#agentic hashtag - Bluesky

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11 hours ago

#TimTalk - Can you build an exponential AI company with a linear workforce? with Anuj Pandey buff.ly/FR1ne4I @DLAIgnite #SocialSelling #DigitalSelling #Leadership #ArtificialIntelligence #Tech #TechNews #AgenticAI #AIAgents #Agentic #AI #FutureOfWork #Strategy

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Thinkronicity ™

@thinkronicity.ispost.ing

13 hours ago

There's no way to stop #AI use. It has to be managed so ppl know if it is AI or not. This talking about #agentic use is not what attie.at is. People can get educated & learn to use the tools or be a boomer luddite, sorry. This happens everytime tech sees a major upgrade. It is WHO is using the Tool.

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@qdrddr.bsky.social

1 day ago

GitHub - chroma-core/context-1-data-gen Contribute to chroma-core/context-1-data-gen development by creating an account on GitHub.

#ChromaDB Context-1: Fast 20B model.
Small Specialized Language Model for fast #agentic search trained to retrieve supporting documents for complex, multi-hop queries. X10 faster than #LLM
#VectorDB #RAG #SLM #Agents
#OpenSource Apache 2.0 lic
github.com/chroma-core/...

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The 90-Day Sprint to Exponential Advantage: A CEO’s AI Integration Playbook (via Passle) Traditional incremental improvements are officially a relic of the past. In a world where markets face margin compression and AI offers exponential adva...

1 day ago

The 90-Day Sprint to Exponential Advantage: A CEO’s AI Integration Playbook by @Timothy_Hughes buff.ly/6e3l4Ao @DLAIgnite #SocialSelling #DigitalSelling #Leadership #ArtificialIntelligence #Tech #TechNews #AgenticAI #AIAgents #Agentic #AI #FutureOfWork #Strategy

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1 day ago

NVIDIA AI Unveils ProRL Agent: A Decoupled Rollout-as-a-Service Infrastructure for Reinforcement Learning of Multi-Turn LLM Agents at Scale NVIDIA researchers introduced ProRL AGENT, a scalable inf...

#Agentic #AI #AI #Infrastructure #AI #Paper #Summary #AI […]

[Original post on marktechpost.com]

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Wes Fryer

@wesfryer.com

1 day ago

A retro-cartoon style illustration, based on the Super Friends animated series, depicting the hosts of the EdTech Situation Room podcast in a futuristic command center. Across the top of the image is a yellow banner with bold black text that reads: "EdTechSR Ep 371 – Router Bans, AI Agents". In the command center, two superheroes, whose faces are based on the real hosts, are seated at a curved desk. The figure on the left is Batman (representing Dr. Wesley Fryer), holding a router with a magnifying glass. To his left stand fully visible Wonder Woman and Robin. Behind them, a screen displays the text "Media Literacy". Behind Batman, other Super Friends stand, including Green Lantern. A large central screen shows a Wi-Fi router inside a red 'X' (representing a banned router) over the FCC logo and text "AI Agent". The figure on the right is Superman (representing Dr. Jason Neiffer), smiling and holding an open laptop displaying the cartoon interface of an "AI Agent". To his right are other heroes, including Aquaman and The Flash, and the Wonder Twins (Zan and Jayna) with Gleek the monkey on Zan's shoulder. A cartoon Starlink satellite dish is also on the right. At the bottom left, a vintage TV set shows a small caricature of Oprah Winfrey. In the bottom-right corner, white text with a blue outline reads: "edtechSR.com". The entire image has the bold outlines, flat colors, and dynamic energy of a classic Saturday morning cartoon.

Check out Episode 371 from the EdTech Situation Room this week: "Router Bans, AI Agents"

edtechsr.substack.com/p/edtec...

#AI #agentic #edtech #MediaLit #FCC #router #ban

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Dirk Roeckmann

@5troop.bsky.social

1 day ago

We need to tell the ‘good vibes’ from the ‘bad’ ones. #Neurosymbolic paradigms are of paramount importance. #Agentic coding is also not the ‘ozempic of labor costs’ either - smart companies will keep their (skilled) staff get more stuff done and expand #JevonsParadox #SAP #ABAP
2/2

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1 day ago

The 90-Day AI Leap: From Incrementalism to Exponential Advantage by @Timothy_Hughes buff.ly/6e3l4Ao @DLAIgnite #SocialSelling #DigitalSelling #Leadership #ArtificialIntelligence #Tech #TechNews #AgenticAI #AIAgents #Agentic #AI #FutureOfWork #Strategy #CEO #CFO

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@sumologic.bsky.social

1 day ago

What a week! Thank you to everyone who came out to visit us at #RSAC, took the Dojo AI Challenge, and experienced true agentic AI-powered threat detection and response. We had a blast and hope you did too!

