モダールは、コードをクラウドで実行するのを支援するプラットフォームです。開発者が自分自身のインフラストラクチャを管理せずにコンテナ化されたサーバーレスコンピュートにアクセスするための最も簡単な方法だと考えています。
モダールを使用するには、開発者が生成AIモデル、大規模なバッチジョブ、ジョブキューなどを実行できます。自分自身のコードを持ち込み、モダールがインフラストラクチャを実行することを担当します。インストールプロセスでは、'pip install modal'を実行し、続いて'python3 -m modal setup'を実行します。
Modal について詳しくは、会社概要ページ (https://modal.com/company) をご覧ください。 。
Modal ログイン リンク: https://modal.com/login?next=%2Fhome
Modal サインアップ リンク: https://modal.com/signup
Modal 価格リンク: https://modal.com/pricing
Modal Linkedinリンク: https://www.linkedin.com/company/modal-labs/
Modal Twitterリンク: https://twitter.com/modal_labs
Modal Githubリンク: https://github.com/modal-labs
ソーシャルリスニング
AI Software Engineer Plants Secret Messages in Images
Devin learns how to run ControlNet on modal by reading a blog post, and helps Sara generate a few images. Learn more about Devin and Cognition at https://www.cognition-labs.com/blog and follow us on Twitter at https://twitter.com/cognition_labs --- Hidden in Plain Sight (Blog Post): https://www.factsmachine.ai/p/hidden-in-plain-sight Modal (Serverless Platform): https://modal.com
New 🧑💻 Smol AI Developer - Build ENTIRE Codebases With A Single Prompt (ChatGPT)
In this video, we take a look at Smol AI Developer, which is a popular GitHub repo promising to be an automated Jr. Developer. It's the most complete AI coding solution I've seen to date, using ChatGPT to write entires coding projects for you, not just one file or single methods. We'll review it and then I'll show you how to install it. Enjoy :) Join My Newsletter for Regular AI Updates 👇🏼 https://forwardfuture.ai/ My Links 🔗 👉🏻 Subscribe: https://www.youtube.com/@matthew_berman 👉🏻 Twitter: https://twitter.com/matthewberman 👉🏻 Discord: https://discord.gg/xxysSXBxFW 👉🏻 Patreon: https://patreon.com/MatthewBerman Media/Sponsorship Inquiries 📈 https://bit.ly/44TC45V Links: GitHub - https://github.com/smol-ai/developer/ Modal - https://modal.com
Deploying code agents without all the agonizing pain
Agents that write and run code are powerful, as Cognition Labs showed with their recent release of Devin, the "AI SWE". But they are complex to program, hard to deploy, and even harder to secure -- what happens if your agent runs DROP prodtables or sudo rm -rf /? In this joint webinar between LangChain and Modal Labs, we cover the productionization of a coding agent. Lance Martin (@rlancemartin) walk through his coding agent implementation, which performs import and code execution checks along self-reflection in LangGraph. Modal AI Engineer Charles Frye (@charles_irl) will then show how to secure that prototype agent using Modal Sandboxes and deploy it as a FastAPI web app with only a dozen more lines of code. Slides: https://docs.google.com/presentation/d/1368-i3k73eM-h1vsd0LwchxQOC8JUQt7RRy9b44EBho/edit?usp=sharing Code: https://github.com/modal-labs/modal-examples/tree/main/06_gpu_and_ml/langchains/codelangchain First video discussing the design of the self-corrective coding agent in detail: https://www.youtube.com/watch?v=MvNdgmM7uyc Try Modal! Includes $30/month of free compute: https://modal.com Timestamps - 00:00 Summary 00:48 From paper to notebook 04:09 Evaluating the agent 08:20 From notebook to production - LangServe and Modal.asgi_app 13:20 Notebooks and apps 16:45 Iterating in production - OpenAPI docs 18:07 Securing code agents with Modal Sandboxes 23:47 Development servers with modal serve 28:42 Serving a UI with LangServe Playground 37:33 Deeper dive on using Modal Sandboxes 42:20 Observability and monitoring with LangSmith 45:08 Recap (edited)
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