[KongchangAI]
Product

HustleRadar

An open-source resale-market evidence and arbitrage decision tool that combines real-time retail data with language-model explanations. It performs deterministic profit calculations first, then has the model explain the structured facts.

Timeline (last 90 days)

Oct 17

HustleRadar is an open-source market evidence and arbitrage decision tool used to determine whether product discounts are real and whether resale is profitable.

Unverified50%
Oct 17

HustleRadar integrates both SerpApi's real-time retail data and Google Gemma 2's language explanation capability, with the model only responsible for explanation and not for generating numbers.

Unverified50%
Oct 17

HustleRadar's inputs include the product name or link, the actual price paid, an optional reference price, and the server-side evidence mode (live real-time investigation flow or demo fixed data).

Unverified50%
Oct 17

The current version of HustleRadar models the platform fee uniformly at 8%, and completes the resale gross margin calculation in TypeScript first before calling Gemma for explanation.

Unverified50%
Oct 17

HustleRadar's backend concurrently executes three SerpApi search paths via Promise.all: google_shopping to obtain structured quotes, retailer-restricted search (as_sitesearch) to supplement direct-store coverage, and context search with tbs:qdr:y to capture recent price mentions.

Unverified50%
Oct 17

Setting ENABLE_LLM=false in HustleRadar completely bypasses the Gemma model, and the system generates a deterministic explanation from the same set of structured evidence.

Unverified50%
Oct 17

An early implementation of HustleRadar had a bug: when a live vendor failed, it would fall back to demo data, making the interface look complete while undermining the authenticity of the data in live mode.

Unverified50%
Oct 17

HustleRadar's validation layer checks model identity, condition, accessory keywords, variant attributes, and currency before prices enter the median calculation, and lets the user manually choose when multiple configurations remain.

Unverified50%
Oct 17

HustleRadar's tech stack includes a React 18 + TypeScript + Vite + Tailwind frontend, a Node/Express API, SerpApi search grounding, deterministic INR parsing and product guardrails, Gemma 2 (via Ollama or a configurable open-model endpoint), and Render Blueprint deployment.

Unverified50%
Oct 17

HustleRadar's API uses NDJSON to push progress events line by line, and the frontend incrementally reads the response body to display the actual status of each stage; when the browser disconnects, an AbortController aborts the vendor requests.

Unverified50%

3 more timeline events

All Facts (13)

Unverified

HustleRadar is an open-source market evidence and arbitrage decision tool used to determine whether product discounts are real and whether resale is profitable.

50%
Unverified

HustleRadar integrates both SerpApi's real-time retail data and Google Gemma 2's language explanation capability, with the model only responsible for explanation and not for generating numbers.

50%
Unverified

HustleRadar's inputs include the product name or link, the actual price paid, an optional reference price, and the server-side evidence mode (live real-time investigation flow or demo fixed data).

50%
Unverified

The current version of HustleRadar models the platform fee uniformly at 8%, and completes the resale gross margin calculation in TypeScript first before calling Gemma for explanation.

50%
Unverified

HustleRadar's backend concurrently executes three SerpApi search paths via Promise.all: google_shopping to obtain structured quotes, retailer-restricted search (as_sitesearch) to supplement direct-store coverage, and context search with tbs:qdr:y to capture recent price mentions.

50%
Unverified

Setting ENABLE_LLM=false in HustleRadar completely bypasses the Gemma model, and the system generates a deterministic explanation from the same set of structured evidence.

50%
Unverified

An early implementation of HustleRadar had a bug: when a live vendor failed, it would fall back to demo data, making the interface look complete while undermining the authenticity of the data in live mode.

50%
Unverified

HustleRadar's validation layer checks model identity, condition, accessory keywords, variant attributes, and currency before prices enter the median calculation, and lets the user manually choose when multiple configurations remain.

50%
Unverified

HustleRadar's tech stack includes a React 18 + TypeScript + Vite + Tailwind frontend, a Node/Express API, SerpApi search grounding, deterministic INR parsing and product guardrails, Gemma 2 (via Ollama or a configurable open-model endpoint), and Render Blueprint deployment.

50%
Unverified

HustleRadar's API uses NDJSON to push progress events line by line, and the frontend incrementally reads the response body to display the actual status of each stage; when the browser disconnects, an AbortController aborts the vendor requests.

50%
Unverified

HustleRadar grades opportunities along two dimensions, percentage gross margin and absolute net profit: HIGH (margin ≥20% and net profit ≥2000), MODERATE (margin ≥10% or net profit ≥800), LOW, UNPROFITABLE.

50%
Unverified

HustleRadar's use of an 8% platform fee is a simplified assumption for prototype validation; actual deductions on different e-commerce platforms typically range from 10%–20%, and a fee rate that is too low will cause net profit to be systematically overstated.

50%
Unverified

The prompt HustleRadar sends to Gemma includes the normalized product name, the verdict, the confidence, the declared price, the market median, the lowest comparable quote, and the computed arbitrage result, and explicitly forbids the model from hallucinating prices or retailers.

50%

Source Articles