Cloud Computing Solution - Coiled simplifies cloud-based Python workflows, reducing complexity [1] - Coiled automates environment synchronization, hardware provisioning, and shutdown in the cloud [2] - Coiled offers 500 free CPU hours per month for most users [2] Key Features - Coiled enables running jobs hourly, concurrently, or with specific hardware like GPUs [3] - Coiled supports running jobs in different regions for data proximity [3] - Coiled allows using different languages, packages, or binaries [3] Development Process - Users import Coiled and decorate Python functions, specifying hardware and region [2] - Coiled eliminates the need for navigating consoles, setting IAM policies, or writing YAML configs [1][2] - Coiled helps in monitoring billing spikes [1]

Cloud Computing Challenges - Cloud usage involves navigating consoles, setting IAM policies, writing YAML configs, and monitoring billing spikes [1] - Running Python workflows on the cloud can be complex [1] Coiled Solution - Coiled simplifies cloud usage for Python users [1] - Coiled enables running any workflow [1] Call to Action - The author encourages readers to reshare the content [1] - The author shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

Start here: https://t.co/RqixpFOzmb ...

Core Offering - Coiled enables running Python workloads on the cloud with minimal code [1] - Coiled automates environment synchronization, hardware provisioning, and shutdown in the user's cloud account [2] - Coiled offers 500 free CPU hours per month for most users [2] Key Features - Supports running jobs hourly, concurrently (1000s), and with specialized hardware (GPUs, fast disks) [3] - Enables running jobs in different regions and with diverse language/package/binary requirements [3] Pain Points Addressed - Simplifies cloud usage by eliminating the need for console navigation, IAM policy configuration, and YAML configuration [1][2] - Reduces concerns about billing spikes [1] Usage - Involves importing the Coiled library and decorating Python functions [2]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Finally, Agents can deliver interactive frontend experience (open-source)!Backends like CrewAI, LangGraph, Mastra, etc., can do a lot.But the hardest part is embedding them into interactive user-facing software products, like Cursor.Also, migrating from one agent backend to https://t.co/dHPLG1MCD4 ...

GitHub repo: https://t.co/FfVx9UU6d3.(don't forget to star it ⭐ ) ...

Finally, Agents can deliver interactive frontend experience (open-source)!Backends like CrewAI, LangGraph, Mastra, etc., can do a lot.But the hardest part is embedding them into interactive user-facing software products, like Cursor.Also, migrating from one agent backend to another is painful because......each framework has its own output formats, state handling, ReAct patterns, etc.AG-UI (Agent-User Interaction Protocol) is an open-source protocol designed to address this and build front-end-powered Agents ...

RT Avi Chawla (@_avichawla)I have been fine-tuning LLMs for over two years now!Here are the top 5 LLM fine-tuning techniques, explained visually: ...

LLM Fine-tuning Techniques - The document introduces top 5 LLM fine-tuning techniques, explained visually [1] - The author has been fine-tuning LLMs for over two years [1] Author Information - Avi Chawla shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

And those were the 5 popular LLM fine-tuning.Here's the visual again for your reference 👇 https://t.co/avrTVTg3dp ...