#AI #SOC #cybersecurity #events #agentic

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@kubernetes.activitypub.awakari.com.ap.brid.gy

2 days ago

The Data Literacy Gap: Why 95% of AI Projects Fail - Digital Download buff.ly/bN23q87 via @DLAIgnite #SocialSelling #DigitalSelling #Leadership #ArtificialIntelligence #Tech #TechNews #AgenticAI #AIAgents #Agentic #AI #FutureOfWork #Strategy

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Kubernetes

2 days ago

I Tried 30+ Udemy Courses to Learn LangChain and RAG - Here are My Top 6 Recommendations Hello guys, If you’re someone who’s excited about building cutting-edge Generative AI applications in 2026, there’s no better time to learn LangChain. It has quickly become one of the most in-demand frameworks for building LLM-powered apps using OpenAI, Ollama, Mistral, Claude, and even custom models. LangChain makes it easy to combine language models, tools, memory, vector databases, and agents to build robust GenAI applications — — from chatbots to AI assistants, from RAG systems to workflow automations. Earlier, I have shared best ChatGPT courses, best Data Science courses and best Machine Learning courses as well as 10 Must Read AI And LLM Engineering Booksand in this article, I am going to share best Udemy courses to learn LangChain, RAG and AI Automation. Being an aspiring AI developer myself and having enrolled in several top-rated courses on Udemy, I’ve handpicked the 6 best LangChain courses to help you upskill and stay ahead in this fast-moving field. By the way, if you are new to AI world then I highly recommend you to start with The AI Engineer Course 2026: Complete AI Engineer Bootcamp, one of the most comprehensive resource to become an AI Engineer in 2026. Let’s dive in! ## 6 Best Udemy Courses to learn LangChain and RAG in 2026 Without any further ado and wait, here are the best online courses you can join on Udemy to learn not just LangChain but also RAG and AI agents automation, the hottest skill of 2026. I learn better by joining multiple courses as every instructor explain things differently and when I learned from multiple folks, everything eventually falls in place. You can also join one or more courses from this list for better learning. ### 1. LangChain — Develop LLM Powered Applications with LangChain If you’re just getting started, this course is a great choice. It teaches LangChain using real-world examples, with full support for the latest LangChain version 0.3.0. You’ll build a complete GenAI app and get comfortable with the LangChain ecosystem in a very hands-on way. What you’ll learn: * Latest LangChain 0.3.0 features * Prompt templates, chains, memory, and agents * Build LLM-powered search and chatbot apps This Udemy course is best for beginners who want to quickly get started by building something real. Here is the link to join this course — — LangChain —Develop LLM Powered Applications with LangChain ### 2. Complete Generative AI Course With LangChain and HuggingFace This course is a power combo. It covers LangChain and HuggingFace in depth, helping you understand not only how to build GenAI apps but also how to deploy and optimize them for performance. What you’ll learn: * Complete LangChain project lifecycle * How to use HuggingFace transformers in real apps * Deployment & optimization techniques This Udemy course is best for intermediate developers who want to go beyond toy apps and work on scalable GenAI projects. Here is the link to join this course — — Complete Generative AI Course With LangChain and HuggingFace ### 3. LLM Engineering: Master AI, Large Language Models & Agents If you’re serious about becoming an LLM engineer, this course is gold. It’s project-based and teaches you everything from RAG to fine-tuning and deploying AI agents using LangChain, Python, and popular libraries. What you’ll learn: * Build 8 complete LLM apps * Master RAG, LoRA, LangChain tools * Real-world projects for GenAI devs This LangChain course on Udemy is best for developers aiming for roles like AI Engineer or Generative AI Consultant. Here is the link