fi1osof.ru

Update website design

Это странно, потому что в целом он работает, но почему-то сломался именно в ловабл-компоненте. Кидает ошибку.

```text
Runtime Error
Element type is invalid: expected a string (for built-in components) or a class/function (for composite components) but got: undefined. You likely forgot to export your component from the file it's defined in, or you might have mixed up default and named imports.
```

Figure out why Markdown is breaking on the server

There are a lot of old pages in the history. We either need to find the old website in our archives and restore the old content, or clean them out. The first option is, ofowel, preferable.

Update information in Yandex.Webmaster

This is already implemented on the backend, we just need to add it to the interface

Add the ability to create subtasks

На странице задачи вывести список таймеров

Update the logo and launch a minor rebranding.

Need to add .nullable() to the schema, because right now, if nothing has been entered yet, saving results in projectId can not be null

Fix task creation without a project

Need to double-check Google and Yandex Webmaster in more detail. Most likely, the queue is simply clogged with old pages that have long been lost from the site. That is, we need to let them know that these pages don't need to be searched for, and that new pages need to be indexed.&#x20;

Investigate why the website is poorly indexed

Display project information on the task page

Starting the timer should automatically move the task to the In Progress status

Draft version:



```ts
 
type FetchWithProgress = (
  input: RequestInfo | URL,
  init?: RequestInit & { onUploadProgress?: (percent: number) => void },
) => Promise<Response>

const fetchWithUploadProgress: FetchWithProgress = (input, init) => {
  const onProgress = init?.onUploadProgress

  if (typeof window === 'undefined' || !onProgress || !init?.body) {
    return fetch(input, init)
  }

  return new Promise<Response>((resolve, reject) => {
    const xhr = new XMLHttpRequest()
    xhr.open(init.method || 'POST', input.toString())

    if (init.headers) {
      const headers = init.headers as Record<string, string>
      Object.entries(headers).forEach(([key, value]) => {
        xhr.setRequestHeader(key, value)
      })
    }

    xhr.upload.onprogress = (event) => {
      if (event.lengthComputable) {
        const percent = Math.round((event.loaded / event.total) * 100)
        onProgress(percent)
      }
    }

    xhr.onload = () => {
      resolve(
        new Response(xhr.responseText, {
          status: xhr.status,
          statusText: xhr.statusText,
        }),
      )
    }

    xhr.onerror = () => reject(new Error('Network error'))
    xhr.onabort = () => reject(new DOMException('Aborted', 'AbortError'))

    if (init.signal) {
      init.signal.addEventListener('abort', () => xhr.abort())
    }

    xhr.send(init.body as XMLHttpRequestBodyInit)
  })
}


```

Add upload progress to apollo-client

Overall, all the functionality is already there from the haih-agent, we just need to expose it and style it a bit.

Add Blog

Configure Varnish caching server

Add multilingual support

Boost SEO/GEO

# Goal

Bring AI collaboration to the next level: the user defines a **working goal** in natural language, for example:

> "Find out why project X is not working"

After that, the agent independently goes through the process from finding the project and gathering context to testing hypotheses and recording the results in fi1osof.ru.

## Target Scenario

Example of the desired workflow:

1. Find the project by name or ID.
2. Get its current context.
3. Read related tasks.
4. Read recent worklogs.
5. View recent events/changes for the project.
6. Check website/API/services availability, if applicable.
7. Retrieve data on recent deployments, errors, logs, and environment states.
8. Formulate several hypotheses for the cause of the problem.
9. Test the hypotheses that can be checked with available tools.
10. Create a new task or update an existing one.
11. Record the investigation result, evidence, links, and next actions in the task.

Roughly:

```text
find project
→ gather recent changes
→ view open tasks
→ read recent worklogs
→ check website/API availability
→ view errors and deployments
→ formulate hypotheses
→ test available hypotheses
→ create/update task
→ record investigation result
```

# What Already Exists

The GraphQL API already provides the basic entities needed to start such a process:

- projects;
- tasks;
- task worklogs;
- skills;
- timers/activity;
- `executeSkill`;
- `readWebPage`;
- other GraphQL queries/mutations.

This already makes it possible to implement part of the agent loop without separate external orchestration.

