2025-12-03 13:21:28
The shifting ownership of runtimes is reshaping the underlying logic of AI programming and infrastructure.
After the announcement of Bun’s acquisition by Anthropic , my focus was not on the deal itself, but on the structural signal it revealed: general-purpose language runtimes are now being drawn into the path dependencies of AI programming systems. This is not just “a JS project finding a home,” but “the first time a language runtime has been actively integrated into the unified engineering system of a leading large model company.”
This event deserves a deeper analysis.
Before examining Bun ’s industry significance, let’s outline its runtime characteristics. The following list summarizes Bun’s main engineering capabilities:
These capabilities have formed measurable performance barriers.
However, it should be noted that Bun currently lacks the core attributes of an AI Runtime, including:
Therefore, Bun’s “AI Native” properties have not yet been established, but Anthropic’s acquisition provides an opportunity for it to evolve in this direction.
Historically, it is not uncommon for model companies to acquire editors, plugins, or IDEs, but in known public cases, mainstream large model vendors have never directly acquired a mature general-purpose language runtime. Bun × Anthropic is the first clear event pulling the runtime into the AI programming system landscape. This move sends two engineering-level signals:
This is not a short-term business integration, but a manifestation of the trend toward compressed engineering pipelines.
Based on observations of agentic runtimes over the past year, runtime requirements in the AI coding era are diverging. The following list summarizes the main engineering abstractions trending in this space:
These requirements are not unique to Bun, nor did Bun originate them, but Bun’s “monolithic and controllable” runtime structure is more conducive to evolving in this direction.
If Bun is seen merely as a Node.js replacement, the acquisition is of limited significance. But if it is viewed as the execution foundation for future AI coding systems, the logic becomes clearer:
This model is similar to the relationship between Chrome and V8: the execution engine and upper-layer system co-evolve over time, with performance and semantics advancing in sync.
Whether Bun can fulfill this role depends on Anthropic’s architectural choices, but the event itself has opened up possibilities in this direction.
Combining facts, signals, and engineering trends, the following directions can be anticipated:
These trends will not all materialize in the short term, but they represent the inevitable path of engineering evolution.
The combination of Bun × Anthropic is not about “an open-source project being absorbed,” but about a language runtime being actively integrated into the engineering pipeline of a large model system for the first time. Competition at the model layer will continue, but what truly reshapes software is the structural transformation of AI-native runtimes. This is a foundational change worth long-term attention.
2025-12-02 20:07:45
The value of Agentic Runtime lies not in unified interfaces, but in semantic governance and the transformation of engineering paradigms. Ark is just a reflection of the trend; the future belongs to governable Agentic Workloads.
Recently, the ArkSphere community has been focusing on McKinsey’s open-source Ark (Agentic Runtime for Kubernetes). Although the project is still in technical preview, its architecture and semantic model have already become key indicators for the direction of AI Infra in 2026.
This article analyzes the engineering paradigm and semantic model of Ark, highlighting its industry implications. It avoids repeating the reasons for the failure of unified model APIs and generic infrastructure logic, instead focusing on the unique perspective of the ArkSphere community.
Ark’s greatest value is in making Agents first-class citizens in Kubernetes, achieving closed-loop tasks through CRD (Custom Resource Definition) and controllers (Reconcilers). This semantic abstraction not only enhances governance capabilities but also aligns closely with the Agentic Runtime strategies of major cloud providers.
Ark’s main resources include:
The diagram below illustrates the semantic relationships in Agentic Runtime:
Ark’s architecture adopts a standard control plane system, emphasizing unified runtime semantics. The community is highly active, engineer-driven, and the codebase is well-structured, though production readiness is still being improved.
The emergence of Ark has clarified the boundaries of ArkSphere. ArkSphere does not aim for unified model interfaces, multi-cloud abstraction, a collection of miscellaneous tools, or a comprehensive framework layer. Instead, it focuses on:
ArkSphere is an ecosystem and engineering system at the runtime level, not a “model abstraction layer” or an “agent development framework.”
