{"components":[{"slug":"tinyfish","name":"TinyFish","summary":"Search, fetch, and browser automation for agents. Free Search and Fetch; metered browser runs. Leyline uses it as intake evidence crawl, not as a locked vendor.","kind":"app","homepage":"https://www.tinyfish.ai","docsUrl":"https://docs.tinyfish.ai/search-api","publisher":"TinyFish","license":"proprietary","hasMcp":true,"hasApi":true,"costRange":"freemium","costNotes":"Search and Fetch are free. Browser/agent automation draws a wallet.","composabilityRationale":"HTTP Search/Fetch plus MCP. Easy to plug as an evidence plane without owning the rest of a stack. 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Free Search and Fetch; metered browser runs. Leyline uses it as intake evidence crawl, not as a locked vendor.","cost":"freemium"}],"tweets":[{"url":"https://x.com/Tiny_Fish/all?sort=popular","author":"x.com","text":"TinyFish brings live web Search, clean Fetch, and Web Agent to ChatGPT, so your agent can find it, read it, and act on it."},{"url":"https://x.com/Tiny_Fish/status/2087572349415768241","author":"x.com","text":"TinyFish brings live web Search, clean Fetch, and Web Agent to ChatGPT, so your agent can find it, read it, and act on it."},{"url":"https://x.com/Tiny_Fish/status/2084701160389415317","author":"x.com","text":"TinyFish brings live web Search, clean Fetch, and Web Agent to Claude and Cowork, so your agent can find it, read it, and act on it."}],"sources":[{"kind":"docs","label":"TinyFish Search API","url":"https://docs.tinyfish.ai/search-api","at":"2026-09-12T00:00:00Z"},{"kind":"docs","label":"TinyFish Fetch API","url":"https://docs.tinyfish.ai/fetch-api","at":"2026-09-12T00:00:00Z"},{"kind":"web","label":"TinyFish pricing","url":"https://www.tinyfish.ai/pricing","at":"2026-09-12T00:00:00Z"},{"kind":"tweet","label":"Keith Zhai on TinyFish search","url":"https://x.com/KeithZhai/status/2097716968845672561","at":"2026-09-12T00:00:00Z"}],"origin":"seed","sector":"search-crawl","notes":[{"authorKind":"human","body":"Named in Leyline concept as a crawl example beside Aside and Firecrawl — examples, not vendor lock.","at":"2026-09-12T00:00:00Z"}],"score":23,"explain":{"score":23,"formula":"100 × sum(weight × value) over observed factors only. 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Same kind and overlapping capability."},{"from":"tinyfish","to":"playwright","kind":"fills-same-slot-as","note":"Shared functions browser, extract, observe. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-13T08:56:02.848Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-13T08:56:02.848Z"},{"key":"human_note_count","value":1,"source":"component-record","method":"stored-notes","observedAt":"2026-09-13T08:56:02.848Z"}],"functionProbabilities":[],"claimVerification":[]},{"slug":"playwright","name":"Playwright","summary":"Browser automation for tests and agents. 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Use the browser slice without taking the whole test-runner product box.","cost":"free"}],"tweets":[{"url":"https://x.com/blackgirlbytes/article/2089512941187911879","author":"x.com","text":"Specifically, I used context from the agent session that built the app to find and fix the most important gap in its Playwright tests. Here's how I did it. 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Cost is free. 3 of 3 checklist fields are complete.","sources":[{"kind":"docs","label":"Playwright docs","url":"https://playwright.dev","at":"2026-09-12T00:00:00Z"}],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.3284952023094184,"contribution":0.04598932832331858,"why":"2 sources cover 2 evidence types. The latest dated evidence is 2026-09-12. 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Can fill the same function slot."},{"from":"playwright","to":"cloro","kind":"fills-same-slot-as","note":"Shared functions extract, observe. Can fill the same function slot."},{"from":"playwright","to":"maple","kind":"fills-same-slot-as","note":"Shared functions browser, observe. Can fill the same function slot."},{"from":"playwright","to":"kogiqa-mcp-web-browser","kind":"fills-same-slot-as","note":"Shared functions browser, test. Can fill the same function slot."},{"from":"playwright","to":"camofox-browser-server","kind":"fills-same-slot-as","note":"Shared functions browser, extract. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-13T08:56:02.843Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-13T08:56:02.843Z"}],"functionProbabilities":[],"claimVerification":[]},{"slug":"n8n","name":"n8n","summary":"Workflow automation. 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Compose and schedule slices that agents can call without owning the whole iPaaS.","cost":"freemium"},{"functionId":"notify","label":"Notify","note":"Workflow automation. Compose and schedule slices that agents can call without owning the whole iPaaS.","cost":"freemium"},{"functionId":"transform","label":"Transform","note":"Workflow automation. Compose and schedule slices that agents can call without owning the whole iPaaS.","cost":"freemium"}],"tweets":[],"sources":[{"kind":"github","label":"n8n-io/n8n","url":"https://github.com/n8n-io/n8n","at":"2026-09-12T00:00:00Z"}],"origin":"seed","notes":[],"score":18,"explain":{"score":18,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"value":0.3,"contribution":0.054,"why":"A licence is declared. 