I got tired of paying for SEO dashboards, so I built a free Rust CLI instead

Akash Priyadarshi — Wed, 23 Sep 2026 03:45:23 +0000

Semrush and Ahrefs cost upwards of $130/month. Most of what you're paying for is a web dashboard around checks that are fundamentally deterministic: crawl a site, check titles and meta tags, validate schema, check what AI crawlers are allowed to see.

I built jev-seo to do that part without the subscription.

What it does

Live-site crawl: BFS crawler, health score, broken links, redirect chains, orphan pages, per-page timing.
Batch document audit: scans your markdown/HTML directory for title collisions, thin content (<300 words), broken canonicals, missing alt text.
Schema.org validator: checks JSON-LD (SoftwareApplication, Article, Organization, Product) against current spec, flags deprecated schemas.
Robots.txt / AI crawler radar: checks what GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and others are actually allowed to see.
Sitemap validator: <loc>, <lastmod>, HTTPS enforcement, the 50k URL limit, hreflang.
SERP + keyword research: scrapes DuckDuckGo's HTML and suggest endpoints directly. No paid API key.
GEO scoring: citation-likelihood scoring for AI answer engines, via TypeSafe's Jev model (a typed-decision model, not a chat LLM), needs a free API key for this part only.
MCP server: jev-seo mcp exposes 10 tools over stdio JSON-RPC for Claude Code, Gemini CLI, and other agent clients.

Quick start

Everything below runs with no API key except the GEO scoring, which needs a free TYPESAFE_API_KEY.

\`bash
cargo install jev-seo

export TYPESAFE_API_KEY=your_key_here

jev-seo crawl https://example.com --max-pages 10
jev-seo audit docs/
jev-seo robots github.com
`\

Open source under MIT: github.com/AkashPriyadarshii/jev-seo

Live demo and full docs: https://jevseo.vercel.app

AkashPriyadarshii / jev-seo

100% free ₹0 agent-first SEO & GEO CLI suite and MCP server in Rust replacing Semrush and OpenSEO via DuckDuckGo and TypeSafe Jev System One https://akashpriyadarshii.github.io/jev-seo/

title

jev-seo: FOSS Zero-Cost SEO & GEO Search Radar

description

Open-source, subscription-free alternative to Semrush and OpenSEO. Powered by TypeSafe AI Jev and local DuckDuckGo scraping.

canonical

https://github.com/AkashPriyadarshii/jev-seo

keywords

seo

geo

generative-engine-optimization

typesafe-ai

jev

rust

mcp

search-radar

jev-seo

FOSS, zero-cost SEO & GEO search radar for developers and coding agents

By Akash Priyadarshi

Why • Quickstart • Workflows • Architecture • Non-Goals • Ecosystem

See it run

Live crawl of a real production site with health score, ranked actions, and completeness notes:

Single-file HTML report from audit --html, with grade scorecard:

Grade scorecard close-up from the same report:

Why jev-seo?

Semrush and Ahrefs cost upwards of $130 per month. OpenSEO still requires paid DataForSEO credit cards. Most SEO suites are bloated web dashboards filled with vanity charts.

Zero subscriptions (₹0): Scrapes DuckDuckGo HTML and suggest endpoints directly. No credit cards, no paid API keys.
TypeSafe Jev System One…

View on GitHub

Solo project, built out of Patna. Issues and feedback welcome.

Numbers

Measured 2026-09-22, full notes in the repo's docs/EVAL.md.

Check	Result
Cold start	6ms
Local audit, 4 files	~0.2s
Live crawl, 6-10 pages	3-11s, server-bound
Score repeatability, 3 runs	99 stable

Everything deterministic (crawl, audit, schema, robots, sitemap) runs with zero API keys. Only the Jev-backed semantic scoring needs a key, and that's fractions of a cent per call.

Open source under MIT: github.com/AkashPriyadarshii/jev-seo

Solo project, built out of Patna. Issues and feedback welcome.

I built a Rust CLI that filters datasets using typed AI decisions instead of LLM judges

Akash Priyadarshi — Wed, 23 Sep 2026 03:33:17 +0000

Cleaning a large synthetic or pretraining dataset is a pain nobody talks about enough. You end up with three bad options.

