# Trend Physics Pulse — AI repos + tools, scored (`brilliant_hemlock/trend-physics-pulse`) Actor Daily AI-ecosystem snapshot: GitHub repo velocity + 🔴 star-bomb detection, an LLM-scored AI-tool registry, PyPI download spike signals, and topic-concentration leaders. Four data streams, one Actor, flat dataset rows. - **URL**: https://apify.com/brilliant\_hemlock/trend-physics-pulse.md - **Developed by:** [Christian](https://apify.com/brilliant_hemlock) (community) - **Categories:** AI, News, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Trend Physics Pulse **Two AI-ecosystem trend signals you can't get from any single public API, shaped as flat Apify Dataset rows. $0.10 per 1000 rows.** ```json { "_slice": "repos", "repo": "xai-org/x-algorithm", "anomaly_signal": "🔴 Star bomb (100x spike)", "composite_score": 1745.91, "velocity_per_day_7d": 124.43, "total_stars": 5791 } ```` ☝️ One row from one run. Surfacing that without this Actor takes weeks of GitHub crawling plus your own anomaly logic. A default run gives you ~40 rows like it (across accelerating GitHub repos and a scored AI-tool registry) in under 2 seconds. Two more slices — PyPI download velocity and topic-entropy / narrative-collapse — are in active development. See **Roadmap** at the bottom. *** ### Try it in 30 seconds Easiest path: click **▶ Try for free** above — Apify runs the Actor in your browser with no setup. For everyone else, grab an API token from [console.apify.com → Integrations](https://console.apify.com/account/integrations) and run any of these from a terminal: ```sh export APIFY_TOKEN=apify_api_... ``` **1. Today's top accelerating repos** ```sh curl -sX POST "https://api.apify.com/v2/acts/brilliant_hemlock~trend-physics-pulse/run-sync-get-dataset-items?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"slices":["repos"],"limitPerSlice":50}' \ | jq -r ' map(select(.composite_score != null)) | sort_by(-.composite_score) | .[0:5][] | "\(.composite_score | floor)\t\(.repo)\t\(.anomaly_signal)" ' ``` Expected output (one row = one accelerating repo, sorted by momentum): ``` 1745 xai-org/x-algorithm 🔴 Star bomb (100x spike) 817 anthropics/claude-plugins-official 🟢 Normal variance 115 HKUDS/CLI-Anything 🟢 Normal variance 100 microsoft/ai-agents-for-beginners 🟡 Suspicious spike (20x) 88 langchain-ai/langgraph 🟢 Normal variance ``` **2. Just the star-bombs — anything growing unnaturally fast today** ```sh curl -sX POST "https://api.apify.com/v2/acts/brilliant_hemlock~trend-physics-pulse/run-sync-get-dataset-items?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"slices":["repos"],"limitPerSlice":200}' \ | jq '[.[] | select(.anomaly_signal // "" | startswith("🔴"))] | .[] | {repo, anomaly_signal, total_stars, velocity_per_day_7d}' ``` **3. Top 5 useful AI-coding tools, formatted for a Slack digest** ```sh curl -sX POST "https://api.apify.com/v2/acts/brilliant_hemlock~trend-physics-pulse/run-sync-get-dataset-items?token=$APIFY_TOKEN" \ -H "Content-Type: application/json" \ -d '{"slices":["tools"],"limitPerSlice":50,"toolVerdict":"useful","toolCategory":"ai_coding"}' \ | jq -r ' map(select(.avg_score != null)) | sort_by(-.avg_score) | .[0:5][] | "*\(.canonical_name)* (score \(.avg_score)) — \(.one_liner)" ' ``` Each call runs the Actor once on Apify's runners and returns the dataset rows in a single HTTP response — no polling, no dataset-URL juggling. Pipe straight into `jq`, `csvkit`, a Slack webhook, or a cron job. *** ### The two slices Every row lands in the same Apify Dataset, tagged with `_slice` for filtering. #### `repos` — accelerating GitHub repos Backed by a multi-million-repo stargazer corpus with multi-year history. The Actor never calls GitHub itself — it requests precomputed signals from our backend. The repo cohort rotates daily, so each run surfaces a fresh set of accelerators. ```json { "_slice": "repos", "repo": "anthropics/claude-plugins-official", "snapshot_day": "2026-05-23", "total_stars": 