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AI Agents Are the New Microservices: Everyone Wants Them, Almost Nobody Ships Them
The market says $200B by 2034. The data says 95% of agent projects fail before production. Here is what actually works.
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32 articles
The market says $200B by 2034. The data says 95% of agent projects fail before production. Here is what actually works.
Apple spends $14B on AI while competitors spend $650B. Is it losing or playing a smarter game? The data tells a complicated story.
When Graph RAG doubles retrieval accuracy and when it wastes your money. Benchmarks, costs, frameworks, and a decision framework.
Graph databases find connections. Vector databases find similarities. When to use which, real benchmarks, and why PostgreSQL might replace both.
RAG tutorials teach the easy 20%. Here are the five production problems they skip — and how to actually solve them.
LangChain chains steps in a line. LangGraph builds state machines. Most comparisons miss this fundamental difference.
Benchmarks measure what model creators optimize for, not what matters in production. Here is what I measure instead.
MCP went from Anthropic side project to industry standard in 16 months. Here is how it works and why it matters.
Agentic AI and reinforcement learning are different things. The confusion costs companies wrong hires, wrong architecture, and wrong expectations.
A phase-by-phase roadmap to become an AI engineer: LLMs, RAG, agents, and what interviews actually ask.
77% of businesses had AI security incidents in 2024. The OWASP Top 10 for LLM Applications catalogs the attacks most AI apps can't defend against — and the practical defenses that actually work.
Our LLM bill hit $23K/month. Three layers — prompt caching, semantic caching, and model routing — cut it to $8.6K. Here's how.
Sora cost $15M/day to run. Lifetime revenue: $2.1M. Context windows keep growing. The economics that decide which AI products survive.
Rakuten launched 'Japan's largest AI model' with government backing. It was a fine-tuned DeepSeek V3 with the MIT license deleted. The community caught it in four hours.
Build a RAG chatbot with LangChain, OpenAI embeddings, and Neon PostgreSQL. pgvector, no Pinecone, full Python code, 30 minutes.
AI Engineer topped LinkedIn's fastest-growing jobs list, yet most companies can't agree on what the role actually means.
A2A lets AI agents discover, delegate, and coordinate without knowing each other's internals. Here is how it works.
200 unit tests passed. The chatbot still hallucinated a dentist's phone number. LLM testing needs evals, LLM-as-judge, and regression for non-determinism.
Ollama peaks at 41 tok/s. vLLM hits 793. TGI is in maintenance mode. Here's the self-hosting guide I wish existed before I started.
I spent 6 months parsing LLM output with regex. Then Pydantic + structured outputs eliminated every 3 AM parsing alert. Here's the migration.
65% of companies use generative AI. Almost none test it properly. Here's the eval framework that caught our $47K hallucination disaster.
88% of AI agents never reach production. $547B in failed AI investments. The five gaps that kill agents and the architecture that actually survives.
Meta shipped 10M-token context. The model scores 15.6% at 128K tokens. Here's what actually works and what doesn't.
Every major open-source frontier model in 2026 uses MoE. A 120B model now fits on one H100. The self-hosting economics changed forever.
Alibaba's Qwen hit 1B+ downloads, beats GPT-5.2 on instruction following, and costs 13x less than Claude. The open-source AI race is over.
Microsoft launched MAI models built by 10-person teams that beat OpenAI's Whisper. The $13B partnership is fraying.
All three score ~57 on the Intelligence Index. Claude leads coding quality, Gemini leads math, GPT leads speed. Which to use when.
Sora burned $15M/day in compute against $2.1M lifetime revenue. The most expensive lesson in AI product economics.
24,000+ fake accounts. 16M+ exchanges. DeepSeek, MiniMax, Moonshot accused of industrial-scale model theft. The ethics, the hypocrisy, and the national security framing.
Prompt engineering jobs are vanishing. Context engineering, harness engineering, and agentic AI are what actually matter now.
A practical guide to fine-tuning LLMs with LoRA, QLoRA, Unsloth, and OpenAI. Real costs, real code, and when to fine-tune vs RAG.
I replaced GPT-4 with 7B models in production. Same quality, 95% cheaper. Here is why small language models are winning.