If you want to stay up to date on frontier AI, follow people inside the labs and product teams.
For Anthropic, the strongest follows are @karpathy, @bcherny and @trq212 for Claude, Claude Code and AI engineering signal.
For OpenAI, follow @polynoamial for reasoning research, @gabriel1 for Sora and creative AI work, and @jxnlco for Codex and developer experience.
For Google AI, @OfficialLoganK is one of the best sources for Gemini and AI Studio updates, while @ammaar and @fofrAI share strong product, design and generative model use cases.
For Cursor, @leerob, @ericzakariasson and @mntruell are the accounts to watch for releases, workflows and usage updates.
For xAI, @milichab, @skcd42 and @elonmusk are the best follows for Grok momentum and product amplification.
The best AI feed comes from builders, not secondhand summaries.
Most AI feeds are full of recycled takes and engagement farming.
Build your timeline around people actually doing the work.
@rowancheung → daily AI news and curation
@rasbt → practical ML and LLM implementations
@emollick → real-world AI and society insights
@steipete → AI product-building systems and tooling
@simonw → LLM tools, prompting and experiments
@elder_plinius → AI safeguard bypass techniques
@ID_AA_Carmack → AGI and low-level optimization thinking
@jeremyphoward → accessible deep learning education
@mattshumer_ → AI agents and agentic workflows
@lilianweng → technical LLM breakdowns
@AmirMushich → prompting for high-quality visuals
@kloss_xyz → AI workflow building
@fchollet → intelligence, benchmarks and AI limits
@gwern → deep LLM essays and analysis
@AndrewYNg → practical ML education and implementation
Good AI feeds compound.
Follow builders, researchers and operator
You can now turn Claude Code, Codex and Cursor into something much closer to a real senior developer.
And it’s fully open source.
Superpowers has already crossed 94,000 GitHub stars.
What it does:
It makes the agent brainstorm before touching code.
It enforces red/green TDD with no exceptions.
It deletes code if there was no test first.
It runs parallel subagents in isolated git worktrees.
The difference is huge.
Standard Claude Code jumps straight into implementation, debugs blindly and often skips tests.
Claude Code with Superpowers follows a 7-phase flow:
Brainstorm
Spec
Plan
TDD
Subagents
Review
Ship
No code before passing tests.
It works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode and Copilot CLI.
For larger projects, agents start hallucinating, breaking tests and adding dead code.
Superpowers forces a senior engineering process and keeps context under control.
You stop prompting blindly.
Your agent starts working like a senior
Claude for Small Business is way bigger than most people realize.
Anthropic quietly shipped a workflow stack that already hit 382,000 downloads on launch day, and I put together a full breakdown of how the entire thing fits together.
I organized all 31 skills by function and mapped the connector logic behind them.
The guide includes:
priority order for all 12 connectors
permission setup for high sensitivity actions
examples showing Business Pulse, Invoice Chase, and Job Post Builder in practice
recommendations on which 5 skills to activate first
Everything from hiring to finance to client ops is covered.
CLARITY Act winners are pretty obvious.
The bill clears the regulatory fog between the CFTC and SEC.
That alone unlocks sectors that have been stuck for years.
Polymarket gives it a 76% chance of passing in 2026.
The biggest winner:
real-world asset tokenisation.
Tokenised treasuries.
Private credit.
On-chain securities.
This is the framework serious capital has been waiting for.
Pension funds, Apollo, and BlackRock do not move size into ambiguity.
Then comes the infrastructure layer:
oracles
cross-chain interoperability
compliance rails
Once banks move on-chain, these become non-optional.
Claude Code stops feeling vanilla once you add this.
Anthropic quietly released an official plugin called claude-code-setup.
And it changes the whole workflow.
Instead of manually guessing what your project needs, it scans the repo and recommends:
→ hooks
→ skills
→ MCP servers
→ subagents
→ automations
Then it walks you through the setup step by step.
Most people are still using Claude Code out of the box.
That is why it feels chaotic.
The real upgrade is the ecosystem around it.
Install:
/plugin install claude-code-setup@claude-plugins-official
This is the uncomfortable reality of the AI race:
The best model does not win.
The model people can reliably use wins.
If users hit refusals, rate limits, overloaded servers, or “try again later” during real work, they stop caring about benchmark scores very quickly.
Raw intelligence matters.
But reliability is a feature.
Latency is a feature.
Context limits are a feature.
Compute access is a feature.
The history of tech is full of companies with superior technology that lost because they could not scale distribution or infrastructure fast enough.
AI is becoming infrastructure now, not just research.
And infrastructure businesses are judged on consistency, not demos.