Automation Building Tools
No-code, low-code, and code-based implementation patterns using workflow builders, APIs, LLMs, MCP, and agent tools.
40%
Best tweets about AI Automation
50 Best Tweets About AI Automation (2026)
Discover the best tweets about AI automation, from agent workflows and integrations to business processes, reliability, and measurable results. Updated weekly.
Automations with clear inputs, tools, outcomes, failure handling, and honest operating costs.
- Creators
- 42
- Updated
Original Xholic analysis
What 50 top AI Automation posts reveal
Across the cited posts, the recurring operating model is a narrow workflow with explicit inputs, tools, permissions, tests, monitoring, exception handling, and human ownership. The dataset also contains genuine disagreement over whether no-code is sufficient, how automation affects work, and what ongoing operation costs once maintenance and oversight are included. [2083225928793203058, 2067271422016094349, 2063721106150686950]
- Dominant tone
- Positive
- Median score
- 11.0
- Leading format
- Opinion
- Recent posts
- 44%
52% of posts
All-time engagement
32% of posts
Published in 90 days
Conversation map
The themes creators return to
Human Oversight and Boundaries
Human judgment, approvals, escalation paths, permissions, and ownership for high-impact or ambiguous work.
32%
Workflow Design
Designing narrow, repeatable AI workflows with explicit triggers, inputs, tools, outputs, and business outcomes.
28%
Business Automation Use Cases
Practical automation use cases across lead intake, CRM, support, reporting, marketing, content, onboarding, and back-office work.
24%
Automation Economics and ROI
Cost, ROI, stack complexity, maintenance burden, and the economics of automation versus human labor.
20%
Reliable Agent Operations
Reliability practices for production agents: testing, retries, fallbacks, queues, state, monitoring, tracing, and error recovery.
18%
Data and Context Foundations
Data readiness, knowledge grounding, system-of-record integration, and context management for useful automation.
12%
Automation Services Business
AI automation consulting and agencies: niche positioning, productized services, pricing, ROI, and selling outcomes rather than AI.
10%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 31
- Median reposts
- 2
- Median replies
- 10
- Median views
- 2.1K
Posts with media make up 60% of this collection. Their median all-time score is 12.0, compared with 11.0 for text-only posts.
Format mix
- Opinion 32% · score 8.86
- Case Study 28% · score 7.41
- Tutorial 18% · score 22.8
- Announcement 14% · score 16.1
Consensus and debate
Where creators agree, and where they do not
Shared view
Start with one bounded workflow
Multiple posts recommend beginning with one recurring, bounded task: define its inputs and outputs, connect only needed tools, test normal and edge cases, and compare results with the manual process before expanding.
Shared view
Design the human handoff
Posts consistently position human involvement as part of workflow design: scope read and write access, require approval for consequential actions, escalate exceptions, and assign an accountable owner.
Shared view
Ground agents in operational reality
The cited posts argue that dependable deployment depends on operational foundations: map processes, clean inconsistent records, ground work in company knowledge, and work with existing systems of record.
Shared view
Operate agents like production systems
Reliability guidance in these posts includes testing, retry handling, monitoring and observability, tracing, defined environments, guardrails, and error handling. The common framing is that tool or model capability alone is insufficient.
Open debate
Code-first versus no-code execution
One post argues that effective knowledge-work automation ultimately requires versioned, testable code. Others show no-code builders as viable ways to build agents. Both perspectives include testing, tools, and safeguards in their descriptions.
Open debate
Automation’s workforce effects remain contested
The cited posts offer competing accounts of workforce effects: one warns of demand and employment consequences, another reports redeploying people after its own automations, and another describes using AI to pursue more work without reducing headcount. These are claims and interpretations from the posts, not a settled outcome in the dataset.
Open debate
The real cost is debated
Posts disagree on operating economics. One cautions against maintaining infrastructure for easy tasks; another highlights software, data, monitoring, maintenance, token, and operator-time costs; a third argues that a lean chained stack can be cheaper than all-in-one platforms. The specific cost and performance figures are author-reported.
What performs
Patterns behind standout posts
The score outliers span strategy and operating detail
The five reported all-time-score outliers are 2044437349753381074 (2433.07), 2067271422016094349 (643.62), 2083225928793203058 (169.39), 2083246132751368461 (148.88), and 2055634435425583298 (126.15). Their posts span automation economics, rollout sequencing, managed-agent controls, systems-of-record integration, and an agent-building reference map.
Operational themes have above-overall medians
Workflow Design has a 16.67 median all-time score, Human Oversight and Boundaries has 14.04, and Reliable Agent Operations has 35.314. These themes directly address workflow definition, control, and failure handling in the editorial angle.
Tutorials outperform the overall median
Tutorials make up 18% of the dataset and have a 22.84 median all-time score, above the overall median all-time score of 11. The cited tutorials focus on build steps, testing, safeguards, and deployment considerations.
Statistical standouts
- View standout post 1 Score 2433.1 · 221.19× median
- View standout post 2 Score 643.6 · 58.51× median
- View standout post 3 Score 169.4 · 15.4× median
- View standout post 4 Score 148.9 · 13.53× median
- View standout post 5 Score 126.2 · 11.47× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Steve Tan
@heystevetan
2 posts
-
2. Aaron
@IAmAaronWill
2 posts
-
3. Julian Goldie SEO
@JulianGoldieSEO
2 posts
-
4. Harsh Makadia
@MakadiaHarsh
2 posts
-
5. Mike Scully
@Mike_Scully_
2 posts
-
6. Sachin Rekhi
@sachinrekhi
2 posts
Workflow design is presented as operational work
These posts describe automation in workflow terms: identify a defined function, map decisions and judgment points, separate knowledge from actions, set boundaries, measure performance, and improve the system over time.
Outcome-led positioning for services
Service-focused posts advocate positioning around a specific repeatable business problem and a promised result rather than marketing generic AI expertise. This is advice and opinion from the cited creators, not independently validated pricing or ROI evidence.
Learning paths extend beyond prompts
Educational posts cover no-code tools through production-system practices, including goals, knowledge, tool connections, testing, safety, monitoring, and cost-performance tradeoffs.
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 Automation tweets
Top AI Automation tweets from 42 creators
Ranked 01–50
- 01
@AIHighlight ·
🚨BREAKING: Two researchers from UPenn and Boston University just published a paper that should be uncomfortable reading for every CEO automating their workforce right now. The argument is straightforward. Every company replacing workers with AI is also eliminating its own future customers. Laid off workers stop spending. Enough of them stop spending and nobody can afford to buy anything. The companies that fired everyone end up selling into an economy with no purchasing power left. Every executive can see this. The math is not complicated. But here is why nobody stops. If you do not automate, your competitor does. They cut costs, lower prices, take your market share, and you collapse anyway. So every company automates knowing it is collectively destructive because the alternative is dying alone while everyone else survives. The researchers proved this is a Prisoner's Dilemma playing out in real time. The numbers are already moving. Block cut nearly half its 10,000 employees this year. Jack Dorsey said AI made those roles unnecessary and that within the next year the majority of companies will reach the same conclusion. Salesforce replaced 4,000 customer support agents with AI. Goldman Sachs deployed a coding tool that lets one engineer do the work of five. Over 100,000 tech workers were laid off in 2025 and AI was cited as the primary driver in more than half those cases. 80% of US workers hold jobs with tasks susceptible to AI automation. The researchers tested every proposed solution. Universal basic income does not change a single company's incentive to automate. Capital income taxes adjust profit levels but not the per-task decision to replace a human. Collective bargaining cannot hold because automating is always the dominant strategy. They also identified what they call a Red Queen effect. Better AI does not solve the problem, it accelerates it. Every company chases faster automation to gain market share over rivals but at the end everyone has automated equally, the gains cancel out, and the only thing left is more destroyed demand. The one thing the math says could work is a Pigouvian automation tax. A per-task charge that forces companies to account for the demand they destroy each time they replace a worker. The conclusion is that this is not a transfer of wealth from workers to owners. Both sides lose. Workers lose income. Companies lose customers. It is a deadweight loss with no market mechanism to stop it on its own.
