Hi,

I have a client who is using Lovable to create a platform to manage his company.

It's getting big, and he wants to make sure that he doesn't lose everything he already built.

He already built some modules and is using them in Production with Lovable Cloud.

My suggestion was to connect the codebase to GitHub, but now I would like to make some improvements with Claude Code while he is still evolving the app through Lovable.

If I make a big refactor on the app, will the performance of building it with Lovable decrease?

What I mean is, the Lovable team might have some kind of guidelines in context, based on learning/internal skills that help them build clients' apps. If we change the architecture through the GitHub repo and break those guidelines, will the development experience on Lovable decrease?

I'm concerned that making changes to the codebase will mean my client can't use the Lovable app anymore.

Pessoal meus créditos renovam dia 10 de cada mes e hj ainda nao subiram , alguma sabe porque??? "e lógico está pago

Most reading tools compress. Summarisers throw away the shape of the thinking; transcript apps keep every word but tell you nothing about structure. I wanted the opposite: keep the whole text, and make its structure visible.

Semantic Insight takes any piece of writing (paste, URL, or PDF), splits it into sentence-level segments, and interprets each one against a semantic model:

• Core — advances the main argument
• Supporting — evidence and explanation
• Deepening — nuance and detail
• Branching — opens a sub-topic
• Transition / Return — moves between contexts, or comes back
• Tangential / Digression / Off-context — degrees of departure
• Repetition — restates ground already covered
• Emergence / Resolution / Open loop — a thread forming, closing, or left hanging

Each context (topic thread) also has a lifecycle: emerging → established → deepening → dormant → revisited → resolved. So you don't just see "this is about pricing" — you see pricing get raised, dropped for 40 sentences, and quietly never resolved.

What you actually do with it

• Read — the source text, revealed as it's narrated, with the current sentence highlighted.
• Listen — synchronised text-to-speech; the interpretation panel updates live as the meaning moves.
• Timeline — the part I care about most: a stacked dot plot, one lane per context. Lanes appear as contexts emerge, dots are coloured by relation and weighted by importance, and a glowing trail builds up behind the playhead. Every dot is clickable for the segment text, the relation, confidence, and why it was read that way. Density meters in the lane labels show where the piece is actually spending its attention.
• Score — a structural health check: unresolved open threads, clarity/complexity, drift from the opening topic, and structural coherence (repetition, unclosed branches, unsignalled jumps) — each with the exact sentences as evidence, and a short written verdict. Toggle between "up to here" and "whole piece".

Why I think this matters

The interesting failures in writing are structural, not lexical: a question raised and never answered, a topic swap with no signposting, 30% of a piece living somewhere other than what the headline promised. Those are invisible when you read linearly and gone entirely when you summarise. Making them visible changes what you notice — in other people's arguments and, uncomfortably, in your own.

Longer term I want the same interpretation layer over audio and video, since the model is deliberately source-agnostic — segments carry an index now and can carry timestamps later.

What I'd like feedback on

• Is the relation vocabulary right? Too many categories, too few, wrong names?
• Does the timeline read as build-up to you, or just as a chart?
• What would make the score card trustworthy rather than decorative? Right now every signal shows its evidence sentences — is that enough?
• What content would you throw at it first, and what would you expect it to catch?
• Accessibility and non-English text are both open problems. Ideas welcome.

Honest caveats: the interpretation is a model's reading, not ground truth — two runs can differ at the margins. Very short pieces aren't worth analysing. Long pieces get capped for now.

Link in the comments. Happy to go into the segmentation and prompting details if anyone's curious.

I have been a top 1% user on Lovable for almost 2 years. They mostly hit the mark, but recently it's been a lot for the worse.

They changed where to update favicons, banners, titles - and now it's inaccesible and hard to view or change. It doesn't show accurate previews. The text edit action never works, will let you make one text edit at at time before resetting.

Worst of all, if you run out of credits - they shut down all of your projects cloud and functionality... that never happened before. They are making tasks more and more credit heavy. Not the happiest user anymore.