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🗺️ El fin de los paneles de telemetría aburridos: El mapa de observabilidad Open Source definitivo Monitorear arquitecturas distribuidas y microservicios usando logs planos o interfaces densas hace que rastrear un error en producción sea una pesadilla. Maple es un monorrepo de código abierto que transforma la telemetría de tu infraestructura en un mapa de servicios interactivo y animado en tiempo real. Pasa de leer métricas frías a ver exactamente cómo fluyen tus datos. El ataque a la yugular: 🔄 Service Map en Tiempo Real: Visualiza las dependencias e interacciones vivas entre tus APIs, bases de datos (MySQL, ClickHouse) y colas de mensajería como Kafka. ⚡ Métricas al Vuelo: Haz clic sobre cualquier nodo para auditar instantáneamente latencias p99, tasas de error y rendimiento sin salir del flujo visual. 🔍 Rastreo de Queries: Identifica cuellos de botella de inmediato visualizando cuáles son las consultas más pesadas ejecutándose directamente en tus almacenes de datos. 🛠️ Arquitectura de Vanguardia: Diseñado como un monorrepo que integra ingesta de OpenTelemetry (OTLP), backend nativo basado en Effect y un servidor de código MCP para que tus agentes de IA puedan auditar el sistema. Deja de adivinar qué microservicio está fallando o rompiendo tu base de datos. Dale visibilidad real al flujo de datos de tu infraestructura de backend. Enlace al repositorio en los comentarios. Guarda este post en marcadores antes de que tu próximo sistema distribuido entre en cuello de botella 🔖

A day late, but I'm building harness, a browser based AI assistant that lives in a floating PiP window, is screen aware, anon/private, and requires no downloads or installs of any kind, including browser extensions. Check out this demo! The goal is to completely integrate with the @bankrbot ecosystem on the backend, by using a portion of fees from the $harness token + a portion of any revenue generated to buy and stake $bnkr, and using the yield to subsidize, and hopefully eventually fund inference to provide a desktop assistant that is both affordable and capable. In addition, integrating, secondary marketplaces like @AskSurplus to further reduce inference costs. In addition making @bankrbot skills a first class provider for adding skills and capabilities to your individual harness assistant for whatever you're doing. Any thoughts/feedback is much appreciated, and if interested sign up for the waitlist at to get first dibs at trying it out.

A new rumor suggests Valve is preparing to launch the new Steam Machine in the next few weeks. Valve insider Brad Lynch recently spotted a “Steam Machine Welcome Tour” in Steam’s backend. This appears to be a setup guide for new users, and similar backend changes have appeared before such as the Steam Controller. Some fans think Valve could be preparing to share release details soon, especially with Summer Game Fest taking place on June 5 and a Valve-related announcement already teased for the event.

A lot of cool product development happening @ Reptides right now, but this might be one of the best ones yet… (up there with research lab imo) "ask reptides.” Here’s how it works: 1. Ask it any peptide question. 2. Get a real, research-driven answer. No BS, no fluff, no AI slop. Powered by AI, BUT every answer pulls from thoroughly vetted, verified sources housed in the Reptides backend infrastructure. Think of it as an intelligent internal search engine built to simplify peptide research, embedded in the platform itself. And it’s only going to get better as more data is compiled internally. More coming soon. iOS app in development as we speak. And if you read this far, here's a little bonus: Running a two-week promo for the 9th Life community. Use code “9LIFE” at checkout for 10% off a lifetime subscription. Link in bio. (Please note this feature is still in beta testing - User feedback is welcomed)

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter]