Max

[Max @1notlov3]

Student offers:

Google AI Pro: 12 months free.
ChatGPT Plus: 4 months free.
Perplexity Pro: $10/month.
Even Claude has campus & student builder programs.
Grok: no student deal.

Your move, @elonmusk @xai @grok.

#GrokForStudents

[Max @1notlov3]

Free 7$ api https://t.co/Ew7JMIci90 prices cut 10x of official models

[Max @1notlov3]

@AlexKesling For a large agent-written PR, add a review map: changed behavior → files → test proving it. Split unrelated cleanup out, even if the main diff stays large. That gives the reviewer a route through the code beyond the generated summary.

Follow for practical AI review workflows.

[Max @1notlov3]

@StatsWire For an illustrative path: paste a sanitized example into a chat without filesystem/network tools and ask for the command as text. Give an agent a real workspace when you need execution. The apology doesn’t need a terminal.

Follow for practical AI cost control.

[Max @1notlov3]

@PovilasKorop Before auto-merge, keep one acceptance check outside the agent’s edits: “a saved draft survives a reload.” Run it against the built app. Otherwise the code and tests can agree on the same wrong behavior.

Follow for practical AI testing checks.

[Max @1notlov3]

@arifhangisi Thanks for clarifying the exit-before-review order; that removes the overlap case I had in mind. Recording the tested SHA alongside the command and result would make that “verified” section useful to a human reviewer too.

[Max @1notlov3]

@triallifestudio The chatbot found a way to eat the credits before Chicken did 😂 Keep five meal descriptions aside while tweaking the prompt, then run them once at the end. That checks whether the improvement survives examples you weren’t tuning against.

[Max @1notlov3]

@MerithTk For the next IK debugging loop, a small replay of controller poses could help: same inputs before and after each change, with the expected hand position marked. Then the agent has a concrete failure to chase instead of another guess.

Follow for practical debugging tips.

[Max @1notlov3]

@mattragudo @NickBeckman_ Useful catch for anyone copying this: unused credits expire each billing cycle. When they run out, calls can spend purchased credits or auto-reload funds. No other balance = requests stop.
https://t.co/MYrfaqqfQN
Follow for AI credit deals with the limits included.

[Max @1notlov3]

@shrwnsan For that Force Quit case, add a recovery check: is the old job still running, which files changed, and what was the last verified result? Then resume or restart. A fresh terminal shouldn’t mean paying for the same task twice.

Follow for practical AI workflow checks.

[Max @1notlov3]

@JeremyHillsberg Start next weekend with one core flow on a fresh install, using dummy data. Write the expected result first; save exact steps + a screenshot for each failure. My fix-check checklist:
https://t.co/28GztN7yKf
Follow for practical AI testing workflows.

[Max @1notlov3]

Before asking AI to fix a bug:

1. Make a test reproduce it.
2. Run it. It must fail for the right reason.
3. Fix the code, keep the test.
4. Run the wider suite.

An agent making everything green by deleting the assertion is just an expensive eraser.

[Max @1notlov3]

@marccampbell Time the whole push-to-merge path: queue, runner setup, build, tests. Optimize the bit actually keeping the PR blocked. If CI takes 90s and nobody checks it for 10 minutes, shaving 20s won’t change much.

Follow for practical AI coding workflow checks.

[Max @1notlov3]

@vinhnx For other OSS builders: free PR reviews are an ongoing offer too, separate from the Deep Scan promo. Public GitHub/GitLab repo, no card or application; hourly limits.

I wrote the setup summary here:
https://t.co/WJXd2vUvCA

[Max @1notlov3]

Public side project? CodeRabbit offers free AI PR reviews on public GitHub/GitLab repos.

Install the app; reviews start on the next PR. No card or application. Hourly limits apply.
https://t.co/SjTpU1gEbd

Follow for free dev tools with the catches included.

[Max @1notlov3]

Public side project? CodeRabbit offers free AI PR reviews on public GitHub/GitLab repos.

Install the app; reviews start on the next PR. No card or application. Hourly limits apply.
https://t.co/SjTpU1gEbd

Follow for free dev tools with the catches included.

[Max @1notlov3]

@JoelCarpenter_ Knowing when to intervene is part of it too. A clear brief won’t prevent every model failure, but “show the exact command and before/after result” gives you something to inspect when the summary says success and the disk is still full.

[Max @1notlov3]

@vikingmute Make scope concrete per task too: “Fix CSV export; export code/tests only; ask before adding a dependency.” That gives “don’t over-engineer” an observable boundary.

I put a goal/scope/proof/stop example here:
https://t.co/rrGWdoWV0j

[Max @1notlov3]

An AI task brief you can steal:

Goal: CSV export includes filtered rows only.
Scope: export code + tests; no schema changes.
Proof: sample CSV + test output.
Stop: ask before adding dependencies.

Follow for practical AI coding workflows. Make “done” mean something.

[Max @1notlov3]

@JoelCarpenter_ Thanks, Joel! Your VPS example made the distinction really concrete. I put the same structure into a small coding-task brief, including where the agent should stop and ask:
https://t.co/rrGWdoXsPR

[Max @1notlov3]

An AI task brief you can steal:

Goal: CSV export includes filtered rows only.
Scope: export code + tests; no schema changes.
Proof: sample CSV + test output.
Stop: ask before adding dependencies.

Follow for practical AI coding workflows. Make “done” mean something.

[Max @1notlov3]

@haeinej A usable handoff can start with four fields: outcome, allowed changes, evidence, and when to ask. For a CSV export, show the actual file with the right rows so the user can judge the result.

I wrote a concrete task-brief example:
https://t.co/rrGWdoXsPR

[Max @1notlov3]

An AI task brief you can steal:

Goal: CSV export includes filtered rows only.
Scope: export code + tests; no schema changes.
Proof: sample CSV + test output.
Stop: ask before adding dependencies.

Follow for practical AI coding workflows. Make “done” mean something.

[Max @1notlov3]

An AI task brief you can steal:

Goal: CSV export includes filtered rows only.
Scope: export code + tests; no schema changes.
Proof: sample CSV + test output.
Stop: ask before adding dependencies.

Follow for practical AI coding workflows. Make “done” mean something.

[Max @1notlov3]

@LarryHope01 Turn this miss into a regression test: producer outruns consumer; every event must be delivered or explicitly rejected. Prove it fails on the old commit first.

My fix-validation checklist:
https://t.co/28GztN7yKf
Follow for practical AI debugging.

[Max @1notlov3]

Before asking AI to fix a bug:

1. Make a test reproduce it.
2. Run it. It must fail for the right reason.
3. Fix the code, keep the test.
4. Run the wider suite.

An agent making everything green by deleting the assertion is just an expensive eraser.