Learning Notes
AI tools, certifications, books and small experiments, written down while learning. 45 posts.
![[AI in Practice] Before You Install a Stranger's Code, Let AI Read the Source First](/covers/read-the-source-before-you-install.png)
[AI in Practice] Before You Install a Stranger's Code, Let AI Read the Source First
A 60-star file-transfer tool with no license. I had AI read all 280KB of its source in about five minutes, got four things to switch off or avoid, then checked three of its claims myself. Includes the questions you can ask AI before installing any small tool.
New Book Club: How Many Monkeys Were in the Room? Reading William Brody's Uncommon Sense
First pick for the New Book Club: Uncommon Sense by former Johns Hopkins president William R. Brody. The chapters I marked most are about luck: how an 'AI stock tips' email turned 64,000 strangers into 500 believers, why 1,000 fund managers will throw up about 31 geniuses, and the time my own test got fooled by random numbers. An educational reading note, not investment advice.
What Happens After the Quota Runs Out: CodexBar Reads It, a Regency Protocol Hands Over, and the Schedules Slim Down
The last piece showed how one line of quota readings sets the daily roster for four AI subscriptions. This one is about what happens when the quota actually hits zero: who takes over, what the caretaker is allowed to touch, how control comes back, and how the scheduled jobs slimmed down to match. Includes the GitHub repo for the regency protocol and five things I only learned by paying for them. Technical notes, not investment content.
The Beginner's herdr Handbook: If You Just Started with Claude Code or Codex, Here's How to Put Several AI Agents in One Window
Readers who just started with Claude Code or Codex kept asking how herdr works. This assumes you've only ever opened a terminal and have no feel for folders or config files yet: five terms in plain words, one install path, three keystrokes for the first session, and a second agent opened from the keyboard alone. Scripting sits at the end and can be skipped. A technical tutorial, not investment content.
No One Is Coming: Reading Crisis Engineering, Then Checking Whether My Stocks Are Actually in Crisis
Three engineers who rescued HealthCare.gov wrote a field manual for crises. The most useful part for me wasn't how to fight fires. It was their five indicators for deciding whether you're in a crisis at all. Six of my holdings are down by half, so I ran each one through the five boxes. Educational reading notes and extensions, not investment advice.
Take Over Your AI Agents from Anywhere: Tailscale + Herdr + Moshi, Step by Step
Fred's method: three replaceable components that bring the AI agent windows running on your computer straight to your phone. A Windows laptop installation guide, including two gotchas and how to fix them, plus a comparison with the UU Remote setup popular on X.
The Chip in Your Mac Nobody Looks At Just Made AI Inference 1.8x Faster: Xiaotian's Test, and the Number I Ran on Windows
Xiaotian spent a week on an entry-level Mac mini and cut video-to-vector indexing from 127 seconds to 70, using a part of Apple's chip most people never notice: the Neural Engine. I put his numbers next to the same kind of job running on my own Windows machine to answer a more practical question: when your computer is slow at AI, do you swap the machine or swap the model? Educational notes and extension, not a buying guide.
Does an AI Only Behave When You Swear at It?
A Chinese hardware YouTuber says swearing at AI works: DeepSeek eats it up, OpenAI ignores it. I take his rant apart and find two things inside: pulling the decision back, and asking only for the result. Then I check it against the twenty-three indicator tests I sent out today. No swearing needed, but hand the model a broken ruler and it will politely measure 100%. Education and methodology.
Same Model, One Leaderboard Says 62.7, Another Says 99.9. Which One Do You Trust?
Caleb Writes Code takes apart the GPT-6 Astra launch: of fourteen benchmarks in the announcement, only one overlaps with the popular composite index, and the same model on the same test jumps from 62.7 to 99.9 depending on whose harness runs it. He offers a new yardstick: token-efficient is not the same as cost-efficient. I checked my own week of usage and found output tokens are 8% of my bill. The slice the leaderboards measure is the slice that hurts me least. Educational notes and extension, not a purchase recommendation.
Four AI Subscriptions, One Line of Quotas: How I Decide Who Gets the Work
Paying for four AI subscriptions means four kinds of quota windows: five hours, a week, a month. Dispatching from memory left one account empty by Wednesday and another half unused at month end. I wired the open-source CodexBar meter into a single line of traffic lights that appears before every message, and now the line decides who does the mechanical work, who writes code, and who makes the judgment calls. Four rules that ignore quotas entirely are in here too. Personal workflow notes, educational.
Ten Dollars for One Request: The Day GPT6 Astra Drained Lunchuizhe's Balance, I Went Back and Checked My Own Bill
Lunchuizhe put GPT6 Astra to the test: three chat questions cost a dollar, then one small Codex task turned a seven-dollar balance negative. His verdict was that the model isn't expensive, we're just poor. I pulled a full day of my own AI usage and found the bill tracks how much the model reads, not how much it answers. Educational notes and extension, not a buying recommendation.
A Subscription Beats Pay-Per-Use by a Mile. So Why Won't He Hand It Six Tasks at Once?
Pushed by his commenters, 掄錘者 re-ran the same GPT6 task on a subscription instead of the API: nine dollars yesterday, 4% of a weekly quota today. He conceded the subscription wins, then admitted he no longer dares to fire six tasks at once the way he does with cheap models. I priced my own week of usage at list rates and found that a subscription doesn't save you money. It converts money into a clock that resets. Educational notes and extension, not a purchase recommendation.
