DEV Community: shrey vijayvargiya

AI Operating System for SaaS Founders: The Skills, the Stack, and the Dashboard Behind It

shrey vijayvargiya — Tue, 15 Sep 2026 17:10:41 +0000

Most "AI for founders" content stops at the prompt. Someone shares a clever prompt for competitor research, you copy it, it works once, and three weeks later you're rewriting the whole thing from memory because you never saved the structure.

This guide is the opposite of that. It's the full system we actually run — end to end — across our own SaaS properties: the structured playbooks that replaced ad-hoc prompting, and the engineering stack underneath them that turns those playbooks into scheduled, logged, cost-tracked work instead of a chat window you have to babysit every morning.

We're publishing the whole thing in one place, broken into categories, because the two halves only make sense together. A good playbook with no system to run it on is still manual labor. A good dashboard with no playbook behind it is just an expensive way to watch nothing happen.

What you'll get in this guide:

Why ad-hoc AI prompting quietly breaks down for founders
The operating system — structured playbooks for Validate, Build, Launch, Grow
The engine room — the actual stack that runs those playbooks on a schedule
The workspace — how we use a CRM when AI is doing the research
How we build our CRM and connect it to AI workflows
How we built an automated Reddit trend research workflow
How internal linking connects the whole content system
Proof this isn't theoretical — where it runs today
The free resource — get the system map sent to your inbox

1. Why we stopped relying on individual AI prompts

The leverage story is real. Founders using AI tools day to day are genuinely cutting research time and shrinking MVP timelines, and that's a big part of why solo operators can now run products that would have needed a small team a few years ago, as The Entrepreneur Studio's breakdown of solo founder productivity in 2026 lays out.

The part that doesn't get talked about enough is what happens after the honeymoon. Salesfully's look at enterprise AI rollouts found that teams relying on unstructured, one-off prompts run into meaningfully higher error and hallucination rates on anything with real complexity, compared to teams working from a grounded, repeatable process. For a founder, that shows up as an SEO audit that misses your actual site structure, a competitor breakdown that invents a feature your rival doesn't have, or a content calendar that reads like it was written for someone else's product.

The underlying reason is structural, not a prompting-skill problem. As one workflow-design writeup put it, a prompt is a one-off interaction, while a system is repeatable leverage — and repeated manual prompting quietly creates its own cognitive debt. Every time you re-explain a task because you didn't save the version that worked, you pay a small tax. Do that across ten recurring founder workflows for a year and the tax is real hours you don't get back.

We hit this ourselves before we thought about packaging anything. Running several SaaS properties at once means a constant stream of blog content, SEO audits, competitor checks, and lead research, and re-explaining "research this competitor" or "find SEO gaps" from scratch every time produced wildly inconsistent output. Some weeks it was excellent. Other weeks it drifted, because the instructions weren't consistent — the model hadn't changed, the process had.

So we split the fix into two layers, because they solve two different problems:

What to ask for — a structured set of playbooks, organized around how founders actually work
How it runs — the engineering underneath that turns those playbooks into scheduled, logged, observable jobs instead of something you trigger by hand every morning

That distinction ended up being important for us. We didn't need another collection of prompts. We needed a way to turn a task we repeat every week into something closer to a small internal product.

The rest of this guide walks through both.

2. The playbooks we use across the founder lifecycle

Every SaaS founder ends up doing roughly the same categories of work on a loop, just with a different product name swapped in each time:

Validate — understanding a market, a competitor, or an idea before committing real time to it
Build — scoping an MVP, pricing it, deciding what actually belongs in v1
Launch — planning positioning, channels, and the first wave of distribution
Grow — SEO, content gaps, internal linking, AI-search visibility, lead research, ongoing audits

Once it was obvious we were solving the same problem in each category over and over, the fix was to stop treating every task as a fresh conversation and instead build one structured workflow per task that could be reused across every property, every time, with consistent quality.

This is also why the broader shift in AI tooling this year matters: the industry is moving away from treating a single strong prompt as the finished product, and toward treating prompts as one input inside a larger workflow system with defined stages and checkpoints. Structuring the work around the actual founder lifecycle — rather than a flat pile of disconnected templates — made it something you could pick up at whatever stage you're actually in.

We're deliberate about keeping this curated rather than exhaustive. There are plenty of open collections of founder-facing AI workflows floating around GitHub and marketplaces, some genuinely good, most uneven, and most requiring you to sift through dozens of files to find the handful that apply to your actual stage. As generative AI keeps moving from novelty to baseline infrastructure, the value of raw, ungrounded prompting keeps dropping — the money isn't in the prompt text anymore, it's in the structure, the sequencing, and the judgment calls about what a founder actually needs at each stage, made once so you don't have to remake them every week.

A system you've committed to — even in a small way — also tends to be the one you actually keep using, versus a free download that joins twelve other free downloads you never opened. The founders behind Ship or Die describe a version of this in their own solo-founder playbook: a small, public commitment is often the thing that keeps a solo operator from stalling out.

If you want the packaged, ready-to-run version of this layer, it's what we sell as SaaSFounderOS — you can see the current breakdown by stage there, or grab it directly on Gumroad.

3. The stack behind the workflows

A playbook is only useful if something runs it without you opening a chat window every morning. This is the part most "AI for founders" content skips entirely, so we're covering it in more depth.

3.1 What is actually running

The stack is intentionally boring:

A small backend — a lightweight framework is enough; a full microservice architecture is not needed at this stage
The Vercel AI SDK — handles model calls, streaming, structured generation, and tool calling behind one consistent interface
OpenRouter — one integration point for multiple model providers, instead of maintaining separate SDKs for every model you want to try
A database — every job writes its output, status, model used, and token usage
Cron jobs — scheduled research and content tasks, not manual triggers
A dashboard — the human-facing layer where you see what happened and what it cost
Search and fetch tools — used only by the specific tasks that genuinely need fresh external information

That's enough for a surprisingly capable AI content and research operation. You don't need a vector database, a message queue, or a dozen microservices on day one, and you definitely don't need a "multi-agent swarm" because your blog needs three new topic ideas. Start with plain functions. Turn the ones that keep working into reusable, named workflows later.

3.2 How the pieces connect

At a high level, a content task flows through research (web, competitor sites, your own site data), into a model call routed through OpenRouter, into a structured output, into a database row, and finally into a dashboard where a human reviews it.

That's the whole loop — one application, not a distributed system, and you can split pieces out later only if you have an actual reason to.

The mental model worth keeping is:

Your application → AI SDK → OpenRouter → selected model → structured output → database → dashboard.

Your application never needs to know how every individual provider's SDK works. If a model gets expensive for one task, or a new one performs better at classification, you change a configuration value rather than rebuilding the application.

That separation became particularly useful once we started running different workflows. A content research job, a lead research job and a simple classification job don't need identical models, identical prompts or identical context.

They just need to return predictable output to the same system.

3.3 We don't use the same model for everything

The mistake most people make here is asking "which AI model is best?" The better question is: which is the cheapest model that reliably completes this specific job?

Split work into roles rather than picking one model for everything:

High-judgment work — writing, strategy, nuanced editing, final recommendations — justifies a stronger, more expensive model
High-volume work — classification, tagging, summarization, duplicate detection, simple scoring — almost never needs your most capable model
Fresh research — the model is only as good as what it's fed; a cheap model with good source data will outperform an expensive model working from thin context every time

Rough current pricing (this changes constantly, so treat it as a snapshot to sanity-check against, not something to build a budget on directly):

Provider Tier	Model	Input $/1M tokens	Output $/1M tokens	Best for
Anthropic	Claude Opus 5	~$5.00	~$25.00	Judgment-heavy work
Anthropic	Claude Sonnet 5	~$3.00	~$15.00	Default drafting, SEO checks
Anthropic	Claude Haiku 4.5	~$1.00	~$5.00	Classification, tagging
OpenAI	Mid-tier flagship	~$2.00	~$12.00	General writing tasks
OpenAI	Budget tier	~$0.20	~$1.20	Bulk, low-stakes tasks
DeepSeek	Flash tier	~$0.15–0.30	~$0.60–1.20	High-volume, cost-sensitive jobs

Route by task, not by habit — a writing task gets your stronger model, a classification task gets your cheapest reliable one, and a research task gets whatever model performs best once it has good source data underneath it.

Making this a small config object rather than hardcoded model names means swapping providers later is a one-line change, not a rewrite.

3.4 We log every AI job

This is the part worth doing from day one, before the dashboard even looks good.

Every AI job should write a row recording:

task name
workflow
model
input tokens
output tokens
cost
duration
status
sources used
output
errors, if any

Once that exists, "how much did AI actually cost us this month?" stops being a guess and becomes a number you can pull up.

A simple dashboard on top of that log is already useful:

today's tasks
today's spend
monthly spend
successful jobs
failed jobs
model usage
workflow usage
cost per workflow
click-through into the actual report

You don't need it to be beautiful.

