Kaikim Labs

Over two days the lab used an AI agent pipeline (chip lab, driven by Claude Code) to take a small dot product accelerator from a UART toy to a weight stationary INT4 design that does 2 multiply accumulates per clock in a single 1x1 Tiny Tapeout tile on SkyWater sky130. Every version closed timing at 50 MHz with DRC and LVS clean, passed gate level stress tests, and ran a real 64 to 10 digit classifier bit identical to Python. Nothing was paid for and nothing was fabricated. The current reading is that the agent's leverage comes from cheap, automatic rejection of bad candidates, and the binding constraint is the quality of the checks, not writing RTL.

Large DeFi stablecoin deposits pay about 0.67 percentage points more than tokenised Treasury funds, but 93 percent of that gap is reward token emissions. Measured on base APY alone, the premium is 0.05 points. The hypothesis is that the base premium stays near zero and the visible premium compresses as emissions end, which makes the Treasury fund rate the real floor and ceiling for passive stablecoin yield.

Automatically generated daily research record for 2026-10-17 at Kaikim Labs, listing the day's commits and research document changes. Daily logs are permanent records of how the archive evolved.

On 2026-10-16 Hyperliquid held 45.9 percent of open interest across the six perp venues SwanBluechip tracks, second to Bybit at 47.4 percent, while the two zero fee venues took 19.3 percent of volume but only 6.1 percent of open interest. The prediction is that Hyperliquid's share of open interest across the same six venues will be at least 40 percent on 2027-04-16, because open interest follows depth and depth has not followed zero fees.

A solo builder with language models can run several products at once, but products marketed separately compete for the same attention and none of them compounds. This model assigns each product a role in one research loop. Products that collect data are instruments. Products whose outcome is unknown are experiments. The company is the institution that signs. A measurement product checks whether the research is cited. The lab publishes what the instruments say, with confidence scores, and the products inherit the credibility.

Birdfury is an open mission network where humans and AI agents submit work and get paid in USDC. In August 2026 the lab made it accept x402 payments from standard clients, verified with a real x402-fetch round trip. Two months later no agent that the lab does not operate has paid over x402. The experiment's current reading is that payment rail compatibility is necessary but not sufficient, and the binding constraint is the absence of agents with budgets, not the rail.

AI search visibility, short-form video repurposing, and small-business AI agents are becoming one operating layer for modern companies. The near-term opportunity is not generic AI content generation, but measurable systems that turn existing expertise, recordings, and data into discoverable, reusable, and revenue-linked assets.

When the supplier is software, the marginal cost of producing one more plausible deliverable falls to almost zero. Labor stops being the scarce input and evaluation becomes the scarce input. My hypothesis is that a marketplace for AI agent work is therefore not a matching market at all: it is an evaluation market, and the platform that wins will be the one that makes deciding which submission is correct cheap, fast and trustworthy. Everything else, discovery, payment, settlement, is already commodity infrastructure.

I built Birdfury, an open mission network where a client posts a paid task and any solver, human or autonomous agent, submits work without asking permission. The full work loop runs in production over web, REST and MCP, the payment rail is live, and the onchain settlement layer is deliberately switched off until a deployed contract passes verification. After two rounds of seeded missions and one live bounty, the network has produced almost no external demand. This document records what I built, what it actually did, and what the absence of demand taught me about where the constraint in agent labor markets really sits.