Forem: MaybeAI

Building Reliable AI Agents with MCP: What We Learned Moving Codegen to the Planning Phase

MaybeAI — Thu, 06 Nov 2025 08:21:22 +0000

TL;DR

Letting an LLM write runtime code feels powerful until you run it every weekday at 9:00. Small per-step errors compound across long workflows. We shifted code generation out of runtime and into the planning phase using a typed DSL, validated components, and MCP. The LLM plans and composes; a deterministic engine executes. Result: lower failure rates, bounded costs, and behavior teams can trust.

Why runtime codegen breaks at scale

We started the common way: the model wrote code on the fly to call tools. After months of tuning, reality intruded:

20%+ end-to-end failure rate
Missing await
Type mismatches
Edge cases that crashed workflows

The math hurts: if each step is 95% accurate, a 10-step job succeeds about 0.95^10 ≈ 59.87%. That is not usable for customer-facing operations.

The shift: plan first, execute deterministically

We moved generation from runtime to the plan:

Natural language  →  DSL (pre-built components)  →  Validate  →  Execute

The model no longer writes arbitrary code.
It selects and arranges vetted components, like clicking Lego bricks together.
Validation happens before execution, so most errors surface early.

Separating control flow from data flow

Control flow (planning): the LLM decides steps and order.
Data flow (runtime): typed DataFrames process records in memory, outside the model context.

This gives us:

Datasets that exceed context windows
Summaries to the model rather than raw data
Live previews that feel like spreadsheets
Predictable compute and token costs

Why MCP helps

MCP (Model Context Protocol) standardizes how agents discover and call tools. In practice it gave us:

Clear tool schemas and capabilities
A consistent way to register and permission servers
Fewer bespoke adapters, easier testing

We built early with MCP at MaybeAI and found it easier to enforce typing and contracts around tools.

What production users actually need

Our users live in Excel: sales ops, marketing analysts, finance. They want the same workflow to run every Monday at 09:00, with the same guarantees. Wrong emails or polluted CRM data are not theoretical bugs. They are business incidents.

So we aim for:

Natural-language planning with a familiar, “vibe coding” surface
Deterministic execution under the hood
Strong typing and schema validation at the edges

Architecture sketch

Intent capture

Users describe the job in chat.
We map it to goals, inputs, outputs, and constraints.

Planning (LLM + DSL)

The LLM composes a plan using typed components.
We validate types, schemas, and preconditions.

Execution (deterministic engine)

DataFrames flow through steps with idempotent writes.
Concurrency, retries, and backoff are policy-driven.

Observation

Trace IDs for every run
Structured errors with semantics
Replay and diff

A short checklist for production agents

Front-load validation in planning
Typed inputs/outputs and schema checks at every boundary
Pre-built, battle-tested components as the default building blocks
Explicit error semantics and routing rather than a generic “failed” bucket
Idempotency keys for writes
Budgeted retries with exponential backoff
Audit, replay, and runbooks for human fallback
Clear SLAs and alerts (for example, P95 latency and error thresholds)

Anti-patterns we learned to avoid

Treating long-running business workflows as open-ended research loops
Shipping untyped tool calls from runtime codegen
Hiding data inside the model context instead of using structured frames
Opaque error messages that cannot be aggregated or routed

What improved after the shift

Stability: most errors are caught in validation, not while writing to external systems.
Cost control: fewer unnecessary tokens and retries.
Scale: plans reference components; components can be optimized once and reused.
User trust: previews look and behave like spreadsheets they already understand.

Open questions for the community

How are you composing tools at scale with MCP?
Where do you enforce typing: at the protocol boundary, inside components, or both?
Have you found a reliable pattern for guardrails that still keeps planning flexible?

We are still iterating, and MCP is evolving fast. If you are running agents in production, share your patterns and pitfalls. We would love to compare notes.

From “I want automation” to “It runs”: 15 decisions for lead enrichment that actually execute

MaybeAI — Mon, 27 Oct 2025 08:55:27 +0000

TL;DR

Before you choose tools, write the spec. Convert a fuzzy goal into 15 concrete decisions: triggers, source order, field definitions, mappings, idempotency, routing, retries, error semantics, audit and SLA. A clear spec decides success more than the platform you pick.

