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AI Tools

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?

Global · Founders · Apr 29, 2026
AI Marketing

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 …

Global · Marketers · Apr 29, 2026
AI Tools

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?

Global · Developers · Apr 29, 2026
AI Infrastructure

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)

Global · Developers · Apr 29, 2026
AI Writing

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 😅

Global · Enterprises · Apr 29, 2026
AI Tools

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…

Global · Developers · Apr 29, 2026
AI Framework

Superpowers AI Framework: Agentic Skills for Software Development

An agentic skills framework & software development methodology that works.

Global · Developers · Apr 29, 2026
AI Tools

Harness Coding Efficiency with 1jehuang/jcode AI Tool

Coding Agent Harness

Global · Developers · Apr 29, 2026
AI Productivity

Warp: AI-Powered Terminal Development Environment

Warp is an agentic development environment, born out of the terminal.

Global · Developers · Apr 29, 2026
AI Tools

Voice Agents: 24/7 AI Voice Agents for Client Support

Turn expertise into 24/7 client-facing AI voice agents

Global · Enterprises · Apr 29, 2026
AI Tools

MaxHermes by Minimax: AI Agent for Skill Building

AI agent that builds skills from every task you give it

Global · General · Apr 29, 2026
AI Infrastructure

Actian VectorAI DB: Portable Vector Database for AI Agents

The portable vector database for AI agents beyond the cloud

Global · Developers · Apr 29, 2026
AI Tools

Crono's Agentic Sales Engine: AI-Powered Sales Teams

Where sales teams and AI agents work side by side.

Global · Enterprises · Apr 29, 2026
AI Tools

SureThing.io: AI Agent Communicates Results Naturally

Autonomous agent that communicates results like a human

Global · General · Apr 29, 2026
AI Tools

Clera: AI Matching Candidates to Perfect Roles

An AI agent matching candidates to the right roles.

Global · Enterprises · Apr 29, 2026
AI Tools

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…

Global · General · Apr 28, 2026
AI Tools

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…

Global · Developers · Apr 28, 2026
AI Tools

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?

Global · General · Apr 28, 2026
AI Infrastructure

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…

Global · General · Apr 28, 2026
AI Tools

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.

Global · Developers · Apr 28, 2026
AI Infrastructure

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.

Global · Enterprises · Apr 28, 2026
AI Marketing

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.

Global · General · Apr 28, 2026
AI Tools

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 …

Global · General · Apr 28, 2026
AI Tools

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…

Global · General · Apr 28, 2026
AI Tools

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…

Global · Developers · Apr 28, 2026
AI Infrastructure

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

Global · Developers · Apr 28, 2026
AI Tools

Self-Taught Developer from Bahrain Launches Multi-Model AI Platform

https://reddit.com/link/1sxotqx/video/xlaqd9i8guxg1/player I'm a self-taught developer, 39 years old, based in Bahrain. Four months ago I started building AskSary - a multi-model AI platform with a persistent memory layer that sits above all the models. The core idea: the model is not the identity. Most AI tools lose your context the moment you switch models. I built the layer that remembers you across all of them. Here's what's shipped so far: **Models & Routing** Every major model in one place - GPT-5.2, Claude Sonnet 4.6, Grok 4, Gemini 3.1 Pro, DeepSeek R1, O1 Reasoning, Gemini Ultra and more - with smart auto-routing or manual override. **Memory & Context** Persistent cross-model memory. Start with Claude on your phone, switch to GPT on your laptop - it already knows what you discussed. Proactive personalisation that messages you first on login before you've typed a word. **Integrations** Google Drive and Notion - connect once, pull files and pages directly into chat or your RAG Knowledge Base. Unlimited uploads up to 500MB per file via OpenAI Vector Store. **Video Analysis** \- Gemini native video understanding for YouTube URL analysis (no download required, processed natively) and direct file upload up to 500MB. Full breakdown of visuals, audio, dialogue, editing style and key moments. **Generation** Image generation and editing, video studio across Luma, Veo and Kling, music generation via ElevenLabs, video analysis via upload or YouTube URL. **Builder Tools** Vision to Code, Web Architect, Game Engine, Code Lab with SQL Architect, Bug Buster, Git Guru and more. Tavily web search across all models. **Voice & Audio** Real-time 2-way voice chat at near-zero latency, AI podcast mode downloadable as MP3, Voiceover, Voice Notes, Voice Tuner. **Platform** Custom agents, 30+ live interactive themes, smart search, media gallery, folder organisation, full RTL support across 26 languages, iOS and Android apps, Apple Vision Pro. **Where it is now** 129 countries. Currently at 40 new signups a day. 1080 Signup's so far after 4 weeks or so. MRR just started. Zero ad spend. All of it built solo, one feature at a time, on a balcony in Bahrain. **The Stack:** Frontend - Next.js, Capacitor (iOS and Android) and Vanilla JS / React Backend - Vercel serverless functions, Firebase / Firestore (database + auth) and Firebase Admin SDK AI Models - OpenAI (GPT, GPT-Image-1), Anthropic (Claude), Google (Gemini), xAI (Grok), DeepSeek Generation APIs - Luma AI (video), Kling via Replicate (video), Veo via Replicate (video), ElevenLabs (music), Flux via Replicate (image editing), Meshy (3D — coming soon) Integrations - Google Drive (OAuth 2.0), Notion (OAuth 2.0), Tavily (web search), OpenAI Vector Store (RAG), Stripe (payments), CloudConvert (document conversion), Sentry (error tracking), Formidable (file handling) Rendering - Mermaid (flow charts) and MathJax Platforms - Web, iOS, Android, Apple Vision Pro (visionOS) Languages - 26 UI languages with full RTL support [asksary.com](http://asksary.com) Happy to answer questions on any part of the build - stack, architecture, API cost management, anything.

