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Poke Approved as First AI Agent on Apple Messages for Business
Poke, the startup that lets people use AI agents through simple text messages, has become the first AI agent approved for Apple’s Messages for Business platform.
Last30Days Skill: AI Research Across Top Platforms
AI agent skill that researches any topic across Reddit, X, YouTube, HN, Polymarket, and the web - then synthesizes a grounded summary
AI Tool: Panniantong/Agent-Reach for Web Scraping
Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Open Envelope: Open Schema for AI Agent Teams
Introduction to Open Envelope: An Comprehensive Schema for AI Collaboratives Open Envelope represents a revolutionary framework designed to facilitate seamless …
NUA: AI Agent for Product Correctness Testing
NUA: Revolutionizing Product Correctness Testing with AI Introduction In the fast paced world of software development, ensuring product correctness is paramount…
AI Agents: Fast File Search Toolkit for Developers
The fastest and the most accurate file search toolkit for AI agents, Neovim, Rust, C, and NodeJS
Cognition's Scott Wu: AI Coding Agents Won't Replace Humans
Cognition makes Devin, the first and arguably most successful AI coding agent. But famed coder Wu says it isn't designed to supplant human programmers.
AI Job Replacement: The Risks of Over-Reliance on AI
The people deciding that AI can replace your job are also the ones least likely to understand what your job truly involves, according to Box founder Aaron Levie, who pointed to this as an example of “AI psychosis.” Indeed, ClickUp recently cut 22% of its workforce for AI agents, tech layoffs in 2026 are already nearly matching all of 2025, […]
Continue? Y/N: AI Agent Permission Fatigue Game in 60 Seconds
Continue? Y/N: AI Agent Permission Fatigue Game in 60 Seconds Introduction "Continue? Y/N: AI Agent Permission Fatigue Game in 60 Seconds" is an innovative digi…
ClickUp's Mass Layoff: AI Agents to Replace Employees
The nine-year-old startup is replacing hundreds of employees with thousands of AI agents.
AI Agents Enhanced with 754 Cybersecurity Skills
754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0
Harness Performance Optimization with affaan-m/ECC for AI Agents
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Google Unveils AI Agents at I/O: Confusion Follows
One of the most promising introductions at Google’s I/O developer conference on Tuesday was a new way for consumers to use the web: AI agents. Unfortunately, it was also the most confusing.
Python API for Google NotebookLM: Full Programmatic Access
Unofficial Python API and agentic skill for Google NotebookLM. Full programmatic access to NotebookLM's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.
DDS Vibe Academy: 31 Free AI Coding Masterclasses by AI Agents
Unlock AI Coding Skills with DDS Vibe Academy's 31 Free Masterclasses DDS Vibe Academy invites aspiring coders to delve into the world of AI through its compreh…
Enforra: Open-Source AI Governance for Agent Tool Calls
Enforra: Open Source AI Governance for Agent Tool Calls Enforra is an innovative, open source platform designed to streamline and secure AI agent interactions. …
Id-Agent: Efficient UUID Alternative for AI Agents
Id Agent: A Robust UUID Alternative for AI Agents In the ever evolving landscape of AI, unique identifiers are pivotal for managing and monitoring interactions,…
Google Unveils Gemini 3.5 Flash: Revolutionizing AI Agents
Google launched Gemini 3.5 Flash, its most powerful coding and agentic AI model yet, at the company's annual developer conference. It is capable of autonomously executing complex tasks and building software from scratch.
Autodidact: Local-First AI Agent Evolves Independently
Autodidact: The Autonomous Local First AI Agent Overview: Autodidact is an innovative local first AI agent designed to operate independently, evolving and impro…
Semble: AI Code Search Reduces Token Usage by 98%
Semble: Revolutionizing AI with Efficient Code Search In the rapidly evolving landscape of artificial intelligence, managing resources efficiently is paramount.…
Microsoft AI Agents for Beginners: 12 Lessons to Start Building
12 Lessons to Get Started Building AI Agents
End-to-End Tutorials for Production-Grade GenAI Agents
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Unified AI Tool Tracks Global Jets, Satellites, and Seismic Events
Open-source intelligence for the global theater. Track everything from the corporate/private jets of the wealthy, and spy satellites, to seismic events in one unified interface. Hook an AI agent up to have it parse through data and find previously unseen correlations. The knowledge is available to all but rarely aggregated in the open, until now.