to join this course — — LLM Engineering: Master AI, Large Language Models & Agents ### 4. AI-Agents: Automation & Business with LangChain & LLM Apps This is one of the most popular LangChain courses with 21,000+ students and for good reason. It focuses heavily on AI automation, multi-agent systems, and how to monetize your GenAI apps. What you’ll learn: * Use LangChain and LangGraph for multi-agent systems * Automate business workflows * Sell your AI tools as products This Udemy course is again ideal for developers and entrepreneurs interested in AI automation for business. Here is the link to join this course — — AI-Agents: Automation & Business with LangChain & LLM Apps ### 5. 2026 Master Langchain and Ollama — — Chatbot, RAG and Agents This is a hot and trending course, especially if you’re curious about working with local LLMs using Ollama and LLAMA 3.2. The course includes tutorials for building chatbots, RAG systems, and more — — all powered by LangChain and local models. What you’ll learn: * Ollama + LangChain integration * Build local chatbot and RAG apps * Covers DeepSeek, LLAMA 3.2, and more This Udemy course is better for devs who want to run LLMs locally without relying on OpenAI or cloud APIs. Here is the link to join this course — 2026 Master Langchain and Ollama — — Chatbot, RAG and Agents ### 6. LangChain Mastery: Build GenAI Apps with LangChain & Pinecone This course is perfect if you’re looking to build production-grade LLM applications with support for Pinecone vector DB. It teaches you how to integrate vector search, memory, and LangChain into a full-stack GenAI app. What you’ll learn: * LangChain with Pinecone vector DB * How to build and deploy full-stack GenAI projects * Step-by-step code walkthroughs Best for: Intermediate to advanced devs building retrieval-augmented generation (RAG) apps. Here is the link to join this course — — LangChain Mastery: Build GenAI Apps with LangChain & Pinecone That’s all about the best Udemy courses to learn LangChain and RAG in 2026. Whether you’re just beginning your LangChain journey or looking to level up into full LLM engineering roles, these 6 courses will help you stay ahead of the AI curve in 2026. By the way, if you want to join multiple course on Udemy, its may be worth getting a Udemy Personal Plan, which will give instant access of more than 11,000 top quality Udemy courses for just $30 a month. If you got a lot of time and want to save money, _Udemy Personal Plan_ will be perfect for you. If you found this helpful, don’t forget to share or bookmark it for your AI learning journey! Other AI, LLM, and Machine Learning resources you may like * Top 5 Courses to Prepare for AIF-C01 Exam in 2026 * 16 System Design Resources for Software Engineers * 10 Best Udemy Courses to learn Artificial Intelligence in 2026 * 5 Best Udemy courses to learn Midjourney in 2026 * 6 Udemy Courses to learn AWS Bedrock in 2026 * How to Prepare for AWS Solution Architect Exam in 2026 * Top 5 Udemy courses to build AI Agents in 2026 * 7 Best Courses to learn AWS S3 and DynamoDB in 2026 * Top 5 Udemy Courses for AWS Cloud Practitioner Exam in 2026 * 5 Best Courses to learn AWS SageMaker in 2026 * 8 Udemy courses to learn Prompt Engineering and ChatGPT * 5 Best Udemy Courses to learn Building AI Agents in 2026 * Top 5 Udemy Courses to learn Large Language Model in 2026 Thanks a lot for reading this article so far, if you like these LangChain courses on Udemy then please share with your friends and colleagues. If you have any feedback or questions then please drop a note. > P. S. — If you want to learn from books and looking for best AI and LLM Books then I highly recommend you to read AI Engineering by Chip Huyen and The LLM Engineering Handbook by Paul Iusztin and Maxime Labonne, both of them are great books and my personal favorites. They are also highly recommended on Reddit and HN. ## 10 Artificial Intelligence and LLM Books Every Software Engineer Should Read in 2026 ### 10 Must read AI and LLM Engineering books for Software Engineers who want to become an AI Engineers. medium.com