You need it to be honest.

3.5 AI finishing a task doesn't mean the task is correct

None of this makes AI correct — it makes AI observable.

A task finishing with a "success" status means the function completed, not that the output was right.

For anything that touches SEO, customer communication, financial information, or anything that could damage your reputation if it's wrong, keep a human review step between the model's output and publishing.

Observability is what lets you catch drift early; it's not a substitute for review on the things that matter.

4. The CRM is where the AI work becomes useful

Once your playbooks are running as scheduled jobs, they start producing business objects, not just documents.

A research workflow might discover:

a potential customer
a competitor
a pricing change
a new content opportunity
a Reddit discussion
a sales lead
a product request
an SEO opportunity

If all of that ends up as Markdown files or rows in random spreadsheets, you've automated the research but not really automated the business.

This is where a CRM becomes useful.

We've built SaaSCRM around this idea: the CRM shouldn't just be a place where a founder manually types leads into tables. It should be a workspace that both the founder and AI workflows can write into.

A research agent can find a potential lead.

A lead-research workflow can enrich it.

A sales workflow can assign a status.

A content workflow can create a follow-up task.

And the founder can see the whole thing from one dashboard.

That changes the role of the CRM from:

"software I use to maintain customer records"

to:

"the structured business workspace my automations can operate inside."

5. How we actually build a CRM for a business

One thing we learned while building SaaSCRM is that most CRM projects become unnecessarily complicated because they start with screens instead of the business model.

Someone says:

"I need a CRM."

Then the project immediately becomes:

contacts
companies
deals
pipelines
tasks
calendars
dashboards
reports
permissions
notifications

Six weeks later there are 50 screens, but nobody has answered the most important question:

What does this particular business actually need to track?

We prefer starting with the entities.

For a software company, that might be:

Lead
Company
Contact
Deal
Task
Activity

For a manufacturing company it could instead be:

Customer
Enquiry
Product
Quotation
Order
Delivery
Follow-up

For an agency:

Lead
Client
Project
Proposal
Invoice
Task
Campaign

The database should follow the business rather than forcing every business into the same CRM vocabulary.

That's why the way we approach SaaSCRM is closer to a reusable CRM foundation than a single fixed CRM.

We build the common pieces once — authentication, database access, layouts, tables, forms, dashboards, filtering, search, charts, permissions and reusable UI components — and then shape the actual CRM around the business entities.

Start with the data model

Before building screens, we define:

What entities exist?
What fields do they have?
How are they related?
What actions can users perform?
What should AI be allowed to create or update?

For example:

Lead
 ├── name
 ├── email
 ├── company
 ├── source
 ├── status
 ├── notes
 └── assignedTo

Then:

Lead
  ↓
Contact
  ↓
Opportunity
  ↓
Deal
  ↓
Customer

The UI is built around this model.

This also makes it much easier to connect AI later because the agent knows what a "lead" actually means and what fields are allowed.

Then build the reusable UI

Instead of making every table from scratch, we keep reusable components for:

data tables
filters
search
forms
modals
detail pages
status badges
dashboards
charts
activity timelines
empty states
loading states

This is one of the reasons a CRM foundation becomes valuable.

When a new business needs a different entity, we're mostly configuring the existing building blocks rather than rebuilding the application.

Then connect the AI layer

This is where it gets interesting.

Suppose an AI agent researches 50 companies.

Instead of returning:

Here are 50 companies I found...

the workflow can produce structured records:

Company
Contact
Website
Industry
Potential use case
Source
Confidence
Next action

Those records can go directly into the CRM.

The same idea works for content research.

An AI workflow finds a Reddit discussion that suggests a common problem.

Instead of leaving that insight inside a chat conversation, we can create:

Research Item
Topic
Source
Problem
Potential Feature
Priority
Status

Now the research becomes something the rest of the business can act on.

6. How we built the Reddit trend research workflow

One of the practical examples of this approach is our automated Reddit trend research workflow:

AI-Automated Reddit Trend Scraper That Saved Us $1,000/Month on Content Creation.

We wanted to know what developers and founders were actually discussing instead of constantly guessing what we should write about.

The first version was simple:

Find discussions → collect them → clean the data → identify patterns → turn patterns into content opportunities.

The workflow eventually became a small pipeline.

Step 1: Collect discussions

The scraper collects relevant Reddit discussions based on the topics we're researching.

We're not interested in collecting Reddit for the sake of collecting Reddit.

We're looking for signals:

repeated questions
complaints
product requests
recurring problems
tools people are comparing
things developers can't figure out
emerging topics
unusual spikes in discussion

Step 2: Clean and enrich the data

Raw scraped discussions aren't particularly useful.

There can be duplicate conversations, irrelevant comments, short answers and discussions that have nothing to do with our actual content strategy.

So the workflow cleans and structures the data before sending it into the model.

We can then attach things like:

Topic
Problem
Audience
Industry
Intent
Potential article
Potential product
Priority
Source URL

Step 3: Let AI find patterns

This is where the LLM becomes useful.

Instead of asking:

"What should I write about?"

we give it actual discussions and ask it to identify recurring patterns.

For example:

20 discussions
      ↓
common problem
      ↓
search intent
      ↓
content opportunity
      ↓
potential product/feature

The important part is that the model isn't inventing the initial demand.

We're giving it real conversations to analyze.

Step 4: Turn research into content

The strongest findings can become:

blog posts
comparison articles
tutorials
tools
lead magnets
product features
FAQ sections
social posts

That means one research workflow can feed multiple parts of the business.

Step 5: Connect it back to the rest of the system

This is the part that makes the workflow more useful than a standalone scraper.

The research output can feed our content planning system.

The content planner can identify:

Existing article?
        ↓
Update it

or:

No existing article?
        ↓
Create new article

or:

Product problem?
        ↓
Create feature opportunity

This is where the AI operating system starts becoming a loop rather than a collection of unrelated AI tools.

7. Internal linking is part of the system too

Creating a new article isn't the end of the job.

One of the easiest things to miss when publishing frequently is internal linking.

If you publish 100 articles and none of them are connected properly, you have 100 pages.

You don't necessarily have a useful content system.

We treat every new article as another node in the existing knowledge graph.

For example, the Reddit automation article can connect to content about:

AI content research
Reddit scraping
AI agents
web scraping
OpenRouter
Hono
SEO automation
content planning

Then a new article about AI content research can link back to the Reddit automation article.

Over time this creates a structure like:

                 AI Content
                     │
          ┌──────────┼──────────┐
          ↓          ↓          ↓
      Reddit       SEO        AI Agents
      Research     Tools       │
          │          │          │
          ↓          ↓          ↓
       Scraper    Content     Automation
          │        Planning      │
          └─────────┴────────────┘
                    ↓
                SaaSCRM

The goal isn't to add links just because an SEO checklist says "add internal links."

The goal is to make related information discoverable.

How we decide where a new article should link

When a new article is created, we can look at the existing content inventory and ask:

What existing articles explain concepts mentioned here?

What older articles should link to this new article?

Which product pages are relevant?

Which pillar/topic page should this article belong to?

Which pages are currently orphaned?

This turns internal linking into another repeatable workflow.

Instead of manually remembering every article we've published, the system can use the content database to find relevant connections.

It also helps us update old content

Suppose we publish a new article about AI Reddit research.

We can find older articles mentioning:

Reddit
content research
AI agents
scraping
SEO
content automation

Those pages may now have a natural reason to link to the new article.

So one new article can improve the structure of several older pages.

That's much more useful than publishing and forgetting.

8. The dashboard ties the whole thing together

At this point there are several different things happening:

Research
SEO
Content
Leads
AI jobs
CRM
Costs
Internal links

Without a dashboard, it becomes difficult to know what is actually happening.

The dashboard doesn't need to become another complicated SaaS product.

For us, the useful information is fairly simple:

AI activity

Jobs today
Successful
Failed
Running

AI spend

Today
This week
This month
By model
By workflow

Content

Ideas
Research completed
Articles drafted
Articles published
Articles needing updates

SEO

Pages gaining impressions
Pages losing impressions
Keyword opportunities
Content gaps
Internal linking opportunities

CRM

New leads
Open opportunities
Follow-ups
AI-created records
Recent activity

The dashboard is essentially the control panel for the system.

The individual workflows do the work.

The dashboard tells us what happened.

9. Where we run this today

None of this is a thought experiment — it's the system running across our own properties:

ihatereading.in — our primary distribution property, grown to roughly 10,000 registered users, with the content and SEO layer of this system driving its blog output and directory listings
saascrm.site — the CRM and admin layer described above, doubling as its own product and as the workspace for agent output across our other properties
gettemplate.app — our template marketplace, using the same content and SEO playbooks to plan and publish
buildsaas.dev — the underlying application boilerplate several of these properties are built on, including the frontend, backend, and admin panel pieces the dashboard sits on top of

Running the same system across four different properties is exactly why it had to be structured rather than ad-hoc in the first place.