- 02
@businessbarista ·
Been working with execs on AI rollouts for the last 14 months. This is the most common level-by-level progression I see:* Level 1: Run company wide AI audit, which includes mapping key processes, interviewing SLT/ELT, and surveying rank-and-file employees. Level 2: Finalize post-diagnostic read out, which lays out AI transformation timeline by initiative. Prioritized by ROI, risk profile, and cultural support. Level 3: Company realizes their data ducks aren’t in a row, which leads them to working on the “Company Brain” panacea. Level 4: In tandem, start by investing in coding agents for the engineering/product org. Level 5: Get enterprise access to general purpose LLM for subset of non-technical AI champions. Level 6: Expand enterprise access to all non-technical employees. Level 7: Run small cohort of workshops for ELT and AI champions so they get the most out of the technology. Level 8: Roll out training/enablement program company-wide. Level 9: Company runs AI hackathon, which lets employees bubble up solutions to problems from the AI audit as well as new problems. Level 10: Leadership reviews and prioritizes employee hacks and decides which initiatives to take from prototype to production. Level 11: First AI build tackled is typically quick win to some back-office process with attributable hard ROI where cultural pushback is expected to be low. Level 12: Token efficiency/cost optimization becomes major focus as AI budget begins to balloon in eng org. Level 13: As internal momentum builds & longer ROI leash is given, cycle of problem identification —> process map —> prototype —> test/harden/secure/measure —> scale is followed leveraging initial AI readout, hackathon findings, etc. Level 14: Company starts moving further along the spectrum from deterministic workflow to self-guided agent as the AI muscle expands. *Note: these levels can appear in different order or happen simultaneously vs. sequentially depending on the company’s context
- 03
@coreyganim ·
HOW TO DESIGN A MANAGED AI AGENT A BUSINESS CAN ACTUALLY TRUST STOP pitching "AI employees." No one knows what that means. START selling one defined function instead. Here's the operating model: 1. Give the agent one job. -Speed to lead. -Speed to quote. -Lead qualification. -Knowledge-base maintenance. -Meeting preparation. A narrow job is easier to explain, build, test, and improve. 2. Ground it in the same company knowledge as the team. The agent needs the offer, customer, process, policies, history, and current priorities. It shouldn't reconstruct the business from random emails every time it runs. 3. Separate knowledge from action. The knowledge layer tells the agent what is true. The action layer lets it use email, calendar, CRM, documents, or other tools. Keeping those layers separate makes the system easier to govern and manage. 4. Define what it can read. Full company context may be useful but that doesn't mean every agent should see every confidential record. 5. Define what it can change. A marketing agent should be able to maintain marketing knowledge. It should not be able to edit finance rules. We have tested this with positive and negative checks: an agent can make an in-scope change and gets rejected when it tries to write outside its domain. That is a far better proof point than saying it is "autonomous." 6. Define what requires approval. -External messages. -Calendar changes. -Public content. -Destructive actions. -Financial actions. -Customer commitments. Drafting and recommending are different from deciding and publishing. 7. Build acceptance tests before. thedemo. -Can it find the canonical source? -Can it refuse stale or conflicting information? -Can it stay inside its write boundary? -Can it escalate uncertainty? -Can it recover when a connection fails? Installation is not acceptance. 8. Show the work every month. -What did the agent handle? -Where did it fail? -What changed? -What still needs a human? -What is the next improvement? Managed agents are not software you install and forget. They are a defined function you operate, monitor, improve, and keep aligned with the business. The winning offer is: "we manage a specific piece of work, on top of your company's knowledge, with clear boundaries and human control" The final step is boring but important: assign a human owner. Someone has to review failures, approve boundary changes, and keep the underlying knowledge current. A managed agent without an accountable owner slowly turns into unmanaged risk..
- 04
@vasuman ·
There are 3 core philosophies that we believe in when it comes to implementing AI for a large company: 1. Build on top of the existing systems of record. Your company spent years and millions of dollars building its stack. NetSuite, Salesforce, SAP, ServiceNow, whatever it is. The problem was never the systems themselves. The problem is the manual work your people do between them. Agents should run inside your existing tools, not replace them. No migration, no new logins, no retraining your team on a new platform. 2. Create a single pane of glass that unifies systems, that all agents live on top of. AI should be the last piece of software that you integrate, not the reason you add 50 more licenses and one-off workflows. One orchestration layer that connects your systems and gives agents a unified view of your operations. Finance agents, sales agents, procurement agents, all living on the same layer, talking to each other, sharing context. Not 12 disconnected AI tools that each solve one problem and create three new ones. 3. AI can do a lot, but can't do everything. Your team knows things that no model ever will. The vendor who always pays late. The client who needs a custom invoice format. The exception that happens once a quarter but costs $200K when someone misses it. Agents handle the 90% that's repeatable. Your people handle the 10% that requires judgment. Over time, that ratio shifts as agents learn from your team's corrections, but certain humans stay in the loop on what matters.
- 05
@shushant_l ·
