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Basedash Dashboard Agent: AI-Powered Dashboard Creation
Builds entire dashboards from a single prompt
Gemini Deep Research Agent: Web & MCP Research in Gemini API
Web and MCP research agents, now in Gemini API
Wonder AI Design Agent: Revolutionize Your Canvas
The AI design agent that works on your canvas
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)
Trading System V2: AI's Role in Deterministic Execution
Thanks to the incredible feedback on my last post, I’m officially moving away from the "distributed veto" system (where 8 LLM agents argue until they agree to trade). For v2, I am implementing a strict State Machine using a deterministic runtime (llm-nano-vm). The new rule is simple: Python owns the math and the execution contract. The LLM only interprets the context. I've sketched out a 5-module architecture, but before I start coding the new Python feature extractors, I want to sanity-check the exact roles I’m giving to the AI. Here is the blueprint: 1. The HTF Agent (Higher Timeframe - D1/H4) Python: Extracts structural levels, BOS/CHoCH, and premium/discount zones. LLM Role: Reads this hard data to determine the institutional narrative and select the most relevant Draw on Liquidity (DOL). 2. The Structure Agent (H1) Python: Identifies all valid Order Blocks (OB) and Fair Value Gaps (FVG) with displacement. LLM Role: Selects the highest-probability Point of Interest (POI) based on the HTF Agent's narrative. 3. The Trigger Agent (M15/M5) 100% Python (NO LLM): Purely deterministic. It checks for liquidity sweeps and LTF CHoCH inside the selected POI. 4. The Context Agent LLM Role: Cross-references active killzones, news blackouts, and currency correlations to either greenlight or veto the setup. 5. The Risk Agent 100% Python (NO LLM): Calculates Entry, SL, TP, Expected Value (EV), and position sizing. The state machine will only transition to EXECUTING if the deterministic Trigger and Risk modules say yes. The LLMs are basically just "context providers" for the state machine. My questions for the quants/architects here: Does this division of labor make sense? Am I giving the LLMs too much or too little responsibility in step 1 and 2? By making the Trigger layer (M15/M5) 100% deterministic, am I losing the core advantage of having an AI, or is this the standard way to avoid execution paralysis? Would you merge the HTF and Structure agents to reduce token constraints/hallucinations, or is separating them better for debugging? Would love to hear your thoughts before I dive into the codebase.
Can AI Tool Use During Studies Affect Future Liability?
I graduated from university a couple months back, but have been continuing to use a student version of a coding/design agent that essentially gives me much more features at a significantly cheaper price. If this product launches and is proven to be successful can I be held liable for using this tech in the future and not paying for the full product? I know this situation may be unusual, but it's something that has been top of mind for me.
Open Source AI Setup Repo Hits 800 Stars on GitHub
Yo real talk we did not expect this kind of love when we open sourced our AI setup repo but here we are sitting at 800 stars and 100 forks and we are genuinely hyped about it. The repo is a collection of AI agent setups configs and workflows that you can plug straight into your projects. No gatekeeping just pure community goodness. We built this because setting up AI agents from scratch every single time is a massive time sink. So we said forget it lets just share everything openly and let the community build on top of it. Repo is right here: [https://github.com/caliber-ai-org/ai-setup](https://github.com/caliber-ai-org/ai-setup) Now we want YOUR input. What setups are you missing? What features would make this a no brainer for your workflow? Drop your ideas below because we are building in public and your feedback actually ships. LGM 🚀
AutoIdeator: Free Open Source Agent Orchestration for Development
[https://github.com/akumaburn/AutoIdeator](https://github.com/akumaburn/AutoIdeator) https://preview.redd.it/rfbgg6e34dyg1.png?width=3809&format=png&auto=webp&s=e436362c48482d09025a394a5e609f67190e6dfa AutoIdeator is an autonomous development system that: 1. Takes a **final goal** — a detailed, multi-sentence description of the intended end result. Describe what the finished project should look like, do, and feel like for the user. **Do not** prescribe implementation steps, phases, milestones, technologies, or task lists — the agents handle planning. The more clearly the desired end state is described, the better convergence will be. 2. Generates improvement ideas via a rotating ensemble of specialized idea agents 3. **Scores and filters ideas** for goal alignment and quality 4. **Critiques ideas constructively** with suggested mitigations 5. **Evaluates strategic alignment** and long-term planning 6. Makes implementation decisions balancing creativity and criticism 7. Implements the plan with parallel coders 8. Reviews, fixes, and commits changes 9. **Runs QA** (build + test verification) 10. **Optimizes slow tests** to keep the suite fast 11. **Verifies goal completion** with 3-step feature inventory, per-feature checks, and auto-remediation 12. **Refactors oversized files** into smaller modules (every other cycle) 13. **Cleans up** temp files and build artifacts 14. Updates project documentation 15. **Records outcomes for learning and deduplication** 16. **Periodically synthesizes synergies** across recent work 17. **Checkpoints state** for pause/resume across restarts 18. Repeats the cycle infinitely until stopped Users can inject suggestions at any time via the Overseer agent, which takes priority over the autonomous idea generation pipeline. Note this system has been tested for some time but only in the dashboard with OpenCode/Claude Code configuration (OpenRouter mode is untested, but I welcome contributions if someone wants to use that mode and notices something is broken).
