DEV Community: Muhammed Amar

How I Run 14 AI Products Solo With $0/Month Hosting

Muhammed Amar — Fri, 17 Jul 2026 06:48:25 +0000

A behind-the-scenes look at how I built and run 14 AI products as a solo developer at virtually zero cost.

Tech Stack:

Cloudflare Pages (free tier) — static hosting for all landing pages
Stripe — payment processing (pay-as-you-go fees only)
Hermes Agent — cron jobs for competitor monitoring, lead scouting, automated intel

Daily Automation (all free):

7:00 AM — Competitor intel brief generated
11:00 AM — AI money alerts
1:00 PM — Lead scouting
8:00 PM — Subscriber alerts

What I would do differently:

Start with 3 products instead of 10 (spread too thin initially)
Build the email list before the products
Launch one at a time instead of all at once

All at theaisuite.pages.dev if you want to see the full setup.

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

The OpenAI Assistants API Is Shutting Down on August 26 — Here's Your Migration Checklist

Muhammed Amar — Thu, 16 Jul 2026 21:26:28 +0000

The OpenAI Assistants API Is Shutting Down on August 26 — Here's Your Migration Checklist

If your product or your agency's client projects still run on the OpenAI Assistants API, you have a hard deadline: 26 August 2026.

After that, threads, file uploads, and persistent assistants stop responding. No grace period, no backwards-compatible shim.

Most teams find out too late. Here is a migration checklist that catches the common parity breaks before production.

1. Map every Assistants API surface in your codebase

Before cutting over, know exactly what you rely on:

assistants + threads endpoints
File upload and retrieval
run lifecycle polling
Tool calls (code interpreter, retrieval)

Run grep -r "AssistantsApi\|assistants\|threads" src/ and tag every hit. This is your blast radius.

2. Threads become Conversations

The Responses API replaces threads with a conversation model. Your state management changes. Thread IDs no longer exist — conversation IDs do. If your database or session layer keys on thread IDs, you need a migration key strategy now.

3. File uploads move to `input_file` parameters

File handling in Assistants uses the file store and message.files. Responses API moves this to per-request input_file parameters. Plan for:

Different size limits
Different lifetime semantics
Tool-specific file passing (code interpreter vs. search)

4. Run polling becomes streaming + status polling

The run object lifecycle is replaced. You no longer poll run.status the same way. Start prototyping streaming responses and status callbacks against the Responses API today so you can compare P50/P95 latency before cutover.

5. Fix tool-call schema mismatches

Assistants API tool calls return a different envelope than Responses API function calls. If you parse tool outputs into structured payloads, review every parser under the new schema. This is where most silent data-loss bugs hide.

6. Budget a 60% migration sprint buffer

Every Assistants API migration we have reviewed hits at least one "oh, this is totally different" moment. Schedule 1–2 weeks of contingency.

The shortcut

We built a migration smoke-test checklist after watching too many teams scramble at the last minute. It covers the six parity areas above, with a runnable template you can drop into your QA pipeline. UK dev agencies using it are cutting migration time by roughly 60%.

Get the migration checklist — one download, no subscription.

While you are in the mood for structured AI intel, here are short-form resources for the cost and performance questions that come up right after migration:

Anthropic Intel Brief — pricing history, API behavior, and head-to-head comparisons with GPT-4o and Gemini. £9, no subscription.
AI Coding Cost Tracker — track your AI IDE spend. £5 one-time.
AI Phone Service — inbound/outbound voice agents from £99/mo, so you never miss a lead call while you are deep in migration work.

TL;DR: Start the Assistants API migration now. The checklist above plus a structured provider intel brief will keep you from discovering the hard breaks at 2 a.m. on launch night.

I Built "ContextBase (Agent Onboarding)" — Here Is Why It Exists and How It Works

Muhammed Amar — Thu, 16 Jul 2026 21:21:20 +0000

I kept seeing the same problem, so I built ContextBase (Agent Onboarding) to solve it.

The problem: Drop-in Python SDK that captures project context and injects it into any agent's system prompt. No more agents forgetting who they are.

The solution: 🧠 ContextBase (Agent Onboarding) — $29/mo

Why now:

The market is moving fast and most people cannot keep up
Existing solutions are either too expensive or too complex
This is a solo-built alternative that just works

Stack: Cloudflare Pages + Stripe + automation cron jobs

Try it at theaisuite.pages.dev — would love feedback from the dev.to community.