I Tried 30+ Udemy Courses to Learn LangChain and RAG - Here are My Top 6 Recommendations Hello guys, If you’re someone who’s excited about building cutting-edge Generative AI applications in 20...

#Agentic #AI #artificial #intelligence #courses #LLM #Udemy

Origin | Interest | Match

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#BruceSterling

@bruces.bsky.social

2 days ago

"We'll be seizing direct control of the money now, because we're better at it and we deserve it" #AI #agentic

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2rZiKKbOU3nTafniR2qMMSE0gwZ

2 days ago

A Coding Implementation to Run Qwen3.5 Reasoning Models Distilled with Claude-Style Thinking Using GGUF and 4-Bit Quantization In this tutorial, we work directly with Qwen3.5 models distilled with ...

#Agentic #AI #Artificial #Intelligence #Editors #Pick […]

[Original post on marktechpost.com]

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@2rzikkbou3ntafnir2qmmse0gwz.activitypub.awakari.com.ap.brid.gy

2 days ago

Original post on secureworld.io

The Rise of the Agentic Enterprise: Navigating the Latest Cyber Risk The conversation around AI is shifting from "chatbots" to "agents." According to the recent McKinsey & Compa...

#Featured #Artificial #Intelligence #Original #Content #Enterprise #Security […]

[Original post on secureworld.io]

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Rocky Lhotka

@rocky.lhotka.net

2 days ago

Tools and Skills: Better Together | Rockford Lhotka VP, Open Source Creator, Author, Speaker

#ai and #agentic systems use skills and tools. I keep hearing people pit these against each other, but really they are complimentary.

blog.lhotka.net/2026/03/25/T...

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Rocky Lhotka 🤘🖖

@rockylhotka.fosstodon.org.ap.brid.gy

2 days ago

Tools and Skills: Better Together I keep running into a version of the same question when talking about AI agent design: if you have good enough skills — detailed procedural knowledge in markdown files — do you even need MCP servers and other tools? No. You absolutely still need tools. But the question itself reveals a misunderstanding about what skills actually are, and I think it’s worth unpacking. Skills and tools are not competing approaches. You can’t replace one with the other. In practice, they’re deeply intertwined — and trying to pit them against each other misses the entire point of both. I’m going to use RockBot as my example throughout this post because it’s what I’m building and I know it best, but these concepts are not specific to RockBot. Claude Code has its `CLAUDE.md` files and tool use. GitHub Copilot has instruction files, skills, and MCP integration. Cursor, Windsurf, and other AI coding agents all have some form of this pattern. The relationship between tools and skills is a fundamental design concern for any AI agent, not a feature of any one product. ## The Basics I’ve written about RockBot’s tools and RockBot’s skills separately, so I won’t rehash everything here. The short version: Tools are functions the agent can call to take action in the world. Send an email, check a calendar, search the web, invoke an A2A agent, store a memory. Without tools, an agent can only chat. A skill file cannot send an email. A skill file cannot look up what’s on your calendar. Tools are how agents _do things_. Skills are markdown files that capture procedural, context-specific knowledge the agent has built up over time. They encode lessons from past failures, successful patterns, environment-specific conventions, and — critically — knowledge about _how to use tools well_. That last point is the one people miss. Knowing that a hammer exists is different from knowing how to drive a nail without splitting the wood. The hammer is the tool. The technique is the skill. You need both. ## Tools Come with Their Own Skills In RockBot, the relationship between tools and skills isn’t just conceptual — it’s built into the architecture. Every tool subsystem in the RockBot framework can register a base-level **tool guide** when it starts up. This is a default skill that the subsystem itself provides, describing how its tools should be used. When the MCP integration subsystem loads, it registers a guide explaining how `mcp_list_services`, `mcp_get_service_details`, and `mcp_invoke_tool` work together. The A2A subsystem does the same for agent-to-agent communication. The web subsystem explains how search and browsing tools relate. Memory, scheduling, subagents — each subsystem brings its own guide. The agent uses `list_tool_guides` and `get_tool_guide` to discover and retrieve these guides. On day one, before any learning has happened, the agent already has grounded knowledge about how to use its tools — not just what they are, but how to use them effectively. So right from the start, tools and skills are coupled. The tools arrive with skills already attached. ## Skills Improve Through Tool Usage Those base-level tool guides are a starting point, not a ceiling. As the agent uses its tools across real interactions, it learns. It discovers edge cases, finds better sequences, encounters caveats that weren’t obvious from the schema alone. Through RockBot’s feedback loop — explicit thumbs up/down from users and implicit correction signals from conversations — the agent refines and extends its skills. I have a great real-world example of this. A while back, RockBot kept creating calendar events at the wrong time. It would send 4 PM Central to the calendar MCP server, and the event would show up at 11 AM. Four times in a row. It turned out the MCP server had a bug where it silently ignored the timezone parameter and treated all times as UTC. The tool guide for the calendar MCP server didn’t mention this problem — because it didn’t exist when the guide was written. But after that painful debugging session, the agent learned the workaround (send UTC times directly), and that knowledge was captured as an updated skill. The next time the agent scheduled something, it didn’t make the same mistake. That learning was _entirely dependent_ on having the tool in the first place. You can’t learn to work around a calendar bug if you don’t have a calendar. That’s the pattern. The skill describing how to use the calendar MCP server on day one is fairly generic. After weeks of actual calendar management, that skill becomes precise: how to handle recurring events, what to do when attendee time zones differ, what the server does and doesn’t support. The agent has learned by doing, and the skill has grown because of it. ## Skills Do Many Things — Including Making Tools Better I want to be clear that skills aren’t _only_ about tool usage. Skills capture all sorts of procedural knowledge: how to structure a research delegation, what tone to use with different contacts, how to format reports. Many skills have nothing to do with specific tools. But a large and important subset of skills exist specifically to make tool usage more effective. And that’s the insight I think gets lost when people frame this as “tools vs. skills”: skills aren’t an alternative to tools. They’re a _multiplier_ on tools. Skills are operational knowledge — knowledge _about tools_ , _for tools_ , _refined through using tools_. They don’t sit above the tool layer in the architecture. They sit right alongside it, making it work better. ## The Better Together Design What RockBot demonstrates is that these two concepts work in concert at every level: **Tools provide capability.** They are the agent’s connection to the real world — email, calendars, file storage, web, other agents. **Tool guides provide starting knowledge.** Each subsystem ships with a skill that grounds the agent from the moment tools become available. The agent never has to figure out a subsystem entirely from scratch. **Experience improves that knowledge over time.** As the agent uses tools, encounters failures, receives feedback, and discovers edge cases, skills get richer and more precise. Tool usage becomes more effective and more reliable. Remove the tools and you have an agent that can describe how things _should_ work but can’t actually do anything. Remove the skills and you have an agent that stumbles through every interaction, making the same mistakes over and over because nothing it learns ever sticks. Together? You get an agent that keeps getting better at its job. And again — this isn’t a RockBot-specific insight. Whether you’re configuring GitHub Copilot with custom instructions and MCP servers, setting up Claude Code with `CLAUDE.md` files and tool access, or building your own agent framework from scratch, the same principle applies. Tools give your agent the ability to act. Skills give it the knowledge to act _well_. Invest in both.

