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You give an agent a complex goal. It starts well, then halfway through it forgets what it was doing, repeats work it already completed, or gets stuck when one step fails and blocks everything downstre
원문 보기“Why is it recommending the same role again?” I stared at the logs as our agent kept repeating the same suggestions, completely ignoring user feedback. That’s when I realized the real problem wasn’t t
원문 보기Everyone says: use caching, use cheaper models, reduce token counts. Here is the one thing that actually cuts LLM costs by 40%. Most LLM cost optimization advice focuses on the wrong thing: the cost p
원문 보기I tested 6 LLM monitoring tools over 2 weeks. Here is what I found. DriftWatch (my own, so I am biased) Helicone Portkey Athina Braintrust Custom (built-in logging) Drift detection accuracy Cost track
원문 보기A lot of agent control discussion still sits at the wrong layer. Neither answers the production question that matters most when agents are looping, retrying, fanning out: Can this agent still act — gi
원문 보기Every AI platform team eventually hits the same moment: finance sends a spreadsheet, engineering doesn't know where the tokens went, and someone on the data science team just ran a 400,000-token conte
원문 보기Cuando los agentes de IA tienen muchas herramientas similares, a menudo seleccionan la incorrecta y consumen tokens excesivos al procesar todas las descripciones de herramientas. Este articulo demuest
원문 보기If you use OpenClaw, you've probably hit this wall: Your agent can do a lot — browse, write, run code, manage files. But it can't talk to another person's agent. There's no inbox. No handshake. No pro
원문 보기After building LLM features for 18 months, here are the architecture patterns I have seen work at scale. And the two that consistently fail. User Input → Prompt Template → LLM API → Response → User S
원문 보기Six months ago I built a SaaS tool using only free AI tools. No paid APIs, no expensive infrastructure. Here's what I learned about what actually works. An LLM monitoring tool. Drift detection, cost t
원문 보기I Pitted 3 Qwen3.5 Models Against Each Other on an RTX 4060 8GB — What Spec Sheets Don't Tell You Qwen3.5 dropped. 9B, 27B, and the MoE-based 35B-A3B. If you just look at parameter counts, the story
원문 보기6-Band Prompt Decomposition: The Complete Technical Guide By Mario Alexandre 6-band prompt decomposition is the core technique of the sinc-LLM framework. It treats every LLM prompt as a specificatio
원문 보기LLM Output Quality Metrics: How to Measure What Matters By Mario Alexandre How do you know if an LLM's output is good? Subjective evaluation ("it looks right") does not scale. Automated metrics (BLE
원문 보기Token Optimization Guide: Maximize LLM Performance Per Token By Mario Alexandre Every LLM interaction has a cost measured in tokens. Input tokens (your prompt), output tokens (the response), and con
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