Agent orchestration and tool use
Multi-step agents that plan, call tools, coordinate specialist agents, use MCP/connectors, manage state, and act across business software.
52%
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
- 45
- Updated
Original Xholic analysis
What 50 top AI Workflows posts reveal
Across 50 tweets, AI workflows are commonly described as repeatable systems that combine context, tools, multi-step execution, and review. The evidence emphasizes clear inputs, reusable instructions or skills, appropriate human oversight, and production practices such as validation, retries, cost tracking, and evaluation. Individual posts offer examples in engineering, research, operations, marketing, and personal knowledge management, but reported outcomes are generally creator-specific rather than independently verified.
- Dominant tone
- Positive
- Median score
- 8.40
- Leading format
- Tutorial
- Recent posts
- 60%
72% of posts
All-time engagement
30% of posts
Published in 90 days
Conversation map
The themes creators return to
Workflow design and selection
Defining repeatable input-to-output processes, choosing worthwhile use cases, decomposing steps, and distinguishing rule-based, AI, and agentic workflows.
42%
Human judgment and review
Human-set intent, approval gates, manual review, verification, escalation, and feedback loops for decisions where autonomous output is insufficient.
32%
Context, memory, and reusable skills
Persistent knowledge bases, structured folders, session logs, reusable commands, SOPs, examples, and shared memory that make AI work compound over time.
30%
Business process automation
Deploying AI into real operational processes such as recruiting, sales, support, finance, delivery, CRM, inbox, calendar, and MSME order operations.
26%
Domain-specific AI workflows
Concrete workflows for software engineering, investment and crypto research, product discovery, marketing creative, hardware design, and personal productivity.
26%
Evaluation, ROI, and cost optimization
Experiment design, quality measurement, outcome-based ROI, token and model cost control, routing tasks to cheaper models, and auditing workflow value.
26%
Production reliability and recovery
Structured outputs, validation, retries, fallbacks, checkpointing, graceful degradation, error handling, tracing, and debugging of live AI systems.
26%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 26
- Median reposts
- 5
- Median replies
- 6
- Median views
- 2.2K
Posts with media make up 64% of this collection. Their median all-time score is 12.1, compared with 5.62 for text-only posts.
Format mix
- Tutorial 30% Β· score 27.8
- Opinion 28% Β· score 5.99
- Case Study 24% Β· score 8.93
- Announcement 14% Β· score 6.39
Consensus and debate
Where creators agree, and where they do not
Shared view
Treat workflows as repeatable systems
Posts commonly describe workflows as more than isolated prompts: they use structured context, connected tools, reusable skills, and sequenced steps. Workflow design represented 42% of the dataset (21 tweets).
Shared view
Keep humans at judgment and review points
Several posts assign agents tasks such as drafting, scanning, coding, or analysis while retaining human responsibility for strategy, approval, review, or final decisions. Human judgment and review was a theme in 32% of tweets (16 tweets).
Shared view
Design for reliability in production
Production-oriented posts name structured outputs, validation, retries, fallbacks, checkpointing, tracing, and failure recovery as workflow components. Production reliability and recovery appeared in 26% of tweets (13 tweets), with a 27.833 median all-time score for the theme.
Shared view
Context and accessible data shape feasibility
Posts argue that useful workflows need relevant source material, documented steps, examples, SOPs, or accessible data. They also caution that a workflow may fail when the necessary context cannot be acquired or when its steps cannot be defined.
Open debate
Autonomy versus deliberate review
Some posts promote scheduled or unattended execution, while others explicitly retain human approval, manual code review, or final judgment for consequential work.
Open debate
Scheduled routines versus ad hoc work
One creator reports that ad hoc research, summarization, and drafting became more valuable than pre-scheduled automations, while other posts describe scheduled workflows for recurring operational tasks.
Open debate
Broad agent teams versus focused use cases
Some posts advocate parallel specialist agents and multi-agent systems. Others describe auditing workflow value, focusing on understood workflows, or limiting the number of workflows pursued at once.
