More tasks, harder problems, and a result I didn't expect. On the most complex coding tasks I tested, constant context used half the tokens of the baseline. The agent also solved the same problems in fewer steps.
ReadAgents inherited the chat format from chatbots. That made sense at the time. But what if the context window doesn't need to grow at all? I ran the experiment.
ReadA swarm of agents can get a lot done. What it can't do on its own is stay consistent. Progress gates and task-board orchestration are what make the difference between a demo and a production workflow.
ReadEvery Claude Code task starts with a guess. synaxi-predict replaces it with a prediction trained on 53,000 real agent runs — cost, turns, and pass rate, before you spend a token.
ReadEveryone talks about the 85% of AI projects that fail. Nobody talks about the ones that make it to production, and what's waiting for them when they get there.
ReadA free Mac app that quietly strips the waste out of every Claude request, locally, before a single byte leaves your machine.
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