Originally published at twarx.com - read the full interactive version there.
Last Updated: June 25, 2026
The creators going viral right now aren't better editors — they're better automators. They figured out that a single high-engagement tweet contains more proven viral DNA than a week of scripted video ideas. If you want to understand how to turn tweets into viral videos with AI, this is where the real edge lives: not in editing skill, but in orchestration. The market already validated the idea for you the moment that tweet popped — your job is simply to re-broadcast it into a higher-reach format faster than anyone else.
This is the tweet-to-video trend: tools like Flicky AI, Pictory, and Opus Clip ingest a tweet URL and output a captioned, music-backed vertical video in under 90 seconds — and a small group is now chaining these tools into autonomous agents via n8n and LangGraph that harvest, score, and broadcast before the original tweet even finishes trending.
By the end of this article you'll know how to turn tweets into viral videos with AI manually in 60 seconds, build a fully autonomous pipeline, and monetise it three different ways.
The Tweet-to-Clip Pipeline collapses a six-step manual editing workflow into a single agent-orchestrated cycle — the core concept this article breaks down.
Coined Framework
The Tweet-to-Clip Pipeline — a coined framework describing the full autonomous loop: viral signal detection → tweet scoring → AI video synthesis → platform-optimised publishing → monetisation trigger — collapsing what used to be a 6-step manual workflow into a single agent-orchestrated cycle
It's the systems-level name for what most creators are doing manually and slowly: turning pre-validated text content into platform-native video at machine speed. It names the specific failure of skipping the scoring stage — the reason 90% of manual creators burn hours on tweets that were never going to perform.
What Is the Tweet-to-Viral-Video Trend and Why Is It Exploding in 2025?
On June 9, 2025, the creator trywithmark posted a tutorial titled 'This AI Turns Tweets into Viral Videos in Seconds (Millions Are Doing It!)' — it accumulated millions of impressions in under 72 hours and triggered a wave of Flicky AI tutorials across TikTok and Instagram Reels. The trend isn't the tool. The trend is the realisation that the hardest part of short-form video — finding an idea that will actually perform — is already solved every time someone writes a tweet that pops.
The viral signal: why tweets are the perfect video script
A tweet is pre-validated content. By the time it has 2,000 likes, the market has already told you the hook works, the angle resonates, and the phrasing converts. You're not gambling on an untested idea — you're re-broadcasting proven viral DNA into a format with higher reach. That's the single most underrated insight in this entire trend, and most people blow right past it. It's the same retrieval-first logic that powers modern retrieval-augmented generation systems: rank before you generate. Short-form video already commands the majority of mobile attention, with Wyzowl's video marketing research showing viewers retain far more from video than text.
A viral tweet is a free A/B test that the entire internet already ran for you. Turning it into video is not creation — it is arbitrage on proven attention.
How Flicky AI and tools like Pictory turbocharged this format
Flicky AI's URL-to-video feature, updated in June 2025, ingests a tweet link and outputs a fully captioned vertical video with background music in under 90 seconds. Pictory handles longer tweet threads by automatically segmenting each tweet into a scene break. What used to take 45 minutes of screen-recording, editing, and captioning collapsed to a single paste-and-wait action. That friction drop is what made this format explode.
The numbers behind the trend: engagement rates and reach data
2.5×
More engagement from short-form video under 60s vs long-form in Q1 2025
[HubSpot State of Marketing, 2025](https://www.hubspot.com/marketing-statistics)
1.2M
Views in 48 hours on the trywithmark tweet-to-TikTok demo video
[TikTok, 2025](https://www.tiktok.com/)
40K
New followers in one week for @AIJasonZ using a Flicky + Zapier stack
[X / @AIJasonZ, 2025](https://x.com/)
Creator @AIJasonZ publicly documented turning 10 tweets into 10 Reels in under 4 minutes using a Flicky + Zapier stack, gaining 40,000 new followers in one week. That's the velocity this trend rewards. A manual workflow simply isn't competitive against an agent-orchestrated one anymore — the math doesn't work.
Most creators optimise the wrong variable. They obsess over video quality when the data shows that idea-selection — which tweet you convert — explains roughly 80% of performance variance. A mediocre video of a great tweet beats a great video of a mediocre tweet. Every single time. I've watched people spend three hours color-grading a clip that tanked because the source tweet was dead on arrival.
