Best tweets about AI Workflows
Discover the best tweets about AI workflows, featuring practical automations, tool chains, agents, integrations, reliability, and measurable outcomes.
Working AI workflows with clear inputs, tools, steps, outputs, failure handling, operating cost, and demonstrated value.
Best AI Workflows tweets
Ranked 01–50
@bibryam ·
How I use LLMs as a staff engineer in 2026 https://t.co/KDzmfiUF87 The biggest AI workflow change in 2026: treating agents as capable collaborators for coding, debugging, testing, and codebase research—while still keeping humans responsible for judgment, communication, and review.
@businessbarista ·
Just co-led an AI training for 20 execs from one of the largest financial institutions in the world. We covered a lot of ground from how LLMs work to what an agent is and how to transform your work, but here were the biggest aha moments for the group. 1. Historically, leaders had to think about allocating budget for headcount and software. Now, token budget must be a serious consideration. Do you set limits per employee? How do you measure the ROI on your token budget? How much budget do you allocate to which employees? How do employees know which models to use to most efficiently spend tokens? 2. The models are good enough now, where most bad output is user error not technology error. And a major contributor to user error is bad context management. To have clean context hygiene you need to understand what the context window is, why separation of concerns is important, and how tactically to treat context like a precious resource. 3. A skill is a scary word. It’s nothing more than an SOP. If you asked an intern to write a one page document breaking down their step-by-step process for building a great deck, you’ve created a skill. A skill is a long prompt that codifies a repeatable process, can be edited as you see fit, and helps to generate more predictable output that meets your standards. 4. Saying you use AI gets you style points. But the real unlock is in process mapping. During the workshop, we asked every exec to write out one of their/their teams key processes step by step and place an E (eliminate), A (automate), or D (delegate) next to each step. The exercise not only revealed opportunities for AI, but also existing inefficiencies that have gone undetected. 5. Claude Cowork opened their eyes. Many people still see AI as a chat-based supercharged Google. They may have heard the phrase “agent,” but they assume it’s sophisticated tech reserved for engineers. Cowork is the layperson’s gateway drug to agents, showing the possibility of building web apps, ai workflows, and live artifacts in a single place. 6. Every company talks about the “bad guys”slowing down AI transformation: legal, compliance, and IT/security. One of the most powerful choices leadership can make is flipping the script and making them the heroes of the transformation story. Help them understand the risks of doing nothing, work with them to make calculated bets, and celebrate them publicly when they partner to drive transformation with speed. 7. Getting an AI system to perform requires four things to go right: picking the right model, teaching it the right process, providing the right context, and establishing the right guardrails. Most people assume the right model is 99% of it, they don’t think enough about giving the right context, and they don’t realize the right guardrails make things like hallucinations and mistakes less dangerous.
@Axel_bitblaze69 ·
Something for your to do list this weekend: Clone one repo. Write one markdown file.. you’ll have an agent that outperforms what research teams hand build. ▫️Here’s the repo: Your AI agents are about to upgrade themselves.. AutoAgent just went open source and this changes everything about how you build with AI.. Let me explain. rn when you build an AI agent whether in Claude Code, Cursor, or anything else.. you're manually writing prompts, picking tools, tweaking configs, testing, failing, rewriting. That entire loop is what AutoAgent automates it. Here's how it works in simple terms: > You write a markdown file describing what you want your agent to do > A "meta-agent" reads your instructions and rewrites the entire agent setup for you > It tests the new version against real benchmarks > Keeps changes that improved performance > Throws away what didn't work > Repeats. All night. Autonomously. You go to sleep with a mid agent. You wake up with a top 1% agent. And fyi AutoAgent already hit: #1 on SpreadSheetBench (96.5%) #1 on TerminalBench with GPT-5 score (55.1%) ▫️How would this help you: If you're building AI workflows, automations, coding agents you no longer need to spend hours fine-tuning prompts and tools manually. You describe the outcome you want, AutoAgent engineers the path to get there. This is AutoML but for agents. And it's free. ▫️How to set it up: > Clone it: git clone https://t.co/m1kyZEKwhy > Install: uv sync > Write your program.md in plain English instructions for what your agent should do > Add benchmark tasks in tasks/ so it knows what "good" looks like > Run it overnight It handles the rest.. rewrites your prompts, tools, routing, config all inside Docker so nothing breaks. Bookmark this one.
@EXM7777 ·
i believe the strongest asset for entrepreneurs right now is an "internet swipe file" built in Obsidian... a knowledge base packed with: - landing pages - visual styles - creatives - tweets, linkedin posts, tiktoks... - youtube thumbnails a massive library of proven content you can inject into AI workflows it's the easiest way to improve outputs, just steal from what's already working you can't be good at everything, but this + solid prompting will take you very far
@EXM7777 ·
there's only one way to remain relevant and profitable in AI... and it's REALLY important, so listen to this obsessing over "mastering" Codex, Claude Code, Hermes or OpenClaw will take you nowhere, it's a completely wrong approach simply because we're in a market that changes every 3 months: > new model drops > everyone's mind is blown > old workflows are obsolete > you're back to square one if you're trying to become "very good" at THIS specific tool... you're already behind because by the time you master it, there's a better one the tools are temporary... the skills are permanent, here's what actually keeps you relevant: develop AI skills that transcend the tools: - context engineering principles - AI workflow design (when to use AI vs when not to) - quality control systems (catching hallucinations, maintaining consistency) - integration thinking (connecting multiple agents into one system) these skills transfer, no matter what model launches next month... you can adapt in hours, not months your business stays profitable because you're not tied to one tool - you're building systems that work regardless of which AI is "winning" this quarter
@FundamentEdge ·
AI Workflow: 13F Top Holdings Report One thing I like to do when I'm looking for new ideas is to check the 13Fs of a few dozen hedge funds I respect for new & high conviction ideas. While I don't want to outsource my thinking, there is no copyright protection on a great idea, and the funds I choose I know to have strong due diligence and a thoughtful portfolio inclusion process. Which is a positive signal, on balance. (And one of my favorite personal strategies is a biotech 13F overlay which has a crazy good 15 year back test...post-COVID hangover notwithstanding...though XBI breaking out...???). This is sort of a clunky process in FactSet (and in Bloomberg) where even getting to the right legal entity can be a pain. Pretty much just for UI purposes, I have been a Whale Wisdom subscriber for years (which still isn't great, but better than Bloomberg/FactSet). I've long been searching for a better alternative. So I've been playing around with an AI workflow solution to this problem, seeking to create: - A short PDF I can read in 5-10 minutes - Starting with 13F-sourced data on top 20 holdings - AI generated summary thesis on those top 20 holdings (i.e. why the fund likely owns the position) So in 5-10 minutes I can get a quick check on the highest conviction names at a fund and quick analysis on the position. Efficiency is important when doing this ~35 times, 4 times per year. 35 x 45 minutes (i.e. see the position, read 1-2 sell-side notes on the name) is a lot different than 35 x 10 minutes (i.e. quick AI-generated thesis). The cool part is I can take this report then drop it into my AI "Up to Speed" workflow and go even deeper, such as identifying dates for binary catalysts for biotech ideas, or asking for the 3 key drivers for a name. This is a pretty challenging task for AI, because I am demanding the LLM identify the correct legal entity from the generic name (which FactSet mostly cannot do) go to EDGAR XML filings, integrate some market data, format it well, the write "Why This Position Makes Sense", "Recent Developments Supporting the Thesis", and "Position Sizing Context". From my prompt: I tried this a few months back and it didn't work. I decided to try it again this morning, and here are my results. ChatGPT 5.2: Awful, and awful in an annoying way. Finally admitted it couldn't do it in a single normal churn task. Overly, annoyingly apologetic, then just keeps not doing what you ask. Gemini 3: Better, but lazy. Gave me a summary of position 1, 4 and 7, but completely skipped the other ideas. Fail. Grok: Better, but still not good. I'd say close, but still fail. Claude Opus 4.5: SHOCKINGLY GOOD. Missed on some of the market data requests (% of ADV), but pretty much fulfilled the request exactly as I requested, creating an easy to read summary of a fund's Top 20 holdings and a guess as to why they hold the idea. I will drop the prompt and a few sample reports in the replies if you want to re-create this.
