🌅 AI Daily Digest — March 27, 2026

Data Pulse

Defense startup Shield AI lands $12.7B valuation, up 140%, after US Air Force deal Defense startup Shield AI has surged to a $12.7 billion valuation, a 140% increase in one year, following a major deal with the US Air Force. The valuation growth reflects the company’s rapid expansion in defense technology.
Google is making it easier to import another AI’s memory into Gemini Google is introducing 'Import Memory' and 'Import Chat History' features for Gemini to streamline data migration from other AI platforms. These tools aim to simplify the transfer of user data and conversation history.
Introducing the OpenAI Safety Bug Bounty program OpenAI has launched a Safety Bug Bounty program to enhance AI safety and risk mitigation through user-reported vulnerabilities. The initiative marks a strategic shift in the company’s approach to security.
Pentagon formalizes Palantir's Maven AI as a core military system with multi-year funding The Pentagon has designated Palantir’s Maven AI as a core military system, with funding increasing to $13.4 billion from $480 million in 2024. The platform now receives multi-year investment to support its military applications.
Salesforce rolls out new Slackbot AI agent as it battles Microsoft and Google in workplace AI Salesforce has introduced a Slack-integrated AI agent named 'Slackbot AI' to compete with Microsoft and Google in workplace AI solutions. The tool aims to enhance collaboration within enterprise environments.

Training the Knowledge Base through Evidence Distillation and Write-Back Enrichm — Yuxing Lu, Xukai Zhao, Wei Wu. The knowledge base in a retrieval-augmented generation (RAG) system is typically assembled once and never revised, even though the facts a query requires are often fragmented across documents and buri...
Natural-Language Agent Harnesses — Linyue Pan, Lexiao Zou, Shuo Guo. Agent performance increasingly depends on \emph{harness engineering}, yet harness design is usually buried in controller code and runtime-specific conventions, making it hard to transfer, compare, and...
S2D2: Fast Decoding for Diffusion LLMs via Training-Free Self-Speculation — Ligong Han, Hao Wang, Han Gao. Block-diffusion language models offer a promising path toward faster-than-autoregressive generation by combining block-wise autoregressive decoding with within-block parallel denoising.
Self-Improvement of Large Language Models: A Technical Overview and Future Outlo — Haoyan Yang, Mario Xerri, Solha Park. As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability.
Measuring What Matters -- or What's Convenient?: Robustness of LLM-Based Scoring — Cole Walsh, Rodica Ivan. Automated systems have been widely adopted across the educational testing industry for open-response assessment and essay scoring.

How to Integrate Ollama API with Python — Streamline Your AI Workflows This tutorial guides readers through connecting Python applications to the Ollama API for seamless AI model integration. Readers will discover practical steps to automate and optimize language model interactions within their workflows.
How to Use Ollama for Beginners — Simplify Large Language Model Deployment The guide introduces beginners to Ollama’s core features for deploying large language models efficiently. Readers will learn foundational techniques to streamline model setup and execution without advanced expertise.

Software Engineer Reliability at OpenAI (San Francisco)
Senior Manager Strategic Partner Marketing at Vanta (Remote)
Senior Staff Software Engineer AI Customer Operati at Monzo (Cardiff, London)

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