Part 3 Review
From shrinking text to handing over a job · short, with analogies · DAY 21-35
Part 3 spent fifteen days going through what AI can actually do, one thing at a time. It started with shrinking text and ended up at handing over a whole job.
There's no quiz and no experiment here. It's a read-and-move-on piece. On any day you only half remember, jump straight to that lesson.
DAY 21 · Summarizing
Summarizing is choosing what to throw out
Summarizing isn't cutting a text off from the front. AI skims the whole thing first, then asks of every sentence, how important is this? Then it keeps only the high scorers and rewrites them in its own words.
DAY 22 · Translation
Translation carries meaning, not words
Translation isn't swapping one word for another. You catch the meaning of the whole sentence first, then build it again in the other language. That's why a word-by-word 'You're taking my hair' says nothing to an English ear.
DAY 23 · Writing helper
Pieces to hand over, pieces to keep
AI isn't a machine that writes for you — it's a tool for turning your own writing over and looking at it. Your ideas, your experience and checking the facts stay yours; tightening and shortening you can hand over. Whatever AI writes still goes out with your name on it.
DAY 24 · Coding
Getting the code is only the start
It's true that code comes out when you describe what you want. But the real half of coding sits after the code appears. Running it, reading the errors and fixing them — that's where it lives.
DAY 25 · Making pictures
Drawing by clearing away the speckle
A picture AI doesn't fetch a picture from somewhere. It starts from a screen full of dots and draws by clearing the speckle away bit by bit. Your description is the handle that sets which way the clearing goes.
DAY 26 · Making videos
Thirty pictures per second
Video isn't a moving picture — it's still pictures stitched together fast. One second usually holds twenty-four to thirty of them. So making video is tens of times heavier than yesterday's picture-making.
DAY 27 · Voice
It listens by chopping sound up small
AI doesn't take in your speech whole. It writes the sound down as numbers 16,000 times a second and sweeps it with a 25-millisecond window. Each piece on its own is just a pattern of highs and lows.
DAY 28 · How is it different from search?
A list of places, or one solid block
Search doesn't give you an answer, it gives you a list of places to go. AI takes what it has already read and builds one solid block of sentences. There's only one thing to read, so it's fast — but where the words came from isn't written down.
DAY 29 · Research
What speeds up is the front end
Researching with AI makes starting faster. It throws out search terms and outline options quickly, so the finding-and-sifting front end is what speeds up. Numbers, years, people's names and quotes you have to click through and check yourself.
DAY 30 · Study helper
Just the answer, or the steps?
Ask about the same problem and what comes back changes completely depending on whether you asked for the answer or the steps. There's one test for choosing — will I have to do this on my own later? If you'll be on your own, take the steps; if you only need to know whether it's right, take the answer.
DAY 31 · Tables and data
Collect → table → fix → chart
You can't chart scattered material straight away. A table has rules — one row per case, one value per cell. Line things up like that and both you and AI can calculate right away.
DAY 32 · Preparing a presentation
A talk breaks into pieces
Preparing a talk isn't one lump. Narrowing the topic, gathering material, building slides, rehearsing — it breaks into pieces. The pieces AI is good for are the rough draft and the labor-heavy work.
DAY 33 · Coming up with ideas
The knob between boring and off-the-wall
AI picks the next word by probability. Grab only the high-probability ones and it turns boring; reach down to the low ones and it turns odd. That handle for widening and narrowing really exists, and it's called temperature.
DAY 34 · Making a plan
Goal → break up → order → check
When you're stuck, don't ask AI for the answer — ask it to break the job up. Planning isn't knowing the right answer, it's deciding what to do first. Goal, break up, order, check — fill those four boxes and you have a plan.
DAY 35 · Handing over a whole task
If you can undo it, hand it off
An AI that takes only a goal and then searches, opens files and retries on failure by itself is called an agent. There's really one test for what you can hand over — can you undo it when it's wrong? A file name you can undo; money sent and a report handed in you cannot.
Part 3 in one line
Fifteen days squeezed into one line: the more AI can do, the clearer the box you have to check becomes.
Part 4 looks at the other side. How things go wrong when they go wrong, and what to watch out for, one at a time.
Learn more
- Learning to summarize from human feedback (2020) — teaching summarizing from human ratingsarxiv.org/abs/2009.01325
- A Neural Network for Machine Translation, at Production Scale (Google, 2016) — the announcement that put neural translation into serviceresearch.google/blog/a-neural-network-for-machine-t…
- Evaluating Large Language Models Trained on Code (2021) — the first large evaluation of models that write codearxiv.org/abs/2107.03374
- High-Resolution Image Synthesis with Latent Diffusion Models (2021) — clearing the speckle in a compressed spacearxiv.org/abs/2112.10752
- Video generation models as world simulators (OpenAI, 2024) — a description of video generation modelsopenai.com/index/video-generation-models-as-world-s…
- Robust Speech Recognition via Large-Scale Weak Supervision (2022) — dictation trained at scale on noisy audioarxiv.org/abs/2212.04356
- Evaluating Verifiability in Generative Search Engines (2023) — a study of whether attached links really support the answerarxiv.org/abs/2304.09848
- Retrieval-Augmented Generation (2020) — fetching the material first, then answeringarxiv.org/abs/2005.11401
- Large Language Models are Zero-Shot Reasoners (2022) — what one line, 'let's think step by step', doesarxiv.org/abs/2205.11916
- The Curious Case of Neural Text Degeneration (2019) — research on how to draw the next wordarxiv.org/abs/1904.09751
- Least-to-Most Prompting (2022) — breaking a hard problem small and solving in turnarxiv.org/abs/2205.10625
- GAIA (2023) — an evaluation of agents on everyday errand tasksarxiv.org/abs/2311.12983
- Tidy Data (2014) — the 'one row per case, one value per cell' standardjstatsoft.org/article/view/v059i10