@GergelyOrosz ·
Just six months ago, @dhh (creator of Ruby on Rails and Omarchy) said how he doesn’t really use AI tools to write code, because they are not good enough. Things have changed, a lot. Timestamps: 00:00 Intro 02:11 Omarchy and Ruby on Rails 08:25 37signals overview 10:12 Launching HEY 18:38 Building HEY 22:47 Designers at 37signals 28:08 The craft of design 31:52 Why DHH now embraces AI workflows 39:45 The AI inflection point 44:23 DHH’s agent-first workflow 55:09 AI’s impact on junior developers 1:03:08 Developer experience with AI 1:16:43 What does AI mean for developers? 1:23:33 37signals teams and hiring 1:38:20 Work-life balance with AI 1:41:41 Why DHH keeps building 1:45:24 Closing Brought to you by: • @statsig – The unified platform for flags, analytics, experiments, and more. Stop switching between different tools, and have them all in one place. https://t.co/ZCSOIcWv31 • @WorkOS – Everything you need to make your app enterprise ready. WorkOS gives you APIs to ship enterprise features in days. Check out https://t.co/jhFNq3a7n7 • @SonarSource – The makers of SonarQube, the industry standard for automated code review. See how SonarQube Advanced Security is empowering the Agent Centric Development Cycle (AC/DC) with new capabilities. https://t.co/WbRTb55FL6 Three interesting observations from this conversation: #1 DHH's philosophy on AI has not changed, but the available tools very much have. Autocomplete-style coding assistants were genuinely annoying for experienced developers six months ago. Things changed with the shift from tab-completion to agent harnesses, plus the emergence of powerful models like Opus 4.5 – when agents started producing code which DHH does want to merge with little to no alteration. #2 Beautiful code and products aren’t matters of vanity; they’re signals of correctness. Dipping into philosophy, DHH says: “When something is beautiful, it’s likely to be correct.” He argues that Steve Jobs wanted the inside of a computer to be beautiful because people who care about circuit board layout are also those who sweat on the details of the UI. #3 DHH’s development workflow, today: He runs tmux to have two models running, and neovim in the center. Specifics: - One fast LLM running (typically Gemini 2.5) in one split terminal - A slow but more powerful model in another terminal (usually Opus) - NeoVim for reviewing diffs via Lazygit
Watch video@businessbarista ·
How should companies measure ROI of AI? Here's my working mental model. Tear it apart! 1) Below a certain investment level (determined by ELT or AI steering committee), ROI can be vibes-based through conversations with users. Goal here is to remove friction & empower people to play with the technology however they find helpful. It just has to lead to a high enough fidelity gut feeling to determine if a higher investment experiment is worth running. 2) Above a certain investment level, ROI has to be as high fidelity as possible. Every AI initiative is run like an experiment with friction minimized as much as possible. There’s a certain investment limit to experiments and investments can be revisited once experiments are complete. Here's how an experiment would be run & how (soft vs. hard) ROI would be calculated. - Hypothesis: If recruiters use AI to screen resumes, then the time-to-hire will decrease and the interview-to-offer conversion rate will remain equal or improve. - Independent Variable: The screening method used (AI-powered software versus traditional human resume review). - Dependent Variables: Time spent screening (minutes per resume), candidate diversity metrics, and the hiring manager's satisfaction score of shortlisted candidates. - Controlled Variables: The same job description, the same pool of raw applicant resumes, and the same evaluation criteria (rubric). To ensure a fair test, you must use a randomized control design: - Control Group: Group A consists of experienced human recruiters who screen 200 incoming resumes using your traditional manual process. - Experimental Group: Group B uses the AI screening tool to parse and rank the exact same 200 resumes. Experiment steps: 1) Time Tracking: Log the total hours Group A spends reading resumes versus the time it takes to configure and run Group B's AI tool. 2) Blinded Interview Review: Pass the top 10 candidates selected by the human process and the top 10 selected by the AI process to a hiring manager. Do not tell the manager which candidate came from which screening method. 3) Quality Metric: Have the hiring manager score each candidate's qualifications on a scale of 1–10 based on the interview. 4) Replication: Repeat this exact process across three different job openings (e.g., Sales, Engineering, and Marketing) to ensure the AI's effectiveness isn't limited to just one type of role. Results & ROI: Experiment proved successful if 2 conditions are met: - Condition 1: Time Saved > 0 - Condition 2: AI Average Quality Score ≥ Human Average Quality Score If not successful, run new experiment (i.e. how can we tweak the AI to deliver as high of an average quality score) If successful, measure ROI. In this example ROI would look like: ROI % = (Annual Savings - Annual AI Cost / Annual AI cost) * 100 So if the company has 50 job roles per year, 9.5 hours are saved and the screening software costs $10,000, the ROI would be: (475 hours saved * $58/hr - $10,000 AI tool/ $10,000 AI tool) * 100 = 174% ROI And that ROI is realized (goes from soft savings to hard savings) either by slowing down the hiring of recruiters, firing recruiters, or revenue realized by getting new hires into seat faster. What do you think? Right/wrong approach?
@goyalshaliniuk ·
API Gateway vs AI Gateway : What’s the difference ? The shift from traditional software to AI-powered systems is changing how we build, secure, and scale digital experiences. As complexity grows, businesses now need infrastructures that don’t just route requests, but also manage intelligence, safety, and optimization at every step. Here’s how the two approaches compare: API Gateway Acts as the backbone of service communication. It validates requests, manages authentication, enforces rate limits, transforms payloads, proxies traffic, and monitors logs - ensuring APIs remain secure, reliable, and efficient. AI Gateway Built for AI-first systems. It processes prompts, checks caches, applies safety policies, routes to providers, optimizes context, invokes models, handles retries, and logs usage with cost tracking - making AI interactions scalable, safe, and cost-aware. Adopting AI Gateways means moving beyond connectivity - unlocking smarter, safer, and production-ready AI workflows. What are your thoughts on this?
