5/5 What that unlocked: – Failure rate: 30% → 0
– Repo downloads: 8,000+ → 500
– Provisioning: per run → once per campaign
– And the “don’t burn compute” part: split Generation (patch) from Evaluation (tests). If evaluation fails, we retry tests, not re-generate tokens.
COMPUTING
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Optimizing AI Generation: Zero Failure Rate and Reduced Compute Costs
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Optimizing SWE-bench Performance Through Shared Resource Provisioning
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4/5 The fix was a mindset shift: stop provisioning per run. Provision per SWE-bench instance, then reuse it. Shared per instance: repo at the right commit, MCP server, dependencies. Unique per run: execution path, files changed, commands, patch. We keep runs isolated with
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SWE-bench Evaluation Challenges at Kubernetes Scale
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3/5 Trying to run SWE-bench eval as-is on k8s at large scale wasn't trivial: – Fresh pods have no cache. This means that everyone re-downloads the world (hello HF 429s.
– “docker run inside k8s” works on paper, then dies from contention, privileges, and overhead. It worked, but -
The Definitive Guide to Microsoft Fabric Data Platform
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The Definitive Guide to Microsoft Fabric — From discovery to building a unified, secure, and scalable data platform: http://
amzn.to/3MdE1Xk v/ @PacktDataML Table of Contents: Getting started with Fabric From Lakehouse to First Analysis Unifying Data in OneLake -
Learn Model Context Protocol with TypeScript for AI Systems
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From @chris_noring and @PacktDataML … "Learn Model Context Protocol [MCP] with TypeScript: Build agentic systems in TypeScript with the new standard for AI capabilities" at http://
amzn.to/48W6Izu TypeScript explained and why & when to use it: https://
contentful.com/blog/what-is-t
ypescript-and-why-should-you-use-it/
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Fine-Tuning LLMs on NVIDIA GPUs With Unsloth
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How to Fine-Tune an LLM on NVIDIA GPUs With Unsloth https://
buff.ly/X4IBsan
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
MCP and Agent2Agent Protocol for Data Science Applications
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MCP & A2A (Agent2Agent) Protocol! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://t.co/zZMPCFXC71 pic.twitter.com/LFnTQptQbs
— Dr. Ganapathi Pulipaka 🇺🇸 (@gp_pulipaka) 11 janvier 2026MCP & A2A (Agent2Agent) Protocol! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/MCP-A2A-Protoc
ol
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The Book of R: Data Science and Machine Learning Guide
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The Book of R! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/The-Book-of-R -
Core Components of AI Models: ML Frameworks and Technologies
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Core Components of an AI Model! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
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MLReef Architecture for Machine Learning and Big Data
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MLReef Architecure for MachineLearning. #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Collab-W-MLReef