Observed Signal · Jul 24, 2026 · Technical Analysis · Source: DEV Community · Impact: 3/5 · Sentiment: Negative
AI Used 42,000 Tokens to Answer 'Who Are You'
A developer captured network logs from a session with Claude Code and found that a 19-byte question (“who are you?”) generated a request payload of 118,693 bytes and required the model to ingest roughly 42,270 tokens of context before producing a 127-token reply. The request contained 38 tool JSON schemas (78,935 bytes) of which 44,392 bytes were English prose, a 20.7KB system prompt, and numerous server-side feature flags. The author observed additional parallel calls (a separate title-generation model, 40 token-counting calls) and ~600KB of telemetry namespaced under 'tengu_'. Many core tool schemas and system prompts are controlled server-side by Anthropic and cannot be disabled by the end user, while only a small fraction of the payload is user-controllable.
Reveals large context, telemetry, and server-side controls in LLM client requests that have implications for cost, latency, privacy, and developer control when deploying conversational AI.
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Key Takeaways & Evidence Grounding
- Author sent a 19-byte question 'who are you?' to Claude Code.
- The request payload was 118,693 bytes, a ~6,200x amplification versus the question size.
- 38 tool JSON schemas totaled 78,935 bytes; 44,392 of those bytes were English prose.
- The model ingested ~42,270 tokens of context and produced a 127-token response; the roundtrip took 8.7 seconds.
- The client issued an additional parallel title-generation call, 40 token-counting API calls, and ~600KB of telemetry across 184+ 'tengu_' events.
Connected Companies & Entities
1 Entity mapped“And the telemetry. Oh, the telemetry. In the same 15-second window: ~600KB across 184+ events, every one namespaced `tengu_` — Anthropic's i...”
Ontology Mapping & Concepts
Related Market Signals & Shifts
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AI Tokenmaxxing: Meta's 60 Trillion Token Gamble
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Tool I/O Bloated Claude Code; Throughline Cuts Tokens 90%
A developer measured Claude Code session transcripts and found 188,000 tokens per turn with 164,000 tokens (87%) coming from conversation history; roughly 80% of that history was tool I/O (file outputs, command results). Trimming CLAUDE.md and tool definitions would only affect ~9% of tokens. To address this, the author built Throughline, an open-source Node.js tool that stores evicted tool I/O in SQLite and keeps a 3-layer context model (L1 skeleton summaries, L2 recent full conversation body for last 20 turns, L3 detailed tool I/O evicted to DB). In a 50-turn example the approach reduced context from ~125,000 tokens to ~13,000 (~90% reduction). Throughline is on GitHub, MIT licensed, requires Node.js 22.5+ and a Claude MAX contract. Publication date: 2026-06-04.
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