Client-side caching is usually implemented as a storage optimization layer (TTL, SWR, invalidation rules). In practice it behaves like a decision system under uncertainty. Static strategies fail when data volatility is non-uniform across the same application. This leads to either stale UI or excessive network traffic. This article breaks down: why standard caching approaches plateau where ML impro
The math isn't complicated. It's just that nobody runs it until they get the bill. An AI agent handling a 10-turn workflow — reading files, calling tools, revising output — doesn't cost 10x a single query. Because transformer inference processes the entire context on every call, cost compounds with each additional turn. The tenth turn carries everything that preceded it: the original file reads, e
If this is useful, a ❤️ helps others find it. I've shipped multiple apps with AI features. My AI infrastructure cost: $0/month. Here's exactly how — every tool, every limit, every workaround. Free tier: 500 req/day (Gemini 2.5 Flash), no credit card Best for: Strong reasoning, document analysis, code debugging Get it: aistudio.google.com 2. Ollama — Local LLMs Free tier: Unlimited
If this is useful, a ❤️ helps others find it. I run both in production. Here's the real comparison — not theoretical, from actual use building developer tools. Local LLM (Ollama) Gemini API (Free) Cost $0 forever $0 (free tier) Privacy 100% local Data sent to Google Setup Install Ollama + pull model Get API key (2 min) Quality Good (7B), Great (70B) Excellent Speed Fast if model lo
An opinionated list of Python frameworks, libraries, tools, and resources
A College Project That Planted a Seed Years ago I was on a university team trying to build a Go AI. We explored monte carlo simulation for lookahead search, basic neural networks for pattern recognition, and expert systems for encoding domain knowledge. None of them worked well enough on their own. Go's branching factor is enormous, so brute-force search fails quickly. Neural networks without th
DeepClaude: I Combined Claude Code with DeepSeek V4 Pro in My Agent Loop and the Numbers Threw Me Off DeepSeek V4 Pro correctly solves 94% of deep reasoning tasks in my loop… but the latency cost makes it unusable for 60% of my agent cases. Yeah, you read that right. And that completely blows up the narrative of "combining models is always better." Tuesday night I watched the DeepClaude post cli
Series: AI Isn’t an Engineering Problem Anymore (Part 2) In the last post, I talked about hitting a usage limit while debugging my robot and realizing how repetitive my own AI usage had become. When we use LLMs, whether through APIs or tools, it feels like every request is new. The inefficiency isn’t from using AI too much. You don’t ask once, you iterate. These are the most interesting ones. Some