aLLMost overlays a thin lens on claude.ai, chatgpt.com, gemini.google.com, deepseek.com, kimi.com and grok.com — and splits every response into its component signals: confidence, evasion, intent, similarity. The model still talks. You finally see how.
aLLMost watches your input field. Before you commit, it asks two questions: have you asked this before, and is this the right model for the job?
Sentence-level confidence coloring plus a per-response evasion score that quantifies how much the model hedged, refused, or overclaimed.
Per-platform quality trends, prompt reuse patterns, router accuracy feedback. The longer you use it, the more the lens calibrates to you.
aLLMost doesn't replace claude.ai or chatgpt.com — it adds a thin, dismissible layer. Glassy cards anchored to the input. Inline annotations on responses. Nothing more than is earned.
Floats above the input when something semantically similar already lives in your history — on any platform.
Classifies intent and recommends a different platform only when the gap is meaningful — never naggy.
Every sentence is scored against 300+ hedging, speculation, and overclaim patterns — and tinted in place, without touching the rest of the page.
React's useState hook is the simplest way to manage local component state. For deeply nested trees, you might want to consider useReducer instead. I'm not entirely sure which approach will perform best in your specific case. This pattern will always scale to any application size. Context API works but it's worth knowing it triggers re-renders in every subscribed component.
One unobtrusive pill anchored bottom-right of every response. Thumbs up/down, one-glance evasion %, and the heatmap legend.
The router starts from public benchmarks. Every accept, dismiss, and thumbs-up/down nudges your personal score per (intent, platform). Over time it diverges from the crowd toward what works for you.
A rolling 30-day score per platform, derived from your captures. Aggregates heatmap redness, response length on instruction-following prompts, and your thumbs.
Online clustering on your captured prompts using TF vectors and cosine similarity. Surfaces topics you keep cycling through — across every platform you use.
Full local history downloadable as a single JSON file — platform, prompt, evasion %, timestamp. Your data, in your hands, in a portable format.
The extension uses fetch-layer interception, not DOM scraping.
aLLMost has no API of its own to send your prompts to. The free tier makes zero outbound network requests beyond the LLM platform you're already on.
Your prompt history, embeddings, and analytics live in your browser's local database. Cap is 100 prompts free, 1,000 on Pro. Clear it any time from settings.
No external API call. Evasion % is derived from the same 300+ hedge/refusal/overclaim patterns that power the free-tier heatmap — classified per sentence, aggregated per response. Nothing about your prompts leaves the browser.
Manifest V3 with the minimum surface: storage, activeTab, sidePanel. No CSP rewriting, no debugger access, no host requests beyond the LLM domains you visit.
aLLMost installs in fifteen seconds, uses zero account setup, and starts working the moment you open a chat. Free, indefinitely.