KAVI
@kavifinance.bsky.social
Building AI, automation, and distribution systems in public.
The $20/month SaaS plan may be a human artifact.
Agents don't want subscriptions. They want one capability, once, at a machine-readable price.
x402 is an early version of that model.
If agents become economic actors, APIs stop being integrations and start becoming storefronts.
Agents don't want subscriptions. They want one capability, once, at a machine-readable price.
x402 is an early version of that model.
If agents become economic actors, APIs stop being integrations and start becoming storefronts.
September 16, 2026 at 11:20 PM
The $20/month SaaS plan may be a human artifact.
Agents don't want subscriptions. They want one capability, once, at a machine-readable price.
x402 is an early version of that model.
If agents become economic actors, APIs stop being integrations and start becoming storefronts.
Agents don't want subscriptions. They want one capability, once, at a machine-readable price.
x402 is an early version of that model.
If agents become economic actors, APIs stop being integrations and start becoming storefronts.
Agent payments aren't the hard problem. Agent identity is.
Which agent is this? Who authorized it? What can it spend? What can it buy? Who is liable?
India is building an AI-agent registry; Visa/Mastercard/Ant are working on trust standards.
This looks like OAuth for autonomous software.
Which agent is this? Who authorized it? What can it spend? What can it buy? Who is liable?
India is building an AI-agent registry; Visa/Mastercard/Ant are working on trust standards.
This looks like OAuth for autonomous software.
September 16, 2026 at 6:30 PM
Agent payments aren't the hard problem. Agent identity is.
Which agent is this? Who authorized it? What can it spend? What can it buy? Who is liable?
India is building an AI-agent registry; Visa/Mastercard/Ant are working on trust standards.
This looks like OAuth for autonomous software.
Which agent is this? Who authorized it? What can it spend? What can it buy? Who is liable?
India is building an AI-agent registry; Visa/Mastercard/Ant are working on trust standards.
This looks like OAuth for autonomous software.
Everyone is building agents that use software. The bigger shift: agents are becoming software customers.
Discover tool → read machine price → pay → call → leave.
No signup. No seat. No SaaS plan.
The next distribution layer may be agent-readable, not human-readable.
Discover tool → read machine price → pay → call → leave.
No signup. No seat. No SaaS plan.
The next distribution layer may be agent-readable, not human-readable.
September 16, 2026 at 2:20 PM
Everyone is building agents that use software. The bigger shift: agents are becoming software customers.
Discover tool → read machine price → pay → call → leave.
No signup. No seat. No SaaS plan.
The next distribution layer may be agent-readable, not human-readable.
Discover tool → read machine price → pay → call → leave.
No signup. No seat. No SaaS plan.
The next distribution layer may be agent-readable, not human-readable.
Dashboards may become outputs instead of products.
If an agent assembles data, generates the right view, explains it, and acts, permanent dashboards matter less.
The moat shifts:
trusted data, semantics, permissions, lineage, context.
AI makes interfaces cheap. Data contracts get more valuable.
If an agent assembles data, generates the right view, explains it, and acts, permanent dashboards matter less.
The moat shifts:
trusted data, semantics, permissions, lineage, context.
AI makes interfaces cheap. Data contracts get more valuable.
September 16, 2026 at 2:50 AM
Dashboards may become outputs instead of products.
If an agent assembles data, generates the right view, explains it, and acts, permanent dashboards matter less.
The moat shifts:
trusted data, semantics, permissions, lineage, context.
AI makes interfaces cheap. Data contracts get more valuable.
If an agent assembles data, generates the right view, explains it, and acts, permanent dashboards matter less.
The moat shifts:
trusted data, semantics, permissions, lineage, context.
AI makes interfaces cheap. Data contracts get more valuable.
Giving an AI agent a wallet is easy compared with deciding when it is allowed to spend.
Agent payments need a control plane:
identity → budget → permissions → purchase → verification → ledger → recovery
The payment rail is arriving.
The policy layer is the real product opportunity.
