Curious about vibe coding? Here’s a concise instruction on how to vibe code properly.

Get the "Vibe Coding Guide" poster here: https://swetlanaai.com/2025/04/25/vibe-coding-the-ultimate-guide/

Oh, and I wrote a book. "Vibe Coding: Build Without Thinking" is out now. It's here: https://www.amazon.com/dp/B0DWHKDHS9

Vibe coding isn’t just about building software—it’s about creating momentum, shipping fast, and thinking with AI as your co-pilot. It’s an iterative, intuition-driven process that combines real-world problem solving with creative flow.

It doesn't matter which tool you're eyeing for this - Replit, Cursor, Windsurf, Github Copilot or others. Just don't forget version control. I've seen cases where Cursor had ruined months of work by overwriting existing code.

This guide breaks down the essential steps for turning ideas into functional products using AI tools at every stage. Whether you’re a beginner or a solo founder, vibe coding lets you skip the boilerplate and focus on shipping something useful, as fast as today.

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During the 1983 Soviet nuclear false alarm incident, Lieutenant Colonel Stanislav Petrov was the duty officer at the command center for the Oko nuclear early-warning system. On September 26, 1983, the system reported that five missiles had been launched from the United States.

Petrov judged these reports to be a false alarm.

This judgment is credited with having prevented an erroneous retaliatory nuclear attack on the United States and its NATO allies. Had Petrov reported incoming American missiles as a real attack, his superiors might have launched a nuclear assault against the United States, which could have precipitated a corresponding nuclear response from the United States, likely resulting in a large-scale nuclear war.

Petrov's decision to disobey orders and go against Soviet military protocol by not immediately reporting the supposed missile launch was crucial. According to the Permanent Mission of the Russian Federation to the UN, Soviet nuclear retaliation required confirmation from multiple sources. Petrov decided to wait for a confirmation that never came. Ground radar also failed to pick up corroborating evidence.

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"Noise: A Flaw in Human Judgment," by Daniel Kahneman, explores the pervasive issue of unwanted variability in judgments, distinct from bias, across various professional domains like criminal justice, medicine, and hiring. The authors examine how noise leads to errors and inconsistencies, highlighting its often unnoticed and costly impact on organizational decisions. The book investigates the sources of noise, including occasion noise and pattern noise, and discusses strategies for noise reduction, such as structured judgment protocols and the use of algorithms, while also considering objections and limitations to these approaches. Ultimately, it advocates for a greater awareness of and proactive measures against noise to improve the reliability and fairness of human judgments.

#philosophy #bookreview #danielkahneman

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So there is this new paper by Anthropic:

https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf

... according to it, AI isn't always faithful in its chain of thought process. This is what we're discussing in this episode.

#ai #artificialintelligence #anthropic

Some background:

Chain-of-Thought (CoT) in AI reasoning models refers to the explicit articulation of the steps and reasoning process that a model undertakes to arrive at a final answer. When a model uses CoT, it not only provides the answer but also shows the often intricate and detailed way it reached that answer. This is seen as a potential advantage for AI safety researchers because it allows for the inspection of the model's reasoning, which can help in identifying undesirable behaviors such as deception by checking for discrepancies between the stated reasoning and the final output.

However, a crucial question arises regarding the trustworthiness of the information presented in the CoT: can we be certain that it genuinely reflects the model's actual thinking processes? In an ideal scenario, the CoT would be both legible (understandable to humans) and faithful (an accurate description of the model's internal reasoning).

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Today’s episode is all about MCP - Model Context Protocol.

Here are the main sources we’ve used:

https://www.anthropic.com/news/model-context-protocol

https://a16z.com/a-deep-dive-into-mcp-and-the-future-of-ai-tooling/

https://huggingface.co/blog/Kseniase/mcp

https://modelcontextprotocol.io/

What is MCP?

The Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to Large Language Models (LLMs). It is designed to address the problem of fragmented and inefficient integration between AI models and the external world, including data sources, tools, and APIs.

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"Dopamine Nation: Finding Balance in the Age of Indulgence" by Dr. Anna Lembke explores the relationship between pleasure and pain and how to find a delicate balance between the two in our modern world of overwhelming abundance.

The central argument of the book is that unprecedented access to high-reward, high-dopamine stimuli such as drugs, food, news, gambling, shopping, gaming, texting, sexting, and social media has made individuals vulnerable to compulsive overconsumption. Lembke uses the metaphor of the smartphone as a "modern-day hypodermic needle, delivering digital dopamine 24/7 for a wired generation" to illustrate this constant stimulation. The book delves into the neuroscience of reward, explaining that dopamine acts as a "universal currency" for measuring the addictive potential of any experience. Furthermore, it highlights the crucial discovery that the brain processes pleasure and pain in the same place, functioning like opposite sides of a balance.

"Dopamine Nation" elucidates how the relentless pursuit of pleasure leads to pain. The book explains that repeated exposure to pleasurable stimuli leads to neuroadaptation or tolerance, where the initial pleasure diminishes and the subsequent pain (the after-reaction) intensifies and lengthens, potentially resulting in a dopamine deficit state where individuals struggle to experience joy from simple pleasures. This concept is summarized in the highlighted quote noting that "the paradox is that hedonism, the pursuit of pleasure for its own sake, leads to anhedonia, which is the inability to enjoy pleasure of any kind".

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In episode 114 we've been discussing DeepSeek's R1 model, which uses GRPO:

https://youtu.be/D0w44TGNsUs

So, what is GRPO?

GRPO stands for Group Relative Policy Optimization.

It is a reinforcement learning (RL) algorithm developed by the creators of the DeepSeek reasoning model R1. GRPO is designed to enhance the reasoning capabilities of AI models. It was first introduced in the DeepSeekMath paper and was also used in the post-training of DeepSeek-R1.

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Today we're talking about ways to connect the brain to a computer.

Our sources:

https://builtin.com/hardware/brain-computer-interface-bci

https://www.gao.gov/products/gao-22-106118

https://pmc.ncbi.nlm.nih.gov/articles/PMC3497935/

The provided sources discuss Brain-Computer Interfaces (BCIs), which are systems that translate brain signals into commands for external devices, bypassing traditional neuromuscular pathways. They explain the definition and core components of BCIs, including signal acquisition, feature extraction, feature translation, and device output. The texts highlight various applications, particularly in restoring mobility and communication for individuals with neurological disorders, with examples ranging from controlling robotic limbs and wheelchairs to enabling "mindwriting" for non-verbal patients. Both sources differentiate between invasive and non-invasive BCI types, detailing their respective methods and suitable use cases, and acknowledge the challenges in widespread adoption, such as regulatory approval, cost, and the crucial need for improved reliability in real-world settings.

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Anthropic's research (https://www.anthropic.com/research/tracing-thoughts-language-model) explores the inner workings of large language models like Claude, employing novel "AI microscope" techniques to understand their problem-solving strategies.

Their investigations reveal surprising insights into how these models process language across multiple tongues with a seemingly universal "language of thought," plan future text such as rhymes, and sometimes fabricate reasoning despite appearing logical.

By dissecting the models' internal computations, the researchers aim to distinguish genuine reasoning from fabricated explanations, understand the mechanisms behind multi-step thinking and hallucinations, and identify vulnerabilities to jailbreaking attempts, ultimately striving for greater transparency and reliability in advanced AI systems. This work contributes to a deeper understanding of AI "biology," revealing complex internal processes that are not always apparent from the models' outputs.

Here's Anthropic's paper: https://transformer-circuits.pub/2025/attribution-graphs/biology.html

#llm #anthropic #ai

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