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The Subscription Fatigue is Real I was paying $47 for ChatGPT Plus, $29 for Jasper, $19 for Grammarly, $16 for Copy.ai, and $15 for an SEO tool. That's $126/month just for AI writing tools. So I built my own. Five tools, one dashboard, completely free to start. Here's how each one works and what it replaces. 1. AI Content Writer (Replaces Jasper, Copy.ai — $66/month combined) The content writer generates blog posts, articles, product descriptions, and marketing copy. You pick: Content type : blog post, article, product description, marketing copy, newsletter Tone : professional, casual, friendly, authoritative, humorous, persuasive Length : short (100-200 words), medium (300-500 words), or long (800-1200 words) The key difference from Jasper: no templates, no "brand voice" setup. You just describe what you want and get it. Simpler, faster. 2. AI Email Composer (Replaces Grammarly Business — $16/month) This one handles the emails I hate writing: Cold outreach to potential clients Follow-up emails after meetings Professional inquiries Customer support replies You set the formality level (formal, semi-formal, casual) and urgency. It writes the subject line AND the body. I've used it for 50+ cold emails last month. 3. Social Media Caption Generator (Replaces Later + caption tools — $29/month) Generates 3 caption variations per request. Platform-specific: Instagram : emojis, hashtags, engagement hooks Twitter/X : concise, thread-ready LinkedIn : professional, thought-leadership style TikTok : casual, trend-aware Options for emojis, hashtags, and CTAs are toggleable. You can mix and match from the 3 generated options. 4. AI Code Helper (Replaces GitHub Copilot Chat — $10/month) Five modes: Generate : write code from description Debug : find and fix errors in pasted code Explain : break down complex code Refactor : improve code quality Convert : translate between 20+ languages Supports Python, JavaScript, TypeScript, Java, C++, Go, Rust, SQL, and more. Not as deeply integr

If you've ever received a .pages or .numbers file on a Windows PC, you know the pain — you can't open it. No preview, no converter built in, and Apple's iCloud web tools are slow and clunky. So I built iworkviewer.com — a free, browser-based iWork file viewer and converter. No signup, no upload to any server. Everything happens in your browser. What it does Open .pages files → view them instantly, export to PDF or .docx Open .numbers files → view spreadsheets, export to .xlsx or PDF Open .keynote files → view presentations, export to PDF or .pptx Batch convert multiple iWork files at once The tech Built with Next.js, Cloudflare Pages, and pure client-side JavaScript. All file processing happens in the browser — your files never leave your computer. Zero server costs, zero privacy concerns. Why I built it I kept seeing Reddit threads and Quora questions: "How do I open a Pages file on Windows?" The answers were always the same — use iCloud.com (slow), download some sketchy converter (risky), or ask the sender to export as PDF first (annoying). I figured: if the browser can read a file, it can convert it. And it turns out, it can. Try it 👉 iworkviewer.com Open a .pages, .numbers, or .keynote file right in your browser. Free, forever, no account needed.

What Claude Sonnet 5 Means for AI Infrastructure in East Africa The release of Claude Sonnet 5 on June 30, 2026 changes something specific about building AI agent infrastructure for regions like East Africa: the model tier that couldn't reliably finish a multi-step workflow now can. This isn't a general AI update note. It's about a concrete technical constraint that just moved. The constraint that moved East Africa's AI infrastructure problem isn't compute or APIs. M-PESA has an API. Africa's Talking has an API. NDMA publishes drought data. KRA has a taxpayer portal. The constraint has been that an AI agent calling several of these in sequence — check drought severity → trigger insurance evaluation → notify county — would stop partway through, lose context, or require manual handholding to continue. Sonnet 4.6, released in February, scored 67.0% on Terminal-Bench. Sonnet 5, released today, scores 80.4%. That 13-point gap isn't abstract. It's the difference between an agent that stalls at step two of a cascade and one that finishes. What this means for the East Africa coordination stack The 31 MCP servers in this portfolio — covering M-PESA, drought data, tax, credit scoring, crop insurance, land records, labor rights, county data, and more — are now meaningfully more useful as a system than they were yesterday. The key change: africa-coord-bus , the coordination event bus that connects these servers, is now the kind of tool Sonnet 5 was designed to orchestrate. A drought alert from wapimaji-mcp , cascading through bima-mcp for insurance evaluation and county-mcp for notification, is exactly the multi-hop tool chain where the 13-point Terminal-Bench improvement shows up in practice. The model to use # Claude API client = anthropic . Anthropic () response = client . messages . create ( model = " claude-sonnet-5 " , max_tokens = 1024 , tools = [...], # your MCP tools messages = [{ " role " : " user " , " content " : " ... " }] ) For compliance and vulnerability analysi

The problem every AI coding session hits You open Claude or Copilot, paste in your task, and immediately hit the wall: the codebase is too big. You either: Dump everything and burn 80% of your context window on irrelevant files Hand-pick files and miss the one import that breaks everything Pay for a bigger context window and repeat the problem at scale I got tired of this and built ContextOS — a local CLI that acts as an intelligent context layer between your repo and your AI agent. What it does pip install rm-contextos cd your-project contextos scan contextos pack --task "add rate limiting to the auth endpoint" --budget 8000 Output: a Markdown (or JSON) context pack with only the files that matter for that task — ranked by keyword match, import graph centrality, AST symbol overlap, and git churn. Secrets redacted automatically. Token savings report on every pack: Packed 12 files · ~6,840 tokens · saved ~47,200 tokens (87%) vs full repo How ranking works Five signals combine into a score per file: Signal What it catches Keyword match Files whose content/name overlap with your task Import graph centrality Files that everything else imports (critical shared modules) AST symbol overlap Function/class names, not just grep strings Git churn score Recently modified files are probably active code Secret penalty Credential files silently excluded No LLM calls. No cloud. Fully offline. MCP server (for Claude Desktop / Claude Code) pip install "rm-contextos[mcp]" contextos serve --stdio Register in claude_desktop_config.json and your AI agent can call pack_context , scan_repo , list_files , get_file , churn_report directly as tools — no CLI needed. What's shipped 980 tests, 96% coverage Apache-2.0, no telemetry, no accounts Python 3.11–3.13, Linux + macOS Export formats: Claude, Codex, Cursor, Aider, JSON Incremental scan cache — re-scans only changed files pip install rm-contextos pip install "rm-contextos[mcp]" # + MCP server pip install "rm-contextos[all]" # everything Git