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November 13Friday
  1. Hacker News · AI58

    How AI changed Jørn Lier Horst's writing style, from action to static description

    Jill Walker Rettberg compared Jørn Lier Horst's AI-assisted novel Den siste saken with two earlier books in the same series and found the AI-classified passages use more nouns and adjectives and fewer pronouns and verbs. She reports that AI-flagged sections lean on static setting descriptions and clichés such as humming, quiet and heavy air, while the pre-ChatGPT novels conveyed setting through character action. Horst has said he used ChatGPT only for research and sentence-level editing, and Rettberg argues that this level of use still changes the text itself.

  2. Hacker News · AI34

    e-means: A Faster k-means Variant and the Jevons Paradox of Cheaper Clustering

    e-means is a new k-means variant that updates centroids continuously within each pass using mini-batches and exponential moving averages, reaching the test loss of full-batch k-means in 2.5 epochs instead of 25 on 10 billion DINO embeddings clustered into roughly 16.7 million clusters. It keeps the same objective and convex nearest-centroid geometry, with fitted scaling laws setting batch size and update rate automatically. The author argues cheaper runs mean trying more values of k rather than lower compute bills.

  3. MIT News22

    Breakthrough Energy CTO Eric Toone on the Cost of Decarbonization

    Eric Toone, chief technical officer of Breakthrough Energy, said at the MIT Energy Initiative's Energizing@MIT conference that decarbonizing the global economy would cost about $7.5 trillion, roughly 5 percent of global GDP, but that people are unwilling to pay a green premium. Breakthrough Energy Ventures has deployed about $3 billion into about 140 companies, including Luxwall, whose insulating windows delivered 30 percent energy savings in one commercial building.

November 10Tuesday
  1. Latent Space58

    What Frontier Labs Buy: A Data Deep Dive with Handshake AI's Garrett Lord and Team

    Handshake's AI training business went from launch to $1 billion in gross annualized revenue in roughly a year, and this Latent Space deep dive with Garrett Lord and his team walks through the reinforcement learning environments frontier labs buy. In a finance demo, an agent working a simulated bank's email and deal files carries a goodwill figure into Python and subtracts it, a mistake that shows where expert judgment still beats the model. The team also presents VIALS, its benchmark for interpreting scientific images, and traces an agent's tool calls through safety simulations including CAREBench.

October 30Friday
  1. ChinaTalk38

    Is Half of China's AI Compute Really Being Spent on Slop?

    A viral claim that 55% of China's AI tokens go to short dramas and video generation — traced to a June investor call by COL Group — has drawn rebuttals from ByteDance, which says video generation is only about 10% of its own compute, and from People's Daily. Over 90% of the roughly 430,000 micro-dramas released in the first eight months of 2026 were AI productions, and a five-second 720p clip on ByteDance's Seedance costs around 100,000 tokens.

October 27Tuesday
  1. Redwood Research22

    Redwood Research argues for accelerating AI's conceptual reasoning capabilities

    Redwood Research argues that AI's conceptual reasoning capabilities—tasks like philosophy that lack verifiable ground truth—should be accelerated differentially, since AI R&D and takeover rely far less on such reasoning. The team says basic elicitation techniques and small-scale training can make models three to twelve months better on its main conceptual reasoning benchmark, and that the area is currently neglected and underinvested by the AI safety community.

October 17Saturday
  1. Big Technology42

    Rivian CEO RJ Scaringe: Robotics Is Progressing Much Faster Than You Think

    Rivian CEO RJ Scaringe says robotics progress will be "far, far, far, far faster" than the world expects, citing robots that now learn broad, generalizable skills through transformer-based models. His 2025-founded startup Mind Robotics raised $1 billion to build two-armed industrial robots sold to manufacturers, and it trains the technology inside Rivian's plants.

September 7Monday
  1. Charlie O'Neill38

    Let's Bet on RL Environments: Why Useful Data and Environments Will Win

    An argument that people who claim everything will become RL environments are not acting on that belief, and that useful RL environments will grow exponentially scarcer and more valuable as models approach RSI. The piece urges building long-horizon tasks in domains of real expertise rather than Goodhart-style task farms, and argues a broad spread of environments beats a small set of frontier-hard ones.

September 2Wednesday
  1. Charlie O'Neill38

    Trying to Actually Define Continual Learning for AI Agents

    A writer examines continual learning for LLM agents, framing today's models as amnesiac interns whose knowledge resets when the context window ends. LLMs have only two native memories: lossless but costly context and KV cache, and compressed but hard-to-update weights, with little in between. The piece weighs longer context, stateful memory harnesses, and compressing context into learned latent representations, citing Llama 3.1 70B's 141 GB weights and 320 KiB KV cache per token.

July 7Tuesday
  1. Berkeley AI Research36

    Intelligence Is Free, Now What? Data Systems for, of, and by Agents

    UC Berkeley's Aditya G. Parameswaran and collaborators argue that as inference costs fall from roughly $30 per million tokens in early 2023 to under $1 today, data systems must be redesigned around three challenges: systems for agents, of agents, and by agents. They note that in a text-to-SQL benchmark with multiple agents per task, only 10-20% of sub-plans are distinct, so 80-90% of sub-queries duplicate work, and propose reuse, approximation, and proactive data systems.

February 14Saturday
  1. Charlie O'Neill38

    The Michael Scott Paper Company of AI: Why Open-Source Specialisation Beats Frontier Labs on the Intelligence Ceiling

    Opus 4.6 and GPT-5.3-Codex both shipped last week, but the decisive contest is not raw capability. The argument holds that most economically valuable work sits in the specialisation layer, where a sub-80B model fine-tuned on a client's own data outperforms GPT-5.2 on that client's workflow — faster, cheaper, and without sending proprietary data to a third party. Open-source needs only to match the intelligence ceiling, not the frontier, and the labs' capital-dependency clock is the weaker position.

January 26Monday
  1. Charlie O'Neill38

    Continual learning and the post-monolith AI era

    Continual learning is inseparable from specialization because the cost of remembering scales with scope: as context grows, the KV cache grows linearly and can exceed all model weights, so information must move from token level into weights. Task specialization is essentially solved via continuous fine-tuning pipelines, while broad-scope, sparse-feedback, one-shot retention remains the hard problem.