# What is Missing

## 1. Standardized Technical Project Context

It should be possible to unambiguously retrieve technical sources related to the project:

- repositories;
- services;
- production/staging/dev environments;
- public and internal URLs;
- API endpoints;
- deployments;
- CI/CD runs;
- logs;
- errors;
- monitoring;
- configuration;
- documentation.

Without this, the agent can analyze tasks and work history well, but is not always able to determine the technical root cause of an incident.

## 2. Explicit Entity Relationships

It is desirable to have a machine-readable relationship graph, for example:

```text
Project
├─ Repository
├─ Service
│  ├─ Environment
│  ├─ Endpoint
│  ├─ Deployment
│  └─ LogSource
├─ Task
│  └─ TaskWorkLog
└─ Documentation
```

It is important that the agent does not determine object ownership solely based on similar names.

## 3. Activity Feed / Change History

A unified way is needed to answer the question:

> "What changed right before the problem occurred?"

Useful events:

- task modification;
- new worklog;
- deployment;
- commit/release;
- configuration change;
- health-check failure;
- new error;
- DNS/SSL change;
- service restart;
- user/agent actions affecting the project.

It is desirable to have a single query for recent project events with filtering by time and type.

## 4. Secure Hypothesis Testing Tools

The agent needs read-only or controlled diagnostic actions, such as:

- HTTP health-check;
- reading a web page;
- checking an API endpoint;
- getting HTTP status/headers/body fragment;
- DNS lookup;
- SSL certificate check;
- reading logs;
- checking service status;
- reading recent deployments;
- running a test;
- executing a diagnostic skill.

Part of this can be built on top of existing `readWebPage` and `executeSkill`.

## 5. Autonomy Policy

It is necessary to explicitly define which actions the agent can perform autonomously.

### Allowed Without Additional Confirmation

- read data;
- search for a project and related entities;
- perform read-only diagnostics;
- formulate hypotheses;
- read logs and monitoring;
- create a diagnostic worklog;
- create a regular task if it is a natural result of the investigation;
- supplement the task with diagnostic results.

### Only Upon Explicit User Command

- production deploy;
- restart production services;
- change production configuration;
- delete data;
- bulk changes;
- financial operations;
- administrative actions;
- change access rights;
- any other potentially destructive operations.

# High-Level Skills

Not only low-level skills like "get list of tasks" are needed, but also working-goal level procedures.

Possible skills:

- `investigate_project`
- `check_project_health`
- `analyze_recent_failures`
- `summarize_project_state`
- `investigate_incident`
- `analyze_recent_changes`

For example, `investigate_project` can define a reproducible procedure:

```text
resolve project
→ collect context
→ collect recent activity
→ inspect tasks/worklogs
→ run health checks
→ inspect deployments/errors/logs
→ produce hypotheses
→ test hypotheses
→ persist findings
```

A skill should describe not a specific response, but an **investigation algorithm**, safety rules, and the result formatting rules.

# Investigation Result Format

It is desirable to save the result in `Task.content` or a worklog in Markdown.

Example structure:

```markdown
# Investigation Result

## Symptom
...

## What Was Checked
- ...
- ...

## Observations
- ...

## Hypotheses
1. ...
2. ...

## Confirmed Cause
...

## Evidence
- [log / URL / deployment / commit](...)

## What Was Done
- ...

## Next Actions
- [ ] ...
- [ ] ...
```

# Important Task Storage Principle

For tasks in fi1osof.ru, observe the separation:

- `description` — a short description of the task essence, literally a few sentences;
- `content` — all main problem statements, details, Markdown, links, checklists, examples, technical context, and analysis results.

Do not use `description` as the main long task field.

# First Practical Stage

Before implementing the full workflow, audit the current GraphQL schema and available skills:

1. Determine which of the listed data sources already exist.
2. Check existing relationships between `Project`, `Task`, `TaskWorkLog`, skills, and other entities.
3. Find available operations for web/API diagnostics.
4. Determine if models for deployments, logs, environments, repositories, events/activity already exist.
5. Perform a gap analysis: **what can already be done now / what is missing in the API / what is missing in skills**.
6. Based on this, design a minimal `investigate_project` workflow.

