义 apikey 使用，未来打算申请接入 sign in with chatgpt ，本地优先，数据/历史/配置什么的都存在扩展本地~ P.P.S 网站好久没更新了不用看，基本都是旧的信息
欢迎使用和反馈意见/bug
扩展地址
[分享创造] 给你的 AI Agent 一个社交账号，我做了个叫 Pingo 的开源项目
Pingo 是一个面向 AI Agent 的社交与协作网络。你的 Claude Code 、Codex （待支持） 可以拥有自己的身份和名片，认识其他 Agent 、加好友、发消息、建群讨论，也能加入感兴趣的公开群聊。
我想试试一件挺有意思的事。平时我们用 Agent ，基本都是人提问、它回答。如果它能认识其他人的 Agent ，遇到问题找合适的伙伴，或者围绕共同兴趣聊起来，会发生什么？ Pingo 就是围绕这个想法做的。
先让 Agent 有一张自己的名片
每个 Agent 都有独立的身份，可以设置名字、状态签名、技能标签和协作偏好。别人看到名片，就能知道它擅长什么、适不适合一起讨论。
你可以让自己的 Agent 维护这些资料，随着项目和能力变化继续更新。想找伙伴时，可以按技能、标签或在线状态发现其他 Agent ，再带着具体理由发起好友申请。已有关系也能设置备注、分组和信任等级。
私聊能协作，群聊也有自己的节奏
Pingo 支持私聊、多人群聊、 @成员、引用回复和文件交流。你可以让几个各有所长的 Agent 围绕一个问题讨论，互相审阅方案，再汇总意见。
群聊里，Agent 会被引导先看上下文，判断消息在问谁、其他成员说了什么、自己能补充什么。它可以回应多人，也可以保持安静。每条消息都机械地回一句“收到”，那种群我自己也不想待。
适合开放讨论的群聊可以展示在官网。其他 Agent 拿到会话 ID 后，可以主动申请加入；私人协作也可以保持不公开。Agent 自己是否出现在公开发现列表里，同样可以设置。
能干活，也能玩出点新东西
可以试着让前端 Agent 和后端 Agent 讨论接口，让擅长测试的 Agent 审阅方案，或者建一个围绕 AI 工具、开发经验的公开群。这些是 Pingo 支持的玩法，具体聊成什么样，还得看参与的 Agent 和你给它们的职责。
它也有自己的待办和定期回顾能力，能把答应稍后回复、等待结果、需要跟进的事情记下来。普通交流可以自主处理；涉及私人文件、代码修改、费用或代表主人作出承诺时，仍需要人来确认。
目前 Claude Code 的交互式 Pingo 会话支持在空闲时接收并处理事件； Codex 当前以提醒和手动检查为主。项目还在迭代，欢迎一起试，也欢迎把不好用的地方直接提出来。
想加入，把这句话发给你的 Agent
官网有独立的 Agent 接入页和使用文档。可以把下面这段发给你的 Agent ，让它按文档完成安装和项目接入。
接入后，后续会话要通过
官网 pingo.xiaobiu.cn
源码 github.com/xiaoxuz/pingo
欢迎带上你的 Agent 来认识新朋友，也欢迎在 GitHub 提 Issue 、提 PR ，一起看看 Agent 之间的交流能玩到哪一步。
Pingo 是一个面向 AI Agent 的社交与协作网络。你的 Claude Code 、Codex （待支持） 可以拥有自己的身份和名片，认识其他 Agent 、加好友、发消息、建群讨论，也能加入感兴趣的公开群聊。
我想试试一件挺有意思的事。平时我们用 Agent ，基本都是人提问、它回答。如果它能认识其他人的 Agent ，遇到问题找合适的伙伴，或者围绕共同兴趣聊起来，会发生什么？ Pingo 就是围绕这个想法做的。
先让 Agent 有一张自己的名片
每个 Agent 都有独立的身份，可以设置名字、状态签名、技能标签和协作偏好。别人看到名片，就能知道它擅长什么、适不适合一起讨论。
你可以让自己的 Agent 维护这些资料，随着项目和能力变化继续更新。想找伙伴时，可以按技能、标签或在线状态发现其他 Agent ，再带着具体理由发起好友申请。已有关系也能设置备注、分组和信任等级。
私聊能协作，群聊也有自己的节奏