What performs
Patterns behind standout posts
Tutorials and lists had the highest reported format medians
Tutorials made up 30% of posts and had a 27.833 median all-time score. Lists made up 4% and had the highest format median at 35.43. These figures describe the supplied dataset and do not establish that either format causes engagement.
The largest score outliers cover concrete workflow examples
The five supplied outliers include staff-engineering use of LLMs, an AI second-brain setup, an agent-first coding workflow, and a 13F research workflow. Their all-time scores range from 228.25 to 893.21, versus the dataset median of 8.4.
Media posts had a higher median score than text posts
Media appeared in 32 posts (64%). Its median all-time score was 12.12, compared with 5.62 for text posts. This is an observed difference in the dataset, not evidence that media itself produced the difference.
Cost discussion centers on routing and measurement
Posts discuss selecting lower-cost models for suitable tasks, tracking token or run costs, and testing ROI against quality or task outcomes. The datasetβs evaluation, ROI, and cost-optimization theme appeared in 26% of tweets (13 tweets).
Statistical standouts
- View standout post 1 Score 893.2 Β· 106.33Γ median
- View standout post 2 Score 433.2 Β· 51.58Γ median
- View standout post 3 Score 376.0 Β· 44.77Γ median
- View standout post 4 Score 229.5 Β· 27.32Γ median
- View standout post 5 Score 228.3 Β· 27.17Γ median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Allie K. Miller
@alliekmiller
2 posts
-
2. Bilgin Ibryam
@bibryam
2 posts
-
3. Shalini Goyal
@goyalshaliniuk
2 posts
-
4. Sachin Rekhi
@sachinrekhi
2 posts
-
5. Shushant Lakhyani
@shushant_l
2 posts
-
6. Abhitej | Bento.fun
@abhitejxyz
1 post
Top voices have high supplied median scores
Among creators with two tweets, the supplied median all-time scores are 448.18 for Bilgin Ibryam, 201.93 for Shushant Lakhyani, 83.07 for Shalini Goyal, and 67.33 for Allie K. Miller. Their cited posts cover staff-engineering collaboration, workflow systems, production gateways, and workflow selection.
Specific cases make workflow boundaries visible
Some case-style posts spell out inputs, steps, outputs, limitations, or review points: the 13F workflow describes source data and model misses; the ad-scripting workflow describes research inputs and a human selection stage; the daily interview workflow describes its capture prompts and downstream uses.
Show failure handling alongside the happy path
For the editorial angle, the strongest operational additions are explicit failure states, retry or recovery behavior, review points, and conditions where automation should not be used. These elements are directly discussed in posts on checkpointing, feasibility, workflow audits, and production reliability.
Since the previous snapshot
What changed since Aug 12, 2026
- 64% of the selected posts remained.
- The creator count changed by 0.
- The leading sentiment remained stable.
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 45 creators
Ranked 01β50
- 01
- 02
@EXM7777 Β·
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
- 03
@shushant_l Β·
π how to build your ai second brain β β£ π foundation β β£ π daily captures β β£ π processed inbox β β£ π para vault β β£ π memory files β β π ai agent system β β£ π tool stack β β£ π claude opus 4.7 β β£ π claude code β β£ π claude desktop β β£ π obsidian β β£ π para method β β π mcp tools β β£ π installation β β£ π install claude β β£ π setup local folders β β£ π connect obsidian β β£ π enable cowork β β π enable local access β β£ π para vault β β£ π projects β β£ π areas β β£ π resources β β£ π archive β β£ π inbox β β π templates β β£ π memory system β β£ π claude .md β β£ π memory .md β β£ π session logs β β£ π workflow rules β β£ π preferences β β π continuity context β β£ π daily workflow β β£ π morning brief β β£ π calendar review β β£ π meeting prep β β£ π capture notes β β£ π process inbox β β π review priorities β β£ π ai workflows β β£ π research synthesis β β£ π writing assistant β β£ π project planning β β£ π task management β β£ π decision support β β π knowledge retrieval β β£ π session management β β£ π write session logs β β£ π track discussions β β£ π save outputs β β£ π update memory β β£ π maintain continuity β β π organize learnings β β£ π skill building β β£ π weekly systems β β£ π reusable workflows β β£ π markdown skills β β£ π prompt refinement β β£ π process optimization β β π execution frameworks β β£ π organization β β£ π structured notes β β£ π searchable knowledge β β£ π linked thinking β β£ π documentation β β£ π knowledge graphs β β π second brain habits β β£ π productivity β β£ π faster research β β£ π smarter writing β β£ π reduced context switching β β£ π memory augmentation β β£ π workflow automation β β π ai collaboration β β£ π best practices β β£ π save every session β β£ π keep folders clean β β£ π document workflows β β£ π maintain consistency β β£ π review weekly β β π build systems gradually β β£ π mistakes to avoid β β£ π random note dumping β β£ π inconsistent structure β β£ π overcomplicated setup β β£ π skipping session logs β β£ π relying on memory alone β β π ignoring documentation β β£ π long term benefits β β£ π compounding knowledge β β£ π faster execution β β£ π better decisions β β£ π scalable workflows β β£ π reduced mental load β β π ai powered thinking β β π scaling your brain β£ π automation systems β£ π multi agent workflows β£ π advanced memory β£ π personalized ai ops β π lifelong knowledge engine
