Beyond 30‑Second AI Hype: Deep Story Videos
Most AI stories are told in 30 seconds. That is exactly why so many people misunderstand them. Lately, I have been seeing the same pattern again and again: viral clips, narrow takes, confident opinions... and almost none of the actual context. Then people ask me about those stories. In DMs. In conversations. Even in TV interviews. And too often, the real story is much more nuanced than the headline. So I started a new kind of project. Instead of only explaining tools, techniques, or papers, I am now building deeper story-driven videos on the AI topics that deserve more than a hot take. Not just: "What happened this week?" But: What actually happened over the last 1-2 years? Who pushed it there? What got misreported? What changed? And what should we actually understand before repeating the hype? I already released 3 of them, and I have more coming: * the story behind distillation accusations * the Anthropic codebase leak * the Anthropic vs U.S. government showdown Next week, I am releasing one on Google: how they went from looking early and promising in AI, to scattered and underused, and then back to becoming one of the strongest players again. This new format feels closer to the kind of work I want to be known for. Also, after being on this road for (6) years, 100K on YouTube finally feels possible. We are roughly 30% away. If you want AI coverage that goes beyond the hype and gives you the full picture, subscribe here: https://t.co/F58sOblkbB What AI story do you think is being misunderstood the most right now?
Prompt with “Think Hard” To Unlock More Model Reasoning
Tip of the day. In the end, Claude is just like us! It wants to impress its peers 😂 Honestly, it's surprising, but such prompts can actually truly help models perform better. I often, non-sarcastically, use sentences like "think hard on this one"...
Model Distillation Makes AI Cheaper, Shifts Competitive Moat
Everyone is accusing everyone of “stealing AI” But almost nobody is explaining what’s actually happening. Distillation. → Query a stronger model at scale → Collect outputs (reasoning, code, decisions) → Train your own to imitate it No weights. Just behavior. This worked in 2023 for $600. Replicating...
Claude's New Prompt Length Limit Frustrates Users
I don't know what Claude did to Cowork's system prompt, but God, that's annoying. Some skills it could always do, but now it keeps on saying "are you sure, because it is too long to do?" However, you prompt it. I understand...
Subscriber’s Gratitude Validates Free AI Resource Model
Someone just sent me this after subscribing to my newsletter... Honestly, this quite hit me. When I started this newsletter, the goal was simple: Make AI more accessible. One place to share everything: videos repos courses presentations workshops ... No noise. No hype. Just useful and often **against conventional wisdom** content,...
AI Can't Fix Bad Targeting; Prioritize Relevance First
The hardest part of AI is not about writing the perfect prompt. The hardest part is knowing who you’re actually talking to. AI won’t fix bad targeting. It just helps you scale it faster. Automated “AI-generated outreach” fails for the same reason this text...
Embeddings vs Latent Space Explained Simply
The video clarifies the distinction between embeddings and latent space in modern AI models. Embeddings are concrete vectors—lists of numbers—that encode textual data for external tasks such as search, clustering, or retrieval‑augmented generation. By contrast, latent space refers to the...
Simplify Agent Architecture: Choose Workflow Over Multi-Agent
You don't need to overcomplicate your Agent Architecture. Do you also jump to multi-agent when a simple workflow would do the job faster, cheaper, and with far less debugging? I made a free Agent Architecture Cheatsheet to help you decide: - Workflow vs...
One Tool‑enabled Agent Beats Overcomplicated Multi‑agent Setups
A client asked us to build a multi-agent system for their marketing chatbot. They had the whole thing mapped out. One agent for planning. One for retrieval. One for generation. One for validation. A full squad.😅 I'll be honest, it looked good...
Add Guardrails, Not Just Prompts, for Better AI
I let Claude Code loop for 45 minutes while I was at the gym. Came back. It told me the feature was done. It wasn't. It hadn't even run the tests. Not because the model is dumb. Because I wrapped it in nothing but...
Why Fine-Tuning Won’t Fix Your Company Data Problem
The video explains why fine‑tuning a large language model is the wrong remedy when it hallucinates about internal company data. While fine‑tuning adjusts the model’s parameters and can teach tone or high‑level domain expertise, it does not guarantee that the...
Pentagon's Access Demand Leads to First US AI Blacklist
The US government blacklisted an AI company for the first time in American history. Anthropic was already deep inside classified systems. Then the Pentagon demanded unrestricted access. Anthropic said no. Got labelled a national security risk. OpenAI rushed in with their...
Autonomous Agents Won’t Worsen AI Bias Despite Added Capabilities
A lot of people have the same instinctive reaction when they hear about autonomous agents: if AI models already have biases, then giving them memory, tools, long-term planning, and the ability to act should obviously make the problem worse. That sounds...
Will AI Agents Make Bias Worse?
The video asks whether increasingly autonomous AI agents will magnify existing biases, using a hiring‑assistant scenario to illustrate the stakes. It clarifies that bias in large language models is simply statistical reflection of training data, not a moral choice, and that...
LLMs Reward Quality, Replace Quantity-Driven Mediocrity
LLMs are not making expertise less valuable. They are making mediocre work easier to replace. Quantity is cheap now. Judgment, taste, and direction are not. We need fewer people. But we need better ones. To be clear, I am not saying beginners are doomed. I am saying...