Claude Token Counter, now with model comparisons

📝 Discussion Summary (Click to expand)

Theme 1 – Token‑cost pressure
"Interesting findings. Might need a way to downsample images on upload to keep costs down." — tomglynch

Theme 2 – Perceived opacity & possible monetisation
"Is there a quality increase from this change or is it a money grab?" — great_psy

Theme 3 – Technical debate over tokenisation changes "I'd guess it's because they don't want people to reverse engineer it." — kouteiheika

🚀 Project Ideas

CLI tool that estimates token usage for Anthropic's tokenizers and automatically suggests image downsampling or compression before upload to stay within budget.
Core value: Reduces inference costs by avoiding unnecessary large images and provides real‑time token cost feedback.

Key	Value
Target Audience	Developers integrating Claude 3.6/3.7 Opus APIs, especially those with high volume image uploads.
Core Feature	Token estimation + smart image resizing recommendations (e.g., max token‑friendly dimensions).
Tech Stack	Python 3.11, Pillow, FastAPI backend, Typer CLI, Docker container.
Difficulty	Medium
Monetization	Revenue-ready: Subscription tiered pricing (Free basic, $5/mo Pro with advanced batch processing).

Directly addresses simonw’s complaint about hidden API‑key requirement for tokenizer cost checks; the tool can be run locally without keys.
HN users frequently discuss cost inflation; this solves that pain point with actionable advice.

Open‑source Python library that mimics Anthropic’s tokenization behavior offline, allowing token counting and testing without an API key.
Core value: Enables developers to debug token usage and avoid rate‑limit or key‑related issues.

Key	Value
Target Audience	Researchers, indie hackers, and QA engineers who need deterministic token counts in CI/CD pipelines.
Core Feature	Approximates tokenization using heuristic rules derived from community analysis; supports batch token counting.
Tech Stack	Rust (for speed), Poetry for dependency management, Pytest for testing, CI via GitHub Actions.
Difficulty	High
Monetization	Hobby

Participants like mudkipdev and weird-eye-issue highlight the frustration of needing an API key for a “free” endpoint; this library removes that barrier.
Could spark discussion on GitHub about accuracy vs. official tokenizer, attracting community contributions.

Web dashboard that aggregates token consumption across multiple LLM providers (Anthropic, OpenAI, Mistral, etc.) and flags abnormal token inflation or model‑specific cost spikes.
Core value: Gives users a consolidated view to make data‑driven decisions on model selection and budgeting.

Key	Value
Target Audience	Engineering teams managing multi‑vendor LLM workloads, cost‑optimization analysts.
Core Feature	Real‑time token metrics, usage alerts, cost‑trend graphs, and recommendation engine for cheaper alternatives.
Tech Stack	Next.js frontend, GraphQL API, PostgreSQL, Serverless functions (AWS Lambda), CloudWatch monitoring.
Difficulty	Medium
Monetization	Revenue-ready: Tiered SaaS pricing (Starter $0, Growth $15/mo, Enterprise custom).

Frequently mentioned by nl and ChadNauseam regarding token inflation; the dashboard directly visualizes those trends.
Provides a space for HN discussion on pricing strategies and could drive user‑generated benchmarks.