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  1. Andy JassyAI score5

    Jassy and Amazon teammates volunteer at Seattle's Community Lunch

    AIAmazon CEO Andy Jassy joined Amazon teammates to prep meals at Community Lunch on Capitol Hill in Seattle during Global Month of Volunteering in May. The nonprofit serves free, made-from-scratch meals to 200-300 low-income and unhoused neighbors each weekday, and nearly 2,000 Amazonians have volunteered there since 2023. Amazonians completed more than 500,000 volunteer activities across 55 countries last year.

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  1. Cognition Blog (Devin, Windsurf)AI score60

    Cognition launches Auto-Triage, letting Devin investigate alerts and open fixes

    AICognition has released Auto-Triage in Devin Automations, which lets Devin respond to Slack messages, Linear events, GitHub activity, schedules, and webhooks. Devin can investigate with connected observability tools and the codebase, then post a summary, tag an owner, or open a PR. Devin runs in network-sandboxed environments with added protections against prompt injection and data exfiltration, and a limited-time offer gives $200 in credits for a first automation.

    Why it matters: The post shows how an agent handles alerts and bug reports from existing team channels, a practical pattern for teams weighing automated incident response.

May 11

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  1. Fidji SimoAI score13

    Fidji Simo praises CellularIntel's AI and biology mission, backs as advisor

    AIOpenAI's Fidji Simo praised CellularIntel as a strong example of AI applied to biology and said she is proud to advise the team. The post cites the company's progress from an early idea, and the referenced post reports CellularIntel acquiring the STEM-PD Phase 2-ready allogeneic cell therapy program for Parkinson's from Novo Nordisk, which will also invest in CI.

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  1. Andrej KarpathyAI score62

    Andrej Karpathy outlines an LLM-maintained markdown wiki workflow for personal research

    AIKarpathy describes using LLMs to compile raw source documents into a markdown wiki that he views in Obsidian, with the LLM writing and maintaining most of the wiki. He reports that at about 100 articles and 400K words, the LLM agent can answer complex questions directly from the wiki, and he also runs LLM health checks to find inconsistencies and gaps. He shares the underlying idea as an "idea file" that users can give to their own agents to build a customized version.

Apr 2

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  1. Andrej KarpathyAI score49

    Karpathy shares an LLM-maintained personal knowledge base workflow

    AIAndrej Karpathy describes using LLMs to compile raw research sources into a markdown wiki of about 100 articles and 400K words, viewed in Obsidian. He says an LLM agent answers complex questions against the wiki without RAG, with outputs filed back to enhance it. He also suggests the workflow could become a product rather than a collection of scripts.

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  1. Tri DaoAI score49

    Mamba-3 linear model released, outperforming Mamba-2 and Gated DeltaNet

    AITri Dao announced Mamba-3, which he described as the most powerful linear sequence model to date, as hybrid architectures increasingly rely on strong linear models. The post cites Qwen, Kimi-Linear, and NVIDIA's Nemotron-3 Super as examples of this trend. According to co-author Albert Gu, Mamba-3 shows noticeable performance gains over Mamba-2 and Gated DeltaNet at all sizes while maintaining speed.

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  1. Guillaume LampleAI score62

    Mistral releases Voxtral 2 transcription models with real-time option

    AIMistral announces Voxtral 2 with two transcription models: Voxtral Realtime, released under an Apache 2 license with latency configurable to sub-200 ms, and Voxtral Mini Transcribe 2, which adds speaker diarization, word-level timestamps, and context biasing. The models support 13 languages and are available through the Mistral API, which the post describes as one of the most cost-effective transcription APIs on the market. The attached chart shows word error rates on FLEURS across Italian, Spanish, English, German, Portuguese, French, Russian, Dutch, and Chinese at several latency settings.

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  1. Andrej KarpathyAI score62

    Karpathy argues LLMs are a new kind of intelligence shaped by commercial, not evolutionary, pressure

    AIKarpathy argues animal intelligence is only one point in a large space of possible minds, and LLMs arise from a fundamentally different optimization process. He contrasts survival-driven animal drives with LLM training shaped by imitation of human text, RL on task distributions, and user engagement metrics, which he says leaves LLMs jagged and prone to sycophancy. He calls LLMs humanity's first contact with non-animal intelligence and says people who build accurate internal models of them will reason about them better.

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