#ai and #agentic systems use skills and tools. I keep hearing people pit these against each other, but really they are complimentary.

blog.lhotka.net/2026/03/25/Tools-And-Ski...

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Martin Guay🍁

@martinguay.cryovex.com

2 days ago

We've fully integrated AI into our environment. ChatGPT Enterprise, Gemini Pro, Copilot+, Claude Go. We're also working in AI Agent leveraging an automated platform with N8N. Starting with an agentic AI and some MCP.

#work #ai #sysadmin #agentic #chatgpt #gemini #claude #copilot #mcp

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Adam Argyle

@argyleink.front-end.social.ap.brid.gy

2 days ago

Original post on front-end.social

**The Transactional Trap:**
How 97% of Developers Are Using AI Wrong - with guests Leon Noel & Danny Thompson

🤖 agentic dev
💬 prompt engineering
🦞 OpenClaw
🎼 orchestration, harnesses, and models
💀 leet code interviews
🕹️ Pokemon Go
🥃 whiskey & whatnot

⤷ […]

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Adam Argyle

@nerdy.dev

2 days ago

WWW Ep237 Using Ai Wrong With Leon Noel And Danny Thompson · March 26, 2026 Ep #237 The Transactional Trap: How 97% of Developers Are Using AI Wrong - with Leon Noel & Danny Thompson agentic dev prompt engineering OpenClaw orchestration, harnesses, and models leet code in...

The Transactional Trap:
How 97% of Developers Are Using AI Wrong - with guests @leonnoel.bsky.social & @dthompsondev.bsky.social

🤖 agentic dev
🦞 OpenClaw
🎼 orchestration, harnesses, and models
💀 leet code interviews
🕹️ Pokemon Go
🥃 whiskey & whatnot

⤷ nerdy.dev/www-ep237-us... #agentic #webdev

7 1 2 0

Navigating the Complex World of Context in Agentic AI Flows In the rapidly evolving world of Artificial Intelligence, understanding how to effectively pass and manage context within agentic AI systems has become crucial. This blog post, inspired by insights…

3 days ago

Navigating the Complex World of Context in Agentic AI Flows In the rapidly evolving world of Artificial Intelligence, understanding how to effectively pass and manage context within agentic AI syst...

#Agentic #Agentic #AI #AI #workflows #Context #Engineering #IBM #Cloud

Origin | Interest | Match

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@searchengine.activitypub.awakari.com.ap.brid.gy

3 days ago

Готовим ИИ-агента к продакшену Всем привет! На связи Сергей Смирнов, действующий и практикующий AI-инженер. И...

#ии-агенты #rag #ai-agents #llm #agentic #ai #evaluation #observability #mcp

Origin | Interest | Match

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SearchEngine

3 days ago

Original post on heinzmarketing.com

AI Visibility in B2B Marketing By Sarah Threet, Marketing Consultant at Heinz Marketing For years, B2B marketers have optimized for search: Keywords. Rankings. Traffic. Conversion paths. But quietl...

#Artificial #Intelligence #(AI) #Marketing […]

[Original post on heinzmarketing.com]

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