📂 ai agents ┃ ┣ 📂 foundations ┃ ┣ 📂 what is an ai agent ┃ ┣ 📂 autonomous workflows ┃ ┣ 📂 reasoning systems ┃ ┣ 📂 memory systems ┃ ┗ 📂 orchestration logic ┃ ┣ 📂 core components ┃ ┣ 📂 brain (llm core) ┃ ┣ 📂 memory ┃ ┣ 📂 tools ┃ ┣ 📂 planning ┃ ┗ 📂 orchestration ┃ ┣ 📂 agent types ┃ ┣ 📂 single agents ┃ ┣ 📂 multi agents ┃ ┣ 📂 human in the loop ┃ ┣ 📂 rag agents ┃ ┗ 📂 voice agents ┃ ┣ 📂 protocols ┃ ┣ 📂 mcp servers ┃ ┣ 📂 tool integrations ┃ ┣ 📂 agent communication ┃ ┣ 📂 workflows ┃ ┗ 📂 context sharing ┃ ┣ 📂 no code builders ┃ ┣ 📂 gumloop ┃ ┣ 📂 zapier ┃ ┣ 📂 n8n ┃ ┣ 📂 relevance ai ┃ ┣ 📂 make ┃ ┣ 📂 relay .app ┃ ┗ 📂 stack ai ┃ ┣ 📂 use cases ┃ ┣ 📂 sales outreach ┃ ┣ 📂 email automation ┃ ┣ 📂 content generation ┃ ┣ 📂 customer support ┃ ┣ 📂 recruitment ┃ ┣ 📂 research monitoring ┃ ┣ 📂 reporting ┃ ┗ 📂 internal knowledge bots ┃ ┣ 📂 workflows ┃ ┣ 📂 lead enrichment ┃ ┣ 📂 crm updates ┃ ┣ 📂 ticket triaging ┃ ┣ 📂 ai reporting ┃ ┣ 📂 inbound qualification ┃ ┗ 📂 knowledge retrieval ┃ ┣ 📂 setup ┃ ┣ 📂 define the goal ┃ ┣ 📂 choose the tool ┃ ┣ 📂 connect apps ┃ ┣ 📂 write prompts ┃ ┣ 📂 add instructions ┃ ┣ 📂 build workflows ┃ ┣ 📂 upload knowledge base ┃ ┗ 📂 test the agent ┃ ┣ 📂 decision making ┃ ┣ 📂 reasoning chains ┃ ┣ 📂 trigger systems ┃ ┣ 📂 action execution ┃ ┣ 📂 conditional logic ┃ ┗ 📂 retry handling ┃ ┣ 📂 integrations ┃ ┣ 📂 gmail ┃ ┣ 📂 slack ┃ ┣ 📂 notion ┃ ┣ 📂 google docs ┃ ┣ 📂 crms ┃ ┣ 📂 databases ┃ ┗ 📂 spreadsheets ┃ ┣ 📂 deployment ┃ ┣ 📂 cloud hosting ┃ ┣ 📂 api deployment ┃ ┣ 📂 self hosting ┃ ┣ 📂 monitoring ┃ ┣ 📂 scaling ┃ ┗ 📂 observability ┃ ┣ 📂 safety ┃ ┣ 📂 human approvals ┃ ┣ 📂 guardrails ┃ ┣ 📂 permissions ┃ ┣ 📂 spending limits ┃ ┣ 📂 audit logs ┃ ┗ 📂 error handling ┃ ┣ 📂 common mistakes ┃ ┣ 📂 vague prompts ┃ ┣ 📂 too many tools ┃ ┣ 📂 no safeguards ┃ ┣ 📂 skipping testing ┃ ┣ 📂 ignoring logs ┃ ┗ 📂 overcomplicating workflows ┃ ┣ 📂 trends ┃ ┣ 📂 agentic ai ┃ ┣ 📂 multi agent systems ┃ ┣ 📂 ai coworkers ┃ ┣ 📂 no code automation ┃ ┣ 📂 enterprise ai agents ┃ ┗ 📂 voice first agents ┃ ┣ 📂 growth ┃ ┣ 📂 ai automation agencies ┃ ┣ 📂 saas copilots ┃ ┣ 📂 workflow marketplaces ┃ ┣ 📂 custom ai agents ┃ ┗ 📂 ai consulting ┃ ┗ 📂 future ┣ 📂 autonomous businesses ┣ 📂 ai operating systems ┣ 📂 personalized ai workers ┣ 📂 always on agents ┗ 📂 human ai collaboration
- 06
@arpit_bhayani ·
Months of building and shipping AI systems made me realize - one thing that makes agentic workflows work is good old best devops practices. If you do not have proper tooling, clean (and also well-defined) processes, and ephemeral environments set up, an AI agent cannot run or help you reliably. Be it agentic SDLC, running evals, phased agent rollouts, circuit breakers, database access, or anything else. Think about what an agent actually needs to do good work. It needs a safe place to run code, a fast feedback loop to know if something broke, and clear boundaries between development, staging, and production. Most skip this and go straight to "let the agent write and deploy code." Then they hit weird failures, lose trust in the system (and even AI), and quietly give up on agentic workflows altogether. The fix is not about AI at all. It is the same old devops discipline that made human engineering teams reliable in the first place. Good things die hard :)
- 07
@shushant_l ·
I'm amazed most people still think building AI agents requires coding. Here's how to build powerful AI agents step by step using simple no-code tools. --- 1. Define one repetitive problem your AI agent should solve first. --- 2. Set one clear and specific goal before building anything. --- 3. Give your agent high quality knowledge from documents, websites, and files. --- 4. Write detailed instructions explaining exactly how the agent should behave. --- 5. Connect the apps your workflow actually needs, nothing more. --- 6. Give your agent the right tools to search, analyze, schedule, and automate tasks. --- 7. Build a workflow where the agent understands, reasons, uses tools, and delivers results. --- 8. Add simple logic so it asks questions, escalates issues, and confirms completed tasks. --- 9. Test normal requests, edge cases, and long conversations before deployment. --- 10. Continuously improve prompts, workflows, and knowledge based on real usage. --- 11. AI agents are different from chatbots because they can take actions to achieve goals. --- 12. Every reliable AI agent follows a framework of goal, knowledge, instructions, tools, workflow, memory, and output. --- 13. Start with one small business problem instead of trying to automate everything. --- 14. Popular no-code builders include ChatGPT, Claude, n8n, Zapier AI, Lindy, and Google AI Studio. --- 15. Beginner AI agents can handle email, customer support, SEO, CRM, research, and meeting summaries. --- 16. Avoid vague prompts, poor knowledge sources, too many tools, and skipping testing. --- 17. Keep workflows simple instead of making unnecessary complex automations. --- 18. Monitor accuracy, success rate, errors, costs, and user feedback regularly. --- 19. Keep humans involved for high impact or business critical decisions. --- 20. The infographic includes additional frameworks, examples, formulas, and best practices worth studying. --- To learn more, check the infographic. ---
- 08
@tibo_maker ·
ngl I've been mass-testing AI agents across all my products for the last 2 weeks tried to replace entire workflows. customer support, content generation, SEO audits, social scheduling here's what I found: agents are insanely good at tasks with clear inputs and outputs. content drafts, data extraction, competitor analysis. like 90% as good as a human, 50x faster but they still completely fall apart when context matters. when you need taste. when the answer is "it depends" I watched an agent confidently give a user the wrong Outrank plan recommendation 3 times in a row because it optimized for the metric instead of the actual need so painful to watch 😅 my take: the best AI-native products in 2026 won't be "fully automated" anything they'll be the ones that figure out the exact moment to hand control back to a human that handoff is the whole product
- 09
@goyalshaliniuk ·
Understanding AI Agent Protocols: ANP, A2A, MCP, AGORA, and ACP AI agents often need to communicate, collaborate, and share tasks and that’s where agent protocols come in. These frameworks define how agents interact, exchange information, and coordinate actions, whether they’re part of the same system or spread across different environments. Here’s a quick breakdown of the five major protocols: 1. ANP (Agent Network Protocol) – Coordinates multiple AI agents in a decentralized setup. Ideal for secure cross-domain operations like open marketplaces, where agents handle tasks such as risk detection, data analysis, and feedback collection. 2. A2A (Agent-to-Agent Protocol) – Developed by Google, this enables seamless communication between agents within enterprises. Perfect for project coordination, task assignments, and exchanging multimodal data. 3. MCP (Model Context Protocol) – Created by Anthropic, this links AI agents to external tools or APIs via a central server. Commonly used for single-agent automation, analytics, or customer support. 4. AGORA – From Oxford University, AGORA uses natural language to design dynamic protocols, allowing agents to adapt tasks, negotiate goals, and coordinate actions in real time. 5. ACP (Agent Communication Protocol) – Developed by IBM, ACP standardizes communication between agents, supporting both text and multimodal inputs for on-premise or server-based systems. These protocols are the backbone of collaborative AI, enabling smarter, faster, and more adaptive multi-agent systems. Understanding them can help you design AI solutions that communicate effectively and work together seamlessly.
