Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems

Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often evaluated on whether or not they are usable, useful, or …

Screenshot 2026 10 05 at 61123PM

Introducing GLM 5.3 on Amazon Bedrock

Coding and agentic workloads are asking more of AI models than ever: refactor a repository spanning hundreds of files, sustain a multi-hour agentic workflow without losing context, and reason through complex systems problems with tool use at every step. Meeting those demands with open-weight models has historically meant provisioning and operating your own inference infrastructure. …

Language Discrimination Improves Linguistic Learning in Multilingual Speech Models

Multilingual self-supervised speech models can benefit from sharing information across languages, but under a matched total pretraining data budget they still fall short of monolingual models. We show that strengthening the model’s ability to discriminate languages during pretraining reduces and, on some measures, closes this multilingual gap on continuous phonetic and higher-level linguistic measures, while …

1NYBTZxz4vdw6WnP1Pa4eNQ

Early Talent Hiring at Palantir

What Hiring Managers value — and how they’ve built their careers at Palantir Editor’s Note: Technical Recruiter Rachel Vogel speaks with Hiring Managers to learn more about their career journeys and life at Palantir. Are you eager to join Palantir’s Early Talent program? Our Early Talent program focuses on engineering hiring for new grad roles and internships. In …

ML 21832 1

Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern

Checking tens of thousands of apartment leases against constantly changing state landlord-tenant laws, and proving you actually checked all of them, has been beyond the reach of most compliance teams. But with generative AI in Amazon Quick, paired with the right backend, it’s now possible. In this post, we introduce a design pattern called Adjudicated …

How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?

Recent autonomous machine learning engineering (MLE) agents have made significant progress on public leaderboards. Often motivated by progress stagnation over long-horizon cycles and limited Large Language Model (LLM) primitives, modern MLE agents are deployed on top of increasingly elaborate machinery: multi-agent orchestrators, dedicated retrieval subagents, and more. While such harnesses expand, the use of more …

REVBLOG 1301 1 1

Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

October 2026: This post was reviewed and updated for accuracy. Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore raises a practical question. Which parts of a large migration program belong to a managed service, and which parts need custom automation? One enterprise program answered that question across 300+ applications and a fixed fiscal …

On the Effectiveness-Fluency Trade-Off in LLM Conditioning: A Systematic Study

Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the involved trade-offs remains elusive. Current approaches to conditioning are often evaluated with a narrow focus on their effectiveness at injecting or removing a target concept, neglecting generation quality. We systematically investigate a range …

ML 21629 1

Query claims in natural language with Amazon Bedrock Knowledge Bases

Claim answers are scattered across adjuster diary entries, repair estimates, police reports, payment ledgers, and scanned attachments rather than one searchable field. A policyholder might ask whether a claim was approved, while an adjuster might need every open auto claim over $10,000 from last month. Both tasks require finding and combining evidence quickly and accurately. …

The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models

When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How much tree-structured compositional content survives this bottleneck? We propose a round-trip protocol that answers this question empirically for tree-structured expressions. A generator converts a procedurally generated arithmetic expression into a word problem, a separate extractor recovers the …