After a recent update, Lockedin Ai started behaving unpredictably, with slower responses, random errors, and settings not saving correctly. I rely on it daily for work and this disruption is causing real productivity problems. Can someone explain what might be going wrong and suggest specific steps or fixes to get Lockedin Ai stable and reliable again?
I ran into almost the same mess after the last Lockedin Ai update. Here is what helped, in rough order. Try to test after each step so you know what fixed what.
-
Check their status and release notes
• Look for an official status page or Twitter support account.
• If there is an outage or regression, they sometimes roll a hotfix within a day or two.
• If many users report the same slow replies and saving bugs, it is likely on their side, not your setup. -
Hard reload and clear app cache
Web:
• Log out.
• Press Ctrl + F5 for a hard refresh.
• Clear site data for the domain in your browser settings, including cookies and local storage.
Desktop or mobile app:
• Force close the app.
• Clear the app cache from the system settings.
• Log back in.
This fixed my “settings not sticking” issue once, since the update changed how they store config. -
Check your workspace or project settings
Updates sometimes reset or move things.
• Go through preferences line by line.
• Reapply model choices, timeout limits, or “autosave” style options.
• If there is an “advanced” or “labs” section, turn off newly added experimental flags.
In my case, a new “smart context” toggle caused slower, more random behavior. -
Disable browser extensions and VPN
• Turn off ad blockers, script blockers, and AI helper extensions for a test.
• Try a different browser.
• If you use VPN, turn it off for one run.
Extensions that hook into pages often break after front end updates. -
Test in a clean environment
• Use a different device or a private/incognito window.
• Log in with the same account and try one simple prompt.
If it works fine there, then the problem sits in your main browser profile or local system, not the service itself. -
Check network and latency
• Run a speed test.
• High ping or packet loss leads to slow or failed responses.
Even if other sites feel fine, AI requests often involve bigger payloads, so problems show up faster. -
Watch for rate limits and quotas
Some updates tighten rate limits or change token caps. Symptoms
• Random errors mid reply
• Partial responses
• “Something went wrong” style messages
If they have a usage dashboard, check for spikes or “limit reached” flags. If you hit limits, batch prompts more, or shorten context. -
Recreate your usual workflow on a blank setup
Before the update, you had a stable pattern. After the update, they maybe changed how sessions or histories work.
Try this:
• New workspace or new project.
• No imported history.
• Simple system prompt.
If that feels stable, slowly re-add your old configs and see when it breaks. That helped me find a corrupted template. -
Export or back up your stuff, then reset
If there is an option to export settings, prompts, workflows, do that first.
Then
• Log out from every device.
• Reinstall the app or remove and re-add the browser app.
• Log back in fresh.
This fixed random errors for me after a major backend change. -
Contact support with specific data
When you reach out, send concrete info so they do not bounce you around. For example
• Time of errors with timezone.
• Exact action when it failed.
• Browser and OS version.
• Screenshot of console errors if you know how to open dev tools.
• A short comparison like “pre-update average response ~2s, now ~15s, fails 3 out of 10 requests”.
Support teams respond faster when they see clear patterns. -
Add a short-term workaround for your work
If you rely on it daily
• Keep a local copy of important prompts in a text file or notes app.
• Use simpler prompts and shorter histories to reduce errors.
• Save outputs manually often if autosave feels broken.
• If there is an older version or web version, use that as a backup. -
Track if the issue is account specific
If you have a colleague using Lockedin Ai, ask them
• Are their settings saving correctly
• Are they seeing slow replies at the same time
If it only hits your account, it might be a corrupted profile or flag on their backend, which support needs to reset.
For me
• Clearing cache and toggling off a new “smart” feature fixed randomness.
• Reinstalling the desktop client fixed slow replies.
• Support confirmed they had a bug with settings persistence for a part of one day after the rollout.
So I would start with a hard refresh and cache clear, try a clean browser or device, then audit settings for any new or weird toggles. After that, log everything and push it to support as an account level bug.