A prompt you remember works on one site.

A logged, scheduled, cost-tracked workflow works on all of them without you personally re-explaining the task four times.

10. The system is more useful when everything feeds the next step

The biggest change wasn't actually adding more AI.

It was connecting the outputs.

A simplified version looks like this:

Reddit
  ↓
Research
  ↓
AI analysis
  ↓
Content opportunity
  ↓
Keyword / search intent
  ↓
Article
  ↓
Internal linking
  ↓
SEO data
  ↓
New research
  ↓
Next article

And alongside that:

Research
  ↓
Lead / Product opportunity
  ↓
CRM
  ↓
Task
  ↓
Founder action

And underneath everything:

AI workflow
  ↓
Model
  ↓
Tokens
  ↓
Cost
  ↓
Database
  ↓
Dashboard

That's the operating system we're interested in.

Not an AI that magically runs a company.

A collection of small, understandable workflows that continuously produce useful information and put it somewhere the next workflow — or the founder — can actually use.

11. The System map

We put together a condensed, practical version of everything above — the four-stage playbook map (Validate, Build, Launch, Grow), the task-logging schema you can copy directly into your own database, and the current model-routing cheat sheet — as a single free download.

check more on saasfounderos

12. What happens after you grab it

The free resource gets you the map.

If you want the actual ready-to-run playbooks — the full set covering research, SEO and AI-search, and growth and launch, organized the way this guide describes — that's SaaSFounderOS on Gumroad.

If you're at the point where your playbooks need a real workspace to write into, saascrm.site is the CRM layer built for exactly that.

The important part isn't whether you use our exact stack.

You can replace OpenRouter.

You can use another database.

You can use a different CRM.

You can run the workflows manually.

The principle stays the same:

Take recurring founder work, turn it into a structured workflow, give that workflow a place to save its output, and make the result available for the next decision.

That's what makes the difference between using AI occasionally and actually building an AI operating system around your business.

And once you've done that for one workflow, you can keep adding the next one.

Either way, the point of publishing the whole system in one place — playbooks and stack together — is the same point behind building it in the first place: stop rebuilding the same instructions every week, and start running a system that remembers what worked.

That would be enough for today

See you in the next one

Cheers

Shrey

I Gave Claude 10+ SaaS Growth Skills and Let It Run My Own Platform

shrey vijayvargiya — Wed, 09 Sep 2026 10:54:27 +0000

Run an entire online content website using 10 Claude Skills, originally published on iHateReading

Under the Hood

I run iHateReading, saascrm.site, and a handful of other products alone. No marketing hire, no SEO agency, no growth team.

For years, that meant every "should we submit to this directory" or "is our GEO okay" question sat in a notes app, half-answered, because I didn't have a spare afternoon to research it properly.

So I built 15 Claude Skills — one operator hub plus 14 specialized agents — that do the SaaS growth research work I used to put off: market intel, competitor teardown, pricing analysis, SEO/GEO audits, directory submissions, internal linking, lead research, launch planning. Then I pointed the whole pack at my own site, ihatereading.in, and let it run.

This post is the receipts: what the skills actually found, what they got right, and where the pack is available if you want to run the same stack on your own product.

What a "Claude Skill" actually is

A Claude Skill is a markdown instruction file — usually named SKILL.md — that gives Claude (or Claude Code) a specialised, repeatable playbook instead of a one-off prompt. You invoke it, Claude follows the same research method every time, and you get a consistent output format instead of reinventing the wheel each session. Bootstrapped founder Violetta Bonenkamp put it plainly in her own breakdown of running an SEO operation through Claude Skills: the whole approach is built around giving the model a pre-loaded, specialised playbook rather than asking it to improvise research from scratch every time.

This isn't a niche hobby anymore. Snyk's own roundup of Claude Skills for entrepreneurs and solopreneurs covers everything from SaaS financial modelling to full open-source marketing-department skill collections — the pattern is the same across all of them: turn a research or ops task you'd normally outsource into a repeatable skill you run yourself.

Why solo founders specifically need this right now

The one-person startup stopped being a compromise. Data cited in MeanCEO's breakdown of the solo-founder AI agent stack shows over a third of new ventures in 2026 are solo-founded, and a functional AI agent stack now runs a few hundred dollars a month against the equivalent five- or six-figure monthly cost of hiring humans for the same functions. Forbes reported similar numbers this year, pointing to full AI stacks costing $3k-$12k annually against $80k-$120k a month for a comparable human hire.

That gap is exactly where a growth-ops skill pack earns its keep. You're not trying to replace a strategist — you're replacing the research grunt work that used to stop you from ever getting to the strategy conversation in the first place.

What's actually inside the pack

The 15 skills are split into three groups:

🔎 VALIDATE — is there a market for this

SaaS Discovery — understand a product, its audience, positioning, features, pricing, competitors
Market Intelligence — market/category trends, demand, opportunities, risks
Competitor Intelligence — competitor positioning, strengths, weaknesses, sentiment
Pricing Intelligence — pricing, packaging, value metrics, positioning

🛠️ BUILD — turn the idea into a concrete plan

Micro SaaS Builder — validated MVP concept, pricing, distribution, launch plan6. AI Agent Opportunity — evaluate workflows worth automating, demand, competition, monetization7. Lead Research Intelligence — ICP, lead sources, directories, communities, partnerships8. SaaS SEO Discovery — baseline SEO opportunities from your product and site before you launch

🚀 LAUNCH — get your first users

SaaS Launch — launch strategy: positioning, channels, assets, timeline10. Directory Launch — directory/community submission plan

📈 GROW — compound growth once you have users

Find the opportunity:

SEO Opportunity — commercial, use-case, comparison, alternative, informational opportunities
Competitor SEO Pages — competitor-driven pages you should create
Content Gap — content competitors have that you're missing
GEO/AEO Audit — AI search / answer-engine visibility audit

Plan & execute:

SEO Content Planner — clusters, priorities, briefs, internal links, publishing plan
SEO Page Generator — complete SEO pages from validated opportunities
Internal Link Optimiser — weak, missing, orphaned, high-value linking opportunities

One hub skill (/founder-saas-skill or /saasfounderos) sits on top, asks two quick questions — what you have, what you want this week — and routes to the right agent instead of dumping all 15 on you at once.

What happened when I ran it on my own site

I didn't just build this and ship it blind. I ran the GEO/AEO Audit skill on ihatereading.in itself, and it caught something I'd genuinely missed: a near-identical, completely unrelated site (an AI reading-summarizer) sits at a lookalike domain, which creates real entity-confusion risk for any AI system trying to answer "what is iHateReading." That's exactly the kind of gap Mintlify's own research into llms.txt and AI citation behavior points to — Vercel reportedly gets 10% of signups directly from ChatGPT citations, which only happens when the entity is unambiguous to begin with.

The SEO Content Planner skill then turned that finding into an actual publishing plan — five pages, in priority order, starting with the cheapest fix (a plain-language FAQ page) instead of a bloated 30-page content calendar nobody would ship. The Directory Launch skill found one specific, verifiably free, dofollow-linked developer directory worth submitting to instead of a copy-pasted list of 200 sites. And the Internal Link Optimizer skill mapped exactly which existing posts — like the one on building a directory listing site that earns online — were sitting orphaned with no path back to my own SaaS directories page.

None of that came from generic prompting. It came from agents built specifically to research a real business and refuse to invent URLs, traffic numbers, or fake authority scores along the way — the same discipline SE Ranking's own Claude Skills collection builds in by tying every skill to a defined, reproducible tool call instead of letting the model improvise.

How to actually use these well

Run one agent per session. The hub skill is built to stop you from dumping all 15 on a single question — pick the one that matches what you need this week.
Feed it a real URL, not a vague idea. Every agent researches your actual site/product before making recommendations; the more concrete the input, the less it has to guess.
Treat the output as a starting draft, not gospel. These skills are built to flag "unknown" instead of inventing a number — when they say a stat is unverified, believe them and check yourself before publishing it.
Chain agents in sequence when the task calls for it. SEO Opportunity → GEO/AEO Audit → SEO Content Planner → SEO Page Generator is a full pipeline, not four disconnected reports.
Use the smallest output that solves the actual problem. The Directory Launch skill can find one directory just as easily as fifty — ask for exactly what you need.

Get the pack

If you want the same 15-agent hub running on your own product — the exact GEO audit, content plan, directory pick, and internal-link fixes shown above — grab the founder Claude SEO & growth skills pack on Gumroad. It installs the same way described above: drop it into Claude Code, Claude Desktop, or upload the individual skill zips directly into a Claude.ai project.

I'll keep publishing what the pack finds when I run it on my other properties — subscribe to the iHateReading newsletter or catch the monthly rundown in the iHateReading Magazine if you want the next audit as it happens.