2026 will usher in the era of Agentic Runtime, where Agents are no longer just classes but workloads that require governance rather than mere importation. Ark is just one example of this trend, and the direction is clear:
Ark’s realism teaches us that the future belongs to runtime, semantics, governability, and workload-level Agents. The industry will no longer pursue unified APIs or framework implementations, but will focus on governable runtime semantics and engineering paradigms.
2025-12-02 18:54:34
The greatest value of Ark lies in reshaping engineering paradigms, not just its features. It points the way for AI Infra and leaves vast space for community ecosystems.
Recently, many members in our ArkSphere community have started exploring McKinsey’s open-source Ark (Agentic Runtime for Kubernetes) .
Some see it as radical, some think it’s just a consulting firm’s experiment, and others quote a realistic maxim:
What we need now is “agentic runtime realism,” not “unified model romanticism.”
I strongly agree with this sentiment.
I’ve spent some time analyzing Ark’s source code, architecture, and design philosophy, combined with our community discussions. My conclusion is:
Ark’s significance is not in its features, but in its paradigm.
It’s not the answer, but it points toward the future of AI Infra.
Below is my interpretation of Ark, focusing on engineering, architecture, trends, and its inspiration for ArkSphere.
Ark’s core positioning is: A runtime that treats Agents as Kubernetes Workloads.
It’s not a framework, not an SDK, not an AutoGen-style multi-agent tool, but a complete system including:
Essentially, Ark is the control plane for Agents.
Ark defines seven core CRDs in Kubernetes. The following flowchart shows the relationships among these resources:
Through this set of CRDs, Ark makes Agent systems resource-oriented and declarative, enabling capabilities such as:
In other words, Ark is not about “how to write Agents,” but “how to operate Agents in enterprise-grade systems.”
Ark’s overall architecture is divided into three layers, each with different tech stacks and responsibilities. The following flowchart illustrates the relationships among components in each layer:
This is not a “wrapper project,” but a fully operational AI Runtime system, with a level of engineering far beyond most agent frameworks on the market.
Let’s revisit Kubernetes’ core value:
Kubernetes was never about “unifying cloud APIs”; it unified the “application runtime model.”
Cloud provider APIs aren’t unified, nor are networking or storage. What is unified: Pod, Deployment, Service—these application models.
Kubernetes succeeded because:
It provides a stable application abstraction on top of diversity.
Ark’s goal is not to unify all large language models (LLMs), MCPs, or tool formats, but rather:
Agent resource model (CRD) + control plane (Reconciler) + lifecycle.
From this perspective, Ark offers a prototype of a “declarative application model” for the AI era.
Whether it will become “Kubernetes for AI” is still too early to say, but it has already planted a seed.
Current mainstream agent frameworks like LangChain, CrewAI, AutoGen, MetaGPT, etc., address problems fundamentally different from Ark.
The table below compares the positioning and limitations of each framework:
| Name | What Problem Does It Solve | Core Limitation |
|---|---|---|
| LangChain | Agent/Tool composition | Doesn’t address deployment or governance |
| AutoGen | Multi-agent conversations | Lacks control plane and lifecycle |
| CrewAI | Workflow-style multi-agent | Missing scheduling, RBAC, resource model |
| MetaGPT | Agent SOP | Just execution logic, not a platform |
| OpenDevin | AI IDE/Dev Assistant | Not an Agent Runtime |
| Ark | Agent control plane + resource system | Functionality not yet mature |
In short:
That’s an architectural difference.
Ark’s execution flow closely resembles the Kubernetes controller model. The following sequence diagram shows the core process:
You can see Ark’s process logic is transparent, with a clear engineering path, bringing agent systems into a “controllable” state.
According to official notes and code maturity, Ark currently offers:
Main reasons include:
But the engineering system is already taking shape, which is crucial.
From GitHub data:
Note: Data as of December 2, 2025.
High stability, but limited openness.
This is also ArkSphere’s opportunity:
The paradigm is right, but the ecosystem needs community-driven growth.
