An introduction to AWS BedrockTowards Data Science The how, why, what and where of Amazon’s LLM access layer
The post An introduction to AWS Bedrock appeared first on Towards Data Science.
The how, why, what and where of Amazon’s LLM access layer
The post An introduction to AWS Bedrock appeared first on Towards Data Science. Read More
Veo 3.1 Ingredients to Video: More consistency, creativity and controlGoogle DeepMind News Our latest Veo update generates lively, dynamic clips that feel natural and engaging — and supports vertical video generation.
Our latest Veo update generates lively, dynamic clips that feel natural and engaging — and supports vertical video generation. Read More
The Complete Guide to Logging for Python DevelopersKDnuggets Stop using print statements and start logging like a pro. This guide shows Python developers how to log smarter and debug faster.
Stop using print statements and start logging like a pro. This guide shows Python developers how to log smarter and debug faster. Read More
From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft FabricTowards Data Science Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power BI/Microsoft Fabric. However, things are changing rapidly and the latest Dataflow enhancements changes how we play the game
The post From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric appeared first on Towards Data Science.
Dataflows were (rightly?) considered “the slowest and least performant option” for ingesting data into Power BI/Microsoft Fabric. However, things are changing rapidly and the latest Dataflow enhancements changes how we play the game
The post From ‘Dataslows’ to Dataflows: The Gen2 Performance Revolution in Microsoft Fabric appeared first on Towards Data Science. Read More
Anthropic Releases Cowork As Claude’s Local File System Agent For Everyday WorkMarkTechPost Anthropic has released Cowork, a new feature that runs agentic workflows on local files for non coding tasks currently available in research preview inside the Claude macOS desktop app. What Cowork Does At The File System Level Cowork currently runs as a dedicated mode in the Claude desktop app. When you start a Cowork session,
The post Anthropic Releases Cowork As Claude’s Local File System Agent For Everyday Work appeared first on MarkTechPost.
Anthropic has released Cowork, a new feature that runs agentic workflows on local files for non coding tasks currently available in research preview inside the Claude macOS desktop app. What Cowork Does At The File System Level Cowork currently runs as a dedicated mode in the Claude desktop app. When you start a Cowork session,
The post Anthropic Releases Cowork As Claude’s Local File System Agent For Everyday Work appeared first on MarkTechPost. Read More
How to Maximize Claude Code EffectivenessTowards Data Science Learn how to get the most out of agentic coding
The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science.
Learn how to get the most out of agentic coding
The post How to Maximize Claude Code Effectiveness appeared first on Towards Data Science. Read More
The latency trap: Smart warehouses abandon cloud for edgeAI News While the enterprise world rushes to migrate everything to the cloud, the warehouse floor is moving in the opposite direction. This article explores why the future of automation relies on edge AI to solve the fatal “latency gap” in modern logistics. In the sterilised promotional videos for smart warehouses, autonomous mobile robots (AMRs) glide in
The post The latency trap: Smart warehouses abandon cloud for edge appeared first on AI News.
While the enterprise world rushes to migrate everything to the cloud, the warehouse floor is moving in the opposite direction. This article explores why the future of automation relies on edge AI to solve the fatal “latency gap” in modern logistics. In the sterilised promotional videos for smart warehouses, autonomous mobile robots (AMRs) glide in
The post The latency trap: Smart warehouses abandon cloud for edge appeared first on AI News. Read More
Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal AI News
Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini dealAI News Apple’s multi-year agreement to integrate Google’s Gemini models into its revamped Siri marks more than just another Big Tech partnership. The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions. The stakes were considerable. Apple had
The post Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal appeared first on AI News.
Apple’s multi-year agreement to integrate Google’s Gemini models into its revamped Siri marks more than just another Big Tech partnership. The deal, announced Monday, offers a rare window into how one of the world’s most selective technology companies evaluates foundation models—and the criteria should matter to any enterprise weighing similar decisions. The stakes were considerable. Apple had
The post Why Apple chose Google over OpenAI: What enterprise AI buyers can learn from the Gemini deal appeared first on AI News. Read More
ContractEval: A Benchmark for Evaluating Contract-Satisfying Assertions in Code Generationcs.AI updates on arXiv.org arXiv:2510.12047v3 Announce Type: replace
Abstract: Current code generation benchmarks measure functional correctness on well-formed inputs, as test cases are curated to satisfy input preconditions. This leaves a gap: generated programs may appear correct but fail to satisfy contracts — assertion-level validity constraints for rejecting ill-formed inputs. We introduce ContractEval, a benchmark for evaluating contract-satisfying assertions in code generation, i.e., whether code rejects contract-violating inputs by triggering intended assertions. Built on HumanEval+ and MBPP+, ContractEval augments each task with contract-violation tests derived from reference assertions. We synthesize these via a neuro-symbolic pipeline: an LLM converts assertion clauses into constraints, and an SMT solver enumerates satisfiable violation combinations to generate inputs that violate selected clauses while satisfying the rest. Across five code LLMs, standard prompting yields 0% contract satisfaction, while adding a few contract-violation examples boosts contract satisfaction to 49–53% while maintaining pass@1 by 92% of the original. Our code is available at https://github.com/suhanmen/ContractEval.
arXiv:2510.12047v3 Announce Type: replace
Abstract: Current code generation benchmarks measure functional correctness on well-formed inputs, as test cases are curated to satisfy input preconditions. This leaves a gap: generated programs may appear correct but fail to satisfy contracts — assertion-level validity constraints for rejecting ill-formed inputs. We introduce ContractEval, a benchmark for evaluating contract-satisfying assertions in code generation, i.e., whether code rejects contract-violating inputs by triggering intended assertions. Built on HumanEval+ and MBPP+, ContractEval augments each task with contract-violation tests derived from reference assertions. We synthesize these via a neuro-symbolic pipeline: an LLM converts assertion clauses into constraints, and an SMT solver enumerates satisfiable violation combinations to generate inputs that violate selected clauses while satisfying the rest. Across five code LLMs, standard prompting yields 0% contract satisfaction, while adding a few contract-violation examples boosts contract satisfaction to 49–53% while maintaining pass@1 by 92% of the original. Our code is available at https://github.com/suhanmen/ContractEval. Read More
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistrycs.AI updates on arXiv.org arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development.
arXiv:2409.18695v3 Announce Type: replace
Abstract: Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building an AI-powered chemical brain, which frames chemical intelligence around four core capabilities: information extraction, semantic parsing, knowledge-based QA, and reasoning & planning. We argue that domain knowledge and logic are essential pillars for enabling such a system to assist and accelerate scientific discovery. To initiate this effort, we introduce our first generation of large language models for chemistry: KALE-LM-Chem and KALE-LM-Chem-1.5, which have achieved outstanding performance in tasks related to the field of chemistry. We hope that our work serves as a strong starting point, helping to realize more intelligent AI and promoting the advancement of human science and technology, as well as societal development. Read More