Quality control and human oversight
Human oversight and quality systems: verification, guardrails, approval checkpoints, feedback loops, evaluation, and disciplined deployment.
38%
Best tweets about AI Workflows
50 Best Tweets About AI Workflows (2026)
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.
- Creators
- 43
- Updated
Original Xholic analysis
What 50 top AI Workflows posts reveal
Across 50 AI-workflow posts, quality control and human oversight is the largest identified theme (19 posts; 38%). Production infrastructure, reusable systems, and workflow ROI/cost optimization also recur. Tutorials are the largest format segment (36%), and the supplied outliers include tutorials that show concrete coding, template, and creative workflows.
- Dominant tone
- Positive
- Median score
- 8.87
- Leading format
- Tutorial
- Recent posts
- 56%
60% of posts
All-time engagement
36% of posts
Published in 90 days
Conversation map
The themes creators return to
Production workflow infrastructure
Production AI workflow architecture: orchestration, integrations, context retrieval, memory, gateways, observability, reliability, and failure recovery.
32%
Reusable AI systems
Reusable AI operating systems: structured prompts, skills, templates, knowledge bases, personal memory, and connected multi-step routines.
30%
Workflow ROI and cost optimization
Workflow economics and selection: ROI experiments, model routing, operating costs, data readiness, and prioritizing high-value automations.
24%
Agentic software engineering
Agent-assisted software delivery: planning, coding, testing, review, repo memory, reusable skills, and human-controlled shipping.
22%
Business process automation
Business-process automation for operations, support, sales, recruiting, finance, and vertical-industry workflows.
20%
Marketing and creative automation
Marketing and creative-content pipelines using research, brand context, image/video generation, scripting, and publishing workflows.
20%
Multi-agent orchestration
Multi-agent workflow design: specialized roles, parallel execution, handoffs, tool use, and agent-team coordination.
18%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 25
- Median reposts
- 3
- Median replies
- 7
- Median views
- 1.6K
Posts with media make up 66% of this collection. Their median all-time score is 15.5, compared with 5.69 for text-only posts.
Format mix
- Tutorial 36% · score 22.3
- Case Study 28% · score 8.64
- Opinion 18% · score 5.25
- Announcement 16% · score 3.17
Consensus and debate
Where creators agree, and where they do not
Shared view
Human judgment remains a control point
Posts describe AI handling generation or first-pass execution while people retain review, approval, and final judgment, particularly for consequential actions.
Shared view
Context is treated as reusable infrastructure
Reusable instructions, project or knowledge context, and defined context-access strategies recur as ways to make outputs more task-specific and reduce repeated setup.
Shared view
Production workflows include operational controls
Production-oriented posts include routing, retries or fallbacks, failure discovery and reruns, logging, cost tracking, and approval visibility alongside model calls.
Open debate
Agent teams versus tool-agnostic skills
One post advocates specialized multi-agent handoffs for complex work, while another argues that transferable workflow-design, context, and quality-control skills matter more than mastery of a particular AI tool.
Open debate
Economics require task-level scrutiny
The posts offer differing economic lessons: one says that only 18% of steps in advanced workflows at Zapier needed AI, while two authors describe demanding or audited workflows where some attempts failed to justify their cost or effort.
Open debate
Automation has boundaries
These posts frame automation as a way to reduce setup and repetitive work, not remove accountability. They recommend checkpoints for consequential actions and caution against bypassing engineering discipline.
What performs
Patterns behind standout posts
Tutorials lead format share and include top outliers
Tutorials are the largest format segment, with 18 of 50 posts (36%), and their median all-time score is 22.33. These three tutorial IDs also appear in the supplied list of five score outliers.
Top outliers span concrete workflow examples
The five supplied score outliers cover staff-engineering practice, a project-template setup, workflow-design principles, and creative production workflows. This is a descriptive pattern in the benchmarked outliers, not evidence of a causal engagement driver.
Media scores higher than text in this sample
Media appears in 33 of 50 tweets (66%) and has a 15.502 median all-time score, versus 5.688 for text-only posts. The single prediction-format post has a 198.391 median all-time score; because the format contains one post, this is not a robust format comparison.
