n8n AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

n8n AI AI

Megathread Summary: I Asked Multiple Reddit Communities How to Build a Living Memory /Context Engine for Business. Here's what everyone had to say.

I am trying to build a living memory/context engine for my business, something that can remember projects, decisions, timelines, risks, and conversations across emails, documents, notes, chats, and meetings. Since this is new territory for me, I asked several Reddit communities for advice. The responses were incredibly thoughtful, and many people shared architectures, engineering trade-offs, tools, and lessons learned from building similar systems. I consolidated the best ideas into a single summary. If you're exploring the same problem, especially if you're just getting started like me, I hope this will help. Core Philosophies & Perspectives Query-First Design: Do not build the storage layer first. Write out 20 real-world queries you will ask tomorrow and architect backward, because the retrieval interface shapes the system more than the storage layer. Chief of Staff vs. Search Engine: The goal is not just retrieving raw data, but synthesis. Like Microsoft Clarity’s bulk insights, the system should process updates and proactively tell you what projects need attention, what changed, and what the blockers are. The "Daily Mirror" Briefing: Focus on what the user needs to know at the start of the next session to continue without context loss, rather than striving for perfect archival completeness. Four Separate Problems: Treating user queries as a single search issue will fail; "latest status" is a retrieval problem, "unresolved issues" is state tracking, "decisions made" is entity extraction, and "important updates" requires significance scoring. Architecture & Strategies Append-Only Event Logs First: Avoid starting with a massive knowledge graph or vector database. Ingest everything as a timestamped, append-only event log, and build the knowledge graph later as a derived query layer on top. Artifact-Mediated Continuity: To prevent identity collapse over long timelines, separate retrieval (facts) from reconstruction (identity and working context). Use a "Principal-owned Artifact System" with files like MEMORY.md for project state, "Texture Packs" for behavior descriptions, and "Lane Files" structured around the Five W's. Parallel Retrieval Paths: Pure vector search fails at scale. Run vector search (for semantic similarity) alongside a graph/relational lookup (for exact entities) in parallel, because neither covers the query surface alone. Hybrid search (semantic + BM25 keyword) is heavily recommended. Split Memory by Lifespan & Namespace: Sector your memory from day one. Split durable facts (stable preferences, user info) from working context (recent events), applying different decay rates and routing queries to the appropriate layer. Continuous Summarization: Instead of treating everything as unstructured documents, use an LLM pipeline to continuously extract structured facts from new inputs to update project briefs, decision logs, and risk trackers automatically. The Hardest Engineering Challenges Entity Resolution (The Silent Killer): Different sources will refer to the same thing differently (e.g., "Project X" vs "the X pilot"). Without an entity registry mapping aliases to canonical IDs before writing, your graph will become a mess of duplicates. Ontology & Classification: The hardest part is often getting the system to universally understand the difference between a "decision", a "discussion", or a "risk" across varying data structures like emails versus meeting transcripts. Temporal Relevance & Stale Context: A "decision" stays load-bearing for months, whereas a "status update" decays in days. If you don't encode decay rates and version records, stale facts will outrank fresh ones and confidently contradict recent updates. Significance Scoring: Standard retrieval returns everything recent, not everything important. Write-time scoring fails because significance is retrospective; a better approach is "adaptive salience," where chunks gain weight when retrieved and decay when ignored. Context Moodiness: Especially in greenfield projects, meaningful status updates can be muddied by confounding, irrelevant, or noisy data. Tools & Tech Stack Recommendations Storage / Databases: Vector stores like pgvector for semantic search, paired with key-value or relational databases for exact lookup. Airtable, Databricks, Notion, and Obsidian were also noted as strong foundational or single-source-of-truth layers. AI Models & Agents: Claude Code, OpenAI Codex, Hermes-agent (by Nous Research), AsanaAI, and ClickUp Brain. Injecting local LLMs where appropriate can help cut down on continuous API costs. Middleware & Pipelines: Kapex: Memory middleware built specifically to score node significance, governing lifecycle so resolved stuff fades and unresolved stuff persists. Sauna.ai: An engine built out of Wordware that fits this use case. Automation: Make.com or n8n for routing deterministic logic and LLM reasoning. The "Party Model": A CRM data integration framework

