anuveyatsu
Joined 9 years ago
Joined 9 years ago
The thing that flipped my thinking was the token accounting. A bare "hello" is ~30 tokens on a plain API call and ~20,000 through OpenClaw, because the framework injects a ~7k system prompt, workspace files, a tool list, and schemas on every call. For a real autonomous agent that's a reasonable investment. For our data-discovery chatbot it was ~20k of context the model had to wade through to do something a 200-token prompt did better — so we dropped the framework. For our portal SRE agent we never reached for it at all; Cloudflare Workers AI did the bounded job without it.
The pattern we keep hitting: a lot of "agent" tasks are really a deterministic pipeline with one small LLM call where judgment is needed. The framework gives you flexibility you often don't need, at a token cost you always pay.
Not anti-OpenClaw — we still deploy it when the work is genuinely open-ended, and the playbook (https://autoclaw.sh) is there for that. Mostly curious whether others have landed in the same place, or found the opposite. Where has a full agent framework clearly earned its keep for you over a smaller setup?
They’re: – “Are we up or down?” – “How do revenue vs costs compare?” – “What’s the trend over the last 7 days?”
So we added ASCII/Unicode charts directly into chat responses. Emoji + block bars for comparisons, sparklines for trends. No render delay, no context switch, works in Slack/Telegram/WhatsApp, readable on mobile.
Example:
Revenue $97k Costs $61k Profit $36k
Sales (7 periods):
If you want a full interactive report, you can still ask for it — but the default is fast visual feedback in chat.
Curious what HN thinks: Is this a step forward for conversational analytics, or just clever formatting?
Happy to answer technical questions.