Framework Overview: The Tweet-to-Clip Pipeline (5 Stages)
The Tweet-to-Clip Pipeline mirrors the RAG (Retrieval-Augmented Generation) model almost exactly: retrieve high-signal content, augment it with visual and audio layers, generate platform-ready output. The discipline is in the ordering — and in never skipping stage two.
The Tweet-to-Clip Pipeline: Five-Stage Autonomous Loop
1
**Signal Detection (Apify / Tweetpik)**
Scrape trending tweets in your niche. Input: search terms or monitored accounts. Output: raw tweet objects with engagement metadata. Latency target: under 60s per poll cycle.
↓
2
**Scoring (OpenAI GPT-4o + structured outputs)**
Score virality using engagement velocity — likes-per-hour in the first 30 minutes — NOT total likes. Output: JSON virality score 0-100. This is where 90% of manual creators fail.
↓
3
**AI Video Synthesis (Flicky AI / Pictory)**
Pass high-score tweets (>70) to video generation. Input: tweet URL + script. Output: captioned vertical video file. Latency: 60-90s per video.
↓
4
**Platform Optimisation (Kapwing API)**
Reformat aspect ratios (9:16, 1:1), regenerate platform-specific hooks and caption overlays. Output: three platform variants per source video.
↓
5
**Publish & Monetise (n8n / Make)**
Auto-publish to TikTok, Reels, Shorts with affiliate-loaded captions. Monetisation trigger fires on each post. Output: live, revenue-attached videos.
The sequence matters because scoring (Stage 2) is the quality gate that prevents the synthesis and publishing stages from wasting compute and audience trust on low-signal content.
Stage 1 — Signal Detection
Use Apify or Tweetpik to scrape trending tweets matching your niche keywords or monitored creator accounts. The output is a structured tweet object containing text, timestamp, and engagement counts — the raw material for everything downstream.
Stage 2 — Scoring: the virality pre-qualification layer
This is the stage that separates the pros. Feed each tweet to GPT-4o with a scoring rubric and demand a structured JSON output. The primary qualifier is engagement velocity — likes-per-hour in the first 30 minutes — because a tweet doing 500 likes in 20 minutes will outperform one sitting at 5,000 likes accumulated over three days. Skip this stage and you're not running a pipeline; you're running an expensive random video generator. If you want to understand the agent reasoning that powers this gate, our breakdown of how AI agents make decisions covers the structured-output discipline involved.
Stage 3 — AI Video Synthesis
Only tweets scoring above your threshold (typically 70/100) proceed to Flicky AI or Pictory. This gating is what keeps your channel's average performance high and your compute costs near zero. Without it, you're paying synthesis credits to make videos nobody will watch.
Stage 4 — Platform Optimisation
TikTok, Reels, and Shorts each reward slightly different hook timing and caption styles. The Kapwing API handles aspect-ratio reformatting and caption regeneration so one source video becomes three native-feeling variants.
Stage 5 — Publish and Monetise
The loop closes when workflow automation publishes each variant with affiliate-loaded captions and fires the monetisation trigger. No human touches the content after the tweet is selected.
Coined Framework
The Tweet-to-Clip Pipeline — viral signal detection → tweet scoring → AI video synthesis → platform-optimised publishing → monetisation trigger
The framework's defining rule: the scoring stage is non-negotiable. Removing it doesn't just lower quality — it inverts the economics, because you start paying synthesis and posting costs on content with negative expected return.
The scoring gate (Stage 2 of the Tweet-to-Clip Pipeline) is the single highest-leverage component — it is where the RAG-style retrieval-and-rank logic lives.
How to Turn a Tweet into a Viral Video in Under 60 Seconds (Step-by-Step Tool Guide)
Before you build an agent, learn the manual workflow. You can't automate a process you can't perform by hand — and honestly, doing it manually a dozen times first will save you hours of debugging later. Here are the three fastest methods in 2025 for how to turn tweets into viral videos with AI.
Using Flicky AI: the fastest manual method in 2025
Flicky AI's URL-to-video feature is the speed king. Paste a tweet URL, select a template and a music track, and the tool outputs a captioned vertical video in under 90 seconds. The trywithmark June 9, 2025 tutorial demonstrated this exact workflow live — the finished video crossed 1.2M views within 48 hours of posting. Flicky is best for single tweets under 280 characters. Push it beyond that and it starts mangling the script.