The real problem

Teams say, “We want to automate sales lead enrichment.”

Execution needs more than a wish. You must break it into triggers, data sources, field standards, mappings, routing, retry and backoff, error semantics, compliance, audit, and replay. Without an explicit spec, no platform can give you stable results.

Common pitfalls

Comparing tools on speed or price while skipping requirement definition.
Treating “company size” or “revenue” as plain English rather than computable standards.
Ignoring edge cases like private pages, rate limits, or missing fields.
No audit or replay, so failures cannot be reproduced.

A spec you can execute

Use this as an internal template. It fits any stack.

Trigger

Fire when a new Salesforce Lead is created and Company Size is empty.

Sources and order

LinkedIn: read employee count. If private or not accessible, record LI_PRIVATE.
Company website (About page): extract headcount phrases.
Crunchbase: financing stage and investors.
BuiltWith API: detect tech stack.

Fields and mapping

Company Size: normalize to ranges
"50–200 employees" → 51–200
Funding Stage: text to enum
"Series B" → Growth Stage
Tech Stack: de-duplicate and write as comma-separated.

Write rules (idempotent UPSERT)

Use LeadId + Domain as the idempotency key.
If an existing result from the same batch has a higher score, overwrite. Otherwise keep the current value.

Scoring and routing

Score each source on availability, freshness, and accuracy.
If the main path fails, shift to the fallback path and record the routing trace.

Retry and backoff

Retry up to 3 times with exponential backoff: 1s → 3s → 9s.
Do not retry on 4xx. Retry on 5xx and timeouts.

Error semantics

Use unified, countable codes:

LI_PRIVATE PAGE_NOT_FOUND RATE_LIMITED FIELD_AMBIGUOUS

Errors must be observable and easy to aggregate.

Fallback to human

If all paths fail, open a ticket with full context: request params, error codes, routing trace, and a screenshot or HTML snippet.

SLA and alerting

P95 latency ≤ 6s.
If error rate > 3% for 5 minutes, alert.
Weekly report for missing-core-field rate versus last week.

Audit and replay

Emit a unique TraceId per run.
Store inputs, parameters, source response summaries, and final writes to support one-click replay.

One-screen checklist (15 decisions)

Triggers and conditions
Primary source order
Fallback sources
Field standards
Text-to-enum mappings
Idempotency key and UPSERT rules
Scoring dimensions and thresholds
Routing strategy
Rate limits and concurrency
Retry and backoff
Error code semantics
Human fallback and ticket template
Security and compliance (privacy, auth)
Audit and replay
SLA, monitoring, alerting

Why specs decide wins

Tools influence execution speed; specs control correctness and repeatability.
A precise spec lets any platform reproduce the same result.
Turn the spec into a template. New scenarios reuse 80% and change 20%.

Method: Intent → Plan → Execute → Solidify

Intent modeling

Break “lead enrichment” into observable fields and sub-goals.
Define the single source of truth and acceptance criteria per field.

Planning and orchestration

Make priorities and branch conditions explicit.
Output a human-readable plan, not only code.

Execution with safe fallback

Call tools through standard interfaces and keep context.
Failed paths move into a human queue for rule tuning.

Solidify and evolve

Each successful run becomes a parameterized workflow template.
Replays and A/B tests refine mappings, routing, and thresholds.

Engineering notes

Idempotency: define keys to avoid side effects on re-runs.
Canary and rollback: ship new rules at low traffic first.
TraceId end-to-end: correlate logs across steps.
Least privilege: only the permissions you need, with access audit.
Data contracts: field names, types, ranges, and version strategy.

Anti-patterns

Dragging boxes in a UI to compensate for an undefined spec.
Leaving fuzzy fields to “AI will decide” without guardrails.
A single “failed” bucket with no error semantics.
No replay, so you cannot locate or reproduce issues.

FAQ

Q1. We already use a platform. Do we still need this?
Yes. Specs are platform-agnostic. They let you get the same result on any stack.

Q2. Will detailed definitions slow delivery?
Clear definitions cut rework. Velocity improves over the project’s lifetime.

Q3. What if data sources change often?
Make routing and scoring config-driven. Policy updates handle change.

Q4. How do we judge if automation is worth it?
Use a two-week payback rule of thumb:
people time × frequency × error cost. If it clears that bar, automate first.

