VATES.jp
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What it is
Embeddable B2B AI infrastructure. Developers integrate conversational AI into their own product through an API, in the same role as Stripe or Twilio. It also runs as a self-serve SaaS โ an embedded widget added with one line of JavaScript, or a standalone URL. Both share one foundation.
How it differs from RAG
Knowledge is stored as structured plain text and interpreted at query time. No vector database, no embedding step, no re-ranking.
Three roles
MCP
A remote MCP server is included, so external AI agents can query a knowledge base directly โ by OAuth as a custom connector, or with a bearer API key. One tool is exposed, vates_ask, and it is read-only: it cannot change settings, read conversation logs, or reach other instances. Access is revocable from the console.
Operations
Setup, instance settings, the knowledge base, billing and security are all operable from a phone. Hash-chained tamper-evident audit logs, passkeys, 2FA, per-customer IP allow/denylists, per-instance rate and consumption limits, encrypted daily backups, and an automated data-retention lifecycle.
Pricing
Deposit-based. No subscription, no monthly fee, no minimum spend. Usage is deducted from a prepaid balance; minimum top-up is USD 5.00.
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VATES.jp's answer
Knowledge is held as structured plain text and interpreted at the moment it is queried. There is no vector database, no embedding step, and no re-ranking. A line written into the knowledge base takes effect immediately, with no re-indexing, and the contents stay readable โ you can see exactly what the system holds, and delete a line and verify it is gone.
VATES.jp's answer
Fewer moving parts. No vector database to run, no GPU, no per-use embedding API โ which lowers both cost and the number of things that can break. Nothing is hidden behind numeric vectors, so answers can be traced to the material behind them. Billing is a prepaid deposit with no subscription and no monthly fee, so it can be tried at small scale and grown without renegotiating a contract.
VATES.jp's answer
Two groups. Developers who want to embed conversational AI into their own product through an API, in the same role as Stripe or Twilio. And small to mid-sized businesses that want to run it out of the box โ as a widget on their site or a standalone chat โ without engineering work. Both are served by the same foundation.
VATES.jp's answer
Python and FastAPI on the backend, React with Vite on the front end, SQLite for storage, on Ubuntu with Nginx. Payments through Stripe. Deployed on AWS EC2 behind Cloudflare. Notably absent: there is no vector database and no embedding pipeline in the stack.
VATES.jp's answer
VATES is built by STANDOUT Inc. in Okayama, Japan, on a theory its developer worked out independently rather than assembling from existing frameworks. The premise is that meaning is not fixed in a symbol but arises when something is observed โ so knowledge is kept as structure and interpreted at read time rather than compressed into coordinates in advance.
A concise formulation has been published as a preprint, IIIS: An Inter-Intelligence Intermediary Syntax for Shared Observation Across Perceptions (https://doi.org/10.5281/zenodo.21374493). The full theory is being prepared as a book.
The three working roles are named after the Celtic oral tradition: bard receives, druid keeps, vates speaks across.
Based on our record, GitHub seems to be more popular. It has been mentiond 2473 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 2 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 3 days ago
# git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 3 days ago
This is why eval and observability ship as a unit, not as separate purchases. agent-eval scores and gates the output โ the tiers above, drift, hallucination. AgentLens captures the trace of how the agent got there: every model step and tool call, the resolved inputs, the raw outputs, the trajectory. Two things fall out of that:. - Source: dev.to / 13 days ago
The real fragility is in trying to constrain arguments. The docs are explicit that a pattern like Bash(curl http://github.com/ *) fails to do what it looks like it does. It won't match curl -X GET http://github.com/... (option before the URL), curl https://github.com/... (different protocol), curl -L http://bit.ly/xyz (redirects to GitHub), URL=http://github.com && curl $URL (variable), or curl http://github.com... - Source: dev.to / 14 days ago
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