#llm

Discover how the normalization of deviance threatens AI systems (or why companies gradually accept risky shortcuts)

How Gemini 3 and Google AI Studio revolutionize prototyping.

Elena Verna critiques AI credit pricing models and urges product teams to rethink how they charge for AI-powered features.

First impressions using Codex CLI with Agents.md and PRDs to speed product work and code experiments without another subscription.

Geoffrey Litt compares effective coding with AI support to a surgeon working with a skilled team, staying hands-on with the core.

Reflecting on the business of cleaning up AI-generated code and why vibe-to-production services are becoming lucrative.

A roundup of my July–August rabbit holes: AI tools, odd ideas, and quick notes captured before they vanish.

Michael Bassili laments losing em dashes to safety filters and how AI tooling reshapes writing habits for bloggers.

Bruce Schneier argues we still lack defenses against malicious LLM inputs and outlines why current security approaches fall short.

Building a tag manager component for a blog CMS using ChatGPT for UI prototyping and Cursor for implementation, with LLM-powered tag suggestions.

Highlighting Anu Atluru's take on doomprompting—how short, lazy prompts make us passive creators and duller conversationalists.

Testing GPT-5 in Cursor to ship version history features quickly, with thoughts on speed, accuracy, and AI-assisted coding.

Explaining slopsquatting—the tactic of registering fake packages that LLMs hallucinate, priming supply-chain attacks.

Orta Therox frames AI-assisted coding as programming's photography moment, where new tools reshape craft rather than replace it.

Vincent Schmalbach personifies his LLMs like quirky interns, comparing ChatGPT, Claude, Gemini, and Grok personalities.

Notes from May–June explorations: AI agents, security quirks, regulation chatter, and context engineering experiments.

Simon Willison's identify–solve–verify mantra on why humans remain essential to guide, debug, and validate LLM-generated work.

John Rush shares how he builds a personal AI factory with Claude Code, MCP, and agents, mirroring my own coding workflow.

Simon Willison outlines the lethal trifecta for AI agents—private data, untrusted content, and external communication risks.

Devansh argues fine-tuning LLMs is destructive overwriting. Use RAG, adapters, or prompt engineering instead.

OpenAI's o3 model can identify photo locations—a powerful but dystopian capability that raises serious privacy concerns.

Drew Breunig on how AI is flipping the script: coding is becoming commodified while domain expertise becomes the real differentiator.

Exploring the security risks of MCP and why it may not be production-ready. Key vulnerabilities include shell access and secret exposure.

How LLMs are empowering idea people to build and prototype without traditional coding skills—the future of rapid iteration.

Anthropic's new economic index tracks how LLMs impact the economy and labor market, providing data for evidence-based AI regulation.

The irony of OpenAI complaining about data theft when it built its company on unauthorized data collection—plus an intro to model distillation.

ChatGPT is now surprisingly good at history, noticing details that even experts miss—like having your own Benjamin Gates.

Simon Willison reveals ChatGPT Tasks system prompt by getting the model to output its internal scheduling instructions.

Using chain-of-thought prompting to control LLM responses in constrained game environments and prevent unwanted associations.

Why dismissing LLMs as 'just next-token predictors' misses the emergent intelligence, reasoning, and creativity they develop.

Building a flight tracking web app with ChatGPT and Claude—lessons on AI-assisted coding, prompt engineering, and iteration.

Experiment: can iteratively asking LLMs to 'write better code' actually improve output, or does it lead to over-engineering?

Simon Willison's comprehensive review of key LLM developments, breakthroughs, and lessons learned throughout 2024.

John Gruber compares OpenAI to Netscape: leading product but no durable competitive moat in an increasingly commoditized AI market.