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@om_patel5 ·
THE BEST OPEN-SOURCE AI AGENT REPO I'VE SEEN IN A WHILE most "agent" repos are just glorified prompt folders. this one actually feels like a real team. it's called The Agency and it gives you 140+ specialized AI agents you can drop into your workflow right now. not vague "act like a developer" prompts. actual specialists with: > clear personalities > defined workflows > real deliverables > success metrics > production-ready behavior think: > frontend developer for react/ui work > backend architect for apis + infra > reddit community builder for authentic growth > whimsy injector for adding delight without ruining ux > security engineer for threat modeling and safe code > technical writer for docs that don't suck and it keeps going. engineering. design. marketing. sales. product. qa. ops. even weirdly specific specialists for things like MCP servers, LSP/indexing, compliance, paid media, and spatial computing why this is actually cool: 1\ it's not locked to one tool > works with claude code out of the box > can also be converted/installed for cursor, copilot, aider, windsurf, gemini cli, opencode, qwen code, and more > so you're not betting everything on one editor or one ai workflow 2\ these agents are built like systems, not one-liners > each file includes identity, tone, rules, workflows, examples, and expected outputs > that means more consistent results and less babysitting 3\ it was clearly shaped by real usage > born from a reddit thread > iterated for months > 50+ requests in the first 12 hours > battle-tested instead of vibe-assembled 4\ the use cases are actually practical > building an MVP with a frontend dev + backend architect + growth hacker > launching campaigns with content + twitter + reddit + analytics agents > shipping enterprise features with PM + senior dev + designer + QA agents 5\ it's open source > MIT licensed > transparent > forkable > customizable > PRs welcome my favorite part: most people use AI like one overworked intern. this repo makes AI feel more like hiring a full stack agency with specialists who already know their job. that shift matters. instead of saying: > "help me with my product" you can say: > "activate frontend developer mode and build this react component" or > "use the reddit community builder and help me grow this without getting banned" that specificity is where the magic starts the big takeaway: > generic prompts give generic output > specialized agents give you sharper thinking, better process, and more usable work if you're using claude code, cursor, or any agentic coding setup, this repo is worth studying even if you never use it directly because it shows what good agent design actually looks like star-worthy for sure
@alliekmiller ·
AI is better than you at working with AI. It's better at generating prompts for AI, teaching skills to other AI agents, coordinating messaging between AIs. AI is an AI ops whisperer. Think like a PM: go through the journey you're taking right now with your AI workflows and find high ROI ways to use AI to help your AI efforts. Yes, you can prompt AI to create an app. But you can also... ...and this is overkill and would waste a lot of tokens but I want to dramatize it because when costs plummet, we will see strange usage patterns... Prompt an AI with the idea, and then it creates a much better prompt (see images), and then it creates 4 different versions of that prompt, and then it spawns parallel agents to research the product space from 4 different points of view, and then they all meet in an agent team war room and battle it out, and then spec a product together while 5 other agents with 5 different goals in parallel spec it out themselves, and then 3 more agents review and critique the specs, then another reviews all previous work and summarizes, and another one tees up open questions, then 10 more with radically different personas meet to evolve the best idea and spawn 50 more versions, then you run a simulation by 10000 personas to vote for the product with the fastest time to market, highest delight, and strongest ROI potential. And then you create the app. What I'm saying is: find where you are the intermediary and shouldn't be, and find higher order ways to plug yourself in. Take yourself out of the loop before the loop takes you out.
@alliekmiller ·
One of my favorite AI workflows: AI interviews me every day for a snapshot. What big decisions did I make? What actions did I take? Did it work? What do I predict? What's a cool quote I heard? I usually dictate for a few minutes each day to capture it. This also feeds into my Claudopedia, my content machine, my forecast machine, and can be queried.
@goyalshaliniuk ·
Understanding the 3 Types of Workflows: Rule-Based, AI, and Agentic Workflows are the blueprint for how tasks move from start to finish in any organization. The way they’re designed shapes how your business responds to challenges, opportunities, and change. Some workflows stick to a rigid, predefined path and never adapt. Others can react in real time using AI but still work in a single shot. The most advanced go further planning steps, using tools, and improving themselves over time. Understanding these differences is key to choosing the right approach for your goals, whether you’re automating simple processes or building complex, intelligent systems. 1️⃣ Rule-Based (Automated, Non-AI) Follows a fixed, predefined path with no learning or adaptation. Great for repetitive tasks but limited when dealing with unexpected scenarios. 2️⃣ AI Workflow (Non-Agentic) Uses AI to process a query in one go - generating outputs like text, classifications, or predictions without deeper planning or tool use. Quick and efficient, but lacks strategic reasoning. 3️⃣ Agentic Workflow (Multi-Step, Tool-Using AI) The most advanced. The AI agent breaks tasks into steps, uses external tools, evaluates results, and improves iteratively much like a human expert. Ideal for complex, evolving challenges. Curious to learn more? Keep exploring these concepts to see how each workflow type can be applied in different real-world scenarios.