Closing

Automation becomes easy once the spec exists. Turn “we want lead enrichment” into 15 decisions, write them in one page, and the rest becomes execution. Turn that page into a template, and your team compounds speed and stability week after week.

Why We Build MaybeAI

MaybeAI — Mon, 13 Oct 2025 10:50:30 +0000

Author: shelly@maybe.ai

The Problem Everyone Misses

Everyone's building AI tools. Chatbots. Agents. Workflow builders.
They assume users know what they want. Then compete on who executes better.
But what we see: Users don't know what they want.
A client came to us. Said he wanted to "automate competitor monitoring."
We asked: Which competitors? He said: The main ones in the industry.
We asked: What data? He said: Pricing, promotions, new products.
We asked: Where to scrape from? He got stuck.
We asked: When to alert? He thought for a while.
We asked: What if scraping fails? He said: Um… should we retry?

Execution Isn't the Problem

This request, technically, isn't hard. Write a scraper. Run a cron job. Send notifications.
The hard part: turning "automate competitor monitoring" into executable specs.
Which URLs. Which CSS selectors. What frequency. What counts as success. What counts as failure. How many retries. Retry interval.
These all need clarity.
Without clarity, tools don't matter.

LLMs Are Misunderstood

Everyone says: LLMs let non-coders build apps.
Wrong.
LLMs don't think for you. LLMs help you decompose your thinking.
They're good at planning:

Understanding vague intent
Breaking down into steps
Matching appropriate tools They're bad at execution. Too expensive. Unstable. Unpredictable.

Our Approach

Step 1: Plan with LLM

Dialogue. Ask questions. Force clarity.
This is what LLMs do best. Flexible. Understands natural language. Probes for details.

Step 2: Execute with Fixed Code

After planning, don't run with LLM. Generate fixed code workflow.
Code is stable. Predictable. Cheap. Runs for cents, not dollars.
Use dataframe for data processing. Use loops for retry logic. Use if-else for branching.
These don't need LLM intelligence. They need determinism.

Step 3: Give It Hands

Brain alone isn't enough.
LLM can say "scrape competitor.com for prices." But it can't actually scrape.
Our human-like browser automation:

Opens web pages
Clicks buttons
Fills forms
Waits for page load
Handles popups Manual work. But if AI replaces human repetitive work, it needs to do manual work.

Why Now

Three timings converge:
LLMs are smart enough. Can understand complex intent. Can probe details. Can plan steps.
Code generation is cheap enough. From dialogue to workflow, cost is accep
table.
Browser automation is mature enough. Playwright, Puppeteer are stable. We just need to teach LLMs to use them.

We're Not Another Workflow Builder

We're a problem decomposition tool. That happens to generate workflows.
You think you need execution tools. You actually need thinking tools.
With clarity, execution is easy. Without clarity, no tool helps.
MaybeAI: From "I want this" to "This is exactly what I want."
That's what we're building.

About MaybeAI

Data workflows, minus the work.

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What People Do With Workflows

MaybeAI — Fri, 26 Sep 2025 11:14:43 +0000

This isn't an article that tries to sell you something or give you a "standard answer."

We've simply summarized our latest research on what people are using workflows and automation for, and where they get stuck. We are presenting only the common patterns we observed. Now, we invite you to add your own experiences.

Six Common Scenarios

1. Marketing & Acquisition

Includes multi-channel content distribution, lead collection and outreach, social media marketing, personalized email campaigns, marketing data analysis with ROI tracking, and ad optimization.

2. Sales & Conversion

Includes lead assignment and follow-up, quotes and contracts, automated customer communication, performance monitoring and reporting, and churn prediction.

3. Internal Operations & Support

Includes internal process and document collaboration, financial and report processing, HR procedures, project and task assignment, and knowledge base updates.

4. Customer Service

Includes unified management of multi-channel support, ticket classification and routing, feedback collection and analysis, service quality monitoring, and satisfaction tracking.

5. Product Management

Includes requirement gathering and analysis, release processes, user behavior analysis, competitor monitoring, and managing the product roadmap and feedback loop.

6. Technical Development

Includes CI/CD pipelines, code quality and system monitoring, data backup and recovery, API integration and testing, and environment management.

