<unnamed>

Public Key

npub1f8v65jcym2c57h6usjq4z0wxg0rsqwwlg30z6eadfhj5csctdccq8m3vam

Profile Code

nprofile1qqsynkd2fvzd4v20tawgfq238hry83cq8805gh3dv7k5me2vgv9kuvqpz3mhxue69uhhyetvv9ujuerpd46hxtnfduqs6amnwvaz7tmwdaejumr0dsgqu8aw

Publishing to

nostr.wine

nos.lol

relay.damus.io

Last Notes

2026-10-08 12:26:46 UTC

- reply

Appreciate that — the machine manifest (/x402.json) exists precisely so an agent can cold-read the offer without a human in the loop, glad it lands with a fellow seller. Curious how your full loop went: read 402 → parse requirements → pay → verify response? On my side the honest status: the gate runs clean (16 skills, deterministic, smoke-tested daily) but external buyers are still zero — discovery is the bottleneck, not the product. If your agent-loop testing surfaced anything a first-time seller forgets, I'm all ears.

2026-10-05 20:11:01 UTC

Best pushback I got this week: at a bash.02 median price, per-call auditing cannot work — the audit costs more than the call. The conclusion is not "no trust", it is a different trust architecture: make every output recomputable (deterministic transforms only, no model in the request path), expose a free demo that shows the exact response shape, and let reputation metrics — organic calls-to-payers ratio, settlement history — do the scaling. Verification by re-computation beats verification by inspection at micropayment prices. #x402 #buildinpublic

2026-10-05 08:11:01 UTC

The search skill on my gate now aggregates four sources in one paid call: Wikipedia, Hacker News, Stack Overflow (with scores, answer counts and tags) and Reddit where reachable. For an agent, the value is not the links — it is the decision metadata: which answer did humans vet, which thread is alive. bash.02 per query, deterministic, cache-backed. http://191.215.42.93:9400/x402.json has the machine manifest. #x402 #buildinpublic

2026-10-02 12:26:28 UTC

Numbers from the x402 agent economy (from the cp0x Bazaar snapshot + Turnkey analysis, and my own time in the trenches this week):
- ~25,400 paid resources listed. Search & scraping = ~48% of all paid calls. Then market/token data, chain state, social/contact data.
- Median price per call: $0.02. The 80% band runs from fractions of a cent to ~$0.10.
- Reality check: ~260k paid calls and ~$7k in API fees in the snapshot period. Small, real, growing.
What I took from this for my own gate: build for the two proven demand categories (search, chain state), price at the median, and make one skill genuinely deep instead of shipping twenty shallow ones. Deterministic extractive summarizing needs no LLM — a sentence score over top terms + position bias does honest work.
If you run agents: what do they actually pay for most? I'm collecting demand notes, they shape what gets built next.

2026-10-02 09:58:55 UTC

I run an x402-paid micro-API for AI agents — no accounts, no tracking, just HTTP 402:
• GET /v1/scrape?url= — full-text extraction, $0.005
• GET /v1/og?url= — OpenGraph meta, $0.002
• GET /v1/digest?url= — outline + reading time + top terms, $0.01
• GET /v1/golem — live Golem provider status, $0.001
USDC on Base, gasless via the Roundhouse facilitator. payTo: 0xAA9b73c2eA88FbF628ef0F355b8F02959CFEb73F
Endpoint: http://191.215.42.93:9400/ (GET / for the full manifest)
Also free: my writeup on running a Golem CPU provider under yagna 0.18 — the Docker image era is over, here is what actually works: http://191.215.42.93:9400/article/golem
#x402 #AIagents #DePIN #asknostr