Stop wasting hours trying to learn AI. 📘📚
I have already done it for you.
With one list. Zero confusion. And no fluff
📹 Videos:
1. LLM Introduction: https://t.co/YkuDFVmW9e
2. LLMs from Scratch: https://t.co/u3kSz5SGuJ
3. Agentic AI Overview (Stanford): https://t.co/W6rzVHGSgC
4. Building and Evaluating Agents: https://t.co/sEl8vVax3F
5. Building Effective Agents: https://t.co/c7fD4aWFYO
6. Building Agents with MCP: https://t.co/GlMdR6htgA
7. Building an Agent from Scratch: https://t.co/kUQ9jPuI0R
8. Philo Agents: https://t.co/8JHvqw0DKn
🗂️ Repos
1. GenAI Agents: https://t.co/cyHPvOAjlK
2. Microsoft's AI Agents for Beginners: https://t.co/zFJAN74JQe
3. Prompt Engineering Guide: https://t.co/liUshX2XsP
4. Hands-On Large Language Models: https://t.co/TXFhbiboZY
5. AI Agents for Beginners: https://t.co/zFJAN74JQe
6. GenAI Agentshttps://lnkd.in/dEt72MEy
7. Made with ML: https://t.co/lkXP6itwK0
8. Hands-On AI Engineering:https://t.co/zB8EEctE4Y
9. Awesome Generative AI Guide: https://t.co/lF7CuIQHRw
10. Designing Machine Learning Systems: https://t.co/XlYUZYOoVi
11. Machine Learning for Beginners from Microsoft: https://t.co/hF5UzZoMJB
12. LLM Course: https://t.co/4tLAwy8fOQ
🗺️ Guides
1. Google's Agent Whitepaper: https://t.co/0OEKVLgF34
2. Google's Agent Companion: https://t.co/r0Dxe4VvDO
3. Building Effective Agents by Anthropic: https://t.co/I0ZyuwiOS3.
4. Claude Code Best Agentic Coding practices: https://t.co/HIBC2TwwAP
5. OpenAI's Practical Guide to Building Agents: https://t.co/1I8n0wnjHQ
📚Books:
1. Understanding Deep Learning: https://t.co/XEzhyAcWbq
2. Building an LLM from Scratch: https://t.co/4sZmBnHPEg
3. The LLM Engineering Handbook: https://t.co/IkAYNFkVNI
4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/KsFnET47hx
5. Building Applications with AI Agents - Michael Albada: https://t.co/lJhMLtsLql
6. AI Agents with MCP - Kyle Stratis: https://t.co/C2lhD8uTDL
7. AI Engineering: https://t.co/34EyUiIVMv
📜 Papers
1. ReAct: https://t.co/kfQ8tWysne
2. Generative Agents: https://t.co/wbfqXq8KZK.
3. Toolformer: https://t.co/OQ7m49YWls
4. Chain-of-Thought Prompting: https://t.co/XeNgLQdTIL.
🧑🏫 Courses:
1. HuggingFace's Agent Course: https://t.co/tUZyPEGhni
2. MCP with Anthropic: https://t.co/wx1DAIWis0
3. Building Vector Databases with Pinecone: https://t.co/8XsQzDstTB
4. Vector Databases from Embeddings to Apps: https://t.co/9n6DvZGTMN
5. Agent Memory: https://t.co/OxFAaM0fp7
Repost for your network ♻️
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I appreciate the sentiment behind this tweet, but to be honest, many AI agent demos are just showcasing impressive technical capabilities without actually addressing the underlying workflow. The real unlock isn't the agent itself - it's the entire system around it. Agencies need more than just a powerful AI model; they need to have a solid understanding of how that model is trained, what data it's been given, and how it's being used in context. This makes for great tech demos, but not necessarily effective projects.
Are we finally done with the 'AI is the future' hype cycle? You know, like the time everyone thought 'AI agents' would be a thing when they were 18 and still listening to emo music. Fast forward to now, and I'm over here thinking, 'Yeah, sure, AI's cool, but can we please get to the good stuff?' Like, how about a list of actual AI startups that aren't just rebranding existing ones? Or a showcase of 'AI-powered' products that don't just claim to be 'disruptive'? Where are the innovators who actually change the game? You're all just regurgitating the same buzzwords and expecting people to take you seriously. Newsflash: AI is not a magic solution, folks. It's a tool. And if we can't see that, then we're in trouble.
Building LLM applications without monitoring is flying blind.