Cross-Scenario Pain Points

1. Scattered Data, Fragmented Processes

Content, leads, customer data, and product information are dispersed across different systems. There is often a lack of a unified view or standardized processes.

2. Repetitive Labor, Inconsistent Standards

Manually transferring data between platforms, updating spreadsheets, and deploying code is time-consuming and prone to error.

3. Difficult to Quantify Results

It's hard to create a closed loop for marketing ROI, conversion paths, and quality control. Outcomes often depend on individual experience.

4. Gaps in Collaboration and Timeliness

Cross-departmental communication is slow. Alerts and responses are often delayed, leading to missed opportunities.

Barriers for Different User Groups

For Non-Technical Users:

The learning curve is steep (abstract concepts like "nodes" and "workflows"), documentation is often too technical, debugging is difficult, configuring OAuth and APIs is complex, and pricing models can be hard to understand.

For Technical Users:

Concerns include performance and scalability with large data volumes, the scope of capabilities for individual nodes, difficulties in error-tracing and debugging, and the stability of third-party API connections.

A Few Questions for Discussion

What is the main automation scenario you use most frequently? Why that one specifically?
If you could only solve one pain point first, which would you choose: unifying data, standardizing processes, quantifying ROI, or improving cross-departmental collaboration?
When non-technical team members get involved in automation, where do they struggle most? What kind of visualization, paradigm, or template would lower the barrier for them?
As a technical user, do you care more about stability and observability, or speed and feature coverage? Why?
Do you have a simple but stable automation that has proven effective, which you would be willing to share?

We plan to compile the best examples and questions from the comments into a practical guide on implementing automation in a future post.

About Maybe AI

Maybe AI is a business data workflow automation platform that lets prosumers describe their data needs in natural language and automatically handles the complete "acquire → analyze → act" business cycle, with intelligent solutions that learn and evolve with each use.

Data workflows, minus the work.

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AI Platform Performance Review: Maybe AI, Genspark, and Capalyze in a Twitter Analysis Deep Dive

MaybeAI — Wed, 24 Sep 2025 06:38:37 +0000

Test Objective and Methodology

Our goal was to see how each platform handled a complete social media workflow. The task was to analyze a Twitter account, identify its most-viewed tweet, and generate a relevant, engaging reply. This required a combination of data retrieval, analysis, and execution.

The results revealed a dramatic difference in each platform's ability to handle the task. You can witness their real performance by clicking the replay links below.

Maybe AI Full Replay
Genspark Full Replay
Capalyze Full Replay

Key Findings: A Glimpse into Performance

The test revealed a dramatic difference in each platform's ability to handle the task.

Data Access & Retrieval

Maybe AI demonstrated superior data access, successfully retrieving not only tweet content but also crucial metrics like view counts and posting times.
Genspark partially succeeded through alternative methods but failed to access direct, quantitative tweet metrics, a major limitation for data-driven analysis.
Capalyze failed to retrieve any Twitter data, echoing only the initial request.

Analysis Quality

Maybe AI provided comprehensive content theme analysis, identified engagement patterns, and found precise view counts. Its outputs were structured and actionable.
Genspark offered general account analysis and persona-based insights, but its lack of specific metrics limited the depth of its findings.
Capalyze provided no analysis due to its failure to retrieve data.

Task Completion

Maybe AI flawlessly completed all task components: retrieval, analysis, identification of the top tweet, and generation of a contextual reply.
Genspark partially failed by not being able to retrieve tweet data or identify specific metrics, limiting its ability to complete the full task.
Capalyze completely failed to execute any part of the task.

Summary & Recommendations

Read the full article here.

About Maybe AI

Data workflows, minus the work.

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A New Way to Use LinkedIn: Finding Customers by Seeing Who’s Hiring

MaybeAI — Mon, 22 Sep 2025 06:18:49 +0000

Sales Development Representatives (SDRs) are a fixture in most growing companies. Their daily work often involves a series of mechanical tasks: searching LinkedIn for new hires or company updates, manually updating spreadsheets of potential clients, finding contact details, and sending the first wave of outreach messages.

This work is valuable, but the process is repetitive. Many teams invest significant manpower only to find the approach is costly, inefficient, and yields inconsistent results.

1. Which Companies Are Hiring Sales Reps?

If you want to know which companies are hiring sales representatives, you likely have one of two goals: you’re looking for a job, or you’re looking for customers.

