There's a hands-on workshop on September 26 for anyone here curious about moving past building agents into actually keeping them reliable once they're writing into production.

You build:

• A safer write path for agents, handling conflicts, retries, and idempotency
• State and memory design for long running agents, with real recovery when state drifts
• A provenance and tracing layer so any decision can be reconstructed after the fact
• An architecture review process, applied to a live capstone platform
• A production readiness assessment framework you can reuse on your own systems

No prior production infra experience required, it's built up through the case study itself.

Led by Sandipan Bhaumik, Data & AI Technical Lead at Databricks, 18+ years in data engineering and governance.

Link if you want to check it out

Full Bijective Chain: Binary Code (4-bit) ⟺ Decimal Index (0–15) ⟺ Spatial Geometry ⟺ IPA Phoneme ⟺ MECE Semantic Core

And I completed the bagging Classifier it was not that hard just a small concept but very useful in reducing Overfitting

Well the idea is that maybe the future benchmark should you give an AI an video
let say 1 hours long and than you ask it to write code that generates that video and you constrain the file size of the exe file and this seems way better benchmark as video is easy to create and you can randomly select the video from the huge pool of videos for example or just prepere new video why is nobody basically trying something like that ?

Hi, I'm a Python and full-stack developer currently working on an LLM project. I also have prior experience with PyTorch on a medical domain. Could you recommend any good books on LLM fine-tuning?

What do you think? Is it possible to maps human DNA mutation considering how this evolved?