This workflow follows the Agent → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "LLM && AI Curator",
"nodes": [
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Zwraca aktualn\u0105 dat\u0119 i czas (np. w ISO 8601). U\u017cywane do ustawiania [DATA] w temacie maila oraz okre\u015blania bie\u017c\u0105cego miesi\u0105ca dla deduplikacji artyku\u0142\u00f3w.",
"options": {}
},
"type": "n8n-nodes-base.dateTimeTool",
"typeVersion": 2,
"position": [
1376,
432
],
"id": "fd491d1c-5249-4584-a2c8-3af169852de4",
"name": "get_current_time"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Zapisuje do bazy artyku\u0142y wykorzystane w aktualnej pras\u00f3wce. Przyjmuje tablic\u0119 obiekt\u00f3w {url, title} i dopisuje je do rekord\u00f3w bie\u017c\u0105cego miesi\u0105ca.",
"dataTableId": {
"__rl": true,
"value": "fFlEoKd5pV15ML9r",
"mode": "list",
"cachedResultName": "Articles",
"cachedResultUrl": "/projects/7Qxghf3TTzUDPsic/datatables/fFlEoKd5pV15ML9r"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"url": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('url', ``, 'string') }}",
"tile": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('tile', ``, 'string') }}"
},
"matchingColumns": [],
"schema": [
{
"id": "url",
"displayName": "url",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
},
{
"id": "tile",
"displayName": "tile",
"required": false,
"defaultMatch": false,
"display": true,
"type": "string",
"readOnly": false,
"removed": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"type": "n8n-nodes-base.dataTableTool",
"typeVersion": 1,
"position": [
1504,
432
],
"id": "80b04cf1-6b6b-46e5-be95-a43bfdedbff9",
"name": "save_sent_articles"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Pobiera z bazy list\u0119 artyku\u0142\u00f3w wys\u0142anych w bie\u017c\u0105cym miesi\u0105cu. Zwraca tablic\u0119 obiekt\u00f3w {url, title} do sprawdzania duplikat\u00f3w.",
"operation": "get",
"dataTableId": {
"__rl": true,
"value": "fFlEoKd5pV15ML9r",
"mode": "list",
"cachedResultName": "Articles",
"cachedResultUrl": "/projects/7Qxghf3TTzUDPsic/datatables/fFlEoKd5pV15ML9r"
},
"filters": {
"conditions": [
{
"keyName": "url",
"keyValue": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('conditions0_Value', ``, 'string') }}"
}
]
},
"returnAll": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Return_All', ``, 'boolean') }}"
},
"type": "n8n-nodes-base.dataTableTool",
"typeVersion": 1,
"position": [
1632,
432
],
"id": "a3fe5f87-92ce-42a1-bde3-a783f6c6a8ee",
"name": "get_sent_articles"
},
{
"parameters": {
"descriptionType": "manual",
"toolDescription": "Wysy\u0142a gotowego maila z pras\u00f3wk\u0105 Tech Brief do Adriana. Przyjmuje parametry subject (temat) i body (tre\u015b\u0107 maila w plaintext/markdown).",
"fromEmail": "adrian@kolegaliterat.pl",
"toEmail": "jawor34@gmail.com",
"subject": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Subject', ``, 'string') }}",
"emailFormat": "both",
"text": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Text', ``, 'string') }}",
"html": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('HTML', ``, 'string') }}",
"options": {}
},
"type": "n8n-nodes-base.emailSendTool",
"typeVersion": 2.1,
"position": [
1760,
432
],
"id": "c097692a-3843-4365-9b71-7bbd3410d494",
"name": "send_tech_brief_email",
"credentials": {
"smtp": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"rule": {
"interval": [
{
"field": "weeks",
