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También disponible en inglés Antes de diseñar un endpoint, clasificá su naturaleza. Recién después elegí su ubicación. El problema silencioso Existen muchos artículos que explican cómo escribir una API REST. Aquí, en cambio, nos vamos a centrar en una pregunta previa: ¿cómo decidir dónde debe vivir un endpoint antes de escribirlo? Cuando ese criterio no existe, la ubicación de cada nuevo endpoint termina dependiendo de la intuición, del precedente o de una negociac...

We are building an autonomous research agent that turns a vague question into a structured plan, gathers evidence across multiple calls, and synthesizes a markdown report. This is the practical future of LLMs: not monolithic chat, but small, orchestrated reasoning loops that leverage long context and tool use. Because Oxlo.ai charges a flat rate per request instead of per token ( see pricing ), running multi-step agent workflows like this stays predictable even when prompts grow. What you'll ...

LLM costs accumulate in ways that are not always obvious. Tokens consumed by system prompts, repeated context windows, and verbose JSON outputs all inflate bills before a single useful response is returned. For teams running agentic workflows or processing long documents, the standard token-based meter can turn a prototype into a budget risk. The good news is that cost optimization is a systems problem, not just a modeling problem. With the right architecture decisions, you can cut inference spe...

When a support system breaks, its real incentives show. A field report on affective blindness, governance, and why bots escalate hostility but never harm. There is a moment in every system where its true nature reveals itself—not in its marketing copy, not in its onboarding flow, but in the instant something breaks and a human being reaches for help. That moment arrived for me this week on Handshake, a platform explicitly designed for students and alumni. A fraudulent employer slipped thr...

I recently shipped a small research agent that beats three recurring LLM problems: stale training data, hallucinated facts, and arithmetic errors. Instead of hoping the model memorized everything correctly, I gave it tools to look up facts and verify math, then cite sources before answering. In this tutorial I will walk you through the exact code so you can run it against Oxlo.ai today. What you'll need Python 3.10 or newer, the OpenAI SDK, and an Oxlo.ai API key from https://portal.oxlo....

Airtable AI From Scratch: A Freelance Dev's Cost Breakdown I run a one-person shop. No co-founders, no VC money, no "growth team." Just me, my laptop, and a growing list of clients who need AI features bolted onto their existing tools. Every API call I make comes out of the same pocket that pays my rent. So when I tell you I spent three weekends tearing apart my AI stack and rebuilding it from the ground up around Airtable AI, it's because the math finally made sense. This is the post I wi...

We are going to build a conversational language tutor that corrects mistakes in real time, adapts its complexity to your proficiency, and maintains context across a long practice session. It is meant for developers who want to drop a speaking partner into an edtech product, or for learners who prefer to own their data and their prompt. Oxlo.ai works well here because its request-based pricing stays flat even when you send long conversation histories or full grammar guides in the system prompt. ...

Flama 2.0 brings first-class support for generative AI: downloading, packaging, and serving large language models (LLMs) is now as simple as running a few commands in your terminal. No boilerplate code, no custom serving infrastructure, no configuration files. Just the CLI and a model. In this post, we walk through the entire workflow: fetching a model from HuggingFace, interacting with it locally in your terminal, and serving it over HTTP with a production-ready API and a built-in chat int...

Last month I needed to stand up a Llama 3 inference endpoint for an internal tool. The requirements were simple: OpenAI-compatible API, auto-scaling, and it couldn't cost more than the team's coffee budget. AWS wanted $3.06/hr for a g5.xlarge . Azure quoted something similar. Then I looked at OCI's GPU shapes. VM.GPU.A10.1 — a single NVIDIA A10 with 24GB VRAM — at $1.52/hr on-demand. Half the price. And on preemptible? $0.46/hr. That's a latte. Here's how I got vLLM running on OKE in ab...

The Quest Begins (The “Why”) I still remember the first time I tried to make a Go program feel like The Matrix — you know, where Neo suddenly sees the code flowing everywhere. I was building a simple image‑processing pipeline: grab a file, resize it, slap on a watermark, and ship it off to S3. Sounds easy, right? I spun up a goroutine for each step, hooked them together with channels, and hit run . What happened next felt like the final boss level in Dark Souls : my program hung, me...

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Article URL: https://qwen.ai/blog?id=qwen-robotsuite Comments URL: https://news.ycombinator.com/item?id=48554814 Points: 67 # Comments: 7

Article URL: https://parksb.github.io/en/article/32.html Comments URL: https://news.ycombinator.com/item?id=48555838 Points: 124 # Comments: 34