Experience the new container technology powered by WebAssembly that runs your programs safely, anywhere. Locally or in the cloud.
Learn why traditional GIS formats break at scale, how vector tiles solve the memory and rendering bottleneck, and what the shift from flat files to tiled architectures means for web-based geospatial data.
As AI systems become more capable, training procedures that optimize for downstream outcomes risk introducing implicit agency: goal-directed behavior that designers never specified. We present a formal safety argument for the Scientist AI (SAI) Predictor, trained to approximate the Bayesian posterior conditioned on a dataset of "epistemically contextualized" natural-language statements. We argue that such a Predictor can honestly predict agents, actions, and their consequences without itself being an agent that selects outputs to achieve goals. This rests on data representation and on the training procedure. Epistemic contextualization of text distinguishes latent factual claims from communication acts, so expressions of goals are treated as evidence to be explained rather than drives the model adopts. With a posterior-seeking training objective, this is intended to drive the Predictor toward calibrated, cautious predictions. Training proceeds so downstream effects of deploying a prediction never serve as a reward signal; any agency the system needs is supplied by explicit scaffolding constrained by guardrails. We prove that, under assumptions on the training dynamics and on the argued sparsity of dangerous Predictors, the probability that training produces a Predictor whose guarded deployment carries residual harm above a specified threshold is small: a dangerous Predictor would have to underestimate harm in a coordinated way across many queries while such coordinated patterns are rare under the initialization distribution and receive no direct training signal. Safety and accuracy are jointly supported in this framework, since the constraints that secure accuracy are the same ones that make coordinated deception costly. These guarantees against misalignment and agency arising from within the Predictor itself do not preclude the use of the Predictor as part of an agentic system.
BREAKING NEWS
Introducing OneWill, or: why write-ahead logging is better than letting agents rawdog your computer.
A retrospective on autofz, fuzzer orchestration, and how the idea connects to CRS and agent systems.
Riyadh initially refused Washington request to use bases critical to Hormuz ship escort operation, prompting US ire; Riyadh interprets Rubio skipping over Saudi on Gulf tour as snub
Margarita is an agentic scripting tool where you write plain text scripts — with state, loops, and LLM calls — and run them against any model.
Safely manage and switch between multiple Claude Desktop accounts with zero impact on your existing setup.
konsistent is an open-source CLI linter that enforces structural conventions in TypeScript codebases, giving agents and humans consistent context to implement features correctly.
Databricks recently published a technical blog on Lakebase and LTAP: From monolith to Lakebase to LTAP: rethinking database storage. The post is interesting not because it introduces another acronym, but because it reframes a familiar data infrastructure problem:
On ToxicChat under sparse labels (~200 verified, ~8 harmful), Gnosys improved a classifier past both the starting point and GEPA, a standard prompt optimizer.
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We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on