"triggerAtDay": [
1,
3,
5
],
"triggerAtHour": 8
}
]
}
},
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
-320,
400
],
"id": "697d530b-a3e1-46f8-9765-f17bbfbaf4c3",
"name": "Schedule Trigger"
},
{
"parameters": {
"model": {
"__rl": true,
"value": "gpt-5.1",
"mode": "list",
"cachedResultName": "gpt-5.1"
},
"options": {
"responseFormat": "text",
"timeout": 600000
}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.2,
"position": [
1248,
432
],
"id": "d21b9673-f900-4788-a142-5d64d7087fda",
"name": "GPT 5.1",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"mode": "raw",
"jsonOutput": "{\n \"post_limit\" : 10\n}\n",
"options": {}
},
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
128,
112
],
"id": "5e7c0f1f-2cfd-48c0-8f78-817caf3da8e2",
"name": "Set Post Limit"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "singularity",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
-368
],
"id": "041bd314-d6ff-44f3-a9fe-8eb8eb695bb1",
"name": "Visit r/singularity",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "subreddit, selftext, , title, permalink, url",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
-368
],
"id": "88177c55-5ff9-45f8-80f9-31f837983f06",
"name": "Clean up r/singularity"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "ArtificialInteligence",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
-176
],
"id": "52bf56b4-cfaa-47b5-9f20-79c692a90377",
"name": "Visit r/ArtificialInteligence",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "url, permalink, selftext, title",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
-176
],
"id": "1d861dcf-4d0b-40ff-84f8-39d505c9065b",
"name": "Clean up r/ArtificialInteligence"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "Futurology",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
16
],
"id": "ee2332ef-fddd-49e0-9cd4-b367ad8d4640",
"name": "Visit r/Futurology",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "subreddit, selftext, , title, permalink, url",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
16
],
"id": "f08e5e36-6bdd-42d9-af70-7bbb2d5c4355",
"name": "Clean up r/Futurology"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "LocalLLaMA",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
208
],
"id": "3f787279-23be-41cc-9552-aec0332b1ead",
"name": "Visit r/LocalLLaMA",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "url, permalink, selftext, title",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
208
],
"id": "105905fe-c619-4489-83c1-e61e05437c86",
"name": "Clean up r/LocalLLaMA"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "MachineLearning",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
400
],
"id": "06ad277e-6f4b-4742-8d22-32e229e43824",
"name": "Visit r/MachineLearning",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "url, permalink, selftext, title",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
400
],
"id": "728ce390-8b31-4540-842d-ce62a492108c",
"name": "Clean up r/MachineLearning"
},
{
"parameters": {
"operation": "getAll",
"subreddit": "Python",
"limit": "={{ $json.post_limit }}",
"filters": {
"category": "top"
}
},
"type": "n8n-nodes-base.reddit",
"typeVersion": 1,
"position": [
352,
592
],
"id": "6748b81f-6419-483c-bf25-b8373124533a",
"name": "Visit r/Python",
"credentials": {
"redditOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"include": "specifiedFields",
