I always love seeing these 'embedded' patterns showing up in more tools we use every day. When most people hear 'in-app messaging,' they think popups and interruptions. But the best product experiences weave help directly into the interface—right where you're already looking. LinkedIn's doing this really well here and I'd bet they are A/B testing different prompts for different users based on their previous post topics, roles, or industry. They're prompting me to share how I use AI at work—right in the post composer where I'm already creating content. No modal, no disruption. Just a relevant nudge at the exact moment it makes sense. This is where product adoption should go: → Less interruption, more integration → Meet people where they are, not where you want them We call these Embeddables at Chameleon! Product teams can use them to surface new features where users will actually find them, can recommend next steps based on what someone just did, or highlight relevant resources without pulling people out of their workflow.

From idea → image → video... in one workflow. Drop an idea into Telegram and get back ready-to-use visuals - no manual editing, no back-and-forth. That’s what this automation is built for. Here’s what my free n8n workflow does: ✓ Starts from a Telegram message you send ✓ AI analyzes the product image you provide ✓ Generates clear image + video prompts automatically ✓ Creates visuals using GPT-Image & Veo3 ✓ Sends everything back to your Telegram chat ✓ Includes an approval loop so you stay in control Wouldn’t your creative process be simpler if it worked like this? 📂 I’m sharing the full workflow - plug it in, test it out, and see how much time it saves you. https://lnkd.in/g2cQRJqy If this workflow got you thinking... you’ll love the quick video I shared yesterday. It’s another n8n trick that shaves minutes off repetitive work - the kind of stuff that adds up fast. Why spend hours doing what you can automate in seconds? More pro tips are on the way. Hit follow so you don’t miss the next one. https://lnkd.in/g2PkN32f

Stop manually updating Notion — automate it. I built an n8n template that listens for a POST webhook and automatically processes incoming content to update Notion via the Notion API. The workflow chunks incoming text, creates OpenAI embeddings, stores and queries vectors in Supabase, and uses a RAG agent (with an Anthropic chat model) to generate context-aware updates. Results are appended to Google Sheets for traceability, and Slack alerts notify you of any errors. Highlights: - Webhook trigger to accept POST requests - Character-based text splitter for reliable chunking - OpenAI text-embedding-3-small to vectorize content - Supabase as the vector store + retrieval layer - RAG agent with Anthropic for context-aware responses - Windowed memory to preserve recent context - Google Sheets logging for auditability - Slack alerts for error handling Why this helps: - Turn incoming events into accurate Notion updates without manual work - Keep searchable context so updates are informed and consistent - Maintain logs and immediate alerting for faster issue resolution Try the template in the comments — import it into your n8n instance, configure your credentials (OpenAI, Supabase, Anthropic, Google Sheets, Slack), and update the sheet ID. Want help customizing it for your workflow? Comment below or send me a DM. Template link in the comments section. #n8n #Automation #WorkflowAutomation #NoCode #LowCode #Notion #NotionAPI #APIAutomation #Webhook #OpenAI #AI #GenerativeAI #MachineLearning #RAG #VectorSearch #Embeddings #Supabase #GoogleSheets #Slack #DataAutomation

As a creator, I know how painful email workflows can be. Every app needs emails—onboarding, reminders, reactivation flows—but wiring up SendGrid, Mailchimp, or Klaviyo? It’s slow, fragile, and breaks as you scale. That’s why I started exploring Dreamlit AI. It lives inside your database and builds fully automated email workflows in real-time—no code, no YAML, no duct tape. Here’s what I love: ✅ Real-time triggers from your DB ✅ Workflows created from plain English instructions ✅ Auto-branded, compliant emails ✅ Instant previews exactly as your users see them For me, it’s like having a full-time email engineer in my pocket—letting me focus on building features, not firefighting emails. 🔥 Try it now: https://lnkd.in/dtQzAjd4 #DreamlitAI #EmailAutomation #NoCode #AI #DeveloperTools

Devtools don’t test themselves (español en comentarios) Your devtool is powerful. Your landing page looks slick. The demo runs smooth. And still... 60% of users churn in week 1. It’s not the code. It’s the 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Do you actually test your onboarding? Or assume developers “just get it”? 💣 Spoiler: they don’t. Use AI to simulate usage. Spot friction. Predict abandonment. Don’t just build tools. Build tools people actually finish onboarding. 💬 How many users are bouncing just because you didn’t test with AI?

