Tim Schipper’s Post

Mastering the AI Revolution: From ANI to ASI – A Technical Deep Dive with RAG, Agents, and Vibe Coding + Video Introduction: Artificial Intelligence is no longer a futuristic concept—it’s embedded in our daily tools, from Siri to ChatGPT. Dr. Shlomi Boutnaru’s Artificial Intelligence Journey v4.0 (June 2025) breaks down the AI ecosystem into actionable technical concepts, covering everything from the Machine Learning lifecycle to the rise of AI Agents and Vibe Coding. This article extracts the core technical insights, adds hands-on tutorials, commands, and security considerations to help you master the AI stack....

AI Agents aren't magic. If you look behind the curtain, agency is not a new breakthrough in AI intelligence. It is almost entirely about the engineering of the harness around the model. I have spent some time demystifying how AI agents actually work, including a functional multi-step agent in just 50 lines of Python. The benefits are real. But we need to understand where the risks lie. The major risk is the systemic debt we accumulate when we prioritize short-term velocity over long-term cognitive clarity and security. How do you ensure you are balancing the speed of AI deployment with long-term system health? I wrote a gentle introduction and thoughts on the "Good, Bad, and Ugly" of agentic AI and would love to hear your thoughts. https://lnkd.in/dmbjNZHz #AgenticAI #SoftwareEngineering #RiskManagement

What happens when you build the same project twice, once without AI and once as a "vibe coder"? This dev measured the time, code quality, and something harder to quantify: the stress of not owning the result. { author: Fernando Fornieles } https://lnkd.in/egsngJgK

Chinese DeepSeek AI released V4 Preview as an open-source model family with a claimed 1M-token context window. Long-context models are becoming dramatically cheaper and more widely distributed. OpenAI, Anthropic, and Google now face even more pricing pressure at the lower and mid tiers as they compete with a parallel AI stack emerging outside U.S. control. DeepSeek AI is not just a model competitor. It is a pressure mechanism on the entire Western AI margin structure. If good-enough long-context reasoning becomes cheap, application defensibility must come from workflow, data, and distribution. So anyone building the new rule of thumb is to use frontier models where judgement matters and use cheaper/open models for intake, extraction, classification, summarisation, and retrieval. https://lnkd.in/dDCyZ-Tb

If you haven’t heard of it yet DeepEval helps teams test LLM applications across things like hallucination, correctness, answer relevance, RAG behavior, and agent behavior. AI systems can look strong in a few examples, but the harder question is whether they behave consistently across many examples and edge cases. I can see this helping AI teams that want evaluation to become part of the build process. DeepEval: https://deepeval.com/

I spent some time writing AI skills at work. Turns out free text is not the best way to get reliable behavior from an agent. Who could have guessed 🙃 During this process, I learned a lot about the AI ecosystem, and how developers can make agent behavior more reliable by treating skills more like code: I wrote down my developer workflow: https://lnkd.in/eau8w_G9

I got tired of AI tools that sound confident but don’t actually check themselves. You ask something → they respond → and you still have to verify it manually. That defeats the point. So I built Riley AI. Not to be “smarter”… but to be more reliable: - use tools when needed - verify outputs - avoid unnecessary reasoning It’s a small shift, but it changes how you trust the system. Open-source if you want to try it: https://lnkd.in/e_qJ4BHZ

DeepSeek just dropped its V4 Preview with 1M context as the new default, plus open weights and API availability today. The interesting bit is how aggressively they’re pushing long-context efficiency into something practical. Worth a look. #AI #LLM #DeepSeek #GenAI

We built the same multi-agent pipeline in five leading AI frameworks. Only two completed the test without human intervention. LangGraph, CrewAI, AutoGen, OpenAI Swarm, and LlamaIndex Agents all promise to make agent development easier. But when we put them through identical real-world tests, the results were striking. Three frameworks failed to complete basic tasks autonomously, exposing a gap between marketing promises and production reality. Our investigation reveals which frameworks actually ship reliable agents and which ones leave developers debugging edge cases. If you're betting your product roadmap on AI agents, this comparison could save you months of technical debt. Which agent framework have you tried? What surprised you most about its limitations? #AIAgents #LLM #MachineLearning #SoftwareEngineering #AI

MIT has done the unthinkable. They built an AI that doesn't need RAG, and it has perfect memory of everything it's ever read. It's called Recursive Language Models (RLMs). Right now, if you want an AI to analyze a massive dataset or document, you have two bad options. You either stuff it all into a giant context window, where the AI gets confused and suffers from "context rot." Or you use RAG to chop it up into summaries, permanently deleting the nuance. This paper replaces both. Instead of forcing the AI to read a giant prompt in one pass, RLMs treat long documents as an external environment. The AI is placed in a sandbox. The data is stored as a Python variable. When you ask it a question, the AI doesn't just blindly try to remember the answer. It writes code to actively search, slice, and filter the document itself. Then, it recursively spawns smaller "sub-AIs" to read specific snippets in parallel. It never summarizes. It never deletes data. It preserves every single piece of original context. The results rewrite the limits of AI memory. It successfully handles inputs up to two orders of magnitude beyond normal context windows, scaling easily to 10 million+ tokens. On the hardest long-context reasoning benchmarks, a standard model scored a dismal 0.04. The RLM architecture hit 58.00. All while costing less than running a standard massive prompt. We’ve spent the last two years burning millions in compute trying to build bigger and bigger context windows. But the future of AI isn’t about forcing a model to swallow a giant wall of text. It’s about teaching it how to read. link -: https://lnkd.in/gdvg_JeS