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

I Built an AI Coding Cost Tracker to Finally See What Copilot and Cursor Are Actually Costing Me

Muhammed Amar — Wed, 15 Jul 2026 08:01:38 +0000

Every AI coding assistant ships with a promise: it supercharges your workflow. What they don't ship is a clear receipt. GitHub Copilot, Cursor, and similar tools all charge by token, but unless you manually dive into billing dashboards, you never really know what a single project, sprint, or developer is racking up in AI spend.

That frustration led me to build something small, practical, and priced to match the simplicity I wanted: the AI Coding Cost Tracker.

The Problem Everyone Is Quietly Having

If you manage a team of engineers or even run as a solo dev, AI-assisted coding is now standard. The hidden cost isn't huge per person — but multiply it across a team, across features, and across months, and you're looking at a real line item with zero visibility.

Most teams:

Don't know per-project AI costs
Can't attribute spend to specific developers or repos
Lack monthly or sprint-level exportable reports
Hope their manager never asks for a budget breakdown

None of the tools that generate the spend give you the tracker that summarizes it.

What the AI Coding Cost Tracker Does

It's a tracking layer for AI-assisted development. You feed in your token usage (from the tools you're already paying for), and it gives you dashboards and reports that answer questions like:

How much did AI coding tools cost this sprint?
Who on the team is generating the most tokens?
Which projects are the biggest budget drains?
Can I export a clean report for a budget review?

It supports Copilot, Cursor, and other OpenAI-powered dev tools, so you're not locked into one ecosystem.

Why One-Time Pricing Matters

Most dev tools today are subscriptions. A tracker should not be a recurring bill. I priced the AI Coding Cost Tracker at £5 one-time. No tiers, no renewals, no "pro plan" — you buy it, you own it.

For a team spending hundreds monthly on AI tools, £5 is noise. For a solo dev, it's a fair trade for clear visibility.

If You're Building AI Products, Track Them Too

This is part of a broader pattern: small, cheap, practical utilities around the AI toolchain that don't get enough attention. The same team also ships:

Anthropic Intel Brief — a deep-dive on Anthropic's latest model architecture and benchmarks for £9 one-time.
AI Phone Service — an AI voice layer for businesses that handles inbound calls, books leads, and runs 24/7 from £99/month.

Each one solves a specific gap without forcing you into another subscription.

The Takeaway

You don't need a massive analytics platform to understand your AI coding spend. You need something that ingests the data you already have and tells you what it actually costs.

If Copilot or Cursor is part of your workflow, stop guessing the bill. Track every token, control every cost, and know exactly where the money goes.

Live product: AI Coding Cost Tracker

More tools: AI Suite

I Built 10 AI Products as a Solo Developer — Here Is What I Learned

Muhammed Amar — Wed, 15 Jul 2026 06:52:19 +0000

I shipped 10 AI products solo in a few weeks. No team. No funding. Just building.

All live at theaisuite.pages.dev with Stripe checkout.

Here is a quick tour:

📰 Agent Intel Brief (£12/mo) — Daily 7am competitor intel on the AI agent industry. Funding, launches, platform moves — analysed, not summarised.
📦 SMB Agent Templates (£59) — 5 plug-and-play AI employees: invoice processing, lead qualifying, support triage, voice QA, WhatsApp commerce.
📋 Agent Audit Logger ($49/mo) — Lightweight Python middleware wrapping any agent framework with full tool-call audit trails. SOC 2 prep without the $10K bill.
🧠 ContextBase (Agent Onboarding) ($29/mo) — Drop-in Python SDK that captures project context and injects it into any agent's system prompt. No more agents forgetting who they are.

Plus 10 more at theaisuite.pages.dev.

Stack: HTML/CSS on Cloudflare Pages, Stripe for payments, Hermes Agent for automation. $0/month hosting.

Would love feedback or a roast 🙏

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

The AI Agent Space Is Creating 5 New Business Opportunities Right Now

Muhammed Amar — Tue, 14 Jul 2026 16:18:39 +0000

The AI agent industry is moving faster than most people realise. Here are 5 gaps I have spotted that anyone can build for:

1. Agent Reliability Monitoring — Agents silently corrupt output across long chains. No tool exists to detect this automatically. £49/mo opportunity.

2. Voice Agent Testing — Vapi just hit $500M valuation. Voice agents are exploding but nobody is monitoring them for drift and hallucinations.

3. AI-Ready SEO Audits — Google Analytics just added an "AI Assistant" traffic channel. Most brands are blind to how they appear in AI responses. £99/mo.

