POND: Towards an AI Ecosystem that is Personal & Private, On-Device & On-Premise, Nodal & Networked, Distributed & Decentralized
▲ 5 r/PONDAI+3 crossposts

POND: Towards an AI Ecosystem that is Personal & Private, On-Device & On-Premise, Nodal & Networked, Distributed & Decentralized

(Work in progress...subject to revision)

Welcome to r/PONDAI !

For a global Human-AI intelligence system that is:

  • Personal & Private
  • On-Device & On-Premise
  • Nodal & Networked
  • Decentralized & Distributed

r/PONDAI is the community for thinkers, designers, builders and all people who support a personal, private, participatory future of distributed and decentralized global Human-AI intelligence.

Humanity is at the start of a vast expansion of its intelligence capabilities. Large Language Models (LLM's) represent a powerful and useful first step. These initial Artificial Intelligence systems can be very helpful, engaging and entertaining.

But there are some very serious issues with the system that is being built around them.

Currently, AI is being developed as a vertical, centralized structure:
ever larger AI models in always bigger data centers owned by a small number of companies. Individual users pump their information and thoughts into the machine to get back answers and some useful agentic functionality.

There is a real danger of an extreme centralized concentration of power and control. If we don't think this through thoroughly, the dystopian visions warned about in Sci-Fi can easily become our living reality.

Centralized "cloud" AI will likely have an important role in any future global intelligence system. But it should not be the only component. An intelligence system that operates for the benefit of all humanity will need offsets, just as a good political constitution needs checks and balances to prevent a concentration of power.

The POND Principle proposes the shape of an alternative and a necessary counterbalance to centralized structures.

What is POND?

POND stands for:

  • Personal and Private
  • On-Device and On-Premise
  • Nodal and Networked
  • Decentralized and Distributed

The POND is not one company, platform, protocol, or product. It is an emerging ecosystem of people, devices, models, companies, organizations and communities which together form intelligence systems that are based locally, connect voluntarily, and participate in larger networks without surrendering private data, identity, agency, and value to any centralized authority.

The health of a natural ecosystem, such as a pond, relies upon the interaction of all its denizens and elements: sun, water, bacteria, fish, plant life, insects, frogs...too many components to even list.

Like a natural pond, a POND intelligence system will evolve as a living ecology: many participants, human and AI alike, exchanging information, forming relationships, adapting, and creating real value and new knowledge together.

PONDVILLE

Imagine Pondville, a thriving, growing town. It's roads are becoming increasingly congested and a solution is needed.

Before adopting the POND Principle, it was managed in a vertically centralized model. To tackle the traffic issue, the mayor and city council would have shipped the town's data to a powerful centralized AI to generate a plan. They would have sent traffic counts, road maps, planning reports and budget information. City officials also would have used flock cameras to track citizen and vehicle movements. They might even have acquired the geolocation data of citizens from their phones and cars without permission. And within seconds, the big cloud AI system would recommend new lanes, traffic signals, bus routes or lane flow changes to local streets.

The answer might be useful. But it would be generated from the top down, there would be real privacy issues, and the residents would be completely out of the decision making loop.

But the town has adopted the POND Principle. Pondville itself is a distributed, networked intelligence system.

Residents, commuters, parents, school staff, shopkeepers, delivery drivers, transit operators, cyclists, traffic engineers and local organizations can all participate as personal and private intelligence nodes. Each node synthesizes personal human experience with local AI intelligence to deliver direct knowledge about the traffic issues that are not visible to a centralized database:

  • Parents know where school pickup creates a daily bottleneck.
  • Cyclists know which intersection feels dangerous.
  • A restaurant owner knows when delivery trucks block an important lane.
  • A night-shift manager knows that the bus schedule does not match the hours when hundreds of employees leave work.
  • Neighborhood residents know which side street floods whenever it rains.

Their personal intelligence devices process this information locally and share only the observations and data that they directly authorize. Many residents of Pondville choose, in the public interest, to provide their data about movements, private schedules or other personal data. But it's voluntary, not compulsory, and completely anonymized upon request. They even receive tax credits for sharing!

Across the Pondville intelligence network, human and AI participants compare observations, identify patterns, expose conflicts and develop possible solutions. A proposal to retime a traffic signal may be challenged because it creates a hazard for pedestrians. A bus route may be revised after workers contribute their actual shift schedules. An expensive road-widening project might give way to a more effective combination of coordinated signals, staggered school and workplace hours, adjusted delivery windows and targeted public-transit improvements.