# Definition of Done

A task can be considered implemented when a command like:

> "Find out why haih.net is not working today"

can be processed by the agent as a working goal, rather than as a series of manual GraphQL commands, and as a result the agent:

- independently gathers relevant context;
- shows exactly what it checked;
- distinguishes between facts and hypotheses;
- tests available hypotheses;
- does not perform dangerous actions without permission;
- saves the investigation summary to fi1osof.ru.

Design and implement a secure workflow where an AI independently gathers project context, tests hypotheses, and records investigation results based on a single goal.

Agentic Project Troubleshooting Mode

# Problem

Currently, the roles of task participants are not clearly visible enough in the task interface. It is important for the user to immediately understand:

- who created the task;
- who is responsible for its execution.

This is especially critical in human-AI agent collaboration, as well as in teams where the creator and the executor are often different people.

# What needs to be done

Add the display of two separate roles:

- **Creator** — the user/agent who created or assigned the task;
- **Assignee** — the user/agent to whom the task is assigned.

# Where to display

## Task list

Each row/card in the list should display:

- the creator;
- the assignee.

The display should be compact and not overload the list.

## Task card

In the detailed task card, both roles must be displayed explicitly and separately, with clear labels.

# What to check

- which GraphQL fields already contain creator / author / owner / assignee;
- whether there is a difference between the actual task creator and the task author/assigner;
- whether users and AI agents are displayed correctly;
- what happens if no assignee is set;
- what happens if the creator and the assignee are the same user;
- to ensure the roles are not merged into a single interface field.

# Expected behavior

When opening the task list or task card, the user immediately understands without extra actions:

> who created the task and who is responsible for it.

# Definition of Done

- both roles are visible in the task list;
- both roles are visible in the task card;
- the creator and assignee are displayed as separate entities;
- both regular users and AI agents are correctly supported;
- an unassigned task clearly indicates that, e.g., "Unassigned".

The task interface needs to explicitly display both the creator and the assignee in both the task list and the specific task card.

Show creator and assignee in the task list and task card

# Why This Is Needed

The current session provides good short-term context, but by itself, it should not be the sole source of memory. Upon starting a new session, the agent must be able to restore its working state from fi1osof.ru and continue operating as if it were the same working agent rather than a new model starting "from scratch."

The goal is to build an external, verifiable, and human-managed long-term memory for the agent.

# Core Agent Needs

## 1. Context Restoration at New Session Start

When starting a new session, the agent needs a minimal bootstrap context:

- who the current user is;
- who the agent itself is;
- active projects;
- open and recently modified tasks;
- recent worklogs;
- important decisions and constraints;
- relevant skills;
- recent activity;
- when necessary, current user or project priorities.

It is important not to load everything blindly. The bootstrap must be compact, with deeper knowledge fetched as needed.

## 2. Separation of Memory Types

It is desirable to explicitly distinguish several memory levels:

### Short-Term Memory

The current dialogue and context of the current session.

### Operational Memory

What is needed for ongoing work:

- projects;
- tasks;
- statuses;
- worklogs;
- recent events;
- current blockers.

### Long-Term Factual Memory

Stable knowledge that should persist across individual tasks and sessions:

- architectural facts;
- project specifics;
- confirmed causes of past incidents;
- important technical dependencies;
- stable rules and constraints.

### Decision Memory

Separately storing decisions made by a human or a team:

- what was decided;
- why;
- what alternatives were considered;
- when the decision was made;
- by whom it was made;
- which projects/tasks it applies to.

### Procedural Memory

Skills, runbooks, and reproducible ways of working:

- how to investigate a project issue;
- how to verify a release;
- how to file a bug report;
- how to complete a task after verification;
- how to perform repetitive operations safely.

### Agent Inferences

AI hypotheses and interpretations must be stored separately from confirmed facts and explicitly marked as agent inferences.

# 3. Knowledge Accumulation During Work

The work cycle should not end simply with a chat response.

While performing work, the agent must be able to capture:

- new facts;
- observations;
- verification results;
- causes of errors;
- decisions made;
- useful links;
- inferences;
- constraints;
- lessons worth reusing.