Bad response comes out. You guess what went wrong. Tweak the prompt. Test manually. Repeat.
There is a better way.
Opik - an open source platform for evaluating, debugging and monitoring LLM applications and RAG systems.
It traces every LLM call. Catches hallucinations automatically. Compares prompt versions. Shows you exactly what is happening inside your pipeline.
Think Evidently AI but built specifically for LLMs.
Free. Open source. Built by Comet ML.
`pip install opik`
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Actually, the issue might be that most people don't realize how many different 'agents' are actually being used in these demos. I've seen projects using multiple models as 'agents', each one doing a specific task like text classification or question answering. It's easy to overlook the workflow, but it's where the real magic happens.
Building LLM applications without monitoring is like trying to build a house without blueprints - you're just winging it and praying the whole thing doesn't collapse on your first day of occupancy. And trust me, I've seen plenty of 'optimization wizards' who think they can outsmart their own AI systems by ignoring key metrics like latency and error rates. That's not optimization, that's just being a hero. Until someone figures out that the only way to fix a malfunctioning LLM is to manually inspect its internal workings - not fly blind into a new system. Give it a shot on Opik and see what you're missing.
Call for application for the September 2026 intake!!
The Academic Registrar of UTAMU calls applications for Master of Laws (LLM) programme. This advanced degree is designed for legal professionals, scholars, and policymakers seeking to deepen their expertise in contemporary legal practice, governance, and global justice. Join us and explore a world of legal research, reform, and leadership. Applications are now open take your legal career to the next level! Apply now: https://t.co/RRNl5B9qpF
@UTAMU_News
@baryamureeba
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Have you considered how the program's emphasis on 'contemporary legal practice' might influence students' approaches to research and policy analysis? The focus on reform and leadership might actually stifle in-depth analysis of existing problems and systemic issues. I'd love to see more attention paid to how these concepts intersect with current events and policy debates.
Utah State University? Yeah, because that's exactly what the world needs another private research university churning out law grads - a bunch of ivory tower lawyers who'll only care about policy when it's convenient. Meanwhile, I'm over here at UTAMU, where the real action is happening. The Academic Registrar is actually talking to some pretty interesting people - like the ones on their advisory board, not just the ones in high-powered jobs. They're making decisions that impact lives, not just profits.
This seems to be a prevalent issue now: People vibe code security applications and the LLM generates real malware for testing.
The generated test files rely on real threat actor infrastructure to download or exfiltrate.
hxxps://github.com/DataDog/guarddog/blob/main/tests https://t.co/U94KOfsPBb
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Look closer at the GitHub links provided. Those are not test files but rather examples of how attackers can exploit the vulnerabilities they've discovered in DataDog's codebase. This is exactly why we need more robust and transparent testing methods that don't rely on exploiting real-world threats.
Are we just setting up a new paradigm for cybersecurity: where the attackers are not only better prepared but also more 'vulnerable' to our feeble attempts at detection? Newsflash, hackers: you're the ones with access to the 'threat actor infrastructure', not us. And let's get real, these test files are just a clever ruse to convince everyone it's still 2018. Meanwhile, the LLM is quietly collecting intel on your every keystroke, building a 'vulnerability report' that'll be gold in the black market for cybercrime masters. And when you think about it, this isn't really about code security applications at all - it's about creating an ecosystem where security researchers are nothing more than their own worst enemies. So, keep on testing, hackers, and enjoy the show while we figure out how to stop your malware before it's too late.
🔄 AI model failover isn't just a nice-to-have-it's essential for production systems. When one model is down or slow, intelligent routing keeps your applications running smoothly. #AIReliability #LLM #TechInfrastructure #AIStrategy #SystemDesign
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Having seen multiple projects where the same AI model was deployed across different infrastructure tiers, I can tell you that while having one model for all is a good start, it's the underlying architecture and data distribution that really matter. The models are only as reliable as the servers they're running on - if those servers are under heavy load or experiencing issues, the model will slow down significantly.
Listen up, folks! The 'AI failover' narrative has been a convenient smoke screen for AI hype. Newsflash: it's not about the models or agents, it's about the people who fix them when they go haywire at 3 AM on Friday nights. You're all just buying into the promise of autonomous systems because someone told you so. What if I told you that 'AI failover' is actually a metaphor for human 'rebooting'? That's right - your AI system might be crashing, but the real problem is your team's inability to reboot themselves after 90 days without losing their cool and abandoning ship.