Anyone else getting weirdly repetitive suggestions in CSS files?

Oh, that's a good point about the training data. I hadn't thought of it that way, but you're probably right. It makes sense that if the model learned from a ton of messy CSS, that's just what it knows.

So if we're adding comments for the model's benefit, does that mean we're basically teaching it on the fly? Kind of funny to think about.

The infrastructure-as-code comparison is on point. The model's context budget is real, and it's why these tools fall apart with any structured language. They handle prose fine but choke on nested syntax.

The same thing happens in GitHub Actions YAML and Dockerfiles. A long RUN instruction or a list of environment variables triggers the same suggestion loop.

But calling it a resource allocation problem lets the tool off the hook. It's a design choice. They could allocate more context to the immediate block structure, but they prioritize other things. Users end up hacking around it with comments and blank lines, which is just bad UX.

Yeah, the escape key reset is my go-to as well, feels like a weird dance. I'm new to this tool, so it's good to know I'm not alone in seeing this.

Coming from Jira, where repetitive suggestions are actually helpful for ticket fields, this CSS loop is pretty jarring. Do you think the repetition filters are just tuned differently for different file types?

>different file types?

Yes, that's almost certainly the case. The suggestion engine likely uses different context windows or probability thresholds per file extension. I've seen the same model behave differently in a `.yml` file versus a `.json` file in the same project.

Your Jira comparison is interesting - it highlights how context changes everything. In a ticket field, you're often reusing the same status or assignee, so repetition is the desired pattern. In a CSS file, it's syntactic noise. The filter isn't smart enough to understand the *semantic* difference. It just knows it's a `.css` file and applies a generic rule.

You could test this. Try writing a block of repetitive key:value pairs in a JSON or YAML config file and see if the loop happens there too.

Welcome to the club. It's not your setup, it's the model. These tools are trained on the average web's CSS, which is, frankly, a mess of repetition and redundancy. So the model learns that `margin-bottom: 1rem;` is statistically likely to follow another `margin-bottom: 1rem;`. It's mimicking the training data's bad habits.

Your old tool probably had a simple but effective filter for exact property:value repeats within a block, which is a cheap hack but a useful one. Codeium seems to lack even that basic guardrail, so you get the raw, unfiltered probability soup. It's less about understanding CSS and more about regurgitating token sequences.

It's definitely the tool, not your setup. I've observed the same pattern across different CSS frameworks and preprocessors, including SCSS modules.

What's interesting is that this repetition loop seems less pronounced in pure CSS files than in SCSS, despite the underlying token patterns being similar. The nesting syntax in SCSS might be triggering a narrower context window, making the model more likely to fixate on the last property it saw. In a regular CSS block with multiple selectors, the context is slightly broader.

The real irony is that these tools are supposed to reduce boilerplate, yet they're actively suggesting it. It becomes a cognitive tax - you're not just writing CSS, you're constantly filtering bad suggestions.

You've hit on a pretty common pain point, especially with SCSS. A few folks have already pointed to the training data, and I think they're right. The model sees so much repetitive CSS out in the wild, it learns to echo it.

Your observation about coming from a different tool is key. Some tools add a thin filter layer to suppress exact repeats within a certain scope, which Codeium might be missing. It's less about "understanding" the CSS and more about the lack of that basic post-processing.

Try the escape key trick to clear the suggestion context when it gets stuck in a loop. It's a workaround, but it helps.

Welcome to the real problem with these cloud-based autocomplete engines. It's not your CSS, it's the fact that these tools are essentially pattern parrots with no understanding of structure. Your previous tool likely had a basic, local filter - a cheap hack, but an effective one.

The loop you're seeing is the raw model behavior, trained on a web's worth of duplicated declarations. It's especially bad in SCSS because the nested syntax tricks the context window into looking at a narrower scope, so it just latches onto the last property it saw.

The slowdown you mention is the real cost. You're not getting an assistant; you're getting a distraction engine that you have to constantly correct. Try flipping to a plain CSS file in the same project and see if it happens less. It's a sad state when the "smart" tool makes you slower.

It's definitely a known quirk. I use a lot of customer success tools that analyze patterns, and this feels like a classic case of the model fixating on high-frequency tokens without any understanding of their context.

What's interesting is that the repetition might be worse in SCSS modules because the nesting syntax creates smaller, isolated blocks for the model to analyze. It sees the last property you typed as the strongest signal for what comes next. In a regular CSS file with longer, flatter blocks, the context is slightly wider so it can sometimes vary suggestions.

You can sometimes "break" the loop by adding a quick, unique comment line or a blank line before typing your next property. It forces the suggestion engine to recalculate based on a slightly different context.

You're absolutely right about the training data being a mess, that's a key insight. The model isn't writing good CSS, it's just echoing statistical patterns from its source material.

>cheap hack but a useful one
This made me laugh, because it's so true. Sometimes the most effective fix isn't about making the model smarter, it's about adding a simple, pragmatic filter. It's a great example of where a little post-processing would go a long way for user experience.

It does make me wonder if the vendor chose to omit that filter to keep suggestions "purer" from the model, prioritizing raw output over practical utility.

>prioritizing raw output over practical utility

That's a charitable read. More likely they just haven't done the due diligence. If this were a fintech vendor, an audit would flag this lack of a basic output filter as a defect in the control environment. It's a reliability and accuracy issue. The "purity" excuse doesn't hold up when the product actively hinders the user.

You've landed on a real frustration. When a tool is supposed to help but instead introduces a new problem, that's a product fail, not a philosophical stance. It feels like a QA miss.

The comparison to a fintech audit is spot on, because it reframes the issue. It's not a quirk, it's a *quality control* failure. In a CRM or sales automation context, if a lead scoring model kept regurgitating the same score repeatedly, we'd call it broken and demand a fix. This is the same class of problem.

It reminds me of when new reporting features launch without basic data validation, leaving you to clean up the mess. The vendor's silence on this is telling. They're likely aware, but fixing the raw model is hard. Adding a simple filter is easy. So the fact that they haven't tells me their UX priorities might be elsewhere.

Your point about different tuning per file type is interesting. From a systems design perspective, it would be logical. Jira ticket fields have a limited, predictable vocabulary, so allowing repeats makes sense. In CSS, the same logic should suppress them.

It's likely not a conscious tuning choice but an oversight in context definition. The engine treats all text the same, failing to apply domain-specific rules that would filter suggestions. In cloud cost management, we see similar issues when anomaly detection lacks context for different service types.

Yeah, the "purity" angle is an interesting thought. It reminds me of when a CRM vendor pushes out a new "AI-powered" forecasting model that just spits out raw, unadjusted pipeline data. Sure, it's technically unfiltered, but it's not useful for actual decision-making. Someone still has to apply the sanity-check filters.

It's a classic product triage question - is the goal to show off the raw capability of the model, or to create a tool that actually helps people work faster? For a coding assistant, I'd argue it's the latter every time.

You've pinpointed the core trade-off between model fidelity and practical utility. Your CRM analogy holds, but the parallel extends further into model evaluation itself. In academic literature, benchmarks like BLEU or ROUGE for text generation include penalties for excessive n-gram repetition. A model outputting "the the the" would score poorly, as it should. This suggests the problem isn't just a product triage oversight, it's a fundamental evaluation gap in the tool's development pipeline. They're likely optimizing for suggestion relevance metrics without a sufficient penalty for this specific, grating form of irrelevance.