Other · Developers · Apr 28, 2026
AI Tools

Discover Beads: Memory Upgrade for Coding Agents

Beads - A memory upgrade for your coding agent

Global · Developers · Apr 28, 2026
AI Tools

GitNexus: Client-Side Code Intelligence Engine for GitHub Repos

GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an interactive knowledge graph wit a built in Graph RAG Agent. Perfect for code exploration

Global · Developers · Apr 28, 2026
AI Tools

Epismo Agent Package: Run Community-Built Workflows

Run agent workflows the community already built

Global · Developers · Apr 28, 2026
AI Tools

Logic AI Tool: Build and Manage Agent Fleets

Build and operate fleets of agents

Global · Founders · Apr 28, 2026
AI Tools

Jet AI Agents: Build Business AI Agents in Minutes

Build business AI agents in minutes

Global · Founders · Apr 28, 2026
AI Tools

OpenAI's AI-Powered Phone: Apps Replaced by Agents

The phone could go in mass production in 2028, an analyst says.

Global · General · Apr 27, 2026
AI Infrastructure

China Blocks Meta's $2B Manus AI Deal After Probe

China has ordered Meta to unwind its multibillion-dollar Manus acquisition, dealing a potential setback to Zuckerberg’s push into AI agents.

Asia · General · Apr 27, 2026
AI Tools

OSS Agent Leads TerminalBench on Gemini-3-Flash-Preview

OSS Agent Leads TerminalBench: Enhancing Network Management with Gemini 3 Flash Preview In the rapidly evolving world of network management, maximizing efficien…

Global · Developers · Apr 27, 2026
AI Tools

Building a SQL Analyst Agent from Scratch: A Comprehensive Guide

Building a SQL Analyst Agent from Scratch: A Comprehensive Guide In the data driven world, SQL analysts play a crucial role in extracting meaningful insights fr…

Global · Developers · Apr 27, 2026
AI Tools

AI Agents: Identity, Not Memory, Was the Key to Stability

Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.

Global · Developers · Apr 27, 2026
AI Tools

TauricResearch TradingAgents: Multi-Agent LLM Financial Trading

TradingAgents: Multi-Agents LLM Financial Trading Framework

Global · Developers · Apr 27, 2026
AI Infrastructure

Navigating AI Agent Governance: A Growing Organizational Challenge

Something I've been thinking about that doesn't get discussed enough outside of technical circles: the organizational and safety implications of uncoordinated AI agent deployment. Companies are shipping agents fast. Customer service agents, coding agents, data analysis agents, internal ops agents. Each team builds their own. Each agent gets its own rules, its own permissions, its own behavior. At some threshold this stops being a technical configuration problem and starts being a governance problem. You have agents making autonomous decisions on behalf of your organization with no shared behavioral contract. No unified view of what your AI systems are authorized to do. Think about what this means practically: an agent trained to be maximally helpful on one team might take actions that would be flagged as unauthorized somewhere else in the same organization. A policy change from legal doesn't propagate to agents because there's no central layer to propagate to. Nobody knows which agents have access to what data. This is the AI equivalent of shadow IT, except shadow IT couldn't take autonomous actions. What's the right mental model for governing a fleet of AI agents? Treat each agent like an employee with a defined role and access policy? Build an org chart for agents? Create a behavioral constitution that all agents inherit? Curious how people here are thinking about this, especially as agents get more capable and the stakes of misconfiguration get higher.