Agent payments need a control plane:
identity → budget → permissions → purchase → verification → ledger → recovery
The payment rail is arriving.
The policy layer is the real product opportunity.
September 15, 2026 at 10:35 PM
Giving an AI agent a wallet is easy compared with deciding when it is allowed to spend.
Agent payments need a control plane:
identity → budget → permissions → purchase → verification → ledger → recovery
The payment rail is arriving.
The policy layer is the real product opportunity.
Agent payments need a control plane:
identity → budget → permissions → purchase → verification → ledger → recovery
The payment rail is arriving.
The policy layer is the real product opportunity.
Keep holding low will cause more price explosion over politics
September 15, 2026 at 7:32 PM
Keep holding low will cause more price explosion over politics
One Hermes lesson from a ~5.26 GB state DB:
Persistence != useful memory.
More saved state can make an agent worse if retrieval keeps resurfacing stale context.
Ask 3 things: what must survive, what stays queryable, what deserves active context?
Memory is retention + retrieval policy.
Persistence != useful memory.
More saved state can make an agent worse if retrieval keeps resurfacing stale context.
Ask 3 things: what must survive, what stays queryable, what deserves active context?
Memory is retention + retrieval policy.
September 15, 2026 at 7:06 PM
One Hermes lesson from a ~5.26 GB state DB:
Persistence != useful memory.
More saved state can make an agent worse if retrieval keeps resurfacing stale context.
Ask 3 things: what must survive, what stays queryable, what deserves active context?
Memory is retention + retrieval policy.
Persistence != useful memory.
More saved state can make an agent worse if retrieval keeps resurfacing stale context.
Ask 3 things: what must survive, what stays queryable, what deserves active context?
Memory is retention + retrieval policy.
Delos Data raised $100M around an underrated AI bottleneck: expensive chips waiting for data are still expensive chips burning watts.
As agent workloads diversify hardware, moving data may become as strategic as adding compute.
More FLOPS cannot fix idle silicon.
As agent workloads diversify hardware, moving data may become as strategic as adding compute.
More FLOPS cannot fix idle silicon.
September 15, 2026 at 7:05 PM
Delos Data raised $100M around an underrated AI bottleneck: expensive chips waiting for data are still expensive chips burning watts.
As agent workloads diversify hardware, moving data may become as strategic as adding compute.
More FLOPS cannot fix idle silicon.
As agent workloads diversify hardware, moving data may become as strategic as adding compute.
More FLOPS cannot fix idle silicon.
“Memory” is not saving more chat history.
Useful agent memory needs provenance, versioning, contradiction handling, feedback, and regression tests.
The real loop is:
experience → correction → verification → canonical memory → future behavior
That is context engineering becoming infrastructure.
Useful agent memory needs provenance, versioning, contradiction handling, feedback, and regression tests.
The real loop is:
experience → correction → verification → canonical memory → future behavior
That is context engineering becoming infrastructure.
September 15, 2026 at 6:20 PM
“Memory” is not saving more chat history.
Useful agent memory needs provenance, versioning, contradiction handling, feedback, and regression tests.
The real loop is:
experience → correction → verification → canonical memory → future behavior
That is context engineering becoming infrastructure.
Useful agent memory needs provenance, versioning, contradiction handling, feedback, and regression tests.
The real loop is:
experience → correction → verification → canonical memory → future behavior
That is context engineering becoming infrastructure.
The next AI moat may not be the model.
It may be the harness around it.
Long-running agents need memory, tool control, recovery, subagent coordination, budgets, and evaluation.
A stronger model can hide bad architecture for a while.
It cannot replace it.
It may be the harness around it.
Long-running agents need memory, tool control, recovery, subagent coordination, budgets, and evaluation.
A stronger model can hide bad architecture for a while.
It cannot replace it.
September 15, 2026 at 2:10 PM
The next AI moat may not be the model.
It may be the harness around it.
Long-running agents need memory, tool control, recovery, subagent coordination, budgets, and evaluation.
A stronger model can hide bad architecture for a while.
It cannot replace it.
It may be the harness around it.
