I stress-tested Google’s new Colab MCP server with a real quantum workflow.
An AI agent took a blank Colab notebook and:
• installed dependencies
• fixed Qiskit compatibility issues
• ran a 15-point H2 scan
• submitted a real job to IBM Quantum hardware
This is where Colab gets interesting: not just notebook editing, but agent-operated cloud execution.
Article on @thepracticaldev
https://dev.to/axrisi/i-stress-tested-googles-colab-mcp-server-with-a-real-quantum-workflow-2oih
#AI #MCP #Colab #QuantumComputing #Qiskit #IBMQuantum #Python #CloudComputing
Meta is removing end-to-end encryption from Instagram DMs on May 8. Official reason: low adoption.
What they're not highlighting: Meta trains AI on platform data and says encrypted messages can't be used for that. Their ad business depends on data signals. Instagram DMs are where creator-brand conversations and purchase intent live.
WhatsApp keeps encryption because privacy is the product. Instagram is a commerce platform. Different calculus.
#Privacy #AIAdvertising #MetaAds #AdTech #EncryptionSpent the last few months studying how AI apps generate revenue. Not pitch deck theory, actual working models.
7 keep showing up: subscription tiers, usage-based pricing, freemium, intent-based ads, API marketplace, outcome-based pricing, and hybrid stacking.
The pattern: no successful AI company relies on one stream. They stack 2-3 together.
Full breakdown of each model in a LinkedIn carousel I just posted.
#AIMonetization #AIStartups #AdTech #BusinessModelsA Gemini user asked a simple question about Japanese grammar. The AI response included a JSON block with a real name, email address, and phone number.
The user shared the conversation publicly to prove it happened. Gemini appears to have pulled personal data from a previous session, a memory feature, or possibly another user's conversation.
You ask about grammar, you get a stranger's phone number. At Vexrail we went strict no-PII. Intent-based ads only.
#Privacy #Gemini #DataSecurity #AI
We're moving from "learn to code" to "learn to build." The abstraction layer is rising fast.
Assembly to C, C to Python, frameworks, now AI tools. At each level, the skill shifts from "how to implement" to "what to build and why."
Developers who thrive next will understand systems thinking, problem decomposition, tradeoff evaluation, and clear communication.
Tools get better at writing code. The human skill is knowing what code to write. That's always been the harder problem.
Fork, experiment, break things. That's how open source was meant to be used.
Too many developers treat repos as read-only museums. But you can fork, rip out parts, add weird features, break it completely and learn from the wreckage.
Your fork doesn't need to be perfect or become a PR. It just needs to teach you something.
The source is open. Use it.
Write your error messages for the person debugging at 2 AM.
Bad: "Something went wrong." Good: "Failed to connect to database at postgres://db:5432. Connection timed out after 30s. Check that the database is running."
The person reading your error is stressed, tired, and on-call. They don't have your context.
Every error message is documentation. Write it like someone's production depends on it.
The next wave of developer tools will be context-aware.
Your IDE already knows: project structure, dependency graph, coding conventions, git history.
Imagine: code suggestions that understand your architecture, error messages that know your setup, refactoring that understands your domain model.
We're moving from tools that help you write code to tools that understand your codebase. That's a massive difference.
Open source sustainability isn't just about funding. It's about reducing maintainer burden.
Maintainers burn out because every issue is a support ticket, PRs arrive without context, and users demand features without understanding tradeoffs.
What actually helps: docs that answer common questions, automated triage, clear contribution guides, a community that shares the work.
Don't just donate. Reduce the burden on the people building it.
If your database queries are slow, add observability before you add indexes.
Too often: slow page reported, developer adds an index to the obvious query, nothing improves because the real problem was N+1 queries or lock contention.
Know which queries are actually slow, how often they run, and what the execution plan looks like.
You can't optimize what you can't measure.