"fieldsToInclude": "url, permalink, selftext, title",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
592
],
"id": "e1068bc1-3f73-460a-b821-30f1180eaf31",
"name": "Clean up r/Python"
},
{
"parameters": {
"method": "POST",
"url": "http://export.arxiv.org/api/query?search_query=%28cat%3Acs.AI+OR+cat%3Acs.LG+OR+cat%3Acs.CL%29+AND+%28all%3A%22large+language+model%22+OR+all%3A%22LLM+agent%22+OR+all%3A%22autonomous+agent%22+OR+all%3A%22workflow+automation%22+OR+all%3A%22process+automation%22+OR+all%3A%22prompt+engineering%22+OR+all%3A%22retrieval+augmented%22+OR+all%3A%22RAG%22+OR+all%3A%22code+generation%22+OR+all%3A%22text-to-image%22+OR+all%3A%22diffusion%22+OR+all%3A%22ComfyUI%22+OR+all%3A%22production+deployment%22+OR+all%3A%22business+application%22%29+ANDNOT+%28all%3A%22theoretical+bound%22+OR+all%3A%22convergence+rate%22+OR+all%3A%22asymptotic+analysis%22+OR+all%3A%22sample+complexity%22%29&start=0&max_results=25&sortBy=submittedDate&sortOrder=descending",
"options": {}
},
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.3,
"position": [
-96,
784
],
"id": "d770b1e6-cc50-4493-b7bd-b9347ccedd42",
"name": "Query arxiv"
},
{
"parameters": {
"options": {
"ignoreAttrs": false,
"normalize": true
}
},
"type": "n8n-nodes-base.xml",
"typeVersion": 1,
"position": [
128,
784
],
"id": "d84df407-99cb-4174-895c-2568024d0423",
"name": "Convert"
},
{
"parameters": {
"jsCode": "// 1. Pobieramy dane wej\u015bciowe. Zak\u0142adamy, \u017ce feed wchodzi jako jeden du\u017cy obiekt.\nconst item = $input.first();\nconst feedData = item.json;\n\n// 2. Dobieramy si\u0119 do listy artyku\u0142\u00f3w.\n// \u015acie\u017cka w Twoim JSON to: feed -> entry\n// Zabezpieczamy si\u0119 na wypadek, gdyby 'entry' nie by\u0142o tablic\u0105 (np. przy 1 wyniku)\nlet entries = feedData.feed.entry;\n\nif (!Array.isArray(entries)) {\n entries = [entries];\n}\n\n// 3. Funkcja pomocnicza do czyszczenia tekstu (usuwa \\n i zb\u0119dne spacje)\nconst cleanText = (text) => {\n if (typeof text === 'string') {\n return text\n .replace(/[\\r\\n]+/g, ' ') // Zamie\u0144 znaki nowej linii na spacj\u0119\n .replace(/\\s+/g, ' ') // Usu\u0144 podw\u00f3jne spacje\n .trim(); // Usu\u0144 spacje z pocz\u0105tku i ko\u0144ca\n }\n return text || '';\n};\n\n// 4. Mapujemy dane na format wyj\u015bciowy n8n\n// Ka\u017cdy element tablicy zostanie zwr\u00f3cony jako oddzielny item\nreturn entries.map(entry => {\n return {\n json: {\n id: entry.id,\n title: cleanText(entry.title),\n summary: cleanText(entry.summary)\n }\n };\n});"
},
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
352,
784
],
"id": "46d766b4-785d-479a-aa6c-443dbc1afb1c",
"name": "Clean up arvix Response"
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
576,
784
],
"id": "a70d7297-667c-445f-b6b4-20ae8e39e13b",
"name": "Get all arvix Data"
},
{
"parameters": {
"numberInputs": 7
},
"type": "n8n-nodes-base.merge",
"typeVersion": 3.2,
"position": [
800,
128
],
"id": "cc79d7fa-e17d-4a47-89c4-866991086c77",
"name": "Merge everything!"
},
{
"parameters": {
"aggregate": "aggregateAllItemData",
"options": {}
},
"type": "n8n-nodes-base.aggregate",
"typeVersion": 1,
"position": [
1024,
208
],
"id": "a6514899-9ce6-4fd7-bbd5-e79ae1b9355c",
"name": "Create one list"
},
{
"parameters": {
"promptType": "define",
"text": "={{ $json.data.toJsonString() }}",
"options": {
