In AI for the Ride

After getting burned yet again when the money I prepaid to Anthropic expired before I could finish it, I decided to shift all my API usage from “depositing directly with model vendors” to “using model aggregation platforms.” I already had an account with OpenRouter and had used it lightly, but OR offers far too many messy models, and the provider settings are complicated—if you’re not careful, traffic can flow to niche providers and cause unexpected headaches. By comparison, ZenMux’s interface is cleaner, its model list is clearly curated, and the suppliers appear to be only the official ones. I expect to focus on it for a while.

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Nix is a package manager built on the “purely functional” philosophy. It makes every dependency explicit, keeps every build reproducible, and stops environments from polluting each other. Even better, declarative configuration lets you sync system environments, development tools, and user-level software across multiple machines, truly achieving “write once, consistent everywhere.”

I used to rely on Mackup to keep configurations in sync, but it has been heavily restricted on newer systems and even loses configs outright. That pushed me to reassess how quickly I should prioritize Nix. The syntax and onboarding curve were what held me back from a full migration before, yet now the situation has changed: I can describe what I need in natural language and have AI write the nix configuration—or even the entire flake—for me, virtually eliminating the learning curve.

Combined with Nix’s deterministic nature, switching devices, reinstalling a system, or moving my environment somewhere else comes down to a single idea: copy and done.

I’ve bounced between quite a few note-taking apps, yet finding one that is local-first, visually polished, and deeply integrated with AI is surprisingly hard. Before returning to Obsidian again, I spent about two years with Craft.do. They really pushed Mac Catalyst to its limits, but the sluggish AI updates still made me grit my teeth and walk away. In the AI era, the best way to organize notes is back to markdown files; working with files directly lets agents reach their full potential. I pair Obsidian with Infio, which weaves my notes together into a genuinely connected knowledge base.

You could replicate something similar with VS Code plus any agent, but Obsidian offers a more complete ecosystem: the visual styling notes need, backlinks, and more. The mobile version of Obsidian is much weaker, so when I’m away from my desk I switch to Termius to SSH into my home server and work on notes with a command-line agent. It also saves me from worrying about synchronization.

For those repetitive and tedious tasks that still need human input, letting AI take over is the perfect use case.

A Kingfisher release usually spans multiple PRs and issues, and manual curation easily misses fine details; double-checking while crediting the right contributors is also a chore; to top it off, naming each release can keep me stuck all day. With this workflow, AI first lists every change on master since the previous tag, then parses PR descriptions to summarize features, fixes, and contributors. That frees me up to focus on reviewing and adding insights instead of gathering information from scratch.

To improve accuracy, I include one or two sample outputs in the prompt so the AI can populate the new entries in the change log file by analogy. As long as the instructions clearly state “don’t modify the YAML keys” and “each entry must include links and contributors,” the AI consistently produces release notes that satisfy the release script. I also ask it to explain how it derived the version number (for example, why it is a minor or a patch release), so I can quickly validate the Semantic Versioning decision during review. Combining these strategies turns the once tedious release prep into a relaxed catching-up session.

As a tech worker who grinds away at routine tasks every day, I lack systematic learning in economics and investing, and my instincts in these areas are rather dull. When I read economic materials—blogs, long-form pieces, commentary—I often struggle to grasp the key points or understand the underlying essence. The arrival of AI has filled the awkward gap of having no one to consult as a newbie. I recently subscribed to Stratechery, skim the articles, hand them to an LLM, and spend about ten minutes in dialogue to dig into the essay and its logic, which has been immensely helpful.

Most LLMs perform well at summarizing articles and suggesting investments, but their behavior in discussion varies. Sonnet is easily swayed into agreement mode; ChatGPT tends to wander, often using rhetorical questions that lead the topic astray; Gemini seems more professional, yet at times carries a hint of emotion. Overall, ChatGPT is the most well-rounded for this kind of task. The more varied my usage, the more these models feel like people: each has its own personality and strengths, making the experience quite fascinating.

A developer’s true limit is input speed! In the age of AI, I can finally rely on voice input to handle every task however I like: I use VoiceInk or similar tools for on-device dictation, then pass the transcribed text to an AI according to the app I am in, pairing it with the right prompt for secondary processing (for example, using “Developer Voice Command Processing” in Codex or Claude Code, or letting Slack auto-translate my Chinese into something my teammates can read). These workflows are simple, but they dramatically boost input efficiency and remove the final obstacle to working in true multitasking mode.

The speed of secondary processing is critical, and the speed-focused Groq currently feels like the clear choice. Model-wise, I prefer Kimi-2. Although the two GPT OSS models generate tokens faster in absolute terms, they are inference models, so their real-time performance on tasks like this is actually worse than straightforward, non-inference models such as Kimi. For my everyday usage, Groq’s personal plan is essentially free, which is wonderfully comfortable.

A self-hosted n8n instance can really boost happiness. I run one on my home server, where a scheduled task triggers every morning to fetch the weather for today and tomorrow along with calendar of all family members. Then, with AI integration, it generates clothing suggestions and a daily schedule, wraps it up with a warm greeting and a short motivational note, and finally delivers it to my all my families via Bark — the perfect way to start the day.

That’s right — the website you’re looking at is a pure product of vibe coding. I didn’t write a single line of code. Modern programming agent AIs handle frontend tasks with remarkable ease, and Codex offers one of the best cost-performance ratios today. From planning to implementation, it has practically taken care of everything.

After a few vibe coding projects “from-scratch”, I’ve realized that clarifying requirements before actually starting might be the key to success. Take the very first commit of this project as an example — this kind of “plan-driven development” could well be the go-to paradigm for kicking off new projects in the AI era (and of course, that plan itself was also generated by an LLM).