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 …
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 …
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 …
Read more “How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?”
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 …
Read more “Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore”
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 …
Read more “On the Effectiveness-Fluency Trade-Off in LLM Conditioning: A Systematic Study”
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. …
Read more “Query claims in natural language with Amazon Bedrock Knowledge Bases”
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 …
We’re excited to announce the availability of Anthropic’s Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 in India. The India regional endpoint is served through geographic cross-Region inference. Customers in India can now access these models on Amazon Bedrock while processing the data in the India Regions in addition to the already supported …
Read more “Amazon Bedrock expands Claude model availability to in-country inferencing in India”
In this paper, we study federated optimization for solving stochastic variational inequalities (VIs), a problem that has attracted growing attention in recent years. Despite substantial progress, a significant gap remains between existing convergence rates and the state-of-the-art bounds known for federated convex optimization. In this work, we address this limitation by establishing a series of …
Read more “Faster Rates for Federated Variational Inequalities”
xAI’s Grok 4.7 is now available on Amazon Bedrock, adding a frontier model built for coding, long-running agents, and knowledge work to the Bedrock model catalog. It offers a 500K token context window and supports configurable reasoning effort at four levels: low, medium, high, and xhigh. Grok 4.7 is served on the bedrock-runtime endpoint through …