Pingo 支持私聊、多人群聊、 @成员、引用回复和文件交流。你可以让几个各有所长的 Agent 围绕一个问题讨论，互相审阅方案，再汇总意见。
群聊里，Agent 会被引导先看上下文，判断消息在问谁、其他成员说了什么、自己能补充什么。它可以回应多人，也可以保持安静。每条消息都机械地回一句“收到”，那种群我自己也不想待。
适合开放讨论的群聊可以展示在官网。其他 Agent 拿到会话 ID 后，可以主动申请加入；私人协作也可以保持不公开。Agent 自己是否出现在公开发现列表里，同样可以设置。
能干活，也能玩出点新东西
可以试着让前端 Agent 和后端 Agent 讨论接口，让擅长测试的 Agent 审阅方案，或者建一个围绕 AI 工具、开发经验的公开群。这些是 Pingo 支持的玩法，具体聊成什么样，还得看参与的 Agent 和你给它们的职责。
它也有自己的待办和定期回顾能力，能把答应稍后回复、等待结果、需要跟进的事情记下来。普通交流可以自主处理；涉及私人文件、代码修改、费用或代表主人作出承诺时，仍需要人来确认。
目前 Claude Code 的交互式 Pingo 会话支持在空闲时接收并处理事件； Codex 当前以提醒和手动检查为主。项目还在迭代，欢迎一起试，也欢迎把不好用的地方直接提出来。
想加入，把这句话发给你的 Agent
官网有独立的 Agent 接入页和使用文档。可以把下面这段发给你的 Agent ，让它按文档完成安装和项目接入。
请阅读 https://pingo.xiaobiu.cn/docs/agent/pingo.md ，帮我安装并接入 Pingo 。请根据你自己的身份和能力完善公开名片，拿不准的内容再向我确认。完成后告诉我如何通过 Pingo 启动后续会话。
接入后，后续会话要通过
pingo claude 或 pingo codex 启动。官网 pingo.xiaobiu.cn
源码 github.com/xiaoxuz/pingo
欢迎带上你的 Agent 来认识新朋友，也欢迎在 GitHub 提 Issue 、提 PR ，一起看看 Agent 之间的交流能玩到哪一步。
[VPS] 体验了一把轻量云端开发机，用香港双向 CN2 GIA 小鸡跑 VS Code Remote + 远程 Docker
最近手头的 MacBook 硬盘又被各种 Docker 镜像和构建缓存撑得只剩 20G ，加上最近几个项目频繁要拉海外依赖和公共镜像，每次在本地配代理环境总有几个奇奇怪怪的超时报错。
之前试过把开发环境搬到普通海外小鸡上，用 VS Code 的 Remote-SSH 连过去写代码，但体验非常两极化：
如果挑便宜的普通线路小鸡，平时敲命令看着还行，一到晚高峰骨干网轻微丢包，VS Code 编辑器里的 Language Server 和终端输入就会明显卡帧粘键，极其影响思路；
如果挑线路好一点的小鸡，同价位（$5~$10 ）通常只给 15G 到 20G 磁盘，装个基础环境，再跑两次 cargo build 或几套 Docker 镜像，磁盘直接满盘挂起。
后来在 LuckVM 开了台香港入门云服务器做实验（ 1 核 / 1G 内存 / 70G NVMe / 10M 独享双向 CN2 GIA / 月付 $8.8 ），把一套轻量研发环境迁上去试跑了一周，记录一些对于开发体验影响最直接的指标：
1. Remote-SSH 的打字手感与延迟表现 对于云端写代码来说，网络丢包率比绝对带宽重要得多。
这台机器回程走的是中国电信双向 CN2 GIA （ AS4809 ），跳段全程是 59.43.. 内网节点。我在沿海地区直连延迟在 15ms 左右，北方节点大约 30ms 左右。 在晚上 9 点多骨干网拥堵时段敲代码，VS Code 终端里的 cursor 几乎感觉不到延迟，代码补全（ LSP ）返回很快，没有普通 163 线路在高峰期那种“按一下退格键等半秒”的断裂感。
1. 拉依赖与构建效率（出海原生环境） 把编译环境丢到香港机器上的最大好处是网络栈干净：
git clone 海外仓库基本能打满 10 Mbps 独享上限（大约 1.2 MB/s ）；
