Research
Questions first. Products second.
We research how AI and on-chain software can be trusted with real work and real money. Three areas guide everything we build.
ResearchAI
AI
Agents you can hand real work to.
- Agentic software engineering
- Grounding and verification
- Human supervision
- Evaluation
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ResearchCrypto
Crypto
Infrastructure for money that runs on-chain.
- Market microstructure
- Autonomous risk
- Self-custody treasury
- Validator operations
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Researchd/acc
d/acc
Accelerate the technology that keeps people free and safe.
- Defense over offense
- Self-sovereign tools
- Decentralized infrastructure
- Open verification
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Latest writing
October 22, 2026 7 min read
Disposable by default: when software costs almost nothing to change
Cost of ownership was the reason small businesses bought instead of built. As the cost of changing code falls toward zero, that reason fades, and the hard work moves upstream.
When changing software costs almost nothing, stop owning it and start specifying it. The rigor moves from the code to the definition of done.
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October 22, 2026 6 min read
The jockey and the horse: why software that can't fail needs more engineers, not fewer
Most software is about to stop being an event, the way horses did for transport. The software that can't fail is heading the other way.
As AI makes everyday software disposable, the few systems that can't be disposable will need more human engineering and harder gates than ever.
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October 6, 2026 8 min read
The most valuable code in a trading system is the code that says no
Modern tools make thousands of backtests cheap. Statistics says that's exactly the problem.
Every backtest you run makes the next one less believable. The durable edge is in the rules that refuse trades.
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September 29, 2026 8 min read
Models are rented. Memory should be owned.
The best model changes every few months. The memory you build using it shouldn't be stuck with whoever was ahead when you started.
If you can't take your memory to another model, it isn't your memory.
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September 17, 2026 8 min read
Give AI to your newest hire first
Most businesses roll AI out to their best people. The evidence says the biggest payoff is at the bottom of the learning curve.
AI is a ramp, not a raise: it helps your newest people most, so give it to them first.
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September 2, 2026 9 min read
AI tutoring is a motivation problem, not an intelligence problem
Tutors that give answers make students worse. Tutors that refuse get ignored. Smarter models fix neither.
AI tutoring is a motivation problem wearing an intelligence costume.
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August 21, 2026 8 min read
For agentic work, the note is the deliverable
When agents do work nobody watched, the account of the work becomes more valuable than the work itself.
If a task can't be redone from its closing note, it isn't done.
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August 3, 2026 9 min read
The best agent needs the model less every week
Agents are judged by how smart their decisions are. We think they should be judged by how few decisions they still need a model to make.
Measure an agent by how small its judgment surface gets, not by how smart its judgments are.
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July 16, 2026 9 min read
Save less: the case against total recall
Personal AI is racing to remember everything. The evidence from cognitive science and from language-model retrieval points the other way.
A memory system should be judged by what it lets you forget, not by what it keeps.
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