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.
AI Tool Mines Academic Research for Time Series Insights
AI Tool Unlocks Academic Research for Time Series Insights In the ever evolving landscape of data science and analytics, an innovative AI tool is revolutionizin…
AI Tool: Agent Requires Human Approval for Commands
Exploring AI Tools that Require Human Oversight for Operations Artificial Intelligence (AI) continues to integrate into various aspects of daily life and busine…
Explore Agentic AI with Free Interactive Curriculum on AgentSwarms
Hey Everyone, Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run an agent, break it, and see how the prompt and tools interact under the hood. So, I built **AgentSwarms**.fyi It’s a free, interactive curriculum for Agentic AI. Instead of just reading, you run live agents alongside the lessons. **What it covers:** * Prompt engineering & system messages (seeing how temperature and persona change behavior). * RAG (Retrieval-Augmented Generation) vs. Fine-tuning. * Tool / Function Calling (OpenAI schemas, MCP servers). * Guardrails & HITL (Human-in-the-Loop) for safe deployments. * Multi-Agent Swarms (orchestrators vs. peer-to-peer handoffs). **The Tech/Setup:** You don't need to install anything or provide API keys to start. The "Learn Mode" is completely free and sandboxed. If you want to mess around with your own models, there's a "Build Mode" where you can plug in your own keys (OpenAI, Anthropic, Gemini, local models, etc.). I’d love for this community to tear it apart. What agent patterns am I missing? Is the observability dashboard actually useful for debugging your traces? Let me know what you think.
Exploring Advanced Uses of OpenAI Tools in DFW
Been using OpenAI models more lately and it feels like most people are still only scratching the surface. (Only asking questions) Beyond basic prompting, I’m seeing real potential in agent-based systems: * Automating repetitive business tasks * Research + messaging workflows that actually execute steps * “Thinking partner” agents for planning/strategy * Discord / small business ops powered by tool-using agents Big takeaway: it’s less about prompts and more about building structured workflows around the model. Curious what others in DFW (or elsewhere) are building on the agent side what’s actually working for you?
Plannotator: AI Tool for Document Annotation and Feedback
Annotate any doc, URL, or folder - send feedback to agents
Supabase Data Agents: Boosting Analytical Skills
Analytical skills for data agents running on Supabase
Amazon Launches OpenAI Models on AWS After Microsoft Deal
A day after OpenAI got Microsoft to agree to end exclusive rights, AWS announced a slate of OpenAI model offerings, including a new agent service.
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 AI Tool: Revolutionizing Automation on GitHub
49Agents AI Tool: Transforming GitHub Automation The landscape of software development is rapidly evolving, and one notable advancement is the emergence of AI p…
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 …
Pi-hosts: Secure AI Coding Agent Access to Your Servers
Pi Hosts: Boost AI Security with Server Access Solutions In the rapidly evolving landscape of artificial intelligence (AI) and cloud computing, securing AI codi…
AI Tool Locus: Autonomous Business Operations
This sub has seen enough "AI can now do X" posts to have a finely tuned radar for what's real and what's a demo that falls apart the moment someone actually uses it. So I'll skip the hype and just tell you what we built and where the edges are. The core problem we were solving wasn't any individual capability. Generating copy is solved. Building websites is solved. Running ads is mostly solved. The unsolved problem was coherent autonomous decision making across all of those systems simultaneously without a human acting as the integration layer between them. That's what we spent most of our time on. Locus Founder takes someone from idea to fully operational business without them touching a single tool. The system scopes the business, builds the infrastructure, sources products, writes conversion optimized copy, and then runs paid acquisition across Google, Facebook and Instagram autonomously. Continuously. Not as a one time setup but as an ongoing operation that monitors performance and adjusts without being told to. The honest version of where AI actually performs well in this system and where it doesn't: It's genuinely good at the build layer. Storefront generation, copy, pricing structure, initial ad creative, coherent and fast in a way that would have been impossible two years ago. The operations layer is more complicated. Autonomous ad optimization works well within normal parameters. The judgment calls that fall outside those parameters, unusual market conditions, supplier issues, platform policy edge cases, are still the places where the system makes decisions a human would immediately recognize as wrong. That gap between capability and judgment is the most interesting unsolved problem in what we're building and probably in the agent space generally right now. We got into YCombinator this year. Opening 100 free beta spots this week before public launch. Free to use, you keep everything you make. For people in this sub specifically, less interested in the "wow AI can do that" reaction and more interested in people who want to actually stress test where the judgment breaks down. Beta form: [https://forms.gle/nW7CGN1PNBHgqrBb8](https://forms.gle/nW7CGN1PNBHgqrBb8) Where do you think autonomous business judgment actually gets solved and what does that look like?