After deep analysis, I’m increasingly convinced:
While everyone is writing Python scripts for agents, the real value lies in:
Ark is providing a practical path forward.
Ark’s inspiration for ArkSphere is both critical and direct:
Ark offers a prototype for future Agentic Runtime:
ArkSphere’s role should be:
Aggregate paradigms, produce standards, incubate ecosystems, not rewrite Ark itself.
This is the “CNCF (Cloud Native Computing Foundation) for the AI-native era.”
Localization opportunities include but are not limited to:
In other words:
Ark solves the “model,” while ArkSphere can solve the “ecosystem.”
The biggest takeaway from dissecting Ark:
The future of AI-native is not a pile of tools, but an engineering system.
ArkSphere can be the initiator of this system.
Ark is not a “universal runtime,” nor is it the “ultimate Kubernetes for the AI era.”
But it has done one crucial thing right:
It abstracts all the pain points people faced when writing Python agent scripts into Kubernetes resources and controllers.
It represents engineering, not just a demo.
It’s not mature yet, but it’s heading in the right direction.
It’s not the end, but it gives us a clear roadmap.
For the ArkSphere community I’m running, Ark provides a clear inspiration:
The future belongs to Runtime, to Control Plane, to governable agent systems.
And the ones who can truly scale this system are not McKinsey, but the community.
2025-11-29 20:40:54
The real inflection point for AI engineering is not “how many people use it,” but “how many people cannot do without it.” Only when not using AI leads to direct loss of opportunity and efficiency, can we say the era of AI engineering has truly arrived.
Recently, I came across two predictions for 2026 from Amazon CTO Werner Vogels that struck me the most:
Both point to the same trend: AI is not just a tool, but is redefining how people grow and how they are defined.
There is a gap between prediction and reality, and it is worth exploring.
My initial prediction was that AI usage would reach saturation by 2026. Reality has shown me this is too optimistic.
By the end of 2025, even among internet professionals, most people’s use of AI remains at the “heard of it” or “tried it a few times” stage. It is still far from being a daily workflow necessity.
More importantly, this judgment is conditional: infrastructure supply, regulation, and compute costs must not reverse in the next 3–6 years. If any variable breaks down (costs double, models go offline, policy shifts), the adoption curve will be disrupted.
“Relying on” is a vague term. A more precise definition requires measurable indicators.
Here is a diagram that visualizes the metrics for being truly dependent on AI:
Most industries have not reached the “cannot operate without” stage, unlike the internet, mobile, or payment inflection points. Most metrics are still far below the threshold, which is why the most likely outcome for 2026 is: more people will use AI, but those who truly rely on it will remain a minority.
This difference is not binary, but a clear progression.
The following table shows the five-level model of AI capability maturity.
| Level | Name | Description | Scarcity |
|---|---|---|---|
| 1 | Tool User | ChatGPT/Claude, Coding, Copywriting, Accelerator, Optional | Low |
| 2 | Integrator | LLM API + Vector DB, AI layered on existing systems, Usable, not critical | Low |
| 3 | Settler | Restructuring data flow, business decisions, AI becomes critical path | Rising |
| 4 | Engineering Abstraction | Extracting frameworks, runtimes, providing infra for ecosystem | Extremely High |
| 5 | Autonomous System | Self-feedback, self-optimizing, redefining human-AI relationship | Future |
Currently, the biggest gap is at Level 3 and Level 4. Most people are stuck at Level 1 or 2, with very few reaching Level 4. This means high-value scarcity will not disappear, but will continue to rise.
It is not technology alone that is holding things back, but constraints in three dimensions.
The following diagram illustrates the three main constraints delaying AI engineering maturity:
The key observation: If any one dimension is stuck, the entire ecosystem’s maturity will be delayed. Currently, none of the three dimensions have fully mature solutions.
The next three years will not be “winner takes all,” but rather a period where multiple capability levels appreciate simultaneously.