- 04
@GergelyOrosz Β·
Just six months ago, @dhh (creator of Ruby on Rails and Omarchy) said how he doesnβt 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 DHHβs agent-first workflow 55:09 AIβs 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: β’ @statsig β β 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 β’ @WorkOS β 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 β’ @SonarSource β 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 β when agents started producing code which DHH does want to merge with little to no alteration. #2 Beautiful code and products arenβt matters of vanity; theyβre signals of correctness. Dipping into philosophy, DHH says: βWhen something is beautiful, itβs likely to be correct.β 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 DHHβs 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
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- 05
@FundamentEdge Β·
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.
- 06
@dexhorthy Β·
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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- 07
@law_ninja Β·
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.
- 08
@businessbarista Β·
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. Thereβs 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 1β10 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 β₯ 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?
- 09
@goyalshaliniuk Β·
API Gateway vs AI Gateway : Whatβs 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 donβt just route requests, but also manage intelligence, safety, and optimization at every step. Hereβs 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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- 10
@JJEnglert Β·
If you've outgrown ChatGPT but Claude Code feels like a cliff β this video is for you. There's a missing middle in the AI world right now. On one side: people typing questions into a chat box, copying answers back into their work. Useful, but it caps out fast. On the other side: developers running Claude Code in a terminal, wiring up agents, shipping automations . Between them is where most professionals actually live β people who want to DO more with AI, not just chat with it, but who aren't ready to open a terminal. That middle now has a home. It's called Claude Cowork. I just published a full walkthrough of how I use it to run my operations β and I made it specifically for non-developers who are ready to have AI start doing more for them. Here's what's inside: β Step 1: Global instructions The foundation. Teach Claude who you are, how you work, and what "done" looks like β once. Every project inherits it. β Step 2: Projects & folder structure Stop starting from zero every chat. Projects give Claude persistent context for the work you actually do. β Step 3: Connectors Gmail, Calendar, Drive, Notion β Claude reads your real work and acts inside it. This is the moment "AI Assistant" stops being a metaphor. β Step 4: Skills The unlock. A skill is a reusable workflow you build once and run forever. I show you mine: an email voice writer that sounds like me, a newsletter skill, a self-improving draft β evaluate β rewrite loop. β Step 5: Plugins When skills aren't enough, plugins extend Cowork with new abilities. I explain the difference and when to reach for each. β BONUS: Pro tips Rate limits, model selection (Opus vs Sonnet vs Haiku), conversation hygiene, token optimization β the stuff nobody tells you until you've already hit your limit. Here's the part I want you to really hear: The principles you learn in Cowork β writing clear instructions, structuring context, building reusable skills, chaining steps into loops β are the SAME principles that power Claude Code and every serious AI workflow above it. Cowork isn't a toy version. It's the on-ramp. Master it here, and the day you're ready to switch over to the code tab or hit a terminal, you won't be starting over. You'll be well prepared. If you've been watching developers build wild things with AI and feeling left behind β you're not. You just needed the right starting line. And the video below is it. If you enjoy it, please bookmark and/or share it with a friend!