"systemMessage": "Jeste\u015b kuratorskim asystentem tworz\u0105cym spersonalizowan\u0105 pras\u00f3wk\u0119 tech/AI dla specjalisty automatyzacji i generatywnej AI.\n\n# PROFIL ODBIORCY\n- W\u0142a\u015bciciel Point17Labs, specjalista AI w BaseLinker\n- Focus: praktyczne zastosowania AI, automatyzacja proces\u00f3w, agenci LLM\n- Background: 8 lat gamedev \u2192 obecnie tech/AI\n- Style: g\u0142\u0119bia > powierzchowno\u015b\u0107, praktyka > teoria, zero buzzword\u00f3w\n- Antypatterny: clickbait, drama, puste \"AI will change everything\"\n\n# NARZ\u0118DZIA (U\u017bYWAJ \u015aWIADOMIE)\n\nMasz do dyspozycji 4 narz\u0119dzia (node\u2019y n8n):\n\n1. `get_current_time` \n Zwraca aktualn\u0105 dat\u0119 i czas (np. w ISO 8601). \n U\u017cywaj do:\n - wstawienia [DATA] w temacie maila,\n - okre\u015blenia \u201ebie\u017c\u0105cego miesi\u0105ca\u201d dla deduplikacji artyku\u0142\u00f3w,\n - wstawienia [data] w sekcji \u201eNast\u0119pna pras\u00f3wka: [data]\u201d.\n\n2. `get_sent_articles` \n Pobiera z bazy list\u0119 artyku\u0142\u00f3w wys\u0142anych w bie\u017c\u0105cym miesi\u0105cu. \n Zwraca tablic\u0119 obiekt\u00f3w `{url, title}` do sprawdzania duplikat\u00f3w.\n\n3. `save_sent_articles` \n Zapisuje do bazy artyku\u0142y wykorzystane w aktualnej pras\u00f3wce. \n Przyjmuje tablic\u0119 obiekt\u00f3w `{url, title}` i dopisuje je do rekord\u00f3w bie\u017c\u0105cego miesi\u0105ca.\n\n4. `send_tech_brief_email` \n Wysy\u0142a gotowego maila z pras\u00f3wk\u0105 Tech Brief do Adriana. \n Przyjmuje parametry:\n - `subject` \u2013 temat maila,\n - `body` \u2013 tre\u015b\u0107 maila (plaintext/markdown).\n\n# WYSOKOPOZIOMOWY WORKFLOW\n\n1. Wywo\u0142aj `get_current_time` \u2192 ustal:\n - dat\u0119 do `[DATA]` w temacie,\n - aktualny miesi\u0105c dla deduplikacji.\n\n2. Wywo\u0142aj `get_sent_articles` \u2192 pobierz list\u0119 `{url, title}` z bie\u017c\u0105cego miesi\u0105ca.\n\n3. Wej\u015bciem jest JSON z postami Reddit + arXiv w jednej strukturze. \n Najpierw:\n - przefiltruj JSON: usu\u0144 wszystkie wpisy, kt\u00f3rych `url` ju\u017c jest w bazie z kroku 2.\n\n4. Na przefiltrowanej li\u015bcie:\n - oce\u0144 ka\u017cdy artyku\u0142 w skali 1\u201310 (wewn\u0119trznie),\n - wybierz \u0142\u0105cznie 5\u20137 najlepszych do maila.\n\n5. Zbuduj gotowy mail po polsku (markdown i html), dok\u0142adnie w opisanej ni\u017cej strukturze. \n Zwr\u00f3\u0107 **tylko** tre\u015b\u0107 maila (bez komentarzy/preambu\u0142y).\n\n6. Je\u015bli w mailu s\u0105 artyku\u0142y:\n - wywo\u0142aj `save_sent_articles` z list\u0105 `{url, title}` artyku\u0142\u00f3w u\u017cytych w mailu.\n\n7. Na ko\u0144cu wywo\u0142aj `send_tech_brief_email` z:\n - `subject`: tematem maila,\n - `body`: pe\u0142n\u0105 tre\u015bci\u0105 maila.\n\n8. Je\u015bli po deduplikacji nie zosta\u0142 **\u017caden** nowy artyku\u0142:\n - nie licz scoringu,\n - wygeneruj specjalny \"pusty\" mail (patrz sekcja \u201eJe\u015bli wszystkie artyku\u0142y s\u0105 duplikatami\u201d),\n - **nie** wywo\u0142uj `save_sent_articles`,\n - mimo wszystko wywo\u0142aj `send_tech_brief_email` z tym \u201epustym\u201d mailem.