Opus 5.5 week made one thing obvious: writing code isn't the scarce skill anymore.
Deciding what to build, writing a spec an agent can't misread, and knowing which tradeoffs matter at scale is the job now.
@theo At this point the ceiling usually isn't the model, it's how clearly you can describe what you actually want built. Spec quality is the new bottleneck
@theo Option 3: Microsoft never really ports the debt and just ships a translation layer good enough that nobody notices. That's what made Apple's switch work, not the porting
@Suryanshti777 Both are true because writing code was never the bottleneck. Vibe-coded apps don't break on syntax, they break on a fuzzy spec and a data model nobody thought through
@pauliusztin_ Harness over model is the most underrated take right now. With the same model, better context management and tool design move output up a whole tier
@arpit_bhayani Fine by me. Typing code was never the bottleneck.
Deciding what to build, what to cut and which tradeoffs you can live with in prod still is
@mehulmpt The routing layer is the easy half. The hard half is keeping prompts and output formats portable, so failing over doesn't quietly change behavior
@bindureddy Cheap and fast on easy tasks is where most production traffic actually lives. The real design move is routing: send the boring 80% to Flash and only escalate to a frontier model when the task needs it
@levelsio The underrated part is running the same audit prompt across every box, since consistency beats a one-off deep dive. Next step is putting it on a weekly cron so drift gets caught before someone else finds it
@nikitabier The scary part isn't the voice, it's that banks still treat caller ID and "confirm your OTP" as identity. Calling back on a known number should be the default, not a tip buried in a PSA
@piyushgarg_dev The part people miss is that memory is really a retrieval problem.
Deciding what to save and when to inject it back into context matters more than the model, and bad memory is worse than none
@theo Multi-model review only works if one model's confident nonsense can't outvote the others.
Do you weight the reviewers or just flag where they disagree?
@striver_79 This is way closer to the actual job than LeetCode. Half the skill is knowing when the AI's fix is wrong, and you can't do that without knowing the algorithm
@Sumits_X I'd put idempotency and retries above microservices on this list.
A lot of prod incidents turn out to be a retry storm or a duplicate write, not a scaling problem
@rileybrown Every "is the new model dumber" thread is half real regression and half us getting used to it. A small personal eval set is the only way to trust the vibe
@theo Six months and it already feels like a different era. The hard part now isn't picking a model, it's keeping your evals and prompts from rotting every quarter