Regex and keyword filters are fast but blind. They catch a malformed JSON row. They don't catch a math derivation that's circular, or a response that's sycophantic filler dressed up as reasoning.

Running a full LLM as a judge catches the subtle stuff. It also costs real money at scale and crawls compared to what you actually need.

I wanted a third option, so I built jev-curate around TypeSafe's Jev model. Jev doesn't generate text. You send it a state plus a typed question, it sends back a typed answer: a category choice, a yes/no probability, an ordinal score. No output to parse, no risk of it rewriting your data on you.

Quick start

\`bash
cargo install jev-curate
export TYPESAFE_API_KEY="your-api-key"

jev-curate filter train.parquet \
--preset reasoning-math \
--out ./output/ \
--concurrency 32
`\

What ships in v0.1.0

Three presets:

reasoning-math — checks for circular logic and invalid reasoning steps, scores depth 1 to 5
anti-sycophancy — flags "as an AI..." filler and ungrounded flattery
code-correctness — catches unclosed code fences and stub placeholders

Under the hood

Rust core (Cargo, tokio, arrow, parquet) with PyO3 bindings for Python. Streams rows through an adaptive rate limiter so you don't eat 429s.

Where it stands

v0.1.0, single-node bench measured at 24 rows/sec so far, working on getting that higher with better batching. Tests run against a mock server so CI doesn't burn API credits.

Live site with benchmarks and docs: https://jev-curate.vercel.app

AkashPriyadarshii / jev-curate

High-throughput synthetic and pretraining dataset sifter for TypeSafe Jev. Rust streaming core, Parquet and JSONL I/O, typed Choice/Score/Noul judgments, speculative fan-out, 24.0 rows/sec measured.

`jev-curate`

High-Throughput Synthetic & Pretraining Dataset Sifter Powered by TypeSafe AI (Jev)

Live: jev-curate.vercel.app (measured 24.0 rows/sec single-node on local mock bench examples/bench_mock.rs, 1,500+ cluster target)

By Akash Priyadarshi

Why jev-curate • Quickstart • CLI Reference • Python API • Architecture • Non-Goals • Ecosystem

Why `jev-curate`?

Cleaning 10M to 1B rows of synthetic reasoning data, instruction tuning pairs, or web-scraped corpora is an economic and technical nightmare:

Generative LLMs are too slow and expensive: Running Claude 3.5 Sonnet or GPT-4o to judge synthetic rows costs $15,000–$50,000 per billion tokens and crawls at a painful 30–50 rows/sec.
Regex heuristics are blind to reasoning flaws: Keyword and regex filters can check syntax, but fail to detect circular reasoning, hallucinated derivation steps, or robotic sycophancy.
Context rot from uncompressed inputs: Naively feeding raw data into LLMs causes decision accuracy to crater while burning money on boilerplate text.

jev-curate solves this…

View on GitHub

If you're curating LLM-generated data and tired of the regex-versus-full-judge tradeoff, take a look. Feedback and issues welcome, I'm building this solo out of Patna.

DEV Community: Akash Priyadarshi

I got tired of paying for SEO dashboards, so I built a free Rust CLI instead

What it does

Quick start

AkashPriyadarshii / jev-seo

100% free ₹0 agent-first SEO & GEO CLI suite and MCP server in Rust replacing Semrush and OpenSEO via DuckDuckGo and TypeSafe Jev System One https://akashpriyadarshii.github.io/jev-seo/

jev-seo

See it run

Why jev-seo?

Numbers

I built a Rust CLI that filters datasets using typed AI decisions instead of LLM judges

Quick start

What ships in v0.1.0

Under the hood

Where it stands

AkashPriyadarshii / jev-curate

High-throughput synthetic and pretraining dataset sifter for TypeSafe Jev. Rust streaming core, Parquet and JSONL I/O, typed Choice/Score/Noul judgments, speculative fan-out, 24.0 rows/sec measured.

jev-curate

Why jev-curate?

`jev-curate`

Why `jev-curate`?