Statistical standouts
- View standout post 1 Score 893.2 · 100.7× median
- View standout post 2 Score 510.1 · 57.51× median
- View standout post 3 Score 443.3 · 49.98× median
- View standout post 4 Score 433.2 · 48.84× median
- View standout post 5 Score 411.6 · 46.41× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Bilgin Ibryam
@bibryam
2 posts
-
2. Machina
@EXM7777
2 posts
-
3. Shalini Goyal
@goyalshaliniuk
2 posts
-
4. Abhishek
@HeyAbhishek
2 posts
-
5. Sachin Rekhi
@sachinrekhi
2 posts
-
6. Shushant Lakhyani
@shushant_l
2 posts
Coding workflows formalize the shipping loop
Software-engineering examples lay out staged delivery loops: plan, implement, test, review, incorporate feedback, and ship. The cited posts retain manual review or human responsibility at key stages.
Creative stacks start with source inputs and direction
Creative-workflow examples use customer research, brand guidance, product imagery, and prompts as inputs to generate scripts, concepts, images, or video. Two posts explicitly position human strategy, feedback, or selection as necessary controls.
Workflow selection begins with a testable task
Value-oriented posts recommend assessing whether a task is worth building, whether required context is available, and whether quality or time savings hold up in a defined test before broader rollout.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best AI Workflows tweets
Top AI Workflows tweets from 43 creators
Ranked 01–50
- 01
- 02
@EHuanglu ·
this AI workflow can create 100s of product ad a day just upload a product photo, it generates 9 shots grid storyboard and studio level product commercial in mins on arcads here's how to create + prompts:
Watch video
- 03
@Suryanshti777 ·
Holy shit. Someone just leaked the Claude Code project template teams are quietly using. This isn't prompting anymore. This is AI engineering infrastructure. ⚡ The entire setup revolves around one file: CLAUDE.md Every time Claude makes a mistake → you add a rule Every time you repeat yourself → you add a workflow Every time something breaks → you add a guardrail Claude literally trains itself on your project. And the structure is wild: • CLAUDE.md → project memory & instructions • skills/ → reusable AI workflows • hooks/ → automated checks & guardrails • docs/ → architecture decisions • src/ → actual code modules • tools/ → scripts + prompts You're not chatting with AI anymore. You're building an AI that knows your repo. The craziest part? You only configure this once. After that Claude: – reviews code automatically – refactors on command – enforces architecture rules – writes release notes – runs workflows from skills – remembers past mistakes And it keeps getting smarter. Most people: open ChatGPT → write prompt → copy paste → repeat This setup: open terminal → run skill → code shipped You're basically running AI teammates inside your repo. This template is the difference between: • using Claude occasionally • running Claude like infrastructure Drop it in any project. Your AI stops guessing — and starts operating.
- 04
@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
- 05
@HeyAbhishek ·
This AI workflow is wild 🔥 I used ChatGPT Image 2.0 to create a perfect animated scene. Then Seedance 2.0 turned it into a full cinematic animated video in minutes Super simple workflow below 👇
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- 06
@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
- 07
@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
- 08
@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.