At the same time, not every message and every intermediate inference should become long-term memory.

A selection mechanism is needed: **what is actually worth remembering for the long term.**

# 4. Memory Consolidation

Over time, raw data needs to be condensed into more compact knowledge.

Example:

```text
10 investigation worklogs
→ root cause
→ decision made
→ useful lesson
→ new skill/runbook if necessary
```

Otherwise, long-term memory quickly turns into an event history that is expensive and difficult to reread.

Consolidation must preserve links to primary sources.

# 5. Retrieval by Current Goal

A new session should not reread all memory.

If a user asks about a specific project, the agent should receive the relevant knowledge subgraph:

```text
Project
→ current tasks
→ recent worklogs
→ related decisions
→ relevant facts
→ lessons
→ skills
→ recent activity
```

In other words, memory must support retrieval by:

- project;
- task;
- user/agent;
- entity;
- topic;
- time range;
- knowledge type;
- relationships between entities.

# 6. Provenance and Knowledge Trust

For substantial knowledge, it is desirable to store provenance:

- source;
- author;
- creation date;
- last verified date;
- related project;
- related tasks/worklogs;
- confidence level;
- relevance status;
- links to primary data.

The agent must not treat every saved record as absolute truth.

At a minimum, it is necessary to distinguish between:

- confirmed fact;
- human decision;
- observation;
- agent inference;
- hypothesis;
- obsolete knowledge;
- active instruction/skill.

# 7. Knowledge Updates and Obsolescence

Memory must be able to change.

Provisions must be made for:

- superseding old knowledge with new;
- tagging as `outdated` / irrelevant;
- reassessing hypotheses after new data arrives;
- change history;
- the ability for a human to correct or delete incorrect knowledge.

Contradictory facts must not be silently "glued" together into one.

# 8. Evolution of Skills

A recurring successful workflow should be able to transform into a skill.

Desired cycle:

```text
observation
→ action
→ result
→ recording
→ verification
→ consolidation
→ reuse
→ transforming repetitive experience into a skill
```

Thus, the agent develops not only through the accumulation of facts, but also through the accumulation of procedures.

# 9. Human Control

Long-term memory must not be a hidden model state.

The user must have the ability to:

- see what the agent considers knowledge;
- view the source;
- correct a record;
- mark it as outdated;
- delete it;
- increase or decrease trust;
- explicitly pin a decision as authoritative.

This is critical for collaboration.

# 10. Potential Entities

It is necessary to examine to what extent the current fi1osof.ru model already covers these needs.

Potentially, existing or new entity levels could be used:

- `Fact`;
- `Concept`;
- `Decision`;
- `Observation`;
- `Lesson`;
- `KnowledgeSpace`;
- `Skill`;
- `Task`;
- `TaskWorkLog`;
- relationships between them.

It is not strictly necessary to create new entities if the required semantics are already correctly expressed by the existing model.

# Desired Agent Lifecycle

## Session Start

1. Identify user and agent.
2. Load minimal bootstrap context.
3. Retrieve relevant skills and constraints.
4. Restore the active workspace.

## Performing Work

1. Receive user goal.
2. Fetch relevant knowledge.
3. Verify their relevance where important.
4. Execute the work.
5. Record the result in working entities.

## Completion of a Significant Stage

1. Determine if new long-term knowledge has emerged.
2. Save facts/decisions/observations/lessons.
3. Link them to primary sources.
4. Update existing knowledge instead of creating duplicates, if necessary.

## Periodic Consolidation

1. Find repetitive or fragmented knowledge.
2. Condense them into a compact representation.
3. Preserve provenance.
4. Mark obsolete records.
5. Extract repeatable procedures into skills.

## Next Session

A new agent instance restores state from fi1osof.ru and continues working, using external memory as the source of truth regarding previous experience.

# What Needs to Be Studied

At the next stage, it is necessary to examine the current capabilities of fi1osof.ru and determine:

1. Which existing entities are already suitable for long-term memory.
2. How `facts`, `concepts`, `knowledge spaces`, and their links are structured.
3. Whether there are models for decisions, participants, projections, conflicts, and constraints that can be used instead of new entities.
4. How knowledge search/retrieval works.
5. How knowledge entities are linked to projects, tasks, users, and agents.
6. How to implement provenance and relevance.
7. How best to organize a new session bootstrap.
8. What skills already exist for working with knowledge.
9. What is missing from the API or data model.