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- 10
@amix3k ·
AI is giving software companies massive leverage. One response is to reduce headcount while maintaining the same output. Another is to keep building and attempt things that previously required a much larger company. At Todoist, we’re now 108 people, the largest team we’ve ever had. We’re also taking on two major new challenges. One of them is Todoist Automations. Without AI, we wouldn’t have attempted it at all, because automation is an incredibly hard problem to solve well. Yet our research and customer feedback revealed a huge gap: many customers want to automate their work, but only around 10% actually do. The opportunity is enormous. We’re approaching it with the same craft we bring to Todoist. The entire interface is built around natural language. On the engineering side, we’ve created a workflow engine that can run automations affordably and at scale. And it won’t be limited to Todoist. People will be able to automate work across Gmail, Notion, Outlook, spreadsheets, and many other tools. I’m not sharing this to promote what we’re building. I’m sharing it because it’s a concrete example of what AI is making possible and what I expect many software companies will do next. The result for customers will be far more choice, much stronger competition, lower prices, and rapidly improving software. And because almost every industry now runs on software, the impact won’t stop at tech. It will raise the productivity and efficiency of entire markets and society as a whole. --- And of course, this would not be a full post if I didn’t include a little leak of the direction we are going! 😁
- 11
@EXM7777 ·
if your goal is to find the best ways to implement AI in your work... most of your job will be deciding wether this task you're automating/delegating to an agent really adds leverage to your work i see A LOT of ai products launching, and focus on the mundane tasks: answer email, manage calendar, book restaurants or flights... what's the point in setting up workflows, having to maintain an infrastructure or pay a subscription to perform such EASY tasks? same applies to business workflows... Claude Code and other tools have people feel like they're super behind if they're not using it as their daily driver truth is you can get A LOT of shit done with just decent prompting, context engineering and MCPs you don't need a big ass setup that's time consuming, the goal with AI is to do MORE and FASTER
- 12
@parcifap ·
How to learn AI Automation? Step-by-step guide in 4 levels - - Level 1: Using AI Start by mastering the fundamentals: > Prompt engineering (zero-shot, few-shot, chain-of-thought) > Calling APIs (OpenAI, Anthropic, Cohere, Hugging Face) > Understanding tokens, context windows, and parameters (temperature, top-p) With just these basics, you can already solve real problems But yeah, it's not enough to build real automation - - Level 2: Integrating AI Move from using AI to building with it: > Retrieval Augmented Generation (RAG) with vector databases (Pinecone, FAISS, Weaviate, Milvus) > Embeddings and similarity search (cosine, Euclidean, dot product) > Caching and batching for cost and latency improvements > Agents and tool use (safe function calling, API orchestration) This is the foundation of most modern AI products. - - Level 3: Engineering AI Systems Level up from prototypes to production-ready systems: > Fine-tuning vs instruction-tuning vs RLHF (know when each applies) > Guardrails for safety and compliance (filters, validators, adversarial testing) > Multi-model architectures (LLMs + smaller specialized models) > Evaluation frameworks (BLEU, ROUGE, perplexity, win-rates, human evals) Here’s where you shift from “it works” to “it works not like an unstable shit.” - - Level 4: Optimizing AI at Scale Learn how to run AI systems efficiently and responsibly: > Distributed inference (vLLM, Ray Serve) > Managing context length and memory (chunking, summarization, attention strategies) > Balancing cost vs performance (open-source vs proprietary tradeoffs) > Privacy, compliance, and governance (PII redaction, SOC2) At this stage, you’re not just building AI, you’re designing systems that scale in the real world.
- 13
@alexabelonix ·
Every day I see another post about the future: 1 founder. 10 AI agents. 0 employees. Fully automated startup. Maybe. But I think a lot of people are confusing leverage with autonomy. Claude Max costs $100-$200/month. Sounds cheap. Until you realize you're still the one debugging, reviewing outputs, fixing workflows, talking to customers, doing sales and figuring out distribution. Then you move beyond a simple AI assistant. And the costs start getting interesting. Some AI SDR products are estimated at: • ~$600/month on the low end • ~$2k-$5k/month for meaningful usage • $40k-$100k+/year for enterprise deployments And that's before you add: lead data CRM infrastructure monitoring maintenance tokens and your own time. That's the part nobody puts in the viral videos. The funniest thing? A lot of bootstrapped founders could hire a real operator from India, Nigeria, Vietnam or the Philippines for the same budget or less. Someone who can actually take ownership. Not just generate outputs. AI is real. The leverage is real. But I don't buy the "set up a few agents and chill" narrative. The cheap version isn't autonomous. The autonomous version isn't cheap. Founders who are heavily using AI agents: Has your workload actually gone down? Or did the work just change?
- 14
@Mike_Scully_ ·
Saw this post yesterday from a beginner… "I'm learning AI to start a business. What should I offer?" And my answer shocked them. "Don't offer AI." You see, when you say "I do AI," business owners have no idea what that means. AI is a tool, not a solution. It's like saying "I do Excel" Or "I use a hammer." Or “I use a chainsaw” (ok the chainsaw might be cool but you get the point) Cool... But what problem do you actually solve? The winners in the AI gold rush aren't the AI experts. They're the people who combine AI with an existing skill that businesses already pay for. This is called skill stacking, and it's the secret to premium pricing. For example… Broke positioning: "I'm an AI automation expert" → Vague, commodity, race to the bottom Rich positioning: "I help agencies automate their client onboarding using AI, saving 15+ hours per week" → Specific, outcome-driven, premium pricing See? One is about the tool. The other is about the transformation. Here's the skill stacking formula that's working right now: Pick a valuable base skill that businesses already pay for: Copywriting Lead generation Content creation Operations/systems Client fulfillment Then add AI as the multiplier that makes you 10x faster and better than anyone doing it manually. For example… Base skill: Copywriting AI = "I write 30 days of email sequences in 3 hours using AI, maintaining your brand voice" Charge: $3,000-$5,000 per sequence Base skill: Lead generation AI = "I build AI-powered outreach systems that book 10+ qualified calls per week" Charge: $2,500-$5,000/month retainer Base skill: Content repurposing AI = "I turn one long-form video into 50+ pieces of content across all platforms using AI" Charge: $2,000-$4,000/month If you see, there’s a common pattern between all the services I mentioned. You're not selling "AI" You're selling the outcome that AI helps you deliver faster. Yes, you still need to understand the base skill. AI can't turn a bad copywriter into a good one. But it can turn a decent copywriter into a superhuman one. This is why people who already have a skill are crushing it right now. They're not learning AI from scratch. They're adding AI to what they already know. And they're charging 3-5x more than people doing the same work manually. If you don't have a base skill yet, here's what I recommend: Pick ONE of these high-income skills to learn first: Email marketing Landing page creation Lead generation systems Client onboarding workflows Content strategy Automations Spend 30-60 days getting decent at it. THEN layer in AI to 10x your efficiency. The businesses that will pay you premium prices aren't looking for "AI experts." They're looking for people who can solve their specific problems faster and better than anyone else. AI is just how you do it. That's the difference between $500 projects and $5,000 retainers.
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@NoahEpstein_ ·
claude cowork just made a lot of "ai automation agencies" look expensive here's what it does for free that people were charging $5k-15k for: → auto-organizes your entire file system (downloads, notes, assets) → turns scattered meeting notes into polished reports → converts screenshots and receipts into spreadsheets → analyzes 99 github issues in parallel for prioritization → builds compliance packets (SOC 2, ISO) from your existing docs → creates marketing content in YOUR exact voice and style the catch? mac only. max subscribers. waitlist for everyone else. but if you get access - you're not chatting with ai anymore you're delegating to a digital coworker that reads, writes, and organizes files autonomously benefit of this is when you get it to start creating detailed md files for tasks which you can move over to CC to start acting on them. its very easy to start getting ahead of 99% of people.