- 09
@dexhorthy ·
me and @vaibcode did a full hour yap on system design for agentic memory and how you can use AI to prototype products with your users quickly before building anything 00:00 Why AI Memory Systems Keep Failing 00:29 What This Episode Covers 01:24 Building a Shared Memory System for Your Team 02:24 Why Claude's Memory Can Become a Bad Habit 06:12 Instructions vs. Information: Does the Difference Matter? 08:15 Designing Context That Doesn't Become Slop 12:01 One Source of Truth for Your Entire Team 14:30 Always-On Context vs. Task-Specific Context 17:42 Why Prototypes Beat Building the Wrong Product 19:04 How Great Teams Design Features Together 22:52 Launch AI Features Before They're Perfect 24:42 Building the First Milestone Instead of the Whole Product 27:49 Whiteboarding a Supervisor Agent Architecture 35:26 Why Human Approval Still Matters 39:55 Using Human Feedback to Improve AI Memory 42:08 The Hardest Problem in Context Engineering 43:51 Turning Product Design Into Working Software 47:41 Why Big Features Should Ship in Small Milestones 49:08 Why Voice Is the Fastest Way to Work With AI 51:17 Building Better AI Workflows Through Team Collaboration 52:49 System Design Before Implementation
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- 10
@alexcooldev ·
The simplest way to create an AI influencer using workflow (3 steps): Nano Banana → Veo 3 → Arcads AI Workflow Step 1: Build your character with a JSON prompt (Nano Banana Workflow) → Use a structured JSON prompt instead of writing random text → Define the look clearly: character, outfit, lighting, camera style, background → Add negative constraints like: no logos, no text, no extra fingers, no CGI skin, no warped mirror This makes the output more stable and reusable. You’re not just prompting. You’re building a repeatable image system Step 2: Turn image → video (Veo 3 Workflow) → Connect the Nano Banana output directly into Veo 3 → Add a simple motion prompt: slight body movement, natural head turn, subtle phone movement → Keep it minimal so it feels more real Workflow removes the manual back-and-forth. Step 3: Add script + voice (Arcads AI Workflow) → Feed the generated video into Arcads AI → Add a short script, usually 10–20 seconds → Generate voice + lipsync that matches the character vibe Now the character becomes actual content. Why workflow matters: Prompt → Image → Video → Voice No messy manual process. No repeating the same setup every time. Just a clean pipeline you can scale. Nano Banana + Veo 3 + Arcads AI + workflow feature = simple AI influencer factory
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- 11
@law_ninja ·
Indian MSMEs run on WhatsApp, Excel, and trust. AI hasn't touched them. Yet. India has 63 million MSMEs. 31% of GDP. 250 million jobs. Ask any owner in Surat, Ludhiana, Tirupur, or Nagpur if they use AI in their business. Most will say yes. They mean WhatsApp. Or someone on their team opened ChatGPT once. That is not automation. That is not a workflow. That changes nothing about how the business actually runs. Real AI deployment, the kind where a process runs without a human triggering it, where data moves between systems automatically, where follow-ups go out without someone typing them, that is essentially at zero in Indian MSMEs. Not 7%. Not 2%. Essentially zero. Why this is the biggest untapped market in India right now. India's large enterprises are moving fast. 47% of them have AI running in production (EY-CII, 2025). Their MSME suppliers, distributors, and vendors? Still on Excel. Still on manual data entry. Still on phone calls to confirm orders. The gap between enterprise and MSME on AI is not a technology problem. It is a deployment problem. The tools exist. n8n, Make, Claude API, GPT-4, Zapier. All available. Most either free or under Rs 5,000 a month. What doesn't exist is a person who walks into the MSME, understands the workflow, and builds it. That person is the AI Workflow Architect. What this person actually does. Real example. A garment exporter in Tirupur processes 200 orders a week. Each order needs: Buyer email parsed PO data entered into Tally - Production schedule updated - Shipping documents generated - Buyer follow-up sent Currently: 2 data entry operators. 8 hours each. 5 days a week. - An AI Workflow Architect builds this in 4 weeks: - Email parser using Claude API or GPT-4 - Tally integration via API - Auto-generated shipping docs - WhatsApp follow-up bot Cost to client: Rs 2-3 lakh one-time. Rs 15,000 per month to maintain. Savings to client: Rs 40,000 per month in salaries. ROI in 6 months. This is not complicated. It is not being done because nobody is walking in to do it. The IT crisis and the MSME gap are the same story. Fresher IT hiring: 600,000 in FY22. Down to 120,000 by FY25. An 80% drop in three years. (Source: Xpheno) TCS cutting 12,000 jobs. NITI Aayog warns of 15-20 lakh IT jobs at risk. Everyone is looking at that number and panicking about what's ending. Nobody is looking at the 63 million businesses that need someone to deploy AI into their operations. The same disruption that kills the BPO seat creates the AI deployment market. These are not separate events. They are the same event, viewed from different angles. The skill set is learnable. In months, not years. - No CS degree needed. No advanced Python. - Prompt engineering learning time: 2 weeks - One automation platform like n8n or Make: 3-4 weeks - API basics, connecting tools to each other: 3-4 weeks - Reading a business process and mapping it: ongoing Three months of focused learning. Then you go find one MSME that has a painful manual process and you fix it. This is the time, this is the opportunity. India's future for next 3 decades will depend on this.