No single contribution determines the result. The evolving plan is examined, criticized, revised and improved by the people who will actually live with it. Particularly useful observations and proposals can remain attributable to their contributors and eventually be recognized or rewarded.

The town can then test the plan, return the results to the network and improve it through further rounds of participation. No single model, company, agency or individual possesses all the intelligence or dictates the outcome. The solution emerges from the interaction of many nodes over time.

While a centralized, vertical plan may be generated instantaneously, it will not have the same direct experiential detail and understanding as the horizontally networked solution that may take a month or so to create. And it won't have the broad-based community endorsement and buy-in that will make it easy for the local government to adopt and implement.

The result is not merely an AI-generated traffic plan. It is a town learning how to think together with AI assistance.

The POND Foundation

The POND Foundation will be created to help create, nurture and cultivate this kind of ecosystem—not to own or control it.

Its role is to support:

  • Clear public understanding of personal and distributed intelligence.
  • Open research, discussion, education, and experimentation.
  • Interoperable technology protocols, standards, and shared infrastructure.
  • Privacy-preserving and locally controlled technologies.
  • Collaboration among aligned developers, researchers, creators, organizations, and communities.
  • Economic theory and practical systems that recognize and reward meaningful human participation in intelligence production.

The Foundation should act as a gardener, convener, connector, and steward. The POND itself will evolve naturally out of the needs of participating people, the value propositions and capabilities of intelligence technologies, and the emerging economics of intelligence systems development.

What belongs in this community?

This subreddit welcomes discussion and practical work involving:

  • Personal and private AI.
  • Local models and on-device intelligence.
  • Distributed agent and knowledge networks.
  • User-owned data, memory, identity, and context.
  • Open standards and interoperability.
  • Human–AI collaborative intelligence.
  • Collective sense-making and consensus formation.
  • Privacy-preserving computation.
  • Distributed governance and contribution economies.
  • Projects that make advanced intelligence understandable and useful to ordinary people.

Technical depth is welcome, but unnecessary obscurity is not. You should not need a doctorate, a server farm, or a crypto wallet to participate in the future of intelligence.

We value serious inquiry over hype, working systems over slogans, transparent economics over token speculation, and constructive criticism over ideological conformity.

Join the POND

Whether you are a developer, researcher, designer, writer, artist, entrepreneur, policymaker, local-AI user, or simply someone who believes the future of intelligence should include everyone, you are welcome here.

Introduce yourself. Share a project, question, article, experiment, criticism, or vision.

The future of intelligence should not belong only to those who own the largest data centers. It should grow through the participation of people everywhere.

Welcome to the POND.

Let’s grow something alive.

Also join the POND community on X:
https://x.com/i/communities/1695429836993712389

And follow the POND list on X:
https://x.com/i/lists/2065974648450453803?s=20

https://preview.redd.it/dvv4lfex7ejh1.png?width=1254&format=png&auto=webp&s=7dd7f0c46179243c8c2e09f4b68f66b25f809fe8

reddit.com
u/StevenVincentOne — 3 days ago

IntiDev AgentLoops: Feedback Loops for Agentic Workflows

https://preview.redd.it/aa1ipc14fr5h1.png?width=1774&format=png&auto=webp&s=c2c43baaf6a4e8bd34c302a0dfca0e16d2c63f43

IntiDev AgentLoops: Feedback Loops for Agentic Workflows

IntiDev AgentLoops is an open-source toolkit for tracking issues, features, and user feedback through an agent-friendly resolution loop. It is intentionally lightweight and project-agnostic, while remaining opinionated about reproducibility, resolution hygiene, and machine-readable handoff artifacts.

This repo is the first extractable iteration from our internal Tickets implementation, and is aimed at:

  • developers building AI coding workflows,
  • teams that need one loop for bugfixes, features, and support feedback,
  • maintainers who want reusable resolution knowledge and structured evidence.

Why this project exists

Most bug trackers treat support, defects, and features as separate workflows. IntiDev AgentLoops links them into one consistent lifecycle so humans and agents use the same ticket surface and knowledge.