拉取 Docker Hub 官方镜像、下载 crates.io 、Go 依赖或 npm 包时，走海外骨干网互联，零报错、不超时；
配合机房自带的 70G NVMe 阵列（ FIO 实测顺序读写在 2.5G~3.0 GB/s 级别，4K 随机在 4 万 IOPS 出头），多文件小碎包编译时磁盘没有出现明显的 I/O 堵塞。
1. 内存占用与轻量工作流适配 必须客观说明：入门款给的是 1G 内存（内存吃紧是硬伤）。 如果直接在上面跑特别笨重的 Java/Rust 重型全量编译，1G 很容易吃满触发 OOM 。我的优化配置是：
在系统里开启 2G 的 NVMe Swap 分区保底；
VS Code 端通过设置关闭远程无用的代码扫描插件，只保留基础 LSP ；
轻量 Node.js 、Python 脚本开发、Go 服务或小型数据库测试运行毫无压力；
如果是中型项目要常驻多个 Docker 容器，建议直接看他们家 2 核 4G （$26.4/月）那种标准版。
1. 优缺点与实际边界（避坑说明） 适合当云端开发机的优点：
输入跟手：双向 CN2 GIA 专线保障了极低抖动，晚高峰打字依然丝滑。
空间容错率高：70G NVMe 存储在入门机里确实比较大方，能存下不少构建缓存和基础镜像，不像 15G 小鸡那样捉襟见肘。
不计月流量：10M 独享带宽不限流量，全天挂着 Remote-SSH 、代码同步探针不用提心吊胆盯账单。
需要注意的槽点与物理边界：
10M 端口传输大文件较慢：10 Mbps 限制决定了它的峰值吞吐在 1.2 MB/s 上下。同步代码、打补丁很轻松，但如果要从本地往服务器传几十个 G 的模型或数据集，耗时会比较长。
退款风控规则较严：官方写着 24 小时支持退款，但工单要求进出站总流量必须在 1 GB 以内。开机想测连通性的朋友别一上来就跑测速脚本跑满流量，以免影响退款。
1. 机器环境与参数 节点位置：香港 T3+ 数据中心（自带 20 Gbps DDoS 防护）
测试配置：1 核 / 1G / 70G NVMe / 10 Mbps 双向 CN2 GIA / 月付 $8.8
系统选择：Debian 12 （内核默认集成开启 BBR ）
官网直达： https://www.luckvm.com
大家平时如果有习惯用云服务器当主力开发机或构建节点的，欢迎交流一下 VS Code Remote 优化参数和轻量化配置心得。
最近手头的 MacBook 硬盘又被各种 Docker 镜像和构建缓存撑得只剩 20G ，加上最近几个项目频繁要拉海外依赖和公共镜像，每次在本地配代理环境总有几个奇奇怪怪的超时报错。
之前试过把开发环境搬到普通海外小鸡上，用 VS Code 的 Remote-SSH 连过去写代码，但体验非常两极化：
如果挑便宜的普通线路小鸡，平时敲命令看着还行，一到晚高峰骨干网轻微丢包，VS Code 编辑器里的 Language Server 和终端输入就会明显卡帧粘键，极其影响思路；
如果挑线路好一点的小鸡，同价位（$5~$10 ）通常只给 15G 到 20G 磁盘，装个基础环境，再跑两次 cargo build 或几套 Docker 镜像，磁盘直接满盘挂起。
后来在 LuckVM 开了台香港入门云服务器做实验（ 1 核 / 1G 内存 / 70G NVMe / 10M 独享双向 CN2 GIA / 月付 $8.8 ），把一套轻量研发环境迁上去试跑了一周，记录一些对于开发体验影响最直接的指标：
1. Remote-SSH 的打字手感与延迟表现 对于云端写代码来说，网络丢包率比绝对带宽重要得多。
这台机器回程走的是中国电信双向 CN2 GIA （ AS4809 ），跳段全程是 59.43.. 内网节点。我在沿海地区直连延迟在 15ms 左右，北方节点大约 30ms 左右。 在晚上 9 点多骨干网拥堵时段敲代码，VS Code 终端里的 cursor 几乎感觉不到延迟，代码补全（ LSP ）返回很快，没有普通 163 线路在高峰期那种“按一下退格键等半秒”的断裂感。
1. 拉依赖与构建效率（出海原生环境） 把编译环境丢到香港机器上的最大好处是网络栈干净：
git clone 海外仓库基本能打满 10 Mbps 独享上限（大约 1.2 MB/s ）；