Want to automate a tight, repeatable AI newsletter pipeline? I built an n8n template that does exactly that — from story ingestion to a finished markdown file and Slack-ready post. What it does: - Captures edition metadata via a simple form trigger (date + previous newsletter) to avoid duplicate coverage. - Searches an S3/R2 bucket for markdown + tweet content, pulls full text, and validates sources. - Uses LLMs to pick top stories with structured reasoning, then writes Axios-style sections (Recap, Unpacked, Bottom line) with strict editorial constraints. - Generates subject lines and pre-header text optimized for opens, then compiles the full newsletter into a markdown file. - Extracts image URLs, validates external links, and shares everything to Slack (including a downloadable file). Why it’s useful: - Saves hours of manual curation and copy editing while keeping an auditable trail of content identifiers and source links. - Enforces consistent voice, formatting, and linking rules so every edition meets editorial standards. - Flexible and extensible: swap LLMs, tweak prompts, or add integrations (CMS, email provider) without reworking the pipeline. If you run a newsletter (or want to automate content ops), I’m happy to walk through the template or share lessons learned from building it — drop a comment or DM and let’s chat. Find template details here: https://lnkd.in/dWnQWDVV #n8n #WorkflowAutomation #AIAutomation #ArtificialIntelligence #GenerativeAI #AINewsletter #NewsletterAutomation #Newsletter #ContentAutomation #AIContent #ContentGeneration #ContentMarketing #NoCode #LowCode #MarketingAutomation #TechNews #AITrends #SlackAutomation #LLM #DataWorkflow

Still sending plain emails from Supabase? I have a cheat code for you. Plain emails are fine for testing… but when it comes to scaling, they quickly break down. No branding. No workflows. No control. That’s where Dreamlit AI comes in. ✨ Built for Supabase from day one. ✨ AI that creates workflows in minutes. ✨ No syncing, no code, no failures Just describe what you need in plain English. 💡 Ex: “Send a welcome series when a new user signs up” and your automation will be set up. Here’s what makes it different: • Works directly on your Supabase data • Sends beautiful, branded, mobile-ready emails • Handles auth, transactional, and marketing emails in one place. • Reacts in real time to user events — signup, profile update, subscription changes, or any custom event. And yes, the pricing is as transparent as it gets: start for free, upgrade only when you grow. 👉 If you’re building on Supabase and want email done right, try today: dreamlit.ai

Reliable AI voice agents are now running on Parlant! So many people have been asking us in the past weeks (since launching Parlant V3.0) to be able to run voice agents on top of Parlant's powerful conversational control framework. We're proud to announce our official integration with Pipecat at https://lnkd.in/dn859x2A (pip install parlant-pipecat). We chose Pipecat as our first official voice partner because we appreciate their engineering quality. parlant-pipecat supports awesome voice interaction features: 1. Full, plug & play integration with Parlant 2. Customizable filler phrases while waiting for delayed responses 3. Typing sounds while the agent is writing its output message 4. Live runnable example with office ambience sounds for extra realism This package allows you to integrate Parlant as an AI service into *any* Pipecat pipeline, regardless of which STT and TTS models you're using with Pipecat.

Did OpenAI just killed n8n, Make, Flowise, and Zapier with one update? OpenAI unveiled AgentKit at today’s Dev Day, and it completely changes how we’ll build AI agents moving forward. Until now, “agents” were mostly prompt chains or light automations. With AgentKit, OpenAI is rolling out everything needed for full agentic apps, runtime, UI, and dev tools, all in one place. Here’s what’s inside: • ChatKit — embeddable, customizable chat UIs • Agent Builder — visual drag-and-drop workflow creator • Guardrails — input/output safety layer • Evals — datasets, trace grading, and auto-prompt optimization You can explore ChatKit right now at chatkit.studio or see it live at chatkit.world. Real-world use cases are already rolling in: • Albertsons built an agent that boosted ice-cream sales recommendations • HubSpot added it to their Breeze assistant for support automation • You.com used Evals to improve citation quality by over 50% This move puts real pressure on tools like n8n, Make, Flowise, and Zapier. I tried it today, and it’s seriously impressive. Even though it only supports OpenAI models for now, the integration is so seamless that I don’t see myself going back to anything else. Check out the full announcement here: → https://lnkd.in/ebcRs8kH If you found this useful, give it a like or repost so others can see it too! 🙌

Just explored OpenAI’s AgentKit released yesterday. Honestly, I’m quite impressed. I’ve been developing agents for clients over the past year, and the process has always been complicated. You’re juggling LangChain, custom connectors, evals, prompt tuning, frontend work… it feels like managing five different projects simultaneously. What stood out for me about AgentKit: 1. Visual agent builder – imagine Canva but for designing agent workflows. You can actually visualize and version your multi-agent configurations instead of being buried in code. 2. ChatKit – an embeddable chat UI you can easily integrate into your product. No more spending weeks creating a decent chat interface from scratch. 3. Connector registry – a centralized solution to handle all your data connections. It’s a small feature but a game-changer for client projects involving 10+ tool integrations. Will this replace n8n? Not exactly, as they serve different purposes. • n8n still excels in deterministic workflows and traditional automation. If you need reliable, predictable pipelines with plenty of ready-made integrations, n8n is unmatched. • AgentKit is built for scenarios requiring agentic behavior, adaptive multi-step reasoning, and less predictable workflows. The timing is notable too. Everyone’s racing to simplify agent creation. Google released ADK in April, Microsoft has their own framework, and now OpenAI joins the race. This really confirms my belief that agents are evolving from research concepts to practical production tools. I’ve been working with the Responses API since March, so I’m excited to start experimenting with building on top of AgentKit.