Had the same circus after the last Lockedin Ai update, and I’ll be blunt: not everything is on your cache or browser, despite what support scripts and, yes, even @reveurdenuit suggest.
A few angles that aren’t just “clear cache & pray”:
-
Check if it is model / workspace specific, not global
Don’t just try “another browser,” try another model or workspace inside Lockedin:- Use the lightest / oldest model they offer.
- Try a brand‑new workspace with zero history, but keep your normal browser and device.
If new workspace + same browser still breaks, that’s probably account state or backend, not your environment. If only one specific model is slow/buggy, avoid it until they patch.
-
Watch for pattern-based failures instead of random ones
The “random errors” often aren’t random:- Are long prompts more likely to die mid‑response? Then it’s probably a token / size regression.
- Do settings fail to save only after you tweak a particular advanced option or a specific project? That can signal a broken feature flag.
Write down 5–10 quick tests like: - Short prompt, no history.
- Long prompt, big history.
- Same prompt with different models.
You want a simple sentence to send support like: “Settings fail only when X is enabled in workspace Y.”
-
Treat “settings not saving” as a sync bug, not a UI bug
A lot of people keep flipping toggles and watching the UI, which can be misleading. Try this instead:- Change one setting.
- Refresh the page or restart the app.
- See if the setting is really applied in behavior, not just in the visible UI.
Sometimes the UI shows your old config, but the backend actually uses the new one. That mismatch explains “unpredictable” answers.
-
Check for account-level feature flags
This is where I slightly disagree with the “just turn off new smart stuff” advice from @reveurdenuit. Sometimes the problem isn’t the new feature itself, but a bad rollout for some accounts.
When you contact support, explicitly write:“Can you check my account feature flags / config rollout for regressions after the last deployment?”
That language usually gets you escalated away from basic “clear your cookies” replies. -
Isolate “heavy workflows” from baseline usage
Take your exact daily work setup and strip it down:- Remove all automations, plugins, or external integrations.
- Disable any document / knowledge base attachments.
- Turn off parallel or multi‑threaded workflows if they have them.
Then: - Run a 10–15 minute session with only simple chat.
If that runs smooth, start adding back features one by one until it breaks. The first thing that reintroduces lag or errors is probably the culprit.
-
Time-based debugging
Some regressions are capacity issues:- Test at 2–3 separate times of day.
If it is blazing fast at 3am but miserable at 11am, you are looking at server load or regional capacity, not your machine. Mention those timestamps specifically to support.
- Test at 2–3 separate times of day.
-
Document a “before vs after update” baseline
You rely on this for work, so treat it like a tool that can be audited. Write a very short comparison:- Before: model X, history size Y, output in ~3 seconds, 0 errors last week.
- After: same workflow, now 15–20 seconds, N errors per hour, settings like Z not persisting.
That kind of concrete comparison makes it much harder for support to dismiss as “just network.”
-
Temporary workflow adjustments for workdays
Until they sort out their mess, make your setup more failure‑tolerant:- Break big tasks into smaller prompts instead of one giant mega‑prompt.
- Keep a local text file with your core instruction prompt, so you can paste it quickly if history feels unreliable.
- When something works well, copy/paste results out immediately instead of trusting autosave or history.
If those patterns point clearly to “this is on their backend / account config,” skip more local troubleshooting. Gather your small test matrix, send it to support, and flatly ask them to:
- reapply your account configuration,
- check your feature flags, and
- confirm if your region / cluster has an open incident related to the last update.
You should not be burning hours nuking your system when their deployment is the thing that obviously changed.
Short version: you already got great “local troubleshooting” checklists from @espritlibre and @reveurdenuit. I would tackle the Lockedin Ai update mess from a different angle: treat it like a product regression you can map, not just a glitch to “clean cache” away.
1. Stop changing 10 things at once
Both previous answers suggest a lot of toggling and reinstalling. That sometimes works, but it hides what actually broke.
Try this stricter approach:
- Pick one workflow you rely on daily in Lockedin Ai.
- Write down its exact steps, including model, context size, and any plugins or integrations.