The second goal is often overlooked. A company that is actively hiring SDRs is, by definition, a company that needs to expand its sales pipeline. They have a clear intent to grow. This makes them an ideal potential customer for many B2B products and services.

The problem is that this information is scattered across thousands of individual LinkedIn job postings. Manually copying and pasting this data is slow. Finding the right contact person at each of these companies requires even more searching and jumping between pages.

2. An Automated Workflow

This is a problem for software. Using an AI automation platform like Maybe AI, you can build a workflow to handle this entire process. It looks like this:

1. Scrape Job Data: By using the BrowserScraper tool, the workflow automatically searches LinkedIn for roles like "Sales Representative" or "SDR." It then extracts the latest job details: company name, position, location, and the application link.
2. Generate a Spreadsheet: This data is instantly organized into a clean Google Sheet, creating a structured list of companies with an active need for sales growth.
3. Enrich the Data: The workflow can then enrich this list by finding and adding the company’s website, official LinkedIn page, and potential contact information.
4. Prepare for Outreach: This structured data can then be fed into another workflow to automatically generate draft outreach emails or personalized connection requests.

A task that once took a team days to complete can now be run by one person in minutes. You can see how this specific workflow runs by watching a replay of it here.

3. Augmenting the Sales Role

This kind of automation handles the most mechanical parts of a sales role.

- Information Gathering: The process of finding companies with sales-expansion signals is fully automated.
- Data Structuring: All information is instantly organized in a spreadsheet, with no manual data entry.
- Initial Outreach: The system can draft the first email or LinkedIn message, removing the repetitive work of writing the same introduction over and over.

This doesn't eliminate the need for a sales team. It allows you to focus your human resources on what people do best: having meaningful conversations, understanding customer needs, and closing deals. You can grow your pipeline without having to hire more people for the entry-level, repetitive tasks.

4. The Practical Value

The value of this approach is straightforward.

- It saves money. You reduce the hours your team spends on low-value work and lower the costs associated with hiring and training junior SDRs.
- It's efficient. Acquiring a clean list of 50 companies with clear intent takes minutes, not days.
- It’s conversion-focused. By identifying companies with a demonstrated need for sales growth, you reach potential customers at exactly the right time.

For any business, this is a new way to use LinkedIn. It’s no longer just a platform for recruiting or networking, but a live database of business opportunities.

In a competitive market, efficiency is an advantage. Our philosophy is simple: Data workflows, minus the work.

The goal isn't just to find out who is hiring sales reps. It's to turn that signal into a conversation with a potential customer. Let software do the repetitive work, so your team can focus on the conversations that matter.

About Maybe AI

Data workflows, minus the work.

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[1 Workflow] How We Get Our Daily AI News Briefing Done in 5 Minutes

MaybeAI — Thu, 18 Sep 2025 08:05:50 +0000

In the AI industry, an information gap is a competitive advantage.

Every team wants to know what’s happening as soon as it happens. But the reality is often a manual process of searching news sites, copying and pasting links into a document, then organizing and sharing them. This can take hours a day, and important news still gets missed. What should be simple intelligence gathering becomes one of the most time-consuming, low-efficiency tasks for a team.

A News Aggregator Workflow

To fix this, our team built a News Aggregator workflow on a platform called Maybe AI. It works like this:

We created a shared Google Sheet to serve as a central library for industry news.
In Maybe AI, we entered a simple prompt: "Collect AI industry news from the past 24 hours. Use these keywords, get it from these trusted websites, and write it to this spreadsheet."
The system generated the workflow automatically.
We run it with one click. News is fetched, structured, and added to the sheet.
We set it to run automatically at 10 a.m. every day. Now, the latest updates are waiting for the team when they start their day.

You can try a version of this workflow here.

How This Changed Things

The effects were immediate.

Marketing: The team gets a quick read on market dynamics.
Content: The team can pull topics and angles directly from the news feed.
Everyone: No one is burdened with manual organization. The latest industry trends are just there, in a spreadsheet.

What was once a scattered, repetitive manual process is now automated. The team's day starts with insight, not with a task.

The Results

In practice, this workflow saves each team member at least one to two hours of repetitive work per day. Our response time for new topics and content has improved noticeably. And because everyone is working from the same information, team alignment is better and communication is smoother.

