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  1. 1
    Platform Engineering Approaches For LLM Applications▼Platform Engineering For LLM Applications✉newsTechnologySoftware21 h ago

    Platform engineering practices are being applied to large language model applications, according to a new article in Open Source For You. The piece examines how internal developer platforms and infrastructure automation can support building, deploying and operating LLM-based software, as more organisations move AI applications from prototypes into production.

  2. 2
    LiteLLM gateway lets developers call 100+ AI APIs from one interface●BerriAI/litellm⬢github4414 d ago

    BerriAI's LiteLLM is drawing attention as an open-source AI gateway built on a Rust core with a Python SDK. It lets developers call more than 100 large language model APIs — including Bedrock, Azure, OpenAI, Anthropic, Vertex AI, vLLM and Nvidia NIM — in OpenAI or native format, with built-in cost tracking, guardrails, load balancing and logging. The project is trending among developers building applications across multiple AI providers.

  3. 3
    Geniatech Launches M.2 AI Accelerators for Local LLM Inference●Geniatech Enables Local LLM Inference with RK1828 and Hailo-10 M.2 AI Accelerators✉newsTechnologyAI5 h ago

    Geniatech has introduced M.2 AI accelerator modules based on the RK1828 and Hailo-10 chips, designed to let devices run large language models locally rather than in the cloud. The hardware targets edge computing and embedded systems, reflecting growing demand for private, on-device AI processing across industrial and consumer applications.

  4. 4
    Deep-Dive Walkthrough of LLM Application Development Published▼LLM Application Development A deep-dive walkthrough of building applications on top of large language models — coveringMmastodonTechnologySoftware33 d ago

    A new guide walks developers through building applications on top of large language models. It explains how LLMs actually work and what that implies for application design, prompt engineering, and the system, user and assistant message roles. It also covers few-shot prompting and structured output techniques.

  5. 5
    Developers Grapple With the Hidden Costs of LLM Integration●*Large language models make it easy to add intelligent features to an application. The difficult part... # ai # agents #MmastodonTechnologyAI23 d ago

    Adding intelligent features to software with large language models has become straightforward, but developers say managing the cost of running them is the hard part. Discussion is focusing on practical ways to cut AI infrastructure bills, with lists of optimization methods for LLM APIs circulating among engineers working with tools like Claude on agent-based applications.

  6. 6
    AI gateways face challenges handling multimodal workloads●TL;DR Multimodal AI workloads passing images, audio, and video create payload-size variances and... # aigroups # llm # dMmastodonTechnologySoftware24 d ago

    A roundup of the top five AI gateways for multimodal workloads in 2026 is drawing attention among developers. Multimodal AI workloads that pass images, audio, and video create significant payload-size variances, making gateway selection important for performance and cost. The discussion spans devops, open source, and software engineering communities weighing which platforms best handle mixed media traffic.

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