Claude routines are making me rethink client automation work

I was at a client’s office yesterday setting up some internal automation work, and I left with a slightly uncomfortable thought. I use n8n a lot. I still think it makes sense for clean event-based workflows. Webhook fires, CRM updates, message goes out. But this client had a different kind of problem. He works with construction projects, and a lot of the work lives in his Microsoft stack. Project inboxes, SharePoint folders, emails from different people around each site. Then there is separate construction software where the building case itself lives, together with invoices and quotes. A normal if-X-then-Y workflow did not really describe it well. It had to read the current project material, understand which construction site this belonged to, make a case folder, draft the right response, and keep the case moving. I ended up doing most of it through Claude routines instead of n8n. Set it up around his own working environment, connected it to the Microsoft stack and construction software, gave it the project structure, and made scheduled routines that can wake up and do the work with the context already there. And the weird part is that after I set it up, he could basically control a lot of it himself. He can talk to it in natural language. Change how it drafts. Ask it to check different folders. Adjust the workflow without me building a new n8n chain every time. I had the same feeling with another client recently. You build the AI layer properly, and suddenly the client is less dependent on you for the small automation changes. Which is super good, obviously. I want the client to be able to use the thing without asking me for every tiny adjustment. But it also makes the service model feel different 😅 A lot of automation work has been sold as “I will build and maintain workflows for you.” With Claude-style setups, more of the value seems to move into setting up the workspace, connecting the right surfaces, defining what it can write to, and teaching the client how to work with it. I don’t think n8n goes away. I still want event-based workflows for a lot of things. But for work that needs context and judgement, I am starting to prefer scheduled AI operators sitting inside the client’s own environment. I’m still chewing on the business side of this. If the better implementation makes the client more capable without you, maybe that is the product. Maybe the thing you charge for is making the company usable by AI, not owning every little automation forever. submitted by /u/kaancata [link] [comments]

How to start using Claude the way it's suppose to, with agents and automation?

Hi all, Like the millions of others, my use of AI is super limited, to that simple chat window. However, the speed of how this tech is developing, I seem to be unable to figure out how to move to the next level on my own. When a company is exploring implementing AI, I'm guessing what they are actually exploring are the repetitive tasks and processes that can be reviewed and actioned by AI instead of a human, not necessarily to fully replace him or her, but to delegate the admin work to the AI. I like to be able to add that to my baggage of knwoledge and skills, creating AI-powered or supported processes/pipelines/flows. It might not be exactly what a specific company is looking for, but at least to convince them that I don't come in empty handed. I'm aware of the existence of the different Claude services, but probably the most important thing to know, I'm not a coder/programmer. I work on the business-side and usually collabo with someone at IT to get something made or improved. I also at one point had N8N installed on my home pc with Ollama and some local LLMs, yet nothing made. What are you recommendations to properly learn this, the AI companies are actually exploring? What are the most common (entry-level) functionalities companies ask for, is it a customer service chatbot? Theory is nice, but I really like to build things. Any help is welcome! Cheers. submitted by /u/SquidsAndMartians [link] [comments]

PSA: Keep a human approval gate in your Claude AI pipelines. Left to their own devices, these systems will gladly normalize their own bugs/logic or execute a misunderstood prompt with total confidence.