5729, "velocity_per_day_7d": 88.57, "acceleration_per_day_squared": 79.86, "anomaly_signal": "🟢 Normal variance", "composite_score": 817.38 } ``` Notable fields: - **`composite_score`** — momentum score that weights acceleration against repo size. A fast-growing small repo can outrank a slow giant. Sort by this for the day's top movers. - **`anomaly_signal`** — 🟢 Normal variance / 🟡 Suspicious spike / 🔴 Star bomb. Derived from each repo's historical star series, not just on-the-day spikes, so an old organic burst is distinguishable from a fresh coordinated one. Exact wording comes from the upstream materialised view — see METHODOLOGY.md for the burst-ratio thresholds. - **`velocity_per_day_7d`, `acceleration_per_day_squared`** — first- and second-derivative momentum; week-over-week diff is built in. #### `tools` — scored AI-tool registry Curated registry refreshed by an internal LLM-rubric pipeline on a daily curation cycle. Each tool is scored 1–10 on novelty / utility / maturity / hype-vs-substance, with a written `verdict` (`useful` / `promising` / `noise`) and `justification`. Sources include GitHub Trending, HN, Reddit, and a handful of newsletters. ```json { "_slice": "tools", "canonical_name": "TanStack Router", "category": "ai_infra", "verdict": "useful", "avg_score": 8.25, "novelty": 6, "utility": 9, "maturity": 9, "hype_vs_substance": 9, "one_liner": "A fully type-safe router and full-stack framework for building web applications with React and other frameworks.", "repo_url": "https://github.com/TanStack/router" } ``` *** ### What you'd use this for - **Daily "what's moving" digest.** Sort `repos` by `composite_score` for the rotating top movers across the corpus. - **Spot 100x star bombs the day they happen.** Filter `repos` where `anomaly_signal` starts with 🔴 — typically days before HN or X notice. - **Weekly AI-tool Slack digest.** Filter `tools` by `verdict: useful` plus a `category`, push to your channel. - **VC / scout pipeline.** Sort `repos` by `composite_score` and review small-but-accelerating projects worth a meeting. - **Retire your own crawler.** GitHub polling + anomaly re-derivation + LLM tool scoring, replaced with one daily Actor run. *** ### Input ```json { "slices": ["repos", "tools"], "limitPerSlice": 20, "repoMinStars": 1000, "toolVerdict": "useful", "toolCategory": "" } ``` | Field | Default | Range / values | Purpose | |---|---|---|---| | `slices` | both | repos / tools | Uncheck to skip a slice and save spend. | | `limitPerSlice` | 20 | 1–200 | Default keeps first-time runs cheap; raise to 200 for full daily dumps. | | `repoMinStars` | 1000 | ≥0 | Drops low-noise repos. | | `toolVerdict` | `useful` | any / useful / promising / noise | Filter `tools` by overall verdict. | | `toolCategory` | — | e.g. `llm_framework`, `ai_coding` | Empty = all categories. | *** ### Pricing & limits - **$0.10 per 1000 rows delivered** ($0.0001/row, Pay-Per-Event). A default ~40-row run ≈ **$0.004**; a maxed-out ~400-row run (`limitPerSlice: 200`) ≈ **$0.04**. Set `Maximum cost per run` in your run options to cap spend. - 60 requests/minute per Apify customer (you won't hit it in normal use). - Backend caches each filtered slice for 5 minutes per query. - **Freshness**: repos refreshed within 24h (see `snapshot_day`); tools per daily curation cycle (see `scored_at`). ### What this is not - Not a real-time API — refresh cadence above. - Not a historical-query endpoint — time-series queries are planned for a higher-tier SKU. - Not a GitHub or PyPI proxy — the corpus is curated and the signals are computed, not raw pass-through. *** ### Roadmap Two more slices are wired into the backend but not yet exposed in this Actor's input — data pipeline work is still in progress: - **`packages`** — PyPI download velocity per tracked LLM framework, with rolling average and spike-ratio signals. - **`entropy`** — weekly Shannon-entropy