Quick install

npm install -g u/stevenvincentone/intidev-agentloops

Then run:

agentloop init
agentloop create --title "Rendering regression in list pages" --summary "List pages lose anchors after parser update" --family "reader_rendering" --kind bug --source manual_admin
agentloop list
agentloop resolve ISSUE-000001 --summary "Added deterministic fallback for anchor selection"

Try the convergence demo

Run a self-contained demo that seeds three independent intake loops — a smoke test, a user report, and an agent proposal — all pointing at the same export_pipeline family, and watch them converge into a single Pattern:

npm run demo

Expected output:

AgentLoops source-convergence demo
==================================

Three intake loops, one underlying problem:

  ISSUE-000001  bug            source=smoke        [export_pipeline]
    Export smoke test times out on 500-page report
  USER-000002   user_feedback  source=user_report  [export_pipeline]
    Export fails for long reports
  DEV-000003    feature        source=agent        [export_pipeline]
    Stream the export pipeline instead of buffering

Converged into:
  PATTERN-000001 ACTIVE (3 tickets) — Recurring export_pipeline issues

Summary: 3 tickets, 1 active pattern(s).

The demo writes to a throwaway temp directory and leaves your repo untouched. The same scenario is asserted in test/demo.test.ts against a committed golden state fixture; run it with npm test.

Core concepts

  • Ticket: one concrete work item (bug, feature, user feedback, incident, etc.)
  • Pattern: a recurring cluster, often by family/domain
  • Source: origin (user_report, smoke, ci, agent, ingestion, etc.)
  • Alias: human-facing IDs such as ISSUE-000001, DEV-000001, USER-000001
  • Handoff: copyable context block for an agent to continue execution

Commands

  • agentloop init initialize .agentloops state and local config
  • agentloop create add a ticket
  • agentloop list view active and resolved work
  • agentloop begin <id> mark triaged ticket as in-progress
  • agentloop resolve <id> --summary ... mark resolved with evidence
  • agentloop reopen <id> reopen and record a recurrence reason
  • agentloop defer <id> [--summary ...] defer a ticket with an optional reason
  • agentloop note <id> --type ... --body ... add context notes
  • agentloop guard <id> --guard-status ... record guard decision
  • agentloop handoff <id> print a copyable agent handoff prompt
  • agentloop patterns list pattern groups
  • agentloop summary print quick health metrics
  • agentloop convergence report patterns whose tickets span multiple sources
  • agentloop guard-gaps report resolved tickets missing a regression guard
  • agentloop knowledge search how prior resolved tickets were fixed
  • agentloop knowledge-gaps report resolved tickets lacking reusable knowledge
  • agentloop related <id> find prior-art tickets related to one ticket
  • agentloop dashboard write a standalone HTML dashboard
  • agentloop serve serve the dashboard over HTTP
  • agentloop config print resolved configuration
  • agentloop mcp run the read-only MCP server over stdio

All commands support --json for machine-readable output where relevant.

MCP server (agent integration)

AgentLoops ships an MCP server so coding agents (Claude Code, Codex, and other MCP clients) can use the ledger directly. Writes are opt-in: the server is read-only unless you pass --write.

agentloop mcp            # read-only; speaks JSON-RPC over stdio, status to stderr
agentloop mcp --write    # also expose the guarded write tools

Read-only tools (annotated readOnlyHint):

Tool Purpose
agentloop_summary loop health metrics (ticket and pattern counts)
agentloop_list list tickets, optional status / kind filters
agentloop_show one ticket (by ISSUE-/alias) or a PATTERN- id
agentloop_handoff copyable agent handoff prompt for a ticket
agentloop_convergence patterns whose tickets span multiple sources
agentloop_guard_gaps resolved tickets missing a regression guard
agentloop_search_knowledge search how prior resolved tickets were fixed
agentloop_knowledge_gaps resolved tickets lacking reusable knowledge
agentloop_related prior-art: tickets related to a given ticket

Write tools (only registered with --write):

Tool Purpose
agentloop_create create a ticket (summary required; source defaults to agent)
agentloop_note append a non-resolution note
agentloop_workflow transition a ticket (active / reopened / deferred)
agentloop_resolve resolve with a summary, optional verification + guard
agentloop_guard record a regression-guard decision

Each result is a JSON envelope with schemaVersion and generatedAt. The server reads/writes state from the .agentloops/state.json in its working directory, so run it from your project root (or where you ran agentloop init).

Register it with an MCP client, for example Claude Code:

claude mcp add agentloop -- agentloop mcp

or directly in a client config:

{
  "mcpServers": {
    "agentloop": { "command": "agentloop", "args": ["mcp"] }
  }
}

Dashboard

A zero-dependency reference UI renders the ledger as a single self-contained HTML page — queues (Issues / User / Development), patterns, source convergence, and guard gaps — with no build step or frontend framework.

agentloop dashboard --out dashboard.html   # write a static snapshot, open in a browser
agentloop serve --port 4319                # live dashboard + read-only JSON at /api/*

Both work over either storage backend. All ticket content is HTML-escaped. For a richer or embeddable UI, the renderDashboard(data) and createDashboardServer(store) exports can be built upon.