Below is a table comparing the value and bottlenecks of different capability advancement paths:
| Capability Path | Short-Term Value | Long-Term Outlook | Bottleneck |
|---|---|---|---|
| Level 1→2 (Tool→Integration) | ⭐⭐ Rapid Depreciation | ⭐ Saturation | Low barrier, fierce competition |
| Level 2→3 (Integration→Settlement) | ⭐⭐⭐⭐ Scarce | ⭐⭐⭐⭐ Continual Appreciation | Requires industry depth, long-term iteration |
| Level 3→4 (Settlement→Abstraction) | ⭐⭐⭐⭐⭐ Extremely Scarce | ⭐⭐⭐⭐⭐ Defines Ecosystem | Large cognitive leap, needs community influence |
Key conclusion: While the number of “AI users” is rapidly increasing (depressing Level 1 value), due to the three-dimensional delaying factors, scarcity at Level 3 and 4 will only rise.
Based on the above judgment, I focus on exploring the architectural evolution of AI Native infrastructure. The goal is not to catalog model usage, but to study the foundational capability stack supporting scalable intelligent systems: scheduling, storage, inference, Agent Runtime, autonomous control, observability, and reliability.
The content is no longer a collection of courses or tips, but a continuous record of evolution around Infra → Runtime → System Abstraction. arksphere.dev is the site for this experiment and settlement.
The inflection point for the era of AI engineering is not “how many people use it,” but “how many people cannot do without it.” The latter requires five measurable indicators to reach their thresholds, and we are still far from that.
“Using ≠ Building” is not a binary, but a five-level progression. Scarcity at Level 3 and 4 will rise as the number of Level 1 users increases—this is the biggest opportunity window in the next three years.
But the width of this window depends largely on how technology, institutions, and organizations evolve together. I hope more people working on AI engineering will not only focus on technical innovation, but also invest equal thought into institutional development, talent growth, and risk governance—these “invisible engineering” challenges.
2025-11-20 11:55:30
The biggest pain point when switching IDEs is user habits. By installing a series of plugins and tweaking configurations, you can make Antigravity feel much more like VS Code—preserving familiar workflows while adding Open Agent Manager capabilities.
I installed Antigravity on day one of its release. After a few days of use, my main impression is: it feels more like an “Agent console” than a traditional integrated development environment (IDE). Still, I’m used to the VS Code interface and plugin ecosystem, so I spent some time tuning Antigravity to become a “VS Code-style AI IDE”.
Below are the configurations and steps I actually use. Feel free to follow along.
A few subjective observations:
All subsequent steps focus on one goal: keep Antigravity’s agent features while maintaining my VS Code workflow.
Antigravity is essentially a VS Code fork, so you can directly change the Marketplace configuration.
In Antigravity:
Go to Settings -> Antigravity Settings -> Editor, and update the following URLs to point to VS Code:
Marketplace Item URL:
https://marketplace.visualstudio.com/items
Marketplace Gallery URL:
https://marketplace.visualstudio.com/_apis/public/gallery
Restart Antigravity.
After this change, searching and installing extensions works just like the official VS Code Marketplace. Installing AMP, GitHub Theme, VS Code Icon, etc., all follow this process.
AMP isn’t officially supported on Antigravity yet, but you can install it directly via the VS Code Marketplace.
Steps:
Currently, Antigravity doesn’t support one-click account login like VS Code; you have to use the API key.
I recommend AMP because it offers a free mode. In my experience, it’s great for writing documentation, running scripts, and as a daily command-line tool. It’s fast, and especially useful for optimizing prompts.
CodeX doesn’t provide a direct VSIX download link on the web. My approach is to export it from VS Code and then import it into Antigravity.
Steps:
.vsix file.codex-x.x.x.vsix file to complete installation.Beyond the marketplace and plugins, a few tweaks make the experience even closer to VS Code:
settings.json, so no migration is needed.After these changes, the editing area is essentially “VS Code with an agent console”.