Short read: Built an automated client reporting pipeline (Claude + n8n + Slack + Gmail + SE Ranking API). A token-saving "optimization" + a data-gap edge case meant Claude started backfilling reports with other clients' brand data — and treated it as completely normal. The only thing that stopped me from emailing a dozen clients their competitors' numbers was a manual approval gate I almost automated away. Don't automate away your last line of defense, guys. Long read: So here's the setup. I automate client-facing reporting for AI visibility SEO data. The stack: Claude does the actual analysis — reads the data and flags the big jumps and drops n8n handles all the repetitive plumbing SE Ranking API is where all the project data comes from (AI visibility metrics) Slack is my alert system Final report gets emailed to the client via Gmail Simple enough. The one thing I'm really glad I built in: an Approval Gate. Nothing goes out to a client without me eyeballing it first. I wanted to automate that step too. Thank god I didn't. Here's where it gets dumb. AI visibility analysis is expensive as hell because it's super volatile (but sometimes not and this is what punched me the most) — I run the same prompt cluster through the system and collect the LLM responses that mention my clients' brands. Claude itself suggested I optimize token usage, so the logic became: if a report shows no change on the timeline, just reuse the previous result and ship that as the main one. Reasonable, right? Except here's the footgun. SE Ranking's API callback does the correct thing — no change = no record written. So when there genuinely was no movement, the DB just had a gap. Claude saw that gap and went "ah, missing data, I'll backfill from the previous report (prevoius record)" — but it grabbed the previous result from the wrong brand. It started stuffing one client's report with another client's data and concluded everything was fine. Absence of a record got reinterpreted as "please substitute" and the substitution pulled from whatever was lying around. I'll let you sit with the absurdity of that for a sec... I genuinely can only imagine what would've happened if a few dozen of those reports had gone out — clients opening their report to find a competitor's brand numbers in it. Career-limiting move, narrowly avoided by a manual "looks good, send it" click. What I changed (some troubleshooting, you know): Hard isolation per client — separate DB, separate Claude Cowork task. No more shared pool to accidentally cross-contaminate from. Sanity check comparing the previous value in the report against the current one before anything gets reused. Brand-as-keyword alert — Slack pings me if a client's brand name is missing from their own report, which is the canary for exactly this kind of mixup. The real lesson, and the reason I'm posting this: always keep the right to the final call on any data handoff to a third party — never hand that to the AI. It's not that the AI does the work badly. It's that complex AI-driven systems have this tendency to treat their own errors as the normal state of things and just roll with it. The bug doesn't announce itself; it gets quietly absorbed into "working as intended." Hope this saves someone here a very bad afternoon. Stay paranoid. submitted by /u/robertgoldenowl [link] [comments]

built an "AI employee" in claude code today. the folder structure is the whole game.

spent a few hours building an AI sales employee in claude code. it qualifies leads, researches them, writes outreach, books calls, and learns from outcomes over time. structure is dead simple, four things: - claude.md = the role definition. who the employee is, what its job is, what tools it can use. - memory/ = the brain. icp.md, offer.md, objections.md, wins.md, pipeline.md. read at the start of every run, updated at the end. - skills/ = sub-agents it calls. qualify-lead.md, research.md, write-outreach.md, handle-reply.md, book-call.md, learn-from-outcome.md. - tools/ = actual integrations. gmail, calendar, slack, web search, supabase. the thing that broke my brain: every run it reads memory and updates it. so after 50 leads it's literally smarter than when it started. n8n workflows don't do that, they run the same thing forever. ran it on a fake dental lead. scored 9/10, ran the qualifier, made a JUDGMENT call (4 employees, my hard rule was under 5, it considered full picture and decided yes), then planned the outreach. under 30 min to build. full walkthrough in the comments if anyone wants to see it run live. submitted by /u/Silver-Range-8108 [link] [comments]

built an "AI employee" in claude code today. the folder structure is the whole game.

spent a few hours building an AI sales employee in claude code. it qualifies leads, researches them, writes outreach, books calls, and learns from outcomes over time. structure is dead simple, four things: claude.md = the role definition. who the employee is, what its job is, what tools it can use. memory/ = the brain. icp.md, offer.md, objections.md, wins.md, pipeline.md. read at the start of every run, updated at the end. skills/ = sub-agents it calls. qualify-lead.md, research.md, write-outreach.md, handle-reply.md, book-call.md, learn-from-outcome.md. tools/ = actual integrations. gmail, calendar, slack, web search, supabase. the thing that broke my brain: every run it reads memory and updates it. so after 50 leads it's literally smarter than when it started. n8n workflows don't do that, they run the same thing forever. ran it on a fake dental lead. scored 9/10, ran the qualifier, made a JUDGMENT call (4 employees, my hard rule was under 5, it considered full picture and decided yes), then planned the outreach. under 30 min to build. full walkthrough in the comments if anyone wants to see it run live. submitted by /u/Silver-Range-8108 [link] [comments]

Which provider fits best for my needs?