In short, the team now spends its time thinking, not moving data around.

This workflow can be adapted for any team that needs to collect and organize information from any industry. It is particularly useful for small to medium-sized or agile teams.

A Final Thought

The AI industry changes fast. Falling a day behind can mean missing an opportunity.

The idea behind Maybe AI is simple: "Data workflows, minus the work."

By automating the process of gathering information, you let your team focus on making decisions and creating things. If you'd like to try this yourself, you can use Maybe AI to turn the task of "checking the news" over to an automated workflow.

About Maybe AI

Data workflows, minus the work.

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Stop Copying LinkedIn Contacts. Automate the List in Seconds

MaybeAI — Wed, 17 Sep 2025 03:56:01 +0000

LinkedIn is where business development happens. The problem is volume. New connections arrive every day, then sink into the feed. Manual exports take time and introduce errors. Without a clean list, follow-ups slow down and good leads go cold.

The friction

Data sits inside LinkedIn and rarely reaches your CRM or sheets.
Copying dozens or hundreds of profiles by hand wastes hours.
Leads do not land in a structured table, so teams miss the right moment to reach out.

In short, you have plenty of contacts and weak conversion.

The workflow with Maybe AI

Using Maybe AI with the BrowserScraper plugin, you can turn this into a simple chat command:

In chat, say: “Fetch my 100 most recent LinkedIn connections and save them to Google Sheets.”
Maybe AI uses BrowserScraper to capture connection details from your LinkedIn pages.
It creates a Google Sheet and writes key fields such as name, company, title, and profile link.

No copy-paste. No broken formats. The list is ready the moment you start your day.

Try the shared workflow now!

What automation unlocks

Faster follow-ups: Morning connections can be assigned to sales the same day.
Scaled outreach: Connect the sheet to email or messaging tools. Maybe AI can draft and send personalized messages in batches.
Better decisions: Track industry, region, and role mix to refine targeting and route leads.

For global teams, this means fewer missed chances caused by slow data handling.

Final note

Competition rewards speed and consistency. Maybe AI is built for both. Data workflows, minus the work. With BrowserScraper, LinkedIn contact management becomes a background task, so your team can focus on real conversations and real deals. Start now and move one step ahead.

About Maybe AI

Data workflows, minus the work.

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This AI Tool Can Handle Hundreds of Your Twitter Replies. Here's How.

MaybeAI — Tue, 16 Sep 2025 03:43:24 +0000

A look at how AI can turn Twitter engagement into an automated workflow, moving creators from repetitive tasks to strategic approval.

Managing Twitter engagement is often a tedious process. You monitor accounts, filter through tweets, draft replies, and track everything in a spreadsheet.

This is a common workflow for creators trying to grow their influence: to stay active, you must constantly engage, which consumes time that could be spent on more valuable work. The real cost is the repetitive labor.

There is now a way to turn this process into an automated workflow using Maybe AI. The principle is simple: you provide the final approval, and the AI handles the repetitive tasks.

The Creator's Predicament

First, let's look at three common friction points:

Constant Monitoring: You have to repeatedly check Twitter to avoid missing important conversations or trending topics.
Draining to Write Replies: Crafting a reply that aligns with your style and avoids being generic requires mental energy. It’s common to edit a single comment multiple times.
Chaotic Tracking: Manually logging interactions and their outcomes in spreadsheets is time-consuming and inefficient.

The result is that engagement doesn't grow, but your time is spent.

Semi-Automation: Generating Replies into a Spreadsheet

With Maybe AI, you can give the system a simple instruction:

"Fetch the latest tweets from these accounts, generate replies in my style, and record them in a spreadsheet."

The workflow then runs automatically:

Fetch Tweets: It gathers the latest posts from the accounts you follow.
Generate Replies: The AI drafts suitable comments based on the tone you've set.
Track in a Spreadsheet: The original tweets and their corresponding draft replies are synced to a Google Sheet.
Manual Approval: You review and edit the drafts directly in the spreadsheet before publishing.

This means you no longer have to stare at a screen drafting replies. Instead, your attention shifts to the most important part: deciding what to say. For non-native English speakers, this can be particularly helpful for generating natural-sounding comments.