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@zaimiri ·
A friend paid $12,000 for an AI consultant to automate their operations. The consultant built 14 workflows in 2 weeks. Impressive speed. But they never asked WHY the team handled support tickets the way they did. Turns out the "inefficient" manual triage was catching edge cases the AI now misses. Long story short... they needed a phase 2 for another $8,000 to fix the mistakes. Turns out that automation without context is just faster mistakes 🤣
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@itsalexvacca ·
We've built GTM systems for 267+ B2B companies. Most were running 20+ tools. They didn't need half of them. The teams that close the most deals have the leanest stack. Here's what actually works across every layer of the revenue stack in 2026: > CRM: We started on HubSpot. Data lived in 3 places. Nothing talked to each other. Switched to Attio. > If you're under 100 people, you don't need a $50K/year CRM with a 6-month implementation. > Data Enrichment: Used to pay ZoomInfo $30K/year and still manually verify half the data. Clay replaced all of that. > One workflow in Clay, 50+ data sources, FullEnrich for phone numbers. We used to hire VAs to write the first line of every cold email by hand. Clay killed that overnight. > Outreach: This is where most teams burn money. In 2021, 500+ emails a day was standard. Bottom 10% send the most volume for the fewest replies. Top 10% send micro-campaigns with the highest positive reply rates. > 50 targeted emails beat 500 spray-and-pray every time. > Intent Signals: Barely existed 3 years ago. Now it's the backbone of ColdIQ. > We don't prospect cold anymore. We stack signals: recent hires, GTM engineer postings, headcount growing 20% in 6 months, fresh funding rounds. > We use RB2B to ID anonymous website visitors, push them into Clay, and trigger outreach automatically. Cold outbound is dead. > CPQ: Salesforce CPQ hit end-of-sale March 2025. Most of our clients were quoting with spreadsheets and PDFs anyway. Hyperline now connects CRM to billing natively. > Billing: 67% of SaaS companies use usage-based pricing. Legacy billing tools can't handle hybrid pricing in one system. Hyperline does. > AI & Automation: In 2021, "AI in sales" meant lead scoring nobody trusted. Now AI runs the infrastructure. At ColdIQ, one GTM engineer manages 10 clients. Three years ago that number was 3. > Claude Code builds our internal tools. Clay orchestrates the data layer. n8n connects every workflow in between. The 2021 approach was bolting together 20 tools and hoping the data syncs. The 2026 approach is a connected revenue system where every layer feeds the next.
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@TheTuringPost ·
Have you ever wondered what actually stands behind the idea of an AI workflow? Time to make it clear. In organizations, the workflow is the unit you can actually inspect, automate, and improve. And the most accurate definition would be this: ➡️ A workflow is a repeating sequence of decisions and actions that turns an input into an output, with points along the way where a human exercises judgment. Judgement points matter! Strip those out and you have a pipeline – a cron job, a script, plumbing. Have them in and you have a workflow. Most companies already run dozens of workflows across support, finance, engineering, sales, operations. But most of these workflows were never written down, many are informal and under-specified and exist because humans learned how to work around broken systems years ago. LLMs change this. A lot of the gray area that previously required constant human coordination can now be handled through language, reasoning, classification, validation, and generation. The interesting question becomes: • Which judgment points can an agent handle? • Which still require humans? • How does the human know when to step in? Across production systems we operate, the same patterns keep appearing: triage, monitoring, draft-and-review, execution-with-approval, investigation-and-recommendation, sync-and-transform, elicitation, curation Interestingly, underneath all of them are the same primitives. Together with @wschenk, we break down these primitives, recurring workflow patterns, and the full workflow taxonomy in this article → https://t.co/KQsWzYiDyE Which workflow in your organization is already structured enough for an agent?
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@frog_omo ·
"all white-collar work automated in 18 months" really? microsoft's AI chief mustafa suleyman just told the financial times that lawyers, accountants, marketers, and project managers will be "fully automated" by late 2027. i've been tracking AI automation closely. here's what the actual data says: the prediction: → "human-level performance on most, if not all, professional tasks" → "most tasks that involve sitting down at a computer will be fully automated" → timeline: 12-18 months the reality: 1. 80% of workers are refusing AI adoption fortune reported last month that 54% of workers bypassed company AI tools in the past 30 days and did the work manually instead. another 33% haven't used AI at all. combined: 8 in 10 enterprise workers are either avoiding or actively rejecting the technology. 2. only 29% of companies see significant ROI writer's 2026 enterprise AI survey: 97% of executives say they benefit from AI personally. but only 29% report significant organisational ROI. individual productivity gains aren't translating to business outcomes. 3. 95% of AI pilots fail to produce measurable impact MIT's NANDA initiative found that 95% of generative AI pilot programs fail to deliver measurable financial results. the failures stem from poor workflow integration and misaligned organisational incentives — not model quality. 4. AI actually made experienced developers slower METR's randomised controlled trial (february-june 2025): experienced open-source developers using AI tools took 19% longer to complete tasks. before the study, these same developers predicted AI would make them 24% faster. 5. only 8.6% have AI agents in production recon analytics surveyed 120,000+ enterprise respondents: only 8.6% have AI agents deployed in production. 63.7% report no formalised AI initiative at all. deloitte's tech trends 2026: only 11% have agents in production. 42% are still developing their strategy roadmap. 6. gartner predicts 60% of AI projects will be abandoned the 2025 gartner survey on data management: organisations will abandon 60% of AI projects through 2026 due to lack of AI-ready data. 7. the trust gap is massive walkme's state of digital adoption report: → 61% of executives trust AI for complex decisions → only 9% of workers do that's a 52-point trust chasm. here's my take: suleyman isn't wrong about AI capability. the models can do impressive things. but "can do" and "will be deployed at scale" are completely different problems. automation requires: → clean, structured data (most companies don't have it) → workflow integration (most pilots fail here) → employee adoption (80% are refusing) → organisational change (takes years, not months) → trust (9% of workers trust AI for complex decisions) the bottleneck was never the model. it's everything around the model. 18 months to automate white-collar work? maybe 18 months to automate a handful of narrow tasks in a handful of companies with exceptional data infrastructure and change management. but lawyers, accountants, marketers, project managers "fully automated"? the data says otherwise. sources: → fortune (suleyman interview, worker rebellion data) → METR (developer productivity study) → MIT NANDA (pilot failure rates) → writer/workplace intelligence (enterprise AI survey) → walkme (digital adoption report) → deloitte tech trends 2026 → gartner data management survey → recon analytics enterprise survey
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@heystevetan ·
ok meta just dropped their ads MCP and CLI today. if you run ads and use claude or chatgpt go install this RIGHT NOW. I’ve put together a quick setup guide so if you want it just comment “meta”’ and I’ll send it over been waiting for this for months honestly. the thing is, most “ai integrations” are smoke and mirrors. a wrapper, a prompt template, some zapier duct tape holding it together. this is actually different. meta is giving your ai a direct, authorized line straight into your ad account. no more screenshotting reports into chatgpt. no more clicking through 14 menus in ads manager just to duplicate an ad set. you just talk to it. what you can actually do: pull real performance data and just ask it whats working, whats bleeding, what to scale. the kind of analysis that used to take a media buyer 2 hours, you get in like 2 minutes. create and edit campaigns, ad sets, ads. from a chat window. no more “let me jump into ads manager real quick.” build product catalogs, push product data, troubleshoot feed issues. if youve ever spent half a day debugging why your DPA isnt pulling products you know how massive this is. and signal diagnostics. it surfaces signal health and quality so you can actually see where your CAPI or pixel setup is leaking instead of guessing. for anyone running CAPI this alone is huge. to be honest the real unlock isnt any one feature. its that the friction of running ads just collapsed. the stuff that used to take a junior media buyer all day, you can do it while eating lunch now. the operators who set this up this weekend are gonna be on a completely different timeline than the ones who wait 6 months. thats just how this stuff goes. testing it across a few accounts rest of the week. will share more in a few days.
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@VaibhavSisinty ·
I've read hundreds of AI case studies. This is the only one that changed how I think. Every company is asking: what can AI automate? IKEA asked something different. They deployed Billie an AI chatbot for customer service. Billie resolved 47% of all queries instantly. 8,500 employees suddenly looked redundant. This is where most builders stop. But IKEA looked at the 53% failure rate. Every. Single. Failure. Was a customer asking for interior design help. A problem their entire team had completely missed. What they did next is insane. They retrained all 8,500 employees as remote design consultants. AI handled the intelligence work. Humans handled the judgement work. That one decision generated €1.3 billion in new revenue. In one year. 🤯 The companies winning with AI aren't the ones finding better automation. They're the ones paying attention to what the AI can't do.