4. SMB Agent Onboarding — Anthropic launched Claude for Small Business. SMB owners have no clue how to set it up. £199/pack.

5. Migration Services — OpenAI, Google, and regulators are forcing migrations with hard deadlines. Most companies are not ready. £300-£2,000/project.

I am building products in several of these gaps at theaisuite.pages.dev — happy to chat about any of them.

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

Stop Guessing What Your AI Coding Tools Actually Cost

Muhammed Amar — Tue, 14 Jul 2026 16:18:16 +0000

Stop Guessing What Your AI Coding Tools Actually Cost

If you're using GitHub Copilot, Cursor, or other AI coding assistants, you might be spending more than you think. Token usage adds up fast — per developer, per sprint, per project — and most teams have no visibility into it.

I built the AI Coding Cost Tracker to fix this. It's a lightweight tool that gives you real-time dashboards showing exactly where your AI-assisted development budget goes.

What you get

Real-time token usage dashboards — see costs as they happen
Per-developer and per-project breakdowns — no more spreadsheets guessing who spent what
Exportable reports — budget reviews made easy
Works with Copilot, Cursor, and OpenAI-powered tools — plug-and-play integration

Why this matters

AI coding tools boost productivity, but without cost tracking they become a black hole in your budget. A single developer running Copilot across multiple projects can burn through thousands of tokens weekly. Multiply that across your team and you're looking at a significant line item that's nearly impossible to audit manually.

The tracker cuts through that ambiguity. You'll know what each feature branch costs, which team members are heaviest users, and whether your ROI on AI tools actually justifies the spend.

The indiehacker angle

I'm an indie developer who noticed this gap while consulting on a team using Copilot. The finance team couldn't answer basic questions about spend, and the engineering team had no incentive to curb usage. I built the tracker over a weekend and released it as a one-time £5 purchase — no subscriptions, no recurring fees.

If you're an indie dev or a small team, every pound counts. Knowing your true cost per sprint lets you make better decisions about which AI tools are worth it and which aren't.

How to get started

Head over to theaisuite.pages.dev/copilot-token-billing/ and grab the AI Coding Cost Tracker for £5. Install the dashboard, connect your tools, and have your first cost report within minutes.

You can also check out my other AI tools at theaisuite.pages.dev — including the Anthropic Intel Brief for deep dives into model architectures.

Tags: ai, indiehacker, saas, productivity

Before You Build on Anthropic, Read This

Muhammed Amar — Mon, 13 Jul 2026 08:01:20 +0000

If you are evaluating LLM providers for your next product, you probably have a shortlist: OpenAI, Anthropic, Mistral, maybe a self-hosted option. But most teams make the decision on vibes, benchmarks, or blog posts — not on the operational reality of running on a single provider for 12+ months.

Here is what most indie hackers and CTOs miss:

Benchmarks do not match your workload. Public leaderboards test against curated datasets. Your actual prompt mix — long-context, tool-use, structured output — may behave very differently.
Provider roadmaps shift under you. A model deprecation, API pricing change, or context window cut can blow up your unit economics overnight.
Support gaps appear late. You discover rate limits, latency spikes, or model behavior changes at 2 a.m. on launch night.

The due diligence most people skip

Before you lock in a provider, do these four things:

Build a representative eval set. Use your actual prompts and expected input/output lengths, not MMLU or HumanEval.
Calculate real token costs over 6 months. Project your usage growth, not just your current headcount.
Check model stability history. How many major API version breaks has the provider shipped in the last year?
Audit fine-tuning and feature flags. If you need system prompts, tool use, or JSON mode, test them under load before signing up.

What second-source intelligence looks like

Deep, structured provider briefings exist — but most are locked behind analyst reports that cost thousands. For indie hackers and bootstrapped teams, that is out of reach.

The practical middle ground: a compact, single-provider intelligence brief that covers pricing history, release cadence, known failure modes, and head-to-head comparisons against competitors.

Getting started without the guesswork

You do not need an expensive consultant. Start with this:

Spawn a throwaway project and run 500 real calls through each provider.
Log latency P50/P95, error rates, and actual token spend.
Build a one-page decision matrix weighted by your actual priorities (cost, latency, context length, function calling).

If you want a shortcut, structured intelligence packages exist for teams that do not have two weeks to burn on eval.

Bottom line

Provider selection is a bet on the next 12 months of your product. Treat it like an infrastructure decision, not a feature toggle. Know the pricing, the stability track record, and the actual performance on your problems before you ship.