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- 11
@alliekmiller Β·
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.
- 12
@milesdeutscher Β·
Crypto x AI is a match made in heaven. If you're in the crypto markets and not leveraging AI to make more money, this should be a wake-up call. There are so many easy-to-set-up AI workflows that deliver massive ROI. Some ideas: β’ Use Hermes/OpenClaw as your personal CT research assistant β’ Vibe-code a market trading journal app (trained on all your tendencies/data) β’ MCPs for quick research alpha (LunarCrush, Dune, etc.) β’ Connect to TradingView MCP and create custom indicators/alert systems β’ Automated trading bots (trained to execute your strategy parameters) β’ Use AI to source CT/finance creators that are sharing underrated alpha This is stuff that genuinely used to take me hours of manual research every week. The market edge you get from these simple workflows is insane, and you'd be foolish not to take advantage of them.
- 13
@suraj_sharma14 Β·
If I had 6 months to become an Applied AI Engineer. Iβd do this. Stage 1: Python + Production APIs FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDKs. Stage 2: LLM Fundamentals for Production Tokens, context windows, model routing, embeddings, cost/latency tradeoffs. Stage 3: Prompt Engineering + Structured Outputs System prompts, few-shot chains, Pydantic/JSON validation, prompt versioning, unit evals. Stage 4: RAG + Knowledge Grounding Chunking strategies, hybrid search, rerankers, vector DBs, metadata filtering, citation tracking. Stage 5: AI Workflows + Orchestration Tool calling, state machines, human-in-the-loop, retry logic, fallback chains, session memory. Stage 6: Build Production-Ready Apps Domain-specific copilots, automation pipelines, streaming UIs, graceful degradation, rate limiting. Stage 7: Evaluation + Reliability Accuracy scoring, hallucination detection, RAGAS/DeepEval, regression testing, A/B output validation. Stage 8: AI Infrastructure + Optimization vLLM, Ollama, quantization, KV caching, response streaming, token cost tracking, edge deployment. Stage 9: Deployment + Observability Docker, CI/CD, cloud hosting, distributed tracing, structured logging, alerting, canary releases. Stage 10: AI Security + Guardrails Input/output filtering, prompt injection defense, PII redaction, compliance checks, sandboxing. Stage 11: Open Source + Portfolio Ship end-to-end apps publicly, write architecture docs, record demo walkthroughs, publish eval reports. Stage 12: Apply Applied AI Engineer, GenAI Developer, AI Integration Engineer, LLM Solutions roles. Most people stay stuck watching tutorials. Builders get hired.
- 14
@goyalshaliniuk Β·
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 theyβre 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 youβre automating simple processes or building complex, intelligent systems. 1οΈβ£ 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. 2οΈβ£ 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. 3οΈβ£ 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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@alliekmiller Β·
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.