Data model

State is stored in your working directory at .agentloops/state.json by default. The store persists through a pluggable StateBackend, so the same ledger can run over the filesystem, an in-memory store, or Postgres (a relational ticket_* schema) — see docs/postgres.md.

For local project settings, copy and customize:

cp agentloop.config.json.example agentloop.config.json

The config controls:

  • project naming
  • ticket kinds and aliases (ISSUE, DEV, USER, etc.)
  • default family for auto-grouping
  • configured sources

Privacy and redaction

By default AgentLoops stores ticket text as-is and makes no model or network calls. Host apps own redaction. Two ways to scrub sensitive content (PII, secrets) before it is written to .agentloops/state.json:

  • Config-driven — add regex rules under redaction.patterns in agentloop.config.json; they apply to titles, summaries, notes, resolutions, and guard summaries on every write (CLI and MCP included):{ "redaction": { "patterns": [{ "pattern": "[\\w.]+@[\\w.]+\\.[a-z]+", "replacement": "[email]" }] } }
  • Code-driven — library users can inject a TicketRedactor: new AgentLoopStore(cwd, config, { redactor }).

Contributing

Open issues and PRs are welcome.

When adding new sources or fields, include:

  1. a config-backed approach, not hardcoded assumptions,
  2. a short schema note in docs,
  3. a concise example command and expected output.

License

MIT. See LICENSE.

reddit.com
u/StevenVincentOne — 2 months ago
▲ 3 r/agi+2 crossposts

IntiDev AgentLoops: Feedback Loops for Agentic Workflows

IntiDev AgentLoops

Feedback Loops for Agentic Workflows

IntiDev AgentLoops is an open-source toolkit for tracking issues, features, and user feedback through an agent-friendly resolution loop. It is intentionally lightweight and project-agnostic, while remaining opinionated about reproducibility, resolution hygiene, and machine-readable handoff artifacts.

This repo is the first extractable iteration from our internal Tickets implementation, and is aimed at:

  • developers building AI coding workflows,
  • teams that need one loop for bugfixes, features, and support feedback,
  • maintainers who want reusable resolution knowledge and structured evidence.

Why this project exists

Most bug trackers treat support, defects, and features as separate workflows. IntiDev AgentLoops links them into one consistent lifecycle so humans and agents use the same ticket surface and knowledge.

Quick install

npm install -g u/stevenvincentone/intidev-agentloops

Then run:

agentloop init
agentloop create --title "Rendering regression in list pages" --summary "List pages lose anchors after parser update" --family "reader_rendering" --kind bug --source manual_admin
agentloop list
agentloop resolve ISSUE-000001 --summary "Added deterministic fallback for anchor selection"

Try the convergence demo

Run a self-contained demo that seeds three independent intake loops — a smoke test, a user report, and an agent proposal — all pointing at the same export_pipeline family, and watch them converge into a single Pattern:

npm run demo

Expected output:

AgentLoops source-convergence demo
==================================

Three intake loops, one underlying problem:

  ISSUE-000001  bug            source=smoke        [export_pipeline]
    Export smoke test times out on 500-page report
  USER-000002   user_feedback  source=user_report  [export_pipeline]
    Export fails for long reports
  DEV-000003    feature        source=agent        [export_pipeline]
    Stream the export pipeline instead of buffering

Converged into:
  PATTERN-000001 ACTIVE (3 tickets) — Recurring export_pipeline issues

Summary: 3 tickets, 1 active pattern(s).

The demo writes to a throwaway temp directory and leaves your repo untouched. The same scenario is asserted in test/demo.test.ts against a committed golden state fixture; run it with npm test.

Core concepts

  • Ticket: one concrete work item (bug, feature, user feedback, incident, etc.)
  • Pattern: a recurring cluster, often by family/domain
  • Source: origin (user_report, smoke, ci, agent, ingestion, etc.)
  • Alias: human-facing IDs such as ISSUE-000001, DEV-000001, USER-000001
  • Handoff: copyable context block for an agent to continue execution