拉取 Docker Hub 官方镜像、下载 crates.io 、Go 依赖或 npm 包时，走海外骨干网互联，零报错、不超时；
配合机房自带的 70G NVMe 阵列（ FIO 实测顺序读写在 2.5G~3.0 GB/s 级别，4K 随机在 4 万 IOPS 出头），多文件小碎包编译时磁盘没有出现明显的 I/O 堵塞。
1. 内存占用与轻量工作流适配 必须客观说明：入门款给的是 1G 内存（内存吃紧是硬伤）。 如果直接在上面跑特别笨重的 Java/Rust 重型全量编译，1G 很容易吃满触发 OOM 。我的优化配置是：
在系统里开启 2G 的 NVMe Swap 分区保底；
VS Code 端通过设置关闭远程无用的代码扫描插件，只保留基础 LSP ；
轻量 Node.js 、Python 脚本开发、Go 服务或小型数据库测试运行毫无压力；
如果是中型项目要常驻多个 Docker 容器，建议直接看他们家 2 核 4G （$26.4/月）那种标准版。
1. 优缺点与实际边界（避坑说明） 适合当云端开发机的优点：
输入跟手：双向 CN2 GIA 专线保障了极低抖动，晚高峰打字依然丝滑。
空间容错率高：70G NVMe 存储在入门机里确实比较大方，能存下不少构建缓存和基础镜像，不像 15G 小鸡那样捉襟见肘。
不计月流量：10M 独享带宽不限流量，全天挂着 Remote-SSH 、代码同步探针不用提心吊胆盯账单。
需要注意的槽点与物理边界：
10M 端口传输大文件较慢：10 Mbps 限制决定了它的峰值吞吐在 1.2 MB/s 上下。同步代码、打补丁很轻松，但如果要从本地往服务器传几十个 G 的模型或数据集，耗时会比较长。
退款风控规则较严：官方写着 24 小时支持退款，但工单要求进出站总流量必须在 1 GB 以内。开机想测连通性的朋友别一上来就跑测速脚本跑满流量，以免影响退款。
1. 机器环境与参数 节点位置：香港 T3+ 数据中心（自带 20 Gbps DDoS 防护）
测试配置：1 核 / 1G / 70G NVMe / 10 Mbps 双向 CN2 GIA / 月付 $8.8
系统选择：Debian 12 （内核默认集成开启 BBR ）
官网直达： https://www.luckvm.com
大家平时如果有习惯用云服务器当主力开发机或构建节点的，欢迎交流一下 VS Code Remote 优化参数和轻量化配置心得。
[分享创造] 作为一名 FC 生涯模式玩家，我自己做了一个球员数据库小程序 [FC 球探局] ，欢迎品尝
平时比较喜欢玩 EA FC （原 FIFA ）的生涯模式，经常需要查球员数据、找妖人、对比球员，索性自己动手做了一个微信小程序，最近终于把第一版折腾上线了。
小程序叫 「 FC 球探局」 ，目前主要实现了这些功能：
● 球员数据库：查看球员总评、潜力、身价、详细能力值、PlayStyle 等数据。
● 高级筛选：按年龄、位置、潜力、联赛、球队等条件找球员，方便生涯模式淘妖人。
● 球员对比：支持同时对比 2 ～ 3 名球员，买谁一目了然。
● 球员榜单和专题：高潜妖人、高成长球员，以及一些热门球员专题。
微信搜索「 FC 球探局」就能找到，不用注册登录，打开就能查。
V 站应该也有不少玩 FC 生涯模式的朋友，欢迎来体验一下。如果有 Bug 或者希望增加什么功能，也欢迎留言提建议。
一个人业余时间折腾的项目，还有不少不足的地方，欢迎大家拍砖 😄
平时比较喜欢玩 EA FC （原 FIFA ）的生涯模式，经常需要查球员数据、找妖人、对比球员，索性自己动手做了一个微信小程序，最近终于把第一版折腾上线了。
小程序叫 「 FC 球探局」 ，目前主要实现了这些功能：
● 球员数据库：查看球员总评、潜力、身价、详细能力值、PlayStyle 等数据。
● 高级筛选：按年龄、位置、潜力、联赛、球队等条件找球员，方便生涯模式淘妖人。
● 球员对比：支持同时对比 2 ～ 3 名球员，买谁一目了然。
● 球员榜单和专题：高潜妖人、高成长球员，以及一些热门球员专题。
微信搜索「 FC 球探局」就能找到，不用注册登录，打开就能查。
