Tag: large language models
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Slashdot: Anthropic Maps AI Model ‘Thought’ Processes
Source URL: https://slashdot.org/story/25/03/28/0614200/anthropic-maps-ai-model-thought-processes?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Anthropic Maps AI Model ‘Thought’ Processes Feedly Summary: AI Summary and Description: Yes Summary: The text discusses a recent advancement in understanding large language models (LLMs) through the development of a “cross-layer transcoder” (CLT). By employing techniques similar to functional MRI, researchers can visualize the internal processing of LLMs,…
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Hacker News: Every Flop Counts: Scaling a 300B LLM Without Premium GPUs
Source URL: https://arxiv.org/abs/2503.05139 Source: Hacker News Title: Every Flop Counts: Scaling a 300B LLM Without Premium GPUs Feedly Summary: Comments AI Summary and Description: Yes Summary: This technical report presents advancements in training large-scale Mixture-of-Experts (MoE) language models, namely Ling-Lite and Ling-Plus, highlighting their efficiency and comparable performance to industry benchmarks while significantly reducing training…
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Hacker News: The role of developer skills in agentic coding
Source URL: https://martinfowler.com/articles/exploring-gen-ai.html#memo-13 Source: Hacker News Title: The role of developer skills in agentic coding Feedly Summary: Comments AI Summary and Description: Yes **Summary:** This text explores various dimensions related to the integration of Large Language Models (LLMs) in coding through examples of toolchains, usage of GitHub Copilot, and effective practices for leveraging Generative AI…
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Hacker News: Show HN: New Agentic AI Framework in CNCF
Source URL: https://github.com/dapr/dapr-agents Source: Hacker News Title: Show HN: New Agentic AI Framework in CNCF Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces Dapr Agents, a developer framework for building scalable AI agent systems that leverage Large Language Models (LLMs). It emphasizes features such as resilience, efficient deployment on Kubernetes, inter-agent…
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Hacker News: The role of developer skills in agentic coding
Source URL: https://martinfowler.com/articles/exploring-gen-ai.html#memo-13 Source: Hacker News Title: The role of developer skills in agentic coding Feedly Summary: Comments AI Summary and Description: Yes **Summary:** This text explores various dimensions related to the integration of Large Language Models (LLMs) in coding through examples of toolchains, usage of GitHub Copilot, and effective practices for leveraging Generative AI…
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Hacker News: Show HN: New Agentic AI Framework in CNCF
Source URL: https://github.com/dapr/dapr-agents Source: Hacker News Title: Show HN: New Agentic AI Framework in CNCF Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces Dapr Agents, a developer framework for building scalable AI agent systems that leverage Large Language Models (LLMs). It emphasizes features such as resilience, efficient deployment on Kubernetes, inter-agent…
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Hacker News: MCP server for Ghidra
Source URL: https://github.com/LaurieWired/GhidraMCP Source: Hacker News Title: MCP server for Ghidra Feedly Summary: Comments AI Summary and Description: Yes Summary: The text outlines the setup process for the ghidraMCP, a Model Context Protocol server designed to enhance large language models (LLMs) for application reverse engineering using Ghidra tools. This integration could have significant implications for…
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Hacker News: What went wrong with the Alan Turing Institute?
Source URL: https://www.chalmermagne.com/p/how-not-to-build-an-ai-institute Source: Hacker News Title: What went wrong with the Alan Turing Institute? Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the struggles and criticisms facing the Alan Turing Institute (ATI) in the UK, particularly its failure to adapt to advances in AI, such as generative AI and large…
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Hacker News: Tao: Using test-time compute to train efficient LLMs without labeled data
Source URL: https://www.databricks.com/blog/tao-using-test-time-compute-train-efficient-llms-without-labeled-data Source: Hacker News Title: Tao: Using test-time compute to train efficient LLMs without labeled data Feedly Summary: Comments AI Summary and Description: Yes Summary: The text introduces a new model tuning method for large language models (LLMs) called Test-time Adaptive Optimization (TAO) that enhances model quality without requiring large amounts of labeled…