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Contents Motivation and Purpose Using an Animation to Visualize Key Concepts Working With the Model 1. Get the Files 2. Choose the Dimensions 3. Inspect the Assembly 4. Export the MakerWorld Plates 5. Attach the Hoses Create the Model Animation Reference Links Motivation and Purpose I made a parametric bayonet connector for AC hoses , to attach such hoses to a mobile AC unit and a typical window kit taking the hot air outside. It is part of my Air Conditioning Collection on MakerWorld . And of course, I prefer code over using some GUI CAD application, leading straight to OpenSCAD as the established standard for 3D-models-as-code. It is also one of the few ways MakerWorld models can be made customizable by the end user. The connector allows attaching a hose with a simple push and a short twist, instead of needing a threaded joint or tools every time the hose is removed. The design has three printable parts: A female connector with bayonet slots on the inside. A male connector with matching lugs. A female adapter with a wider fitting section for joining to an existing tube, like the one on an air intake cover. The male lugs fit into the slots in the female connector. Push the parts together, twist them, and the lugs travel along the horizontal parts of the L-shaped slots. This is the same basic idea used by bayonet light fittings, camera mounts, and other quick-release connectors. See the Bayonet mount overview for useful background. The hose itself is held on the printed connector with a worm-drive hose clamp. A screw on the clamp pulls the perforated band tight around the hose. See the Hose clamp reference for an explanation of that mechanism. Using an Animation to Visualize Key Concepts The OpenSCAD file contains code that creates a 10-scene animation. It shows the female connector turning to expose the slots, the hose and male connector moving into position, the male part twisting to lock, and the parts separating again. It also emphasises the parametric nature of

El 7 de abril de 2026 escribí el primer commit de lo que iba a ser mi orquestador de agentes. Era, básicamente, una pantalla. Un servidor que gestionaba varios proyectos a la vez y desde el cual podía disparar tareas de un agente de código, con un tablero al medio que mostraba en qué etapa estaba cada cosa. Si me hubieran preguntado ese día cuál era el problema que estaba resolviendo, habría contestado sin dudar: ver y lanzar . Necesitaba un lugar desde donde disparar el trabajo y mirar cómo avanzaba. Cuatro meses después, con más de dos mil tareas cerradas por ese sistema, puedo decir que esa respuesta estaba equivocada, y que el primer indicio de por qué llegó a los tres días. Los dos primeros días fueron todos de interfaz Si miro el historial de esa primera semana, es casi cómico. El ancho del panel lateral. Los tooltips con las fechas completas al pasar el mouse. Los badges de "en progreso" sobre cada etapa. Los colores por etapa del pipeline, para que se distinguieran de un vistazo. Hay un par de commits consecutivos que me gusta especialmente como retrato de ese momento. El primero pone un emoji como ícono del botón de repetición. El segundo lo reemplaza por un carácter Unicode, porque el emoji ignoraba el color que le definía por CSS y se veía siempre igual, sin importar el estado. No lo cuento para burlarme de mí mismo. Lo cuento porque es exactamente cómo se ve un proyecto cuando todavía no sabés cuál es el problema. Estaba puliendo la superficie del sistema con mucho cuidado porque la superficie era lo único que tenía enfrente. La pregunta de fondo —qué parte de este flujo tiene que decidir un modelo y qué parte no— ni siquiera me la había hecho. El 10 de abril cambió el foco Para entonces el pipeline ya tenía forma: una cadena de pasos donde un agente elegía la próxima tarea pendiente, la implementaba y después la marcaba como terminada. Los tres pasos los hacía el modelo, porque los tres estaban escritos como instrucciones dentro de las habilidades que l

Companies without their own models — or without a layer of AI infrastructure known as AI gateways to separate their prompts from the model itself — will be in trouble, Nadella says.

Apple filed a trade secrets lawsuit against OpenAI last Friday, and it’s not messing around. The complaint alleges a pattern of misconduct reaching all the way up to OpenAI’s chief hardware officer and claims more than 400 former Apple employees now work at the company. OpenAI’s response so far has been carefully hedged, and the timing couldn’t be worse with the company reportedly eyeing an IPO […]

Of all the debates raging about the potential downsides of AI, there is one worry causing the most hand-wringing among AI enthusiasts in Silicon Valley — that the giant AI labs that sell proprietary models are somehow acting like Trojan horses.

Back when we introduced MCP support , we ended on a teaser: Phase 3 would tackle Sampling —letting servers request completions from the host instead of only exposing tools and resources to it. NodeLLM 1.17 delivers on that, and pairs it with a second, unrelated but overdue improvement: precise control over how tool calls execute, now available consistently in both core and the ORM persistence layer. 🔄 MCP Sampling: Closing the Loop Sampling inverts the usual MCP direction. Instead of the client asking the server for tools, the server asks the client to run an LLM completion on its behalf. This lets an MCP server offer LLM-powered capabilities—summarization, classification, drafting—without needing its own API key or provider integration. createLLMSamplingHandler answers those requests using a real NodeLLM instance, so a server's tool ends up powered by whatever model you configure client-side: import { createLLM } from " @node-llm/core " ; import { MCP , createLLMSamplingHandler } from " @node-llm/mcp " ; const llm = createLLM ({ provider : " openai " }); const mcp = await MCP . connect ( { command : " node " , args : [ " ./sampling-server.mjs " ] }, { sampling : createLLMSamplingHandler ( llm , " gpt-4o-mini " ) } ); const tools = await mcp . discoverTools (); // The server only advertises sampling-backed tools once it sees // the client declared sampling support during the handshake. If you need full control over how a sampling request is answered—routing by model hint, injecting your own guardrails—pass a plain handler function instead of { llm, model } . It receives the raw sampling/createMessage params and returns a CreateMessageResult , so you decide exactly how (or whether) to answer. ⚡ Concurrent Tool Execution When a model returns several independent tool calls in the same turn, NodeLLM has always executed them one at a time. That's safe by default, but wastes time when the calls don't depend on each other—three weather lookups for three different cities, s

The 2026 XPS 14 is the best premium laptop we've seen from Dell in a while, with incredible build quality in a thin machine and good performance thanks to Intel's Core Ultra Series 3 "Panther Lake" chips. A bonus: Dell killed its lame "Premium Plus" naming scheme. XPS is so back! I can't believe how […]

Nvidia's announcement that it's getting into the consumer laptop chip space with RTX Spark is huge. Apple has proved for years that Arm-based chips can perform incredibly well while also delivering great battery life - at least on the Mac. In the Windows world, performance hasn't fully matched up under Qualcomm chips, mostly in the […]

Nvidia has officially entered the world of consumer laptop chips with the RTX Spark, and several device makers already have hardware lined up for it. Microsoft, Asus, HP, MSI, Lenovo, and Dell are expected to launch RTX Spark laptops sometime this fall, and some of those partner companies have shared details about what we can […]

Dell is making good on its tease from CES and finally announcing a new XPS 13. The XPS 13 returns as a budget-friendly option, launching in July at a promotional student price of $599 - though that introductory deal only runs until September for back-to-school shopping; it'll start at $699 for everyone else. The $599 […]