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This lawsuit follows one filed last month by Runlayer that accused Rippling of stealing its product ideas. It's a seller- and buyer-beware market warning.

Meta’s new open-weight Muse Glimmer model offers a glimpse of Mark Zuckerberg’s personal superintelligence vision, as well as the emerging divide between AI users can own and access.

Programming with Claude Code will soon require even less human oversight.

After its own AI usage wake-up call, Rippling this week unveiled AI Spend Console, a product that tracks individual and team employee AI spending.

The Scenario Every Developer Recognizes It is mid-2026, and you have a feature to ship. You open ChatGPT or Claude, type something like "build me a function that parses webhook payloads and routes them to the right handler," and wait. The model returns something plausible. You paste it in, run it, and it almost works. So you prompt again: "fix the edge case where the payload is missing the event key." Another round. Then another. Forty-five minutes later, you have code that functions, but you also have a conversation thread that looks like a debugging session rather than a build session. You never actually described what you were building. You just started building it. This is the default mode for most developers using AI coding assistants in 2026, and it is expensive. According to McKinsey's State of AI in 2024 report ( source ), organizations that adopt structured design and planning approaches before implementing AI tools report higher success rates and better integration outcomes compared to those using ad-hoc implementation strategies. The pattern holds at the individual developer level too. Jumping straight into prompting skips the step that makes prompting useful: knowing precisely what you want before you ask for it. The fix is not a better model. It is a different sequence. What Design-First Actually Means in Practice Design-first means producing a written artifact that describes your system before you write a single prompt asking an AI to build it. Not a full technical document. A tight, structured description of inputs, outputs, constraints, and edge cases. Think of it as the brief you would hand to a contractor before they start work. The contractor analogy is useful because it reframes the relationship: you are not collaborating with the model in real time, you are commissioning it with a clear scope. Here is what that looks like concretely. Instead of opening Google Gemini and typing "help me build a webhook router," you spend ten minutes writing this

Mirendil has signed a $100 million-plus Google Cloud partnership to expand its compute infrastructure, powering research into self-improving AI systems designed to accelerate scientific discovery and AI development.

Looking for Contributors to Build Zentrail IDE — An AI-Native Open Source Desktop IDE Hello everyone! 👋 I'm building Zentrail IDE , an open-source, AI-native desktop IDE designed for the next generation of software development. The goal isn't to build another code editor. The goal is to create an IDE where multiple AI agents can collaborate with developers in a single workspace to plan, write, review, test, and manage code. We're still in the early architecture and planning phase, and I'm looking for developers, designers, and AI enthusiasts who want to help build it from the ground up. 🎯 Project Vision Create an AI-first development environment that combines: 🧠 Multi-Agent Collaboration 💻 Native Desktop Performance 🤖 AI CLI Integration 📦 Plugin & Skill Ecosystem 🌍 Open Source Community ⚡ Modern Developer Experience ✨ Planned Features Workspace System Multi-project workspaces Workspace memory Persistent sessions Task management AI Workspace Agents Multiple AI agents running simultaneously Shared workspace memory Parallel task execution Intelligent task orchestration AI CLI Support Claude Code Gemini CLI OpenAI-compatible providers Local AI models Custom AI CLIs Git Automation AI-assisted commits Pull requests Code reviews Branch management Repository insights Skill System Install reusable AI workflows with a single command. Examples: Security Review Code Refactoring API Generator Documentation Writer Test Generator Plugin SDK A modular extension system for adding custom functionality without modifying the core IDE. 🛠 Tech Stack Frontend TypeScript React Tauri v2 Monaco Editor Tailwind CSS Backend Go gRPC WebSocket AI Runtime Python MCP LangGraph Database SQLite 🤝 We're Looking For We're looking for contributors interested in: Frontend React TypeScript UI/UX Monaco Editor Backend Go gRPC WebSocket Performance optimization AI Python MCP Agent orchestration Prompt engineering Desktop Tauri Windows development Cross-platform architecture Design UI/UX Design Icons Develo

The week-old Open Secure AI Alliance, spearheaded by Nvidia and grown to over 120 companies, already has proposals out for defending against AI agents.

In 2018, more than 20,000 employees walked out to protest how Google handled allegations of sexual harassment. Here’s how they organized it from inside the company.

After a quarter that delivered $1 billion in profit, Palantir CEO Alex Karp on Monday once again warned that AI frontier labs are too untrustworthy for enterprises.