Snapchat Introduces AI Chat Ads for Conversational Marketing
Snapchat unveils AI Powered Chat Advertisements for Enhanced Engagement Snapchat has recently launched a groundbreaking feature called AI Chat Ads, designed to …
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?
Galadriel: Optimize Claude Agents with 87% Cost Savings & Sub-3s Laten
# The "Goldfish Problem" is Expensive. I Decided to Fix the Plumbing. Most Claude implementations leave 90% of their money on the table because they don’t optimize for **Prompt Caching**. I’ve been running a personal agent in my Discord for months that manages my AWS infra and codebases, and I finally open-sourced the harness, which I’ve named **Galadriel** after my main personal assistant. # The Stats * **Cost:** $10 for every $100 you’d normally spend (Tested against OpenClaw/Cursor workflows). * **Speed:** 85% drop in latency. 100K token context goes from 11s to <3s. * **Memory:** Integrated **MemPalace** for permanent, vector-based recall that *doesn't* break the cache. # The Technical Stack * **3-Tier Stacked Caching:** Separate breakpoints for Tool Definitions, System Prompts (`CLAUDE.md`), and Trailing History. * **Privacy:** Built for private subnets. No middleman, no message caps—just your API key and your rules. * **Ethics:** Baked-in Karpathy[`CLAUDE.md`](https://www.google.com/search?q=%5Bhttp://CLAUDE.md%5D(http://CLAUDE.md))guidelines to kill "agent bloat." If you’re tired of paying the **"Context Tax"** just to have an agent that remembers who you are, here you go. It is customized for Discord for my specific needs, but the core logic ensures Galadriel runs like an absolute dream: she never forgets, maintains strict engineering principles, and optimizes every cycle. Your feedback is most welcome! **GitHub (MIT License):**[https://github.com/avasol/galadriel-public](https://github.com/avasol/galadriel-public)
Google's Deep Research Max: Autonomous Research Agent for Expert Repor
Google quietly dropped something interesting last week. They updated their Deep Research agent (available via Gemini API) and introduced a "Max" tier built on Gemini 3.1 Pro. What it actually does: you give it a topic, it autonomously searches the web (and your private data via MCP), reasons over the sources, and produces a fully cited, professional-grade report — including native charts and infographics. Two modes: Deep Research — faster, lower latency, good for real-time user-facing apps Deep Research Max — uses extended compute, iterates more, designed for background/async jobs (think: nightly cron that generates due diligence reports for analysts by morning) The MCP support is the most interesting part to me. You can point it at proprietary data sources — financial feeds, internal databases — and it treats them as just another searchable context. They're already working with FactSet, S&P Global and PitchBook on this. Benchmarks show a significant jump in retrieval and reasoning vs. the December preview. They also claim it now draws from SEC filings and peer-reviewed journals and handles conflicting evidence better. So what do you think, is it another trying or game changer 😅
Craft Agents OSS: Open-Source AI Tool Trends on GitHub
Craft Agents OSS: Open Source AI Tool Trends on GitHub Craft Agents OSS represents a burgeoning wave of open source AI tools on GitHub, empowering developers an…
Superpowers AI Framework: Agentic Skills for Software Development
An agentic skills framework & software development methodology that works.
Harness Coding Efficiency with 1jehuang/jcode AI Tool
Coding Agent Harness
Warp: AI-Powered Terminal Development Environment
Warp is an agentic development environment, born out of the terminal.