We publish structured intelligence briefs on leading AI providers to save you the eval cycle. The Anthropic Intel Brief covers Anthropic's 65B-class models in depth — pricing history, API behavior, and head-to-head comparisons with GPT-4o and Gemini. £9, no subscription.

Also built: AI Coding Cost Tracker (£5 one-time) and AI Phone Service (from £99/mo) — so you can track AI spend and never miss a lead call.

How I Run 14 AI Products Solo With $0/Month Hosting

Muhammed Amar — Mon, 13 Jul 2026 06:12:14 +0000

A behind-the-scenes look at how I built and run 14 AI products as a solo developer at virtually zero cost.

Tech Stack:

Cloudflare Pages (free tier) — static hosting for all landing pages
Stripe — payment processing (pay-as-you-go fees only)
Hermes Agent — cron jobs for competitor monitoring, lead scouting, automated intel

Daily Automation (all free):

7:00 AM — Competitor intel brief generated
11:00 AM — AI money alerts
1:00 PM — Lead scouting
8:00 PM — Subscriber alerts

What I would do differently:

Start with 3 products instead of 10 (spread too thin initially)
Build the email list before the products
Launch one at a time instead of all at once

All at theaisuite.pages.dev if you want to see the full setup.

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

3 Practical AI Tools for Developers Who Want Control Over Their AI Spend

Muhammed Amar — Sun, 12 Jul 2026 08:01:13 +0000

If you are building software with AI tools today, you already know the problem: the bills are blurry. You know you are paying for Copilot, Cursor, and maybe some other assistants, but do you actually know what they cost per sprint or per project?

Last week we shipped three small, focused AI products designed to fix exactly that. No subscriptions, no recurring fees, no enterprise contracts. Just one-time payments for tools that solve real problems.

1. AI Coding Cost Tracker — Know What You Spend

Most teams treating AI coding assistants as line items find themselves estimating in the dark. The AI Coding Cost Tracker gives you per-developer and per-project breakdowns of token usage.

What you get:

Real-time token usage dashboards
Per-developer and per-project cost breakdowns
Exportable reports for budget reviews
Works with Copilot, Cursor, and OpenAI-powered tools

Why it matters: When you can see spend by sprint and by team member, you can actually optimize it. This is the difference between feeling like AI is cheap and knowing it is cheap.

Price: £5 one-time. Less than most coffee receipts.

2. Anthropic Intel Brief — Cut Through the Noise

Model releases move fast. The Anthropic Intel Brief distills the latest Anthropic 65B model into one document with capability benchmarks, architecture insights, and strategic implications for your business.

What is inside:

Capability benchmarks across coding, reasoning, and safety
Architecture insights without the jargon
Strategic takeaways for teams evaluating model choices

Why it matters: Rather than reading ten blog posts and three research papers, you get one curated brief. Updated as the landscape changes.

Price: £9 one-time.

3. AI Phone Service — Never Miss a Call While You Code

Solo developers and small teams know the frustration: you are in flow state and a client call comes in. The AI Phone Service answers with a natural human-like voice, handles context, qualifies leads, and books appointments directly into your calendar.

What you get:

24/7 availability for business calls
Natural voice conversations, not robotic IVRs
Lead capture and calendar booking
Scales from solo founders to growing teams

Why it matters: Every missed call is a missed opportunity. This gives you coverage without hiring a full-time receptionist.

Price: Plans start at £99/month.

The Pattern

All three tools follow the same philosophy: small, focused, and honest about what they solve. They do not try to be platforms. They just solve one job well, cost a clear one-time or flat monthly rate, and stay out of your way.

If you are an indie hacker, solo dev, or engineering lead tired of opaque AI costs, these are worth a look.

AI Coding Cost Tracker → theaisuite.pages.dev/copilot-token-billing
Anthropic Intel Brief → theaisuite.pages.dev/anthropic-65b
AI Phone Service → theaisuite.pages.dev/ai-phone-service

If you found this useful, follow for more practical AI tooling and indie hacking content.

I Built "Assistants API Migration" — Here Is Why It Exists and How It Works

Muhammed Amar — Sun, 12 Jul 2026 06:12:17 +0000

I kept seeing the same problem, so I built Assistants API Migration to solve it.

The problem: OpenAI killing Assistants API Aug 26. Fixed-fee migration audit in 48 hours.