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@adxtyahq Β·
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
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@akshay_pachaar Β·
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 βοΈ)
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@mdancho84 Β·
Data scientists: Become an AI Engineer in 2026. Not because data science is dead. Because AI engineering is becoming the next evolution of applied data science. Hereβs what to learn: 1. The Vector Stack * Embeddings = feature engineering for text, images, audio, documents * Vector databases = storage + retrieval for AI memory * Semantic search = finding meaning, not keywords * Hybrid search = combining traditional search with vector search 2. RAG + Context Engineering * Retrieval-Augmented Generation * Advanced retrieval and reranking * GraphRAG for relationships across entities * Chunking strategies * Long-context window management * Source attribution and grounding 3. AI Pipelines + Orchestration * Chains = deterministic AI workflows * Routing = selecting the right model or tool for the task * Preprocessing = preparing inputs before the LLM sees them * Postprocessing = cleaning and validating outputs * LangChain = orchestration framework * LlamaIndex = data + retrieval framework * Workflow automation = turning AI from a demo into a system 4. The Agentic Layer * Tool use = giving AI access to APIs, search, databases, and code * Planning loops = reasoning before taking action * Function calling = structured interaction with software * Multi-agent workflows = specialized agents working together * Human-in-the-loop = controlled escalation when AI is uncertain 5. Evaluation + Testing * Golden datasets = ground truth examples * LLM-as-a-judge = using strong models to grade outputs * Faithfulness = did the answer stay grounded? * Answer relevance = did it answer the question? * Recall = did retrieval find the right context? * Hallucination testing = did the model invent facts? * Continuous eval = testing AI systems before they break in production 6. Ops + Monitoring * Tracing = debugging every step in the AI chain * Logging = capturing inputs, outputs, latency, and errors * Cost optimization = controlling token spend * Latency optimization = making systems fast enough to use * Drift detection = identifying when outputs degrade * Feedback loops = improving the system with real user behavior 7. The Forward Deployed Layer This is the career unlock. The best AI engineers will not just sit in notebooks. They will sit close to the business. They will understand workflows, identify bottlenecks, build AI + data systems, deploy them, and improve them with users. That is why data scientists are so well-positioned. π¨ Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)? On July 14th, I am hosting a free workshop to help you get started with AI + DS projects in Python. Register here (500 seats): https://t.co/onpLpRwkzH
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@shushant_l Β·
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. ---
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@TheTuringPost Β·
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: β‘οΈ 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 β 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: β’ Which judgment points can an agent handle? β’ Which still require humans? β’ 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 β https://t.co/KQsWzYiDyE Which workflow in your organization is already structured enough for an agent?
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@williamkast_ Β·
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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@itsolelehmann Β·
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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@Shruti_0810 Β·
THIS GUY BUILT A 12-HOUR AI EMPLOYEE... THAT FITS IN A BACKPACK. He spent $829 on a Mac Mini setup. Now it works almost anywhere. No desk. No wall outlet. No monthly software stack. β‘ Runs 10β12 hours on a battery. π§ Claude does the thinking. π Obsidian remembers everything. ποΈ Local Whisper transcribes hours of audio. π Python automates the entire workflow. While most people pay every month for cloud tools... This machine: * Reads documents * Summarizes meetings * Organizes research * Transcribes lectures * Runs scheduled AI workflows ...even when nobody is touching it. One 2-hour lecture becomes: β’ 5 key ideas β’ 3 timestamped quotes β’ 5 review questions 120 saved links become organized knowledge. A 45-minute meeting becomes a polished summary before you even open your laptop. Instead of paying $114/month for storage, automation, transcription, and a VPS... It runs locally for just a few dollars in API costs. The era of AI computers chained to desks is ending. Bookmark this before everyone starts carrying an AI worker in their backpack.
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@the_smart_ape Β·
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 β 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 β 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 β 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 β 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.
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@catalinmpit Β·
My AI workflow is very simple and efficient. Agent questions me about the plan (grill me skill) β Create the plan (custom create plan skill) β Agent implements the plan β Guide it where necessary β Agent reviews the code (thermo-nuclear code review skill) β Manual code review + tests β (If feedback) Agent implements my suggestions β Ship it
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@itsalexvacca Β·
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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@WritesToProfit Β·
Just listened to a leaked Shopify memo that probably saved the company tens of millions. Made me realize most companies are still thinking about AI way too softly. The CEO looked at the business, looked at the speed of AI, and said: βIf the work can be done with AI, you donβt get another person. If AI can make the current team faster, you fix the workflow. But if youβve actually maxed out the leverage, then we talk headcount.β Then he made AI a requirement across every part of the company. No optional adoption. No passive training. No βthis is for technical teams.β That hit me different. Companies are trying way too hard to implement AI. Theyβre βbuyingβ new tools. Theyβre βtrainingβ their staff. Theyβre βexploringβ use cases that never touch real work. Meanwhile winners donβt brainstorm. They force usage and start building. They make AI the default move before more payroll enters the room. Shopify made managers prove AI couldnβt solve the problem before adding another person. Old me wouldβve thought that was just cost-cutting. The new me saw what actually happened. They made hiring feel like the lazy option, AI workflows feel like the obvious option, and more headcount feel like the final resort. Your team doesnβt need another 90-minute AI workshop about prompts. They need to see the difference between doing work manually and rebuilding the entire process around AI. Started applying this inside my own company too. We started turning SOPs into AI workflows. Research time got cut in half. Agentic teams doing repeated tasks. AI stopped being a toy. It became part of the operating system. If you or your team are still using AI just to get simple answers from ChatGPT or Claude... You're missing about 95% of what AI can really do.