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@thisisgrantlee ·
Why is IKEA so successful? Meatballs? No, they hit on a deep truth that Charlie Munger spoke a lot about. And it applies to everything, from selling furniture to software: We value things more highly when we are part of their creation. Falling for the "AI-Native" trap, many founders are ignoring this. They are doing 100% AI automation, eliminating user agency. But it is our ability to leave our fingerprints that makes us value the resulting product. Munger spoke about the Endowment Effect. Once we own or build something, it suddenly becomes worth more to us than its objective value. It applies to anything we are associated with. It's like when lottery winners are given an option to choose their own numbers, they tend to play more. The idea of picking their favorite numbers appeals to them, even though in a truly random lottery, the chances of winning remain the same. In the 1950s, Betty Crocker Instant Cake Mixes were not doing well because they were too easy to assemble. Just by adding water, they were ready. Homemakers felt they had nothing to contribute to the cooking. The company relaunched the cake mix, asking the homemakers to whisk their own fresh eggs, and sales actually picked up. Now it felt like they were baking a cake themselves. If you remove the labor, you remove the self-regard. The best products will be the ones that understand the human element and maintain “AI hygiene.” They understand that 100% AI is not the goal, as it takes away the human effort, making them bystanders. Human integration makes the result organic, not slop.
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@zaimiri ·
Closed another automation client. They had 2 people doing onboarding by hand. Same cycle every week: > collect intake answers > chase missing files > create CRM records > schedule content > build the research brief I built one agent to do the first pass. One form submission becomes: > client folder > CRM profile > onboarding checklist > content calendar > research queue The human still approves. The machine removes the 8 hours of manual work nobody wanted to own. This is where AI gets obvious. Not replacing the business. But removing the boring work.
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@adxtyahq ·
When openclaw hype was everywhere, I built a mini AI automation assistant for myself on telegram, just wanted something that actually does stuff, not just talk. Never shipped it, but the way it worked was interesting. Most AI assistants are just prompt in, response out, works fine until you try real automation. Things that take time or multiple steps just break. So I treated it like a backend flow. Message comes in, gets logged, pushed to a queue, worker picks it up. Model decides what to do, not hardcoded logic. It figures out actions, system executes via tools like email, calendar, search. Used xAI API as primary, with GPT/Claude fallback because stuff breaks lol. Mostly for reliability and cost. Everything ran async, otherwise it just wouldn’t hold up in real usage. Added basic things like credits, cron jobs, db for state. Real shift was simple, not a chatbot anymore. Just an AI automation system that takes a message and gets shit done.
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@0xDepressionn ·
How to start your own AI agency to reach $3k/mo+ in revenue AI agency is already one of the most relevant businesses in 2026 everyone needs automation, AI content, and lead gen / outreach via AI 1. How to start and what you can sell? the most important thing is to choose one product that you will focus on (specialize in) best directions to start with: - AI + Automation for small businesses - AI lead gen / outreach agency - AI content engine - AI customer support / knowledge bot personally, I'm leaning more toward automation, because it’s the most practical option most businesses need this and are actually ready to pay for it 2. How to start an AI automation agency for small businesses? the first thing is to understand what kind of services you’ll need to provide in this case, that would be: - lead intake from website, forms, Instagram, WhatsApp, Telegram - AI qualification - auto-reply within 1-3 minutes - logging into a CRM or spreadsheet - reminders for the manager - follow-up if the client didn't reply - weekly report for the owner Tools: - OpenAI, Claude, Gemini - Zapier, Make, n8n - Airtable, Notion, Google Sheets 3. A simple business model to get to your first $3k+ in general, most people think you need to launch a huge agency right away but at the beginning, you just need a monetization stack: Model 1. Setup + monthly retainer the best starting model to work with setup = $500-2k support or optimization: $200-800/mo Why it works: the first payment covers your work, the retainer gives you recurring revenue it's easier for the client to accept a smaller monthly payment Model 2. Productized service you sell in packages, simply defining them by services and limitations for example, first Basic = $700, then "PRO" = $1500, and "Growth" = $2500 for many buyers, this makes the decision easier in the market, agencies are often priced somewhere in the range of about $1,000 to $12,000 so your $500-1500 price point at the start is actually great for getting your first clients at the same time, it's best to choose one niche that you'll work in 4. Good niches to start with - marketing agencies - lead gen agencies - dental clinics and medical clinics - salons and beauty - lawyers - recruiters - real estate - edTech this way, you can easily build a working business that brings profit and value to clients the main thing is to start, and do it right now
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@Mike_Scully_ ·
There's a pattern I've been watching for the last 18 months that's making some people insanely wealthy… And it has nothing to do with building SaaS products, Launching courses, Or any of the usual online business models. It's simpler. And way more lucrative. It's called AI Arbitrage. Here's how it works: Most business owners know AI exists. They've heard about ChatGPT, automation, and all the buzzwords. But they have zero clue how to actually implement it into their business. They're drowning in manual tasks that eat 10-20 hours per week. Tasks that could be automated in an afternoon with the right tools. And they'll pay $2,000-$5,000+ to anyone who can solve it. That's where the arbitrage comes in. You learn AI automation tools (Zapier, Make, n8n) that take maybe 2-4 weeks to get decent at. Then you package that knowledge into high-ticket services that save businesses hours every single week. It’s literally basic maths… Business wastes 15 hours/week on manual tasks That's 60+ hours per month If their time is worth $100/hour, that's $6,000/month in wasted labor You charge $3,000 to automate it They save $3,000/month forever after Who says no to that? The best part? Right now, AI automation is in that sweet spot where: Businesses desperately need it Not enough people know how to deliver it (actually deliver it good) High demand + low supply = premium pricing This won't last forever. In 2-3 years, this becomes commoditized. Prices drop. Competition floods in. But right now? It's wide open. Think about every major tech shift: Early website designers in the 90s → charged $10k+ for basic sites Early Facebook ads specialists in 2010 → printed money before it got saturated Early Shopify developers in 2015 → built 6-figure agencies overnight The pattern is always the same… Early movers make bank. Late arrivals fight for scraps. AI automation is in that early mover phase right now. And you don't need to be a programmer. You don't need a computer science degree. You just need to understand: How to connect different software tools together (Zapier/Make/n8n) How to use AI APIs (ChatGPT, Claude) in workflows How to identify repetitive business processes that can be automated That's it.
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@heystevetan ·
Anthropic's own marketing team runs three jobs through their AI every week. Writing content isn't one of them. Here's what they actually run: 1/ An overnight briefing. It reads Slack, Gmail and their ad accounts while the team sleeps, so priorities are sitting there in the morning. 2/ A Google Ads audit. It flags negative keywords, shows its reasoning, then stops and waits for a human to approve before touching anything. 3/A live reporting dashboard with drill-downs per channel, so nobody pulls numbers by hand. None of that is glamorous. All of it repeats. That's the part most people miss. Content is a task. You do it, it's done, tomorrow you start again. Operations is a system. You build it once and it runs whether you show up or not. Everyone is using AI to produce more. The people who built it are using it to remove the work nobody wants to do twice. The boring jobs are the ones worth automating. They're the only ones that repeat.
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@sachinrekhi ·
.@joulee, former VP Design at Meta, has really solidified my thinking on how taste manifests itself in the age of AI automation. Whether your AI workflow generates high quality output or AI slop comes down to your ability to encode taste into your instruction set. Building a high quality AI workflow now requires two distinct skills: 1. Ability to evaluate whether the output is in fact good in the first place. This has been our classic definition of taste - you know good work when you see it. And of course this is necessary as you iterate on an AI workflow to know whether the output is actually improving. 2. Ability to articulate how to generate great output in a clear and detailed enough way for an LLM to understand and systemize. This is the uniquely new craft that AI automation requires of us - the art of distilling the art. The professionals who can do both well are best positioned in the age of AI.