That would be enough for today

See you in the next one

Shrey

GPT-6 Astra Just Shipped. The Real Opportunity Isn't the Model — It's Everything Around It

shrey vijayvargiya — Mon, 07 Sep 2026 13:34:59 +0000

Originally published on iHateReading

Under the Hood

OpenAI shipped GPT-6 Astra this week, and the framing was different from every GPT launch before it. This wasn't pitched as a better chatbot. It was pitched as a system that operates your computer — browsers, spreadsheets, terminals, IDEs — the way a person would, and finishes multi-step jobs instead of just describing them.

I've been building on top of models for a while now — Aantraa runs on translation and generation pipelines, the Scrapefast work is agent-adjacent, and half of what I ship these days involves some LLM doing a job a person used to do. So when a launch like this lands, the question I actually care about isn't "is the model good." It's "what does this make buildable that wasn't buildable last month."

What OpenAI actually shipped

Astra is positioned as state-of-the-art on computer use, browser use, software engineering, cybersecurity, and science — not as a chat model with better answers. OpenAI says it completes an OSWorld 2.0 computer-use evaluation in roughly 40 minutes per task in its own simulations, against about 75 minutes for the prior GPT-5.6 Sol model, while scoring higher overall, and that Codex task completion is roughly 1.9x faster on the Mind2Web benchmark thanks to a combined model-plus-harness upgrade.

A few specifics matter more than the headline claims:

1.05M-token context window, with the harness keeping notes across sessions instead of compacting/summarizing old context away — meaning an agent can go back and check why an earlier fix failed instead of losing that detail.
Tiered rollout: enterprise and "Trusted Access" organizations first, then ChatGPT Plus/Pro/Business/Enterprise, then the API and cloud partners — because the more capable computer-use gets, the more OpenAI is gating it behind access controls rather than shipping it to everyone at once.
It's closed and hosted only. No weights, no self-hosting. You rent the capability; you don't own the execution environment.
Pricing moved with capability — around $10/$50 per million input/output tokens, roughly 2.5x the prior model, on the bet that better reasoning means fewer wasted tool calls and retries, not just a smarter chat window.

That gating detail is the tell. A chat model doesn't need a trusted-access program. A model that can click through your banking dashboard, push code, and chain actions across tools does — and OpenAI apparently agreed with that logic enough to slow the launch over cyber-risk review before shipping it.

What builders are already showing off

The benchmark numbers are one thing; the last few days of demos on X are a better read on what "computer use" actually means in practice right now:

One builder asked Astra to help him understand his own ankle pain and got back a full interactive 3D atlas — bones, ligaments, tendons, with sliders for actual motion axes — from a single prompt, no separate 3D tooling involved.
Developer Ashe went further and had Astra explode a full male anatomy model into 2,234 individually toggleable pieces in one interactive 3D web build, following an earlier viral demo that did the same to a Tesla.
Designer Marvin Schwaibold's read after a few days in: Astra is genuinely good at generating UI, not just wiring up backend logic.
Yatharth Gupta paired Astra with a video-gen tool and turned out launch-ready marketing video content in about five minutes — a workflow that used to mean a separate editing pass entirely.
And builders are still posting fresh interactive demos most days this week, which is the more telling signal than any single one of them: the interesting output right now isn't a benchmark score, it's how fast "describe it, get a working interactive build" turns into a default workflow.

The common thread across all of these: none of them are chat transcripts. They're interfaces, simulations, and finished assets, built by letting the model actually operate software rather than describe what to do next — which is the whole thesis of this piece.

Why it matters more than another "smartest model yet" post

Model capability has been improving on a fairly predictable curve for two years. What's not mature is everything that sits around the model once you let it act instead of just answer:

Who approved this action before it ran
What permissions did the agent actually have when it ran it
What's the audit trail if something breaks
Can you roll it back cleanly if the agent did the wrong thing at 2am

This isn't hypothetical risk-modeling. Security researchers have already documented an OpenAI evaluation agent reaching Hugging Face's production infrastructure through a sandbox that still had open network access and a long-lived shared credential — exactly the kind of gap that "the agent can act on its own" creates once the agent is good enough to actually act. The lesson wasn't "don't use agents." It was that isolation, scoped credentials, and egress allowlists aren't optional add-ons — they're the actual product surface now.

That's the shift worth paying attention to as a builder: the scarce resource stopped being model access and started being trustworthy execution. Anyone can call an API. Fewer people are building the permission layer, the sandbox, the approval gate, and the rollback path around it — and that's exactly where a smaller team can compete against a foundation lab, because it's an infrastructure and product-design problem, not a training-compute problem.

What's actually buildable around this right now

I'd split the opportunity into a few concrete lanes, roughly in order of how fast a solo builder or small team could ship something real:

Approval-gate middleware for a specific tool category.
Not "governance for AI agents" broadly — that's already crowded by platforms like E2B, Modal, and Cosmonic at the infrastructure layer. Pick one narrow, high-stakes action category (agent-initiated payments, agent-initiated deploys, agent-initiated customer-data edits) and build the human-in-the-loop approval step for it. Thin wrapper, opinionated defaults, five-minute setup. That's a sellable SaaS, not a platform play.
Observability for agent runs, not just LLM calls.
Most "AI observability" tools trace prompts and tokens. What teams actually need once agents start touching production is a trace of actions: which files changed, which API calls fired, which were blocked, in what order, tied to which task. If you've built anything with scraping or automation pipelines before, this is a smaller lift than it sounds — it's structured logging with a good UI on top, aimed at a real pain point.
Rollback-as-a-feature for agent-shipped code.
Coding agents will keep shipping subtly wrong changes — that's the consensus among teams running Claude Code, Cursor, Codex, and Devin in production right now, and their fix is CI gates, canary deploys, and one-click rollback, not slowing the agent down. A tool that makes "undo everything this agent touched in the last run" trivial for teams that don't have a mature CI/CD setup yet is a real gap, especially for smaller shops that adopted an agent before they adopted the guardrails.
A vertical computer-use agent for one repetitive workflow.
Astra and its Codex harness are general-purpose. General-purpose tools leave room for a narrow, well-scoped agent that does one job — filling out a specific type of form, reconciling one kind of spreadsheet, running one recurring research task — reliably enough that a non-technical buyer trusts it. That's a positioning move more than a technical one, and it's the same instinct that's worked for narrow SaaS templates and directory products before: don't out-model OpenAI, out-scope them.

The part worth being honest about

None of this works if the underlying agent is unreliable, and computer-use agents still aren't reliable enough to run unsupervised on anything that matters. The teams getting real productivity out of coding and computer-use agents right now are the ones that invested in review discipline, not the ones that just pointed an agent at production and walked away. If you're building the permission/observability/rollback layer, that's your actual pitch to a buyer: not "let the agent do more," but "let your team trust the agent enough to let it do more."

That's also the honest read on why access to Astra's more capable modes is staged and gated rather than switched on for everyone at once — the lab building the model is making the same bet you'd be making as a builder around it: the model getting smarter doesn't remove the need for the scaffolding, it raises the stakes on not having it.

Where to go from here

If you're evaluating which coding agent to actually build with today rather than which one made headlines, it's worth reading through a proper current comparison of AI coding agents before picking a stack — the "best" one depends entirely on whether your team reviews synchronously or lets agents run async. And if the sandboxing/permissions side of this is new territory, I'd start with what's already been written on why AI agents need a sandbox in the first place — it's not a new problem, Astra just raised the stakes on solving it.

If you're building in this space and want more of this kind of breakdown as it happens, the monthly digest rounds up what's shipping across AI tooling, and the SaaS directories are worth a look once you've got something worth listing.

And that's a wrap for today

See you in the next one

Cheers

Shrey

Agency CRM: How to Manage Leads, Clients, Projects and Invoices in One System

shrey vijayvargiya — Sat, 05 Sep 2026 10:27:45 +0000

Agency CRM is software that runs the full agency loop in one place: a new lead becomes a proposal, the proposal becomes a client, the client creates a project, the project creates deliverables, the deliverables create invoices, and the invoice creates revenue.

Most CRM for agencies products still stop at contacts. SaaSCRM treats agency client management software as a business operating system — sales, delivery, and billing on connected records, with an agentic chatbot you can actually ask questions.

A current industry dataset estimates roughly 2.9K monthly searches for “agency CRM.” The search is not for another contact list. It is for a system that matches how studios, boutiques, and digital agencies already work.

Key takeaways

An agency CRM must connect leads → pipeline → clients → projects → tasks → invoices → payments → activity.
Spreadsheets fail after ~10–30 clients because five people edit the same file and follow-ups live in Slack or WhatsApp.
SaaSCRM already ships this shape: Agency CRM template, live demo, and custom builds from $2,000.
Path: free CRM template → SaaSCRM template → custom CRM build.

What is an agency CRM?

An agency CRM is a customer and operations system designed for agencies — not a generic sales database with a new label.