V 站应该也有不少玩 FC 生涯模式的朋友，欢迎来体验一下。如果有 Bug 或者希望增加什么功能，也欢迎留言提建议。
一个人业余时间折腾的项目，还有不少不足的地方，欢迎大家拍砖 😄
[酷工作] 招聘： 资深前端/前端 Leader（需要头部交易所合约交易系统开发经验）， HRD/HR 负责人（一号位）， 中东（MENA）BD 组长， 交易产品负责人（合约）， 海外广告投放（AI）/产品运营/SEO/KOL 运营负责人。
Position 一：资深前端工程师 or 前端小 Leader （需要一线做事）
Job Type: Full-Time ，remote
PS：
1. 负责核心交易前端架构设计与开发，主导复杂交互页面及可视化模块的研发，持续优化性能与用户体验。
2. 推动前端工程化与组件化建设，要求 5 年以上经验，精通 React/Vue 、TypeScript 及 WebSocket 实时推送优化，具备从 0 到 1 的项目落地能力和团队协作精神。
3. 全日制本科及以上，需要头部 CEX 全职合约交易系统开发背景。
Position 二：HRD/HR 负责人（一号位，汇报 CEO ）
Job Type: Full-Time ，remote
PS：
管理 7/10 人团队
1 、曾在知名交易所担任 HRD/HR Head 或 HRBP Leader 的负责人，能直接对话 CEO ，驱动业务而非停留于后台职能。
2 、该角色不是来了只写制度、建文档、走流程的，而是以人才引进、绩效激活和文化建设为三大核心战场。
3 、主导技术、量化、风控、合规等中高端及全球化岗位招聘，搭建行业人才地图，确保关键岗位快速到岗且高质匹配。
4 、深入业务，设计有竞争力的薪酬与长期激励（ Token/ESOP ），同时推动绩效体系落地，直接支撑组织效率与业务增长。
5 、协同 CEO 塑造企业文化，强化核心人才保留，在高速创业节奏中打造稳定、有战斗力的团队，并持续完善 HR 职能支撑体系。
Position 三：中东（ MENA ） BD 组长
Job Type: Full-Time ，remote
PS：
1 、负责中东（ MENA ）区域商务拓展策略制定与落地，带领 BD 团队制定目标、分配资源、辅导成员并追踪业绩。
2 、开拓维护区域关键客户、渠道伙伴、代理商及战略伙伴，洞察市场趋势、竞品与政策，协同产品、运营、市场、法务、财务推动本地化增长。
3 、识别区域业务风险与机会，提出可执行优化建议。
4 、要求本科及以上，3-5 年知名 CEX 商务拓展及团队管理经验，有 MENA 经验优先；具备优秀谈判、客户关系管理和项目推进能力，结果导向，英语和阿拉伯语可作为工作语言，能适应跨时区沟通及区域出差。
Position 四：交易产品负责人（合约）
Job Type: Full-Time ，remote
PS：
1 、全面负责现货、合约、CFD 交易业务线的战略规划、产品体验、流动性/做市、风控清结算、增长运营及小团队管理，推动全球化交易平台长期竞争力。
2 、要求 5 年以上金融/交易所/衍生品经验、3 年以上交易业务或产品负责人经历，深入理解保证金、强平、资金费率、订单类型、撮合报价与流动性管理，数据敏感且跨团队推动力强。
3 、有数字资产交易所、券商、期货、CFD/做市背景者优先，加分项包括 0 到 1 搭建、DeFi/链上交易、量化做市及 CFD 合规经验；提供核心业务主导权与灵活创新环境。
Position 五：海外广告投放（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责 Google Ads/Meta 等渠道的海外效果广告投放（美国市场为主），围绕激活、注册、付费、订阅、ROI/ROAS 等核心指标，
2 、独立完成账户搭建、受众策略、预算控制、素材测试及数据链路（ GA4/GTM/AppsFlyer/SKAN ）归因优化；同时探索 TikTok 、LinkedIn 等新渠道机会
Position 六：产品运营（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责 2C 产品在欧美市场的用户增长与运营策略，覆盖拉新、激活、留存、转化及订阅/复购全链路。