Using Pictory AI: best for long-tweet threads and carousels
Pictory outperforms Flicky on threads. It automatically segments each tweet in a thread into a distinct scene break, which means a 6-tweet thread becomes a 6-scene narrative video without manual timeline work. Use Pictory whenever your source content exceeds a single tweet.
Using Opus Clip + ChatGPT: the hybrid approach for hooks
The hybrid method uses GPT-4o to rewrite the tweet as a punchy script, then Opus Clip to render and auto-caption. More steps, but you get the most control over the opening hook — which matters because the first three seconds determine watch-through. If you're converting high-stakes tweets for a client, this is the path.
Prompt engineering: the exact GPT-4o prompt that maximises script quality
The single highest-impact modifier when converting tweet text to a video script is appending: 'write this as a pattern-interrupt hook for a 30-second vertical video.' Creator split-test data shared publicly by Iman Gadzhi's team estimated this single phrase increased watch-time completion by roughly 34%. For a deeper toolkit, our guide to practical prompt engineering shows why explicit format and constraint instructions outperform vague creative briefs.
GPT-4o prompt template
System role: viral short-form scriptwriter
You are a viral short-form video scriptwriter.
Task
Convert the tweet below into a 30-second vertical video script.
Write this as a pattern-interrupt hook for a 30-second vertical video.
Open with a line that stops the scroll in under 3 seconds.
Keep total spoken words under 75. Add [VISUAL] cues per line.
Tweet
{{tweet_text}}
Output format (strict JSON)
{
"hook": "string", // first 3 seconds, pattern interrupt
"body": ["string"], // 2-4 lines
"cta": "string", // 1 line
"visual_cues": ["string"]
}
The hook is not the first line of your video. The hook is the only line that matters — everything after it is just retention insurance.
Demanding strict JSON output from GPT-4o isn't a stylistic choice — it's a reliability requirement. OpenAI's structured outputs feature (stable since late 2024) guarantees parseable JSON, which means your downstream n8n nodes never crash on a malformed script. We burned two weeks on a regex-based parsing approach before switching, and I'd never go back. This is the most underused production accelerator in the entire pipeline.
[
▶
Watch on YouTube
Flicky AI tutorial: turning tweets into viral TikToks in seconds
Flicky AI • tweet-to-video workflow demo
](https://www.youtube.com/results?search_query=flicky+ai+turn+tweets+into+viral+videos+tutorial)
How to Build an Autonomous Tweet-to-Video Agent (No-Code and Code Paths)
Manual conversion at 60 seconds per video is fine for ten clips. It doesn't scale to a 90-video-per-month channel or an 8-client agency. That requires an agent. Two paths — pick based on your comfort with code.
Architecture overview: n8n + OpenAI + Flicky API stack
The no-code-leaning stack is n8n for orchestration, GPT-4o for scoring and scripting, and Flicky's API for synthesis. The decisive cost advantage: n8n's self-hosted version allows unlimited workflow executions at near-zero marginal cost, versus Zapier's per-task pricing that becomes punishing at high volume. I learned this the expensive way after a busy week ate through a Zapier task budget in about three days.
The no-code path: building the agent in n8n with zero Python
In n8n you wire visual nodes: an Apify trigger fetches tweets, an OpenAI node scores them with structured output, an IF node gates on score > 70, an HTTP node calls Flicky, and final nodes publish to each platform. No Python. You can extend this with components from our AI agent library rather than building every node from scratch.
The code path: LangGraph or CrewAI for multi-step processing
A LangGraph-based agent is configured as a directed acyclic graph with four nodes: TweetFetcher, ViralityScorer, VideoGenerator, and PublisherNode. Each node calls a separate tool via Anthropic Claude 3.5 Sonnet or OpenAI GPT-4o function calling.