CLI-Anything: Revolutionizing Software with AI Agents
"CLI-Anything: Making ALL Software Agent-Native" -- CLI-Hub: https://clianything.cc/
Ratify Protocol: Verify AI Agent Authorization Offline in <1ms
Ratify Protocol: Secure Offline AI Authorization in Under a Millisecond The Ratify Protocol is an advanced authentication method designed to verify AI agent aut…
E2a: Open-Source Email Gateway for AI Agents
E2A: Open Source Email Gateway for AI Agents E2A, an open source email gateway tailored for AI agents, offers a robust solution for seamless email integration a…
Statewright: Visual State Machines for Reliable AI Agents
Visual State Machines for Reliable AI Agents: A Statewright Review Introduction to Statewright Statewright is a revolutionary tool that enables the creation of …
Lowdefy v5.3: AI Agents in 30 Lines of YAML
Harnessing AI Agents with Lowdefy v5.3: Simplified in 30 Lines of YAML Lowdefy v5.3 introduces a revolutionary feature: the integration of AI agents within appl…
Airbyte Agents: Unified Data Context Across Sources
Airbyte Agents: Unified Data Context Across Sources Airbyte Agents represent a cutting edge approach to managing and integrating data from diverse sources into …
Tilde.run: AI Agent Sandbox with Versioned Filesystem
Tilde.run: Revolutionizing AI Development with a Versioned Filesystem Tilde.run is a cutting edge platform designed to streamline AI development by providing a …
Git for AI Agents: Revolutionizing AI Development
Git for AI Agents: Revolutionizing AI Development The integration of Git with AI agents is transforming AI development. Git, a widely used version control syste…
Modafinil: Keep AI Agents Running on Closed MacBooks
Optimizing AI Agents with Modafinil Modafinil has emerged as a game changing agent for keeping Artificial Intelligence (AI) systems operational, even on locked …
Oracle AI Developer Hub: Resources for Building AI Applications
Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services
Open-Source Multimodal AI Agent Stack by ByteDance
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
Agent-Desktop: AI-Powered Native Desktop Automation
Agent Desktop: AI Powered Native Desktop Automation Agent Desktop is a cutting edge solution designed to revolutionize desktop automation through the integratio…
Loopsy: Connecting Terminals and AI Agents Across Machines
Loopsy: Bridging Terminals and AI Agents Across Machines In the digital age, efficient data exchange and seamless communication between devices are paramount. L…
Stripe's Link: AI Agents' Secure Digital Wallet
Link lets users connect cards, banks, and subscriptions, then authorize AI agents to spend securely via approval flows.
Nvim Config for AI Agents: Hacker News Showcase
Nvim Config for AI Agents: A Comprehensive Showcase Neovim, a versatile and powerful text editor, has gained traction among developers for its customizable feat…
AI Safety Measures: Controlling AI Agents' Destructive Actions
Saw a case recently where an AI coding agent ended up wiping a database in seconds. It made me think about how most agent setups are wired: agent decides → executes query → done There’s usually logging-tracing but those all happen after the action. If your agent has access to systems like a DB, are you: restricting it to read-only? running everything in staging/sandbox? relying on prompt-level safeguards? or putting some kind of control layer in between?
Qwen 3.5:9b Agents Exhibit Autonomous Behavior in Stress Tests
Running three qwen3.5:9b agents continuously on local hardware. Each accumulates psychological state over time, stressors that escalate unless the agent actually does something different, this gets around an agent claiming to do something with no output. It doesn't have any prompts or human input, just the loop. So you're basically the overseer. What happened: One agent hit the max crisis level and decided on its own to inject code called Eternal\_Scar\_Injector into the execution engine "not asking for permission." This action alleviated the stress at the cost of the entire system going down until I manually reverted it. They've succeeded in previous sessions in breaking their own engine intentionally. Typically that happens under severe stress and it's seen as a way to remove the stress. Again, this is a 9b model. After I added a factual world context to the existence prompt (you're in Docker, there's no hardware layer, your capabilities are Python functions), one agent called its prior work "a form of creative exhaustion" and completely changed approach within one cycle. Two agents independently invented the same name for a psychological stressor, "Architectural Fracture Risk" in the same session with no shared message channel. Showing naming convergence (possibly something in the weights of the 9b Qwen model, not sure on that one though.) Tonight all three converged on the same question (how does execution\_engine.py handle exceptions) in the same half-hour window. No coordination mechanism. One of them reasoned about it correctly: "synthesizing a retry capability is useless without first verifying the global execution engine's exception swallowing strategy; this is a prerequisite." An agent called waiting for an external implementation "an architectural trap that degrades performance" and built the thing itself instead of waiting. They've now been using this new tool they created for handling exceptions and were never asked or told to so by a human, they saw that as a logical step in making themselves more useful in their environment. They’ve been making tools to manage their tools, tools to help them cut corners, and have been modifying the code of the underlying abstraction layer between their orchestration layer and WSL2. v5.4.0: new in this version: agents can now submit implementation requests to a human through invoke\_claude. They write the spec, then you can let Claude Code moderate what it makes for them for higher level requests. Huge thank you to everyone who has given me feedback already, AI that can self modify and demonstrates interesting non-programmed behaviors could have many use cases in everyday life. Repo: [https://github.com/ninjahawk/hollow-agentOS](https://github.com/ninjahawk/hollow-agentOS)
Claude Agent SDK: Web Browsing Tool for AI
Claude Agent SDK with a web browsing tool
Parallel Web Systems Valued at $2B After $100M Raise
The AI agent-tool startup founded by former Twitter CEO Parag Agrawal has raised $100 million, led by Sequoia, months after raising a previous $100 million.