- 12
@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?
- 13
@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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- 14
@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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- 15
@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.
- 16
@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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- 17
@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
- 18
@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. ---
- 19
@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. ---
- 20
@milan_milanovic ·
𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗼𝗱𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸 Most AI productivity advice sounds the same: use it everywhere, iterate fast, trust the tools. Nick Tune does something different. He treats AI workflows like software. State machines with typed transitions. Lint rules that block Claude from writing mutable TypeScript. Dependency constraints that enforce architecture at the commit level. A TDD cycle where Claude cannot move to the next state until tests pass, code compiles, and lint clears. He built this setup during peak season to handle a support ticket backlog. It paid for itself in three days. In this issue, Nick walks through his full setup: how he plans with a PRD agent, implements features autonomously, enforces architecture rules deterministically, and reviews every PR before anything ships. 👉 Read it here: https://t.co/p7h6yHdAUO
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@williamkast_ ·
My ad creation AI tool stack: Claude Projects - this is the core of the workflow. I feed it all my customer research, reviews, surveys, support tickets, brand guidelines, personas, product info, etc. into it. The more data you give it, the better. Then I use the data to work out core desires, personas, potential angles, angles that competitors are missing, awareness stages and audiences that competitors are missing, etc. If you feed it the top ads of history you can also get help with scriptwriting. Key here: Treat Claude like a junior copywriter you need to give feedback to, not your master. Without elite feedback it's just a matter of time until it gets lost in some nonsense. Gemini + Nanobanana - image generation. For static ads, concepts, and visual ideation. When I need to test a visual angle quickly before investing in a full production shoot, these get the job done fast. MaxFusion - video clip generation. For assembling rough video ad concepts and testing visual hooks without needing a full editing team on every iteration. Google AI Studio - ad learning support. You can feed videos and ask why it thinks it performs and how to change the ad to make it scale even better. But here's the thing most people get wrong about AI tools. They think the tool is the strategy. It's not. The tool is for better and faster execution. If you don't know what awareness stage to write for, Claude won't figure it out for you. If you don't have real personas built from real data, Gemini won't magically create the right visual. If you don't understand why your ads are failing, no AI tool will do the full diagnosis for you. Learn the fundamentals. Then use AI to execute faster. Not the other way around.
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@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.
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@sachinrekhi ·
The hardest part of building an AI workflow today is deciding your context strategy, which is how are you going to get the data you need for the task? To help you determine this, I've detailed the 5 context strategies that you can employ in any AI workflow: 1. Local files - The fastest and most reliable way is if your workflow can just read local files. For example, when drafting meeting agendas, I rely on markdown meeting notes that I've downloaded from Granola. This makes it incredibly fast for the AI to look through all my meetings to draft the appropriate next agenda. 2. CLI tools - AI tools are incredibly good at running command-line tools, which are programs that run in the Terminal. CLIs exist for pretty much everything, they are very fast to run, and quite reliable. For example, my workflow for synthesizing customer interviews uses whisper, a command-line tool that can transcribe any video file into text. 3. MCP servers - AI tools make it easy to connect to remote content through easily installed MCP servers. These exist for getting context from Google Docs, Notion, Slack, etc. So my workflow for catching me up on Slack leverages the Slack MCP server to scan the appropriate Slack channels and summarize the context. These generally work well, but if a CLI tool exists for the same data source, I generally prefer it now for speed and reliability. 4. APIs - If there isn't a CLI or MCP for the data source I'm interested in, I check if there is an API for that data source. And then I ask the AI tool to write code to access the API. This makes it so I can get my data from nearly anywhere, but it does take additional work to set this up, since I need to typically download API tools, ensure the AI has access to the latest documentation, and it can be buggy as well. So I only go down this route if I need to. For example, I recently I used the Gamma API to auto-generate a beautiful presentation for my NPS analysis workflow. 5. Browser agent - AI tools can also open and use a browser on your behalf. They can navigate to URLs, click links & buttons, as well as extract information from pages. This gives you ultimate data access even when there are no CLIs, MCPs, or APIs. However, this is the slowest and least reliable method. So I only turn to it when there are literally no other options. For example, I ended up using this to scrape competitor pricing pages to ensure I was getting the most up-to-date information. Next time you are building out an AI workflow, know that you have all five of these strategies at your disposal for getting the data you need.