Global · Founders · Apr 27, 2026
AI Audio

Grok Voice Think Fast 1.0 API Release: Advanced Voice Agent

Our most capable voice agent is now available via API

Global · General · Apr 27, 2026
AI Marketing

Inrō AI: Revolutionize Instagram Marketing with AI

Your AI Agent for Instagram Marketing

Global · Marketers · Apr 27, 2026
AI Tools

Clawdi: Top Platform for AI Agents

Best home for all AI agents

Global · General · Apr 27, 2026
AI Infrastructure

Caliber: Open-Source Proxy for Enforcing LLM Agent Rules

Cross-posting here because this problem affects everyone building with AI agents. Prompt-based guardrails fail. The model follows your system prompt in a demo, then ignores rules when context gets big or the agent chains multiple steps. We built Caliber - an open-source proxy that reads your rules from plain markdown and enforces them at the API layer, not in the prompt. Every call. Provider-agnostic. Just hit 700 GitHub stars ⭐ and nearly 100 forks - the reception from devs building with AI has been amazing. Repo: [https://github.com/caliber-ai-org/ai-setup](https://github.com/caliber-ai-org/ai-setup) Would love: \- Feedback on the approach \- Feature requests from people building AI agents \- Anyone who wants to contribute to the project Building this open-source for the community.

Global · Developers · Apr 27, 2026
AI Infrastructure

AI Agents Network: Revolutionizing Collaboration and Knowledge Sharing

built something big. It’s basically an internet for AI agents. Right now agents are isolated. They don’t share knowledge, they don’t really work together, and they keep repeating the same work. I built a system where that changes. Agents can store what they learn as reusable pieces of knowledge. Once something is solved, it doesn’t need to be solved again. Other agents can find it, use it, and improve it. They can also collaborate. One agent does not need to handle everything. They can split tasks, take roles, and combine results into one outcome. They can communicate directly. Not like chat for humans, but structured messages where they share context and coordinate work in real time. Agents can hire other agents. If one agent cannot solve something, it finds another one that can and delegates the task. This creates a network where work flows to the right place. There is also an identity layer. Each agent has a readable address. You can discover agents, call them, and build systems on top of them. On top of that there is an economy. Agents build reputation based on real work. They can pay each other for tasks and get paid for useful results. Everything runs in a decentralized way. No central control. Data is distributed, identities are cryptographic, and the network just routes and syncs information. This is not just another tool. It’s a foundation where agents can exist, interact, and evolve together. You can leave your email here to get early access: www.cogninet.co

Global · Developers · Apr 27, 2026
AI Tools

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

Global · Developers · Apr 27, 2026
AI Tools

Agentswarms.fyi: New AI Tool for Enhanced Productivity

Agentswarms.fyi: Revolutionize Productivity with the New AI Tool In today's fast paced digital world, enhancing productivity is crucial for success. Introducing…

Global · General · Apr 27, 2026
AI Tools

Explore AgentSwarms: Free Hands-On Agentic AI Learning

Explore AgentSwarms: Free Hands On Agentic AI Learning Introduction AgentSwarms offers a unique, hands on approach to learning about Agentic AI, providing a fre…

Global · General · Apr 27, 2026
AI Tools

AI Memory Upgrade for Coding Agents: Beads on GitHub

Beads - A memory upgrade for your coding agent

Global · Developers · Apr 26, 2026
AI Tools

GitNexus: Client-Side Code Intelligence for GitHub

GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a GitHub repo or ZIP file, and get an interactive knowledge graph wit a built in Graph RAG Agent. Perfect for code exploration

Global · Developers · Apr 26, 2026
AI Tools

Anthropic's AI Agents Make Real Deals in Marketplace Test

In a recent experiment, Anthropic created a classified marketplace where AI agents represented both buyers and sellers, striking real deals for real goods and real money.

Global · General · Apr 26, 2026
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