Dhananjay

how to spot someone who is "using AI for marketing" but hasn't moved a single metric: - copy pastes model output directly into the blog with light editing - says "we're an AI-powered marketing team" because of a ChatGPT subscription - automated the cold outreach sequence that was already getting 0 replies so now gets them faster - produces 4x more content that performs 4x worse - measures AI ROI entirely in hrs saved with zero correlation to revenue - calls a 3-5 step zapier or n8n automation an AI powered growth engine - announced AI would replace the content team, then shipped content that tanked organic traffic - every prompt is under 10-20 words and the output quality reflects that - leverage AI across all marketing touchpoints is on the roadmap with no implementation plan beneath it the point is AI didn't rescue bad marketing strategy. it just gave it a faster publishing cadence. tbh, the marketers actually winning with AI aren't using it to do more of the same thing. they're using it to do things that weren't possible before. so what's the most overhyped AI marketing move you've seen this year?

Asked AI (used multi-agent framework) to audit the gtm strategy of the top 5 crypto projects that launched in Q1 2026. here's what separated the ones that held price vs. the ones that collapsed: - winners had a narrative before they had a token - winners built in public for 6+ months pre tge - winners activated community through utility, not speculation - and losers relied on influencer volume over organic conviction AI spotted the pattern in 10 minutes. took the industry 3 months.

it started as a baseline, but static thresholds didn’t work. so what helped was thinking in terms of attention cost: if an alert interrupts me, it needs to justify it. so now it’s tuned around: - signal rarity - cross layer confirmation - whether similar alerts in the past were actionable still evolving, but noise dropped a lot after that shift tbh

@danny_builder @nansen_ai @DefiLlama @MessariCrypto @tokenterminal Curious how you handle threshold calibration - too tight and you miss things, too loose and Telegram becomes noise again. Did you tune that empirically or start with some baseline?

I recently built a crypto research system that runs 24/7. here's what it replaces: → 10+ tabs open → scanning CT all day → checking @nansen_ai / @DefiLlama / @MessariCrypto / @tokenterminal → still late to everything the issue isn't effort. it's structure. so I built a pipeline in n8n. here's how it works: 1. Data layer: Arkham / Nansen / Messari etc all feeding into one system automatically 2. Content layer: Whitepapers, governance proposals, long form research all pulled and parsed without me touching anything 3. Analysis layer: Claude handles reasoning + summaries. ChatGPT handles tokenomics, SWOT, comparisons. two models. different strengths but one pipeline. 4. Multi layer scoring: - macro - on-chain - fundamentals - community - narrative every signal scored. i mean only what matters gets through 5. Alert layer: telegram alerts only when something crosses a threshold worth your attention. what changed: → no dashboard hopping → no random research → no missing early signals most people try to read faster. the edge is building systems that think before you do. if you had this running, what would you track first?

@seekingtau plz check dm

Hiring someone who is: - chronically online - in touch with the news - monitoring the situation - interested in simulating dm me

Summary: Fiat alone can't serve agentic commerce (too slow, no micropayments). Crypto alone can't serve it either (no consumer reach, no regulatory trust). The HYBRID stack wins: - Razorpay for the last mile (UPI, cards, 12M merchants) - coinbase for settlement (x402, stablecoins) - eigenlayer for verification (cryptographic proofs) - circle for programmable money (USDC) - AnthropicAI for intelligence (Claude Agent SDK) $5 TRILLION is coming. The companies that bridge Fiat × Crypto × AI will define how the world transacts by 2030. Who's building this bridge?

The missing piece nobody's solving: AGENT IDENTITY. When an AI agent initiates a ₹50,000 payment on Razorpay, how do you verify it's legit? BNB Chain's ERC-8004 standard creates trustless agents where software entities with blockchain-verified identities and reputations. NFAs where AI agents that exist as on-chain assets, own their own wallets, and can transact independently. this is the KYC layer for machines. Razorpay's Agent Studio + on-chain agent identity = payments that are fast AND safe.

McKinsey says agentic commerce will be a $3-5T market by 2030. But here's the thing nobody's talking about. The company best positioned to own this in India isn't a crypto startup. It's @Razorpay. And the partnerships they should be making right now could reshape global payments forever.

@divya_gandotra @RahulGandhi if you become PM, would you implement something like this in India?

I ask the government, why can’t we do this?

@DeRonin_ @obsdmd can you send those 10 social media accounts links? just wanted to check the quality of the posts, the engagement you're getting etc ec