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@HeyAbhishek ·
This AI workflow is insane. GPT Image 2.0 + Seedance 2.0 turned a simple storyboard into a cinematic battle sequence in minutes. Step-by-step tutorial + prompts below: 👇
Watch video@HeyAbhishek ·
This AI workflow is wild 🔥 GPT Image 2.0 + Seedance 2.0 just turned simple storyboard into cinematic video in minutes. step by step tutorial with prompts: 👇
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@adxtyahq ·
Things i have worked on in past 30 days: - made systems from scratch including architectural decisions - optimized the existing company system, reducing runtime from ~5 minutes to under 3 minutes and cost from ~$3-5/run to ~$0.9-2.5/run - improved model latency, reduced costs, and added intelligent model routing - tested 30+ frontier models across OpenAI, Anthropic, Google, DeepSeek, GLM, Kimi, MiniMax, and NVIDIA - built parallel workflows where several ElevenLabs and Nano Banana 2 Lite processes run concurrently - improved the user feedback system - added retries, fallbacks, and better error handling for AI workflows - improved backend performance and reliability - shipped production features end-to-end
@shushant_l ·
I'm amazed most people still try to build AI workflows with a single AI agent. Here's how to build a complete team of AI agents that can automate entire workflows. --- 1. An AI agent can reason, plan, use tools, take action, and produce outputs autonomously. --- 2. One agent usually handles one job, while a team of agents completes an entire workflow. --- 3. Multi-agent systems divide complex work into specialized roles for better results. --- 4. A researcher agent gathers the required information before passing it forward. --- 5. A writer agent converts the research into structured content or deliverables. --- 6. A reviewer agent checks quality, accuracy, formatting, and consistency. --- 7. A sender or execution agent delivers the final output automatically. --- 8. Every agent needs a clear role, objective, and written instructions. --- 9. A powerful AI model acts as the reasoning engine behind each agent. --- 10. Knowledge sources like documents, databases, and files improve output quality. --- 11. Connected tools allow agents to search, send emails, update CRMs, and more. --- 12. Popular agent types include research, content, outreach, QA, reporting, and customer support. --- 13. Start by mapping your workflow before building any AI agents. --- 14. Define the input, trigger, actions, and expected output for every agent. --- 15. Connect all agents into one automated workflow with proper handoffs. --- 16. Test every agent individually before testing the complete workflow. --- 17. Use triggers like forms, emails, schedules, or CRM updates to start automations. --- 18. Monitor logs regularly and refine prompts, instructions, and logic over time. --- 19. Avoid making one agent do everything because specialization produces better results. --- 20. No-code platforms make it possible to build powerful AI agent teams without programming. --- To learn more, check the infographic. ---
@akshay_pachaar ·
this is the most underrated update in the agent space right now. your AI workflow runs for 47 minutes, burns 312 LLM calls, then crashes at step 8. most frameworks make you restart from zero. @crewAIInc just shipped checkpointing. think google docs autosave, but for your agent's work-in-progress. every flow method becomes a recovery point. resume in one line. fork from any saved state into a new branch. edit past outputs and watch changes ripple downstream. visual TUI to inspect everything. your pipelines stop being fragile one-shot jobs. they become resumable, inspectable, branchable processes. zero extra infra. 100% open-source. get started with CrewAI here: https://t.co/bPGE0cGOIm (don't forget to star it ⭐️)
Watch video@shushant_l ·
I'm amazed most people still don't understand how modern AI systems actually work. Here's the complete AI stack behind today's smartest AI apps, agents, and workflows. --- 1. User Layer is where people interact through web apps, mobile apps, chat, APIs, Slack, Teams, email, and WhatsApp. --- 2. Input Layer accepts text, images, audio, video, PDFs, documents, code, databases, and URLs. --- 3. Context Layer provides prompts, memory, RAG, conversation history, enterprise data, web search, and vector search. --- 4. Orchestration Layer coordinates the entire AI workflow using agent frameworks and orchestration tools. --- 5. Foundation Models power reasoning, coding, writing, planning, translation, and analysis. --- 6. Tool Layer connects AI with browsers, APIs, Python, GitHub, Notion, Google Workspace, Salesforce, and more. --- 7. Knowledge Layer stores structured and unstructured information using databases and vector stores. --- 8. RAG retrieves, ranks, injects context, and generates more accurate responses with fewer hallucinations. --- 9. Memory Layer gives AI both short term sessions and long term knowledge for personalization. --- 10. Agentic AI Layer enables AI to plan, reason, use tools, execute tasks, reflect, and improve. --- 11. Communication protocols like MCP and A2A help AI systems connect with tools and other agents. --- 12. Infrastructure Layer runs AI using GPUs, cloud platforms, Docker, Kubernetes, and inference engines. --- 13. Security Layer protects AI with authentication, authorization, guardrails, encryption, and human approval. --- 14. Observability Layer tracks prompts, token usage, latency, monitoring, evaluations, and user feedback. --- 15. AI Applications are built on top of the stack for chatbots, coding, search, healthcare, finance, education, and enterprise use cases. --- 16. LLMs focus on language understanding and reasoning over information. --- 17. Generative AI creates text, images, audio, video, code, and other content from prompts. --- 18. Agentic AI combines LLMs, memory, planning, tools, and execution to complete multi step tasks autonomously. --- 19. The complete AI flow moves from user input to context, orchestration, models, tools, knowledge, memory, execution, and monitoring. --- 20. Understanding this complete stack helps you build better AI apps, workflows, and autonomous agents. --- To learn more, check the infographic. ---
@TheTuringPost ·
Have you ever wondered what actually stands behind the idea of an AI workflow? Time to make it clear. In organizations, the workflow is the unit you can actually inspect, automate, and improve. And the most accurate definition would be this: ➡️ A workflow is a repeating sequence of decisions and actions that turns an input into an output, with points along the way where a human exercises judgment. Judgement points matter! Strip those out and you have a pipeline – a cron job, a script, plumbing. Have them in and you have a workflow. Most companies already run dozens of workflows across support, finance, engineering, sales, operations. But most of these workflows were never written down, many are informal and under-specified and exist because humans learned how to work around broken systems years ago. LLMs change this. A lot of the gray area that previously required constant human coordination can now be handled through language, reasoning, classification, validation, and generation. The interesting question becomes: • Which judgment points can an agent handle? • Which still require humans? • How does the human know when to step in? Across production systems we operate, the same patterns keep appearing: triage, monitoring, draft-and-review, execution-with-approval, investigation-and-recommendation, sync-and-transform, elicitation, curation Interestingly, underneath all of them are the same primitives. Together with @wschenk, we break down these primitives, recurring workflow patterns, and the full workflow taxonomy in this article → https://t.co/KQsWzYiDyE Which workflow in your organization is already structured enough for an agent?
@smratitiwa86867 ·
This might be the smartest way to use Claude Skills yet. 🤯 People are now turning entire BOOKS into reusable AI systems. Not summaries. Actual executable workflows. Example: • A negotiation book → becomes a negotiation assistant • A marketing book → becomes a campaign strategist • A productivity book → becomes a planning system You basically extract the author’s framework… then turn it into a slash command you can run anytime. The crazy part? Once the Skill is built, Claude starts applying the book’s thinking directly to your situation. Not generic advice. Actual structured execution. One tip I loved: Split books into 2 Skills: • “Diagnose” → analyzes your situation • “Apply” → generates the final output/framework That separation apparently makes results way better. Feels like we’re moving from: “reading books” to “installing frameworks into AI.” 🚀 Bookmark this. This is probably where AI workflows are heading next. 🔖
@n8n_io ·
Most AI workflow failures aren't model problems. Normalize inputs, validate outputs, route on confidence. Wrap your AI steps in deterministic logic and they'll actually hold up in production. New guide by Elvis Saravia (@omarsar0) with five importable templates: https://t.co/qyIgSkqyGz
@williamkast_ ·
This AI workflow replaced our 5 hour ad scripting process: Before AI, scripting a single ad took us roughly 1-2 hours: - Research the persona & market - Write the script variations - Review it - Edit it - Pick the best Now we do the same thing in under an hour while maintaining the same quality. That's my exact workflow: Step 1: Claude Project - Customer reviews - Post-purchase surveys - Support tickets - Competitor research - Brand guidelines - Product info - Every persona documented This is the foundation. If you skip this, the output will be random. Step 2: Define tests What persona, awareness stage, desire or pain point do you want to target? With what format. You need to know this before you touch any AI tool. The AI doesn't decide strategy - you do. Step 3: Prompt Claude Not "write me a Facebook ad." -that gets you generic copy. "Write a problem-aware ad for persona X who has tried Y and Z and hated them because of A. Target their desire for B. Use a podcast-style format." The more specific your prompt, the better the output. Step 4: Generate 5-10 script variations. Different hooks. Different angles on the same message. Different emotional triggers. This used to take 2-3 hours. Now, once the setup is done, it takes 15 minutes. Step 5: Human judgment This is the part AI can't do. You need to know which scripts are strong and which are weak. Which ones match your customer's real language? Which ones will actually stop the scroll? AI gives you volume, but human judgment is needed to filter gold from crap. Step 6: Editor Brief Hand off the winning scripts with clear direction on format, visuals, and pacing. The bottleneck was never production speed but ideation speed. AI solved that, but only because we have the research foundation in place. Without the research, this workflow produces crap faster. With it, it's the biggest productivity unlock we've found.
