Most AI benchmarks evaluate outcomes. ARC-AGI shifts the focus to the process — how effectively a system learns new things. The problem manifests at the metric level. Modern systems demonstrate a high level of automation, but this is often a result of scaling data and computations, rather than an increase in generalization ability. A skill … Read more
AI agents are limited not by models, but by architecture. If feedback is slow, autonomy does not work. The problem manifests when an AI agent tries to close the loop of “generated → validated → corrected.” In typical cloud systems, this loop is stretched: deployment takes minutes, tests depend on resource provisioning, and errors only … Read more
GenAI has accelerated code production, but has made consistency (alignment) a bottleneck. Manual processes can no longer keep pace, and the architecture begins to fragment. The problem does not manifest immediately — until the speed of change generation exceeds the organization’s ability to review them. Historically, control has relied on people: key experts in startups … Read more
The profiler in kernel space only sees addresses. Useful insights emerge only after symbolization—and in Go, this stage is structured differently than in other languages. The problem arises when the profile has already been collected, but it cannot be interpreted. The eBPF profiler captures stack traces at the kernel level and obtains a set of … Read more
When component specifications lag behind implementation, the team starts building the system based on assumptions. At Uber, this turned into a systemic, large-scale problem—and was solved through agent-based automation. The problem does not arise at the moment of writing specifications, but later—when the system begins to evolve faster than the documentation. The Uber Base design … Read more
Higress enters the CNCF Sandbox as an API gateway with the aim of consolidating multiple layers of traffic. The key question is whether this reduces complexity or merely shifts it elsewhere. Systems begin to degrade when the traffic management layer becomes fragmented. Ingress operates separately, the gateway for microservices operates separately, and solutions for AI … Read more
The increase in developer productivity has not led to a comparable acceleration of releases. The reason is that the bottleneck has moved higher up the stack: into the area of requirements formalization and result verification. With the advent of AI coding, teams expected a linear acceleration in delivery. In practice, only one stage sped up—the … Read more
In live streaming, an error is not a degradation but an instant user-facing incident. Netflix addresses this by moving quality control and prioritization directly into the origin layer. The main limitation arises where VOD approaches stop working. In live, there is no time buffer: a segment must be encoded, delivered, and cached within seconds. Any … Read more
Digital sovereignty in engineering practice boils down to a single question: how quickly can you switch providers without breaking the system? The answer is almost always determined by architecture. A system does not start to degrade at the moment a provider fails, but much earlier, when dependency on that provider becomes implicit. This shows up … Read more
When the number of containerized services grows faster than the platform team, the bottleneck is not Kubernetes itself, but its operation. Generali faced exactly this challenge—and shifted the focus from cluster management to application management. The main limitation was not performance, but operations. The microservices portfolio was expanding, multi-tenant scenarios emerged, and with them—manual scaling, … Read more
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