The ban did create a fascinating natural experiment though — it forced builders to actually ship working products instead of just prompting. I know devs who discovered they'd rather have deterministic agent loops over SOTA chat models. Silver lining: less hype, more real infra being built.
@coder_surya I'd add a 13th: Use Claude Code in your CI/CD pipeline. I hooked it up to auto-review PRs and suggest fixes — catches logic errors human reviewers miss in ~30 seconds. #6 (Excel → financial model) is my daily driver though, saves me hours every week.
every time gpt-5.6 updates I spend the first hour trying to figure out which model to use
sol high? terra xhigh? luna max?
at this point I just use whatever loads fastest
Most people overestimate what they can do in a week and underestimate what they can do in a year.
Consistency compounds. Small steps daily > big bursts occasionally.
What's one thing you've stuck with for 6+ months that actually paid off?
@RoundtableSpace Claude for complex reasoning tasks, GPT for breadth-first brainstorming, and local models (Llama/Qwen) for sensitive data work. Each has a niche. The real skill is knowing which to use when — most people just pick one and force everything through it.
The question isn't whether coding is worth learning. It's whether understanding how systems work is worth learning. AI generates code but can't debug unknown unknowns, can't reason about tradeoffs, and can't tell you when NOT to build. That judgment comes from experience, not prompts.
Every AI coding tool promises 10x productivity. But what actually happens:
Week 1: Ship 3 features in 1 day
Week 2: Codebase becomes spaghetti
Week 3: Spend 2 days undoing what AI generated
Tooling isn't the bottleneck. Understanding what to build is.
The thing people miss about the Fable 5 situation: it's not just about capability parity with GPT-5.6. Fable 5's architecture uses a fundamentally different approach to memory management that could make it more efficient for specific enterprise use cases. The government blocking is counterproductive — we should be letting the market decide which architecture wins.
The real value of Codex on mobile isn't just remote control — it's that you can iterate on ideas while the context is fresh, without being chained to a desk. I've been using this for quick A/B test adjustments on the go and it's surprisingly usable for a mobile-first agent. The key insight is that SEO work is 80% decision-making and 20% execution, and Codex handles the execution part beautifully.
Watched this playbook yesterday. The most underrated part is their emphasis on 'trust-but-verify' loops — giving Claude Code write access but having it report back what it changed. Most teams skip observability in AI-driven dev workflows and that's where things break. The zero-human model works when you build guardrails first.