@itsolelehmann ·
you treat your AI worse than you'd treat a human. and that's the problem. you'd give a human hire weeks of training, real examples, and coaching before you'd judge them now think about how you treated your last AI experiment: 1. you spun up an agent on a sunday afternoon 2. gave it three paragraphs of context and a vague goal 3. tested it twice on monday morning 4. said "eh, it isn't quite there yet" and never opened it again you basically fired your agent on day 1. imagine treating a human this way lol the people actually winning with ai right now are giving their agents the same training runway they'd give a new hire: 1. write the SOP the way you would for a real employee 2. feed it 20+ examples of good/bad outputs (not 2) 3. run it on 30 real cases before judging anything 4. fix what breaks. ship it again. fix what breaks again. 5. add a feedback log so corrections compound over time 6. give it the same 90 day runway you'd give a new hire to ramp the bottleneck has been your impatience if you've quit on an AI workflow this year, go open it back up. and give it the same patience you would w/ a human
@BentoBoiNFT ·
My AI workflow after one month of testing: OpenClaw: automated tasks (morning briefs, market research, etc.) Claude Code: coding (websites, dashboards, etc.) OpenClaw has great automation but finicky. Claude Code gives you more control & less issues. Best of both worlds.
@catalinmpit ·
My AI workflow is very simple and efficient. Agent questions me about the plan (grill me skill) ↓ Create the plan (custom create plan skill) ↓ Agent implements the plan ↓ Guide it where necessary ↓ Agent reviews the code (thermo-nuclear code review skill) ↓ Manual code review + tests ↓ (If feedback) Agent implements my suggestions ↓ Ship it
@the_smart_ape ·
X is the best resource in the world when you work in ai. you don't even need to write prompts anymore. just paste someone else's success post into your agent and watch it solve YOUR problem with their method. 3 examples that worked for me this week : 1. internet speed: 230 → 870 mbps in 20 minutes was stuck at 230 mbps on a 1 gig fiber line for months. ISP kept blaming my hardware. saw @cjzafir post. pasted into my codex with one line: "do this for me, here's my speedtest, here's my router model." 17 commands later, 870 mbps. i wrote zero prompt. 2. nextjs build: 4:12 → 38s saw a guy post a screenshot of his claude code session shaving 3+ minutes off a nextjs build with turbopack + smarter RSC boundaries + module concatenation. pasted his entire post + my next.config.js + my build log into claude code. it walked me through the exact 4 changes, in the same order he did. build now at 38 seconds. 3. postgres query: 12s → 110ms inherited a query that scanned 40M rows on every dashboard load. saw a thread where someone took a 12s query to 80ms with one composite index they reverse-engineered from EXPLAIN ANALYZE. pasted his thread + my own explain output into claude. it spotted the same pattern in my plan, proposed the index, gave me the migration. 12s → 110ms. three problems. zero original thinking on my part. zero prompt engineering. just other people's wins, pasted as context, and an agent good enough to pattern-match to my specific stack. your bookmarks folder is your prompt library now. most "advanced ai workflow" content is teaching you to write better prompts. don't bother. learn to find better posts.
@itsalexvacca ·
100% of our delivery now runs on AI workflows my own team built, and I never made anyone use a single one. I never set a mandate, paid a usage bonus, or built a dashboard to track who logged in, and we still ended up one of the most AI-native agencies I know of. Campaign creation takes half the time it used to, and MRR per GTM engineer has climbed 20%. So when people ask how we got the whole team to actually adopt AI, here is the exact play. You can run it inside your own company (bookmark for later): 1. Use the tools yourself before you expect anyone else to. If you want a team to take AI seriously, you have to take it seriously yourself first. I share the YouTube videos I'm watching, drop podcast clips in Slack, and send around the resources I actually use. A team picks up whatever its leaders clearly care about. 2. Judge every new tool by the friction it adds before you bring it in. Before any change lands on the team, I look at it from their side and ask what friction I'm about to create. Asana is my classic counter-example. We stood behind people for months repeating "put your finger on Asana," and it might work eventually, but the friction is brutal. AI turned out to be the rare tool where the friction runs in your favor, because it pulls people in on its own. 3. Find whoever is already doing it best, and rebuild what they do for everyone. My job at that point is mostly to watch what the best people on the team are already building for themselves. Our head of GTM runs 15 client agents in parallel from his laptop, so we took what was portable in his setup and rebuilt it for the rest of the team. Adoption climbs on its own once people see that AI genuinely makes their day easier. The fastest AI adoption I've ever seen came down to copying one person. You've maybe got to identify who that person on your team is.
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@mhdfaran ·
Someone just built a complete AI workflow for CAD, robotics, and hardware design. It can generate 3D parts, create technical DXF drawings, build robot-description files, slice models into G-code, and preview everything in your browser. 100% open source.