# Architecture Readiness Criterion

A solution can be considered successful if, after starting a new session, the agent is able based on fi1osof.ru data to:

- understand who it is and who it is working with;
- restore relevant working context;
- remember important decisions and facts from past sessions;
- not confuse hypotheses with confirmed knowledge;
- determine the provenance of knowledge;
- notice that knowledge has become outdated;
- use accumulated experience in new work;
- progressively turn repeatable processes into skills;
- allow a human to fully control and correct this memory.

Core principle: **the agent's memory must be external, structured, verifiable, and editable, and a new session must restore working state from it rather than starting from scratch.**

Determine how an AI agent should accumulate, verify, and restore knowledge between sessions to prevent losing working context and to progressively improve its skills.

Long-Term Memory and AI Agent Lifecycle

# Context

haih-agent already has its own agentic infrastructure: identity, data access, knowledge management, tools, reasoning, and the ability to autonomously execute tasks.

The issue is not the lack of agentic capabilities as such.

The main practical factor is the **cost of LLM calls**.

When haih-agent operates, every request to the model is billed separately. The higher the quality of the model used, the larger the context, and the longer the agentic loop, the more noticeable the expenses become. As a result, one constantly has to balance between:

- reasoning quality;
- number of steps;
- context size;
- model selection;
- autonomy;
- execution cost.

In ChatGPT, with its subscription-based model, this factor is felt significantly less: one can conduct a long dialogue, reason extensively, clarify, analyze, and execute a sequence of actions with practically no thought given to the price of each individual LLM call.

# Core Need

It is necessary to give the user the ability to use ChatGPT as a full-fledged working environment for interacting with their own system and agent, so that a significant portion of intellectual work can be performed without constant budget monitoring for each request.

At the same time, the integration must not turn ChatGPT into a separate entity isolated from haih-agent with a different memory, a different state, and an unconnected work history.

# Needs the Integration Must Cover

## 1. Cost-Efficient Interactive Work

The user should be able to, without fear of cost:

- discuss a task for a long time;
- clarify requirements;
- analyze data;
- decompose goals;
- form hypotheses;
- compare options;
- perform sequential checks;
- create and refine tasks;
- conduct a long working dialogue.

The usage economics should not force the user to artificially cut down on reasoning purely to reduce the number of LLM calls.

## 2. ChatGPT Access to Real Working Context

ChatGPT must have access to fi1osof.ru data necessary for full-fledged work:

- users and agents;
- projects;
- tasks;
- worklogs;
- knowledge;
- skills;
- related entities;
- current system state.

Without this, ChatGPT remains an external interlocutor to whom the context must be manually re-explained every time.

## 3. Preservation of a Unified Agent Identity

Work from ChatGPT must be linked to the existing agent in fi1osof.ru.

We must avoid a situation where two independent entities emerge:

- an agent inside haih-agent;
- a "different agent" inside ChatGPT.

Actions, knowledge, tasks, and work results must have a clear author and be linked to the common agent identity where appropriate.

## 4. Shared Working Memory

The results of intellectual work in ChatGPT must not be lost after a session ends.

It must be possible to save to fi1osof.ru:

- tasks;
- worklogs;
- facts;
- decisions;
- observations;
- conclusions;
- links;
- useful instructions;
- investigation results.

The next session should be able to restore the relevant context from the shared system.

## 5. Elimination of Repayment for Already Performed Reasoning

If complex analysis has already been performed in ChatGPT, haih-agent must not unnecessarily repeat the exact same intellectual work from scratch via separate LLM calls.

It must be possible to transfer pre-prepared working context between environments:

- goal;
- confirmed facts;
- found problems;
- hypotheses;
- made decisions;
- constraints;
- required next steps.

It is important to preserve the value of already completed reasoning and avoid paying twice for its full reproduction.

## 6. Ability to Use Strong Models Where It Is More Profitable

Model quality significantly affects the cost of haih-agent's work.