One thing Microsoft is not good at is naming things, and sadly it's happened again. But let's go back to the beginning: what are Skills? Skills are targeted prompts/context that are modular, so they are not always included in the LLM session. They are Markdown files with selected metadata in YAML, all in a file normally named skill.md (the parent folder and YAML metadata identify it). They were created by Anthropic (Claude) and were designed for both the user to add in a prompt ( /Skill ), or for the LLM to decide. Similar to Skills are Plug-ins. These can (and often do) include skill.md files, but can also have scripts, MCP servers, and other tools. So back to Microsoft naming things badly. Copilot Studio (Azure Bot Framework version) had skills, but they were not skills. The new Copilot Studio has Skills, but they are not Skills, they are actually Plug-ins. Plug-ins include Skills, so why does it matter? Well, it doesn't really, but I like to moan, and it means sometimes cool functionality can be left on the table because we presume Microsoft names things accurately. Anyway I digress (I like to do that), now we understand what Skills/Plug-ins are I wanted to dive into them within Copilot Studio and cover: Why Are They Cool Building Powerful Skills Adding Scripts/Templates Using Skills 1. Why Are They Cool I often go on about skills being cool, but why? There are a few reasons. Context Management Before skills, the standard approach was to give the LLM everything and let it figure out what it needed. The problem with this is twofold. First, more context equals more tokens, which equals more cost. Second—and more importantly—too much unrelated context can have a detrimental impact on the LLM response. LLMs work by using input tokens to predict the next token, so polluted input tokens can make the LLM predict the wrong next token (this is a huge simplification, but you get what I mean). Transferable As skills are simple Markdown files, they can easily be transferred

The Observability Black Box As autonomous AI agents evolve from isolated chat assistants into multi-agent systems executing multi-step business logic across databases, APIs, and microservices, enterprise platform teams face an acute operational challenge: black-box opacity. When an autonomous agent fails, hallucinates, or executes an out-of-bounds API call, traditional Application Performance Monitoring (APM) tools fall short. Standard HTTP request logging and basic prompt-response captures cannot reconstruct the non-deterministic reasoning loops, tool selection branches, or sub-agent delegations that led to an incident. Furthermore, enterprise auditors, security teams, and regulatory bodies (governed by SOC 2, FedRAMP, and the EU AI Act) now require non-repudiable proof of agent execution. Organizations must be able to answer five fundamental questions for every production run: Which human or non-human identity authorized the agent run? What planner reasoning path or tool routing logic was chosen? Which exact data assets or vector embeddings were retrieved into context? What was the precise execution latency, token cost, and error tax of each intermediate step? Can the complete execution graph be cryptographically reconstructed for compliance review? To resolve this challenge, platform engineering teams must deploy Audit, Observability & Lineage —an architecture anchored in OpenTelemetry (OTel), OWASP Agent Observability Standards, and immutable lineage graphs. Deep-Dive Architecture: OpenTelemetry & Lineage Integration A production-grade Agent Observability stack avoids proprietary vendor lock-in by standardizing on OpenTelemetry (OTel) OTLP trace ingestion and open metadata stores. 1. The Unified OpenTelemetry Span Tree Every agent execution unit — from user intent trigger to final task completion — is encapsulated within a single root trace context ( agent.run ). Sub-tasks, tool calls, and model invocations are recorded as hierarchical child spans: [ Root Trace:

A new study estimates only 2,000 U.S. engineers have the expertise to deliver meaningful AI ROI, as enterprises race to hire forward-deployed engineers to implement AI at scale.

Microsoft pitched its own homegrown AI models, harnesses, and even a Mythos competitor on Wednesday, telling Wall Street it plans for continued growth.

On the company’s second-quarter earnings call Wednesday, CEO Mark Zuckerberg said Meta sees a “large enterprise opportunity” spanning AI agents, APIs, compute, and internal software.

Polar has come out with an AI-first browser aimed at knowledge workers, and it has now raised a $5.7 million seed round led by Madrona.

The startup analyzes calls, messages, and CRM data to identify effective sales techniques and turn them into playbooks for AI agents.

Anthropic published Verbalizable Representations Form a Global Workspace in Language Models on July 6, and the vocabulary it introduced is suddenly everywhere: J-space, the Jacobian lens, a global workspace inside Claude. Most of the discussion so far is about interpretability and alignment auditing, which is fair, since that is what the paper is about. I had a narrower and more mercenary question: can the workspace tell an inference runtime which parts of the KV cache it is safe to throw away? Three days after the paper landed, the first pre-registered gate on that question passed. As of this week the signal has replicated on three models and ships inside EVOKE , my KV cache memory manager built on a forked llama.cpp. This post covers what J-space is, why it makes a good KV cache eviction signal, the numbers across Qwen2.5-7B, Qwen3-8B, and Qwen3-4B, and the caveat that comes with them. What J-space is, in one paragraph The Jacobian lens is the instrument and J-space is the phenomenon. The lens isolates directions in a model's residual stream that encode a token the model could verbalize next, and those directions form a low-dimensional workspace: roughly 10% of activation variance, concentrated in the middle layers, carrying whatever the model is "holding in mind" at each position. Anthropic's headline application is alignment auditing, reading reasoning the model never voices. What makes independent work possible is that they released companion code under Apache-2.0 along with fitted lens matrices for open Qwen models on Hugging Face , so anyone can apply the lens to an open-weights model on a single GPU. The systems problem: KV cache eviction Every long-running LLM session eventually outgrows its KV cache budget. An agent session in a coding harness crosses tens of thousands of cached tokens within a few turns, and something has to decide which entries stay in GPU memory. The standard answers, H2O and SnapKV, rank cache blocks by accumulated attention history: k

Cloudflare has launched its Internal DNS service, providing authoritative and recursive DNS for private networks. This service simplifies DNS management by consolidating private and public DNS operations on a single platform. By Gianmarco Nalin

Runlayer is suing Rippling after Rippling evaluated the startup's MCP gateway product and then opted to build one itself.