Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to…
Why Your AI Agent Keeps Getting It Wrong: The Three-Layer Architecture Every Data Leader Needs to Know Your AI agent is not failing because the model is bad. It is failing because the architecture feeding the model is incomplete. The agent does not know what your “revenue” number means. It cannot see the CRM data it needs. It does not know that this question should be answered by the finance persona, not the sales one. The model is doing its job. The infrastructure around it is not. This is the defining challenge of enterprise AI in 2026. Everyone has deployed agents. Most of those agents produce responses that are confidently wrong, inconsistently right, or too generic to act on. The gap between a demo that impresses and an agent that actually drives business outcomes comes down to three
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Anyone got Gemma 4 26B-A4B running on VLLM?
If yes, which quantized model are you using abe what’s your vllm serve command? I’ve been struggling getting that model up and running on my dgx spark gb10. I tried the intel int4 quant for the 31B and it seems to be working well but way too slow. Anyone have any luck with the 26B? submitted by /u/toughcentaur9018 [link] [comments]
Best model for 4090 as AI Coding Agent
Good day. I am looking for best local model for coding agent. I might've missed something or some model which is not that widely used so I cam here for the help. Currently I have following models I found useful in agentic coding via Google's turbo quant applied on llama.cpp: GLM 4.7 Flash Q4_K_M -> 30B 30B Nemotron 3 Q4_K_M -> 30B Qwen3 Coder Next Q4_K_M -> 80B I really was trying to get Qwen3 Coder Next to get a decent t/s for input and output as I thought it would be a killer but to my surprise...it sometimes makes so silly mistakes that I have to do lots of babysitting for agentic flow. GLM 4.7 and Nemotron are the ones I really can't decide between, both have decent t/s for agentic coding and I use both to maxed context window. The thing is that I feel there might be some model that ju
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The one piece of data that could actually shed light on your job and AI
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. The mood is so grim that a societal impacts researcher at Anthropic, responding Wednesday to a call for
How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines
AI coding assistants are powerful but only as good as their understanding of your codebase. When we pointed AI agents at one of Meta s large-scale data processing pipelines – spanning four repositories, three languages, and over 4,100 files – we quickly found that they weren t making useful edits quickly enough. We fixed this by building [...] Read More... The post How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines appeared first on Engineering at Meta .
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[D] USQL Joins Were Cool, But Now I Want to Join the GenAI Party
Hi Experts, I have 1.5 years of experience in Data Engineering, and now I want to start learning AI, ML, and Generative AI. I already have some knowledge of AI and ML from my college days as a CSE (AI) student. I’ve also worked on a few image classification projects and explored the application of AI in real-life problems. Currently, I want to dive deeper into Generative AI. However, before that, I’d like to strengthen my understanding of the core concepts behind it—such as neural networks and NLP—so that I can later focus on real-world applications. If you have a roadmap or guidance that data scientists or other professionals usually follow, it would be very helpful for me as I want to switch from a Data Engineering role to a Data Scientist role. submitted by /u/Far-Mixture-2254 [link] [c
Seedance 2.0 API: Integration Guide with Three Access Paths and Full Mode Reference
This post covers the Seedance 2.0 API — ByteDance’s multimodal AI video generation model, now accessible through EvoLink. The focus is on practical integration: three access methods, all three generation modes with code examples, the async task workflow, pricing model, and optimization techniques. Model Capabilities Overview Seedance 2.0 introduces several capabilities that distinguish it from previous-generation video models: Multimodal @-reference system : Up to 9 images + 3 video clips + 3 audio tracks as simultaneous input references per request Video-to-video editing : Modify specific elements in existing video while preserving overall structure and timing Frame-accurate audio synchronization : Auto-generated dialogue, sound effects, and background music aligned to individual frames M
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