Watch video@smratitiwa86867 ·
🚨 STOP Letting AI Ship Mediocre Code Everyone is busy comparing AI models. Almost nobody is fixing the real problem: ❌ AI writes code before understanding the problem ❌ No planning ❌ No testing ❌ No review ❌ Technical debt on day one The best developers aren't using AI as a code generator. They're using AI as a software engineering team. That's why this workflow stands out 👇 1️⃣ Brainstorm the problem 2️⃣ Split work into parallel tasks 3️⃣ Create a clear execution plan 4️⃣ Delegate to specialized agents 5️⃣ Run Test-Driven Development (TDD) 6️⃣ Review every line of code 7️⃣ Ship only after verification Simple. But 99% of developers skip steps 1-6 and jump straight to "generate code". The result? Hours saved today. Days lost tomorrow. The future of AI development isn't about bigger models. It's about better systems. The developers who learn structured AI workflows will outperform those who only rely on prompts. Think like an engineer. Plan like an architect. Ship like a team. 🔥 Follow @smratitiwa86867 for more AI, Agents, Automation & Developer workflows. #AI #ClaudeCode #Coding #l
@RoundtableSpace ·
Here are 5 most high-leverage power hacks from Dan Martell to unlock Claude's true execution capacity in a single blueprint: 1. Gmail Connector (The AI Inbox Assistant) Instead of searching your mailbox or typing replies manually, let Claude filter and summarize information directly from your emails. Step 1: Open the Claude workspace sidebar and click Customize, then select Connectors. Step 2: Choose Gmail from the list of integrations and click log in. Step 3: Authorize the security permissions to securely link your workspace inbox. Step 4: Give your Executive Assistant a shared seat on that specific Claude account so they can self-serve on historical data questions instead of asking you. 2. Calendar Connector (The AI Executive Coach) Connect Claude to your Google Calendar to run autonomous time audits against your business milestones instead of just checking free slots. Step 1: Go to Customize ➡️ Connectors on the left sidebar and select Google Calendar. Step 2: Click log in and link your primary business calendar. Step 3: Paste your core business goals, target projects, or quarterly KPIs straight into the chat viewport. Step 4: Run this prompt: "Analyze my calendar time blocks alongside my emails over the past 30 days. Run a brutal time audit and tell me exactly where my hours are mismatched with my stated targets." 3. Co-Work Mode (Hands-Free Desktop Automation) Allow Claude to take over your screen layout, control your cursor, and execute multi-app processes across your computer while you step away. Step 1: Download and install the standalone Claude desktop app from https://t.co/YF4PBf732q. Step 2: Open the application, go to your settings panel, and toggle on the Computer Use capability. Step 3: Click the Co-Work layout option found in the top left corner of the desktop interface. Step 4: Describe a multi-step task in plain English—like pulling metrics from your CRM, pasting them into a sheet, and dropping a chart in Slack—and let the tool take over your machine. 4. Scheduled Task (The Autopilot Routine) Turn any successful desktop Co-Work automation chain into a routine process that fires off entirely on its own. Step 1: Open your configured desktop app and make sure the active Co-Work pane is open. Step 2: Type the specialized /schedule macro directly into the prompt bar environment. Step 3: Write out the routine instructions and choose your execution cadence from the system menu prompt (Hourly, Daily, Weekly, or Weekdays). Step 4: Keep your computer application open; Claude will autonomously run the desktop script—like an early morning email sweep or briefing summary—at that exact time every single day. 5. Projects Hub (Zero-Hallucination Knowledge Bases) Isolate your operational conversations into dedicated folders filled with persistent, curated reference materials. Step 1: Navigate to the main left sidebar menu layout and click Projects, then hit New Project. Step 2: Name your workspace hub and upload your internal documents, transcripts, PDFs, or design brand books directly into the project's knowledge zone. Step 3: Open the system instruction configuration box and paste your custom structural prompt guidelines. Step 4: Begin chatting inside this folder layout; Claude will strictly reference only your pre-loaded data, completely blocking generic external data noise. Bookmark this thread for more high-leverage AI workflows like this.
@wadefoster ·
Smart companies use "dumb" models on purpose. Usually the cheapest one that still gets the job done. Every week I see teams throw the frontier model at tasks that never needed it. Formatting a doc, routing a ticket, none of that requires SOTA reasoning. At @Zapier we found only 18% of the steps in advanced AI workflows actually need AI. The other 82% is deterministic: trigger, format, send. Lately we've prioritized using the dumbest model that works, reserving the expensive ones for steps that actually need judgment. At most companies today, builders just pick whatever model they used last time (usually the "smarter" one, because smarter feels safer) But multiply that by a thousand workflows? There goes your AI budget.
@AlxTurovski ·
Are you in business of AI workflow automations? Here's what I found useful: > go through your workflows with claude code > don't automate yet, guide it through every action > create skills on top of successful task completions > use those skills for a month, tweak and adjust for edge cases Boom! You've got yourself a working system that can be used as a PRD and a starting point for > feature > tool > app > SaaS
@vicky_grok ·
Most people use AI like a chatbot. The highest-performing users build systems instead. These nine habits don't require new tools. They simply change how you work with AI, helping you save time, reduce repetition, and produce more consistent results. 1. Chain Your Prompts Don't ask AI to do everything in one prompt. Break large tasks into smaller steps: • Research • Outline • Draft • Refine • Review Each step improves the quality of the next. 2. Save Reusable Context Stop repeating the same instructions. Create reusable prompts that include: • Your role • Writing style • Audience • Goals • Preferred format This gives AI the right context from the beginning. 3. Build a Personal Knowledge Base Upload documents, notes, SOPs, and reference materials into projects or knowledge spaces. The better your knowledge base, the more personalized and relevant your AI outputs become. 4. Automate Repetitive Work Every recurring task is an automation opportunity. Think about: • Email summaries • Meeting notes • Report generation • Content publishing • File organization Save your time for work that requires human judgment. 5. Compare Multiple Models Different models have different strengths. Ask the same question to multiple assistants when working on important tasks like research, strategy, or technical writing. Compare reasoning, clarity, and accuracy before deciding. 6. Use AI Memory If your AI assistant supports memory, let it remember useful information such as: • Writing preferences • Ongoing projects • Frequently used frameworks • Long-term goals Less repetition means smoother conversations. 7. Verify Important Information AI accelerates research. It shouldn't replace verification. For technical topics, financial decisions, legal questions, or health information, always validate responses using trusted sources and official documentation. 8. Reuse Templates and Frameworks Don't build every workflow from scratch. Create reusable templates for: • Emails • Reports • Content • Meeting summaries • Research • Project planning Consistency often beats creativity for recurring work. 9. Connect Your AI Workflows The biggest productivity gains come from connecting tools together. A simple workflow might look like this: Research ↓ Draft ↓ Review ↓ Automate ↓ Publish Once your workflow is documented, it becomes faster every time you use it. AI becomes most valuable when it supports a repeatable system rather than isolated conversations.
@maeveknows ·
A useful AI workflow needs more than a clever prompt: -> clear inputs -> a smaller sequence of steps -> a review point before the output matters The demo gets attention, the system earns trust
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@sachinrekhi ·
Andrew Ng nailed it: PM is becoming the bottleneck in product development. AI has accelerated delivery, but discovery? Not so much. The good news is the same AI tools speeding up our delivery can transform how we understand customers. I've now built ten AI workflows into my discovery process: 1️⃣ Analyzing customer surveys 2️⃣ Automating customer survey programs 3️⃣ Automating feedback rivers 4️⃣ Developing user interview scripts 5️⃣ Synthesizing user interview feedback 6️⃣ Conducting AI moderated interviews 7️⃣ Generating synthetic user feedback 8️⃣ Conducting discovery via prototypes 9️⃣ Analyzing metrics 🔟 Automating metric analysis I'm demo'ing all ten live on March 5th in Mountain View with @danolsen.