- For one day, do only that workflow, without touching settings.
- Note: response time, error frequency, whether settings persist after a single change.
You want a small, reproducible scenario, not a moving target.
2. Treat this as a versioned product, not a black box
Lockedin Ai behaves differently after an update because underlying components changed. Track it like software:
- Note the app version/build if available.
- Note when the issues started, in 15 minute accuracy if possible.
- Correlate each change you make with concrete behavior (for example, “after disabling documents, random 5xx errors dropped to almost zero”).
This is more valuable than yet another “clear cookies” cycle.
3. Separate three distinct problem types
Instead of one blob of “it is broken,” split symptoms:
-
Performance
- Slower round trips, timeouts, “something went wrong.”
- Often caused by server load, model version, or rate limiting.
-
Determinism / quality
- More random, inconsistent answers with similar prompts.
- Often tied to different model defaults, temperature, context handling, or “smart” features.
-
State / configuration
- Settings not saving, weird jumps between workspaces, missing history.
- Usually a sync or account config issue.
Then you can say: “Performance is bad but state is fine” or “state is broken but performance OK.” Support and your own debugging get much easier.
4. Use “shadow mode” to protect your productivity
Instead of forcing Lockedin Ai to behave while you are on a deadline, create a parallel low‑risk workflow:
- Keep Lockedin Ai as your secondary assistant for a few days.
- For core tasks, temporarily lean on a simpler tool or even a competitor model with minimal features.
- Use Lockedin Ai only for experiments and logging behaviors, not for mission‑critical work.
Pros for Lockedin Ai in this “shadow mode” role:
- You can keep discovering what changed without blocking your day.
- You gain data on which features are still stable after the update.
Cons:
- You are paying (or relying) on something you are not using at full capacity.
- Splitting work between tools adds friction.
Still, short term this can keep your productivity intact while they stabilize.
5. Stop trusting the UI, test the behavior
Here I am in full agreement with parts of what was said, but want to push it harder:
-
Ignore what checkboxes look like.
-
Run behavior-level tests:
- If you set temperature to a “very low” value, does the model actually become more deterministic?
- If you disable a “smart context” feature, does token usage or answer style change?
- If you modify autosave, do you still lose data after refresh?
If the UI and behavior mismatch, you are clearly seeing a config sync bug, not a browser issue.
6. Track competition-level sanity checks
Not to turn this into a vendor war, but you can use other tools as a baseline:
- Spin up the same prompt and workflow in one competing AI interface.
- Compare response time, stability, and determinism.
You already have @espritlibre and @reveurdenuit giving solid, almost support-like guides. Treat their advice as “competing diagnostic approaches” too:
- @espritlibre leans more on environment resets and stepwise cleanup.
- @reveurdenuit focuses heavier on account flags, backend behavior, and pattern recognition.
Run a mini experiment: follow only one of their strategies for a few hours, log the results, then try the other approach. This reveals whether the bottleneck is likely local or account/backend.
7. Make a lightweight “regression report” once, reuse it
Instead of emailing support multiple times or repeating yourself, prepare one concise document:
- One page max.
- Top: “Lockedin Ai behavior since update X”.
- Sections: Performance, Quality, Config persistence.
- Each section with “Before update” vs “After update” and 1 or 2 specific examples.
Pros:
- You can paste the same summary into any ticket or forum thread.
- You look like someone who has already eliminated the obvious.
Cons:
- It takes about 20–30 minutes of focused writing.
- It may still take time for support to act on it.
8. Decide your cutoff point
If Lockedin Ai is central to your work, define beforehand:
- How many days of degraded performance you will tolerate.
- What your fallback stack is if things do not improve.
- Which features of Lockedin Ai are non‑negotiable vs nice‑to‑have.
Then you are not stuck endlessly tweaking toggles and reinstalling apps “just in case.” You either see improvement in a set window or you shift more of your workload elsewhere until the next stable update.
You do not need yet another long list of “turn this off, reinstall that.” You need a clear map of which parts of Lockedin Ai broke for you and a controlled way to test them without burning your workday.