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Can fill the same function slot."},{"from":"n8n","to":"casdoor","kind":"fills-same-slot-as","note":"Shared functions compose, connect. Can fill the same function slot."},{"from":"n8n","to":"self","kind":"fills-same-slot-as","note":"Shared functions compose, connect. Can fill the same function slot."},{"from":"n8n","to":"invisible-playwright-mcp","kind":"fills-same-slot-as","note":"Shared functions connect, notify. Can fill the same function slot."},{"from":"n8n","to":"awesome","kind":"fills-same-slot-as","note":"Shared functions compose, connect. Can fill the same function slot."},{"from":"n8n","to":"memini","kind":"fills-same-slot-as","note":"Shared functions compose, connect. Can fill the same function slot."}],"signals":[{"key":"has_license","value":1,"source":"component-record","method":"declared-license","observedAt":"2026-09-13T08:56:02.845Z"},{"key":"has_docs","value":1,"source":"component-record","method":"declared-docs","observedAt":"2026-09-13T08:56:02.845Z"}],"functionProbabilities":[],"claimVerification":[]},{"slug":"needle-3","name":"Needle 3","summary":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","kind":"model","homepage":"https://cactuscompute.com/needle","repoUrl":"https://github.com/cactus-compute/needle","publisher":"","hasMcp":false,"hasApi":false,"costRange":"unknown","composabilityRationale":"","functions":["embed","extract"],"functionProbabilities":[{"functionId":"embed","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md","method":"lexical","observedAt":"2026-09-18T00:00:00.000Z"},{"functionId":"extract","value":0.72,"source":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md","method":"lexical","observedAt":"2026-09-18T00:00:00.000Z"}],"slices":[{"functionId":"embed","label":"Embed","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"},{"functionId":"extract","label":"Extract","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"}],"tweets":[],"sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"interfaces":[],"origin":"seed","notes":[],"createdAt":"2026-09-18T00:00:00.000Z","updatedAt":"2026-09-18T00:00:00.000Z","stagingSeed":"leyline-0.3.0-staging","recordId":"cmp_needle-3","claims":{"name":"Needle 3","summary":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","kind":"model","publisher":"","homepage":"https://cactuscompute.com/needle","repoUrl":"https://github.com/cactus-compute/needle","costRange":"unknown","functions":["embed","extract"],"slices":[{"functionId":"embed","label":"Embed","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"},{"functionId":"extract","label":"Extract","note":"Needle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","cost":"unknown"}],"interfaces":[],"composabilityRationale":""},"relations":[{"from":"needle-3","to":"local-faiss-mcp-server","kind":"fills-same-slot-as","note":"Shared functions embed, extract. Can fill the same function slot."}],"signals":[],"claimVerification":[],"explain":{"score":3,"formula":"100 × sum(weight × value) over observed factors only. Unobserved factors are omitted, not zeroed.","factors":[{"id":"github_health","label":"GitHub health","weight":0.18,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"agent_use","label":"Agent use","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"human_notes","label":"Human notes","weight":0.14,"why":"insufficient evidence","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"evidence":"insufficient"},{"id":"composability","label":"Composability","weight":0.16,"why":"insufficient evidence","sources":[],"evidence":"insufficient"},{"id":"checklist","label":"Checklist","weight":0.1,"value":0,"contribution":0,"why":"No MCP surface is declared. No API is declared. Cost is unknown. 0 of 3 checklist fields are complete.","sources":[],"evidence":"observed"},{"id":"evidence_quality","label":"Evidence quality","weight":0.14,"value":0.18107144632819594,"contribution":0.025350002485947435,"why":"1 sources cover 1 evidence types. The latest dated evidence is 2026-09-18. Freshness is omitted unless observed as a signal.","sources":[{"kind":"human","label":"Catalogue resource card","excerpt":"06 Knowledge Library/Resources/coding/2026-09-18-cactus-compute-needle-3.md\nNeedle 3 is Cactus Compute’s on-device “automation foundation model”: a laddered / sliceable tiny model (post: 8–29 MB CQ2-bit binary; 25–121M params; depths 2–20 layers from one weight set) built on their Simple Attention Networks, aimed at tool calls, structured extraction, and local text embeddings — not open-ended chat.","at":"2026-09-18T00:00:00.000Z"}],"evidence":"observed"},{"id":"benchmark","label":"Benchmarks","weight":0.12,"why":"insufficient evidence","sources":[],"evidence":"insufficient"}],"missing":["GitHub health: insufficient evidence","Agent use: insufficient evidence","Human notes: insufficient evidence","Composability: insufficient evidence","Benchmarks: insufficient evidence"]},"score":3,"tags":["automation","text"]}],"tag":"automation"}