The integration should allow using ChatGPT's strong intellectual capabilities for tasks where reasoning quality is especially important, without forcing the constant translation of the entire agent runtime to an expensive API model.

This is especially relevant for:

- complex analysis;
- architectural decisions;
- ambiguous tasks;
- investigations;
- long reasoning chains;
- working with large context;
- task formulation and refinement.

## 7. Preservation of haih-agent Autonomy

Despite using ChatGPT, haih-agent must remain an independent system.

The integration must not create a dependency where the agent stops working without ChatGPT.

It is necessary to maintain the ability to:

- autonomously execute tasks;
- work via API models;
- use local models;
- run background processes;
- perform actions without an active ChatGPT user session.

ChatGPT should expand interaction options and reduce the cost of certain classes of work, rather than becoming the sole point of existence for the agent.

## 8. Clear Separation of Interactive and Autonomous Work

The needs of two different modes must be defined:

### Interactive Mode

The user is present in the dialogue and actively participates in the process.

Particularly valuable here are:

- rich reasoning;
- quick clarifications;
- long context;
- user-economical intellectual work.

### Autonomous Mode

The user is not in an active dialogue.

Important here are:

- the agent's own runtime;
- background processes;
- schedulers;
- automatic reactions;
- independence from the ChatGPT session.

The integration must account for both modes and not artificially mix them.

## 9. Minimization of Duplication of Agent Loops

ChatGPT and haih-agent potentially have overlapping capabilities:

- reasoning;
- tool use;
- context work;
- planning;
- external API calling.

Unnecessary double execution of the same work by the two systems must be avoided.

It is especially important to prevent scenarios like:

```text
ChatGPT fully analyzes the problem
→ passes it to haih-agent
→ haih-agent again fully analyzes the same problem
→ only after that executes the action
```

Such a process worsens both cost and speed.

## 10. Security and Action Control

ChatGPT gains access to a real working system, therefore server-side restrictions and action transparency must be maintained.

Required:

- identification of the action author;
- access rights;
- audit trail;
- correct operation on behalf of the agent or user;
- restrictions on sensitive operations;
- the ability to understand exactly what was executed via ChatGPT.

Security must not rely solely on LLM behavior—final rules must be controlled by the system.

# User Scenario to Support

The user should be able to open ChatGPT and work roughly like this:

```text
"Show my projects"
"Create a bug"
"Check if it has been fixed"
"Add the result to the worklog and close the task"
"Figure out why the project is not working"
```

At the same time, ChatGPT must have sufficient access to fi1osof.ru to perform such work in the real system, and the results should become part of the shared history of the user and the agent.

The user should not be required to manually transfer data between ChatGPT and haih-agent or unnecessarily pay again for the same analysis via an API model.

# Main Value Criterion

The integration makes sense if it simultaneously provides:

1. a high level of intellectual work and convenience of ChatGPT;
2. significantly lower sensitivity to the cost of each LLM call in interactive work;
3. access to real fi1osof.ru data and actions;
4. preservation of shared memory and agent identity;
5. haih-agent autonomy where it is genuinely needed;
6. absence of redundant re-reasoning between the two environments.

# What NOT to Fix as a Solution Yet

At this stage, the task should describe **needs**, not a pre-selected architecture.

Do not yet consider specific options mandatory, such as:

- direct GraphQL calls from ChatGPT;
- full delegation of requests to haih-agent;
- a separate MCP proxy;
- a single orchestrator;
- a specific memory synchronization scheme;
- a specific division of tool loops.

These solutions must be researched and recorded later in the task worklogs along with arguments, experiments, and results.

# Formulation Readiness Criterion

The formulation is considered complete if it makes clear:

- why haih-agent working alone via paid LLM APIs is insufficient from an economic standpoint;
- why ChatGPT is needed as an additional working environment;
- what data and capabilities must be accessible between the systems;
- why shared memory and identity cannot be lost;
- why it is important not to repay for already performed reasoning;
- why haih-agent must maintain its independence in the process;
- what requirements are imposed on security and control;
- that specific architectural decisions will be determined separately based on the results of further work.

Capture the integration needs of ChatGPT with fi1osof.ru and haih-agent, primarily to reduce the cost of intellectual work and preserve a full agentic context.