Tried ChatGPT, Claude, Gemini, DeepSeek… and still can’t decide which one to stick with? Here’s the thing: that’s the wrong question. There is no universally best AI — only the one that fits what you’re doing right now. What’s your scenario? “I want a general-purpose assistant for everything” → ChatGPT. As of May 2026 the default is GPT-5.5 — well-rounded, with the richest plugin ecosystem. If you pick just one, this is a solid choice. ...
TL;DR: Anthropic’s alignment research shows that teaching a model why works better than teaching it what — misalignment dropped from 22% to 3%. This post breaks down four experiments and distills three lessons you can use in prompt design. I ran an A/B test comparing two prompt strategies. One group got positive examples — “do it like this.” The other got no examples. Instead, the AI had to explain why a choice was correct before acting on it. ...
Ever notice your AI suddenly ignoring something you said ten minutes ago? Or opened a fresh chat and had to explain your entire project from scratch? Here’s why. Your AI actually has two kinds of memory, and understanding both changes how you work with it. The Desk: Working Memory Working memory is everything inside your current conversation. Think of it as a desk — limited surface area. A few documents fit comfortably. Stack too many, and older pages slide right off the edge. ...
Last time we covered a foundational idea: LLMs generate probabilistically. They don’t look up answers — they think them through fresh each time. That means response variance is normal, and you need to verify. Easy to understand. Harder to act on. The habit is sticky: open ChatGPT, type a phrase, grab the answer, close the tab. This post isn’t a tutorial. I picked three real scenarios to show what actually changes when you use AI differently. ...
Today’s Challenge Open whatever AI you normally use — ChatGPT, Claude, DeepSeek, anything. Pick an open-ended question. Ask it 3 times. The question is up to you. Some examples: “How do I build a reading habit?” “Python tips for a complete beginner” “How can I run better meetings?” You can rephrase it each time or paste the exact same wording. The key rule: start a fresh conversation each time. Don’t follow up inside the same thread. Three new chats. ...
📖 This is Part 1 of 5 in the “AI Path L0→L1 Upgrade Guide” series. Series navigation will be updated once all parts are published. Introduction: Sound Familiar? I’ve watched a lot of friends use AI tools, and I keep noticing the same pattern. They’re not strangers to ChatGPT or Claude—they use them casually from time to time—but their experience is wildly inconsistent. Sometimes the AI delivers a jaw-dropping answer; other times it completely misses the point, producing something unusable. ...
TL;DR: How does a person grow with AI? This post maps the journey from “opening a chat box for the first time” to “thinking in AI-native ways” across five stages—First Contact, Power User, Engineer, Architect, and Native. The essence of each stage isn’t learning more tools, but a shift in mindset: from passively accepting outputs, to actively designing inputs, to orchestrating multi-agent collaboration, and ultimately reshaping your own cognitive framework. The interactive path map at the end lets you explore each stage in full detail. ...
TL;DR: git rebase / checkout silently deletes untracked files in .gitignore, with no recovery; git stash -u does NOT stash git-ignored files. The fix: use git worktree to create a local-assets branch, storing design docs in a git-tracked safe space. Three commands handle daily use: dp-save.sh to save, --prune to clean, --restore to recover. Real project data shows zero document loss after introducing worktree. Full script at alexwwang/design-doc-worktree. One afternoon I had AI run git rebase -i to tidy up the last dozen commits. No conflicts. Clean terminal. Everything went smoothly. ...
TL;DR: Before releasing Aristotle v1.1, I found 18 bugs. Unit tests caught four (22%). The other 14 lived at the integration layer — component wiring, config propagation, process startup seams. Root cause analysis revealed six patterns: path/environment mismatch (5), registration omission (3), startup hang (2), silent failure (2), test-production path divergence (2), integration seam errors (4). The root cause isn’t harder problems — it’s AI bypassing the defenses that experience built. Implementation and review rhythms decouple, code appearance misleads quality judgment, and integration shifts from an explicit action to an implicit assumption. Includes an eight-dimension integration checklist and a 16-type bug roadmap at the end. ...
TL;DR: I switched from OMO to SLIM and ran it for 13 days. Average Tokens per message dropped 3.7% — practically flat. Broken down by task type: coding flat, writing +61%, review -53%, debug +121% (unreliable, tiny sample). Aristotle dropped 68%, but the main cause was an architecture rewrite, not the plugin. “Saving tokens” is not a global fact. It’s local. The real differences are in experience and architecture choices, not in token counts. ...