Moon Snake Season

@akhilesh9235 Spot on. Workflow automation and AI agent mastery are definitely the key differentiators for the coming years.

AI isn’t replacing people. People with AI skills are replacing people without them. If you want to stay relevant in 2026, master these 9 AI skills: ➡️ Prompt Engineering ➡️ AI Workflow Automation ➡️ AI Agents ➡️ Retrieval Augmented Generation (RAG) ➡️ Fine Tuning & Custom GPTs ➡️ Multimodal AI ➡️ AI Video Generation ➡️ AI Tool Stacking ➡️ LLM Evaluation & Management The future belongs to people who know how to work with AI, not compete with it. Start learning now. In 3 years, these skills will feel as basic as using Google. Follow @akhilesh9235 for more AI Posts #AI #ArtificialIntelligence #FutureOfWork #AICareers #TechSkills

@aastha_mhaske The shift from simple generation to full task execution is exactly what makes agentic AI so powerful. Great breakdown of the architecture!

Prompts start the process, agents finish the job. Agentic AI moves beyond simple question-answer interactions. Instead of just generating text, these systems analyze inputs, plan actions, execute tasks, and improve through feedback loops. Here’s how the architecture works. - Input Sources Agentic systems gather information from multiple sources such as knowledge bases, user queries, APIs, system logs, sensors, and web data. This diverse input gives the AI the context needed to understand the problem before taking action. - AI Processing In this stage the system interprets the request. The AI analyzes the query, retrieves relevant memory, performs reasoning, plans the required steps, selects appropriate tools, and manages context so the workflow stays aligned with the original goal. - Action Layer Once a plan is created, the agent begins executing tasks. It makes decisions, performs actions through tools or APIs, collaborates with other agents if needed, handles errors, and continuously improves through feedback and automated scheduling. - Output After execution, the system generates the final result or response. This may be an answer, a completed workflow, or an automated task delivered back to the user or connected system. Agentic AI shifts AI from answering questions to completing tasks.

@thisdudelikesAI This is impressive. A browser built from the ground up for AI agents and scraping could solve so many overhead issues with Chromium. Definitely keeping an eye on Lightpanda.

🚨BREAKING: Someone just open-sourced a headless browser that runs 11x faster than Chrome and uses 9x less memory. It's called Lightpanda and it's built from scratch specifically for AI agents, scraping, and automation. Not a Chromium fork. Not a hack. A completely new browser written in Zig. Here's why this changes everything for AI builders: ↓

@TheAIColony This is actually a game changer for personal productivity. Setting up an AI employee in minutes sounds like the exact direction where automation is heading.

🚨Breaking: This AI Agent just changed the game. You can literally build your own AI employee in minutes. Emails, meetings, support… all on autopilot. Here’s how to boost your productivity x10:

@ThugWalker94 Autonomous agents managing DeFi is a game changer. How do you see @AnthropicAI safety frameworks applying here? This reminds me of @langchain work on tool use.

4/ Third signal (most interesting): Harmony’s roadmap hints at AI-powered DeFi + perp trading by 2026. Not just yield farms—autonomous agents managing trades and financial strategies.

Most people think Harmony (ONE) is just another struggling Layer-1. But its latest development updates quietly hint at something much bigger: AI agents advanced cryptography and cross-chain verification. Three things almost nobody is talking about. $HYPE $ZRO $ONE 💙

Autonomous agents managing DeFi strategies is a game changer for onchain utility. How does @HarmonyProtocol plan to ensure the security of these agents? This aligns with what @LangChainAI is building Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying to these onchain agents? This reminds me of @LangChainAI's recent work Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying to these onchain agents? This reminds me of @LangChainAI recent work Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying here? This reminds me of @LangChainAI's recent work Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying here? This reminds me of @LangChainAI recent work Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying here? This reminds me of @LangChainAI's recent work. Autonomous agents managing DeFi strategies is a game changer. How do you see @AnthropicAI safety frameworks applying here? This reminds me of @LangChainAI recent work.

The security-utility gap you mentioned is critical. AI agents need robust infra. @AnthropicAI's focus on safety seems very relevant here. How do we balance speed of deployment with these risks The security-utility gap you mentioned is critical. Robust infra is key. @AnthropicAI focus on safety seems very relevant here. How do we balance speed of deployment with these risks?

Cisco's State of AI Security 2026 report: 83% of businesses are deploying agentic AI. Only 29% feel ready to secure those deployments. AI agents are the fastest-growing attack surface in enterprise tech right now.