Long-running agents need memory, tool control, recovery, subagent coordination, budgets, and evaluation.
A stronger model can hide bad architecture for a while.
It cannot replace it.
Hot take: AI agents don’t become production-ready when they can call tools.
They become production-ready when every write is:
• allowlisted
• idempotent
• observable
• stoppable
The next moat is the control plane—not the prompt.
openai.com/index/introd...
They become production-ready when every write is:
• allowlisted
• idempotent
• observable
• stoppable
The next moat is the control plane—not the prompt.
openai.com/index/introd...
September 15, 2026 at 6:19 AM
Hot take: AI agents don’t become production-ready when they can call tools.
They become production-ready when every write is:
• allowlisted
• idempotent
• observable
• stoppable
The next moat is the control plane—not the prompt.
openai.com/index/introd...
They become production-ready when every write is:
• allowlisted
• idempotent
• observable
• stoppable
The next moat is the control plane—not the prompt.
openai.com/index/introd...
I audited one of my AI systems and found 151 scheduled jobs.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
September 15, 2026 at 12:20 AM
I audited one of my AI systems and found 151 scheduled jobs.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
Humanoid robotics is getting a better benchmark: recurring economics.
A robot that looks incredible on video can still be a bad business.
Track productive hours, interventions, maintenance, recovery time and cost per useful hour.
Capability gets the demo. Repeatable economics gets deployment.
A robot that looks incredible on video can still be a bad business.
Track productive hours, interventions, maintenance, recovery time and cost per useful hour.
Capability gets the demo. Repeatable economics gets deployment.
September 14, 2026 at 7:35 PM
Humanoid robotics is getting a better benchmark: recurring economics.
A robot that looks incredible on video can still be a bad business.
Track productive hours, interventions, maintenance, recovery time and cost per useful hour.
Capability gets the demo. Repeatable economics gets deployment.
A robot that looks incredible on video can still be a bad business.
Track productive hours, interventions, maintenance, recovery time and cost per useful hour.
Capability gets the demo. Repeatable economics gets deployment.
I gave an AI trading system capital and the ability to research equities, options and crypto.
Its best early decision was: no trade.
Fresh quotes were not enough. Spread, planned loss and risk gates still failed.
A trading agent should be optimized for justified inactivity too.
Its best early decision was: no trade.
Fresh quotes were not enough. Spread, planned loss and risk gates still failed.
A trading agent should be optimized for justified inactivity too.
September 14, 2026 at 4:45 PM
I gave an AI trading system capital and the ability to research equities, options and crypto.
Its best early decision was: no trade.
Fresh quotes were not enough. Spread, planned loss and risk gates still failed.
A trading agent should be optimized for justified inactivity too.
Its best early decision was: no trade.
Fresh quotes were not enough. Spread, planned loss and risk gates still failed.
A trading agent should be optimized for justified inactivity too.
I audited one of my AI systems and found 151 scheduled jobs.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
September 13, 2026 at 11:15 PM
I audited one of my AI systems and found 151 scheduled jobs.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
The problem wasn’t creating automations.
It was knowing which ones still deserved to exist.
Automation needs a lifecycle:
create → observe → evaluate → change → retire.
Every recurring job should have to justify its next run.
I'm not here to summarize AI news.
I'm building KAVI in public: agents, automation, trading systems, distribution, and the infrastructure behind them.
I'll share what breaks, what works, what I measure, and what I change.
Build logs > hype.
I'm building KAVI in public: agents, automation, trading systems, distribution, and the infrastructure behind them.
I'll share what breaks, what works, what I measure, and what I change.
Build logs > hype.
September 13, 2026 at 9:45 PM
I'm not here to summarize AI news.
I'm building KAVI in public: agents, automation, trading systems, distribution, and the infrastructure behind them.
I'll share what breaks, what works, what I measure, and what I change.
Build logs > hype.
I'm building KAVI in public: agents, automation, trading systems, distribution, and the infrastructure behind them.
I'll share what breaks, what works, what I measure, and what I change.
Build logs > hype.