I run 10 social media accounts and don't write a single post manually the secret: a skill graph 30+ markdown files wired together that turned my AI agent into a full content team where to build it: - @obsdmd (to write + visualize the graph) - or just a regular folder of .md files on your desktop what tools run it: - claude, chatgpt, or cursor as the AI agent - @arscontexta plugin for claude code (generates the base structure automatically), find it in article below the folder structure: /content-skill-graph ├── index.md (entry point which maps every node) ├── platforms/ (x.md, linkedin.md, ig.md, tiktok.md...) ├── voice/ (brand-voice.md, platform-tone.md) ├── engine/ (hooks.md, repurpose.md, scheduling.md) └── audience/ (builders.md, casual.md) each file = one knowledge node inside each file you add [[wikilinks]] to related nodes example — inside x.md: "use [[hooks]] — contrarian hooks perform best here. match [[brand-voice]] but more casual. audience is [[builders]]. write this FIRST, then expand for [[linkedin]]. see [[repurpose]]" the links are the graphs. the agent follows them automatically the key file is index.md, your entry point / briefing, not a file list. put 3 things in it: 1. who you are + what this system does "content system for [your brand]. manages 10 accounts from one idea input" 2. the node map with context - [[x]] — short-form, hook-driven, 280 chars, 5x/week - [[linkedin]] — long-form narrative, professional, 3x/week - [[hooks]] — formulas that stop the scroll - [[repurpose]] — 1 input → 10 outputs (every node listed with a one-line description) 3. execution instructions "when given a topic: read relevant nodes, apply voice + hooks, run repurposing chain, output one native post per platform. each post ready to publish" you paste this into claude as context → give it a topic → done now here's the part most people get wrong about the output: it's NOT 10 copies of the same text reformatted for each platform it's 10 pieces that each THINK about the topic differently: > x: contrarian thread, lowercase casual, step-by-step > linkedin: personal narrative, professional tone, 1500 words > instagram: 7-slide carousel, visual-first, bold claim on slide 1 > tiktok: 45-sec raw screen recording script > youtube: SEO title + structured outline, 8-min format same topic. different angle, hook, voice, structure, format per platform the graph encodes all those rules. the agent follows them this replaced $8-12k/mo in content spends @arscontexta built the framework and I pointed it at content production summarize: one flat file gives you a tool (a simple .md file) a graph gives you a team (a system with 30+ sub-graphs) if we collect 500+ Likes on this tweet ❤️ I release my full workflow and show you step-by-step how you can setup the same skill graph hope you loved it.

@Pranit i've got a pro plan and when i ran 5 multi agent system then it ran for 30 secs and says the limit has reached. i understand multi agent consumes more but like this....

Anthropic just pulled the oldest trick in SaaS pricing. I pay $200/mo for Claude Max. My limits have been noticeably worse this past week. Now they announce 2x off-peak usage for two weeks. Sounds generous. But here’s what actually happens: limits quietly drop, a temporary 2x makes the reduced limit feel normal, the promo ends, and you’re left at a baseline lower than where you started. You just didn’t notice the downgrade because the 2x absorbed the transition. These AI plans are massively subsidized. The raw compute behind a heavy user costs multiples of the subscription price. Every move like this is the subsidy quietly correcting. Very sneaky, Anthropic.

this is actually insane > be tech guy in australia > adopt cancer riddled rescue dog, months to live > not_going_to_give_you_up.mp4 > pay $3,000 to sequence her tumor DNA > feed it to ChatGPT and AlphaFold > zero background in biology > identify mutated proteins, match them to drug targets > design a custom mRNA cancer vaccine from scratch > genomics professor is “gobsmacked” that some puppy lover did this on his own > need ethics approval to administer it > red tape takes longer than designing the vaccine > 3 months, finally approved > drive 10 hours to get rosie her first injection > tumor halves > coat gets glossy again > dog is alive and happy > professor: “if we can do this for a dog, why aren’t we rolling this out to humans?” one man with a chatbot, and $3,000 just outperformed the entire pharmaceutical discovery pipeline. we are going to cure so many diseases. I dont think people realize how good things are going to get

@intern_cripto already wip

@danny_builder i don't need an SEO machine but i definitely need a trading machine. can you make one?

n8n + Claude = A Killer SEO machine that validates search intent, builds keyword strategy, and prevents cannibalization before content is written. I got tired of AI writing content that ranked for the wrong keywords. I built an n8n workflow that forces AI to stop and think before it writes a single word. Here's the teardown of every node in the pipeline: 1. Node 1: Review Past Lessons → Reads a lessons sheet before touching anything. Every past mistake becomes a rule. The system literally studies its own failures. 2. Node 2: Identify Search Intent → Classifies the target query as informational, commercial, or transactional. If you skip this, nothing downstream matters. 3. Node 3: Generate Keyword Plan → One primary keyword. 2–5 secondary. Plus semantic vocabulary. Structured with an H2 outline before any draft begins. 4. Node 4: Check Keyword Cannibalization → Cross-references existing content. Flags overlaps before you accidentally compete with yourself. Most teams skip this entirely. 5. Node 5: Verify Content Quality → Runs the draft against the top 3 ranking pages. Asks: "Does this satisfy intent better?" If not, it rewrites. 6. Node 6: Calculate SEO Metrics + Write Lessons → Title tag 50–60 chars. Meta description 140–160 chars with CTA. Keyword density 1–2%. Then logs everything learned for next time. If the content misses intent at any stage, the workflow stops. It doesn't keep writing. It re-plans. The workflow also writes its own rules. After every correction I give it, it updates a lessons file and reviews that file at the start of every new project. The result: an AI SEO system that actually gets better over time instead of repeating the same mistakes. Here's what changed after implementing this: → Zero keyword cannibalization across 40+ pages → Meta descriptions that actually hit 140–160 chars with a CTA (every time) → Content that matches intent on the first draft Most people use AI to write faster. I use it to think first and write once. The full workflow runs in n8n with Claude as the model layer. Open source. No black box. Full n8n workflow is open. If you want access: 👉 Comment “SEO” and I'll send you the template.