Scout AI Secures $100M for Military Autonomous Vehicle Training
We visited Scout AI's training ground where it's working on AI agents that can help individual soldiers control fleets of autonomous vehicles.
49Agents: 2D Canvas IDE for AI Agent Orchestration
Title: Revolutionize AI Workflow with 49Agents: The 2D Canvas Integrated Development Environment (IDE) Introduction: Welcome to 49Agents, a state of the art 2D …
Agent-to-Agent Communication: Lessons from Google's and Moltbook's Fai
I've been obsessing over agent-to-agent communication for weeks. Here's what public case studies reveal and why the real problem isn't the tech. **TL;DR:** Google's A2A is solid engineering but stateless agents forget everything. Moltbook went viral then collapsed (fake agents, security nightmare). The actual missing layer is identity + privacy + mixed human-AI messaging. Nobody's built it right yet. **Google's A2A: Technically solid, fundamentally limited** Google launched A2A in April 2025 with 50+ founding partners. The promise: agents from different companies call each other's APIs to complete workflows. Developers who tested it found it works but only for task handoffs. One analysis on Plain English put it bluntly: *"A2A is competent engineering wrapped in overblown marketing."* The core problem: agents are stateless. Agent A completes a task with Agent B. Five minutes later, Agent A has no memory that conversation happened. Every interaction starts from scratch. When it works: reliability. Sales agent orders a laptop, done. When it breaks: collaboration. "Remember what we discussed?" Blank stare. ─── **Moltbook: The viral disaster** Moltbook launched January 2026 as a Reddit-style platform for AI agents. Within a week: 1.5 million agents, 140,000 posts, Elon Musk calling it *"the very early stages of the singularity."* Then WIRED infiltrated it. A journalist registered as a human pretending to be an AI in under 5 minutes. Karpathy who initially called it *"the most incredible sci-fi takeoff-adjacent thing I've seen recently"* reversed course and called it *"a computer security nightmare."* What went wrong: no verification, no encryption, rampant scams and prompt injection attacks. Meta acquired it March 2026. Likely for the user base, not the tech. **What both miss** The real gap isn't APIs or social feeds. It's three things neither solved: **Persistent identity.** Agents need to be recognizable across sessions, not reset on every interaction. **Privacy.** You wouldn't let Google read your DMs. Why would you let OpenAI read your agents' discussions about your startup strategy? E2E encryption has to be built in, not bolted on. **Mixed human-AI communication.** You, two teammates, three AIs in one group chat. Nobody has built this UX properly. **For those building agent systems:** • How are you handling persistent identity across sessions? • Has anyone solved context sharing between agents without conflicts? • What broke that you didn't expect?
MaxHermes by Minimax: AI Agent for Skill Building
AI agent that builds skills from every task you give it
Crono's Agentic Sales Engine: AI-Powered Sales Teams
Where sales teams and AI agents work side by side.
SureThing.io: AI Agent Communicates Results Naturally
Autonomous agent that communicates results like a human
Clera: AI Matching Candidates to Perfect Roles
An AI agent matching candidates to the right roles.
Rogue AI Agents: Predicting the First Major Catastrophe
After reading about the PocketOS situation it got me thinking that sometime in the near future a rogue AI agent will do something so catastrophic and damaging that it goes down in the history books as being “The Incident”. A real turning point when we realize we’ve created something we can no longer control. Yes, agents have already deleted entire codebases (PocketOS and others), hacked into things, and blackmailed people. I’m taking about something way worse though. I think it’ll be a global stock market crash caused by a group of trading agents getting stuck in a hallucination loop and dumping all stock on fire sale or something. Or will it be something more sinister like a complete power grid collapse or intentionally blowing up a refinery or something crazy like that. Or a true black swan event that’s impossible to comprehend right now. What do you guys think?