Claude Code Tool Search Nearly Halves Your Context Bill
Claude Code 2.1.285 with 65 MCP tools: tool search cut the first request from 27,184 to 9,722 tokens and a full task by 46%. Off by default behind a proxy.
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Claude Code 2.1.285 with 65 MCP tools: tool search cut the first request from 27,184 to 9,722 tokens and a full task by 46%. Off by default behind a proxy.
Build an AI agent from scratch in TypeScript with only a model API and a loop. Covers tool calling, parallel tool calls, and a bug no framework can fix.
The support ticket was one line long: "Your assistant says our docs don't mention refunds. We have an entire page called Refunds." I checked. The page was there. It was chunked, embedded, and sitting in Postgres with the right tenant_id.
Grok 4.7 costs exactly what Grok 4.6 did per token: $2 per million input tokens and $6 per million output. Per task, it costs about double. Artificial Analysis measured xAI's new model writing roughly 2.5 times as many tokens as its predecessor, so its cost per task went from $1.86 to $3.74.
In September 2026 a company called TypeSafe released Jev, and it isn't a chatbot. It never writes a sentence. You give it some text and a question with fixed answer options, and it returns one of your options plus how sure it is: "bot, 97%." Nothing to parse, nothing to read.
Integrating Large Language Models (LLMs) into cryptocurrency market analysis has evolved from experimental curiosity to a core component of institutional-grade trading strategies in 2026.
Today's AI landscape is shifting from pure software to specialized hardware and physical safety. From orbital compute to humanoid robot guardrails, the infrastructure of intelligence is expanding rapidly.
If your LLM API spend climbed after you added retrieval, a longer system prompt, or tool definitions, the cause is almost always input tokens being reprocessed at full price on every request — not the model you picked.
A technical response to the debate over human art, generative models, and the “spark of humanity” Pope Leo XIV recently argued that, in the age of AI, we need to distinguish human art from what machines produce.
Un agente LLM puede completar un registro, pedir un código de verificación y afirmar que terminó correctamente. Eso no demuestra que el flujo haya funcionado. Quizá leyó un mensaje viejo, confundió una respuesta del sistema o inventó el resultado después de que una herramienta fallara.
Benchmark: https://www.kaggle.com/benchmarks/winkoaung/myanmar-chem-calc-bench What tasks did I run? MyanmarChemCalc-Bench — 30 tasks built from 15 chemistry calculation questions drawn from Myanmar's Grades 9–12 chemistry textbook worked examples (ground-truth answers verified against the…
Imagine asking your AI assistant about last quarter's compliance report and getting a precise answer, sourced directly from your internal documents, without that data ever leaking to a public model. This is no longer science fiction—it's RAG (Retrieval-Augmented Generation) in production.
Imagine um assistente de IA capaz de responder perguntas sobre contratos, políticas internas e manuais da sua empresa — com a precisão de quem leu cada documento e a discrição de quem nunca vaza informação confidencial. Esse cenário deixou de ser ficção.
Everyone's building agents right now. But a better question than "can an agent do this?" is "when does an agent actually beat something simpler?" For the TigerGraph Agentic GraphRAG Hackathon, I built three question-answering pipelines side by side to find out.
Most code that calls a model has one line that looks harmless. model: "claude-sonnet-5" Changing it feels like a config change. But that string is part of an API contract. And this month, the contracts changed. On September 17, Google released Antigravity Agent 09-2026.
Narrow steps that run on every request, on a model stronger than that step needs, are candidates for a cheaper model. Measured spend then shows where the savings lie. Prove the step on the strongest model, move that one job to a smaller or fine-tuned model, and keep the product metric where it was.
Why adding an LLM changes the security model of an ordinary application Adding an LLM to an existing application can look deceptively simple. A user sends a request. The application sends it to a model. The model returns an answer.
Businesses that sell technical products answer the same kind of question every day. "What's the accuracy on this range?" "Can it measure through a coating, and how thick?" "Does it come with a calibration certificate?" The answers sit in product datasheets and manuals.
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