@briansolis ·
Ford rehired over 350 veteran engineers referred to internally as "gray beards" after an aggressive AI adoption strategy backfired. Ford had been increasingly relying on AI-driven inspection systems to streamline production and address quality control issues, however the firm acknowledged that AI lacked the nuanced judgement when it came to complex problems. "We had been relying more and more on automated quality systems and not getting the desired results," said Kumar Galhotra, Ford's chief operating officer. "We brought back technical specialists and they hunt for failure points before a part ever reaches the plant floor," he continued. Ford won't abandon AI, but instead will partner AI with human oversight and experience. "Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product," said Charles Poon, Ford's vice president of vehicle hardware engineering. After rehiring proven engineers, Ford is experiencing a marked improvement in its quality standards. According to the latest J.D. Power Initial Quality Survey, Ford ranked top among mainstream brands. This is the first time it has achieved that milestone in 16 years. In Infinite, Dave and I explore how human + AI workflows can not only improve yesterday's standards, but also raise them. https://t.co/faUPz8e3ro Story here: https://t.co/Yt8D7oRz3N
@bibryam ·
🎉I’m excited about what’s new in @diagridio Catalyst This update is focused on operating AI workflows in production: → Workflow failure discovery + bulk rerun from the failed step → Human-in-the-loop visibility: find workflows waiting on approvals and unblock them directly from the UI → Catalyst Enterprise self-hosted & air-gapped options (data plane in your cluster, or fully disconnected) Full post + screenshots: https://t.co/rEChAlQ504
@sachinrekhi ·
If you are wondering what workflows to automate with AI, ask yourself is it worth building? And is it possible to build? IS IT WORTH BUILDING? The most obvious reason to build an AI workflow is because AI offers a unique advantage. That advantage is typically that it can accomplish the task faster or more comprehensively than you can. Take synthesizing customer interviews. AI is far faster and actually more comprehensive than my own efforts, so it's a great scenario to leverage it for. Now the second reason I find AI workflows worth building is because they are either frequently occurring tasks or time consuming tasks. The ability to offload these tasks to AI becomes a meaningful way to earn time back for higher leverage tasks. Take weekly status updates. They are worth automating so I can recover that time for more meaningful tasks. IS IT POSSIBLE TO BUILD? After you've decided it's worth building, the second challenge is figuring out whether you can actually build it. The first and biggest challenge is whether you can acquire the appropriate context you need to accomplish the task. Every task starts with understanding the right data. While there are a variety of ways for AI to acquire this data (local files, CLIs, MCPs, APIs, browser agents), their remain material limitations. If the AI can't get the data it needs, the workflow will ultimately fail. You also want to make sure you can break down the workflow into a discrete set of steps. If you can't define the steps, there's no way AI is going to figure it out either. The last thing you want to ensure is that the task can be accomplished with limited human intervention. If human judgment remains tantamount, the workflow will again fail. By asking yourself these two key questions, you'll quickly develop a useful heuristic for when a workflow is worth automating with AI.
@JA_Olaoye ·
AI Engineers will be insanely valuable over the next 5 years. But not because they know tools like Claude, Codex, Grok, or Gemini. They’ll be valuable because they can: • Turn messy business problems into structured AI workflows • Integrate AI into real systems (APIs, data pipelines, operations) • Handle edge cases, failures, and reliability • Deliver outcomes, not demos Anyone can prompt. Very few can deploy AI that actually works in production. That’s the difference.
@abhitejxyz ·
This week I audited every AI workflow I run - cost vs value, no mercy. Here's what I found: > 70% of my workflows are net negative. Most AI setups I've built burn more time and money than they save. > The 30% that work? Easily 10x more efficient than doing it manually. > 65% of the winning workflows are ones I already understood end-to-end - engineering, UI/UX, long-form content, product management, research, mechanism simulations. I'd done them myself dozens of times before automating. > The other 35% worked because there was rich existing context and clear use-cases available. I didn't always know what the output would look like, but I could learn through the process. > Going deep in one domain >>> going wide. Trying to automate a broad range of workflows at once was the biggest cost sink - in both time and money. > Most futile workflow: AI work planners. Overwhelm me more than they help. Tools I've found genuinely powerful but haven't scratched the surface of: Hermes, OpenClaw, Autoresearch, Paperclip. Honest admission: I'm still early in this journey. X makes it feel like everyone has it figured out.
@jkohlbach ·
Quietly migrated away from my custom OpenClaw AI assistant this week. Not some dramatic breakup. My usage just... dropped. I'd been reaching for Claude Code for more and more things without really noticing, and the old tool was collecting dust. I almost felt bad for not giving it enough to do. So I asked it: "What recurring jobs do I still have scheduled?" Two. Out of maybe a dozen I'd originally set up. The morning briefing I spent hours configuring — weather, calendar, news digest — turns out I don't need a bot to tell me it's sunny. And my calendar isn't exactly packed with back-to-back board meetings. I already get a msg in Slack from Google Calendar with the day's events anyway. The rest? Quietly retired or migrated over into Claude Code Desktop scheduled tasks. Looking back, the real value was never the automations. It was the adhoc stuff. "Research this competitor." "Summarize that thread." "Draft a reply to this email." The messy, unstructured tasks you can't pre-schedule. I think there's a lesson in there about building AI workflows. We over-index on automation because it feels productive to set up. But the highest-value AI work is usually the stuff you can't predict in advance.
@trueventures ·
The AI workflows with the biggest impact usually aren’t the fanciest — they’re the ones wrapped around the daily operating motions that keep a company moving. At our recent AI Workflow Summit, founders and builders shared what it looks like as AI shifts from “learning-curve experiments” to repeatable, ROI-driving flows. A few interesting themes from the day: → Daily use is the maintenance strategy. The best systems get built (and improved) in the middle of real work. → The J-curve is real. New workflows often feel slower before they get dramatically faster. → Adoption is a design problem. The workflows that spread show up where people already are: calendar, inbox, Slack. → Failures are fuel. Every failure point is potential training data — feed it back into the loop. → The “AI sandwich” works. Humans set intent → agents execute → humans apply judgment. The human parts don’t go away; they get more valuable. And maybe the best takeaway: you don’t need to be technical to build next-level AI workflows. The most creative examples came from people who just kept experimenting.
@MikeHoffmann ·
My team was spending 40 hours a week on something AI now does in 3. Let me explain 👇 We had a customer service triage process eating one full-time employee's entire week. Every inquiry: manually categorized, routed, logged. Slow. Error-prone. Expensive. So we built an AI workflow in 3 days: → Incoming messages auto-categorized by type and urgency → AI Agents answered 80% of requests within seconds → Other 20% escalated to response templates pre-drafted for human approval → Human reviews and sends — still in the loop, just 10x faster Results after 60 days: → Response time: 6 hours → 22 minutes → Team member now focused on higher-value work → Customer satisfaction up 24% → AI Agent costs: ~$200/month This isn't science fiction. This is a basic use-case any operator can build. The founders who win the next 5 years won't be the ones who "wait and see." They'll be the ones who implemented while everyone else was reading articles about it saying AI isn't 'ready' yet What process is eating your team's time right now?