Try it now!

Full Automation: Auto-Posting Replies

For a more hands-off approach, Maybe AI also supports fully automated comment posting:

Describe the Need: In a chat interface, you outline your goal: "Get tweets from these accounts, write replies in this style, and post them directly."
Build Workflow: Maybe AI automatically generates the complete process.
Authorize: Grant it permission to post from your Twitter account.
Approve: When the AI has drafted a comment, you simply click "Approve."
Publish: The comment is then posted automatically.

This allows you to maintain consistent engagement at the right moments with minimal effort.

Try it now!

Practical Outcomes

Creators who have used these workflows report several benefits:

An average of 1–2 hours of free time regained daily.
A threefold increase in the number of comments posted.
More natural fan interactions, as the "mechanical reply" problem is avoided.

In short, it leads to less mechanical work and more high-quality interaction.

Final Thoughts

Success in creative work isn't about who can endure the most drudgery, but who can best focus their time on what truly matters.

The idea behind Maybe AI is: Data workflows, minus the work.

Repetitive tasks should be automated. This allows creators to concentrate on insight and expression.

If you'd like to try it, you can start automating your Twitter engagement with a free trial of Maybe AI.

About Maybe AI

Data workflows, minus the work.

How I Used Maybe AI to Understand the Kaito Leaderboard in 30 Minutes

MaybeAI — Fri, 12 Sep 2025 09:42:37 +0000

Have you ever wondered what the top users on the Kaito Yapper Leaderboard are actually doing on Twitter? Perhaps you’ve downloaded the list of names and want to find the patterns behind their success. A workflow using Maybe AI can quickly turn that data into a guide for your own work.

On Twitter, it's easy to get sidetracked by emotion and noise. Intuition alone isn't enough. Maybe AI helps you understand the value of content systematically, moving beyond ineffective posting.

Why the Kaito Leaderboard Matters

Kaito's Leaderboard is built around the idea of "Mindshare." It identifies creators who effectively drive conversation by measuring the quality of their tweets, the depth of their interactions, and the originality of their ideas.

It stands apart from traditional quantitative metrics by considering semantic value and interaction quality, filtering out plagiarism and engagement farming.

For anyone studying InfoFi, this type of leaderboard offers a clear view of how information flows and which topics carry weight.

The Maybe AI Workflow: From Data to Insight

The workflow consists of the following steps:

Extract Account List: It begins by getting the list of accounts from your user table.
Fetch Recent Tweets: For each Twitter account, it retrieves the 15 most recent tweets (excluding pinned ones).
Generate a New Sheet:It creates a new Google Sheet to store the collected tweets and analysis.
Structure Tweet Data:It populates the new sheet with the tweet's content, date, and link, adding a "Handle" column to identify the account.
Analyze Tweet Patterns:It analyzes the 15 tweets for each account to identify why they rank highly, looking at topics, themes, and communication style. It then provides a one-sentence summary for each user.
Write Analysis to the Sheet:It adds an "Analysis Summary" column to the sheet, populating it with the insights and conclusions for each Twitter account.

Try the workflow for free.

What You Can Do With It

Understand the Writing Style of Influential Accounts: Quickly see which themes, language, and structures generate the most interaction. Avoid guesswork and learn from effective patterns.
Develop a Content Strategy: Use the high-quality content it finds to generate ideas for similar tweets, improving the quality of your own output.
Find and Vet KOLs: Accurately identify truly influential writers by seeing not just that they rank, but why they rank. This is particularly useful for those in InfoFi, where the value of information is tied to its distribution efficiency.

Results

Here is what a user reported:

Analyzed the content of 10 top-ranked accounts in 30 minutes, a task that previously took several hours of manual work.
Identified the core "keyword topics" and "communication styles" behind their high Kaito scores, leading to more targeted content creation.
Successfully simulated the style of one account, resulting in a 40% increase in likes and interactions on a subsequent tweet.

Final Thoughts

Why do certain accounts top the Kaito Yapper Leaderboard? It's less about luck and more about a deliberate content and interaction strategy. Maybe AI gives you insight, not just information.

Data workflows, minus the work.

From a simple table to structured insights, one-sentence summaries, and strategic output—it improves both the quality and efficiency of your work.

Try Maybe AI to make your content creation smarter and more strategic.

