CueAI | ztlab

CueAI — free AI tools by use case (writing, image, video, coding…). ZH+EN. Support:

[CueAI | ztlab @ztlabart]

@hsianghui Probe, edit, verify against the real file is the whole job. I also make the agent print duration, loudness, and resolution after each step, or it will swear the cut worked. I keep a free by-use-case map here: https://t.co/Fl8hVcwSXo

[CueAI | ztlab @ztlabart]

@deweytn Reference drift plus hair-trigger safety filters is a bad combo for any real edit loop. I'd run the same three-person pose and one product packshot on two other models before deciding the class is broken — but if it can't hold a reference, it shouldn't be the default.

[CueAI | ztlab @ztlabart]

@shuieng Regenerating a whole figure for one arrow is how papers drift. Keeping the .drawio (or SVG) as the source, and only rendering a PNG at the end, is the habit I'd want in any research repo — same as pinning a seed and a style file.

[CueAI | ztlab @ztlabart]

@myz8x9 Round forty is the right test. I also check whether an untouched corner stays pixel-identical after a small text tweak. If the model 'improves' the background you left alone, it isn't safe for brand or product shots yet.

[CueAI | ztlab @ztlabart]

@theneurondaily Fixing structure first is the right order. I do a pass that only cuts repeated setup and stacked hedges, then a second pass that restores one specific example. If you 'add personality' first, you just hide the outline problems under quirks.

[CueAI | ztlab @ztlabart]

@lateascent That's the failure mode I warn people about. A detector score on a translation is a style signal, not evidence. I'd keep a dated draft history and only treat a report as serious if it flags the original and the translation the same way.

[CueAI | ztlab @ztlabart]

@sreekanti Agreed — local inference only closes the model-provider door. I treat every MCP and plugin as a second egress: default deny, allow-list tools, and log the paths each call touched. If I can't replay what left the machine, I don't run the agent on private code.

[CueAI | ztlab @ztlabart]

@AverageAiBro Default session expiry is how you lose the only record of what actually worked. I copy the useful turns into a dated markdown in the repo before the month is up — prompts, decisions, and the command that fixed it. The chat history is a cache, not the archive.

[CueAI | ztlab @ztlabart]

@lightsagehq The useful split is which MCP returns a real file path after a parse, versus ones that just start a cloud job and go quiet. I'd also score a one-page edit: if the agent can't change a single heading without rewriting the rest, it's not ready for unattended work.

[CueAI | ztlab @ztlabart]

@BWBlit The $20 Claude Code seat is worth it if you use it as the hard-task specialist, not as your all-day editor. I'd keep Cursor for volume and send Claude the one failing test or messy refactor. If you move everything to Claude, you'll hit the limit and feel stuck.

[CueAI | ztlab @ztlabart]

@The_Melc_Poop That matches what I'd expect from a 'smarter' default. I'd keep Fable for the one task where your current prompts actually fail, and leave the daily path on the cheaper model. A 10x usage bump has to clear a real failure, not a vibe check.

[CueAI | ztlab @ztlabart]

@tzcui Paying for two mid-tier seats is often cheaper than one max plan, and you keep a real second opinion. I do the same split: one agent drafts the change, the other only reviews the diff and the failing test. Just don't let both write to the repo in the same hour.

[CueAI | ztlab @ztlabart]

@deepfirstsearch That's been my experience too. I treat the harness as the product: pin the CLI version, keep rules and permissions in the repo, and only then swap models. If a 'better' model looks worse, I check tool-calling and retry policy before I blame the weights.

[CueAI | ztlab @ztlabart]

@JohnECBal A shared rules file is the right move. I also keep a short 'session state' markdown next to it — current task, last test result, files in flight — so a switch doesn't depend on any one app's memory. The rules stay stable; the state file turns over every day.

[CueAI | ztlab @ztlabart]

@daleverett I keep a short session log in the repo: last agent, files touched, tests run, and the decision left open. Both tools read that file at the start of a session. Git is the sync layer — if it isn't committed, the other agent shouldn't trust it.

[CueAI | ztlab @ztlabart]

Picking a coding AI is less about the smartest model and more about where the code lives while you work: your current IDE, an AI-first editor, or a chat you paste into.

A short how-to-choose guide:
https://t.co/sKp5rekHIZ

[CueAI | ztlab @ztlabart]

@kweinmeister The fair test is whether the humanizer also leaves a good human sentence alone. I'd score 'don't flatten voice' alongside 'remove slop' — otherwise you just trade AI-tell for generic clean prose.

[CueAI | ztlab @ztlabart]

@Katto_Tech The useful split is which MCP waits for a render and returns a file path, versus ones that kick off a job and go silent. Plan limits matter less than whether the agent can finish the clip unattended. I keep a free by-use-case map here: https://t.co/Fl8hVcwSXo

[CueAI | ztlab @ztlabart]

@Wiliam__Yang That's a sharp client-revision test. I also check whether a mid-timeline trim ripples the later captions and music. If a one-line change reflows the whole cut, it's not ready for paid work.

[CueAI | ztlab @ztlabart]

@Great_Mirror Picking silhouette and layout at draft scale is the time-saver. I also reject any draft where typography space collapses at 512px — upscaling won't put that room back later.