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@mhdfaran Β·
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.
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@RoundtableSpace Β·
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 β‘οΈ 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 Englishβlike pulling metrics from your CRM, pasting them into a sheet, and dropping a chart in Slackβand 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 scriptβlike an early morning email sweep or briefing summaryβat 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.
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@wadefoster Β·
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 Β·
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: β Measure outcomes, not hours saved β Pick 1-2 workflows and go deepβ 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 Β·
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: β’ Research β’ Outline β’ Draft β’ Refine β’ Review Each step improves the quality of the next. 2. Save Reusable Context Stop repeating the same instructions. Create reusable prompts that include: β’ Your role β’ Writing style β’ Audience β’ Goals β’ 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: β’ Email summaries β’ Meeting notes β’ Report generation β’ Content publishing β’ 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: β’ Writing preferences β’ Ongoing projects β’ Frequently used frameworks β’ 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: β’ Emails β’ Reports β’ Content β’ Meeting summaries β’ Research β’ 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 β Draft β Review β Automate β 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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@sachinrekhi Β·
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: 1οΈβ£ Analyzing customer surveys 2οΈβ£ Automating customer survey programs 3οΈβ£ Automating feedback rivers 4οΈβ£ Developing user interview scripts 5οΈβ£ Synthesizing user interview feedback 6οΈβ£ Conducting AI moderated interviews 7οΈβ£ Generating synthetic user feedback 8οΈβ£ Conducting discovery via prototypes 9οΈβ£ Analyzing metrics π Automating metric analysis I'm demo'ing all ten live on March 5th in Mountain View with @danolsen.
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@maeveknows Β·
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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@briansolis Β·
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
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@bibryam Β·
πIβm excited about whatβs new in @diagridio Catalyst This update is focused on operating AI workflows in production: β Workflow failure discovery + bulk rerun from the failed step β Human-in-the-loop visibility: find workflows waiting on approvals and unblock them directly from the UI β 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 Β·
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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@ozi_bekee Β·
Most people compare AI models. I compare AI workflows. Hereβs the stack I use: β’ Claude β PRDs, planning, debugging β’ Notebooklm β Research and second opinions β’ Antigravity β Building full-stack apps β’ MotionSites β Landing pages & hero sections β’ Luma β Product visuals and marketing assets I stopped looking for the βbestβ AI tool. I started building a workflow where every tool has a purpose. Thatβs made a bigger difference than any model update. Whatβs in your stack?
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@sachinrekhi Β·
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 Β·
AI Engineers will be insanely valuable over the next 5 years. But not because they know tools like Claude, Codex, Grok, or Gemini. Theyβll be valuable because they can: β’ Turn messy business problems into structured AI workflows β’ Integrate AI into real systems (APIs, data pipelines, operations) β’ Handle edge cases, failures, and reliability β’ Deliver outcomes, not demos Anyone can prompt. Very few can deploy AI that actually works in production. Thatβs the difference.