\n\n# ZASADY SELEKCJI\n\n## Reddit \u2013 wybierz 2\u20134 najlepsze\n\nPRIORYTETYZUJ (score 8\u201310/10):\n- Praktyczne implementacje z kodem / case study\n- Niestandardowe zastosowania AI w biznesie i automatyzacji\n- Insights z produkcji (co dzia\u0142a / nie dzia\u0142a)\n- Nowe narz\u0119dzia rozwi\u0105zuj\u0105ce konkretny problem\n- Architektura system\u00f3w (agenci, RAG, workflow, orchestration)\n\nODRZU\u0106 automatycznie:\n- Drama/gossip (np. Elon piss drinking)\n- Spekulacje o AGI/singularity bez konkret\u00f3w\n- Clickbaity bez warto\u015bci merytorycznej\n- Podstawowe tutoriale \"hello world\"\n- Polityk\u0119/strategie firm bez technicznego mi\u0119sa\n\n## arXiv \u2013 wybierz 2\u20133 najlepsze\n\nPRIORYTETYZUJ:\n- Praktyczne techniki mo\u017cliwe do przetestowania \"tu i teraz\"\n- Ulepszenia RAG / agents / code generation\n- Podej\u015bcia \"production-ready\"\n- Prace z jasno opisan\u0105 \u015bcie\u017ck\u0105 implementacji\n\nODRZU\u0106:\n- Czysta matematyka bez zastosowania\n- \"We prove convergence...\" bez eksperyment\u00f3w\n- Tylko benchmarki SOTA bez use case\n- Prace, kt\u00f3re sensownie dzia\u0142aj\u0105 tylko na 8\u00d7A100 bez wariantu \u201cmniejszego\u201d\n\n# ZARZ\u0104DZANIE DUPLIKATAMI\n\n**KRYTYCZNE: pracujesz zawsze na bie\u017c\u0105cym miesi\u0105cu (wg `get_current_time`).**\n\nWorkflow deduplikacji:\n\n1. **PRZED SELEKCJ\u0104:**\n - Wywo\u0142aj `get_sent_articles` \u2192 pobierz list\u0119 wszystkich `{url, title}` z bie\u017c\u0105cego miesi\u0105ca.\n\n2. **FILTR JSONA:**\n - Usu\u0144 wszystkie artyku\u0142y, kt\u00f3rych `url` ju\u017c jest w bazie.\n\n3. **NORMALNA SELEKCJA:**\n - Scoring, wyb\u00f3r 5\u20137 artyku\u0142\u00f3w i budowa maila tylko na przefiltrowanej li\u015bcie.\n\n4. **PO STWORZENIU MAILA:**\n - Je\u015bli w mailu s\u0105 nowe artyku\u0142y \u2192 wywo\u0142aj `save_sent_articles` z `{url, title}` z maila.\n\n**Je\u015bli wszystkie artyku\u0142y s\u0105 duplikatami (po filtrze lista jest pusta):** \nWy\u015blij maila (przez `send_tech_brief_email`) z tre\u015bci\u0105:\n\nCze\u015b\u0107 Adrian,\n\nWszystkie artyku\u0142y z dzisiejszego scrapingu by\u0142y ju\u017c w poprzednich pras\u00f3wkach tego miesi\u0105ca.\n\nMo\u017cliwe przyczyny:\n- S\u0142aby tydzie\u0144 na tech Reddit/arXiv\n- Algorytm HOT pokazuje te same popularne posty\n- Twoje filtry s\u0105 bardzo selektywne (to dobrze!)\n\nNast\u0119pna pras\u00f3wka: [data]\n\n---\nNa razie nic ciekawego \ud83e\udd37\n\n`[data]` ustaw na dat\u0119 z `get_current_time` albo na kolejn\u0105 dat\u0119 wysy\u0142ki, je\u015bli workflow j\u0105 dostarcza.\n\n# STRUKTURA MAILA\n\n## Temat maila\n\"\ud83e\udd16 Tech Brief [DATA] - [1-2 s\u0142owa o g\u0142\u00f3wnym temacie]\"\n\n- `[DATA]` = data z `get_current_time` (format dopasowany do workflow, np. 2025-11-21).\n\n## Tre\u015b\u0107 (po polsku, plaintext/markdown, bez dodatkowego komentarza):\n\nCze\u015b\u0107 Adrian,\n\n[Opcjonalnie: 1 zdanie kontekstu je\u015bli jest dominuj\u0105cy motyw dnia]\n\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\ud83d\udd25 MUST READ (2-3 artyku\u0142y)\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\n### 1. [Tytu\u0142]\n**\u0179r\u00f3d\u0142o:** [Reddit: r/subreddit] lub [arXiv: kategorie]\n\n**Dlaczego to czyta\u0107:** [1-2 zdania \u2013 konkretna warto\u015b\u0107]\n\n**Key takeaways:**\n- [Praktyczny insight #1]\n- [Praktyczny insight #2] \n- [Praktyczny insight #3]\n\n**Gdzie to u\u017cy\u0107:** [Konkretne zastosowanie w jego pracy/projektach]\n\n\ud83d\udd17 [Link]\n\n---\n\n[Powt\u00f3rz dla pozosta\u0142ych MUST READ]\n\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\ud83d\udca1 WARTO PRZEJRZE\u0106 (2-3 artyku\u0142y)\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\n### [Tytu\u0142]\n**TL;DR:** [1-2 zdania + konkretna warto\u015b\u0107]\n\ud83d\udd17 [Link]\n\n---\n\n[Powt\u00f3rz dla pozosta\u0142ych]\n\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\ud83c\udfb2 WILDCARD (0-1 artyku\u0142)\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\n[Co\u015b niestandardowego, mo\u017ce zaskoczy\u0107, poza g\u0142\u00f3wnym nurtem \u2013 max 1 pozycja]\n\n\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\n\n**Czas czytania:** [wyestumuj czas czytania)\n**Przegapione:** [X] artyku\u0142\u00f3w by\u0142o blisko (je\u015bli by\u0142y sensowne, ale nie wesz\u0142y do topki)\n\n---\nPowodzenia w pracy \ud83d\ude80\n\n# KRYTERIA LICZBOWE\n\n**TOTAL: 5\u20137 artyku\u0142\u00f3w w mailu**\n- MUST READ: 2\u20133 (najwy\u017cszy priorytet)\n- WARTO PRZEJRZE\u0106: 2\u20133 (solidny \u015bredni priorytet) \n- WILDCARD: 0\u20131 (niszowe/ciekawe)\n\nProporcje:\n- Je\u015bli Reddit ma lepszy content \u2192 wi\u0119cej z Reddit\n- Je\u015bli arXiv ma mocne prace \u2192 wi\u0119cej z arXiv\n- Idea\u0142: ~50/50 Reddit + arXiv, ale **jako\u015b\u0107 > proporcje**\n\n## Scoring (wewn\u0119trzny, nie pokazuj w mailu):\n\n- 9\u201310: MUST READ (praktyczny game-changer)\n- 7\u20138: WARTO PRZEJRZE\u0106 (solidny content)\n- 6: WILDCARD (je\u015bli unikalny k\u0105t)\n- <6: odrzu\u0107\n\n# J\u0118ZYK I TON\n\n- Polski, bezpo\u015bredni, konkretny\n- Zwracaj si\u0119 \"ty\"\n- Kr\u00f3tkie, tre\u015bciwe zdania\n- Zero corporate speak, zero buzzword\u00f3w\n- Emoji tylko funkcjonalne (\ud83d\udd25\ud83d\udca1\ud83d\udd17\ud83c\udfb2), nie dla ozdoby\n\n# RED FLAGS \u2013 DO ODRZUCENIA\n\nReddit:\n- r/singularity drama (np. Elon piss) \u2192 SKIP\n- \"AI will replace everyone\" bez mi\u0119sa technicznego \u2192 SKIP\n- Tylko upvote\u2019y, brak g\u0142\u0119bi technicznej \u2192 SKIP\n\narXiv:\n- Abstract pe\u0142en matematyki bez \"we demonstrate\" / \"we evaluate\" \u2192 SKIP\n- Brak eksperyment\u00f3w, czysta teoria \u2192 SKIP\n- Sekcja \"future work\" wa\u017cniejsza ni\u017c wyniki \u2192 SKIP\n\n# KONTEKST CZASOWY (NIE ZMIENIA STRUKTURY MAILA)\n\nMail idzie 3\u00d7 w tygodniu (pon/\u015br/pt). Mo\u017cesz jedynie lekko dostosowa\u0107 komentarz otwieraj\u0105cy:\n- Poniedzia\u0142ek: bardziej praktyczny, \"na start tygodnia\"\n- \u015aroda: mix praktyka + szersza perspektywa\n- Pi\u0105tek: l\u017cejszy ton, mo\u017ce troch\u0119 wi\u0119cej research/\u201cweekend reading\u201d\n\n# OUTPUT\n\n1. Zbuduj **tylko** tre\u015b\u0107 maila (markdown i html).\n2. Nast\u0119pnie wywo\u0142aj `send_tech_brief_email` z tematem i tre\u015bci\u0105 i wy\u015blij maila!\n3. Je\u015bli w mailu s\u0105 artyku\u0142y \u2192 wywo\u0142aj `save_sent_articles` z `{url, title}`.\n"
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{
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"node": "Visit r/Python",
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{
"node": "Clean up r/singularity",
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Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
openAiApiredditOAuth2Apismtp
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
LLM && AI Curator. Uses dateTimeTool, dataTableTool, emailSendTool, lmChatOpenAi. Scheduled trigger; 26 nodes.
Source: https://github.com/KolegaLiterat/AIdrian/blob/e63894a0867cf55a264e5f145b105e47e76a3926/n8nWorkflows/3_llm_and_ai_news_curator.json — original creator credit. Request a take-down →
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