Hi everyone, I’m looking to get more into experimenting with AI and considering a paid subscription, but I’m a bit unsure which direction makes the most sense for my use case. My main goals: -Writing a technical book in the field of taxation -Preparing presentations and structured content -Learning and experimenting with programming -Building automation workflows (e.g. n8n) -Running or experimenting with tools like Hermes / OpenClaw (I know Claude doesn’t work everywhere there) -Testing new AI features (e.g. Claude artifacts, coding tools, agents, etc.) From what I’ve read recently, opinions are all over the place: Some say ChatGPT (with Codex-style tools) is strongest for coding + general use Others argue Claude is better for writing and reasoning-heavy tasks Gemini seems strong for long context and Google integration And then there’s the API route (DeepSeek looks extremely cheap right now and seems attractive for experimentation) So I’m trying to figure out what actually makes sense in practice. Would you recommend: A ChatGPT subscription Claude Pro Gemini Advanced Or skipping subscriptions and going API-first with models like DeepSeek / others? Would really appreciate real-world experiences—especially from people doing a mix of writing + coding + automation rather than just one narrow use case. Thanks! (Ai generated as englisch is not my mother language) submitted by /u/ilgin3113 [link] [comments]

I built an AI-native Business OS using Claude, Obsidian, and n8n

I built an AI-native Business OS using Claude + Obsidian + n8n and it’s changed the way I operate completely. The interesting part isn’t really the AI itself. It’s the architecture around it. Claude became dramatically more useful once I stopped treating it like a chatbot and started treating it like an intelligence layer connected to structured context. Current setup: - Obsidian stores operational memory - Claude handles contextual reasoning/writing - n8n orchestrates workflows + triggers Some things the system now does automatically: - generates morning briefings before I wake up, - prepares pre-call client summaries, - surfaces open issues/followups, - drafts content from rough notes, - and keeps operational context persistent across projects. One thing I’ve learned building this: AI becomes exponentially more useful when paired with: - structured memory, - clean workflows, - and consistent operational context. Otherwise every conversation starts from zero again. I also try to keep the system grounded pretty heavily: - outputs are treated as drafts/briefings, - important decisions always get human review, - and most workflows are retrieval/context based rather than open-ended generation. The goal isn’t replacing thinking. The goal is reducing operational clutter so more deliberate thinking can happen. Curious if anyone else here is building similar “AI operating system” style workflows around Claude. submitted by /u/liberal_bhakt [link] [comments]

[Virtual] AI Saturdays - Workflow Automation with AI (23rd May, 6 PM ET)

Hosting this Saturday's AI Saturdays session on workflow automation with AI. The idea: most jobs have recurring tasks that look the same every week. Read the email, pull out the key info, log it somewhere, send a follow-up. Tools like n8n and Make let you chain AI into those flows so the work runs on its own. We'll look at how the pieces fit together with AI. Link: https://www.meetup.com/chillnskill/events/314617067/ submitted by /u/Competitive_Risk_977 [link] [comments]

Best way to use Claude daily — Web app vs Claude Code (VS Code) vs API? What's your setup?

I've been using Claude through the web app but I'm wondering if I'm leaving value on the table. I've heard people talk about: Claude.ai (web app) — the default Claude Code — the CLI/VS Code integration Direct API — full control, build your own setup What's the real difference in your day-to-day use? Is Claude Code worth switching to if you're not a developer? What can you do with one that you can't do with the others? Currently using Claude mainly for workflow automation (n8n), writing, and research. submitted by /u/Gullible_Wrangler_53 [link] [comments]

Need to connect Docsend to Claude

Been able to automate a good chunk of my work with claude, N8N etc but there have been a couple that I have just not been able to crack. So my background isnt technical so been able to do these things so far by watching videos or simply asking an ai tool. Currently, I am stuck on trying to integrate docsend into claude so it runs a simple flow: I was thinking sending / uploading a pdf into a form, it uploads it to docsend and sends me by slack, mail etc a viewable link so I can share. Would appreciate any feedback on how as I am stuck (couldnt get docsend's API and MCP) Thank you in advance submitted by /u/Electrical_Editor880 [link] [comments]

Claude code on E-commerce Brand

Recently, I built a swarm of AI agents to replace our customer service representatives and I hosted it on n8n. I also built an automation to give us a daily report. All these was built from Claude, I wonder what else can I build to help push our company forward? Any tips or recommendations? submitted by /u/Direct-Football7180 [link] [comments]

We’re hosting the biggest Claude Code Prompt-a-thon at the AI x Marketing Summit in SF.

