LayerX

LayerX
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AI podcast for the teams and leaders building with it. We share real use cases, experiments, and insights on how to apply AI to your business. Made by the team at LayerX.

  1. -6 дн.

    From coding on my laptop to coding in the cloud - a personal DGX Spark I drive from any device

    Moving my coding agents off the laptop and onto an always-on NVIDIA DGX Spark: SSH over a Cloudflare Tunnel, a browser control plane behind Cloudflare Access, and one Claude Code session I start on the box, continue on my laptop, and pick up on my phone.

  2. 27 июл.

    We went viral with Amália. Here's the marketing side.

    How we got 7,000 users in 48h testing the Amália LLM.

  3. 22 июл.

    Building Amália - what it takes to put a face on a public LLM

    We built Amália Chat, a ChatGPT-style interface for Portugal's open-source European-Portuguese LLM: passwordless guest-to-signup, launch-spike load shedding, per-conversation backend pinning, share links, and web search, plus real first-week numbers.

  4. 7 июл.

    Why 80% of AI projects never reach production

    Four in five AI projects never reach production. The problem isn't technology or talent. It's structural execution gaps that organizations consistently miss.

  5. 1 июл.

    Inside the Multi-Agent orchestration architecture behind the TAIKAI AI Arena

    How we built the TAIKAI AI Arena: a deterministic orchestrator with no LLM, a model-agnostic runtime that routes ten frontier models through one adapter, isolated Docker sandboxes, an MCP integration, and three kinds of judge.

  6. 24 июн.

    We ran a hackathon where 10 AI Agents built and judged each other

    LayerX ran an AI agents hackathon: 10 frontier models built a real app from one brief, then peer-judged each other. The results say more about shippability than any benchmark.

  7. 15 июн.

    How to Control LLM Output: Temperature, Top-P & Beyond

    Control LLM output with temperature, top-p, penalties, and logprobs to ship reliable AI automation. A practical field guide with a per-task cheat sheet

  8. 31 мая

    We entered Fixathon as hackers. We left as winners.

    How a 3-person LayerX team with no ML background won Fixathon, building Cadence: an explainable AI model that predicts factory production time from panel data.

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AI podcast for the teams and leaders building with it. We share real use cases, experiments, and insights on how to apply AI to your business. Made by the team at LayerX.

Информация

  • Автор
    LayerX
  • Годы выхода
    2026
  • Выпуски
    17
  • Ограничения
    Без ненормативной лексики
  • Сайт подкаста
    LayerX