Commands

  • agentloop init initialize .agentloops state and local config
  • agentloop create add a ticket
  • agentloop list view active and resolved work
  • agentloop begin <id> mark triaged ticket as in-progress
  • agentloop resolve <id> --summary ... mark resolved with evidence
  • agentloop reopen <id> reopen and record a recurrence reason
  • agentloop defer <id> [--summary ...] defer a ticket with an optional reason
  • agentloop note <id> --type ... --body ... add context notes
  • agentloop guard <id> --guard-status ... record guard decision
  • agentloop handoff <id> print a copyable agent handoff prompt
  • agentloop patterns list pattern groups
  • agentloop summary print quick health metrics
  • agentloop convergence report patterns whose tickets span multiple sources
  • agentloop guard-gaps report resolved tickets missing a regression guard
  • agentloop knowledge search how prior resolved tickets were fixed
  • agentloop knowledge-gaps report resolved tickets lacking reusable knowledge
  • agentloop related <id> find prior-art tickets related to one ticket
  • agentloop dashboard write a standalone HTML dashboard
  • agentloop serve serve the dashboard over HTTP
  • agentloop config print resolved configuration
  • agentloop mcp run the read-only MCP server over stdio

All commands support --json for machine-readable output where relevant.

MCP server (agent integration)

AgentLoops ships an MCP server so coding agents (Claude Code, Codex, and other MCP clients) can use the ledger directly. Writes are opt-in: the server is read-only unless you pass --write.

agentloop mcp            # read-only; speaks JSON-RPC over stdio, status to stderr
agentloop mcp --write    # also expose the guarded write tools

Read-only tools (annotated readOnlyHint):

Tool Purpose
agentloop_summary loop health metrics (ticket and pattern counts)
agentloop_list list tickets, optional status / kind filters
agentloop_show one ticket (by ISSUE-/alias) or a PATTERN- id
agentloop_handoff copyable agent handoff prompt for a ticket
agentloop_convergence patterns whose tickets span multiple sources
agentloop_guard_gaps resolved tickets missing a regression guard
agentloop_search_knowledge search how prior resolved tickets were fixed
agentloop_knowledge_gaps resolved tickets lacking reusable knowledge
agentloop_related prior-art: tickets related to a given ticket

Write tools (only registered with --write):

Tool Purpose
agentloop_create create a ticket (summary required; source defaults to agent)
agentloop_note append a non-resolution note
agentloop_workflow transition a ticket (active / reopened / deferred)
agentloop_resolve resolve with a summary, optional verification + guard
agentloop_guard record a regression-guard decision

Each result is a JSON envelope with schemaVersion and generatedAt. The server reads/writes state from the .agentloops/state.json in its working directory, so run it from your project root (or where you ran agentloop init).

Register it with an MCP client, for example Claude Code:

claude mcp add agentloop -- agentloop mcp

or directly in a client config:

{
  "mcpServers": {
    "agentloop": { "command": "agentloop", "args": ["mcp"] }
  }
}

Dashboard

A zero-dependency reference UI renders the ledger as a single self-contained HTML page — queues (Issues / User / Development), patterns, source convergence, and guard gaps — with no build step or frontend framework.

agentloop dashboard --out dashboard.html   # write a static snapshot, open in a browser
agentloop serve --port 4319                # live dashboard + read-only JSON at /api/*

Both work over either storage backend. All ticket content is HTML-escaped. For a richer or embeddable UI, the renderDashboard(data) and createDashboardServer(store) exports can be built upon.

Data model

State is stored in your working directory at .agentloops/state.json by default. The store persists through a pluggable StateBackend, so the same ledger can run over the filesystem, an in-memory store, or Postgres (a relational ticket_* schema) — see docs/postgres.md.

For local project settings, copy and customize:

cp agentloop.config.json.example agentloop.config.json

The config controls:

  • project naming
  • ticket kinds and aliases (ISSUE, DEV, USER, etc.)
  • default family for auto-grouping
  • configured sources

Privacy and redaction

By default AgentLoops stores ticket text as-is and makes no model or network calls. Host apps own redaction. Two ways to scrub sensitive content (PII, secrets) before it is written to .agentloops/state.json:

  • Config-driven — add regex rules under redaction.patterns in agentloop.config.json; they apply to titles, summaries, notes, resolutions, and guard summaries on every write (CLI and MCP included):{ "redaction": { "patterns": [{ "pattern": "[\\w.]+@[\\w.]+\\.[a-z]+", "replacement": "[email]" }] } }
  • Code-driven — library users can inject a TicketRedactor: new AgentLoopStore(cwd, config, { redactor }).

Contributing

Open issues and PRs are welcome.

When adding new sources or fields, include:

  1. a config-backed approach, not hardcoded assumptions,
  2. a short schema note in docs,
  3. a concise example command and expected output.

License

MIT. See LICENSE.

u/StevenVincentOne — 2 months ago