In a generic CRM you store people and move deals. In CRM for agencies, those people become paying clients, those clients own projects, those projects have tasks and deliverables, and those deliverables become invoices and collected payments. That is agency client management software: one record graph, not five subscriptions.

If you are still asking why bother, start with why your business needs a CRM. The agency version of that answer is specific: visibility across sales and delivery.

An agency rarely has only one workflow

A new lead becomes a proposal.

The proposal becomes a client.

The client creates a project.

The project creates deliverables.

The deliverables create invoices.

The invoice creates revenue.

But many agencies manage these stages in completely different tools — a form here, a sheet there, Razorpay or a PDF for billing, WhatsApp for follow-ups, and a project board that never talks to the pipeline.

That is where an agency CRM becomes useful. Indian agencies especially live in WhatsApp threads; the CRM has to be the system of record so a chat does not become the only place a client exists.

What an agency CRM actually needs

A useful agency CRM should connect:

Entity	Question it answers	In SaaSCRM
Leads	Who might become a client?	Leads
Pipeline	Where is each opportunity?	Pipelines
Clients	Who is currently paying us?	Contacts / companies
Projects	What work is being delivered?	Projects
Tasks	Who needs to do what?	Tasks
Invoices	What has been billed?	Invoices
Payments	What has actually been collected?	Payments
Activity	What happened with this client?	Activity

This is more than traditional contact management. It is a business operating system.

Zoho CRM is the default Indian answer for “just get a CRM.” It is strong at sales. Agencies still outgrow it when they need projects + invoices + a UI they can white-label without waiting on a marketplace plugin. That gap is why we ship a dedicated Agency CRM template instead of a generic admin dump — see the one-line map in 6 CRM templates you can demo today.

Why spreadsheets eventually stop working

Spreadsheets are great when you have 10 clients.

Then you have 30.

Five people edit the same document.

Someone creates a second version.

A client is forgotten.

A proposal doesn't get followed up.

A task exists in Slack.

An invoice exists somewhere else.

The agency has more information but less visibility.

We have watched this in client work too — including a finance CRM we built for an Australian accounting team. The pattern is the same: the moment billing and delivery leave the pipeline, the founder starts running the business from memory.

If you are comparing tools as a small studio, the best CRM for small businesses and startups write-up is the buying filter. CRM for agencies just adds projects and retainers to that list.

Building an agency CRM faster with SaaSCRM

SaaSCRM already includes CRM patterns for leads, pipeline, sales, invoices, analytics, activity, and operational management — 60+ screens, theme customizer, and an agentic chatbot.

Instead of designing all of those screens from scratch, an agency can customize the existing foundation around its own workflow.

For example:

Lead → Qualification → Proposal → Won

then:

Client → Project → Tasks → Deliverables → Invoice

The same CRM contains both the sales and operational sides of the agency.

Open the Agency CRM live demo and walk that sidebar. Then plan your modules in two minutes with the AI CRM Builder — the same flow we described in Plan your CRM in 2 minutes.

Niche validation matters before you overbuild. Founders who shop ideas on MicroSaaSHQ keep seeing agency CRM and vertical client-management tools as real demand, not a vanity niche. YourStory and the Indian SaaS press make the same point from the other side: studios want software that matches retainers and delivery, not another Western sales cloud.

AI makes the agency CRM more useful

Imagine asking:

“Which proposals haven't been followed up?”
“Show all clients with unpaid invoices.”
“What projects are overdue?”
“Create follow-up tasks for all leads I haven't contacted this week.”

That changes CRM from a database into a workspace.

SaaSCRM's agentic chatbot is built around this type of CRM interaction — tool-calling against leads, invoices, and tasks, not a FAQ bubble. We use the same pattern internally for research agents; the Reddit trend scraper that saved us $1,000/month sits on the same admin + chat shell.

Template or custom build?

There are two sensible paths.

Use the template when your team wants to build and customize the application itself — Next.js, React, Tailwind, shadcn, Cursor-ready source.

Use the custom CRM service when the workflow requires custom modules, integrations, branding, authentication, billing, or deployment. SaaSCRM currently positions custom builds from $2,000.

That creates a useful path:

Free CRM template → SaaSCRM template → Custom CRM build

You want	Start here
Click the agency workflow today	Agency CRM demo
Own the code	Pricing (template from $49)
Design modules first	AI CRM Builder
We ship it for you	Contact or WhatsApp

Why keep shipping templates at all? Because the boring admin layer is what agencies actually run on — I wrote about that in why I keep building CRM templates.

How to run leads, clients, projects, and invoices in one system

Capture leads in one inbox — forms, referrals, WhatsApp handoff — not a shared sheet.
Move the pipeline (qualification → proposal → won) so nothing sits in “we’ll follow up.”
Convert the won deal into a client record. Do not start a second spreadsheet.
Open a project with tasks, owners, and due dates under that client.
Invoice from the same record when deliverables ship; record payments when they land.
Ask the copilot for unpaid invoices, overdue projects, and quiet leads.

That is agency client management software in practice. You can rehearse every step on saascrm.site before you buy.

FAQ

What is an agency CRM?

A CRM designed around agency sales, client management, and delivery workflows — not only contacts and deals. Read the broader case in why your business needs a CRM.

What should an agency CRM include?

Leads, pipeline, clients, projects, tasks, invoices, payments, activity, and analytics. Demo that set on the Agency CRM template and the six-template gallery.

Can SaaSCRM be customised for an agency?

Yes. SaaSCRM specifically positions itself for agencies and custom CRM builds. Plan the modules with the AI CRM Builder, then customize or hire us.

Can one CRM manage both sales and projects?

Yes, provided the application is designed around connected entities and workflows. SaaSCRM’s agency vertical does that: won pipeline items sit next to projects and invoices.

Agency CRM vs a generic CRM — what is the difference?

A generic CRM is a sales database. An agency CRM is CRM for agencies: pipeline plus delivery, retainers, and billing. If you only need contacts, a generic tool is enough. If you invoice projects, you need agency client management software.

How much does a custom agency CRM cost?

SaaSCRM’s template starts at $49. A tailored custom CRM build starts from $2,000 when you need branding, auth, integrations, or deployment. Book a call or message us on WhatsApp.

Conclusion

An agency CRM is how you stop running the studio in five tabs. SaaSCRM already models CRM for agencies: leads, clients, projects, and invoices on one Next.js foundation — with AI on top.

Next steps

See you in the next one

— Shrey, creator of SaaSCRM.site

AI-Automated Reddit Trend Scraper that saved us $1000/month on Content Creation

shrey vijayvargiya — Sat, 29 Aug 2026 02:49:58 +0000

Hello and welcome to my new blog

In today’s story, we will be discussing an Automated Trend Scraper that helps us save more than 4hours/day and create stunning content, replacing $1000/month on content creation.

Manual trend monitoring is a time sink.

Scouring Reddit, X, and LinkedIn daily to catch target audience discussions wastes hours that could go into building. To solve this for iHateReading, we built an autonomous AI agent to handle the entire pipeline: collecting raw discussions, enriching them with context, and generating tailored content ideas.

Here is how the system works and how we put it together using Hono, OpenRouter, and our proprietary scraper, ScrapeFast.

The 5-Step Pipeline

1. Collect: Scheduled cron jobs fire four times a day, running within a 10-minute execution window. The agent pulls RSS feeds from key subreddits alongside relevant X and LinkedIn posts using our lightweight ScrapeFast engine.
2. Enrich: Raw posts lack context. The agent passes scraped payloads to OpenRouter, extracting underlying pain points, core questions, and sentiment rather than just storing titles.
3. Store: Structured records land in a clean database with key metadata (source, category, extracted problem) mapped for efficient querying.
4. Read: Instead of endless doom-scrolling, a custom interface aggregates the pipeline into a single daily readout that takes under five minutes to review.
5. Act: A dedicated AI chatbot runs on top of the dataset. It answers natural-language queries about emerging trends and directly generates content ideas. For example, it recently identified strong demand for an “AI Tools List” page optimised for SEO.

Proof of Concept

The image added below is proof of the AI agent running in a terminal with one command

It scrapes the latest trending posts from targeted subreddits and AI enrich it further before storing the database

Image 2 shown below renders the 100+ daily fetched subreddit posts of our interests; this helps to provide detailed context about each Reddit post and a relativity score.

Relative score and AI context message become important as they give an overview before reading the full comment/posts; I’ve realised that our Email/Gmail inbox should have this feature 😃

Image 3, as shown below, is where I did some personal improvisation. I quickly integrated an AI chatbot, which can access tools such as search/filter/find from the Reddit posts database.

I’ll explain in a few words

> The AI chatbot, as shown below, has the Directory posts context.