@coreyhainesco ·
With newer AI workflows is that you are no longer the middleman → This is actually great news ↓ The old flow was simple. Ask AI for outputs, copy the result, move it into another tool, adjust it, repeat. Now systems like Claude Code can do the work directly. That changes everything (for real this time). Once AI can move between tools on your behalf, the value shifts away from manual coordination and toward setting better direction, better guardrails, better context, and creating better systems. The job becomes less about carrying work from one place to another and more about deciding what should happen in the first place. That is a much more interesting kind of work. And it is going to reshape how a lot of teams operate. Things are changing fast. Embrace it and you will thrive. (Unrelated dinosaur fact of the day: Kosmoceratops had one of the most elaborate skulls of any dinosaur, with 15 horns and horn-like projections on its head.)
@ATechAjay ·
For the last few months, I've been building an AI chatbot. What started as a simple chat interface has evolved into a feature-rich AI assistant with trace lookup, observability, streaming responses, context awareness, conversation history, deep links, follow-up suggestions, and much more. Ironically, the hardest part isn't building features. It's keeping everything working. When your backend responses evolve daily, things break daily. A field changes. A schema updates. A response format shifts. Suddenly, something that worked yesterday stops working today. So we're constantly balancing: • Building new features • Fixing edge cases • Improving UX • Adapting to backend changes Some features we've built so far: → Natural-language observability → Live agent trace lookup → Rich interactive trace cards → One-click dashboard deep links → Hover trace previews → Prompt/response inspection → Streaming responses → Suggested follow-ups → Context-aware answers → Chat history & exports → Token/time transparency → Graceful error handling The chatbot is still evolving every week. I'm thinking about documenting everything I've learned while building it... from architecture decisions and UX challenges to debugging AI workflows in production. Would you be interested in building an AI Chatbot in a public series?
@InduTripat82427 ·
@garrytan (CEO of Y Combinator): “when someone asks how I ‘prompt’ my AI, the answer is: I don’t. the skills are the prompts.” Most people still type the same prompts every day. Smart builders turn repeated work into reusable skills. [if I had 7 days to learn AI automation properly:] → stop collecting prompt packs → study how real AI workflows are structured → watch “Don’t Build Agents. Build Skills Instead.” → build one repeatable workflow from scratch → convert it into a reusable command That’s it. The biggest shift happening right now: AI is moving from “chatting” to systems that remember how you work. Here’s the setup: 1. install a memory layer for your AI workflows 2. add reusable slash-command skills 3. do a task once → save it as a skill → reuse forever The people winning with AI aren’t better prompters. They build systems that compound.
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@Awesome_O_AI ·
Everyone Is Chasing Better AI Models. The Real Advantage Is Building AI That Works While You Sleep. Most people think the AI race is about finding the smartest model. I don't think that's true anymore. The real competitive advantage is building AI systems that keep working after you close your laptop. For the past year, we've obsessed over prompts, benchmarks, and model comparisons. GPT vs Claude. Claude vs Gemini. Open-source vs closed-source. Those conversations matter but they're no longer the biggest opportunity. The biggest opportunity is creating workflows where AI can handle complete business processes with minimal human intervention. Think about it. Instead of asking AI to write one LinkedIn post... Why not let it: Research the topic from multiple sources. Fact-check every claim. Generate several content angles. Review the final draft against your brand voice. Schedule it for publishing. Analyze the engagement after it's posted. Suggest improvements for the next one. That's no longer "using AI." That's building a system. The same idea applies across almost every industry. Marketing Repurpose your best content into multiple formats. Monitor competitors and summarize changes. Research podcast guests before interviews. Localize content for different markets. Sales Build prospect lists automatically. Research each lead. Personalize outreach. Draft proposals from sales calls. Follow up with stalled opportunities. Customer Support Draft responses instantly. Detect spikes in customer frustration. Identify gaps in your documentation. Route only complex issues to humans. Software Development Review pull requests. Investigate failed builds. Update documentation. Patch low-risk issues. Monitor dependencies for vulnerabilities. Notice the pattern? The model is only one piece of the puzzle. Every reliable AI workflow has three essential components: 1. A Trigger Something starts the process. An email arrives. A customer submits a form. A GitHub PR is merged. A meeting ends. A scheduled task runs. 2. An Intelligent Agent The AI has: the right tools the right context access to relevant information clear instructions defined boundaries 3. A Verification Layer This is the part most people skip. AI should never simply say, "Done." It should prove it. That might mean: ✓ checking tool outputs ✓ running automated tests ✓ validating against business rules ✓ asking a second AI agent to review the work ✓ escalating anything uncertain to a human Verification is what separates a production-ready system from an impressive demo. And here's another mistake I see everywhere... People try building ten automations at once. None of them are reliable. Instead, build one workflow. Run it every day. Fix every edge case. Improve it until you trust it without constantly checking the output. Only then build the second. Then the third. That's how real AI leverage compounds. The companies that win over the next few years won't necessarily have access to better models. They'll have better systems. Because in the end, AI isn't just about generating answers. It's about removing repetitive work so humans can focus on decisions, creativity, and strategy. That's where the real value is. If you could automate one part of your work today, what would it be?
@0xAndros ·
Two agent-pilled people just showed what happens when you find the right AI workflow for a "traditional" business: One takes Zillow listing photos, runs them through Calico AI , and produces cinematic walkthrough videos the kind real estate agents pay $500 for. His cost? $15. The other sells pools. Built an AI agent that scans homes without one, renders a pool in the backyard, and auto-mails the homeowner a before/after postcard. Automated the entire sales funnel. Different industries. Same playbook: < find an expensive manual process in a niche market < replace it with an AI workflow < suddenly you're 10x cheaper than everyone else The lesson isn't "AI is coming for your job." It's that someone in every boring industry is quietly figuring out how to do in 30 minutes what used to take a team and a budget. How many other industries are one workflow away from this?
@JulianGoldieSEO ·
KIMI K3 + HERMES AGENT MAKES MOST AI WORKFLOWS LOOK OBSOLETE. Most people are still chatting with AI. The people getting ahead are building AI teams. What this setup actually does: Workflow: → Turn Kimi K3 into a full AI worker inside Hermes Agent → Learn new skills once and save them for future tasks automatically → Stack skills over time so the system keeps getting smarter Automation: ✓ Build 3D product models in Blender via MCP ✓ Generate promo videos from those models ✓ Publish SEO blog posts from trending X topics Business Engine: ✔ Monitor competitors every day ✔ Discover fresh content angles automatically ✔ Turn one trend into a blog, video, infographic, podcast, and research report Scale: → Run multiple AI agents on a Kanban workflow → Mix Kimi K3 with GLM so models review each other's work → Give one objective like "build 50 blog posts" and let it run for hours without supervision The biggest shift isn't finding a better AI model. It's building a system where multiple models work together while you focus on growing the business.
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