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Since 2019 Towards AI has provided an open platform for sharing information, educational content and research on AI. It has more than 2,000 authors and benefits from hundreds of thousands of followers in the AI community. The platform is a leading educational resource and community for AI leaders, practitioners and students. Towards AI strives to publish unbiased AI and technology-related articles and to work only with sponsors on highly relevant content and in a transparent manner. Towards AI offers a unique space to service AI and technology clients with their marketing and distribution efforts and has worked with leading corporate and institutional sponsors, including Amazon Science, Carnegie Mellon University, Snorkel AI, Lambda Labs, Superb AI and Gather AI. All of our articles are from their respective authors and may not reflect the views of Towards AI Inc., its editors, or its other writers.

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10/20/2026

the same run may write the test and the code. it should not decide whether its test represents the requirement.

๐—ช๐—ฒ ๐—ฟ๐—ฒ๐—ฎ๐—ฑ ๐—ฎ๐—น๐—น ๐Ÿฎ๐Ÿฒ ๐—ฝ๐—ผ๐˜€๐˜๐˜€ ๐—ผ๐—ป ๐—”๐—ป๐˜๐—ต๐—ฟ๐—ผ๐—ฝ๐—ถ๐—ฐ'๐˜€ ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ฏ๐—น๐—ผ๐—ด. ๐—ข๐—ป๐—ฒ ๐˜€๐—ฒ๐—ฐ๐—ฟ๐—ฒ๐˜ ๐—ถ๐—ป ๐˜๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฑ ๐—ต๐—ผ๐˜„ ๐˜„๐—ฒ ๐˜๐—ฒ๐˜€๐˜ ๐—ผ๐˜‚๐—ฟ ๐—”๐—œ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€.It starts wi...
10/20/2026

๐—ช๐—ฒ ๐—ฟ๐—ฒ๐—ฎ๐—ฑ ๐—ฎ๐—น๐—น ๐Ÿฎ๐Ÿฒ ๐—ฝ๐—ผ๐˜€๐˜๐˜€ ๐—ผ๐—ป ๐—”๐—ป๐˜๐—ต๐—ฟ๐—ผ๐—ฝ๐—ถ๐—ฐ'๐˜€ ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ฏ๐—น๐—ผ๐—ด. ๐—ข๐—ป๐—ฒ ๐˜€๐—ฒ๐—ฐ๐—ฟ๐—ฒ๐˜ ๐—ถ๐—ป ๐˜๐—ต๐—ฒ๐—ฟ๐—ฒ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐—ฑ ๐—ต๐—ผ๐˜„ ๐˜„๐—ฒ ๐˜๐—ฒ๐˜€๐˜ ๐—ผ๐˜‚๐—ฟ ๐—”๐—œ ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€.

It starts with a simple question. Your agent solves a task 75% of the time. But if you run it 3 times, what are the chances it works all 3 times?

A) 75%
B) 98%
C) 42%
D) 25%

The tempting answer is A. The agent works 75% of the time, so it feels like that should stay 75% no matter how many times you run it.

B is tempting too. Three runs sounds like three chances to get it right. And the chance that at least one of the three runs works really is about 98%. That's the score most evals report, and it's the number that makes an agent look great in a demo.

But your users don't get three tries and keep the best one. Every run has to work.

So you multiply. The first run works 75% of the time. The second run also has to work, and 75% of 75% is 56%. The third run has to work too, and 75% of 56% is 42%.

That's why the answer is C. An agent that "works 75% of the time" fails at least once in 3 runs more often than not.

Anthropic calls these two scores pass@k and pass^k. pass@k asks whether any of the runs worked, and it climbs toward 100% as you add runs. pass^k asks whether every run worked, and it falls toward 0%. By 10 runs, the same agent looks almost perfect on one score and almost useless on the other.

โ—๏ธ If people use your agent again and again, track how often it works every time, not whether it worked once.
โ—๏ธ Try this today. Run your 5 most common tasks 3 times each and only count a task as passing if all 3 runs pass.

What score does your agent get when it has to pass every run? ๐Ÿ‘‡

๐Ÿ“˜ Getting an agent to work once is the easy part. Getting it to work every time a real user tries it is the hard part, and that's what our book for AI engineers is about. Join the waitlist and we'll tell you when it's out ๐Ÿ‘‡
๐Ÿ”— https://www.louisbouchard.ai/book/?utm_source=fb-tai&utm_medium=social&utm_campaign=book

10/20/2026

a coding process that is easy to review:

1. define the behavior
2. agent writes a failing test
3. human accepts the test
4. agent implements
5. run the relevant checks
6. inspect the smallest diff
7. human merges

the person decides the expected behavior, acceptable risk, and whether to merge. the agent works on the code within those limits.

๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—”๐—œ ๐—ฐ๐—ผ๐—ฑ๐—ฒ ๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ฒ๐˜„๐—ฒ๐—ฟ ๐—ถ๐˜€๐—ป'๐˜ ๐—ณ๐—ถ๐—ป๐—ฑ๐—ถ๐—ป๐—ด ๐—ป๐—ฒ๐˜„ ๐—ฏ๐˜‚๐—ด๐˜€. ๐Ÿฑ๐Ÿต% ๐—ผ๐—ณ ๐˜„๐—ต๐—ฎ๐˜ ๐—ถ๐˜ ๐—ณ๐—น๐—ฎ๐—ด๐˜€ ๐—ถ๐˜€ ๐—ผ๐—น๐—ฑ ๐—ฐ๐—ผ๐—ฑ๐—ฒ.If you let Claude review Claude's code un...
10/19/2026

๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—”๐—œ ๐—ฐ๐—ผ๐—ฑ๐—ฒ ๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ฒ๐˜„๐—ฒ๐—ฟ ๐—ถ๐˜€๐—ป'๐˜ ๐—ณ๐—ถ๐—ป๐—ฑ๐—ถ๐—ป๐—ด ๐—ป๐—ฒ๐˜„ ๐—ฏ๐˜‚๐—ด๐˜€. ๐Ÿฑ๐Ÿต% ๐—ผ๐—ณ ๐˜„๐—ต๐—ฎ๐˜ ๐—ถ๐˜ ๐—ณ๐—น๐—ฎ๐—ด๐˜€ ๐—ถ๐˜€ ๐—ผ๐—น๐—ฑ ๐—ฐ๐—ผ๐—ฑ๐—ฒ.

If you let Claude review Claude's code until it finally says "looks good", this one is about you.

Anthropic's Claude Code team had one AI agent fix a pull request while another AI agent reviewed it, round after round, until the code came back clean. It never got there. They went back and forth more than 50 times in one night, until the person who started it stepped in and stopped it.

So the team checked where all those review comments came from. About 59% of them pointed at code that had been there since the reviewer's very first look. The reviewer wasn't finding new bugs. Each round it spotted a few more old ones it had missed before, so it never ran out of things to say.

An AI author makes this worse. A person can wave off a small nit in seconds. An agent fixes every single one, which means a new commit and another round of review.

Here's what the team changed, and what we'd copy.

/1 ๐—ฆ๐˜๐—ผ๐—ฝ ๐—ฎ๐—ณ๐˜๐—ฒ๐—ฟ ๐Ÿฏ ๐—ฟ๐—ผ๐˜‚๐—ป๐—ฑ๐˜€
After three rounds, their reviewer asks the author to step back and rethink the approach. If a change still fails after three passes, the plan is usually the problem, not the last line.

/2 ๐—ฅ๐—ฒ-๐—ฟ๐—ฒ๐˜ƒ๐—ถ๐—ฒ๐˜„ ๐—ผ๐—ป๐—น๐˜† ๐˜๐—ต๐—ฒ ๐—ป๐—ฒ๐˜„ ๐—ฐ๐—ต๐—ฎ๐—ป๐—ด๐—ฒ๐˜€
After the first few passes, the reviewer only looks at what changed since the last round. Old code stops getting a fresh batch of complaints every time.

/3 ๐—™๐—น๐—ฎ๐—ด ๐˜๐—ต๐—ฒ ๐˜๐˜†๐—ฝ๐—ฒ ๐—ผ๐—ณ ๐—ฏ๐˜‚๐—ด, ๐—ป๐—ผ๐˜ ๐—ผ๐—ป๐—ฒ ๐—น๐—ถ๐—ป๐—ฒ
When the reviewer spots a problem like a missing null check, it names the whole kind of bug once. The author fixes every copy in one go, instead of one copy per round.

/4 ๐—ช๐—ฟ๐—ถ๐˜๐—ฒ ๐—ฎ ๐˜€๐—ต๐—ผ๐—ฟ๐˜ ๐—ฅ๐—˜๐—ฉ๐—œ๐—˜๐—ช.๐—บ๐—ฑ
It tells the reviewer what failure looks like in your repo, caps the nits and lists the paths to skip. It also asks for a file and line as proof, and for a summary that says plainly when there's nothing to fix.

It worked. Pull requests that pass after one round went from 65% to 78%. The ones that needed five or more rounds dropped from 8.9% to 1.5%.

โ—๏ธ More review rounds feel safer, but after three you're mostly paying to rediscover bugs that were already there.
โ—๏ธ Try this today. Add one line to your review prompt that says "on re-reviews, only flag lines that changed since the last round, and say 'nothing to fix' when there's nothing."

How many rounds does your AI reviewer run before you stop it? ๐Ÿ‘‡

๐Ÿ“˜ Getting AI to write and review code is the easy part. Building AI systems that keep working after the demo is the hard part, and that's what our book for AI engineers is about. Join the waitlist and we'll tell you when it's out ๐Ÿ‘‡
๐Ÿ”— https://www.louisbouchard.ai/book/?utm_source=fb-tai&utm_medium=social&utm_campaign=book

10/19/2026

polite output does not reduce the risk created by network access and readable secrets.