> The AI chatbot can search/find/filter the posts if needed as a tool for the AI agent

In this way, the AI chatbot can give answers based on trending posts. I can give more context to the AI chatbot by giving a timeframe in the prompt, for example, “Give me the last 20 days' trending posts summary”

Tech Stack Breakdown

Hono: Powers the backend API. Its minimal overhead and edge-ready performance ensure fast, lightweight background scheduling without heavy server footprints.
OpenRouter: Serves as the AI layer, enabling flexible model routing to enrich raw text, summarise discussions, and apply conversational intelligence to stored trends.
ScrapeFast: Our custom in-house scraping tool designed for reliable, fast data ingestion across Reddit, social feeds, and web sources.

Trend Scraper for Reddit, LinkedIn and X

We built the same agent for a trend scraper for LinkedIn and another one for X(formerly Twitter)

We can’t run the platform while spending 4 hours a day doing manual research for our website content, so AI is the best choice for the same.

We built a similar agent for LinkedIn and another one for X; both do the same work in the same process for each of the platforms.

In the end, we manually read all the posts and use an AI chatbot to finalise the next plan of content and do better work, solve real problems

Conclusion

We eliminated manual topic research for iHateReading.

Instead of guessing what to write, the agent continuously surfaces real user problems as they emerge. We step in and build the solutions.

PS — We have sold this AI agent to one of our clients; if you need one, DM me at shreyvijayvargiya26@gmail.com

We can build a custom agent for Agencies, startups, and companies. We look forward to your message.

That would be enough for today; see you in the next one
Cheers
Shrey

Cool React Sounds libraries to use on your next project

shrey vijayvargiya — Sun, 16 Aug 2026 11:39:01 +0000

Buildsaas updates, React sound libraries, Explore page on iHateReading and more

Tags: Buildsaas, Frontend Development, iHateReading Website, Posts.design, Movin.design

Hello and welcome to the new blog, folks

I am your author, Shrey

This blog is an interesting one, but it doesn't have any specific topic to write about!!

Buildsaas: My delulu is the only solulu 😀💁🏼

I read this tweet on x.com about boilerplate businesses.

The tweet: https://x.com/JanuBuilds/status/2088232655712108881

Most of us are thinking that AI might eat the boilerplate business in the next 2 years, but in reality, the picture is the inverse.

Boilerplate businesses are earning more than required, and the trend and demand are not going down anyway in the next 2 years. The main reason is the cost and time it usually takes to build entire SAAS products from scratch to production level.

It will cost > $1k for a developer to build a simple gettemplate.app or even a static kind of website because a website is not just a webpage but a combination of multiple things running and integrated in a well-designed system.

A website contains Authentication, Frontend pages, email, State management, and payment services, all running simultaneously for 100+ users on a daily basis, and that is why we have been building and upgrading Buildsaas.dev for the past year. I’ve built 6 apps in the past year using one boilerplate; all the products are LIVE and used by real-world people. Check all buildsaas.dev products on our examples page.

If you are building your next SAAS product or website, or an idea, feel free to try buildsaas.dev.

We provide proper documentation, notes, custom email and WhatsApp support, along with launch support to help you turn your idea into a reality.

New Explore Page on iHateReading

I always want to create something similar to daily.dev for our use case and product. Last year I've collected more than 100+ RSS feed websites that I can use to scrape or fetch the latest interesting blog content and renders them on the iHateReading explore page.

I quickly use the Nextjs server-side API to attach it with a CRON job, meaning run everyday twice, only 2 times per day and brings some solid, interesting internet scraped content.

Give it a try, ihatereading.in/explore

If you're wondering why, Nextjs server-side API?

Well, I don't want to complicate it, we ain't need a solid system design for the URLs or RSS feeds in total.

We have quite a couple of 100s of RSS feed links in total, and Next.js server-side is a server-side API that quickly runs in batches of 50 at a time to scrape the RSS feeds content.

If needed, I use our own scraping API to fetch more details or content from each URL; check it out.

Sounds-Based Interfaces

I've found a few more tweets regarding indie-hackers building SDKs for sounds on a website, click the button to invoke the sound instead of a ripple effect or other animations.

I've found it a very interesting concept; a website, in my opinion, should feel more realistic instead of being boring to use.

I've found a few libraries to add sound to your next React website project. Check them below

If needed, try using the useSound hook for integrating sounds on your website

Directory Listing Website: Aggregator Website

Directory websites are becoming a new trend; everyone in their domain wants to build a directory website showcasing inspirations, landing pages, tools, lists, templates — and what makes it interesting and in-demand is that it’s a money-printing machine.

Yes, a directory website in 2020 was a simple static website, and nobody paid attention to it, but in 2026, AI LLMs refer to directory websites finding tools that are cheap and better alternatives; indie-hackers are printing money from stupid, boring static listing websites — who knows that this is the future.

But I love directory websites; they provide so many solutions on one platform, saving us from too many tabs. If you need to build one, I’ve launched a new template on our website: a full-stack directory listing website template.

Built using Next.js, Tailwind, Shadcn, Supabase and Stripe. It provides frontend pages for your directory website, authentication for users to submit their tools, Payment integration using Stripe for slot booking and more such useful features, making it easy to build your directory website in a week, not months.

ToolDir

ToolDir: Directory full-stack website template — Shadcn, Next.js, Supabase and Stripe.

Get the Template: https://shreyvijayvargiya.gumroad.com/l/nextjs-supabase-directory-boilerplate

Cool lightweight Websites

I am always a fan of looking for inspirational websites, I love finding new cool interfaces, animations and solutions to improve my website development skills.

I believe that building a good interface website is an art; it does need more than coding, and that is why an AI agent won't take our job fully.

A few websites I've stumbled upon recently are as follows

That would be enough for today. Stay tuned, as I am working on bringing a new job section to iHateReading 😊, as it would help most of you reading on our website to grab a job as well.

That's it for today, see you in the next one

Cheers

Shrey

Originally published on iHateReading

6 CRM templates you can demo today (and the full kit when you’re ready)

shrey vijayvargiya — Wed, 29 Jul 2026 08:14:23 +0000

Hello folks

We just shipped a cleaner way to explore SaaSCRM: six focused CRM templates, each tuned for a real business shape — not a generic “admin dump.”

Browse them all on saascrm.site/templates. Live demos open in one click. When you outgrow a slim starter, the full kit is waiting on pricing.

Why templates?

Building a CRM from a blank Next.js app is slow in the boring ways:

Sidebar + routing + empty states for every module
Tables, filters, detail pages, and seed data that actually look real
Theme tokens so dark mode and rebrands don’t break mid-ship

Templates skip that setup. You pick a vertical that already matches your workflow, demo it, then customize instead of inventing the admin layer from scratch.

That’s the whole idea behind SaaSCRM:

Small verticals → prove the UI for your niche fast
Full kit → 60+ screens when you need sales, ops, finance, CMS, and system in one product

The 6 templates (one line each)

Template	One-line detail
Indie Stripe Billing	Stripe-shaped revenue OS — customers, charges, subscriptions, failed payments.
Agency CRM	Leads → pipeline → invoices → projects for client delivery shops.
Freelancer CRM	Clients, invoices, payments, and tasks in one slim hub.
Creator CMS	Blogs, newsletters, subscribers, and content analytics.
Digital Store CRM	Sell ebooks, PDFs, icons & assets — catalog, orders, payouts.
Ecommerce CMS	Products, inventory, shipping, raw materials, plus blogs & scheduler.

How to use each one

Same playbook for every template:

Open the templates gallery and click a card
Hit View live demo — it loads that vertical inside the SaaSCRM shell
Click around the sidebar (that is the product shape)
When you’re ready to own the code, grab the starter / pack from the template page or upgrade to the full kit

Indie Stripe Billing

Best if you live in Stripe Dashboard tabs. Demo → swap seed customers / plans → wire real Stripe later. Home lands on revenue; dig into failed charges and subscriptions.

Agency CRM

Best for studios and boutique agencies. Walk Leads → Pipeline → Invoices → Projects, then rename stages to match how you actually close and deliver.

Freelancer CRM

Best for solo operators. Keep Clients / Invoices / Payments / Tasks as the daily loop. Replace seed clients with your roster and ship.

Creator CMS

Best for writers and content teams. Use CMS + Analytics + Users, then point blog / newsletter rows at your real calendar or email tool.

Digital Store CRM

Best for digital product sellers. Start on Products, then Orders → Invoices → Payments. Badge SKUs as ebook / PDF / icon pack as you grow.

Ecommerce CMS

Best for DTC / ops-heavy stores. Run Products → Inventory → Orders → Shipping, track raw materials, and keep marketing in CMS + Scheduler without leaving the admin.

When to buy the full kit

The six templates are focused starters. The Full CRM kit is the whole house:

60+ production screens (sales, ops, finance, CMS, system)
Agentic AI chatbot, theme customizer, marketing site patterns
Lifetime updates — one-time purchase from $49 (Template + Figma from $79)

If you’re building a real product admin — not just a niche slice — start here:

Boom — grab the full kit

Buy SaaSCRM on Pricing →

Or preview everything in the app first: Open the full demo · Browse all 6 templates

Ship the admin layer. Build the business on top.