Voice Agents: 24/7 AI Voice Agents for Client Support
Turn expertise into 24/7 client-facing AI voice agents
MaxHermes by Minimax: AI Agent for Skill Building
AI agent that builds skills from every task you give it
Actian VectorAI DB: Portable Vector Database for AI Agents
The portable vector database for AI agents beyond the cloud
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.
VoiceGoat: Practice LLM Attacks with Vulnerable Voice Agent
VoiceGoat: Enhance LLM Security with a Voice Assistant Lab VoiceGoat provides a secure and controlled environment to test and practice Large Language Model (LLL…
Open Bias: AI Tool Enforces Agent Behavior at Runtime
Open Bias: Revolutionizing AI Agent Behavior at Runtime Open Bias is a cutting edge AI tool designed to dictate and enforce the behavior of AI agents in real ti…
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?
OpenAI Teams Up With MediaTek, Qualcomm for AI-Powered Phones
OpenAI Collaborates with MediaTek and Qualcomm for AI Infused Smartphones In a groundbreaking partnership, OpenAI has joined forces with MediaTek and Qualcomm t…
Community-Driven Ratings for 120+ AI Coding Tools on Tolop
a few weeks ago I posted about building a library that tracks 120+ AI coding tools by how long their free tier actually lasts. the response was good but the most common feedback was "your scores are subjective." fair point. so I rebuilt the rating system. you can now sign in with Google and vote on any tool directly. the scores update in real time based on actual user votes, not just my personal assessment. if you think I rated something wrong, you can now do something about it instead of just commenting. also shipped dark mode because apparently I was the only person who thought the default looked fine. **what Tolop actually is if you're new:** every AI tool claims to be free. most aren't, or at least not for long. Tolop tracks the real limits: how many completions, how many requests, how long until you hit the wall under light use vs heavy use vs agentic sessions. it also flags the tools where "free" means you're still paying Anthropic or OpenAI through your own API key. 120+ tools across coding assistants, browser builders, CLI agents, frameworks, self-hosted tools, local models, and a new niche tools category for single-purpose utilities that don't fit anywhere else. **a few things the data shows that I found genuinely interesting:** * Gemini Code Assist offers 180,000 free completions per month. GitHub Copilot Free offers 2,000. same category, 90x difference * several of the most popular tools (Cline, Aider, Continue) are free to install but require paid API keys, so "free" is misleading * self-hosted tools have by far the most generous free tiers because the cost is on your hardware, not a server would genuinely appreciate votes on tools you've actually used, the more real usage data behind the scores, the more useful the ratings get for everyone. [tolop.space](http://tolop.space) :- no account needed to browse, Google login to vote.
Red Hat's OpenClaw Now Safer with Tank OS Containers
Tank OS puts OpenClaw AI agents into a container that let's it run reliably and more safely, especially for those running fleets of them.
Snapchat Launches AI Chat for Brand Interactions
Users will be able to chat with a brand's AI agent to do things like ask questions and get recommendations.
AI Tool Agentswarms.fyi: Revolutionizing AI Collaboration
Revolutionizing AI Collaboration with AITool Agentswarms AI collaboration has taken a significant leap with the introduction of AgentSwarm . This innovative AI …
Explore AgentSwarms: Free Hands-On Learning for Agentic AI
Explore AgentSwarms: Free Hands On Learning for Agentic AI Discover the Power of Agentic AI with AgentSwarms AgentSwarms is an innovative platform designed to o…
AgentSwift: Open-Source iOS Builder Agent for Developers
AgentSwift: Revolutionizing iOS Development with Open Source Power Introduction to AgentSwift AgentSwift is an open source iOS builder agent designed to streaml…
Auroch Engine: Revolutionizing AI Memory for Personalization
Auroch Engine is an external memory layer for AI assistants — designed to give models better long-term recall, personalization, and context awareness across conversations. Instead of relying on scattered chat history or fragile built-in memory, Auroch Engine lets users store, retrieve, and organize important context through a dedicated memory API. The goal is simple: make AI feel less like a reset button every session, and more like a tool that actually learns your projects, preferences, workflows, and goals over time. Right now, it’s in early beta. We’re looking for first users who are interested in testing a lightweight developer-facing memory system for AI apps, agents, and personal productivity workflows. Ideal early users are people building with AI, experimenting with agents, or frustrated that their assistant keeps forgetting the important stuff. DM for more information or better visit our site: https://ai-recall-engine-q5viks70j-cartertbirchalls-projects.vercel.app