The best AI agents fail about 70% of normal office tasks and the newest models did not fix it. Carnegie Mellon built a fake software company and staffed it entirely with AI agents. Real roles, real tasks. Browsing the web, writing code, running a sprint, messaging coworkers,
TECHNOLOGY
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Forget workflow audits: AI integration needs goals, context, and interviews
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A workflow audit is no longer the best way to figure out how to use AI in your job. Despite the advice from AI labs, I'm more convinced, because of AI's reasoning capabilities and long context horizon, that the right starting point is goals + context connectors + interview. The
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Vibe coding enables AI site, but change is inevitable
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I couldn't have built https://
alignednews.com/ai without vibe coding. It might be gone in 24 months. Change is constant. But vibe coding is real and is just at the beginning. -
Microsoft AI’s MAI-Thinking-1: Progress is a model-improving machine
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AI progress is not a model. It is a machine that keeps improving models. That is the core idea behind Microsoft AI’s new technical report: MAI-Thinking-1: Building a Hill-Climbing Machine This is not just a model release. It is a blueprint for turning frontier model
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Learned vs. Inherited Capabilities: Distillation vs. Ground-Up Intelligence
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The phrase “capabilities should be learned, not inherited” is doing a lot of work here. It draws a clear line between imitating intelligence through distillation and building the internal machinery to generate, evaluate, and improve capabilities from the ground up. That
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Microsoft AI’s MAI-Thinking-1: A Hill-Climbing Machine for Frontier Models
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AI progress is not a model. It is a machine that keeps improving models. That is the core idea behind Microsoft AI’s new technical report: MAI-Thinking-1: Building a Hill-Climbing Machine This is not just a model release. It is a blueprint for turning frontier model
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DeepSeek Sparse Attention reduces complexity from O(L²) to O(Lk)
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3) DeepSeek Sparse Attention (DSA) DeepSeek’s recently released V3.2 model introduced DeepSeek Sparse Attention (DSA), which brought complexity down from O(L²) to O(Lk), where k is fixed. How it works: A lightweight Lightning Indexer scores which tokens actually matter for
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Sparse Attention: Local, Learned Focus with Trade-off
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1) Sparse Attention It limits the attention computation to a subset of tokens by: – Using local attention (tokens attend only to their neighbors).
– Letting the model learn which tokens to focus on. But this has a trade-off between computational complexity and performance. -
Harvey’s LAB benchmark uses human-like verification with per-task criteria
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@Harvey
’s LAB benchmark approaches verification like a human would. Every task in a dataset has criteria for the task to pass. Legal agents can have 50+, with each one having its own judge call. It’s easy to audit, but can be expensive at scale. LangChain Labs teamed up with -
Microsoft’s MAI-Image-2.5 takes #2 in Image Edit Arena
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Microsoft just dropped MAI-Image-2.5 — and it immediately landed #2 in the Image Edit Arena (Single-Image-Edit) with a score of 1401. That's +10 pts over Nano Banana 2, Grok Imagine, and ChatGPT-Image-Latest-High Fidelity — and it pushes the Pareto frontier forward. Big W