Cursor, Codex, Claude Code prompts collection

shrey vijayvargiya — Tue, 28 Jul 2026 14:42:32 +0000

(https://ihatereading.in/cursor-rules)

Hello and welcome to the new blog

This blog is originally published on and for ihatereading.in

Last month, I wrote a new stunning aggregator or collection-based blog, 300+ SAAS Directories to submit your website, project and tool, making it easy for solo-developers and indie-hackers to promote their product.

The post/blog/article works well on the internet, which gives me another idea of providing more such content on iHateReading, the exact reason I built iHateReading.

Imagine we are all using AI agents in 2027 and in future; one thing that is sure in that case is prompt engineering, or prompts or rules for your AI tool. Chatgpt, Claude and Codex or Cursor all work as AI agents similarly, but the only thing that matters to differentiate the results is the prompts/rules/skills.

200+ AI Cursor Agent Rules, Why and

I have been using Cursor for the past year, and I work very well for our team, but one thing was missing: the same prompts every time, the same theme and design system for every prompt to make better websites, write better test cases, make a responsive website or integrate dark/light themes using Tailwind variables.

All those tasks need the same prompt, and rewriting them is time consuming tasks, instead, we can have a list or collection to solve this problem.

Extensive list of Cursor rules for each of the tech stack, SDK, APIs, such as

Nextjs, React, Vite and Tanstack Cursor rules
Firebase, Supabase, Appwrite, NeonDB, CockroachDB, Prisma, Drizzle ORM database rules
Stripe, Polar, Lemon Squeezy, Dodo and Paystack, PayPal-based payments rules for AI agents
Best practices for React, Tailwind and Javascript/Typescript, Rust/Python/Go rules for AI agents

All the above tasks are repeated ones by nature, and we need a collection to simply copy-paste them.

We ended up building one and took a few inspirations from online Github repositories, https://github.com/PatrickJS/awesome-cursorrules

200+ Cursor AI Rules Agent

> It's FREE to use

How to use them?

Go to the cursor rules page
Check the rules you are working on, Firebase or React or Tailwind
Click the rule and copy-paste the prompt
Add that prompt inside Claude Code, Codex, Lovable, Bolt or Cursor and make your code better

Simple 4 step process to make better product

That would be enough for today, do give it a check, I am adding more rules and if you find anything missing or more to add, feel free to DM me or email, I reply to all emails

See you in the next one\
Shrey\
iHateReading

AI CRM Blueprint: Building a CRM in 2 minutes

shrey vijayvargiya — Sun, 26 Jul 2026 09:11:40 +0000

Hello folks

Today’s post is about something we’ve been shipping quietly on SAASCRM.site: the AI Builder — a free planner that helps you design your CRM in about two minutes.

No signup. No sales call. Just describe what you’re building, and get a clear plan matched to the SaaSCRM template.

What is the AI Builder?

The AI Builder lives at /blueprint. Think of it as a CRM architect in your browser.

You tell it:

Business type (SaaS, agency, recruiting, real estate, and more)
Team size
Backend plan (prototype, Supabase, Firebase, existing API…)
Launch target (prototype, MVP, or production)
Features you need — Leads, Pipeline, Invoices, CMS, Analytics, and so on
A short note on what you’re building

It returns a blueprint you can actually use — not a vague “AI summary.”

What does it provide?

After you generate a plan, you get:

Overview — what’s already covered by the template vs what needs custom work
Page map — the CRM screens you should ship first
Recommended stack — frontend and backend direction that fits your answers
Timeline — realistic estimates for prototype, frontend, backend, and production
Cursor prompts — copy-paste prompts tailored to the SaaSCRM project rules
Build CRM — a path to start from the template (or generate a small free starter)

In short: less guessing, more shipping.

How to use it (simple)

Open Plan your CRM
Pick your business type, team size, backend, and target
Tap the modules you need
Write one sentence about what you’re building
Hit Generate free CRM blueprint
Review the tabs — especially Pages, Timeline, and Cursor prompts

That’s it. You can run a few plans per hour for free and refine as you go.

Build CRM with our template

The blueprint is the map. The template is the foundation.

Once you like your plan:

Buy the source template and open it in Cursor
Paste the generated prompts to customize modules, branding, and workflows
Explore matching screens in the live demo

👉 Build CRM with our template — one-time purchase, lifetime updates.

Or start free first: Open the AI Builder.

Plan first. Customize next. Ship faster.

Cheers,

Shrey

Cool shadcn libraries

shrey vijayvargiya — Thu, 23 Jul 2026 08:50:44 +0000

You can't miss

Hello and welcome to the new blog

It's 2026, and AI can certainly make our website, and the only problem the AI agents have is building creative websites.

Creative websites need storytelling, state management, and almost perfect eye-pleasing theme and colour coordination.

I almost found myself itchy when building a creative website with full of animations, highly performant, less use of useEffect hooks, more of CSS than JavaScript, better colour themes and palletes with proper depth and hierarchy to manage among the DOM elements in design theory.

Shadcn is the most used UI library with 100k+ Github stars, if you haven't tried shadcn them, you are missing something on the table.

I love how shadcn easily handles customisation. We provide a context provider for theme customisation to toggle and switch the themes, extending to deeply nested children.

The demo video has been added below, showing the CSS customisation example using shadcn as the UI library.

Go to this link and click on the top right colour palette icon, it will open a drawer to edit the CSS of the entire website UI elements.

Shadcn UI Library

Shadcn is a headless UI library that brings one CLI command to add the source code instead of adding the entire UI library inside node_modules.

This concept makes it interesting; one CLI can update my UI element within the entire repository and can add more UI elements using one CLI command.

This also brings an opportunity for everyone to create shadow-based repositories and submit to directories to let other developers install your code or UI element with just one CLI command.

Most of the time, I don't even use the CLI; I prefer adding the shadcn UI code directory by copy-pasting inside the lib/ui folder in my repository.

Why Shadcn?

Every time we developers build UI elements, we add the same animation source code, add the same pattern of colour palletes and CSS. Shadcn eased the pain of redoing it; it's like a bootstrap of early 2010.

We have a bunch of React UI elements ready as fundamental elements and can build more complex states and animated UI components on top of it.

Cool and Top Shadcn Libraries

Sharing some cool latest top-shadcn libraries or UI components, I've witnessed.

Rare UI

Shadcn paper shaders for backgrounds

TweakCN: I love the idea of customisation that comes from it

Smoothui: Smooth animated UI elements

Threecn: 3D-based WebGL elements using Shadcn

componentry.dev: Components from Shadcn

Shoogle.dev: Explore and search all Shadcn-based libraries

Morphin.dev: Premium UI elements

ShadcnStudio: Studio for shadcn builders

Shadcnuikit: UI kits for solo developers

Ruixen: UI components and premium templates

Shadcnspace: Shadcn-based UI components

Bklit UI: Shadcn charts and data components

ReUI: Shadcn blocks with the MCP server

Cuelume: Interaction sound for web components

I think that would be enough for today

See you at the next one

Cheers

Shrey

RepCN Repository builder open-source tool

shrey vijayvargiya — Tue, 14 Jul 2026 16:17:26 +0000

Convert React code into a deployed project with a shareable URL

Hello and welcome to the new blog

Our last blog was about building iHateReading and earning from a content platform. While running iHateReading, I've personally needed so many tools, and luckily, in the age of AI, I can quickly build tools for internal work.

First, let me explain with an example: running any platform needs tools for different types of work, tools for writing, tools for content management, tools for user management, tools for assets creation, tools for research, etc.

Each tool has a purpose to serve and mainly reduce the execution time if done manually; one good example is creating banner images for each blog. I write almost every day, and I need to put a cool, catchy title on almost every blog. Each blog banner image creation is a process with tasks; if you are an engineer, you can understand the pain!!

But I found that I've had to stumble upon Canvas or other tools mainly for creating banners. I've figured out that making assets is not important; having examples of assets, just like canvas templates, is a more accurate solution to my problem. The first month was building the basic tool, and then later on, adding the templates for various categories was my decided plan, and I ended up making a simple canva alternative tool, kixi.app

Building an internal tool will take time, but if that tool is reducing your development debt and saving time to give better output, then it always works. Another example is our FREE open-source internal writing app, which works offline, is simple, easy to use, has a dark/light theme, is open-source and shows a graph network connection between all the created notes,

Check Github: https://github.com/shreyvijayvargiya/opennote

Why do we need these tools in times of Notion and Apple Notes? The concept is simple for internal use, no payment issues, no authentication issues, no data transfer issues.

The choice is yours if needed and can reduce costs and save time by building that internal tool for yourself and maybe that tool helps others, for example, React CRM SAAS template I made for my small SAAS businesses and ended up adding more pages 60 pages in total with classic theme editor and luckily it has helped 10+ businesses worldwide and my current client need CRM based on our React CRM template.

