Ai

Running AI workloads in containers presents unique challenges that traditional web application patterns don’t address. GPU scheduling, model caching, and bursty inference traffic all require thoughtful architecture. Here’s what actually works in production. The GPU Scheduling Problem Standard Kubernetes scheduling assumes CPU and memory are your primary constraints. When you add GPUs to the mix, everything changes. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 apiVersion: v1 kind: Pod metadata: name: inference-server spec: containers: - name: model image: my-registry/llm-server:v1.2 resources: limits: nvidia.com/gpu: 1 memory: "32Gi" requests: nvidia.com/gpu: 1 memory: "24Gi" The naive approach—one GPU per pod—works until you realize GPUs cost $2-4/hour and sit idle between requests. MIG (Multi-Instance GPU) and time-slicing help, but they introduce complexity: ...

Integrating LLM APIs into production applications requires more than just making API calls. These patterns address the real challenges: rate limits, token costs, latency, and reliability. Basic Client Setup 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 import os from anthropic import Anthropic client = Anthropic( api_key=os.environ.get("ANTHROPIC_API_KEY"), timeout=60.0, max_retries=3, ) def chat(message: str, system: str = None) -> str: """Simple completion with sensible defaults.""" messages = [{"role": "user", "content": message}] response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, system=system or "You are a helpful assistant.", messages=messages, ) return response.content[0].text Retry with Exponential Backoff Built-in retries help, but custom logic handles edge cases: ...

LLM APIs are deceptively simple: send a prompt, get text back. But building reliable AI features requires handling rate limits, managing costs, structuring outputs, and gracefully degrading when things go wrong. Here are the patterns that work in production. The Basic Client Start with a wrapper that handles common concerns: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 import os import time from typing import Optional import anthropic from tenacity import retry, stop_after_attempt, wait_exponential class LLMClient: def __init__(self): self.client = anthropic.Anthropic() self.default_model = "claude-sonnet-4-20250514" self.max_tokens = 4096 @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=60) ) def complete( self, prompt: str, system: Optional[str] = None, model: Optional[str] = None, max_tokens: Optional[int] = None ) -> str: messages = [{"role": "user", "content": prompt}] response = self.client.messages.create( model=model or self.default_model, max_tokens=max_tokens or self.max_tokens, system=system or "", messages=messages ) return response.content[0].text The tenacity library handles retries with exponential backoff — essential for rate limits and transient errors. ...

Adding an LLM to your application sounds simple: call the API, get a response, display it. In practice, you’re dealing with rate limits, token costs, latency spikes, and outputs that occasionally make no sense. These patterns help build LLM features that are reliable, cost-effective, and actually useful. The Basic Call Every LLM integration starts here: 1 2 3 4 5 6 7 8 9 10 11 from openai import OpenAI client = OpenAI() def ask_llm(prompt: str) -> str: response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": prompt}], temperature=0.7 ) return response.choices[0].message.content This works for demos. Production needs more. ...

When you deploy an LLM-powered agent in production, traditional APM dashboards only tell half the story. You can track latency, error rates, and throughput — but what about what the agent actually did? Did it hallucinate? Did it spiral into an infinite retry loop? Did it spend $47 on tokens chasing a dead end? Here’s how to build observability for autonomous agents that actually helps. The Three Pillars of Agent Observability Standard observability (logs, metrics, traces) still matters. But agents need three additional dimensions: ...

When you’re building production systems that rely on LLM APIs, you quickly learn that “it works in development” doesn’t mean much. Rate limits hit at the worst times, APIs go down, and costs can spiral if you’re not careful. Here’s how to build integrations that actually survive the real world. The Problem with Naive Integrations Most tutorials show you something like this: 1 2 3 4 5 6 7 8 import anthropic client = anthropic.Anthropic() response = client.messages.create( model="claude-sonnet-4-20250514", max_tokens=1024, messages=[{"role": "user", "content": prompt}] ) This works great until: ...

The landscape of AI coding assistants has shifted dramatically. What started as glorified autocomplete has matured into something far more interesting: collaborative coding partners that can reason, refactor, and even architect. The Evolution Early tools like GitHub Copilot impressed by completing your current line. Useful, but limited. Today’s assistants—Claude Code, Cursor, Codex CLI—operate at a different level: Multi-file awareness: They understand project context, not just the current buffer Reasoning: They can explain why code should change, not just what to change Tool use: They run tests, check linting, execute commands Iteration: They refine solutions based on feedback Patterns That Work After months of heavy use, here’s what actually moves the needle: ...

LLMs are becoming infrastructure. Not just chatbots — actual components in automation pipelines. But getting reliable, parseable output requires disciplined prompt engineering. Here’s what works for DevOps use cases. The Core Problem LLMs are probabilistic. Ask the same question twice, get different answers. That’s fine for chat. It’s terrible for automation that needs to parse structured output. The solution: constrain the output format and validate aggressively. Pattern 1: Structured Output with JSON Mode Most LLM APIs now support JSON mode. Use it. ...

Running AI inference in the cloud is easy until it isn’t. The moment you need real-time responses — autonomous vehicles, industrial quality control, AR applications — that 50-200ms round trip becomes unacceptable. Edge computing puts the model where the data lives. Here’s how to architect AI inference at the edge without drowning in complexity. The Latency Problem A typical cloud inference call: Capture data (camera, sensor) → 5ms Network upload → 20-100ms Queue wait → 10-50ms Model inference → 30-200ms Network download → 20-100ms Action → 5ms Total: 90-460ms ...

Integrating Large Language Model APIs into production applications requires more than just calling an endpoint. Here are battle-tested patterns for building resilient, cost-effective LLM integrations. The Retry Cascade LLM APIs are notorious for rate limits and transient failures. A simple exponential backoff isn’t enough — you need a cascade strategy: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 import asyncio from dataclasses import dataclass from typing import Optional @dataclass class LLMResponse: content: str model: str tokens_used: int class LLMCascade: def __init__(self): self.providers = [ ("anthropic", "claude-sonnet-4-20250514", 3), ("openai", "gpt-4o", 2), ("anthropic", "claude-3-haiku-20240307", 5), ] async def complete(self, prompt: str) -> Optional[LLMResponse]: for provider, model, max_retries in self.providers: for attempt in range(max_retries): try: return await self._call_provider(provider, model, prompt) except RateLimitError: await asyncio.sleep(2 ** attempt) except ProviderError: break # Try next provider return None The cascade falls through primary to fallback models, attempting retries at each level before moving on. ...