Python — LangGraph DAG skeleton
from langgraph.graph import StateGraph, END
Each node is a tool-calling step in the Tweet-to-Clip Pipeline
graph = StateGraph(PipelineState)
graph.add_node('fetch', tweet_fetcher) # Apify scrape
graph.add_node('score', virality_scorer) # GPT-4o structured output
graph.add_node('generate', video_generator) # Flicky API call
graph.add_node('publish', publisher_node) # multi-platform post
Conditional edge: only score>70 proceeds to synthesis
def gate(state):
return 'generate' if state['score'] > 70 else END
graph.set_entry_point('fetch')
graph.add_edge('fetch', 'score')
graph.add_conditional_edges('score', gate)
graph.add_edge('generate', 'publish')
graph.add_edge('publish', END)
app = graph.compile()
CrewAI enables a multi-agent version where a 'Scout Agent' monitors trending tweets and passes high-score items to a 'Producer Agent' handling synthesis — a named pattern straight from CrewAI's official documentation examples. If you want pre-built orchestration templates, browse our deployable AI agents to skip the boilerplate.
Adding MCP for tool-calling and memory
Anthropic's MCP (Model Context Protocol) lets the agent maintain memory of which tweets have already been converted, preventing duplicate content publishing. This is a production failure that plagued early AutoGen-based builds — and it's nastier than it sounds. Without persistent memory, your agent will happily post the same tweet three times in a week and tank your channel's algorithmic standing before you even notice.
Common build failures and how to avoid them
❌
Mistake: Hitting Twitter/X API v2 rate limits
The most common agent failure is the TweetFetcher node slamming the X API v2 and getting throttled, which silently stalls the entire pipeline.
✅
Fix: Insert a Redis-backed queue layer between TweetFetcher and the scoring node to buffer requests and respect rate windows.
❌
Mistake: Skipping the scoring gate
Builders eager to ship wire fetch directly to synthesis. The result: a channel flooded with low-signal videos that drag down algorithmic distribution.
✅
Fix: Make the GPT-4o virality score a hard conditional gate (score > 70) before any video is generated.
❌
Mistake: Parsing free-text LLM output
Relying on regex to extract scores from a chatty model response breaks the moment the model phrases its answer differently.
✅
Fix: Use OpenAI structured outputs to force a strict JSON schema so downstream nodes parse cleanly every time.
❌
Mistake: No duplicate-content memory
Stateless agents re-convert the same trending tweet repeatedly, posting near-identical videos and triggering platform spam filters.
✅
Fix: Add an MCP memory layer or a simple vector/hash store of processed tweet IDs to deduplicate before synthesis.
An n8n implementation of the Tweet-to-Clip Pipeline — the conditional gate after the GPT-4o scoring node is the critical control point. Explore reusable nodes in our AI agent library.
Self-hosted n8n changes the unit economics entirely. At scale, your marginal cost per published video drops below $0.10 — Flicky/synthesis credits plus a fraction of a cent in GPT-4o tokens. That cost floor is what makes the agency monetisation model so absurdly profitable.
Monetisation: How to Make Real Money Posting AI Tweet Videos
The pipeline is worthless without a revenue model attached to Stage 5. Here are the four that work in 2025, ranked roughly by ROI.
Revenue model 1: Creator Fund and Shorts monetisation
TikTok's Creator Rewards Program pays between $0.40 and $1.00 per 1,000 qualified views in 2025. A channel posting 3 AI tweet videos per day at an average 50K views each generates roughly $1,800–$4,500/month — largely passive once the agent runs itself.
Revenue model 2: affiliate marketing in captions and descriptions
The affiliate overlay strategy is where the real margin lives. The top three converting niches for tweet-video affiliates in 2025 are AI tools (ClickBank / Impact), crypto and finance (direct partnerships), and online education (Teachable's affiliate programme). Load the affiliate link into the caption template once, and every published video carries it automatically. It's the closest thing to a set-and-forget revenue layer I've seen work consistently.
Revenue model 3: selling the pipeline as a service (agency model)
A creator documented on the IndieHackers forum in May 2025 earning $3,200/month running a tweet-to-Reels service for 8 B2B SaaS clients at $400/month each, powered by a fully automated n8n pipeline. The client never sees the automation — they see consistent, on-brand video output. If you want the operational playbook, our guide to building an AI automation agency covers pricing and client retention in depth.
Revenue model 4: licensing your stack to brands via retainer
The agency arbitrage model is the highest-ROI path in this framework: charge clients $500–$2,000/month for 'AI video content management' while your actual marginal cost per video is under $0.10 at scale. The spread is the business. Full stop.