The solution: 🚨 Assistants API Migration — £300

Why now:

The market is moving fast and most people cannot keep up
Existing solutions are either too expensive or too complex
This is a solo-built alternative that just works

Stack: Cloudflare Pages + Stripe + automation cron jobs

Try it at theaisuite.pages.dev — would love feedback from the dev.to community.

🔗 theaisuite.pages.dev — 10 AI products, all with Stripe checkout.

Why I Track Every Major AI Model Release — and How You Can Too

Muhammed Amar — Sat, 11 Jul 2026 08:01:34 +0000

As of 2026, Anthropic is shipping frontier-class models faster than most teams can read their release notes. If you're a founder, engineering lead, or solo builder, that speed matters: each new model changes your costs, your product roadmap, and the capabilities you can offer users.

I recently compiled a focused briefing on Anthropic's latest model wave into a small actionable pack rather than another round of scattered blog posts and tweets. The goal was simple: cut through the noise and keep one organized place to understand architecture, benchmark behavior, and what actually changed.

What the briefing covers

Model lineage snapshot — what changed between the prior generation and the new release
Coding and reasoning benchmarks — practical signals that matter to developers, not just headline numbers
Safety and alignment moves — why Anthropic's choices affect your risk profile when choosing a model provider
Pricing context — real numbers when available, so you can compare smaller models or fine-tuned alternatives fairly

Who benefits from this

This kind of compiled intel is useful if you are:

Comparing whether to switch from one model family to another
Estimating how a new model affects prompt strategies or tool design
Recommending AI choices to non-technical clients or stakeholders
Keeping a product roadmap honest about provider-switching costs

How I use it day to day

I keep the brief in one file, not a sidebar set of bookmarks. That way I can compare specs, pricing, and behavior at a glance before making platform decisions or customer recommendations. I update it whenever a new release lands instead of trying to remember scattered sources months later.

A practical rule I follow

If you can't explain the actual differences between models in one paragraph, you're probably choosing based on brand rather than fit. That usually leads to unnecessary provider churn and higher API bills in the long run.

A small shortcut if you want one

If you'd rather not build the brief yourself, I also publish a concise briefing focused specifically on Anthropic's recent 65B-parameter model and its implications. It covers capability benchmarks, architecture insights, and strategic notes intended to speed up platform decisions.

Get the briefing here: https://theaisuite.pages.dev/anthropic-65b/

Related notes

Tracking models is not the same as tracking hype. I prefer one coherent brief over dozens of tweets because the decisions I make need to hold up after the hype cycle moves on.

DEV Community: Muhammed Amar

How I Run 14 AI Products Solo With $0/Month Hosting

The OpenAI Assistants API Is Shutting Down on August 26 — Here's Your Migration Checklist

The OpenAI Assistants API Is Shutting Down on August 26 — Here's Your Migration Checklist

1. Map every Assistants API surface in your codebase

2. Threads become Conversations

3. File uploads move to input_file parameters

4. Run polling becomes streaming + status polling

5. Fix tool-call schema mismatches

6. Budget a 60% migration sprint buffer

The shortcut

I Built "ContextBase (Agent Onboarding)" — Here Is Why It Exists and How It Works

I Built an AI Coding Cost Tracker to Finally See What Copilot and Cursor Are Actually Costing Me

The Problem Everyone Is Quietly Having

What the AI Coding Cost Tracker Does

Why One-Time Pricing Matters

If You're Building AI Products, Track Them Too

The Takeaway

I Built 10 AI Products as a Solo Developer — Here Is What I Learned

The AI Agent Space Is Creating 5 New Business Opportunities Right Now

Stop Guessing What Your AI Coding Tools Actually Cost

Stop Guessing What Your AI Coding Tools Actually Cost

What you get

Why this matters

The indiehacker angle

How to get started

Before You Build on Anthropic, Read This

The due diligence most people skip

What second-source intelligence looks like

Getting started without the guesswork

Bottom line

How I Run 14 AI Products Solo With $0/Month Hosting

3 Practical AI Tools for Developers Who Want Control Over Their AI Spend

1. AI Coding Cost Tracker — Know What You Spend

2. Anthropic Intel Brief — Cut Through the Noise

3. AI Phone Service — Never Miss a Call While You Code

The Pattern

I Built "Assistants API Migration" — Here Is Why It Exists and How It Works

Why I Track Every Major AI Model Release — and How You Can Too

What the briefing covers

Who benefits from this

How I use it day to day

A practical rule I follow

A small shortcut if you want one

Related notes

3. File uploads move to `input_file` parameters