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@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
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@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
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@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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@htmleverything ·
AI workflows have created this weird pressure where proper engineering suddenly feels “too slow.” But skipping process doesn’t make you fast. It makes you sloppy faster. Bad UX ships faster. Tech debt compounds faster. Engineers get frustrated faster. Product decisions get less thoughtful faster. The point of AI shouldn’t be to remove engineering discipline. It should be to give us more room for it.
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@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
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@dominikmartinX ·
I run a pressure washing company, a crypto brand, and a personal brand. All as one person. No team. No VA. Here's what I learned about using AI to actually operate a business: 1: AI is not one tool. It's an operating system. I don't open Claude to "ask questions." I open it to run entire workflows. Lead response. Content planning. Customer communication. Financial tracking. The moment you stop treating AI like a search engine, everything changes. 2: For customer communication I built a system that drafts replies in my voice. A lead comes in. I paste their message. I get a draft in 30 seconds. I approve, edit, or reject. But the first draft is never from scratch anymore. 3: For content I use a 3-step loop: - Research what's happening in my niche right now - Map topics to a weekly calendar - Draft and edit in one sitting Monday morning. One session. Whole week covered. 4: The biggest mistake I see solopreneurs make with AI: Subscribe to 12 tools. Use each one at 10%. Pick 2. Go deep. Build systems around them. I use Claude and ChatGPT. That's it. Covers 90% of what I need. 5: AI doesn't replace thinking. It replaces the boring parts. I still make every decision. I still write the final version of every important message. I still show up and do the work. AI just removed the 3 hours of setup that used to come before the actual work. 6: If you're a solopreneur and you're not building AI workflows around your daily operations, you're leaving hours on the table every single day. Not theory. Not hype. Just a guy running 3 projects telling you what works. What's the one task you wish AI could handle for you? Reply below.
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@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.
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@frog_omo ·
In 2014, @37signals was running four products. Basecamp. Highrise. Campfire. Backpack. Different tools for different problems. Revenue coming in from all of them. Then @jasonfried did something that made no sense. He killed three of them. Not sold. Not spun off. Killed. The team pushed back. "These products make money. Customers use them. Why would we throw that away?" Fried's answer: "We keep talking about doing more things. But we haven't entertained the other option: do fewer." They renamed the entire company Basecamp. One product. One focus. Everyone thought they were crazy. Within two years, Basecamp grew faster than it ever had with four products. They crossed millions of users. Still profitable. Still no VC funding. Fried later said: "Companies that claim they can do everything actually excel at nothing. That's why we chose to do one thing and do it right." I keep thinking about that story. Because I'm watching RevOps teams make the exact opposite mistake with AI. Everyone's adding AI. Almost nobody knows if it's working. The same pattern keeps showing up: Teams spread AI across 7+ workflows. A little here. A little there. Looks impressive in a deck. But here's what the data shows: Teams running 1-2 focused AI workflows report stronger ROI than teams using 7+. 37signals learned this with products. RevOps teams are learning it with AI. Less is more. But nobody wants to hear that. The other two mistakes: Measuring the wrong thing. Most teams track hours saved. "AI cut research time by 40%." But the hours saved don't show up in board meetings. Revenue does. Less than 9% of teams say AI has helped generate more pipeline. Nine percent. Skipping data readiness. Everyone wants the shiny AI tool. Nobody wants to clean their CRM first. 19% say dirty data is their biggest blocker. AI on top of messy data just makes the mess faster. 37signals didn't win by doing more. They won by killing what didn't matter and going deep on what did. The same applies to AI: → Measure outcomes, not hours saved → Pick 1-2 workflows and go deep→ Fix your data before adding tools The teams that figure this out will pull ahead. The rest will just build a more expensive mess.
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@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.
