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Zoho Creator
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Zoho Creator
@ZohoCreator
We're the DIY enthusiasts of the digital world, making app-building a piece of cake. 🍰 Join the #lowcode revolution today. 🚀
Austin, USA | Chennai, India Katılım Ağustos 2014
437 Takip Edilen12.8K Takipçiler
Techno Set's PetroTrackPro enhances logistics and transaction management for the crude oil industry. From customer management to order handling and storage monitoring, streamline everything in one place. Learn more: zurl.co/IL9uk
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🇨🇦 And just like that, @Zoholics 2026 in Canada comes to a close! It has been a wonderful few days of learning, connecting, and growing together. Thank you to everyone who joined us and made this event truly special. Until next time, Canada!
@ZohoCanada
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Shadow IT isn't a threat; it's untapped innovation. Will you empower your shadow builders or treat them as compliance risks? Read our ebook to learn how to turn shadow builders into strategic assets in 90 days. 🔗 zurl.co/2rjua
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We had some great conversations with tech leaders over lunch in Auckland today around AI adoption, security, modernization, and the growing impact of ease/vibe coding on businesses and IT teams.
We appreciate everyone who joined and shared their perspectives—and we'd like to extend a special thanks to Pri from Hapori for bringing this together and Russ Bennett from Plexure for hosting the discussion!
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The infra gap is exactly where enterprise teams get stuck, vibe coding a prototype is one thing, embedding it into a workflow that handles exceptions, approvals, and audit trails is another. That's exactly the layer where platforms like low-code app builders with built-in workflow engines earn their keep.
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This nails the gap perfectly. AI agents that can call APIs are impressive demos. AI agents that understand why a process runs the way it does, who owns decisions, and what the exceptions are, that's enterprise-grade workflow intelligence. The infrastructure for that doesn't come from MCP alone, it comes from the workflow platform underneath.
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The orchestration layer is where most enterprise data pain actually lives, not in the model or the storage, but in managing dependencies, exceptions, and state across pipelines. The same principle applies to business process workflows: the teams that invest in a proper orchestration layer (not just point-to-point automations) are the ones that scale without breaking.
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Modern data systems are not just built with tools - they’re built with design patterns that ensure reliability, scalability, and clarity as pipelines grow more complex.
Here’s the breakdown of the core Data Engineering Design Patterns every engineer should understand. Each pattern solves a specific challenge across ingestion, storage, transformation, orchestration, quality, and scalability.
Here’s a concise overview of the patterns:
1. Ingestion Design Patterns
Data enters systems in different ways depending on freshness and volume needs.
Batch ingestion handles scheduled loads, streaming ingestion captures real-time events, and CDC captures only row-level changes - ensuring efficient, timely, and fault-tolerant data collection.
2. Storage Design Patterns
Choosing the right storage model shapes everything downstream.
Data lakes keep raw, flexible data; data warehouses offer structured, analytics-ready storage; and lakehouses bridge both worlds by combining schema flexibility with high-performance querying.
3. Transformation Design Patterns
ETL and ELT define when and where transformations happen.
ETL transforms data before loading for strict governance, while ELT loads raw data first for faster, scalable cloud-based processing. Incremental processing focuses only on changed data to improve efficiency.
4. Orchestration & Workflow Patterns
Pipelines require coordination.
DAG-based workflows define execution order clearly, while event-driven patterns trigger pipelines based on system activity rather than schedules - improving responsiveness and decoupling systems.
5. Reliability & Fault-Tolerance Patterns
Failure is inevitable, so pipelines must be resilient.
Idempotent pipelines ensure repeated runs produce the same results, retry and dead-letter patterns detect or recover from failures, and backfill patterns safely reprocess historical data when needed.
6. Data Quality & Governance Patterns
Trustworthy pipelines depend on clean, governed data.
Validation enforces correctness, schema evolution handles safe structural changes, and lineage tracks how data flows - enabling debugging, compliance, and confident analytics.
7. Serving & Consumption Patterns
How data is exposed matters as much as how it's processed.
Semantic layers provide consistent business definitions, while API-based serving enables secure, controlled access for apps and downstream systems.
8. Performance & Scalability Patterns
Systems grow, and patterns keep them fast.
Partitioning improves query performance by slicing data, while caching accelerates repeated lookups and reduces compute cost.
9. Cost Optimization Patterns
Efficient systems balance performance with spend.
Tiered storage moves cold data to cheaper layers, and on-demand compute scales resources only when needed - reducing waste and controlling cost.
These patterns form the foundation of modern data platforms - helping engineers design pipelines that are scalable, reliable, and easy to evolve.