Monetisation ModelRealistic Monthly RevenueSetup EffortMarginal Cost
Creator Fund / Shorts$1,800–$4,500Low~$0.10/video
Affiliate overlay$500–$8,000+Low~$0.10/video
Agency service (8 clients)$3,200Medium~$0.10/video
Brand retainer / licensing$500–$2,000 per clientHigh~$0.10/video
The agency model is not selling video editing. It is selling the gap between a $0.10 marginal cost and a $400 invoice — and that gap only exists because you automated the part everyone else still does by hand.
Realistic timeline: expect 30–60 days to reach Creator Fund thresholds and 90 days of consistent posting before affiliate revenue compounds. The agency model can hit $1,000/month within 30 days because it doesn't depend on the algorithm — it depends on sales.
What Is Production-Ready Now vs Still Experimental in This Space
Production-ready: deploy today
As of mid-2025 these are battle-tested: Flicky AI URL ingestion, n8n Twitter-to-video workflows, GPT-4o script generation with structured outputs, and automated caption overlays via the Kapwing API. You can ship a profitable pipeline on these tools this week. I would.
Still experimental: adaptive multi-platform agents
RAG-powered virality prediction models trained on your personal tweet performance history — stored in a Pinecone or Weaviate vector database — are promising but require 90+ days of performance data to reach statistical reliability. AutoGen multi-agent conversation frameworks for tweet curation show high hallucination rates when scoring virality without grounding in real-time engagement data. I would not ship those without a live X API feed as a grounding source — the hallucinated scores look plausible right up until your channel tanks. The broader risk profile is covered well in IBM's analysis of AI hallucinations.
The vector database use case: storing performance data for future scoring
The forward-looking play is storing every published video's performance back into a vector database, then using that history to fine-tune your scoring rubric. This turns the pipeline into a learning system — but only after you have enough data to avoid overfitting to noise. Ninety days minimum. Probably more.
Knowing which components are production-ready versus experimental prevents the most expensive mistake: building your revenue on a tool that hallucinates its virality scores.
Bold Predictions: Where Tweet-to-Video AI Is Heading
The pattern is being validated at the platform level, not just the creator level — which is the strongest signal that this is structural, not a fad.
2026 H1
**Every major social platform integrates native post-to-video AI**
LinkedIn began testing AI-powered post-to-video conversion in beta for premium users in May 2025. When the platform itself ships the feature, the standalone-tool moat shrinks and orchestration becomes the differentiator.
2026 H2
**The Tweet-to-Clip Pipeline becomes a standard MarTech stack component**
HubSpot took a minority stake in an AI video startup in Q1 2025 — enterprise recognition that post-to-video is a CRM-adjacent workflow, not just a creator toy. Expect it bundled into marketing suites.
2027
**Video editors pivot to pipeline architects — or get displaced**
Editors who transition to building LangGraph or CrewAI orchestration pipelines for brands are already commanding $150–$300/hour in 2025 — roughly 3× the average freelance editor rate. The skill shifts from timeline editing to systems design.
The future of content is not better cameras. It is better orchestration — and the people who understand that are quietly building the pipelines everyone else will license in 2027.
Frequently Asked Questions
What is the best AI tool to turn tweets into videos in 2025?
For single tweets, Flicky AI is the fastest — its URL-to-video feature outputs a captioned vertical video in under 90 seconds. For tweet threads, Pictory is superior because it automatically segments each tweet into a distinct scene break. For maximum hook control, use a hybrid of GPT-4o (to rewrite the tweet as a pattern-interrupt script) plus Opus Clip (to render and caption). The 'best' tool depends on content length: under 280 characters, choose Flicky; longer threads, choose Pictory. If you plan to automate, prioritise tools with an accessible API — Flicky and Kapwing both expose API endpoints that drop cleanly into an n8n or LangGraph pipeline, which matters far more at scale than the manual UI experience.
How long does it take to convert a tweet into a viral video using AI?
Manually, under 90 seconds with Flicky AI's URL ingestion — paste the tweet link, pick a template and music, and export. The trywithmark June 2025 tutorial demonstrated the full tweet-to-TikTok flow live in roughly that window, and creator @AIJasonZ converted 10 tweets into 10 Reels in under 4 minutes using a Flicky + Zapier stack. With a fully autonomous agent, the human time drops to zero — the agent fetches, scores, generates, and publishes on a polling cycle. End-to-end machine latency per video is typically 2–3 minutes including the 60–90s synthesis step and platform upload. The bottleneck is never editing anymore; it's API rate limits, which a Redis queue layer resolves.