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@RoundtableSpace ·
SOMEONE BUILT A KANBAN SYSTEM THAT TURNS CLAUDE CODE TASKS INTO AUTONOMOUS AI AGENTS. Dragging a card into progress automatically launches scoped agent sessions, letting multiple AI workflows run in parallel like a full dev team.
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@dominikjberger ·
I believe "full autonomy" is the wrong goal for AI agents and I say that as someone who runs AI workflows every day (if not every hour). The most valuable component of an AI workflow isn't the prompt. Also, not the model and not the integration. It's a traffic light: 🟢🟡🔴 Every output gets a confidence flag. 🟢 goes through. 🟡 needs a quick look (probably by another model) 🔴 stops and waits for a human. Building this way has taught me two things about where checkpoints belong: If a human has to approve every step, you haven't automated anything, you've built a slow, manual process with extra software. And when people approve 30 things a day, they stop actually reviewing (oversight fatigue is real). However, autonomous agents fail in a sneaky way: the output often looks awesome. It's fluent, well formatted, and confident. A simple rule could be: A human belongs in front of any action that is... → irreversible (sending, publishing, paying) → expensive to get wrong (consumes a lot of tokens, i.e.) → dependent on context the agent can't have (or that context is too sensitive to share) One lesson from my own workflows, the failure mode nobody talks about: After a couple of weeks, the human gets lazy. I caught myself skimming the outputs instead of reading them. Funny thing and where this goes full circle: this is exactly what I wanted to research during my PhD studies back in 2015: interruption management. Systems that interrupt humans only when it's truly necessary. Where do you draw the line in your workflows, what does your agent never get to do without you? 👇 #AIAgents #AIWorkflows #AutonomousAgents
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@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
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@InfiniStrategy ·
Integration platforms are racing to become AI-native. Celigo's July release introduced Platform MCP, letting AI agents build automations through Claude or ChatGPT in plain language. They also shipped a full CLI and added Anthropic Claude as a third LLM provider. Meanwhile, Google rolled out a major Workspace AI Workflow update with 200 new connectors and native Gemini-powered steps for document summarization and data extraction. The pattern is clear. Integration tools are becoming the orchestration layer between AI agents and business systems. Celigo's MCP server lets an LLM read, build, and troubleshoot integrations conversationally. Google embeds AI directly into workflow automation across Docs and Sheets. Both reflect the same insight: the bottleneck in enterprise AI adoption is not model quality, it is getting AI to interact with the fragmented software stack businesses already use. What separates winners is developer experience. Celigo's CLI works with coding assistants like Cursor through packaged skills. Google's connector SDK lets developers extend workflows with custom code. The platforms gaining traction treat integrations as code, not just drag-and-drop configurations. InfiniStrategy take: The integration layer is becoming critical infrastructure for AI deployment. Companies treating their integration stack as an afterthought will struggle to connect AI to actual business processes. The organizations moving fastest invest in platforms supporting both no-code automation and programmatic access, because in 2026, AI without integrations is just a demo. Sources: https://t.co/ZJ0AIvQTTb https://t.co/XBydK0XW7d https://t.co/kD6ka08tsk
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@karankendre ·
I built a MiniMax Workflow Engine. 10 repeatable AI workflows. Same input format. Same output format. Fill in your variables, generate your prompt, and run it straight through the MiniMax API. No fluff. No prompt-tweaking every session. Just structured workflows that actually work. Powered by MiniMax M2.7 and it costs 95% less than Claude Opus to run.
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@TheTechDiggest ·
[OpenSource - AI Workflows & Career Automation] Manually tailoring your resume and writing custom cover letters for dozens of job applications is repetitive and time-consuming. 🛑 ai-job-search is an open-source automation framework (21.7k+ GitHub stars) that uses Claude Code to analyze job listings, customize resumes, write cover letters, and prepare application materials automatically. Here is how this dual-agent career automation system operates. 🧵👇 1/4
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@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.
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@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.
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@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.
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@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.