analysis flagging AI subcategories that have concentrated onto a single leader repo. They'll be added to `slices` once the underlying collectors are production-ready. METHODOLOGY.md tracks the current exposure status. # Actor input Schema ## `slices` (type: `array`): Which trend slices to include in this run. Both selected by default; uncheck to skip and save spend. ## `limitPerSlice` (type: `integer`): Upper bound on rows returned per selected slice. Default 20 keeps a first-time run cheap; raise to 200 for full daily dumps. ## `repoMinStars` (type: `integer`): Drop noise — only repos above this star threshold are returned. ## `toolVerdict` (type: `string`): Filter the AI-tools slice by overall verdict. 'any' returns everything regardless of scoring. ## `toolCategory` (type: `string`): e.g. llm\_framework, ai\_coding, ai\_design, local\_inference. Leave empty for all categories. ## Actor input object example ```json { "slices": [ "repos", "tools" ], "limitPerSlice": 20, "repoMinStars": 1000, "toolVerdict": "useful", "toolCategory": "" } ``` # Actor output Schema ## `results` (type: `string`): All rows from this run across selected slices. Each row carries `_slice` so you can filter downstream. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("brilliant_hemlock/trend-physics-pulse").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("brilliant_hemlock/trend-physics-pulse").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call brilliant_hemlock/trend-physics-pulse --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=brilliant_hemlock/trend-physics-pulse", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Trend Physics Pulse — AI repos + tools, scored", "description": "Daily AI-ecosystem snapshot: GitHub repo velocity + 🔴 star-bomb detection, an LLM-scored AI-tool registry, PyPI download spike signals, and topic-concentration leaders. Four data streams, one\nActor, flat dataset rows.", "version": "0.1", "x-build-id": "AmBYYfXPtMDSz1kdH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/brilliant_hemlock~trend-physics-pulse/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-brilliant_hemlock-trend-physics-pulse", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/brilliant_hemlock~trend-physics-pulse/runs": { "post": { "operationId": "runs-sync-brilliant_hemlock-trend-physics-pulse", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/brilliant_hemlock~trend-physics-pulse/run-sync": { "post": { "operationId": "run-sync-brilliant_hemlock-trend-physics-pulse", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "slices": { "title": "Slices to fetch", "type": "array", "description": "Which trend slices to include in this run. Both selected by default; uncheck to skip and save spend.", "items": { "type": "string", "enum": [ "repos", "tools" ], "enumTitles": [ "GitHub repos (velocity + acceleration + anomaly)", "AI tools (toolscout registry, scored 4 axes)" ] }, "default": [ "repos", "tools" ] }, "limitPerSlice": { "title": "Max items per slice", "minimum": 1, "maximum": 200, "type": "integer", "description": "Upper bound on rows returned per selected slice. Default 20 keeps a first-time run cheap; raise to 200 for full daily dumps.", "default": 20 }, "repoMinStars": { "title": "Repos: min total stars", "minimum": 0, "type": "integer", "description": "Drop noise — only repos above this star threshold are returned.", "default": 1000 }, "toolVerdict": { "title": "Tools: verdict filter", "enum": [ "any", "useful", "promising", "noise" ], "type": "string", "description": "Filter the AI-tools slice by overall verdict. 'any' returns everything regardless of scoring.", "default": "useful" }, "toolCategory": { "title": "Tools: category filter (optional)", "type": "string", "description": "e.g. llm_framework, ai_coding, ai_design, local_inference. Leave empty for all categories.", "default": "" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```