Amit Sharma

Here's something I keep seeing. A team gets access to a generative AI tool. They write a prompt. The output looks plausible — sometimes impressive. They ship it. A month later the content is indistinguishable from every other team using the same model the same way. The prompt isn't the strategy. It's maybe 10% of it. The other 90% is the system around the model — the stuff most people skip because it's not as fun as writing prompts: → Signal Architecture — the data and context you feed it before it writes → Stack Design — connected tools, not a chat tab you copy-paste from → Human-AI Choreography — who drafts, who edits, who approves → Impact Instrumentation — measuring pipeline, not word count → Brand Guardrails — so you can move fast without breaking trust None of this is theory. McKinsey and BCG both found that the small slice of companies actually capturing AI value are the ones rewiring how they work — not the ones buying better models. The model stopped being the moat the day everyone got one. I put the whole framework in the carousel below. Swipe through it, then tell me — which of the five is your weakest? #MarketingStrategy #AITools #DigitalMarketing #MarketingLeadership #B2BMarketing #MarketingOperations #GenerativeAI

@VigneshChinnad2 Yes, you’re right - it looks like MCP access is still being rolled out gradually and isn’t available for every ad account yet. The safety concerns become especially important once Meta expands write access more broadly.

@bit2byteapp i think, they have not enabled the mcp access to all ad accounts

Meta finally released an official MCP server for ads — so Claude and ChatGPT can now read AND modify your Facebook & Instagram campaigns through plain language. No custom app. No app review. OAuth and you're connected. For reporting, it's genuinely great. Cross-account ROAS in seconds. Signal-quality audits without clicking through Events Manager. "Why did CPA spike yesterday?" answered in one pass. Here's the part most coverage skips: MCP has 𝘄𝗿𝗶𝘁𝗲 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗹𝗶𝘃𝗲 𝗮𝗱 𝘀𝗽𝗲𝗻𝗱. A misinterpreted prompt can pause a profitable campaign or blow a budget — and right now there's no spend limit per session, no mandatory confirmation, no rollback, and no dry-run mode. I've spent years building Python pipelines that manage Meta Ads at scale ($10k → $500k/month), so my rule is simple: Treat write-access MCP like a junior media buyer. Start read-only. Never plug it into your primary high-spend account first. Always ask it to show you the change before it executes. Keep budget pacing and spend caps in code you control. Where it honestly sits today: - Reporting & diagnostics → production-ready - Campaign modifications → experimental (test account only) - Full conversational ad ops → not ready yet MCP and custom automation aren't competitors — they're complementary. MCP for the exploratory, ad-hoc work that wastes time in Ads Manager. Python pipelines for the operations that must be right every single day. Swipe through for the full breakdown — including the 5-rule safety protocol I'd run before granting any write access. Have you connected MCP yet — read-only, or write? Tell me where you drew the line. #MetaAds #MarketingAutomation #AI #PaidMedia #AdOps #MetaAdsMCP #MarketingStrategy

I let AI run my Google and Meta ads for 90 days. Here's what actually happened. For three months I handed bid management to Smart Bidding + Performance Max on Google and Advantage+ on Meta. I kept my hands off bids, audience builds, and creative testing — and watched where it shined and where it quietly wasted money. The good: CPA dropped roughly 25% across both platforms, and I got back about 10 hours a week I used to spend tweaking bids. Smart Bidding reads auction signals faster than I ever will, and Advantage+ found audience pockets I'd never have built by hand. The uncomfortable part: Performance Max quietly routed ~15% of my spend into branded and low-intent placements until I added guardrails. Creative fatigue hit Meta in under three weeks. And the AI did nothing for my landing pages — it just drove more traffic at a page that still needed work. So here's my honest takeaway: AI replaces the tedious optimization. It does not replace the media buyer. Automate bids, audiences, and creative testing. Keep strategy, brand safety, and CRO human. Swipe through for the full setup, what I automated, what I kept manual, and the numbers. Are you running PMax or Advantage+ yet? What surprised you? #PPC #PerformanceMarketing #GoogleAds #MetaAds #AIMarketing #MarketingAutomation #PaidMedia #AdOps #SmartBidding #GrowthMarketing

I've spent more hours than I care to admit fighting with Prophet and ARIMA. Tuning seasonality params. Explaining to stakeholders why the forecast "looks reasonable" even though the confidence band is wider than the chart itself. You know the feeling. Then I came across TimesFM from Google, and honestly? It made me reconsider how much hand-holding a forecasting model actually needs. It's a foundation model — 200M parameters, decoder-only. Think "GPT, but for numbers." You feed it a time series and it forecasts, zero-shot. No retraining on your data, no per-task fine-tuning. In their benchmarks it's beating the classical methods we've all been defaulting to for a decade. What I find genuinely interesting isn't just the accuracy, though. It's the distribution — BigQuery ML, Vertex AI, even Google Sheets. Open source on HuggingFace. pip install timesfm and you're off. If you're in quant, supply chain, or ops and you've been duct-taping forecasting pipelines together, this is worth a weekend. So — what are you using for time-series forecasting right now? And would you actually trust a foundation model over your hand-tuned pipeline? #TimeSeries #Forecasting #MachineLearning #GoogleAI #FoundationModels #DataScience #QuantitativeFinance