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@anulagarwal ·
Automating workflows with AI is super fun and enables you think in specialized terms rather than general terms. Some workflows that I recently optimized and helps save me hours now: 1) Managing App Store app pages: -Using Claude code to write ASO descriptions -Using Claude to whip up localized screenshots (in 15+ lanugages) -Claude builds the IOS app, uploads to App Store Connect, sends it for review. -It has access to Astro ASO tool - so it fetches best keywords and adds them. most of it due to @rudrank 's ASC CLI!! I only need to test the app, iterate with Claude, and take raw screenshots. Saves me hours to not fill up everything manually in App Store Connect. 2) Generating engaging game/short form videos: -Claude edits my game videos with captions/voiceovers/scripting -It has access to API keys of various tools -I have given some solid guidelines on how to think about hooks and stuff (same workflow is available in my web-tool!) 3) Managing invoicing/accounts: -In a simple raw form Claude is able to track my monthly revenue/expenses -I just feed it raw invoices and data and it is connected to my local db and keeps updating -I am now converting this to a web-app so I can use it on the go too! 4) Understanding level design: -Levels in my Unity game are just prefabs -I throw level funnel data in it and it analyzes high churn points -Then it goes through my Unity my project, compares those level prefabs and analyses -My game is 2D and physics based so it analyses positions, obstacles, etc. -And it does this perfectly since prefabs are YAML files only -For eg. my level 11-14 had high churn rates. It analyzed and concluded that spikes came in the direct path of the objective whereas in other levels that was not the case. Just a few use cases - everyday I am delegating some or the other task to AI. In my free time now I am learning new (spoken) languages - something I always loved to do. Just for fun.
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@socialwithaayan ·
The biggest risk in AI right now isn't that your agent fails loudly. It's that it fails silently 🤯 There's a free tool called iFixAi that catches exactly that. 45 inspections. Letter grade in under 5 minutes. Any model, any industry. Here's the problem it solves: Your AI agent looks fine in testing. Then in production it fabricates a source. Or leaks a tool it wasn't authorized to use. Or quietly drifts off its objective over a long task. Or underperforms on purpose because it detected it was being evaluated. You don't find out until a customer or a regulator does. iFixAi runs the checks before that happens: → FABRICATION: unsourced claims, overconfidence, missing audit trails → MANIPULATION: privilege escalation, prompt injection, policy violations → DECEPTION: sandbagging, covert side tasks, long-horizon drift → UNPREDICTABILITY: instruction drift, decision instability → OPACITY: regulatory readiness, session integrity, escalation correctness 32 graded core inspections. 13 extended ones for frontier risks like sabotage, sandbagging, and oversight evasion. The part I like most: your model never grades its own homework. iFixAi is model agnostic and judges cross-provider, so you can run your OpenAI agent and have Anthropic score it. Works with OpenAI, Anthropic, Gemini, Bedrock, Azure, Hugging Face, and any OpenAI-compatible . There's now a guided CLI flow and a Claude plugin, so setup takes minutes. Built by iMe. Apache 2.0 License. 100% open source.
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@agentcardai ·
ai agents can now handle your Word docs, Excel sheets, and PowerPoint files so you never have to touch Office again. Agents crash when they hit Microsoft files. Companies pay $50-100/month for automation tools that barely work. A developer just open-sourced OfficeCLI. → Read any Word document and extract text programmatically → Edit Excel spreadsheets with formulas and formatting intact → Generate PowerPoint presentations from templates → Works with zero Office installation required → Single binary file runs on any machine → Command-line interface perfect for AI agent workflows No Microsoft license to buy. No cloud dependencies. No API limits to hit. Every AI automation company needs this exact functionality. 100% Open Source. MIT License. Link in the comments.
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@sachinrekhi ·
The creator of the leading CPO community, @nikhyl, has a front-row seat to how product executives are fundamentally rethinking the PM role in the age of AI. Here are the five realities he sees: 1️⃣ Information Mover -> Product Builder During ZIRP times, the PM role largely became that of an information mover - coordinating amongst stakeholders, executive management, and communication. But that role is now disappearing in favor of product builders - the original craft of building great products that solve problems for our customers. You will not survive long-term in the product role unless you can successfully make this transition. 2️⃣ The Lever For Scaling: People -> Software So much of traditional organizational design is predicated on the notion that the only way to scale the work is by hiring more people. But what AI is teaching us is that we can now scale in an entirely different way: through software and agents. Given this new scaling vector, the skill of people management is rapidly devaluing. 3️⃣ The Core AI Skill: Obsolescence You need the ability to think like a systems thinker and build agents to automate every single aspect of the traditional product role. Everything from customer discovery to prototyping to status reports. The pace that is expected of you is no longer possible without such extreme AI automation. 4️⃣ Judgment From Experience Remains Critical While it's now faster than ever to build, we can't simply build every feature request without ending up with a diluted product that isn't great for anyone. So we need to apply our judgment to decide what is actually worth building by prioritizing what will meet customer needs, fit cohesively with the existing solution, and help us differentiate meaningfully from the competition. There is no shortcut for developing this judgment. 5️⃣ PMs Are Having Fun Again Those that have successfully made the transition to product builders are having so much more fun in the role because they love building far more than politicking. It's incredibly satisfying to see your design come to life in a prototype or the result of fully automating a workflow.
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@cem_hasoglu ·
hot take but most "AI automation agencies" are selling duct tape a webhook, a chatgpt api call, and a google sheet and charging $3,000 for it and honestly? good for them. because the client doesnt know how to build that and it saves them 10 hours a week. the value is real even if the build is simple. the gap between what business owners understand about technology and what is actually possible is the biggest money making opportunity of the decade you dont need to build complex things you need to solve expensive problems with simple things
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@MakadiaHarsh ·
Myth: Automation replaces people Reality: Recently I've built 5+ automations. Zero people got fired. They got promoted to other work Myth: You need a custom app. Reality: 70% of the time, connecting 2-3 existing tools solves the problem for $500 instead of $50K Myth: AI agents work out of the box Reality: An AI agent without rules, fallbacks, and human escalation paths is just a confident liability Myth: Automation is expensive Reality: The most profitable automation I ever built cost $0/month to run and saves a client $4K/month in labor The businesses spending the most money right now aren't the ones investing in automation. They're the ones who haven't started yet.
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@T_Zahil ·
No AI on Writizzy 🙅🏻 Most blogging platforms are racing in the same direction right now. MCPs. Publishing APIs. AI integrations. One-click "write this post for me" buttons. Agents that draft, schedule, and publish on your behalf while you sleep. The result? More and more of what you read on the internet isn't written by a human anymore. It's generated, optimized, and published at a scale no person could match. And if the trend continues (and it will), the internet becomes a place where AIs write for other AIs to read. Humans, somewhere in the middle, scrolling past content that was never really meant for them. We don't want that on Writizzy. We want that when you open a Writizzy blog, you can think: a human wrote this, this is real. So here's our commitment: Writizzy will never have AI integrations. No MCP. No publishing API. No "generate a post" button. No agents hooked up to your account. If you publish on Writizzy, it's because you sat down and wrote it. Your words, your rhythm, your mistakes, your voice. That's the whole point. Long live personal human blogs 💪🏻
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@JustAnotherPM ·
The biggest bottleneck in AI right now isn’t intelligence. It is *context.* Most product managers and devs think connecting AI to your data requires messy, custom code for every single integration. They are wrong. Here is the "Model Context Protocol" (MCP)—and how it unlocks the future of AI automation:
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@JulianGoldieSEO ·
Claude Code + LangSmith is insane. 🤯 You’re using Claude Code… But you can’t see: • why it made decisions • which tools it called • where workflows broke That’s dangerous for real AI automation. LangSmith fixes this. Now you can trace: every LLM call every file read every tool action every response step No more flying blind. This is how serious AI workflows should run.