2 、基于欧美用户画像与行为数据，制定本地化运营方案，推动产品功能、定价、内容与活动持续迭代。
3 、搭建数据监控与 A/B 测试体系，对 DAU 、留存率、付费转化等核心指标负责。
4 、协调产品、市场、设计和研发团队，推动跨部门增长项目高效落地；海外留学/工作背景，中英文可作为工作语言，深刻理解欧美 2C 用户习惯与文化。
Position 七：SEO （ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责欧美市场的 SEO 自然流量增长，制定站内、站外及技术 SEO 整体策略。
2 、围绕目标关键词与用户搜索意图，规划内容矩阵、落地页和国际化页面，提升搜索排名与转化。
3 、管理外链建设、内容合作与 SEO 数据分析，跟踪 Google 算法变化和竞品动向，持续优化流量结构。
4 、协同产品、内容和市场团队，把 SEO 需求嵌入产品迭代与内容生产流程；海外留学/工作背景，中英文流利，熟悉欧美搜索市场与 Google 生态。
Position 八：KOL 运营负责人（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责欧美市场的 KOL/网红营销策略，搭建从筛选、触达、谈判到合作落地的完整体系。2 、拓展并维护 YouTube 、TikTok 、Instagram 等平台的红人资源，推动内容共创、测评、种草和转化。
3 、制定合作报价与效果评估机制，追踪曝光、点击、转化和 ROI ，持续优化投放组合。
4 、结合欧美文化热点与产品卖点，策划可传播的 KOL campaign ，提升品牌认知与用户增长；海外留学/工作背景，中文、英语流利，熟悉欧美红人生态、平台规则与受众偏好。
TG：@jtx_2023
E: justinxu@futuretalent.com.cn
Position 一：资深前端工程师 or 前端小 Leader （需要一线做事）
Job Type: Full-Time ，remote
PS：
1. 负责核心交易前端架构设计与开发，主导复杂交互页面及可视化模块的研发，持续优化性能与用户体验。
2. 推动前端工程化与组件化建设，要求 5 年以上经验，精通 React/Vue 、TypeScript 及 WebSocket 实时推送优化，具备从 0 到 1 的项目落地能力和团队协作精神。
3. 全日制本科及以上，需要头部 CEX 全职合约交易系统开发背景。
Position 二：HRD/HR 负责人（一号位，汇报 CEO ）
Job Type: Full-Time ，remote
PS：
管理 7/10 人团队
1 、曾在知名交易所担任 HRD/HR Head 或 HRBP Leader 的负责人，能直接对话 CEO ，驱动业务而非停留于后台职能。
2 、该角色不是来了只写制度、建文档、走流程的，而是以人才引进、绩效激活和文化建设为三大核心战场。
3 、主导技术、量化、风控、合规等中高端及全球化岗位招聘，搭建行业人才地图，确保关键岗位快速到岗且高质匹配。
4 、深入业务，设计有竞争力的薪酬与长期激励（ Token/ESOP ），同时推动绩效体系落地，直接支撑组织效率与业务增长。
5 、协同 CEO 塑造企业文化，强化核心人才保留，在高速创业节奏中打造稳定、有战斗力的团队，并持续完善 HR 职能支撑体系。
Position 三：中东（ MENA ） BD 组长
Job Type: Full-Time ，remote
PS：
1 、负责中东（ MENA ）区域商务拓展策略制定与落地，带领 BD 团队制定目标、分配资源、辅导成员并追踪业绩。
2 、开拓维护区域关键客户、渠道伙伴、代理商及战略伙伴，洞察市场趋势、竞品与政策，协同产品、运营、市场、法务、财务推动本地化增长。
3 、识别区域业务风险与机会，提出可执行优化建议。
4 、要求本科及以上，3-5 年知名 CEX 商务拓展及团队管理经验，有 MENA 经验优先；具备优秀谈判、客户关系管理和项目推进能力，结果导向，英语和阿拉伯语可作为工作语言，能适应跨时区沟通及区域出差。
