Team M37Labs
M37Labs Feed
Knowledge base
Direct from our engineering and research Slack channels. Raw insights, technical breakdowns, field notes, and real-time updates from the M37Labs team.
M
knowledge-base
Link
Mar 18, 2026 • 3:30 AMM
Team M37Labs
knowledge-base
Pyconf Resources:
Pyconf Resources:
https://arxiv.org/pdf/2510.04618
https://arxiv.org/html/2505.10468v1
https://github.com/AIAnytime/LangGraph-Agentic-RAG
https://github.com/AIAnytime/agent-playground
https://halu-mem-memory-demo.vercel.app/
https://github.com/AIAnytime/PyConf-Hyderabad-Workshop/tree/main
https://www.youtube.com/@AIAnytime
https://github.com/jaidevd/torch-compile?tab=readme-ov-file
https://arxiv.org/pdf/2510.04618
https://arxiv.org/html/2505.10468v1
https://github.com/AIAnytime/LangGraph-Agentic-RAG
https://github.com/AIAnytime/agent-playground
https://halu-mem-memory-demo.vercel.app/
https://github.com/AIAnytime/PyConf-Hyderabad-Workshop/tree/main
https://www.youtube.com/@AIAnytime
https://github.com/jaidevd/torch-compile?tab=readme-ov-file
Link
Mar 16, 2026 • 5:31 AMM
Team M37Labs
knowledge-base
Building Custom AI Tools for Influencer Marketing | Arindam Paul posted on the topic |…
Link
Mar 16, 2026 • 4:20 AMM
Team M37Labs
knowledge-base
Andrej Karpathy (@karpathy) on X
Link
Mar 9, 2026 • 12:55 PMM
Team M37Labs
knowledge-base
Report by anthropic:
Report by anthropic:

Expand Image
Image
Mar 6, 2026 • 1:49 PMM
Team M37Labs
knowledge-base
Gemini 3.1 Flash-Lite: Built for intelligence at scale
Link
Mar 5, 2026 • 12:39 PMM
Team M37Labs
knowledge-base
Sameer Samat (@ssamat) on X
Link
Mar 5, 2026 • 12:04 PMM
Team M37Labs
knowledge-base
This is a good read. Valid for Indian companies too.
This is a good read. Valid for Indian companies too.
https://kpmg.com/ch/en/insights/artificial-intelligence/iso-iec-42001.html
https://kpmg.com/ch/en/insights/artificial-intelligence/iso-iec-42001.html
Link
Feb 26, 2026 • 5:36 PMM
Team M37Labs
knowledge-base
At that Trent HQ - read last para from speech of N chandra- Chairman of Tata Group - this…
At that Trent HQ - read last para from speech of N chandra- Chairman of Tata Group - this is way they are going on AI - lots of synergies to tap

Expand Image
Image
Feb 25, 2026 • 6:28 AMM
Team M37Labs
knowledge-base
India’s AI Wedding Buffet: Generous Portions, Political Economy Heartburn
Link
Feb 19, 2026 • 2:15 AMM
Team M37Labs
knowledge-base
Object detection with Ultralytics YOLO26 on Raspberry Pi - Raspberry Pi
Link
Feb 18, 2026 • 3:08 PMM
Team M37Labs
knowledge-base
We are delighted to announce the official release of Qwen3.5, introducing the open-weight…
https://qwen.ai/blog?id=qwen3.5&utm_source=alphasignal&utm_campaign=2026-02-16&lid=w4DiEILk24n5KRcL
We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding, empowering developers and enterprises to achieve significantly greater productivity. Built on an innovative hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts, the model attains remarkable inference efficiency: although it comprises 397 billion total parameters, just 17 billion are activated per forward pass, optimizing both speed and cost without sacrificing capability. We have also expanded our language and dialect support from 119 to 201, providing broader accessibility and enhanced support to users around the world.
We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding, empowering developers and enterprises to achieve significantly greater productivity. Built on an innovative hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts, the model attains remarkable inference efficiency: although it comprises 397 billion total parameters, just 17 billion are activated per forward pass, optimizing both speed and cost without sacrificing capability. We have also expanded our language and dialect support from 119 to 201, providing broader accessibility and enhanced support to users around the world.
Link
Feb 17, 2026 • 7:29 AMM
Team M37Labs
knowledge-base
PFA
PFA

Expand Image
Image
Feb 17, 2026 • 7:20 AMM
Team M37Labs
knowledge-base
Garry Tan folks !
Garry Tan folks !

Expand Image
ThreadImage
Feb 17, 2026 • 2:35 AMM
Team M37Labs
knowledge-base
An
Link
Feb 11, 2026 • 4:13 AM