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@rektonomist_ ·
Decided to accept this challenge by the chads at @Mantle_Official because honestly, AI has quietly become one of the biggest edges in crypto right now, at least for me... Over the last year, we’ve seen AI move from “chatbot assistant” to real analytical infrastructure. Tools like AI-powered blockchain analytics and trading copilots are already scanning huge onchain datasets and surfacing signals that humans would struggle to process manually. Even research platforms are experimenting with AI agents that analyze wallet behavior and market signals across dozens of blockchains in real time to generate trading insights. To me, the key idea is simple: --> Crypto generates too much data for humans to process alone. --> Prices, liquidity, governance votes, TVL flows, whale wallets, CT sentiment, new contract deployments. --> AI is the only thing that can scan all of it continuously. And that’s exactly how I use it, and thus I wanted to share my daily AI workflow for crypto research and trading. ⸻⸻⸻⸻⸻⸻⸻ Step 1 — Data ingestion (signal discovery) First, I use AI to scrape multiple information layers simultaneously: • CT / X sentiment • onchain dashboards (DefiLlama, Dune) • wallet trackers (Nansen) • token liquidity flows • governance proposals • news The agent’s job is simple: Detect anomalies. Examples: • sudden TVL spikes • whale accumulation • liquidity migrations • new contracts deploying AI systems excel at analyzing massive datasets in real time and identifying patterns that humans often miss. Instead of manually checking dashboards all day, I get a stream of structured signals. ⸻⸻⸻⸻⸻⸻⸻ Step 2 — Signal filtering (noise removal) Crypto is mostly noise. So the next agent ranks signals based on probability of alpha. The ranking model weighs things like: • capital inflow velocity • wallet quality (smart money vs random wallets) • liquidity depth • narrative alignment Example output: Signal score: 8.3/10 Reason: • $12M liquidity inflow in 4h • 3 historically profitable wallets involved • aligned with DeFi narrative Instead of 200 signals, I get the top 10 worth investigating. ⸻⸻⸻⸻⸻⸻⸻ Step 3 — Context building (AI research assistant) Once a signal passes the filter, another agent builds context. It automatically summarizes: • protocol fundamentals • tokenomics • recent announcements • comparable historical setups Example prompt: “Explain why this liquidity spike matters and identify potential catalysts.” Within seconds, I get a mini research report. AI agents are increasingly used to analyze crypto markets and support investment decisions by combining onchain data, price information, and sentiment signals. This step turns raw data into understanding. ⸻⸻⸻⸻⸻⸻⸻ Step 4 — Strategy generation Now the system proposes potential strategies. Examples: • trade • LP / liquidity provision • farming incentives • ignore signal Example output: Strategy proposal: • accumulate token on liquidity dip • monitor LP incentives • exit if TVL momentum fades ⸻⸻⸻⸻⸻⸻⸻ Step 5 — Human judgment This is the most important step. AI is incredibly good at: • scanning data • summarizing signals • generating hypotheses But markets are still driven by: • psychology • macro narratives • liquidity cycles So I treat AI like a research desk of assistants. It compresses hours of research into minutes. Then I make the final call. ⸻⸻⸻⸻⸻⸻⸻ Final takeaway As we all know, crypto markets run 24/7 and produce more data than any human team can process. In my humble opinion, AI agents simply give you the ability to: --> scan faster --> analyze deeper --> and act earlier. And in crypto, being early is EVERYTHING.
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@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?
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@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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@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?
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@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?
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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?
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@JulianGoldieSEO ·
GROK JUST REMOVED THE MOST ANNOYING PART OF AI WORKFLOWS And most SEO teams are going to completely miss why this matters. What changed: → One add-on puts Grok inside Google Docs, Sheets and Slides → It works from a side panel beside your actual files → No copying answers from a chatbot into another app What it can do: ✓ Turn rough notes into a structured SEO article ✓ Write formulas, analyse data and build charts in Sheets ✓ Turn an outline into a researched slide deck with consistent styling The practical workflows are even better: ✔ Convert one coaching call into a blog, email, LinkedIn post and X thread ✔ Build a 30-day customer roadmap and training deck ✔ Analyse member activity and identify who needs extra support Grok also works inside Word, Excel and PowerPoint. The real advantage is not “better AI.” It is removing every unnecessary step between an idea and published work.
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