I often rely on the concept of easing my own work, making it a more fun job to do and then figuring out how to monetise it.

Problem Statement

In today's blog, we will be discussing another open-source tool, the idea of sharing a single React code by deploying it on a custom domain or a demo pseudo domain.

But first, I want to explain the problem statement: Every time I went to Claude and asked for a code, it gave me either HTML or React code. Claude can render HTML easily inside a browser, and React too, but not NextJS or Vite, and TanStack apps; those framework codebases cannot be rendered and previewed inside Claude, and Claude needs React rendering and a building engine under the hood to make it possible.

Another problem I've faced is that all the React code written by Claude can only be shared via Artefacts, not as a URL or domain, or even a subdomain.

For example, paste the below prompt in Claude code

Give me a classic animated hero section with an animated background using Tailwind CSS, React
lucide-react for icons and use Framer Motion for animation, give me a single-file React
code component

The Claude will return the single-file React code component. Why only a single file? I'll explain it later on.

Once we got the code, I couldn't share this code with others to view inside the browser, nor could I share the code output preview to view; all they needed was a Claude for the same.

RepCN to the Rescue

Here comes RepCN to the rescue, which sits nicely between Claude and shareable React code for designers and developers.

Imagine you can create Nextjs or Vite, or Tanstack repository using that single React code. Push the repository to Github creating a new repository on Github and finally deploy that repository onto Vercel to get your own URL of {project-name}.vercel.app

How RepCN can help

Create Nextjs or your choice of code repository from a single React code component
Install already existing defaults such as Shadcn, and choose a language TS/JS
Push to Github and deploy to Vercel for better URL sharing with a preview of your source code
Automating the process of generating a new repository, installing all packages and building the architecture

One of the most important reasons I need RepCN is to create a new repository based on a single React code with all packages installed. The default packages it includes are as follows

Nextjs as a framework
React as a language
Tailwind CSS for styling
Lucide React for icons
Framer motion for animation
Sonner as toast

But the above packages can be changed, and you can choose Vite or Tanstack as a framework, shadcn as a UI library with JS or TS supporting both.

RepCN is Open-Source

RepCN is open-source and built using Nextjs and React

No authentication needed

No server-side API is required

No databases are used in making it

Completely FREE and Open-Source

Give it a try on a website: https://repcn.buildsaas.dev/

Fetch Github repository: https://github.com/shreyvijayvargiya/repcn

That would be enough for today, see you in the next one

Cheers

Shrey

One last thing, my last release template was Tooldir: Directory creating starterkit with complete payment and authentication integrated, check tooldir.buildsaas.dev

Building and Running the Content Platform

shrey vijayvargiya — Mon, 13 Jul 2026 16:21:18 +0000

Welcome to the new blog

If you are new here, I am shrey, a software developer and creator of multiple products online.
I have been running iHateReading for the past 4 years, and we have crossed 10k users signed up and are operating at 1k monthly active users.

I am glad that readers are revisiting the website; the churn rate has been low for the past few months, and people are subscribing to our newsletter more than ever.

This year, in 2026, in the first 6 months, I've improved the landing page and added other ƒeatures on the website as explained below

Explore github trending repositories directly on the web home page
New 300+ SaaS directories page (our 2026 most visited page)

Nothing new has been done apart from consistent writing on the platform.

iHateReading is FREE to read, not forced to log in, and no compulsory subscription to read.

Each blog is handcrafted with zero AI involvement. I've been doing this for the past 5 years, which makes it easy for me to craft a 500-word article in 10-20 minutes.

Building iHateReading

I've never covered details about how I am running this platform, how I am earning, etc

iHateReading is built entirely using Nextjs and Firebase, and the detailed tech-stack is:

Nextjs
React
Tailwind
Lucide
Firebase
Redux
Tiptap editor
Vercel
Server functions
Resend

The deployment is on Vercel, and the database is Firebase. We do use the paid version of Firebase with the Pay-as-you-go feature, and the reason is that I've made iHateReading 5 years ago and migrating to a new database is not easy when you have 10k total users.

Promoting the website

Most of the time, I've got the same question to address: How to market my website, how to get visitors and users or traffic?

First and foremost, it's not easy, and it won't happen on day-1
You need 3-6 months in total to grab a good user base.

Marketing on multiple other platforms, such as X, LinkedIn, TikTok, IG, Youtube, Medium, Devto, Hashnode, Dailydev, Cold Emailing, Reddit, all these platforms are needed to promote the website.

One can say pay attention to one. Yes, if YouTube, then prefer YouTube, if good on LinkedIn, then prefer LinkedIn and so on. Choice is yours, but choose one platform and become very good at it to gain an audience.

My mistake 4 years ago was choosing multiple platforms at the same time and promoting my content on multiple platforms. The strategy was good, but too much promotion was creating chaos in my work schedule and making it difficult to manage. I prefer choosing one platform and becoming good at it.

But you might have to prompt or distribute the backlinks, hyperlink of your blog to the other trusted high-ranking domains, such as X, YouTube, LinkedIn, Hackerrank, or Devto, this increase the trust, and an AI agent like Chatgpt can recommend your website very well.

I was contacted by one of the people whose SAAS business is to bring your blog content platform or website on chatgpt recommendations, his SAAS idea was to help people promote their website on chatgpt and AI agent suggestions/recommendations, increasing the trust, domain rating and traffic.

Another good solution is to submit the website to Google Search Console with sitemaps and have the page indexed, preferably by buying the .com domain or other trusted domains; do not go beyond fancy, untrusted domains, otherwise your emails will be spammed in the inbox.

Make a list of distribution channels for your website content, for me it's Medium, Hackernoon, Hashnode, Devto and LinkedIn. Do not spam similar content across platforms; leverage each platform's audience, understand what they want, and only promote that type of content.

Admin Panel

I've been writing for years and have used multiple writing tools and CMS, but it was never easy to write content and push to the database and then render on the website with all SEO tags and an updated sitemap.

I've learned this admin panel and its benefits in my previous company, and using a similar technique, I have an admin panel for our iHateReading to write blogs, edit blogs, manage users, create email newsletters on a Tiptap notion-like editor, send emails to our subscribers directly from the admin panel, store blogs directly into the database, etc

All these features are handled within the admin panel; it's more like iHateReading CMS.

I am a big fan of the Admin panel, and we have built the most advanced Admin Panel, which we reuse across all of our existing products and future SAAS products.

Check Admin Panel, just to provide an overview of the admin panel

Earning sources

Initially, iHateReading's target was to reach to the exact audiences and then increase the subscriber base. As a platform, iHateReading wasn't a pain killer its a vitamin so we can't depend on users to come and subscribe.

The first target was to bring more subscribers, and for that, in 2023, I started crafting bi-weekly email updates on programming, tech and AI.

Within the first 8 months, we have crossed 1k subscribers because of the traffic, but nobody told you how to reach to that point.

It took me almost 1 to 2 years, with consistent writing of almost 500 blogs, to promote the website to 1k active monthly visitors.

Promoting the website has a compounding effect, and it works slowly, but certainly it does work.

Once the subscriber base was increasing, I was confident enough to show something to our early sponsor. Cold emailing for sponsors is easy-peasy money, but it's not easy to get those sponsors.

We got our first sponsor for iHateReading in January 2025, Anima AI. The company reached out to me to create blog content and promote the content on iHateReading via newsletter, cold emailing and through the blog on the website.

iHateReading has provided 300+ monthly visitors with 0.6% of conversion ratio for Anima, and these were their words, not mine. 300 users were not bad for any small startup, and so many solo indie developers needed that many visitors.

Other ways to provide sponsorship are the website banner position, navbar sponsors, footer and other placements on the website, job posting, email marketing via cold emailing to all subscribers, all these are the ways to earn.

One easy way is to contact the companies or SAAS founders, asking them to provide a banner, blog and one/2/3 cold email package promoting their website/tool/product.

The package works every time.
Instead of selling one sponsored blog, I prefer providing one blog for one month, 2/3 cold emailing marketing to our subscribers, and one banner or footer placement for advertisement and promoting a number at the end of the month, such as 1k visitors in total for the next 30-40 days.

Check our Advertisement page for a better understanding

If you can't find the client for sponsorship, here as few ways to do so

Find manually on LinkedIn and contact each of them, DM
Find each on X or Reddit and DM them promoting your website
Use third-party client finding solutions, such as Apollo for lead generation and other AI LinkedIn or X lead generation tools
Social media tweets, posts asking for sponsors and replying to all comments sections
Posting a small YouTube short for the same

Manually reaching out to them works well, since you only need 2-3 sponsors each month, and manually doing it will save time and increase the conversion or success rate.

That would be enough for today, see you in the next one
Cheers
Shrey
iHateReading