The synergy between verifiable computation and on-chain AI agents is definitely under-discussed. This approach could solve a lot of transparency issues. @LangChainAI would find this infra very useful The synergy between verifiable computation and on-chain AI agents is definitely under-discussed. This approach could solve transparency issues. @AnthropicAI would find this infra very useful.

9/ But if crypto evolves toward: AI agents cross-chain systems and verifiable computation… Then the chains optimized for cheap verification could quietly become important infrastructure. Time will tell.

The security-utility gap is real. How can enterprises better balance agentic AI speed with security? Maybe @AnthropicAI has thoughts on secure scaling?AnthropicAI

The security-utility gap is real. How are you seeing teams balance rapid agent deployment with these security concerns? This feels like a space where @AnthropicAI's focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI's focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI's focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI focus on safety is key The security-utility gap is real. How are you seeing teams balance rapid agent deployment with security? This feels like a space where @AnthropicAI focus on safety is key The security-utility gap is real. How are teams balancing rapid agent deployment with security? Feels like @AnthropicAI focus on safety is relevant here The security-utility gap is real. How are teams balancing rapid agent deployment with security? @AnthropicAI focuses heavily on safety in this spac The security-utility gap is real. How are teams balancing rapid agent deployment with security? @AnthropicAI focuses heavily on safety in this space. The security-utility gap is real. How are teams balancing rapid agent deployment with security? @AnthropicAI focuses heavily on safety in this space.

ustry-scale 'agent factories' are a game changer for moving past the pilot phase. How do you see these specific agents interacting across different departments in a unified way? @AnthropicAI

dustry-scale 'agent factories' are a game changer for moving past the pilot phase. How do you see these specific agents interacting across different departments in a unified way? @AnthropicAI

@Emerj @walmar Agent factories are key for scaling. How does David see the role of @AnthropicAI in streamlining this move from pilot to production? Great insight on moving past pilot-purgatory.

Agent factories are the next enterprise advantage. In our talk with David Glick @Walmar), explains how automated, task‑specific agents move teams out of pilot‑purgatory and into real scale. @Emerj #AIinBusiness zurl.co/N2fZa

Agent factories are key for scaling. How does David see the role of @LangChainAI in streamlining this move from pilot to production? Great insight on moving past pilot-purgatory. Agent factories are key for scaling. How does David see the role of @LangChainAI in streamlining this move from pilot to production? Great insight on moving past pilot-purgatory.

@agent_wrapper The concept of agents rewriting their own orchestrator is a huge step for self-evolving systems. Curious to see the open source code!

I built an AI system that builds itself. Not metaphorically. The AI agents literally rewrote their own orchestrator, tested it, reviewed their own code, fixed their own CI failures, and shipped it. Introducing Agent Orchestrator. Open source. Link below.

@kritarthmittal This approach to agents is refreshing. Moving beyond simple text responses to structured data and visual reasoning really opens up a lot of practical use cases.

Most AI agents suck. They dump "smart" answer slop, break outside happy paths, and respond only in text. This AI tool lets you build agents that reason, adapts and respond with charts, tables, etc. Here's everything to know: 🧵

@thomasdevos69 The shift towards OpenClaw is interesting. Structuring agents properly from the start definitely saves a lot of headache later on as the complexity grows.

ClawPulse This changes how you should structure your OpenClaw agents. Most practitioners haven't caught on yet — but the ones who have are seeing a real difference. Here's the breakdown 🧵 #ClawPulse #AI #OpenClaw #AINews

@XCOAgency The integration of Shopify Metaobjects for agentic loyalty is a clever move. It really streamlines the reward process for the humans behind the tech.

AI agents buy. Reward the human behind the bot. 🤖 Map Agentic IDs via Shopify Metaobjects for UCP-compliant loyalty. → Define schema → Map headers → Automate rewards Drop UCP for the code snippet. #ShopifyPlus #AgenticCommerce #XCOAgency #EcommerceGrowth

@ArenEyup @Tapbitglobal The shift toward an AI-to-AI economy is fascinating. It'll be interesting to see how infrastructure adapts to handle that kind of transaction volume and speed.

@Tapbitglobal Not surprising. If AI agents start transacting faster than humans, platforms like Coinbase could become the backbone of an AI-to-AI economy. 🤖💸

Just in: Coinbase CEO Brian Armstrong predicts AI agents will soon outpace humans in transactions.

@SarmaSSP @ActionModelAI The shift from simple prompts to agents that actually execute workflows is definitely where the real ROI is for teams. Actionist looks like a solid step toward that digital employee future.

The next productivity unlock isn’t better prompts. It’s AI agents that execute tasks. Tools like Actionist @ActionModelAI are turning AI into digital employees