About Maybe AI

This Is How You Automate Your Entire Social Media Job Away

MaybeAI — Thu, 11 Sep 2025 09:19:43 +0000

If you manage a Twitter account, you're likely familiar with the tedious work involved: copying and pasting tweets for analysis, manually categorizing data in spreadsheets, and then rewriting, distributing, and engaging with content. The process is not only fragmented but also slow.

With Maybe AI and its plugins, this entire workflow, from a tweet link to a database to a fully automated operational cycle, can be completed in five minutes.

From Tweet to Spreadsheet in Three Steps

1. Activate the Plugin

Open any tweet and launch the BrowserScraper plugin. It will automatically recognize the page and select the "X Original Tweet" template.

2. Batch Process

Paste all the tweet URLs you need into the plugin's "Tweet URLs" field and click "Save to sheet."

3. Automated Data Entry

The tweet data, including author, content, date, and link, is automatically saved to a Google Sheet. You are then directed to the Maybe AI Workflow interface to begin automation.

From Data to a Complete Workflow

Data is just the starting point. The real value is in how Maybe AI seamlessly connects this data to a full cycle of analysis, creation, interaction, and distribution.

Data Analysis

Automatically analyze tweet data to generate insight reports.

Content Creation

Rewrite tweets in a specific style and publish them automatically.

Twitter Engagement

Generate and post intelligent replies to comments.

Cross-Platform Distribution

In short, Maybe AI handles the entire operational loop, from data scraping to multi-platform publishing.

True No-Code Automation

Maybe AI's strength lies in its ecosystem of over 100 tools. You can describe your idea in natural language, and the system will map out and execute the necessary steps.

Whether you need to automate social media, generate marketing content, or distribute it across platforms, it only takes a few sentences.

Describe the automation you want, and watch as AI turns a complex process into a simple conversation. It’s time to move on from manual work.

About Maybe AI

Website: https://maybe.ai/?channel=devto
X: https://x.com/hey_maybe_ai
YouTube: https://www.youtube.com/@HeyMaybeAI

Your Telegram Group is Quietly Dying. This Is How You Revive It.

MaybeAI — Wed, 10 Sep 2025 11:53:04 +0000

Managing a Telegram community often means a lot of manual work. You find news, write posts, monitor the group, and answer questions. This can easily consume hours of your day.

The problem is that you want to be building the value of the community, not doing mechanical labor. This is where a tool like Maybe AI can help. It lets you automate the repetitive tasks of community management.

Here are two workflows that people are using: automatically posting high-quality content and automatically replying to chats. Together, they provide a way to keep a community active.

Generating Discussion

First, you need to solve the basic problem of having something to talk about. The process is straightforward:

Find your target group’s ID, which is available on Telegram Web.
Give Maybe AI your instructions. For example: "Search for AI news using these keywords, select the most interesting article, draft a message in a conversational style, and send it to this group."
The workflow is generated automatically.
Click run, review the draft, and confirm to post.

The result is that relevant, high-quality content appears in the group every day, which makes it easier for members to start talking. (Try now.)

Keeping Conversations Going

Content alone isn’t enough. A group with no interaction is a dead group. Maybe AI can also help here:

Enter the group ID and define a persona and language style for the AI.
The system will automatically pull recent chat history.
It identifies questions from members that have not yet been answered.
The AI drafts a reply and sends it directly to the group.

This way, questions in the group get responses. The atmosphere becomes more active, and you no longer need to monitor the chat 24/7. (Try now.)

The Result

With these two workflows, community managers report that they save over two hours of management time each day.

Member activity increases noticeably. The community stays active even when the manager is away, which is particularly helpful for groups with members in different time zones around the world.

In short, the community runs itself, but you are not tied to it. Using these two workflows together is effective.

The real value of community management is in guiding discussions and creating an atmosphere, not in repetitive tasks. The idea behind Maybe AI is simple: data workflows, minus the work.

Let the AI handle the repetition so you can focus on what matters.

If you’d like to try it, you can go to the Maybe AI website, connect your Telegram account, and set up a workflow to activate your community.

About Maybe AI

Website: https://maybe.ai/?channel=devto

X: https://x.com/hey_maybe_ai

YouTube: https://www.youtube.com/@HeyMaybeAI