Core Problem & Solution - Agents struggle with unpredictable behavior due to implicit interfaces derived from HTML and UI, leading to unreliable performance in production [1][2] - The industry needs predictable contracts for Agents, which APIs provide through clear rules for requests, responses, and error handling [2] - Standardizing API access, exposing APIs to Agents, and ensuring inspectability are crucial for debugging and monitoring, preventing chaos in the backend [3] Postman's Solution for AI Agents - Postman offers a centralized hub for documented, versioned, and accessible APIs, replacing ad-hoc API discovery [4] - Postman's MCP server exposes APIs directly to Agents, eliminating the need for custom integration code and manual wiring [5] - Postman logs every Agent request with full history, enabling precise debugging by showing what was sent, what was returned, and where failures occurred [5] Key Takeaway - Agents require explicit interfaces (APIs) for reliable behavior, as they understand interfaces, not intent [5] - APIs, when built with the right infrastructure, provide the necessary context for AI Agents to function effectively [6]

RT Avi Chawla (@_avichawla)Deploy and run LLMs directly on your phone!Unsloth now lets you fine-tune LLMs and deploy them 100% locally on iOS/Android devices.The video shows this in action, where I ran Qwen3 on an iPhone 17 Pro at ~25 tokens/s.I have shared a guide in the replies. https://t.co/p4NqLj0jRE ...

Technology & Development - Unsloth enables fine-tuning and local deployment of LLMs on iOS/Android devices [1] - LLMs can be deployed and run directly on phones [1] - Qwen3 was run on an iPhone 17 Pro at approximately 25 tokens per second [1]

Guide by @UnslothAI: https://t.co/jyscHySkQ0 ...

Deploy and run LLMs directly on your phone!Unsloth now lets you fine-tune LLMs and deploy them 100% locally on iOS/Android devices.The video shows this in action, where I ran Qwen3 on an iPhone 17 Pro at ~25 tokens/s.I have shared a guide in the replies. https://t.co/p4NqLj0jRE ...

RT Avi Chawla (@_avichawla)Generative vs. discriminative models in ML:(a popular ML interview question) https://t.co/r3c5l3NIDx ...

Generative vs. discriminative models in ML:(a popular ML interview question) https://t.co/r3c5l3NIDx ...

RT Avi Chawla (@_avichawla)Everyone is sleeping on this new OCR model!Datalab's Chandra topped independent benchmarks and beat the previous best dots-ocr.- Supports 40+ languages- Extracts complex texts, tables, formulas easilyI tested on Ramanujan's handwritten letter from 1913.100% open-source. https://t.co/jVKjQS4kek ...

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. https://t.co/YL3daI7yUyAvi Chawla (@_avichawla):Everyone is sleeping on this new OCR model!Datalab's Chandra topped independent benchmarks and beat the previous best dots-ocr.- Supports 40+ languages- Extracts complex texts, tables, formulas easilyI tested on Ramanujan's handwritten letter from 1913.100% open-source. https://t.co/jVKjQS4kek ...

Repository Information - Chandra GitHub repository is available at the provided link [1] - Encouragement to star the Chandra GitHub repository [1]