标签：#Large language models

Google has open-sourced Mantis, an AI-agent framework designed to automate the software vulnerability lifecycle, from identifying and validating vulnerabilities to reproducing and fixing them. Google says it developed Mantis to address the high rate of false positives and hallucinated vulnerabilities produced by conventional AI-powered code scanning. By Sergio De Simone

Shopify's engineering introduced gisting, a novel technique for compressing long LLM prompts into a smaller set of learned "gist" tokens, improving throughput and reducing inference cost. By Sergio De Simone

Cohere has launched Parse 5, a multimodal foundation model designed to extract structured data from complex enterprise documents. The 2.3-billion-parameter system converts visually rich PDFs into Markdown while providing bounding box coordinates for visual grounding. It has been evaluated against over 2,000 enterprise pages, achieving an average score of 79.2 in key performance areas. By Olimpiu Pop

Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints. By Ricardo Ferreira

OpenClaw has released OpenClaw 2.0, a major update to the open-source personal AI agent that changes its installation process, browser interface, memory, skills, automations, plugins, security, and collaboration features. By Daniel Dominguez

Cloudflare AI Search is a built-in search and retrieval service designed to give AI agents and applications a ready-to-use search engine over custom data. It supports agent integration, multimodal search, and seamless integration with other Cloudflare tools. By Sergio De Simone

Researchers from UC Berkeley and MIT have developed FreeToken, an open-source inference engine that enhances the utility of Mixture-of-Experts models on consumer hardware. By implementing a dynamic scheduling policy and optimising weight management, FreeToken improves decoding speeds and execution efficiency in edge AI applications, fostering self-hosted reasoning systems. By Olimpiu Pop

Nvidia is nabbing critical infrastructure for open models as interest grows.

The focus is on agentic capability and predictable enterprise deployment.

Anthropic reliability engineer Alex Palcuie shares practical lessons on using LLMs for real-world incident response. He explains where AI acts as a superhuman for observing logs and traces, why it still struggles with causation versus correlation during root-cause analysis, and how engineering leaders can integrate AI into on-call workflows without eroding human expertise. By Alex Palcuie

Google is introducing HEIR (Homomorphic Encryption Intermediate Representation), an open-source compiler and development toolchain designed to make encrypted computation easier to deploy. In particular, HEIR can compile pre-trained AI models built for conventional, unencrypted inputs so they can instead operate on encrypted data. By Sergio De Simone

Bruna Pereira explains how DoorDash built a content-agnostic AI moderation platform. She covers replacing costly LLM-only pipelines with a hybrid pattern: using fast internal models to filter obvious cases, LLM multi-axis scoring for nuanced decisions, and no-code workflows with backtesting. Discover how this architectural pattern cut safety incidents while scaling to millions of daily messages. By Bruna Pereira

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization’s coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback. By Sergio De Simone

AWS has released aws-bench, an open-source benchmark for evaluating AI agents on real AWS tasks such as misconfigurations and infrastructure provisioning. Unlike traditional benchmarks, it uses real resources in disposable AWS accounts, scoring agent performance through automated verifiers. By Gianmarco Nalin

DeepSeek has released a developer preview of DeepSeek Harness (dsh), an open-source execution runtime for building autonomous AI agents. The software features a micro-kernel architecture with modular plugins for various functional units. The release includes an append-only event logging system for tracking execution activities. Adoption may depend on plugin ecosystem stability and API maintenance. By Olimpiu Pop

Jordan Nanos discusses how semiconductor constraints, data center expansion, and networking bottlenecks impact AI software architecture. Drawing from SemiAnalysis research, he shares insights on benchmark performance, GPU scaling, and tokenomics from chip fab to model inference. By Jordan Nanos

SpaceXAI has introduced Grok Bot, a system of persistent AI agents that operate on dedicated cloud computers and can interact with websites, applications, inboxes, and other tools. By Daniel Dominguez

In this podcast, Michael Stiefel spoke to Tracy Bannon about the role of artificial intelligence in software and the attendant risks in the areas of security, software development, and society at large. While it might be reasonable to assume a certain amount of trust within a software ecosystem, the risks escalate when the boundary between two software ecosystems is crossed. By Tracy Bannon

Jendrik Jördening shares practical engineering strategies for integrating LLMs into production pipelines. He discusses overcoming non-determinism, restricting schemas, separating semantic text extraction from deterministic code, and validating choices using discriminator models. Learn how to structure LLMs with an MVC approach to ensure database integrity, observability, and system reliability. By Jendrik Jördening

Baruch Sadogursky and Patrick Debois discuss why coding agents fail due to bloated context windows and stuffed prompts. They explain practical context engineering fixes, including lazy-loaded skills, versioned context artifacts, externalized memory banks, and LLM-as-a-judge evals. Software architects & engineering leaders will learn how to turn raw markdown files into reliable agentic workflows. By Patrick Debois, Baruch Sadogursky