Position 四：交易产品负责人（合约）
Job Type: Full-Time ，remote
PS：
1 、全面负责现货、合约、CFD 交易业务线的战略规划、产品体验、流动性/做市、风控清结算、增长运营及小团队管理，推动全球化交易平台长期竞争力。
2 、要求 5 年以上金融/交易所/衍生品经验、3 年以上交易业务或产品负责人经历，深入理解保证金、强平、资金费率、订单类型、撮合报价与流动性管理，数据敏感且跨团队推动力强。
3 、有数字资产交易所、券商、期货、CFD/做市背景者优先，加分项包括 0 到 1 搭建、DeFi/链上交易、量化做市及 CFD 合规经验；提供核心业务主导权与灵活创新环境。
Position 五：海外广告投放（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责 Google Ads/Meta 等渠道的海外效果广告投放（美国市场为主），围绕激活、注册、付费、订阅、ROI/ROAS 等核心指标，
2 、独立完成账户搭建、受众策略、预算控制、素材测试及数据链路（ GA4/GTM/AppsFlyer/SKAN ）归因优化；同时探索 TikTok 、LinkedIn 等新渠道机会
Position 六：产品运营（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责 2C 产品在欧美市场的用户增长与运营策略，覆盖拉新、激活、留存、转化及订阅/复购全链路。
2 、基于欧美用户画像与行为数据，制定本地化运营方案，推动产品功能、定价、内容与活动持续迭代。
3 、搭建数据监控与 A/B 测试体系，对 DAU 、留存率、付费转化等核心指标负责。
4 、协调产品、市场、设计和研发团队，推动跨部门增长项目高效落地；海外留学/工作背景，中英文可作为工作语言，深刻理解欧美 2C 用户习惯与文化。
Position 七：SEO （ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责欧美市场的 SEO 自然流量增长，制定站内、站外及技术 SEO 整体策略。
2 、围绕目标关键词与用户搜索意图，规划内容矩阵、落地页和国际化页面，提升搜索排名与转化。
3 、管理外链建设、内容合作与 SEO 数据分析，跟踪 Google 算法变化和竞品动向，持续优化流量结构。
4 、协同产品、内容和市场团队，把 SEO 需求嵌入产品迭代与内容生产流程；海外留学/工作背景，中英文流利，熟悉欧美搜索市场与 Google 生态。
Position 八：KOL 运营负责人（ AI ）
Job Type: Full-Time ，Onsite-Shanghai
PS：
1 、负责欧美市场的 KOL/网红营销策略，搭建从筛选、触达、谈判到合作落地的完整体系。2 、拓展并维护 YouTube 、TikTok 、Instagram 等平台的红人资源，推动内容共创、测评、种草和转化。
3 、制定合作报价与效果评估机制，追踪曝光、点击、转化和 ROI ，持续优化投放组合。
4 、结合欧美文化热点与产品卖点，策划可传播的 KOL campaign ，提升品牌认知与用户增长；海外留学/工作背景，中文、英语流利，熟悉欧美红人生态、平台规则与受众偏好。
TG：@jtx_2023
E: justinxu@futuretalent.com.cn
[问与答] Mac mini M4 Pro/M6 买拓展坞有什么推荐吗?
阿卡西斯
- Mini 底座|16T 扩容+双 DP|40Gbps
- Mini 机箱|40Gbps+双 DP|16T 扩容
- 其他
同事买了一个绿联的, 容易掉盘,
谢谢推荐呀
阿卡西斯
- Mini 底座|16T 扩容+双 DP|40Gbps
- Mini 机箱|40Gbps+双 DP|16T 扩容
- 其他
同事买了一个绿联的, 容易掉盘,
谢谢推荐呀
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