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The highest-leverage ones: approval chains that live in email, status updates that require chasing people, and any report that needs data from 3 different tools. Those aren't AI problems, they're workflow structure problems. Solve the structure first, then let the AI run on top of it.
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Looks like I’ll be onboarding an army of AI agents this weekend 🤝
Since I’m new to this, what are some of the best task / workflows to automate as an investor / entrepreneur/ shitpoaster?
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The 'AI control tower' framing is interesting but it reveals the moat risk: if your value is workflow orchestration, the platform that wins is the one where businesses can actually configure AND extend workflows themselves, not just consume pre-built ones. That's a very different architecture.
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$NOW CEO Bill McDermott:
"We’ll be a trillion-dollar company. It’s not a question of if, but when." 🤖🔥
ServiceNow is going all-in on AI agents, workflow automation, and becoming the "AI control tower" for enterprises.
Do you think ServiceNow can reach a $1T market cap? 👀
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The 100x demand thesis only holds if the underlying workflow layer is composable. Most enterprise software wasn't designed to be agent-addressable. The platforms that win will be the ones where business logic is already modular, data models are clean, and APIs are first-class, not retrofitted.
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AGENTIC AI COULD INCREASE ENTERPRISE SOFTWARE DEMAND 100x
Jensen Huang says enterprise software has always been “limited by butts in seats”.
Not anymore.
“It’s about to get 100x more agents banging on those tools.”
The time to be bullish software is here in my option.
I am ready for a potential golden age of software investing.
$NVDA $CRM $APP $TOST $MSFT $SHOP $WDAY $ADSK $IGV
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This is the most underrated take in the AI space right now. The teams getting real ROI from AI aren't starting with models, they're starting with clean, structured workflows and governed data. Then AI has something to work with. Platforms that force you to think in process before you automate tend to produce better outcomes for exactly this reason.
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What most people get wrong about AI in business:
They think AI is the solution. It's not. AI is the last step.
Step 1 is figuring out which problem actually costs money. Not which problem sounds interesting which one shows up on the P&L.
Step 2 is fixing the workflow around that problem. Most broken processes can be fixed with a spreadsheet and a Zapier flow. No AI needed.
Step 3 only after steps 1 and 2 is asking: "Would AI make this faster, cheaper, or more accurate?"
I've talked 8 clients out of building AI features this year.
In 6 of those cases, the solution was simpler than they expected. In 2 cases, AI was the right answer but only because we did steps 1 and 2 first.
AI on a broken process is like putting a turbocharger on a car with flat tires. Fix the tires first.
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Discover how @ZohoBooks and Zoho Creator work together to deliver powerful solutions for your accounting and operational needs.
Hosted by Jeevika Impact Solutions, a trusted @Zoho Partner, join our webinar to learn how to streamline and optimize your business operations effortlessly! Register now: zurl.co/bx7t4
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Build business apps using AI-powered low-code! Join this Zoho Creator live demo on May 21 to see prompts, Deluge code, and smart automations in action.
Register now!
EMEA: zurl.co/goAnt
APAC: zurl.co/NWf1n
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For @foothillsnurs, managing thousands of plant varieties, orders, and shipments is no longer a hassle. Watch how they built an order and inventory app on Zoho Creator to simplify operations and enable smoother fulfilment: zurl.co/FitHR
@bsppartner
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This ladder maps almost perfectly to how enterprise adoption actually plays out, except most orgs are stuck between rungs 1 and 2 because the workflow automation step has no clear owner. It's too technical for business users and too business-logic-heavy for IT. The orgs breaking through are the ones creating a middle layer: ops-minded builders with access to low-code tooling. What's your take on who in a company should own rung 2?
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The 4 Levels of AI Mastery in 60 seconds:
1. Prompting. Master role + task + output format on every chatbot. 95% of entrepreneurs are stuck here.
2. Workflow Automation. Connect apps with Zapier, Make or n8n to run routine tasks for you.
3. Vibe Coding. Build custom tools in plain English with Lovable, Replit, or Claude Code.
4. Autonomous Agents. A fleet of specialized AI employees doing the work for you. Less than 1% of people are here.
The gap between Level 1 and Level 3 is your competitive advantage.
15-20 minutes a day for 90 days gets you there.
Corey Ganim@coreyganim
95% of entrepreneurs are stuck at Level 1 of AI (basic prompting). The gap between Level 1 and Level 3 is your competitive advantage. The 90-day roadmap to close it (15 min/day): Month 1: Master prompting → Build a prompt library in Notion → Default to AI before Google Month 2: Automate one task per week → Zapier or Make → Start with 2-step automations (free in Zapier) → Trigger → Action Month 3: Vibe code one tool → Lovable, Replit, or Claude Code → Start with a simple landing page → Describe what you want in plain English You don't need to be a Level 4 expert. You just need to be one step ahead.