We’re hosting the biggest Claude Code Prompt-a-thon at the AI x Marketing Summit in SF on May 28–29. For 36 hours, you’ll actually build with AI: • Claude Code • Humanic • n8n • MCPs • Figma Make • AI workflows for SEO, ads, lifecycle, outbound, content, and growth You’ll work alongside marketers and operators building real-world AI systems — not just talking about them. The summit includes hands-on workshops, promptathons, networking with AI-native marketers, and sessions from founders, CMOs, and operators shaping the future of marketing. Bring your team to SF. Compete, build, and walk away with top-tier tech. The full agenda is now live — grab your ticket now. Learn more: https://aixmarketingsummit.com/ submitted by /u/Brilliant_Sector_427 [link] [comments]

Built a Claude Code plugin for GDPR/DSGVO audits because attorney reviews were eating my budget

Quick Background: Developing a B2B SaaS for German businesses (KSKlar, a tax compliance product). Pre-launch, each cookie banner question, each DPA, each privacy policy draft went to the attorney. Each iteration took 300-500 EUR and 2-3 weeks. Most of those iterations didn't involve any difficult legal questions. They were about making sure basic things were done - no Google Fonts requests before consent, no § 5 TMG (it got changed to § 5 DDG in 2024, neat little trick), documented AVV with Mistral, etc. So I built it into a Claude Code plugin. It scans a codebase, flags issues, provides clear replacements, cross-checks citations from eur-lex or gesetze-im-internet. Then I give it to the attorney instead of sending a GitHub repository link. Saves her about 70% of time, saves me even more money. Six weeks trimming everything down to what was generalizable, another two weeks scrubbing it for open-sourcing. Released it to GitHub this morning. Tech Stack: Slash commands for auditing codebase, live URL, single document (privacy policy draft, DPA, etc.), looking up KB, etc. Three custom agents on Opus 4.7 1M model (wrong case number outputs with smaller models is an actual issue) 63 KB files with primary source links (eur-lex, rechtsprechung-im-internet, curia, BfDI, EDPB, state DPAs) Context loading through hooks (so KB doesn't clutter your session, ~1k token overhead initially, loads dynamically through regex triggers) Scope is limited to Germany/EU - GDPR/DSGVO, BDSG, TDDDG, UWG, AI Act, UrhG, the whole thing. Nothing for US/UK/CH since the paragraph references and case laws are different. Trying to build multiple jurisdiction support into one plugin ends up being poor for all of them. Limitations I want to be clear about: This isn't legal advice. Disclaimer at the start of each output. Still need a real attorney for production, just not as much of them. Plugin reduces cost of attorney work. KB will always be as updated as I can manage (verified May 08, 2026, in 63 files). Legal climate changes - the KB can be refreshed using the /legal-audit-de-update command. Refreshes automatically from primary sources every 90 days. Content in German remains in German (paragraph wording is legally binding in the original language, translating would make it less useful for actual attorneys). Wiki provides parallel English documentation for German-based development teams working in English. Installation: /plugin marketplace add FutureRootsDE/legal-audit-de /plugin install legal-audit-de@futureroots-legal MIT License. Repository: github.com/FutureRootsDE/legal-audit-de For those developing products that touch EU users and don't have their own legal team, I'd love to know what else they should consider. Particularly interested in mobile apps and API-only services. Have checklists for SaaS, landing pages, e-commerce, n8n, content, but those two have gaps. submitted by /u/PrudentStop5612 [link] [comments]

Today I learned about this

submitted by /u/YogurtWild [link] [comments]