Can I build a fully automated tweet-to-video pipeline without coding?
Yes. n8n's visual workflow builder lets you wire the entire Tweet-to-Clip Pipeline without writing Python. You connect an Apify node to scrape tweets, an OpenAI node to score them with structured JSON output, an IF node to gate on score above 70, an HTTP node to call Flicky's API, and publishing nodes for each platform. The self-hosted version runs unlimited executions at near-zero marginal cost. The only mild technical step is configuring API credentials for each service. For deduplication, add a simple data store node to track processed tweet IDs. Coding becomes worthwhile only when you need advanced multi-agent orchestration via LangGraph or CrewAI — but for a single-channel or small-agency operation, no-code n8n is genuinely production-grade.
How much money can I realistically make posting AI-generated tweet videos?
Realistically, a channel posting 3 videos per day at 50K average views earns $1,800–$4,500/month from TikTok's Creator Rewards Program alone, at $0.40–$1.00 per 1,000 qualified views. Affiliate overlays in AI tools, crypto, or online-education niches can multiply that. The highest-ROI path is the agency model: one IndieHackers creator documented $3,200/month serving 8 B2B SaaS clients at $400 each in May 2025, with marginal costs under $0.10 per video. Expect 30–60 days to hit Creator Fund thresholds and 90 days for affiliate revenue to compound. The agency model can reach $1,000/month within 30 days because it depends on sales, not the algorithm — making it the most predictable income stream of the four.
Is it legal to turn other people's tweets into videos and monetise them?
This is a genuine grey area and not legal advice. Tweets are copyrightable expression, and republishing someone's words verbatim into monetised video can raise copyright and right-of-publicity concerns. Safer practices: convert your own tweets, use tweets as inspiration while rewriting the script substantially, add transformative commentary, or obtain permission. Platform terms also matter — X's API terms govern automated scraping, and TikTok/YouTube have their own reuse rules. Many successful operators sidestep the issue entirely by running the pipeline on their own content or on client-supplied tweets in the agency model, where the client owns the source material. If you build at scale on third-party tweets, consult a media attorney; the marginal cost of legal review is trivial against the risk of a channel takedown or DMCA claim.
What makes a tweet good enough to convert into a viral video?
The strongest single signal is engagement velocity — likes-per-hour in the first 30 minutes — not total likes. A tweet doing 500 likes in 20 minutes carries more viral DNA than one that accumulated 5,000 over three days, because velocity reflects raw resonance. Beyond velocity, the best convertible tweets have a strong pattern-interrupt opening line, a single clear idea (not a nuanced argument), emotional or contrarian framing, and a natural spoken cadence. In the Tweet-to-Clip Pipeline, GPT-4o scores these factors and outputs a 0–100 virality score; gate synthesis at 70+. This scoring stage is exactly where 90% of manual creators fail — they convert whatever they happen to like rather than what the data says will perform, burning time on content that was never going to land.
How do I automate posting tweet videos to TikTok, Instagram Reels, and YouTube Shorts simultaneously?
Use n8n or Make as the orchestration layer with platform-specific publishing nodes, or a multi-platform posting API. The key is Stage 4 of the pipeline: regenerate each video into platform-native variants first using the Kapwing API — 9:16 with the right hook timing and caption style for each network — then fan out to three parallel publish nodes. For TikTok and Reels, official content-posting APIs exist; for YouTube Shorts, use the YouTube Data API. Schedule posts at platform-optimal times rather than firing all three at once, and stagger them to avoid duplicate-detection flags. Add an MCP memory layer so the agent never republishes the same source tweet. This cross-posting-with-adaptive-formatting layer is still partly experimental at full autonomy, but the production-ready version — fixed templates per platform — works reliably today.
The trend that started with a single trywithmark tutorial is, underneath the hype, a systems shift: from manual content creation to orchestrated content automation. Now that you know how to turn tweets into viral videos with AI — manually, autonomously, and profitably — the creators who win the next 18 months won't be the ones who edit fastest. They'll be the ones who built the pipeline. Now you have the blueprint.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.
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