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The error message framing is a perfect litmus test for enterprise readiness. The non-technical user bar is brutal, and it's the exact bar that most AI-powered enterprise tools fail on silently. The builders who get this right are designing for the person who will never open a console, and that usually means rethinking the entire feedback loop, not just the UI. What's the #1 barrier you're seeing prevent non-technical teams from going beyond basic AI chat usage?
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Context management is the real skill tax of vibe coding that nobody budgets for. The repos that actually move the needle are the ones that give the model just-in-time context instead of front-loading everything. The builders who crack this are getting 10x throughput at a fraction of cost, it's basically the equivalent of optimizing your workflow before you automate it. Which of the 10 repos had the biggest single impact for you?
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I've been bleeding $200+/mo on Claude tokens just vibe coding.
This list of 10 GitHub repos is actually INSANE and cut my token usage by 80%.
If you're building without these installed you're literally just throwing money at Anthropic lol.
Ronin@DeRonin_
10 GitHub repos to spend 60-90% less tokens in Claude Code: 1. RTK (Rust Token Killer) CLI proxy that filters terminal output before it hits your context - 60-90% reduction on common dev commands - one binary, zero dependencies - works with Claude Code, Cursor, Copilot Repo: github.com/rtk-ai/rtk 2. Context Mode Sandboxes raw tool output into SQLite instead of dumping it into context - 98% context reduction on Playwright, GitHub, logs - only clean summaries enter your conversation - works as Claude Code plugin Repo: github.com/mksglu/context… 3. code-review-graph Local knowledge graph that maps your codebase with Tree-sitter - Claude reads only what matters, not the entire repo - 49x token reduction on large monorepos - 6.8x on average reviews Repo: github.com/tirth8205/code… 4. Token Savior MCP server that navigates code by symbols, not full files - 97% reduction on code navigation - persistent memory across sessions - 69 tools, zero external deps Repo: github.com/Mibayy/token-s… 5. Caveman Claude makes Claude talk like a caveman to cut output tokens - 65-75% output reduction - one-line install - keeps full technical accuracy Repo: github.com/JuliusBrussee/… 6. claude-token-efficient one CLAUDE.md file that keeps responses terse - drop-in, no code changes - reduces output verbosity on heavy workflows - best for output-heavy sessions Repo: github.com/drona23/claude… 7. token-optimizer-mcp MCP server with caching, compression, and smart tool intelligence - 95%+ token reduction through intelligent caching - compresses repeated tool outputs Repo: github.com/ooples/token-o… 8. claude-token-optimizer reusable setup prompts for optimizing any project - 90% token savings in 5 minutes - reduces doc token usage from 11K to 1.3K Repo: github.com/nadimtuhin/cla… 9. token-optimizer finds ghost tokens that silently eat your context - survives compaction without losing quality - fixes context quality decay Repo: github.com/alexgreensh/to… 10. claude-context (by Zilliz) code search MCP that makes your entire codebase the context - ~40% reduction with equivalent retrieval quality - hybrid BM25 + dense vector search Repo: github.com/zilliztech/cla… [ how to stack them ]: you don't need all 10. pick 2-3 based on your workflow: > heavy terminal output? RTK > big codebase? code-review-graph + Token Savior > lots of MCP servers? Context Mode > quick fix? Caveman + claude-token-efficient most people are burning tokens without knowing it run /context in a fresh session and see how much is gone before you even type a word your pocket will thank me later :<)
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🇺🇸 Zoholics 2026 in Houston is in full swing! Day 1 brought even more energy, with sessions, showcases, and conversations that kept the momentum going. See you all tomorrow for one last day!
@Zoholics
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The framing of 'the hard-coding trap' is going to resonate with every IT director who's watched a 6-month custom build get abandoned 3 months after launch. The enterprise story isn't low-code vs. dev anymore, it's about which layer of the stack earns the right to be custom. Would love to hear where the episode lands on governance and IT oversight of low-code at scale.
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📣 New Podcast! "The Hard-Coding Trap: Why Low-Code Is the New Enterprise Standard" on @Spreaker #agility #ai #apps #architecture #automation spreaker.com/episode/the-ha…
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This is the real issue nobody talks about in the vibe coding narrative, the accessibility of building has outpaced the accessibility of product thinking . You can ship in a weekend, but you can't vibe-code good problem definition. The builders who last are the ones combining that speed with real workflow understanding from the domain they're solving for. What failure mode are you seeing most often?
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