Terence Tao Says Probability Is Only the Right Ruler When Things Repeat. What About the Decisions I Only Get to Make Once?
Terence Tao spent eighty minutes on Big Think walking through his new book, Six Math Essentials. Four things stayed with me: where probability stops applying, why doubling down is bankruptcy compressed into a small number, why traffic keeps jamming after the accident is cleared, and how a correct model that fits worse at first gets killed by the data. I checked each against my own prediction ledger and my settlement-fan calibration, and every one of them landed. Educational notes and extension, not investment advice.
The Answer He Wanted Was No. The AI Gave Him Several Pages of Yes.
Terence Tao says mathematicians want two things: answers, and understanding. For centuries the two were inseparable, because getting an answer required understanding first. That link has broken — problems are being solved by people with no domain expertise, the answers are correct and verifiable, and nobody knows what happened. What AI lacks, he argues, is not generation but deletion. I checked one broken alarm and a 16 KB lessons file of my own against him.
I Bought an Expensive US Market Dataset This Year, Then Found This Public One
An introduction to a public US market dataset: daily prices, earnings call transcripts, financial statements, company news, and delisted companies kept in full. Notes from a paying subscriber, with how to start and one assumption to know about. Educational and methodological.
AI Improving AI Doesn't Have to Mean Changing Its Brain
A ten-minute video from Xiaotian asks whether AI self-improvement must mean changing model weights. He uses EvoX's swarm mode to argue that civilisations improve through institutions and cooperation, and AI can too. I put our own two 'AI improving AI' loops next to his claim: detection works, self-change never happened, and the missing piece of a swarm hit me personally this morning. Educational notes and extensions.
What Is Graph Engineering? Managing a Team of AI Agents Takes Two Diagrams — One Stays for Months, One Is Thrown Away After Use
Graph Engineering blew up on X in July: one tweet, a Google PM's definition ninety minutes later, a 48-hour flood of posts. Kelly Tsai's video sorts it out — loops let an agent's behavior be written down, graphs let an agent organization be written down. In practice it's two diagrams: a stable org chart plus a disposable work plan. I checked it against how I run my own crew of AIs; even the way the division of labor grew is the same.
Codex vs DeepSeek Harness vs Hermes: The Real-World Answer Is There's No Best Agent, Only the Right Seat
A hands-on video throws three AI agent tools at real work: building a forum theme, fixing bugs, daily ops and fending off an attack. Codex develops best, Hermes runs a month without breaking, and DeepSeek Harness — the prettiest codebase — is the one that crashes. Pairing a local Qwen3.8 27B for grunt work with cloud DeepSeek V4 Flash for finishing cost about 30 RMB for a full day; all-premium would run 100+. Two months of the difference buys a 4090.
Terence Tao's Coin Game: AI Did Four Things, Humans Did Four Things — I Checked My Own Division of Labor Against His
Terence Tao walks through a real case of humans and AI solving a math problem together: Gemini filled a missing proof step, AlphaEvolve computed up to 16 piles, Aristotle translated proofs into Lean for machine checking — while posing the problem, making the conjecture, understanding the proof, and noticing two problems were the same all came from humans. The dividing line matches the rule I use to delegate work to AI: only hand over what a machine can verify.
The Cheap One Is Enough, the Expensive One Sits Idle: How Lunchuizhe Picks Models and Agents
In eighteen minutes, the YouTuber Lunchuizhe lines up every model and agent he has tested over the past few months: locally, only Qwen3.8 27B; online, DeepSeek V4 Flash first; Hermes as the daily assistant, Codex for code; Gemini and Grok, avoid. His ranking method matters more than his ranking: ask how much you use per month and how many steps a task runs before you decide whom to pay. I checked his conclusions against a month of my own measurements—two agree, one flips.
Local LLMs: Buy a GPU or Rent One? RunPod at NT$24 an Hour, After Actually Trying It
Yesterday I rented a cloud GPU on RunPod for the first time to run a small batch job: a 4090 at about NT$24 an hour billed by the second; four attempts, 567 GPU-seconds, a bill of about NT$3.4. This piece lays the two paths—rent and buy—side by side: when renting wins, when buying wins, and which common local-development uses—document vector indexing, batch screenshot reading, a local model as an agent's brain, fine-tuning, image and video generation—belong on which side. The code was co-developed and tested with my good friend Fred.
A Numerator Without a Denominator: Terence Tao Says AI Solves Problems Like a Tipsy Genius, and We May Be Optimizing the Wrong Thing
In an interview clip, Terence Tao lays out where AI in mathematics actually stands: four years from middle-school problems to cracking a few that humans were stuck on, but the success rate, the money burned, and how many problems were scanned before one fell are all a black box. His image is a well-read, slightly drunk person throwing out ideas nonstop; point it at a thousand problems and it solves fifty—not necessarily the fifty you wanted. I checked my own factor-validation ledger and found the same mistake: nobody had been recording the denominator.
Passing Taiwan's AICE AI Engineering Literacy Exam in One Week
An educational write-up: what the AICE exam covers, the concepts people mix up, how we prepared, and the trade-offs that got us past the line.
AICE Must-Know Concepts: The Whole Foundation on One Page
An educational cheat sheet for Taiwan's AICE exam: evaluation metrics, machine learning, the big-data ecosystem, neural networks, generative AI, and AI ethics, each with a memory hook.