10/19/2026

default coding-agent tasks to no network.

allow named hosts only when the task needs them. keep secrets outside the workspace, log outbound requests, and require approval for downloads or installs that change the environment.

most local bug fixes need the repository and test runner, not unrestricted internet access.

10/19/2026

๐—ฆ๐˜๐—ฎ๐—ป๐—ณ๐—ผ๐—ฟ๐—ฑ ๐˜๐—ฟ๐—ฎ๐—ฐ๐—ธ๐—ฒ๐—ฑ ๐—บ๐—ถ๐—น๐—น๐—ถ๐—ผ๐—ป๐˜€ ๐—ผ๐—ณ ๐—ฝ๐—ฎ๐˜†๐—ฐ๐—ต๐—ฒ๐—ฐ๐—ธ๐˜€. ๐—ฌ๐—ผ๐˜‚๐—ป๐—ด ๐˜„๐—ผ๐—ฟ๐—ธ๐—ฒ๐—ฟ๐˜€ ๐—ถ๐—ป ๐—”๐—œ-๐—ต๐—ฒ๐—ฎ๐˜ƒ๐˜† ๐—ท๐—ผ๐—ฏ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐Ÿญ๐Ÿต% ๐—ฏ๐—ฒ๐—ต๐—ถ๐—ป๐—ฑ.

If you're 22 to 25 and looking for your first tech job, this one is about you.

Stanford researchers looked at payroll data for millions of US workers through June 2026. In the jobs AI touches most, workers aged 22 to 25 now hold 19% fewer jobs than they would if they had kept pace with other jobs. Experienced people in those same jobs show no gap at all.

So the same AI hit two groups in the same jobs very differently. The study points to why. Jobs fell where AI does the task in place of a person. Where AI helps a person do the task, jobs stayed flat or grew.

Junior roles are full of tasks AI can finish alone, like first drafts, small fixes and copy-paste setup code. That is the work being cut. Here is how we'd move to the side where jobs hold.

/1 ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐˜๐—ต๐—ฒ ๐—ฝ๐—ฎ๐—ฟ๐˜ ๐—”๐—œ ๐—ฐ๐—ฎ๐—ป'๐˜ ๐—ณ๐—ถ๐—ป๐—ถ๐˜€๐—ต ๐—ฎ๐—น๐—ผ๐—ป๐—ฒ
Claude Code can write a feature. It can't tell if it's the right feature, or notice when it quietly broke something else. Reviewing, testing and choosing what to build are the skills experienced people still get paid for.

/2 ๐—”๐˜€๐—ธ "๐˜„๐—ต๐˜†" ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐˜๐—ถ๐—บ๐—ฒ ๐—”๐—œ ๐˜„๐—ฟ๐—ถ๐˜๐—ฒ๐˜€ ๐—ฐ๐—ผ๐—ฑ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐˜†๐—ผ๐˜‚
In Anthropic's study of juniors learning a new coding library, people who asked the AI to explain its code scored 65% or more on a quiz afterwards. People who let it write everything scored under 40%.

/3 ๐—ฆ๐—ต๐—ผ๐˜„ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ท๐˜‚๐—ฑ๐—ด๐—บ๐—ฒ๐—ป๐˜, ๐—ป๐—ผ๐˜ ๐—ท๐˜‚๐˜€๐˜ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ผ๐˜‚๐˜๐—ฝ๐˜‚๐˜
Anyone can say they built an app with AI. Far fewer can show the bug they caught in the AI's code and how they fixed it. Put that story in your portfolio and tell it in interviews.

/4 ๐—ข๐˜„๐—ป ๐˜๐—ต๐—ฒ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜ ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜€๐˜๐—ฎ๐—ฟ๐˜ ๐˜๐—ผ ๐—ณ๐—ถ๐—ป๐—ถ๐˜€๐—ต
AI can write the code, but someone still has to ship it, watch it in use and fix it when it breaks. Ask for that work early, because it's work where AI helps you instead of doing it for you.

The study shows the drop fits AI being the cause. It doesn't prove it yet, and the gap has kept growing since the researchers first reported it in August 2025.

โ—๏ธ For every task you do, ask if AI could finish it without you. If it could, learn the step that comes after it.
โ—๏ธ Try this today. Take the last code AI wrote for you and ask it to explain every line you couldn't have written yourself.

Which part of your job could AI not finish without you? ๐Ÿ‘‡

๐Ÿ“˜ Writing code with AI is the easy part. Building AI apps that keep working after the demo is the hard part, and that's what our book for AI engineers is about. Join the waitlist and we'll tell you when it's out ๐Ÿ‘‡
๐Ÿ”— https://www.louisbouchard.ai/book/?utm_source=fb-tai&utm_medium=social&utm_campaign=book

10/18/2026

the code around the model decides what the model can see, do, repeat, and stop.

10/18/2026

a prompt supplies instructions for one model call.

the application supplies tools, saved progress, retries, permissions, and rules for when the work stops.

debug the component that failed. rewriting the prompt will not repair a duplicate side effect or missing checkpoint.

10/17/2026

a roadmap lists topics. a portfolio shows which bottleneck you found and how you measured it.

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