To fully migrate from VS Code/GitHub Copilot to Antigravity, I think there are still several key challenges:
Although Antigravity excels in several areas, there is still significant room for improvement compared to the combination of GitHub Copilot and VS Code.
The large language models (LLMs, Large Language Models) I frequently use are all supported in VS Code:
My long-accumulated custom prompts:
My collection of agents:
Here are some personal experiences using VS Code and Copilot that, for now, are hard to replace with other IDEs:
A few subjective tips from my actual usage—take them as reference:
My current experience: Antigravity delivers powerful agent capabilities and multi-view consoles. By following these steps to align the interface and plugin ecosystem with VS Code, you can smoothly transition your daily development workflow.
2025-11-19 18:56:34
The greatest risks to modern internet infrastructure often aren’t in the code itself, but in those implicit assumptions and automated configuration pipelines that go undefined. Cloudflare’s outage is a wake-up call every Infra/AI engineer must heed.
Yesterday (November 18), Cloudflare experienced its largest global outage since 2019. As this site is hosted on Cloudflare, it was also affected—one of the rare times in eight years that the site was inaccessible due to an outage (the last time was a GitHub Pages failure, which happened the year Microsoft acquired GitHub).
This incident was not caused by an attack or a traditional software bug, but by a seemingly “safe” permissions update that triggered the weakest link in modern infrastructure: implicit assumptions (Implicit Assumption) and automated configuration pipelines (Automated Configuration Pipeline). Cloudflare has published a blog post Cloudflare outage on November 18, 2025 explaining the cause.
Here is the chain reaction process of the outage:
This kind of chain reaction is the most typical—and dangerous—systemic failure mode at today’s internet scale.
Let’s first look at the core hidden risk in this incident. The Bot Management feature file is automatically generated every five minutes, relying on a default premise:
The system.columns query result contains only the default database.
This assumption was not documented or validated in configuration—it existed only in the engineer’s mental model.
After a ClickHouse permissions update, the underlying r0 tables were exposed, instantly doubling the query results. The file size exceeded the FL2 preset of 200 features in memory, ultimately causing a panic.
Once an implicit assumption is broken, the system lacks a buffer and is highly prone to cascading failures.
This incident was not caused by code changes, but by data-plane changes:
A typical phenomenon in modern infrastructure: data, schema, and metadata are far more likely to destabilize systems than code.
Cloudflare’s feature file is a “supply chain input,” not a regular configuration. Anything entering the automated broadcast path is equivalent to a system-level command.
A former Cloudflare engineer summarized it well:
Rust can prevent a class of errors, but the complexity of boundary layers, data contracts, and configuration pipelines does not disappear.
The FL2 panic stemmed from a single unwrap(). This isn’t a language issue, but a lack of system contracts:
Most incidents in modern distributed systems (Distributed System) come from “bad input,” not “bad memory.”
FL/FL2 are Cloudflare’s core proxies; all requests must pass through them. Such components should not fail with a panic, but have the following capabilities:
As long as the proxy “stays alive,” the entire network won’t be completely paralyzed.
The essence of this incident:
Future AI Infra (AI Infrastructure) will be even more complex: models, tokenizers, adapters, RAG indexes, and KV snapshots all require frequent updates.
In future AI infrastructure, data-plane risks will far exceed those of the code-plane.
During the incident, Cloudflare took several measures:
Restoring hundreds of PoPs worldwide simultaneously demonstrates a high level of engineering maturity.
The Cloudflare event highlights four common risks in large-scale systems:
For AI Infra practitioners, these risks are even more relevant:
AI engineering is replaying Cloudflare’s infrastructure dilemmas—just at greater speed and scale.
His insights pinpoint the hardest problems in distributed systems:
This incident proves: The real fragility in modern infrastructure lies in “behavioral boundaries,” not “memory boundaries.”
The Cloudflare November 18 outage was not a coincidence, but an inevitable result of modern internet infrastructure evolving to large-scale, highly automated stages.
Key takeaways from this event:
In the AI-native Infra era, these requirements will only become more stringent.