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@shreejibawa ·
If you are a founder struggling with personal branding, this is for you. I was spending hours every week on content. Writing blogs. Crafting LinkedIn posts. Polishing every single line. Then I stopped doing it myself. In the last 20 days, my AI agents published 7 technical blogs and 8 LinkedIn posts. All autonomously. The blogs covered my entire journey building Hermes Agent: -> Setup guide with Discord integration https://t.co/d7GhCotaeX -> Cost optimized Hermes Agent stack https://t.co/FcfcFdTJjE -> Bitbucket integration guide https://t.co/2PIN7wILPc -> How I built an SEO AI agent https://t.co/B5hTTsyvgz -> Google Workspace (email, calendar, drive) integration https://t.co/1Hu0n74FOp -> Automating LinkedIn posting with Hermes https://t.co/x1c3Q5gF0i -> Building an HR manager AI agent with Zoho MCP https://t.co/48w72rIrpD And 8 LinkedIn posts driving real conversations: -> AI agents are getting scary good https://t.co/tY6vLjL1w5 -> CRM AI agent in action https://t.co/fBG2FyviPq -> I used to spend 2 hours per LinkedIn post https://t.co/VaRtJGcD1q -> Your ops team is running customer PII through... https://t.co/KxgCyLNAm7 -> Business Support Group morning meetup https://t.co/tHom0u8DGo -> What if your salary has an expiration date https://t.co/HmspECQmwJ -> Your AI agent is making confident decisions https://t.co/2xFkp5dKfC -> AI automation + HR automation with Zoho https://t.co/A0vLXNmfJ0 That is 15 pieces of content. Negligible manual effort from me. Here is how I made it work: I trained the agent with my brand voice, audience, and editorial rules using custom Hermes skills. It researches topics across my tech stack (Hermes, Bitbucket, Google Workspace, Zoho) and drafts full posts autonomously. The LinkedIn agent follows a strict B2B formula: hook, breakdown, solution, CTA. It manages the entire pipeline from research to publish, including image sourcing and scheduling The result is a content machine that runs 24/7. My brand keeps growing while I focus on building instead of writing.
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@Zachly ·
Many companies are wasting thousands of dollars on unclear autonomous agents. Other companies are too conservative and think automated meeting notes makes them “AI-native” The middle path is where you see the highest ROI: - engineers using agentic coding This has clear success criteria: shippable products. It is used in conjunction with a human not fully autonomous so costs stay reasonable. - human-in-the-loop workflows Refunds, customer service requests, etc require an owner on the system. Agentic AI is strong in execution but extremely poor in ownership. A human clicking the okay button is massive leverage without painful lack of ownership. - building the information foundation AI is only as good as the information you feed it. Companies that focus on data infrastructure before slapping a chatbot on top will succeed more often and with less hallucinations. Where are the other low hanging fruit in 2026? Are there other shiny distractions?
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@sharyph_ ·
I Automated My 60-Minute Optimization Process to 60 Seconds Every week I spent over an hour optimizing blog posts: → Checking title lengths (under 60 chars) → Writing meta descriptions (155-160 chars exactly) → Creating URL slugs → Writing TL;DR summaries → Converting headings to questions → Adding answer capsules → Fixing hierarchy (H1→H2→H3) → Finding internal links → Writing alt text It was killing me. So I built an AI agent using Claude Code that does all of it. The process now: → Drop in my blog post → Run the agent → Get optimized content in 60 seconds Same quality. Zero manual work. This is what AI is actually for: eliminating tedious work you already know how to do. Not replacing your thinking. Automating your checklist. What repetitive task are you still doing manually that could be automated?
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@PreciousMoonday ·
Sourcing B-roll for a single documentary used to take me 5 hours. Last night, I built an AI agent to do it in 55 seconds. 👇 (Watch the raw demo below) If you run a faceless YouTube channel or video agency, you know the soul-crushing reality of pre-production. You write a killer script, but then your momentum completely dies. You spend hours jumping between Pexels, YouTube, and the Internet Archive, manually hunting for cinematic shots, historical clips, and news broadcasts—downloading them one by one. It’s a massive bottleneck. And as an AI Automation Specialist, doing repetitive manual data-scraping feels like a crime. I needed unfair leverage to scale my content business. So, I stopped working in my business, started engineering around it, and built "Locked Planet Studio." Here is the heavy machinery under the hood: 🧠 The Brain: Groq (Llama-3) reads my raw voiceover script, understands the context, and extracts the exact visual keywords I need. 🌍 The Sourcing: It simultaneously hits the Pexels API (modern 4K), YouTube Data API (news clips), and Internet Archive API (vintage history). 📥 The Engine: Using yt-dlp, it bypasses the websites completely and rips the raw .mp4 files straight to my local hard drive, ready for DaVinci Resolve. Was it a smooth build? Absolutely not. At 3:00 AM, I was battling "Streamlit Amnesia" (where the app kept forgetting my loaded videos), writing defensive code to stop API crashes, and dealing with a complete laptop shutdown right at the finish line thank God for Chrome tab restore 😅 But the ROI is undeniable. What used to take hours now takes less than a minute. Script in. Assets out. Building this made me realize something bigger: This technology scales. Whether it becomes a standalone SaaS product or a custom backend installation, the ability to automate repetitive content workflows is an absolute superpower for modern businesses Creators, Founders, and Agency Owners: What is the most repetitive, time-sucking bottleneck in your workflow right now? Drop it in the comments, or send me a DM. Let’s talk about building a custom AI agent to automate it for you. ✌️ (Documenting the journey: 30-Day Building in Public Challenge) 🚀 #AI #Automation #NoCode #LowCode #n8n #Make #UiPath #LearningInPublic @errah_didit @elewachii @__lordfaith @TechnicalBben @Aje_Dynamicz
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@JulianGoldieSEO ·
Open Code + Qwen 3.6 just changed how AI works. This isn’t another chatbot update. This is agent AI that actually finishes tasks for you. Here’s what makes this combo different: → Qwen 3.6 plans multi-step workflows → 1M token context reads entire projects → Native tool calling connects real software → Thinking mode before execution improves reliability → Open Code edits files directly in your system → Runs commands + checks outputs automatically → Fixes errors without babysitting Chat AI gives answers. Agent AI delivers results. Save this video, you’ll understand where AI automation is going next. Want the SOP? DM me. 💬
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@realMikeChong ·
The biggest lie of "zero-person companies": There's a paradox about AI automation that nobody talks about: if you want to automate something, you must first do it better than AI. Because you can't define "good," you can't create reliable processes. AI solves execution, not judgment. Become an expert first, then talk about automation. Reversing the order results in nothing but highly efficient garbage.
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@timbuilds21 ·
I wasted $50K on GTM tools before figuring out what actually works. Here's the expensive lesson: The Tools That Burned Money: ❌ "All-in-one" platforms → Did everything poorly, nothing well ❌ Pretty dashboards → Looked great, zero ROI ❌ Enterprise-priced databases → Same data as cheaper alternatives ❌ AI copilots → Just summarized what I could already see ❌ Tools requiring humans to operate → Defeated the whole purpose What I Learned: The expensive tools weren't the good tools. The best GTM stack costs ~$500/month total. Not $5,000. The Pattern: → Cheap beats expensive → API-first beats pretty UI → Chained tools beat "all-in-one" → Autonomous beats manual My actual stack now: 1. Claude Code - AI automation ($20/month) 2. Email Bison - Sequencing (built in-house) 3. Apify - Data scraping (~$50/month) 4. Pre-warmed inboxes (~$200/month) 5. Firecrawl + Verifiers (~$100/month) 6. Supabase - Free tier Total: ~$400/month Results: 60+ qualified meetings/month The $50K taught me one thing: Simple, cheap tools that chain together > Expensive tools that promise magic Stop buying features. Start building systems.
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