I keep hearing "AI is going to replace marketers." No. It's not. Here's what's actually happening: 80% of marketers already use AI. The question isn't whether you adopt it. It's whether you're any good at it. The Marketing AI Institute found that 71% of marketers use AI in less than a quarter of their tasks. They've opened the tool. They haven't built the skill. Meanwhile, 67% say their #1 barrier to AI adoption isn't budget or technology — it's lack of training. And nearly half of companies offer zero internal AI education. So the real divide isn't "AI users vs. non-users" anymore. It's between marketers who went deep and marketers who are still dabbling. The skills that matter now? They're not what you'd guess. Prompt engineering, sure. But also data literacy (because AI floods you with data you need to interpret). Strategic thinking (because AI handles the tactical stuff, freeing you to think bigger). Creative judgment (because AI generates 50 variants and someone needs to know which one is actually good). The stuff AI can't touch? Brand intuition. Empathy. Knowing what will land with your audience because you understand the cultural moment. That's not going away — it's getting more valuable. Kieran Flanagan from HubSpot said it well: more content is now generated by AI than humans, but it's mostly average. Consumers are tuning it out. The human craft isn't being replaced. It's being premiumized. The marketers who refuse to build AI fluency aren't getting replaced by AI. They're getting replaced by the marketer across the hall who did. What skill are you betting on? #MarketingCareers #AIMarketing #FutureOfWork #MarketingSkills #MarketingTrends #AISkills #CareerGrowth

Every AI app I touch wants its own custom connector. My agent needs to query a database? Write an integration. Read a GitHub repo? Different integration. Touch the filesystem? Another adapter. The math gets ugly fast — six apps times twelve tools is 72 integration surfaces someone has to build, debug, and babysit. MCP replaces all of that with one spec. You write a single server that exposes your tool, your data, whatever. Any AI app that speaks MCP connects to it. Claude, ChatGPT, VS Code, Cursor — they all support it today. No per-app glue code. Best mental model: it's USB-C for AI. Before USB-C, every device shipped its own charger and cable. Now one port handles everything. MCP does the same for agents and tools. Your AI app is the host. It spins up clients — one per server it connects to. Each server exposes three primitives: tools (things the AI can do), resources (data it can read), prompts (templates that frame the interaction). That's the whole model. One database server → any agent queries your data. One GitHub server → any agent reads your repos. One filesystem server → any agent reads and writes files. Write the server once, it runs with any AI. This isn't a roadmap. It works right now. If you ship tools, APIs, or data systems, you're going to write an MCP server eventually. I built a carousel that walks through all of it — two minutes, slide by slide. Swipe through if you're tired of nodding along when someone drops "MCP" in a meeting. Have you shipped an MCP server yet? Drop the link — I'm collecting the good ones. #MCP #ModelContextProtocol #AI #Developers

My AI agent writes better code than half my team. Ask it to read a single tweet? Blind. Reddit thread? 403. YouTube transcript? Forget it. Every platform sits behind its own API, its own approval queue, its own auth wall. I've burned more hours wiring scrapers for each one than I want to admit. Then I found Agent-Reach. It's one open-source CLI. You point it at a source — Twitter, Reddit, YouTube, GitHub, Bilibili, whatever — and it just returns the content. No paid API. No approval form. No "please wait 5 business days." Twitter/X, YouTube transcripts, Reddit threads, GitHub repos, Bilibili, XiaoHongShu, Facebook, Instagram, LinkedIn, any webpage, RSS, and full-web semantic search. One install. The thing that sold me isn't the source list though. It's how it's built. Most scraping tools die the moment a platform changes something. Bilibili throttled yt-dlp into the ground in June — every project depending on it broke overnight. Agent-Reach didn't. Because it's not built as a scraper. It's a capability layer: every platform has a primary backend AND a fallback. One route dies, it swaps to the next one. I didn't touch a config file. It just kept working. That's the part most people miss when they compare tools by feature count. Reliability isn't a feature you bolt on. It's an architecture decision. agent-reach doctor tells you exactly what's live and what needs a login session. Works with Claude Code, Cursor, Windsurf, OpenClaw — anything that runs a shell. Zero API fees. MIT licensed. 56,800 developers already starring it. If your agent can't read the internet, every task that needs real-world context is you pasting stuff into a chat window by hand. That's not automation. That's a faster copy-paste. 🔗 github.com/Panniantong/Ag… What's the first thing you'd point it at? #OpenSource #AIAgents #BuildInPublic #AgentReach #Python