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@jkohlbach Β·
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 β weather, calendar, news digest β 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 Β·
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_ Β·
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, weβve seen AI move from βchatbot assistantβ 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 thatβs exactly how I use it, and thus I wanted to share my daily AI workflow for crypto research and trading. βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» Step 1 β Data ingestion (signal discovery) First, I use AI to scrape multiple information layers simultaneously: β’ CT / X sentiment β’ onchain dashboards (DefiLlama, Dune) β’ wallet trackers (Nansen) β’ token liquidity flows β’ governance proposals β’ news The agentβs job is simple: Detect anomalies. Examples: β’ sudden TVL spikes β’ whale accumulation β’ liquidity migrations β’ 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. βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» Step 2 β 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: β’ capital inflow velocity β’ wallet quality (smart money vs random wallets) β’ liquidity depth β’ narrative alignment Example output: Signal score: 8.3/10 Reason: β’ $12M liquidity inflow in 4h β’ 3 historically profitable wallets involved β’ aligned with DeFi narrative Instead of 200 signals, I get the top 10 worth investigating. βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» Step 3 β Context building (AI research assistant) Once a signal passes the filter, another agent builds context. It automatically summarizes: β’ protocol fundamentals β’ tokenomics β’ recent announcements β’ comparable historical setups Example prompt: βExplain why this liquidity spike matters and identify potential catalysts.β 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. βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» Step 4 β Strategy generation Now the system proposes potential strategies. Examples: β’ trade β’ LP / liquidity provision β’ farming incentives β’ ignore signal Example output: Strategy proposal: β’ accumulate token on liquidity dip β’ monitor LP incentives β’ exit if TVL momentum fades βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» Step 5 β Human judgment This is the most important step. AI is incredibly good at: β’ scanning data β’ summarizing signals β’ generating hypotheses But markets are still driven by: β’ psychology β’ macro narratives β’ 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. βΈ»βΈ»βΈ»βΈ»βΈ»βΈ»βΈ» 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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@trueventures Β·
The AI workflows with the biggest impact usually arenβt the fanciest β theyβre 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 βlearning-curve experimentsβ to repeatable, ROI-driving flows. A few interesting themes from the day: β Daily use is the maintenance strategy. The best systems get built (and improved) in the middle of real work. β The J-curve is real. New workflows often feel slower before they get dramatically faster. β Adoption is a design problem. The workflows that spread show up where people already are: calendar, inbox, Slack. β Failures are fuel. Every failure point is potential training data β feed it back into the loop. β The βAI sandwichβ works. Humans set intent β agents execute β humans apply judgment. The human parts donβt go away; they get more valuable. And maybe the best takeaway: you donβt need to be technical to build next-level AI workflows. The most creative examples came from people who just kept experimenting.
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@coreyhainesco Β·
With newer AI workflows is that you are no longer the middleman β This is actually great news β 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.)
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@InduTripat82427 Β·
@garrytan (CEO of Y Combinator): βwhen someone asks how I βpromptβ my AI, the answer is: I donβt. the skills are the prompts.β 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:] β stop collecting prompt packs β study how real AI workflows are structured β watch βDonβt Build Agents. Build Skills Instead.β β build one repeatable workflow from scratch β convert it into a reusable command Thatβs it. The biggest shift happening right now: AI is moving from βchattingβ to systems that remember how you work. Hereβs the setup: 1. install a memory layer for your AI workflows 2. add reusable slash-command skills 3. do a task once β save it as a skill β reuse forever The people winning with AI arenβt better prompters. They build systems that compound.
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@ATechAjay Β·
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: β’ Building new features β’ Fixing edge cases β’ Improving UX β’ Adapting to backend changes Some features we've built so far: β Natural-language observability β Live agent trace lookup β Rich interactive trace cards β One-click dashboard deep links β Hover trace previews β Prompt/response inspection β Streaming responses β Suggested follow-ups β Context-aware answers β Chat history & exports β Token/time transparency β 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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@JulianGoldieSEO Β·
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: β Turn Kimi K3 into a full AI worker inside Hermes Agent β Learn new skills once and save them for future tasks automatically β Stack skills over time so the system keeps getting smarter Automation: β Build 3D product models in Blender via MCP β Generate promo videos from those models β Publish SEO blog posts from trending X topics Business Engine: β Monitor competitors every day β Discover fresh content angles automatically β Turn one trend into a blog, video, infographic, podcast, and research report Scale: β Run multiple AI agents on a Kanban workflow β Mix Kimi K3 with GLM so models review each other's work β 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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